Object-oriented hand dexterity and grasping abilities, from the animal quarters to the neurosurgical OR: a systematic review of the underlying neural correlates in non-human, human primate and recent findings in awake brain surgery

Introduction: The sensorimotor integrations subserving object-oriented manipulative actions have been extensively investigated in non-human primates via direct approaches, as intracortical micro-stimulation (ICMS), cytoarchitectonic analysis and anatomical tracers. However, the understanding of the mechanisms underlying complex motor behaviors is yet to be fully integrated in brain mapping paradigms and the consistency of these findings with intraoperative data obtained during awake neurosurgical procedures for brain tumor removal is still largely unexplored. Accordingly, there is a paucity of systematic studies reviewing the cross-species analogies in neural activities during object-oriented hand motor tasks in primates and investigating the concordance with intraoperative findings during brain mapping. The current systematic review was designed to summarize the cortical and subcortical neural correlates of object-oriented fine hand actions, as revealed by fMRI and PET studies, in non-human and human primates and how those were translated into neurosurgical studies testing dexterous hand-movements during intraoperative brain mapping.

Methods: A systematic literature review was conducted following the PRISMA guidelines. PubMed, EMBASE and Web of Science databases were searched. Original articles were included if they: (1) investigated cortical activation sites on fMRI and/or PET during grasping task; (2) included humans or non-human primates. A second query was designed on the databases above to collect studies reporting motor, hand manipulation and dexterity tasks for intraoperative brain mapping in patients undergoing awake brain surgery for any condition. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed more appropriate.

Results: We provided an updated overview of the current state of the art in translational neuroscience about the extended frontoparietal grasping-praxis network with a specific focus on the comparative functioning in non-human primates, healthy humans and how the latter knowledge has been implemented in the neurosurgical operating room during brain tumor resection.

Discussion: The anatomical and functional correlates we reviewed confirmed the evolutionary continuum from monkeys to humans, allowing a cautious but practical adoption of such evidence in intraoperative brain mapping protocols. Integrating the previous results in the surgical practice helps preserve complex motor abilities, prevent long-term disability and poor quality of life and allow the maximal safe resection of intrinsic brain tumors.

1 Introduction

Dexterous, effortless and reproducible hand movements represent a determinant feature of human behavior: they allow interaction with the surrounding environment, manipulate and craft objects and tools, generate complex non-verbal forms of communication and satisfy indispensable needs according to external contingencies. Since the first experiments in the early twentieth century on direct cortical stimulation, investigations on the anatomical and functional substrates of pure motor responses first and complex, meaningful hand motor actions later have shown a growing trend with an intensification in the last three decades, fostered by the expanding number of available invasive and non-invasive study modalities. The former, mostly in non-human primates, clarified that motor behavior control lies on the cortico-subcortical sensory and motor input integration throughout reciprocal modulations of primary motor, parieto-premotor and cortico-thalamic loops (Alexander and Crutcher, 1990; Rizzolatti et al., 1998).

In monkeys, previous intracortical micro-stimulation (ICMS), cytoarchitectonic and functional imaging studies clarified the involvement of a frontoparietal system connecting areas of the inferior parietal lobule (IPL) and the ventral premotor (PMv) cortex in the selection process and online control of purposeful goal-oriented hand actions (Belmalih et al., 2009; Gerbella et al., 2011; Maeda et al., 2015; Borra et al., 2017) This large-scale network, centered on vPM and extended to prefrontal and temporal areas, has been defined as lateral grasping network and is supposed to shape the motor output integrating sensorimotor information with higher order inputs as action goals and object’s features derived from the context or retrieved from memory (Borra et al., 2017; Borra and Luppino, 2019). Similarly, consistent data from healthy human subjects showed a homolog topography comprehending areas within the supramarginal gyrus (SMG), the angular gyrus (AG), intraparietal sulcus and the ventral premotor cortex (vPM) at the core of a vast network encoding objects properties, contextual information and behavioral adaptation schemes for generating complex hand-limb motor actions (Jeannerod et al., 1995; Rosenzopf et al., 2022; Sartin et al., 2022). Converging findings from different study modalities agree that non-human and human primates might share a common functional architecture subserving goal-directed actions comprehending prehension and manipulation of objects and tools (Orban and Caruana, 2014). In both species, an analog duality in dorsal frontoparietal connectivity (i.e., a dorsomedial pathway specialized in the visuo-motor integration for reaching and limb lifting and dorso-ventral pathway encoding sensorimotor integration and more direct access to motor output through parieto-premotor and premotor-motor projections) has been identified, segregating the control of specific and complementary features of hand motor schemes (Geschwind, 1975; Goldenberg, 2009; Grafton, 2010; Nelissen and Vanduffel, 2011; Glover et al., 2012; Caminiti et al., 2015). The evolutionary gain in humans led to an expansion of multimodal areas in the frontal, temporal and parietal lobes, probably due to an evolutionary-guided alteration of the macaque’s pre-existing dual-stream frontoparietal network areas. In this view, this modification allowed more complex behavioral responses—which are not affordable for non-human primates—to be encoded (i.e., praxis abilities, complex communicative limb gestures and abstract manipulative tasks; de Waal and Ferrari, 2010; Van Essen et al., 2016; Dressing et al., 2018). Accordingly, fMRI studies in humans described a wider fronto-temporo-parietal network, defined as “the praxis representation network” (PRN), consistently involved in elaborating conceptual and sensorial knowledge into goal-directed and specialized hand motor actions (Frey, 2008; Króliczak and Frey, 2009).

The extensive knowledge about neural correlates of motor functioning has been successfully translated from animal studies to intraoperative brain mapping protocols developed to guide oncological and epilepsy neurosurgery. This intimate relation has been indissoluble since Penfield and colleagues’ first experiments on direct electrical stimulation (DES) of the human cortex eliciting motor responses, influenced by previous pioneering animal experiments Penfield witnessed and collaborated on under Sherrington’s guide in the early 20th century. Since then, animal studies have inspired and provided a solid background to many brain mapping studies in intraoperative scenarios.

Awake surgery employing DES is routinely performed in specialized centers to preserve cortical and subcortical essential components of the motor network to maximally extend the resection, preserving patient’s motor abilities (so-called “onco-functional balance”; Duffau and Mandonnet, 2013). Despite several advancements, a comprehensive exploration of the mechanisms underlying object-oriented dexterous hand movements in pre- and clinical scenarios remains a relevant challenge. The old-world monkeys, like macaques, represent the closest ancestor of Homo Sapiens in which, through invasive neurophysiological and anatomical studies, detailed anatomical and physiological notions on the neural bases of sensorimotor and high-domain cognitive functioning can be carried out and generalized to human models. However, despite undoubted similarities between species, the about 30 million years of independent evolution drove significant differences in brain architecture and function, contributing to the peculiar cognitive capabilities of humans but also entangling complete transpositions of evidence from one species to the other. In addition, invasive methods with high temporal and spatial resolutions constituting the gold standard for formulating causal inference in neural mechanisms are not reproducible—for obvious reasons – in humans.

FMRI applications have been developed to fill this gap in humans; to map specific areas, activated voxels scans and analyses of time-dependent regional activities can be obtained first, then the relation of such areas to the specific function elicited can be indirectly derived with reduced spatial and temporal resolutions compared to the methods above developed in monkeys (for additional details see Hillman, 2014).

Although the translational impact of these neural substrates on the neurosurgical practice is indisputable, the anatomo-functional consistency between preclinical data gathered on primates and the results obtained in neurosurgical settings with brain mapping protocols has not yet been fully analyzed and discussed. A systematic review of this topic is a critical step in shedding light on the influence of cross-species basic science on neurosurgical practice, directly impacting patients’ quality of life and survival expectations. The current study aims to systematically collect evidence on the neural substrates of object-oriented hand manipulation movements in healthy non- and human primates and from intraoperative studies investigating responses after direct electrical stimulation of hand movement-related cortical areas and subcortical structures.

2 Methods

2.1 Information sources and search strategy

A systematic literature review was conducted according to the Cochrane Handbook for Systematic Reviews and was reported based on the PRISMA statement for reporting systematic reviews and meta-analyses (Page et al., 2021). The aim of the current study was clarified through the definition of the following questions:

i. Query 1 (Non-human primate and human grasping investigations; from here referred to as Q1): What is the evidence on the anatomical-functional substrate of objects/tools grasping and hand manipulation skills in non-human primates and healthy humans? What are the homologies and differences between species?

ii. Query 2 (Intraoperative awake grasping and hand manipulation tasks investigations: from here referred to as Q2): What implication have the evidence collected from non-human primates and healthy humans in designing and implementing intraoperative advanced brain mapping paradigms to preserve grasping capacity, hand dexterity and fine motor abilities during awake surgeries? What is the state of the art on the intraoperative mapping and monitoring of grasping and/or any additional fine hand motor task during awake brain surgery?

A systematic literature search was conducted in three biomedical databases: (i) PubMed, (ii) EMBASE, (iii) Web of Science. The search was updated to 28 February 2023 and further updated on 15 September 2023). To the best of our knowledge, no additional study published after this date and available through a literature search in the databases reported above could be included in our study.

2.2 Inclusion criteria

2.2.1 Query 1

For Query 1 (Q1), the following PICO terms were used: “(Grasping) AND ((fMRI) OR (MRI) OR (functional MRI) OR (PET)).” All studies had to respect the following inclusion criteria to be considered in our systematic review (Figure 1):

- To report an experimental investigation on non-human primates or healthy adult human candidates with a sample size equal to or greater than two participants (we excluded single case reports given the poor level of evidence provided);

- To test a hand grasping, reach-and-grasp or hand manipulation task reporting a contrast showing more significant activation levels for the execution task than a control condition. Control conditions include passive view, reach, simple finger movements, object detection and object discrimination);

- To declare a measurement of brain activity during the active execution of the task mentioned above as the study’s primary outcome. Comparative assessment of brain activity during planning or passive tasks was reported as secondary findings if the study’s primary goal fit all inclusion criteria. Studies focused on brain activity during resting or planning phases were excluded;

- To use fMRI or PET to measure neural activity indirectly;

- To have conducted a ROI-base or whole-brain analysis;

- To have performed a univariate, a multivoxel pattern (MVPA), or a functional connectivity analysis (aiming to qualitatively summarize the body of literature irrespectively of the nature of data provided, a consensus among authors was achieved for interpreting the impact of results expressed through different types of analyses; L.T., L.M., L.V., M.R.);

- To report activation areas in Montreal Neurologic Institute (MNI) or Talairach coordinate spaces (TAL); studies reporting findings in native space were excluded.

Figure 1

Figure 1. PRISMA 2020 flow diagram for studies involving non-human primates and healthy human participants investigating brain activated region during grasp-related fine hand gestures.

2.2.2 Query 2

For Query 2 (Q2), the following PICO terms were used: “((Dexterity) OR (grasping) OR (Fine motor) OR (Grip) OR (Haptic) OR (Hand manipulation) OR (manipulation) OR (Hand movement) OR (praxis) OR (apraxia) OR (sensorimotor network) OR (motor network)) AND ((Intraoperative monitoring) OR (IOM) OR (Direct Electrical Stimulation) OR (DES) OR (awake) OR (Intraoperative Mapping)).” All studies had to respect the following inclusion criteria to be considered in our systematic review (Figure 2):

- To report an experimental study investigating any intraoperative assessment of grasping, reach-and-grasp ability or other fine hand movement tasks (i.e., dexterity, haptic-related fine finger movements, precision grip, pinching and whole-hand power grip).

- To test the functions mentioned earlier in human patients undergoing awake surgery for any condition (i.e., intra-axial tumors, vascular lesions in so-called “eloquent areas” or epilepsy surgery) employing brain mapping with or without intraoperative neurophysiological monitoring (IOM).

- To report surgical outcomes regarding function preservation, functional independence after surgery, quality of life and/or extent of resection.

Figure 2

Figure 2. PRISMA 2020 flow diagram for intraoperative awake surgery brain mapping and monitoring of grasping and fine hand manipulation tasks.

2.3 Exclusion criteria

The following exclusion criteria were applied (both Q1 and Q2):

- Publication design: Case reports, abstracts, commentaries, editorial papers, conference papers and publications written in any language but English were excluded

- Contents: all studies not fulfilling the aforementioned inclusion criteria were excluded.

2.4 Selection process

Two authors (L.T. and L.M.) independently reviewed the titles and abstracts of the retrieved articles, classifying them as included, excluded and maybe. During this stage, the articles that did not meet the inclusion criteria were excluded (such as reports written not in English, studies on non-primate animals, in vitro studies, abstracts, reviews, commentaries and case reports). In case of disagreement between the authors, the consensus was reached by full-text jointly-conducted examination. Afterwards, the full texts of the articles classified as “included” and “maybe” were independently assessed by the same authors (L.T. and L.M.). Again, in case of disagreement, the consensus was reached by broad discussion with the senior author (M.R.). No automatic tools were employed during the selection process.

2.5 Review of reports

Due to the heterogeneity in the body of evidence collected for Q1 and Q2, quantitative synthesis was deemed inappropriate. For Q1, a qualitative synthesis of results was provided: results collected from the reviewed articles were compiled through a narrative approach, and an updated description of the anatomical and functional bases of grasping and fine hand motor tasks characteristics and functional implications in non-human primates and healthy humans was provided. Similarly, for Q2, a synthesis of findings will be provided: the state-of-the-art intraoperative neurophysiological monitoring and brain mapping paradigms for hand fine movements/grasping-related tasks preservation during awake surgeries were presented in a narrative form.

Overall, to improve the readability of our qualitative review, major evidence from selected studies will be organized according to their anatomical location and connectivity, following cortical and cortico-subcortical topographical segregation. We are aware this approach will penalize the characterization and discussion of specific studies and their methodological peculiarities; however, we aimed to provide a physician-oriented comprehensive review of the most relevant cortical and subcortical nodes involved in the mechanisms under investigation with a potential impact on the neurosurgical research and clinical practice.

A detailed methodological revision of study designs and their implications was beyond the scope of the current study.

2.6 Graphical contents

The relevant sectors of the lateral grasping network in monkeys and object-oriented hand dexterity network / praxis representative network in humans were collected and reproduced in tridimensional standardized left-hemisphere brain maps. In Monkeys, lateral grasping network sectors, comprehending the exploratory oculomotor network, were drawn on the Mount Sinai cohort data on the INIA-19 template,¹ as previously done by Howells and colleagues (for additional methodological insights, see Rohlfing et al., 2012; Howells et al., 2020). Similarly, the relevant areas in humans were extracted by the Human Connectome Project atlas, multi-modal cortical parcellation (HCP-MMP1.0; Glasser et al., 2016). Intraoperative Data shown were extracted from Vigano et al. and Fornia et al. and rendered within the left hemisphere only (Fornia et al., 2020a, 2023). The figures in the article were designed and produced within the open-source surface render software “SurfIce” (SurfIce, 2015).

3 Results

3.1 Imaging-based experimental findings

For Q1, bibliographic searches on literature databases yielded 3,611 records (PubMed: 2,038; Embase:783; Web of Science: 790). After removing duplicates (971 records) and unrelated manuscripts, 598 were selected for full-text evaluation. Among these, 528 were further excluded, as they did not meet the predefined inclusion criteria. Additional 13 studies were extracted from appropriate references during the screening. Overall, 85 records were included in our systematic review: six on non-human primates and 79 on healthy human candidates. Figure 1 shows the flow diagram of the literature search and study selection.

3.1.1 Non-human primates

Six studies (Nishimura et al., 2007; Hopkins et al., 2010; Nelissen and Vanduffel, 2011; Hecht et al., 2013; Fiave et al., 2018; Nelissen et al., 2018) on non-human primates were completed between 2007 and 2018 and included overall nine macaques and 78 chimpanzees [in Hopkins et al., 2010, only four chimpanzees underwent the behavioral task during PET scanning; 70 chimpanzees were recruited for baseline MRI scan only and further segmentation of hand knob region]. Five studies explored activation areas on the whole brain surface, while Fiave et al. focused only on the left hemisphere. Four studies conducted grasp and reach-and-grasp experiments with (Nishimura et al., 2007; Hopkins et al., 2010) or without visual aid (i.e., “grasping in the dark”; Nelissen and Vanduffel, 2011; Hecht et al., 2013; Fiave et al., 2018; Nelissen et al., 2018). All studies but Hopkins et al. allowed right hand movements only.

Three investigations used an FDG-PET imaging acquisition and further co-registration in MRI-normalized coordinates (Nishimura et al., 2007; Hopkins et al., 2010; Hecht et al., 2013), while the other three studies performed the behavioral task during fMRI (Nelissen and Vanduffel, 2011; Fiave et al., 2018; Nelissen et al., 2018).

The behavioral contrasts reported by the authors are the following: Grasp > Rest (Hecht et al., 2013); Reach-and-Grasp > Rest (Nishimura et al., 2007; Hopkins et al., 2010; Nelissen and Vanduffel, 2011; Fiave et al., 2018; Nelissen et al., 2018); Reach-and-Grasp > Reach (Nelissen and Vanduffel, 2011; Nelissen et al., 2018); Grasp > transitive and intransitive passive observation (Hecht et al., 2013); Reach-and-Touch > Rest (Fiave et al., 2018) and Grasp > Touch (Fiave et al., 2018). Additional information on the study design and findings are reported in Table 1. The results of the individual studies will be summarized in the narrative discussion and in Figures 3, 4.

Table 1

Table 1. Main findings (non-human primates): Abbreviations are reported in the main table.

Figure 3

Figure 3. Grasping Network (non-human primates): Graphical representation of the lateral grasping network in non-human primates (macaques). INIA19 template. Neuromaps Atlas (Macaca mulatta). 46, lateral prefrontal cortex, area 46; AIP, Anterior IntraParietal area; F1, frontal motor area F1, macaque homolog of human M1 area; F5, frontal motor area F5, macaque homolog of human vPM area; anterior (F5a), posterior (F5p) and convexity (F5c); PFG, posterior parietal area PFG; SII, Secondary Somatosensory cortex; Insula, insular cortex (anterior); Interm12r, intermediate segment of rostral frontomesial area 12.

Figure 4

Figure 4. Grasping Network (non-human primates), extended representation: Graphical representation of the extended lateral grasping network in non-human primates (macaques). INIA19 template. Neuromaps Atlas (Macaca mulatta). 45B, part of the macaque homolog of Broca’s area; AIP, Anterior INtraParietal area; F1, frontal motor area F1, macaque homolog of human M1 area; FEF, Frontal eye Field; LIP, lateral IntraParietal Sulcus; PE, posterior parietal are PE; PG, posterior parietal area PG; TE, inferior temporal area TE, rostral and caudal.

3.1.2 Healthy human candidates

Seventy-nine studies were completed between 1996 and 2022, including 1,412 healthy patients (560 females, 9 studies did not specify gender heterogeneity; Grafton et al., 1996; Matsumura et al., 1996; Binkofski et al., 1999a,b; Ehrsson et al., 2000, 2001, 2007; Kuhtz-Buschbeck et al., 2001; Chapman et al., 2002, 2007, 2011; Culham et al., 2003; Grèzes et al., 2003; Ward and Frackowiak, 2003; Frey et al., 2005, 2015; Talati et al., 2005; Shmuelof and Zohary, 2006; Cavina-Pratesi et al., 2007; Grol et al., 2007; Króliczak et al., 2007; Milner et al., 2007; Vaillancourt et al., 2007; Begliomini et al., 2007a,b, 2008, 2014, 2015; Park et al., 2008; Stark and Zohary, 2008; Verhagen et al., 2008; Gallivan et al., 2009, 2011a; Hinkley et al., 2009; Matsuda et al., 2009; Spraker et al., 2009; Cabinio et al., 2010; Kurniawan et al., 2010; Fiehler et al., 2011; Holmström et al., 2011; Hong and Jang, 2011; Kim et al., 2011, 2021; Martin et al., 2011; Monaco et al., 2011, 2014, 2015, 2017; Neely et al., 2011; Glover et al., 2012; Makuuchi et al., 2012; Nathan et al., 2012a; Vingerhoets et al., 2012a; Renzi et al., 2013; Rossit et al., 2013; Fabbri et al., 2014, 2016; Plata et al., 2014; Gutteling et al., 2015; Pavlova et al., 2015; Hamzei et al., 2016; Leo et al., 2016; Marangon et al., 2016; di Bono et al., 2017; Gatti et al., 2017; Przybylski and Króliczak, 2017; Ariani et al., 2018; Cavina-Pratesi et al., 2018; Styrkowiec et al., 2019; Marneweck and Grafton, 2020; Sulpizio et al., 2020; Turella et al., 2020; Bencivenga et al., 2021; Knights et al., 2021, 2022; Errante et al., 2021a; Livne et al., 2022; Michalowski et al., 2022; Ras et al., 2022).

All 79 studies investigated cortical sites of activation in the left and right hemispheres. Seventy-two studies included right-handed patients, and seven included left-handed participants (Begliomini et al., 2008; Cabinio et al., 2010; Martin et al., 2011; Gallivan et al., 2011a; Vingerhoets et al., 2012b; Fabbri et al., 2014; Gutteling et al., 2015); two studies did not declare the participants’ handedness (Knights et al., 2021, 2022). Seven studies on right-handed patients investigated grasping task of the non-dominant hand (Binkofski et al., 1999b; Ward and Frackowiak, 2003; Shmuelof and Zohary, 2006; Hong and Jang, 2011; Kim et al., 2011; Vingerhoets et al., 2012a; Begliomini et al., 2015).

Twenty-two studies performed experiments with no direct view of the target of the grasping task (“i.e. grasping in the dark; Ehrsson et al., 2000, 2001, 2007; Kuhtz-Buschbeck et al., 2001; Shmuelof and Zohary, 2006; Milner et al., 2007; Spraker et al., 2009; Kurniawan et al., 2010; Fiehler et al., 2011; Holmström et al., 2011; Hong and Jang, 2011; Kim et al., 2011, 2021; Neely et al., 2011; Renzi et al., 2013; Fabbri et al., 2014; Marangon et al., 2016; Gatti et al., 2017; Ariani et al., 2018; Cavina-Pratesi et al., 2018; Styrkowiec et al., 2019; Turella et al., 2020). Moreover, eight studies reported specific hand manipulation tasks with (Binkofski et al., 1999a,b; Pavlova et al., 2015; Michalowski et al., 2022; Ras et al., 2022) and without visual aid (Talati et al., 2005; Marangon et al., 2016; Styrkowiec et al., 2019), and two studies acquired functional imaging during a pointing behavioral task (Frey et al., 2005; Cavina-Pratesi et al., 2018).

Additional information, including behavioral contrasts implemented, is available in Supplementry Tables 1–3. The results of individual studies will be summarized in the narrative discussion and reported in Figures 5, 6.

Figure 5

Figure 5. Object-oriented hand manipulation network in humans: Graphical representation of the object-oriented hand manipulation network (within the praxis-representative network) in humans. Atlas, Human Connectome Project-MMP1.0; 3a-4, Brodmann areas 3a-4; 5 m, Brodmann area 5 medial; 6ma, Brodmann area 6ma (preSMA, mesial SFG); 6mp, Brodmann area 6mp (SMA, mesial SFG); 6d, Brodmann area 6d, premotor area dorsal; 6v, Broadmann area 6v, premotor area ventral; AIP, Anterior IntraParietal area; PF, Inferior Parietal Lobule area PF; PGp, posterior portion of human Angular Gyrus.

Figure 6

Figure 6. Object-oriented hand manipulation network in humans (extended view): Graphical representation of the extended object-oriented hand manipulation network (within the praxis-representative network) in humans. Atlas, Human Connectome Project-MMP1.0; a24, subdivision of Anterior Cingulate Cortex; a24pr, subdivision of the anterior part of Middle Cingulate Cortex; LOC, Lateral Occipital Cortex; MTG, Middle Temporal Gyrus; OP1, Operculum Parietal 1 area; p24pr, subdivision of the posterior part of Middle Cingulate Cortex; POS, Parieto-Occipital Sulcus; V3, Visual Area 3.

3.2 Intraoperative awake surgery findings

For Q2, bibliographic searches on literature databases yielded 4,892 records (PubMed: 1,790; Embase:1,686; Web of Science: 1,415). After removing duplicates (1,916 records) and unrelated manuscripts, 287 records were selected for full-text evaluation. Among these, 269 were further excluded, as they did not meet the predefined inclusion criteria. Overall, 20 records were included in our systematic review. Figure 2 shows the flow diagram of the literature search and study selection. The results of individual studies will be summarized in the narrative discussion (see also Table 2 and Figure 7).

Table 2

Table 2. Main intraoperative findings during awake brain mapping procedures (human primates).

Figure 7

Figure 7. Intraoperative brain mapping stimulation sites distribution during DES-induced hand manipulation task (hMT) disruption: Distribution of stimulation sites in the standardized left hemisphere. The sectors are segregated according to the EMG and behavioral response during DES administration and online execution of the hand-manipulation task (hMT). The “arrest response sites” were defined as those anatomical sectors containing effective sites associated with deficient muscle performance, characterized by complete task arrest during DES. The “clumsy response sites” were defined as those anatomical sectors for which effective sites were associated with high variability among muscles, possibly reflecting poorer muscle coordination. The arrest response pattern is clustered in the ventral and dorsal sectors of the precentral (PreCG, PMd), the postcentral gyrus (postCG) and the anterior sector of the posterior parietal cortex (aPPC). In contrast, the clumsy pattern is identified in the dorsal vPM and the PostCG’s mid-portion. Not represented in the figure: Within the arrest behavioral responses, a “suppression response,” determined as a hand movement arrest and a general decrease in muscle recruitment, was identified in the precentral gyrus in correspondence with a sector within the dorsal vPM and in within arrest sites within aPPC, PostCG and anterior supramarginal gyrus. Similarly, a “recruitment response,” characterized by an involuntary movement and a general unspecific increase of muscles activity within an arrest behavioral phenomenon, was localized in a dorsal sector of PMd and a caudal PostCG sector. Mixed muscle effects were also identified within the caudal PostCG sector. Yellow, arrest response; Green, clumsy response.

4 Discussion

4.1 Query 1: Non-human primates

4.1.1 Anatomical substrates of lateral grasping network in monkeys

The classical paradigm for monkey prehension network (namely the “lateral grasping network”) was initially framed on parallel cortico-cortical pathways connecting neurons of the posterior parietal (PPC) and frontal cortex exhibiting similar architecture and functional responses. The ventral premotor area F5 is one of the most relevant areas in object motor grasping and receives inputs from the inferior parietal lobule (IPL) areas, namely AIP, PF, PFG and SII, projecting to the primary motor cortex (F1) and spinal cord (especially F5p; di Bono et al., 2017). On the medial frontolateral convexity, the rostral part of area F2 (F2vr) directly integrates grasping control, specifically during wrist rotation and orientation toward a target. However, it is also involved in reaching, although weaker evidence is available (Dum and Strick, 2002; Gamberini et al., 2009; Hecht et al., 2013). Imaging-based studies investigating the role of F3 (proper-SMA in monkeys) and F6 (pre-SMA area) in primates are less reported: primary evidence from single-neuron studies advoked the involvement of F6 in learning, execution of sequential motor behaviors and the initiation of conscious action. Accordingly, F6 neurons were reported preferentially activated while learning a sequence of actions compared to subsequent execution of the learned scheme in a behavioral study on monkeys (Nakamura et al., 1998). Based on these findings, it is widely accepted that area F6 controls action time and appropriateness.

Several prefrontal cortex areas are connected to the premotor and parietal cortex and areas 46 (46dr and 46d, 46vr and 46v) and area 12r are the most represented on dorsal and ventral banks of the principal sulcus (Gerbella et al., 2013; Saleem et al., 2014; Bufacchi et al., 2023). In a recent study, Luppino et al. reported that prefrontal areas might be subdivided into three caudo-rostral strips according to their primary output connectivity. From rostral to caudal, the first strip carries intrinsic lobar connections within the prefrontal cortex, connecting the frontal pole and orbital prefrontal areas (Borra et al., 2011; Saleem et al., 2014). The caudal part shows significant connections with the lateral intraparietal cortex (LIP), frontal eye field (FEF), supplementary eye field (SEF) and other subcortical nodes involved in gaze control, while the intermediate cluster is mostly interconnected with premotor, parietal areas and subcortical structures involved in grasping and reaching movements (Gerbella et al., 2013; Saleem et al., 2014; Borra et al., 2017). The ventral prefrontal areas (VLPF) share a common connectivity with the temporal lobe, suggesting a major role in processing object semantic features, while the dorsal prefrontal cortex (DLPF) is strongly connected to the parietal and premotor areas, as area 46 is involved in motor and behavioral control of grasping movements (Goldman-Rakic, 1987; Hecht et al., 2013).

The most reported parietal nodes of the grasping network are the anterior infraparietal cortex (AIP) and the adjacent area, PFG, both dense in neurons encoding grasping movements execution (Hyva¨rinen, 1981; Taira et al., 1990). Dorsally, MIP and the dorsal part of area V6A (V6Ad) were later attributed to reaching control (Andersen et al., 1997). The former have dense interconnections, except PFG, which only shows selective connectivity with AIP. The latter also receive input from higher-order associative visual areas in the temporo-occipital cortex (Rozzi et al., 2006). Overall, this sector mediates visuomotor transformation for reaching and grasping, processing target features (size, orientation, position and shape) and activating appropriate potential motor actions. This process, called “affordance extraction, “elicits objects’ visual properties transformation and projection through the dorsal stream (Sakata et al., 1997).

Finally, the inferior temporooccipital and the posterior occipital regions define the “ventral visual stream” involved in visual information processing, object identification and semantic recognition (Tanaka et al., 1996). Nelissen et al. identified cortical activations during action observation at the level of the upper (superior temporal polysensory area or STP) and lower banks (Tea/m sector) of the superior temporal sulcus (STS). STP is a high-order multisensory sector integrating information encoded in multiple sensory modalities, also populated by visual neurons encoding self-produced and external motion features connected to the PFG area in parietal lobe (Baylis et al., 1987). Tea/m sector was identified as a ventral visual node specialized in three-dimensional object and action processing (Orban and Caruana, 2014). Information encoded in STS is projected through Tea/m to AIP and the mirror system in the parietal lobe, providing input of action goal performed by others, intrinsic information to identity the target object as a substrate for affordance extraction, but also along STP-PFG pathway to elaborate the intention and the goal of the observed action (Jellema et al., 2000). An additional area in the parietal operculum, SII area, hosts visual-responsive neurons firing during external action observation, suggesting that it might interact with temporoparietal projections as a multisensory integrating node for motor control and action recognition (Hihara et al., 2015).

4.1.2 Functional modeling of the grasping network in monkeys

The dorsolateral and dorsomedial pathways of the lateral grasping network are essential for sensorimotor integration (i.e., planning and online control) during reaching, grasping, and gaze control (Rizzolatti et al., 1998; Andersen and Buneo, 2002). According to this classical model, a dorsolateral pathway encodes grasping and different grip features, while a dorsomedial pathway encodes reaching and control of the transport/lifting phase (Jeannerod et al., 1995; Caminiti et al., 1998; Culham et al., 2003).

The dorsolateral pathway comprehends AIP (Murata et al., 2000) and subareas F5p/F5c of the ventral premotor cortex (PMv; Murata et al., 1997; Rizzolatti et al., 1998; Nishimura et al., 2007; Nelissen and Vanduffel, 2011; Nelissen et al., 2018). Similarly to AIP, PFG area responded to hand movements observed but even without a target or the presentation of 3D objects, implying a pivotal role in grasp motor scheme planning. Of note, an additional neural category responding to passive viewing of actions performed by others and peri-personal space awareness in PFG (I.e. “mirror neurons”) was characterized (Rozzi et al., 2008; Hopkins et al., 2010; Hecht et al., 2013; Fiave et al., 2018; Nelissen et al., 2018). A core description of the extended mirror network is beyond the aim of the current review (for a review, see Rizzolatti and Craighero, 2004).

AIP was proposed as primer of grasping response by activating visual-dominant neurons, which extract 3D object characteristics and propagate them to F5 visuomotor neurons encoding congruent motor representations for the affordable object. To integrate the modulation of the prefrontal cortex to the intended behavior, Arbib and co-workers proposed the “Fagg–Arbib–Rizzolatti–Sakata (FARS) model” (Fagg and Arbib, 1998; Arbib and Mundhenk, 2005). AIP produces multiple motor representations of object affordances to F5, while modulated by prefrontal inputs, encoding the goal of the individual in affording the target object; this whole process selects the most appropriate motor execution program. The robustness of this model relies on the fact that F5 is not directly interconnected with the inferotemporal cortex but receives dense connections from the ventral prefrontal cortex, which in turn receives input from inferior and posterior temporal areas, as previously described.

The dorsolateral pathway encoded the transformation of intrinsic target properties into appropriate behavioral and motor commands comprehending hand pre-shaping, force adjustment and type of grip during visually-aided grasping (Jeannerod et al., 1995) through visuomotor neurons in AIP, PFG and F5, with significant activation while observing a graspable object or performing a grasping task (Bonini et al., 2014). Lesion studies within this pathway clarified how lesions within AIP and F5p affected hand pre-shaping and wrist orientation, leaving object-reaching ability mostly unaffected. These findings were consistent only during precision grip of small objects, while whole hand prehension showed no deficit, confirming the crucial role of AIP and F5p during complex sensorimotor control or pinching of small objects (Ehrsson et al., 2001; Cavina-Pratesi et al., 2007; Grol et al., 2007; Begliomini et al., 2007a, 2014; Renzi et al., 2013; Monaco et al., 2015). Neighbor area F5c (F5 subarea) lesion, despite having the same visuomotor properties as other F5 subareas, was not responsible for any grasping impairment (Fogassi, 2001; Bonini et al., 2014).

The selection of the object’s meaning also relies on connections from prefrontal area 12r, while the behavioral response based on the overarching goal could be appointed to prefrontal area 46v, which is densely connected with posterior parietal and premotor cortex. Eventually, the affordance selection elicits F5 motor representation, activating F1(the primary motor area). Once affordance is selected and hand shaping programmed, additional features modulate arm-hand movement and grip characteristics. SII region, for example, was activated during movement, especially object grasping and different hand configurations, object orientation and passive view: it is plausible to consider this area as a fundamental sensorimotor integration node for object’s physical and visual properties during reaching and grasping, receiving feedback information used in F5, AIP and PFG for online monitoring and update grasping motor scheme (Hihara et al., 2015).

The dorsomedial pathway connects the PPC, among all V6a (Bosco et al., 2010), VIP and MIP (Johnson et al., 1996) with F2 anteriorly, within the primate dorsal premotor cortex (PMd), and associative visual areas posteriorly (Nishimura et al., 2007; Nelissen and Vanduffel, 2011; Fiave et al., 2018; Nelissen et al., 2018). MIP and AIP are part of both ventrolateral and dorsomedial pathways, confirming the integration and overlap of the two pathways. Visual areas V1–V4 are strongly connected to V6A and, in particular V1 and V2 subareas, are involved in the representation of peripheral visual field showed the most substantial convergence onto V6A, with similar attributes toward peripheral vision and stimulus position in craniotopic coordinates encoding (Daniel and Whitteridge, 1961; Galletti et al., 1999). Despite previous modeling including the precuneus in the dorsomedial stream only, a substantial dorsomedial and dorsolateral pathways overlap was later identified, whose direct function in reaching and grasping remains only partially understood. Tracer studies in monkeys revealed projections to both area F5 and V6A, suggesting a role in coordinating reaching and grasping, in addition to the well documented activation during bimanual performance, complex tasks and (Johnson et al., 1996) as a functional hub in the default mode network (DMN) in monkeys (and humans); however, this area of grasping-reaching overlapping does not seem to correspond with the DMN activation sites in fMRI (Johnson et al., 1996; Fransson and Marrelec, 2008; Mantini et al., 2011). Of note, the frontal eye field (FEF, area 8 m) area is also strongly connected to the precuneus, suggesting a role during sensory guidance of limb and hand movements as much as a hand-eye coordination node (Thompson et al., 1996).

Overall, an integration gradient on the functional level between the dorsomedial and ventrolateral streams seems consistent. Although the ventrolateral stream is primarily integrated with somatosensory areas and the dorsomedial stream the has more robust interconnections to the visual cortex, several areas within each pathway are strongly interconnected. Grasp planning and execution revealed significant activation in the IPL convexity areas posterior to AIP, comprehending area PF, PG, and particularly area PFG, having direct connections to F5 (Bonini et al., 2011, 2014). Similarly, neural activity during grasping was recorded in V6A and PMd subareas (i.e., area F2vr; Fattori et al., 2010, 2012). Conversely, subpopulations of F5 and AIP showed reaching-related coding in additional studies (Lehmann and Scherberger, 2013), supporting an overlapping architecture of the lateral grasping network (Battaglini et al., 2002).

4.1.3 Cortico-subcortical loops integrated with the lateral grasping network in monkeys

The cerebellum and the basal ganglia play a role in motor cognitive tasks execution and control ranging from sensorimotor to complex behavioral integration (Strick et al., 2009; Bostan et al., 2013; Caligiore et al., 2013). They fulfill a functional architecture of input projections from a wide range of cortical areas, despite earlier findings supported the idea that these connections were anatomically and functionally segregated according to the function being modulated (Middleton, 2000; Baldassarre et al., 2013). Hoshi et al. reported a strong bisynaptic connection between the striatum and the thalamus, while Bostan, Dum and colleagues showed that the subthalamic nucleus has similar connections to the cerebellar cortex through the pontine nuclei (Hoshi et al., 2005; Bostan et al., 2010). Tracer studies in monkeys clarified that efferent connections from the cerebellar nuclei are projected to M1 but also on premotor, prefrontal and parietal areas (Percheron et al., 1996). The output streams to M1 and premotor areas within the dorsal dentate nucleus are clustered in a circumscribed “motor” domain while a ventral region of the nucleus showed connections to the prefrontal (non-motor) areas (Shinoda et al., 1992; Dum and Strick, 2003). This functional segregation in the dentate nucleus has a counterpart in the cerebellar cortex. The dorsal “motor” dentate is majorly connected to the anterior cerebellar lobe (lobules III–VI) and paramedian lobule (HVIIB and HVIII), while the ventral dentate is connected with the posterior vermis and the cortex are not involved in motor control, as consistently documented also in imaging studies (Kelly and Strick, 2003). The cerebellar cortex elicits activation of the dentate nucleus, and this functional loop allows cortical areas (i.e., prefrontal, premotor and F1) to interconnect with the dentate nucleus via the pontine nuclei while areas not projecting to the cerebellum showed no major connections from the cerebellum (i.e., area 46v, 12 and TE, for example). This series of closed-loop circuits might represent the anatomical substrate of a multi-level modulation system involving the cerebellum as a significant player. If so, cerebellar output directly influences multiple non-motor cortical areas (i.e., premotor and parietal cortex), suggesting an essential role in various cognitive tasks (Koziol et al., 2014). Similarly, motor and non-motor subregions of STN showed a similar projection onto the motor and non-motor cerebellar cortex (Bostan et al., 2010), while Chen et al. demonstrated that the cerebellum is responsible for a short-latency direct modulation of the striatum (Chen et al., 2014). Although the growing evidence hereby reported, the physiology of cerebellar-striatal interconnections is still poorly understood in contexts other than motor functioning, and further research is prompted.

4.2 Query 1: Human primates

4.2.1 Anatomical and functional substrates of the object-oriented hand manipulation network

The anatomical and functional substrates of dexterous motor behavioral responses, complex non-verbal communicative gestures and abstract manipulative tasks in humans—comprehending those abilities defined as “praxis”—represents the translational evolution of the pre-existing dual streams frontoparietal network (i.e., lateral grasping network) defined in the macaques. With a certain amount of approximation, we can refer to it as “praxis representation network” (PRN) in the human model.

The premotor and prefrontal areas play a significant role in the dorsoventral and dorsomedial pathways for reaching and grasping performance within the network. The human ventral premotor area (PMv or hPMv) has been proposed as the homolog of the rostral part of the nonhuman primate F5 area, with which it shares direct or indirect control of hand movements and mirror-like properties (Cerri et al., 2003, 2015; Ehrsson et al., 2007; Maranesi et al., 2012; Fornia et al., 2018). A recent rTMS study highlighted PMv features by reproducing an impairment of current finger position during precision grip, confirming its involvement in such a task (Davare et al., 2009). As mentioned above about area F2 in monkeys, the dorsal premotor area (PMd) is involved in grasping and reaching tasks, confirming that the dorsomedial frontoparietal circuit serves both grasping and reaching encoding (Raos et al., 2006; Errante and Fogassi, 2019). Indeed, the inactivation of PMv impaired hand pre-shaping, while that of PMd corresponded to interference in object lifting with disruption of grasping-lifting coupling, conforming a different but convergent contribution of these areas in controlling hand/arm performance during grasping, as previously demonstrated in monkeys (Davare et al., 2006; Fornia et al., 2020b). Comparable with animal findings, additional areas involved in hand motor control are the supplementary motor areas (proper- and pre-SMA) and ventrolateral and dorsal prefrontal cortex (VLPFC and DLPFC, respectively). Several imaging studies investigating planning vs. online control of hand motion toward a target attributed a relevant inhibition and behavioral switching control to pre-SMA, DLPFC (Spraker et al., 2009) and VLPFC (Mostofsky et al., 2003; Aron, 2009; Glover et al., 2012; Tabu et al., 2012), comprehending force strength adaptation and dynamic grip modulation (Neely et al., 2011) to grasped objects (Kuhtz-Buschbeck et al., 2001; Holmström et al., 2011). Most of the studies we reviewed on healthy humans collected evidence on dominant hand grasping execution in right-handed subjects with a skew lateralization of cortical activity measured during handgrip only for the involvement of contralateral sensorimotor cortex (and ipsilateral superior cerebellum). Nevertheless, most studies consistently reported bilateral activation of PMv, PMd, SMA, cingulate motor cortex, IPL, insular, vermis and both superior/inferior cerebellar hemispheres, despite previous reports left-lateralized cortical activations in right-handers humans but a widespread bi-hemispheric sensorimotor, premotor and SMA activations in left-handed candidates performing grasping tasks with the dominant and—even more evident—non-dominant hand (Grafton et al., 1996; Ehrsson et al., 2000, 2001; Kuhtz-Buschbeck et al., 2001; Ward and Frackowiak, 2003; Cavina-Pratesi et al., 2007; Vaillancourt et al., 2007; Matsuda et al., 2009; Kurniawan et al., 2010; Fiehler et al., 2011; Hong and Jang, 2011; Martin et al., 2011; Neely et al., 2011; Glover et al., 2012; Makuuchi et al., 2012; Nathan et al., 2012b; Begliomini et al., 2014; Fabbri et al., 2014, 2016; Plata et al., 2014; Monaco et al., 2015; Leo et al., 2016; Przybylski and Króliczak, 2017; Ariani et al., 2018; Cavina-Pratesi et al., 2018; Styrkowiec et al., 2019; Marneweck and Grafton, 2020; Sulpizio et al., 2020; Bencivenga et al., 2021; Errante et al., 2021b; Michalowski et al., 2022). These results must be interpreted in light of several methodological and biological factors: Milner et al., for example, reported that younger participants showed greater deactivation of ipsilateral M1 during hand grip compared to elderly volunteers in both dominant and non-dominant limbs (Milner et al., 2007).

The parietal lobe comprehends areas specialized in encoding and modulating reach- and grasp-related fine hand movements: human medial parietal sulcus (mIPS) and superior parieto-occipital cortex (SPOC) have been proposed as homologs for V6 area complex found in macaques (Pitzalis et al., 2013). Recent studies confirmed the specific role of SPOC area in visually-aided reaching movements (Gallivan et al., 2009; Cavina-Pratesi et al., 2010): its activation during reaching subserves subject hand preferences and stimulus location in peripersonal space. Indeed, left-handers commonly employ both hands when asked to reach and grasp a target object, while right-handers showed a segregation toward a dominant hand use. This preference is also extended to the peripersonal workspace within range of action: right-handers respond with higher SPOC activation when the object is presented within range of their dominant limb, while left-handers exhibit response to objects within range of both sides. Irrespective of hand preference, bilateral SPOC and left precuneus showed visual field polarization, activating most when the object was presented in the inferior visual field. The anterior precuneus (aPCu) is significantly activated during both visually-aid and reaching in the dark, suggesting it does not act as an associative visual area but modulates both visuomotor and sensorimotor input transformation for reaching execution (Filimon, 2010; Gatti et al., 2015). Within this dorsomedial parietal region, the presentation of affordable objects in different visual field sectors elicit reproducible selective activations, not discordant from what is observed in animals: mIPS—which is strongly interconnected with caudal PMd through the dorsomedial stream, is more activated during object reaching execution within the central visual field, while a wider bilateral perieto-occipital junction (POJ) area is elicited during peripheral vision engagement. The latter showed selective connectivity to the rostral PMd (Prado et al., 2005).

In homology with animal models, ventrolateral parietal areas are involved in grasping encoding and performance, namely the rostral part of the lateral bank of IPS (aIPS) and the supramarginal gyrus (SMG; Frey et al., 2005; Culham and Valyear, 2006; Filimon, 2010). An additional area in the superior parietal lobule (SPL) seems related to grasping encoding but overlaps with SPOC, precuneus and mIPS areas. All these share activation signals with PMd in fMRI studies (Tunik et al., 2008; Gallivan et al., 2011b; Fabbri et al., 2014) and co-activate during complex 3D object haptic manipulation (either with and without proper grasping task), together with the right SPL, aIPS, anterior SMG (aSMG) and area SII in the parietal operculum (Jancke, 2001). The parietal opercular region comprehends peculiar associative somatosensory areas, namely OP1 and OP4, as extensive components of the “human grasping/praxis network” (Eickhoff et al., 2006, 2007). According to fMRI data, OP1 and OP4 are concurrently activated during tactile hand stimulation, motor execution and are related to fine object manipulation for target recognition with and without visual aid (Eickhoff et al., 2007; Burton et al., 2008). Further TMS evidence confirmed their involvement in haptic working memory during object identification in the darkness and grasping motor scheme programming. In light of the previous evidence, it has been proposed that OP1 and OP4 are the human homologs for parietal area SII in macaques (human SII).

A connectivity gradient is present in the parietal areas related to the object manipulation network: more dorsal cortical areas – showing higher connectivity with posterior visual areas – might be involved in special processing of visual input integration for action planning when the target object is presented outside the range of action. On the contrary, parieto-ventral areas activation might imply remapping of motor behaviors encoding peripersonal space affordance depending on other than visual sensorial feedback, confirming the consistency with the lateral grasping network model described in monkeys and its dual streams partial segregation (Gallivan et al., 2011a; Renzi et al., 2013; Rossit et al., 2013; Monaco et al., 2017).

The cortico-cortical connections between frontal nodes and PPC within the network consist of long-range association bundles, namely the superior longitudinal fasciculus (SLF) and the arcuate fasciculus (AF) bilaterally. Both injective tracers in monkeys and non-invasive imaging techniques in both species described the anatomical substrate of this densely interconnected network. Probabilistic DTI tractography, among all, permitted an accurate virtual definition of the dorsal stream bundles anatomy, comprehending the classification of its subdivisions (SLF I, II, III) in the living human brain. According to Martino et al., SLF can be anatomically split into three independent fascicles: SLF I connects the superior frontal gyrus and anterior cingulate cortex to SPL and precuneus posteriorly (Martino et al., 2013). SLF II binds the posterior part of the superior and middle frontal gyri and the caudal part of IPL (aIPS and AG). Finally, SLF III connects the inferior frontal gyrus (IFG-BA44, comprehending vPM) to aIPS and IPL. In the interest of simplification, it might be assumed that SLF I represents the cortico-cortical connections of the so-called dorsomedial frontoparietal pathway.

In contrast, the SLF II and SLF III represent the subcortical pathway of the ventrolateral circuit. Finally, the recent description of an oblique frontal white matter bundle, namely the frontal aslant tract (FAT) was proposed as a substrate for the interconnectivity between PMv/IFG and pre-SMA/proper SMA, which are consistently associated with activation related to high-order motor cognition and motor response inhibition (Nachev et al., 2008; Catani et al., 2012; Rojkova et al., 2016).

The temporo-occipital regions in humans and macaques carry structural and functional discrepancies, which complicate a direct comparison between the species, as a direct topographical correspondence is unreliable, given the greater representation of highly associative temporo-occipital areas in the evolution of Homo sapiens compared to primates. Nevertheless, several fMRI studies located probable homologs: the lateral occipital cortex (LOC) is located ventral to the human homolog motion-sensitive middle temporal area (MT) within the posterior part of the inferior temporal (ITG) and fusiform gyrus (FG). LOC is activated during visual processing of shape, faces, action identification and object dimensions relevant to grasping goals among other functions (Monaco et al., 2011); the latter proves LOC to be the human homolog of area TEa/m and surrounding sectors in macaques (Malach et al., 1995; Bell et al., 2009). FMRI studies described the co-activation of AIP, PMv and LOC during object-oriented action planning, with specific temporospatial patterns of activation correlated to hand activation schemes in MVPA analyses (Gallivan et al., 2013). Moreover, LOC elaborates visual and haptic object representations, defining a multimodal object identity, which is further processed in hAIP (Verhagen et al., 2008).

The human homolog of macaque area STP is rostral to MT and dorsal to LOC in humans: it comprehends a broad region within the posterior superior temporal sulcus (pSTS) and middle temporal gyrus (MTG; Allison et al., 2000). While LOC activates more during visualization of different object configurations, pSTS/MTG sector is more related to the kinematic features of target actions and is implicated in the human mirror network as a visual action information processing node (for additional information, see Caspers et al., 2010; Rizzolatti et al., 2014).

Similarly, the exploration of unusual object shapes activates foveal cortex even when shapes have been explored with haptic feedback in the dark, while retinotopic V1 cortex activates when shapes are visualized, eliciting neural activity within the cortical location of the target in the visual field during observation. These findings imply that storage of object perception depends on sensorial modality employed to explore the object itself at the first exposure. Still, tactile hand exploration consistently reactivates early visual cortex (EVC), LOtv, aIPS and PMd irrespective of the sensory modality implemented during the first exploration. This suggests that these areas collect an abstract representation of the object of interest, recalled even when the task is performed in complete darkness (Monaco et al., 2017). Several authors concord that action imagery cannot explain these results, and further studies are necessary to elucidate these results.

The associative temporo-parieto-occipital fibers of the IPL to the posterior STG, MTG and ITG in the human brain are abundant and represented mainly by the posterior segment of the AF or the posterior vertical segment of SLF according to the description by Catani and colleagues. However, an additional pathway interconnecting SMG and STG is carried by the middle longitudinal fasciculus (MdLF; Catani et al., 2005; Martino et al., 2013; Makris et al., 2017). The latter might reproduce the counterpart Tea/m-AIP and STP-PFG connectivity previously described in macaques, confirming the role of these areas in the grasping/action recognition network.

The long-range direct prefrontal-inferotemporal connectivity wire is still a matter of debate. In humans, the inferior fronto-occipital fasciculus (IFOF) might be responsible for direct connectivity between prefrontal areas (i.e., MFG) and LOC, resembling the inter-connectivity documented in macaques between VLPF and the inferotemporal region. Notably, the internal long direct segment of the AF described by Catani et al. represents an additional inferotemporal-prefrontal dorsal connection with no homolog in primates’ brains (Catani et al., 2005; Rilling et al., 2008). Despite evidence from diffusion imaging studies and intraoperative findings during awake surgery attributing this role to the IFOF, there is no such unequivocal evidence to exclude the involvement of AF.

4.2.2 Cortico-subcortical connections and network nodes in humans

Beyond cortico-cortical connections elicited during motor programming and performance of reach-to-grasp and manipulative actions generally included in the network definition, additional subcortical nodes are recognized to play a relevant modulating role. The striatum represents the primary output node within the basal ganglia, and its activation has been measured during hand movements: it receives afferents from frontal, parietal and temporal cortex areas and relays them on the thalamus and brainstem and onto the pallidum for backpropagation to the cortex (Haber, 2003). The frontal lobe connections constitute an ensemble of segregated functional fields with a high degree of overlap documented in humans. Evidence from previous investigations highlighted that the orbitofrontal cortex (OFC) and medial prefrontal cortex (MPFC) – involved in motivational content information for decision-making purposes-connect with the ventral striatum (Kurniawan et al., 2010), while DLPFC projections were found within the central striatum overlapping with OFC-PCFC fields and the dorsolateral portions of putamen and caudate, interconnected with premotor and motor areas (Draganski et al., 2008). The ventral striatum is involved in motivational context analysis for motor performance, while the dorsolateral striatum in pure motor control with substantial overlapping relays among these two nodes (Alexander et al., 1990; Francois et al., 1994; Saleem et al., 2002; Pope et al., 2005; Lehéricy et al., 2006; Vaillancourt et al., 2007).

Furthermore, basal ganglia are co-activated during motor tasks, not strictly requiring a reach or grasp goal but only manipulation of tridimensional objects under visual or haptic sensory guidance. FMRI investigations proved that the Putamen, the Caudate nucleus (CdN), Globus Pallidus (GP) and STN are involved in performing complex hand tasks requiring a precision grip compared to whole hand movements. Marangon et al. found a specific bilateral activation of the putamen and GP during such tasks (with more intense activation peaks in the contralateral hemisphere to the performing hand, however), as later confirmed by Errante and Fogassi during the execution of a skilled manipulation task compared to simple finger tapping (Marangon et al., 2016; Errante and Fogassi, 2019). The subthalamic nucleus (STN) has been investigated with single unit activity recording in patients undergoing deep brain stimulation (DBS) surgery, confirming the involvement of this nucleus in grip force control, suggesting that the basal ganglia might modulate grip properties during grasping tasks, in line with fMRI findings (Vaillancourt et al., 2004; Grafton, 2010; Grafton and Tunik, 2011). The basal ganglia, co-activated with cortical sites, may prosecute the spatiotemporal representation of the hand during object manipulation. Moreover, basal ganglia control movement sequence programming and are involved when a specific sequence of manipulative movements has to be coordinated for a goal-oriented action (Lehéricy et al., 2005; Garr, 2019).

The ventral thalamic nuclei work as relay units for basal ganglia output pathways following a semi-segregated topographical and hierarchical structure discussed before (Hoover and Strick, 1993; Matelli and Luppino, 1996). As reported for frontostriatal connectivity, an analog convergence among thalamic output fibers toward the frontal cortex was demonstrated: medial dorsal (MD) nuclear projections, for example, are primarily directed toward MPFC and OFC areas while ventral anterior (VA) nuclear projections to PMv and PMd, with a coherent overlapping field of nuclear areas projecting to both MPFC-OFC and premotor areas and similarly to both premotor areas and M1. An integrative role for thalamic nuclei has been postulated in the past, especially considering that cortico-thalamic connections outnumber the thalamocortical projections by several orders of magnitude, making it counterintuitive that ventral thalamic nuclei merely relay information back to the cortex (Sherman and Guillery, 1996; Darian-Smith et al., 1999). This hypothesis is also supported by the identification of direct projections between the thalamus and striatum, as proposed by McFarland and Haber and later reproduced in diffusion tractography. The latter confirms the dual (relay-overlay) role of ventral anterior and ventrolateral thalamic nuclei through their vast connections with cortical, striatal and pallidal regions (Behrens et al., 2003; Johansen-Berg et al., 2005). According to this anatomical evidence, it has been proposed that they project but also integrate motor information with dorsal striatal output, which in turn receives direct input from a larger spectrum of cortical areas, contributing to modulate features of motor programming and online control during dexterous motor tasks (i.e., limb transport, grasp and fine object manipulation).

Finally, the cerebellum plays an adjunct role in motor control of reach-to-grasp movements and hand manipulation tasks in humans: it contains a somatotopic motor map of the hand within lobules IV, V and V mainly, and several virtual lesion studies described disturbance of either reaching, grasping or object manipulation (Nitschke et al., 1996; Rand et al., 2000; Grodd et al., 2001; Zackowski et al., 2002; Holmström et al., 2011). Milner et al. and Errante and Fogassi reported similar activation in the anterior and posterior cerebellar cortex within lobules V, VI and VIII-VIIIb during complex hand manipulation tasks with the dominant hand, confirming previous evidence by Schmamann et al. about the somatotopic representation of both distal arms in the cerebellum for hand manipulation performance (Milner et al., 2007; Errante and Fogassi, 2019; Stoodley et al., 2021). Recent imaging studies collected evidence of reported activation within the dorsal and ventral sectors of the dentate nucleus (DN), the main cerebellar output node to the thalamocortical pathway (projecting to parietal and premotor areas (Dimitrova et al., 2006): DN might play a role in voluntary movement correction, irrespective of the presence of sensory (visual, haptic and others) feedback (Weeks et al., 2000). Similarly to what was demonstrated in non-human primates, anteroposterior cerebellar segregation in the nucleus interpositus (IN) and DN regarding the hand-arm representation seems conceivable, with hand skills encoding being activated more anteriorly while limb transport and lift encoding (i.e., reaching) are more posteriorly represented (Mason et al., 1998).

4.3 Query 2: Intraoperative translation

The description of standard DES protocols for motor and cognitive mapping is beyond the aim of the current review (for additional information, see Duffau, 2021; Rossi et al., 2021).

Brain mapping has a centenarian history, starting more than a century ago with the pioneering studies on monkeys by Sherrington, who first described the organization of the Rolandic cortex using DES (Sherrington, 1906). About 30 years later, Penfield and Boldrey demonstrated a somatotopic segregation of the sensory-motor system in humans during awake surgeries in patients with brain tumors (Penfield and Boldrey, 1937). The stimulation of the sensorimotor cortex showed the existence of a “body shape-like” distribution of motor responses for the face, upper and lower limb (i.e., the “Penfield homunculus”) from lateral to medial near the central sulcus. Later evidence suggested a revisited somatotopic organization of the precentral gyrus with different stimulation paradigms: Roux et al. confirmed a medio-lateral gradient for positive motor sites in patients with intact motor systems (Roux et al., 2020). He demonstrated a substantial inter-subject consistency for eliciting simple or stereotyped movement of wrist, hand, global or individual fingers other than oro-facial muscles (evaluated as behavioral responses with no EMG recording) applying a low frequency bipolar DES on the precentral gyrus. These findings corroborate what described by Fornia and colleagues, who described an organized medio-lateral somatotopy in highly elicitable sectors within M1 but a more heterogeneous distribution of positive sites in the premotor cortex with longer response latencies and overall reduced excitability during high frequency DES and EMG recording. Accordingly, the authors presented evidence for a “transition oro-hand zone” localized in the ventrolateral premotor cortex, where output contraction of multiple muscles was more represented compared to M1 (Fornia et al., 2018). A similar concept has been proposed for the somatosensorial cortex and sensorimotor pathway: however, these conclusions are still a matter of debate (Dejerine, 1895; Duffau et al., 2003; Roux et al., 2018). Relevant similarities across the species have been documented also in the motor system sub-structure. Rathelot et al. first described a segregation within primary motor area in macaques according to intrinsic characteristics in terms of excitability during direct electrical stimulation; indeed, M1 is clustered in a caudal sector exhibiting higher excitability (“new M1”) compared to its rostral sector (“old M1”). Most corticomotoneuronal fibers project from new M1 and are fast-conducting projections, while old M1 originate a smaller proportion of corticospinal fibers with lower conductive properties (Rathelot and Strick, 2009).

Viganò et al. reported heterogeneous responses during DES motor mapping over hand knob area in patients undergoing surgery for brain tumors; a rostrocaudal gradient within the hand-knob region was described (the caudal sectors showed higher excitability when compared to the rostral ones; Viganò et al., 2019). This suggests a distinct contribution of these areas to the corticospinal tract. The rostral hand-knob might correspond to the monkeys’ old-M1 (however, this interpretation cannot be supported by architectonical data in the same patients) or, alternatively, to a motor transition area between M1 and PMd, explaining its lower excitatory profile and coherently with fMRI data showing a partial overlap of the premotor cortices and M1 on the convexity of the PreCG.

Even though the consistency of the previous evidence, the continuous somatotopic homunculus has been questioned since then and several methods suggested that M1 is interrupted by regions spreaded around isolated effector-specific (foot, hand, mouth) motor areas with a distinct connectivity pattern, structure and function. Gordon and colleagues recently described the coexistence of an effector-specific circuit constituted by concentric M1 sectors for precise, isolated movements of tongue, fingers and toes for dexterous movement and speech, and a second integrative system, namely the “somato-cognitive action network (SCAN),” interconnecting the motor effector sites and the cingulo-opercular network (CON) for whole body movement planning, neurovegetative preconditioning control, arousal, error correction and pain response among others (Gordon et al., 2023). According to these assumptions, the regions for foot, hand and mouth fine motor movements are, in fact, somatotopically-oriented with concentric architecture (distal appendices at their core, and proximal structures along the perimeter), while the inter-effector sectors coordinates motor-specific areas with the CON to execute whole-body performances.

Exploring behavioral responses during DES stimulation of premotor areas, Penfield and Jasper identified sites (“negative motor areas”) of motor arrest without loss of consciousness (negative motor responses, NMRs) located in the posterior part of IFG (likely PMv) and pre-SMA (Luders et al., 1987; Penfield, 1954). Several modern studies investigated the functional cortical and subcortical anatomy of sites responsible for NMRs (Schucht et al., 2013; Rech et al., 2014, 2019; Monticelli et al., 2020). The superior frontostriatal tract (sFST), running from SMA to the caudate head, might play a role in the motor control network, together with the frontal aslant tract (FAT), which connects pre-SMA and IFG; the latter, however, would be more relevant in the face and speech motor initiation and control, following the rostrocaudal somatotopy within the pre-SMA area (Fontaine et al., 2002; Catani et al., 2012; Rech et al., 2016, 2019, 2020). Confirming the essential role in motor control (comprehending initiation and inhibition to move) within the dorsomedial stream of object-oriented hand manipulation network, Rech and colleagues proved that the preservation of these cortico-subcortical motor connections could prevent permanent motor and hand-coordination deficits, even though transitory speech and motor disturbances were experienced in the early postoperative period (Rech et al., 2014). The transient neurological impairment was reported as motor and language initiation dysfunction, bimanual coordination (Rech et al., 2014) and fine movement deficits during the first weeks after surgery (Rech et al., 2017). From a functional point of view, the elicitation of negative motor responses bears a significant limitation, as the net effect of DES on cortical surface is not entirely understood (Borchers et al., 2012). Indeed, some authors attribute to DES an inhibitory role on motor performance, while others assume that the behavioral inhibitory response is an epiphenomenon given by the perturbation of a positive ongoing motor scheme. The real contribution of SMA/pre-SMA to motor scheme execution during reach/grasp or dexterous hand manipulation tasks and the mechanism underneath is still debatable. This area has also been studied in terms of compensatory reserve after nearby resection for brain tumor removal: Rosenberg et al. reported that patients with solid preoperative fMRI activation within lesioned SMA during a motor task were less likely to experience transient disruption during DES in this area as a consequence of a possible “higher functional reserve” in the adjacent regions able to compensate for the resected portion (i.e., suggesting SMA area could be resected entirely; Rosenberg et al., 2010). Also, they found higher connectivity of the lesioned SMA with other ipsilateral and contralateral cortical regions during fMRI acquisition, ipsilateral M1 and contralateral SMA, among others. When such coupling was absent, patients were more likely to experience transitory functional deficits during DES (suggesting SMA was still functional and wide resection would have induced severe SMA syndrome), driving surgical considerations on extending the resection. However, contralateral SMA activation might also be a clue of lost transcallosal inhibition from the damaged frontal lobe or even indicate that an insufficient compensatory mechanism is in act and some marginal deficit is already present preoperatively. Although contralateral SMA recruitment is associated with functional compensation and faster recovery in stroke patients, SMA functional reorganization in patients with brain tumors and DES-induced SMA functional disruption during awake mapping is poorly predictable based on fMRI data and further studies are demanded (Shimizu et al., 2002; Krainik et al., 2004; Rosenberg et al., 2010).

Altogether, the previous results find a sufficient analogy to animal studies, suggesting that the phenomena occurring during DES in awake conditions are results of perturbating those very functional substrates homologs implicated in object-oriented complex hand motor tasks in monkeys. The previously mentioned DES protocols are widely accepted as the gold standard in motor mapping, with the potential benefit of preserving functional integrity in patients undergoing awake or asleep surgery according to the technique employed. Nevertheless, motor cognition is frequently affected after surgery in the perirolandic area, with a prevalence of post-operative ideomotor apraxia around 30% (Rossi et al., 2018) but specific mapping techniques are far from becoming the standard of care in the neurosurgical practice. Rossi et al. recently proposed a newly developed ecological intraoperative task, namely the hand manipulation task (HMt; Rossi et al., 2018): it consists of a hand-object interaction trial with a small cylindrical handle mounted on a rectangular base resembling a worm screw. The patients were asked during awake motor mapping to grasp the handle with thumb and index finger, hold, rotate and release the object with no visual clue (i.e., haptically driven task), while DES was intermittently applied on regions of interest under EMG monitoring. The authors reported peculiar behavioral and electrical response patterns consistent with previous knowledge of the frontoparietal hand manipulation network (i.e., praxis representation network): DES applied over M1 elicited a tonic hand muscle activation and cessation of handle rotation, while stimulation on S1 caused clonic activations and release of the object. DES produced over SMG and vPM were responsible for complete movement arrest without muscle activation, and a disruption of online awareness of motor execution was documented (vPM; Fornia et al., 2020a). The authors reported a lower incidence of ideomotor apraxia irrespective of the hemisphere at 5 days (28.4% vs. 71.1%) and 1–3 months (8.8% vs. 47.4%) after surgery in patients undergoing intraoperative HMt compared to those undergoing “standard” motor protocol with no significant impact on the extent of resection. The residual percentage of patients affected by long-term apraxia were affected mainly by superior parietal lobule tumors, suggesting a lower task efficiency for mapping posterior parietal sites within the frontoparietal praxis network (especially along the dorsomedial pathway). As the parietal lobe is responsible for multisensorial integration during action programming and execution, plural sensory input might be necessary to elicit an intraoperative response; in this view, haptically-driven HMt might underperform during awake mapping of SMG compared to a visual-aid dexterity task.

Moreover, Fornia et al. provided quantitative evidence of the effect of DES on premotor areas through EMG recording during the execution of the HMt (Fornia et al., 2020b). They reported a complete arrest of movement during HMt when DES was applied over vPM, characterized by a complete and sharp arrest of motor scheme execution and muscle suppression in several or even all muscles involved, especially in the dorsal vPM sector. Several sites of DES on vPM elicited also a partial impairment with execution failure described as “clumsy-like,” characterized at the EMG by a partial and variable impairment of muscle contraction. Interestingly and consistent with its direct kinematic control role, DES on dPM elicited first an early suppression followed by a progressive muscle recruitment. This response was not registered in any other premotor sector. Viganò et al., in the dorsalmost aspect of dPM corresponding to the anterior hand knob sector, reported mixed responses in terms of EMG response patterns: these ranged from complete muscle suppression to mixed suppression-recruitment phenomena with segregated responses between distal and proximal muscles (Figure 7; Viganò et al., 2019).

The authors suggested that the arrest pattern might be related to the perturbation of neural areas directly implicated in motor output execution, both in vPM and dPM. In contrast, the perturbation of vPM alone might disrupt sensorimotor integration for online control of hand movements, causing a clumsy-like behavior. Vigano et al. later reviewed this evidence to identify those subcortical frontal connections involved in the dexterous hand motor control during the performance of the HMt in awake patients (Viganò et al., 2022). The author concluded that transient perturbation of short-range premotor mid-U-shaped fibers, SLF I and II within the dorsomedial stream, the sFST and corticospinal projections of dPM and SMA was related to the complete arrest pattern during DES. At the same time, stimulation on the inferior frontostriatal tract (iFST), arcuate fasciculus and SLF III within the dorsolateral stream was preferentially associated with the clumsy response pattern. The arrest pattern in the more dorsal subcortical stream might reflect an inactivation of nodes more proximal to the motor output: in fact, SMA and dPM have direct projections to the spinal cord and both direct/indirect connections with M1 through local premotor-motor U fibers and longer striato-thalamo-cortical loops. Moreover, this hypothesis might justify the presence of short-term post-operative upper limb motor deficits after resection of the dorsal but not of the ventral frontal region.

Similar response patterns were described during mapping of the posterior parietal cortex; a medial cluster of activations sites elicited complete motor arrest as previously reported in the frontomedial cortical region, while a more lateral cluster of stimulation points evoked a clumsy-like behavioral response during HMt. Again, the arrest pattern might represent the temporary disruption of a hierarchically higher node of the frontoparietal praxis network directly (or more directly) involved in the computation of motor output. On the contrary, clumsy response pattern might indicate a disruption in sensorimotor integration necessary for any dexterous hand movement requiring haptic control. Notably, the medial cluster comprehended areas connected with the angular gyrus and SPL, while the lateral cluster with the anterior SMG (Fornia et al., 2023). Finally, the majority of stimulation sites were identified within hAIP and, secondarily, the adjacent dorsal sector in the anterior intraparietal sulcus (DIPSA), corresponding to the anterior motor-dominant and posterior visuo-dominant homologs of monkey AIP (Figure 7; Orban, 2016).

Rolland and colleagues collected additional evidence on a series of patients harboring tumors adjacent to the right IPL (Rolland et al., 2018). In their series, patients were asked to perform simple repetitive movements of the left upper limb (flexion of the arm, wrist, and fingers, then the extension of the arm with the open hand and fingers for 4 s), together with an additional test (naming test, non-verbal semantic, visuospatial etc.). Awake mapping allowed testing of complex movements, motor control during action and even bimanual coordination. The authors reported transient fine motor impairment in one patient only, with complete recovery within 3 months after surgery, achieving total or subtotal resection in 13 cases. In line with previous experiences, Rolland et al. confirmed the vital importance of preserving cortical and subcortical somatosensory pathways detected by intraoperative DES in awake conditions to prevent pure motor deficits and perirolandic fronto-parietal areas to prevent complex motor behavior impairment.

In asleep setting, a similar conclusion was drawn by Cattaneo and colleagues in a cohort of 17 patients undergoing brain tumor surgery in the parietal lobe (Cattaneo et al., 2020). They applied dual strip DES to the hand-M1 area and the parietal cortex, recording abductor pollicis brevis (APB) MEP responses after posterior parietal cortex conditioning stimulation (additional details on stimulation paradigm are available in Cattaneo et al. (2020). Their results suggest the existence of a direct parietal-motor functional connection with short-latency modulating properties, with two distinctive clusters according to their effect on motor output: a ventral region corresponding to the part of SMG immediately posterior to the inferior postcentral gyrus to the parietal operculum eliciting an excitatory effect on MEPs, and a dorsal cluster within the SPL responsible of an overall inhibitory effect on MEP output. These findings, however, are not directly comparable to those previously discussed in awake patients, as motor responses obtained in the setting provided by Cattaneo et al. in patients administered with propofol regimen depend on mono- and oligo-synaptic connections only, with no clue on the actual effect of multi-synaptic connections on the functioning of these regions, which are suppressed by anesthesia.

Although our elaborated review focused on complex hand motor behaviors, it is understood that encoding and performance of object-oriented dexterous tasks come together with additional high hierarchical cognitive outputs (the definition of a goal, the social context surrounding the hand motor act being computed, the elaboration of the environmental effect of such action among all). During any identifiable task, a spatiotemporal integration of extensive but significantly specialized networks occurs through a dynamic interaction responsible for the continuous redefinition of equilibrium states and the definition of complex behavioral responses. The theory behind these assumptions, the so-called “meta-networking theory,” would represent one of the reasons for the interindividual behavioral variability we can assess in humans, but also explain the attitude to acquire complex abilities compared to non-human networks and neuroplastic phenomena after injury (Herbet and Duffau, 2020). Compared to previous assumptions related to object-oriented hand dexterous skills, a direct translation of evidence from non-human primate models would be of limited use. Identifying appropriate protocols to address such complexity intraoperatively is still under investigation and is beyond the aim of the current study.

The real-time neuropsychological testing (RTNT) has been proposed as a complementary tool to DES during surgical resection to continuously monitor the patient’s overall neuropsychological status throughout the resection phase. RTNT consists of sequential runs of awake neuropsychological assessments performed during surgery: Tomasino et al. proposed a tailored RTNT protocol for resection in the perirolandic and posterior parietal cortex within the frontoparietal praxis network including the Handedness decision task (HDT), the Florida Praxis Imagery Questionnaire (FPIQ), the Action verb naming (AVN) task, the Kissing and Dancing test (KDT) and the Buccofacial praxis and ideomotor praxis (BP/IMP) tests (additional information is available in Tomasino et al. (2022). This approach’s primary goal is to compensate for the risk of so-called negative mapping during DES (previously discussed); the absence of DES-induced transitory perturbance, in fact, might not accurately predict the “non-functionality” of a specific area. RTNT identified a significant decrease in performance during mental rotation of body parts (HDT) and action imagery (FPIQ) testing: for the latter, resection adjacent to the right postcentral gyrus, SMA, SPL and IPL were accounted, while decreased performance in HDT was more likely experienced during resection of the right cingulum, SMA left SPL, left IPL and medial precuneus. No alterations in the remaining cognitive domains were identified during RTNT in areas within the frontoparietal praxis network, suggesting the presence of a wider compensatory distribution. Compared to previous studies investigating the exact cognitive domains with no RTNT, Tomasino et al. reported no long-term motor cognitive deficits, suggesting RTNT allows online monitoring of sensory-motor cognition in favor of a weighted oncofunctional balance (i.e., mean EOR was 91 ± 17%).

4.3.1 Limitations

In this study, we reviewed decades of pioneering investigations through a systematized selection of imaging-based studies, which we believe are more intelligible to a clinical reader—whom we hereby addressed—compared to invasive studies. However, our systematic review has several limitations to be discussed. First, we focused on the evidence provided by fMRI and PET investigations to characterize the extended grasping network in primates and humans. However, a larger body of literature involving invasive and noninvasive stimulation experiments has been discarded during our systematized search according to the selected query design. Second, a more comprehensive narrative approach was preferred in reviewing all these details in the discussion section, including referenced studies beyond those set during our PRISMA literature search to make our review as exhaustive as possible. However, we cannot guarantee that all highly relevant notions about this extended network’s cortical and subcortical structures have been included in the current manuscript. Third, we grouped all non-human primate species to draw conclusions from animal studies: we are aware of peculiarities differentiating monkeys and apes regarding brain structure and functioning. However, given the limited number of animals selected by our query and the purposes of the qualitative narration, a critical discussion of differences between them was deemed unnecessary.

From a methodological point of view, the heterogeneity of the studies discouraged any quantitative analysis: several authors investigated healthy primates and humans with a variety of behavioral contrasts and both uni- and multivariable statistical models, undermining any general comparison among them all and preventing a meta-analysis from being computed. Finally, the intraoperative mapping protocols present their peculiarities, restraining methodological comparisons to be formulated. Further studies are necessary to standardize the intraoperative brain mapping of complex motor behaviors.

5 Conclusion

We provided an updated overview of the current understanding of the extended frontoparietal object-oriented hand manipulation and complex motor behavior network, with a specific focus on the comparative functioning in non-human primates, healthy humans and how the latter knowledge has been implemented in the neurosurgical operating room during brain tumor resection. The anatomical and functional correlates we reviewed highlighted some consistencies, among several relevant differences, in the evolutionary continuum from monkeys to humans, paving the way for a cautious but practical implementation of such evidence in intraoperative brain mapping investigations. Integrating the previous results in the surgical practice might help preserve complex motor abilities, prevent long-term disability and poor quality of life and allow the maximal safe resection of intrinsic brain tumors.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

LT: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft. LM: Data curation, Methodology, Writing – original draft. LV: Conceptualization, Investigation, Methodology, Supervision, Validation, Writing – review & editing. MGal: Data curation, Writing – review & editing. MGam: Writing – review & editing. TS: Writing – review & editing. LG: Writing – review & editing. MC: Writing – review & editing. AG: Writing – review & editing. GC: Supervision, Writing – review & editing, Resources. LB: Resources, Supervision, Writing – review & editing. MR: Resources, Supervision, Writing – review & editing, Conceptualization, Methodology.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The work was supported by funds from PRIN - Bando 2022 PNRR (project no. P20228K3W9) to MR.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnint.2024.1324581/full#supplementary-material

Footnotes

1. ^https://nitrc.org/projects/inia19/

References

Alexander, G. E., and Crutcher, M. D. (1990). Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci. 13, 266–271. doi: 10.1016/0166-2236(90)90107-L

PubMed Abstract | Crossref Full Text | Google Scholar

Alexander, G. E., Crutcher, M. D., and DeLong, M. R. (1990). Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, ‘prefrontal’ and ‘limbic’ functions. Prog. Brain Res. 85, 119–146.

PubMed Abstract | Google Scholar

Allison, T., Puce, A., and McCarthy, G. (2000). Social perception from visual cues: role of the STS region. Trends Cogn. Sci. 4, 267–278. doi: 10.1016/S1364-6613(00)01501-1

PubMed Abstract | Crossref Full Text | Google Scholar

Andersen, R. A., and Buneo, C. A. (2002). Intentional maps in posterior parietal cortex. Annu. Rev. Neurosci. 25, 189–220. doi: 10.1146/annurev.neuro.25.112701.142922

PubMed Abstract | Crossref Full Text | Google Scholar

Andersen, R. A., Snyder, L. H., Bradley, D. C., and Xing, J. (1997). Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annu. Rev. Neurosci. 20, 303–330. doi: 10.1146/annurev.neuro.20.1.303

PubMed Abstract | Crossref Full Text | Google Scholar

Arbib, M. A., and Mundhenk, T. N. (2005). Schizophrenia and the mirror system: an essay. Neuropsychologia 43, 268–280. doi: 10.1016/j.neuropsychologia.2004.11.013

PubMed Abstract | Crossref Full Text | Google Scholar

Ariani, G., Oosterhof, N. N., and Lingnau, A. (2018). Time-resolved decoding of planned delayed and immediate prehension movements. Cortex 99, 330–345. doi: 10.1016/j.cortex.2017.12.007

PubMed Abstract | Crossref Full Text | Google Scholar

Aron, A. R. (2009). Introducing a special issue on stopping action and cognition. Neurosci. Biobehav. Rev. 33, 611–612. doi: 10.1016/j.neubiorev.2008.10.003

Crossref Full Text | Google Scholar

Baldassarre, G., Caligiore, D., and Mannella, F. (2013). “The hierarchical organisation of cortical and basal-ganglia systems: a computationally-informed review and integrated hypothesis” in Computational and robotic models of the hierarchical Organization of Behavior (Berlin Heidelberg: Springer), 237–270.

Google Scholar

Battaglini, P. P., Muzur, A., Galletti, C., Skrap, M., Brovelli, A., and Fattori, P. (2002). Effects of lesions to area V6A in monkeys. Exp. Brain Res. 144, 419–422. doi: 10.1007/s00221-002-1099-4

PubMed Abstract | Crossref Full Text | Google Scholar

Baylis, G., Rolls, E., and Leonard, C. (1987). Functional subdivisions of the temporal lobe neocortex. J. Neurosci. 7, 330–342. doi: 10.1523/JNEUROSCI.07-02-00330.1987

PubMed Abstract | Crossref Full Text | Google Scholar

Begliomini, C., Caria, A., Grodd, W., and Castiello, U. (2007a). Comparing natural and constrained movements: new insights into the Visuomotor control of grasping. PLoS One 2:e1108. doi: 10.1371/journal.pone.0001108

PubMed Abstract | Crossref Full Text | Google Scholar

Begliomini, C., de Sanctis, T., Marangon, M., Tarantino, V., Sartori, L., Miotto, D., et al. (2014). An investigation of the neural circuits underlying reaching and reach-to-grasp movements: from planning to execution. Front. Hum. Neurosci. 8:676. doi: 10.3389/fnhum.2014.00676

PubMed Abstract | Crossref Full Text | Google Scholar

Begliomini, C., Nelini, C., Caria, A., Grodd, W., and Castiello, U. (2008). Cortical activations in humans grasp-related areas depend on hand used and handedness. PLoS One 3:e3388. doi: 10.1371/journal.pone.0003388

PubMed Abstract | Crossref Full Text | Google Scholar

Begliomini, C., Sartori, L., Miotto, D., Stramare, R., Motta, R., and Castiello, U. (2015). Exploring manual asymmetries during grasping: a dynamic causal modeling approach. Front. Psychol. 6:167. doi: 10.3389/fpsyg.2015.00167

PubMed Abstract | Crossref Full Text | Google Scholar

Begliomini, C., Wall, M. B., Smith, A. T., and Castiello, U. (2007b). Differential cortical activity for precision and whole-hand visually guided grasping in humans. Eur. J. Neurosci. 25, 1245–1252. doi: 10.1111/j.1460-9568.2007.05365.x

PubMed Abstract | Crossref Full Text | Google Scholar

Behrens, T. E. J., Johansen-Berg, H., Woolrich, M. W., Smith, S. M., Wheeler-Kingshott, C. A. M., Boulby, P. A., et al. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750–757. doi: 10.1038/nn1075

PubMed Abstract | Crossref Full Text | Google Scholar

Bell, A. H., Hadj-Bouziane, F., Frihauf, J. B., Tootell, R. B. H., and Ungerleider, L. G. (2009). Object representations in the temporal cortex of monkeys and humans as revealed by functional magnetic resonance imaging. J. Neurophysiol. 101, 688–700. doi: 10.1152/jn.90657.2008

PubMed Abstract | Crossref Full Text | Google Scholar

Belmalih, A., Borra, E., Contini, M., Gerbella, M., Rozzi, S., and Luppino, G. (2009). Multimodal architectonic subdivision of the rostral part (area F5) of the macaque ventral premotor cortex. J. Comp. Neurol. 512, 183–217. doi: 10.1002/cne.21892

PubMed Abstract | Crossref Full Text | Google Scholar

Bencivenga, F., Sulpizio, V., Tullo, M. G., and Galati, G. (2021). Assessing the effective connectivity of premotor areas during real vs imagined grasping: a DCM-PEB approach. Neuroimage 230:117806. doi: 10.1016/j.neuroimage.2021.117806

PubMed Abstract | Crossref Full Text | Google Scholar

Bennett, C., González, M., Tapia, G., Riveros, R., Torres, F., Loyola, N., et al. (2022). Cortical mapping in glioma surgery: correlation of fMRI and direct electrical stimulation with Human Connectome Project parcellations. Neurosurg. Focus. 53:E2. doi: 10.3171/2022.9.FOCUS2283

Crossref Full Text | Google Scholar

Binkofski, F., Buccino, G., Posse, S., Seitz, R. J., Rizzolatti, G., and Freund, H. J. (1999b). A fronto-parietal circuit for object manipulation in man: evidence from an fMRI-study. Eur. J. Neurosci. 11, 3276–3286. doi: 10.1046/j.1460-9568.1999.00753.x

PubMed Abstract | Crossref Full Text | Google Scholar

Binkofski, F., Buccino, G., Stephan, K. M., Rizzolatti, G., Seitz, R. J., and Freund, H. J. (1999a). A parieto-premotor network for object manipulation: evidence from neuroimaging. Exp. Brain Res. 128, 210–213. doi: 10.1007/s002210050838

PubMed Abstract | Crossref Full Text | Google Scholar

Bonini, L., Maranesi, M., Livi, A., Fogassi, L., and Rizzolatti, G. (2014). Space-dependent representation of objects and Other’s action in monkey ventral premotor grasping neurons. J. Neurosci. 34, 4108–4119. doi: 10.1523/JNEUROSCI.4187-13.2014

PubMed Abstract | Crossref Full Text | Google Scholar

Bonini, L., Ugolotti Serventi, F., Simone, L., Rozzi, S., Ferrari, P. F., and Fogassi, L. (2011). Grasping neurons of monkey parietal and premotor cortices encode action goals at distinct levels of abstraction during complex action sequences. J. Neurosci. 31, 5876–5886. doi: 10.1523/JNEUROSCI.5186-10.2011

PubMed Abstract | Crossref Full Text | Google Scholar

Borchers, S., Himmelbach, M., Logothetis, N., and Karnath, H.-O. (2012). Direct electrical stimulation of human cortex — the gold standard for mapping brain functions? Nat. Rev. Neurosci. 13, 63–70. doi: 10.1038/nrn3140

PubMed Abstract | Crossref Full Text | Google Scholar

Borra, E., Gerbella, M., Rozzi, S., and Luppino, G. (2011). Anatomical evidence for the involvement of the macaque ventrolateral prefrontal area 12r in controlling goal-directed actions. J. Neurosci. 31, 12351–12363. doi: 10.1523/JNEUROSCI.1745-11.2011

PubMed Abstract | Crossref Full Text | Google Scholar

Borra, E., Gerbella, M., Rozzi, S., and Luppino, G. (2017). The macaque lateral grasping network: a neural substrate for generating purposeful hand actions. Neurosci. Biobehav. Rev. 75, 65–90. doi: 10.1016/j.neubiorev.2017.01.017

PubMed Abstract | Crossref Full Text | Google Scholar

Borra, E., and Luppino, G. (2019). Large-scale temporo–parieto–frontal networks for motor and cognitive motor functions in the primate brain. Cortex 118, 19–37. doi: 10.1016/j.cortex.2018.09.024

PubMed Abstract | Crossref Full Text | Google Scholar

Bosco, A., Breveglieri, R., Chinellato, E., Galletti, C., and Fattori, P. (2010). Reaching activity in the medial posterior parietal cortex of monkeys is modulated by visual feedback. J. Neurosci. 30, 14773–14785. doi: 10.1523/JNEUROSCI.2313-10.2010

PubMed Abstract | Crossref Full Text | Google Scholar

Bostan, A. C., Dum, R. P., and Strick, P. L. (2010). The basal ganglia communicate with the cerebellum. Proc. Natl. Acad. Sci. 107, 8452–8456. doi: 10.1073/pnas.1000496107

PubMed Abstract | Crossref Full Text | Google Scholar

Bostan, A. C., Dum, R. P., and Strick, P. L. (2013). Cerebellar networks with the cerebral cortex and basal ganglia. Trends Cogn. Sci. 17, 241–254. doi: 10.1016/j.tics.2013.03.003

PubMed Abstract | Crossref Full Text | Google Scholar

Bufacchi, R. J., Battaglia-Mayer, A., Iannetti, G. D., and Caminiti, R. (2023). Cortico-spinal modularity in the parieto-frontal system: a new perspective on action control. Prog. Neurobiol. 231:102537. doi: 10.1016/j.pneurobio.2023.102537

PubMed Abstract | Crossref Full Text | Google Scholar

Burton, H., Sinclair, R. J., Wingert, J. R., and Dierker, D. L. (2008). Multiple parietal operculum subdivisions in humans: tactile activation maps. Somatosens. Mot. Res. 25, 149–162. doi: 10.1080/08990220802249275

PubMed Abstract | Crossref Full Text | Google Scholar

Cabinio, M., Blasi, V., Borroni, P., Montagna, M., Iadanza, A., Falini, A., et al. (2010). The shape of motor resonance: right-or left-handed? Neuroradiol. J. 23:337. doi: 10.1016/j.neuroimage.2010.01.103

Crossref Full Text | Google Scholar

Caligiore, D., Pezzulo, G., Miall, R. C., and Baldassarre, G. (2013). The contribution of brain sub-cortical loops in the expression and acquisition of action understanding abilities. Neurosci. Biobehav. Rev. 37, 2504–2515. doi: 10.1016/j.neubiorev.2013.07.016

Crossref Full Text | Google Scholar

Caminiti, R., Ferraina, S., and Mayer, A. B. (1998). Visuomotor transformations: early cortical mechanisms of reaching. Curr. Opin. Neurobiol. 8, 753–761. doi: 10.1016/S0959-4388(98)80118-9

PubMed Abstract | Crossref Full Text | Google Scholar

Caminiti, R., Innocenti, G. M., and Battaglia-Mayer, A. (2015). Organization and evolution of parieto-frontal processing streams in macaque monkeys and humans. Neurosci. Biobehav. Rev. 56, 73–96. doi: 10.1016/j.neubiorev.2015.06.014

PubMed Abstract | Crossref Full Text | Google Scholar

Caspers, S., Zilles, K., Laird, A. R., and Eickhoff, S. B. (2010). ALE meta-analysis of action observation and imitation in the human brain. Neuroimage 50, 1148–1167. doi: 10.1016/j.neuroimage.2009.12.112

PubMed Abstract | Crossref Full Text | Google Scholar

Catani, M., Dell’Acqua, F., Vergani, F., Malik, F., Hodge, H., Roy, P., et al. (2012). Short frontal lobe connections of the human brain. Cortex 48, 273–291. doi: 10.1016/j.cortex.2011.12.001

Crossref Full Text | Google Scholar

Catani, M., Jones, D. K., and Ffytche, D. H. (2005). Perisylvian language networks of the human brain. Ann. Neurol. 57, 8–16. doi: 10.1002/ana.20319

Crossref Full Text | Google Scholar

Cattaneo, L., Giampiccolo, D., Meneghelli, P., Tramontano, V., and Sala, F. (2020). Cortico-cortical connectivity between the superior and inferior parietal lobules and the motor cortex assessed by intraoperative dual cortical stimulation. Brain Stimul. 13, 819–831. doi: 10.1016/j.brs.2020.02.023

PubMed Abstract | Crossref Full Text | Google Scholar

Cavina-Pratesi, C., Connolly, J. D., Monaco, S., Figley, T. D., Milner, A. D., Schenk, T., et al. (2018). Human neuroimaging reveals the subcomponents of grasping, reaching and pointing actions. Cortex 98, 128–148. doi: 10.1016/j.cortex.2017.05.018

PubMed Abstract | Crossref Full Text | Google Scholar

Cavina-Pratesi, C., Goodale, M. A., and Culham, J. C. (2007). FMRI reveals a dissociation between grasping and perceiving the size of real 3D objects. PLoS One 2:e424. doi: 10.1371/journal.pone.0000424

PubMed Abstract | Crossref Full Text | Google Scholar

Cavina-Pratesi, C., Monaco, S., Fattori, P., Galletti, C., McAdam, T. D., Quinlan, D. J., et al. (2010). Functional magnetic resonance imaging reveals the neural substrates of arm transport and grip formation in reach-to-grasp actions in humans. J. Neurosci. 30, 10306–10323. doi: 10.1523/JNEUROSCI.2023-10.2010

PubMed Abstract | Crossref Full Text | Google Scholar

Cerri, G., Cabinio, M., Blasi, V., Borroni, P., Iadanza, A., Fava, E., et al. (2015). The mirror neuron system and the strange case of Broca’s area. Hum. Brain Mapp. 36, 1010–1027. doi: 10.1002/hbm.22682

PubMed Abstract | Crossref Full Text | Google Scholar

Cerri, G., Shimazu, H., Maier, M. A., and Lemon, R. N. (2003). Facilitation from ventral premotor cortex of primary motor cortex outputs to macaque hand muscles. J. Neurophysiol. 90, 832–842. doi: 10.1152/jn.01026.2002

PubMed Abstract | Crossref Full Text | Google Scholar

Chapman, C. S., Gallivan, J. P., Culham, J. C., and Goodale, M. A. (2011). Mental blocks: fMRI reveals top-down modulation of early visual cortex when obstacles interfere with grasp planning. Neuropsychologia 49, 1703–1717. doi: 10.1016/j.neuropsychologia.2011.02.048

PubMed Abstract | Crossref Full Text | Google Scholar

Chapman, H., Gavrilescu, M., Wang, H., Kean, M., Egan, G., and Castiello, U. (2002). Posterior parietal cortex control of reach-to-grasp movements in humans. Eur. J. Neurosci. 15, 2037–2042. doi: 10.1046/j.1460-9568.2002.02021.x

PubMed Abstract | Crossref Full Text | Google Scholar

Chapman, H., Pierno, A. C., Cunnington, R., Gavrilescu, M., Egan, G., and Castiello, U. (2007). The neural basis of selection-for-action. Neurosci. Lett. 417, 171–175. doi: 10.1016/j.neulet.2007.02.033

Crossref Full Text | Google Scholar

Chen, C. H., Fremont, R., Arteaga-Bracho, E. E., and Khodakhah, K. (2014). Short latency cerebellar modulation of the basal ganglia. Nat. Neurosci. 17, 1767–1775. doi: 10.1038/nn.3868

PubMed Abstract | Crossref Full Text | Google Scholar

Culham, J. C., Danckert, S. L., Souza, J. F. X. D., Gati, J. S., Menon, R. S., and Goodale, M. A. (2003). Visually guided grasping produces fMRI activation in dorsal but not ventral stream brain areas. Exp. Brain Res. 153, 180–189. doi: 10.1007/s00221-003-1591-5

PubMed Abstract | Crossref Full Text | Google Scholar

Culham, J. C., and Valyear, K. F. (2006). Human parietal cortex in action. Curr. Opin. Neurobiol. 16, 205–212. doi: 10.1016/j.conb.2006.03.005

Crossref Full Text | Google Scholar

Daniel, P. M., and Whitteridge, D. (1961). The representation of the visual field on the cerebral cortex in monkeys. J. Physiol. 159, 203–221. doi: 10.1113/jphysiol.1961.sp006803

PubMed Abstract | Crossref Full Text | Google Scholar

Darian-Smith, C., Tan, A., and Edwards, S. (1999). Comparing thalamocortical and corticothalamic microstructure and spatial reciprocity in the macaque ventral posterolateral nucleus (VPLc) and medial pulvinar. J. Comp. Neurol. 410, 211–234. doi: 10.1002/(SICI)1096-9861(19990726)410:2<211::AID-CNE4>3.0.CO;2-X

PubMed Abstract | Crossref Full Text | Google Scholar

Davare, M., Andres, M., Cosnard, G., Thonnard, J.-L., and Olivier, E. (2006). Dissociating the role of ventral and dorsal premotor cortex in precision grasping. J. Neurosci. 26, 2260–2268. doi: 10.1523/JNEUROSCI.3386-05.2006

PubMed Abstract | Crossref Full Text | Google Scholar

Davare, M., Montague, K., Olivier, E., Rothwell, J. C., and Lemon, R. N. (2009). Ventral premotor to primary motor cortical interactions during object-driven grasp in humans. Cortex 45, 1050–1057. doi: 10.1016/j.cortex.2009.02.011

PubMed Abstract | Crossref Full Text | Google Scholar

de Waal, F. B. M., and Ferrari, P. F. (2010). Towards a bottom-up perspective on animal and human cognition. Trends Cogn. Sci. 14, 201–207. doi: 10.1016/j.tics.2010.03.003

PubMed Abstract | Crossref Full Text | Google Scholar

Dejerine, J. (1895). Anatomie des centres nerveux. Rueff, Paris 1.

Google Scholar

di Bono, M. G., Begliomini, C., Budisavljevic, S., Sartori, L., Miotto, D., Motta, R., et al. (2017). Decoding social intentions in human prehensile actions: insights from a combined kinematics-fMRI study. PLoS One 12:e0184008. doi: 10.1371/journal.pone.0184008

PubMed Abstract | Crossref Full Text | Google Scholar

Dimitrova, A., de Greiff, A., Schoch, B., Gerwig, M., Frings, M., Gizewski, E. R., et al. (2006). Activation of cerebellar nuclei comparing finger, foot and tongue movements as revealed by fMRI. Brain Res. Bull. 71, 233–241. doi: 10.1016/j.brainresbull.2006.09.015

Crossref Full Text | Google Scholar

Draganski, B., Kherif, F., Klöppel, S., Cook, P. A., Alexander, D. C., Parker, G. J. M., et al. (2008). Evidence for segregated and integrative connectivity patterns in the human basal ganglia. J. Neurosci. 28, 7143–7152. doi: 10.1523/JNEUROSCI.1486-08.2008

PubMed Abstract | Crossref Full Text | Google Scholar

Dressing, A., Nitschke, K., Kümmerer, D., Bormann, T., Beume, L., Schmidt, C. S. M., et al. (2018). Distinct contributions of dorsal and ventral streams to imitation of tool-use and communicative gestures. Cereb. Cortex 28, 474–492. doi: 10.1093/cercor/bhw383

PubMed Abstract | Crossref Full Text | Google Scholar

Duffau, H. (2021). New philosophy, clinical pearls, and methods for intraoperative cognition mapping and monitoring “à la carte” in brain tumor patients. Neurosurgery 88, 919–930. doi: 10.1093/neuros/nyaa363

PubMed Abstract | Crossref Full Text | Google Scholar

Duffau, H., Capelle, L., Denvil, D., Sichez, N., Gatignol, P., Taillandier, L., et al. (2003). Usefulness of intraoperative electrical subcortical mapping during surgery for low-grade gliomas located within eloquent brain regions: functional results in a consecutive series of 103 patients. J. Neurosurg. 98, 764–778. doi: 10.3171/jns.2003.98.4.0764

PubMed Abstract | Crossref Full Text | Google Scholar

Duffau, H., and Mandonnet, E. (2013). The “onco-functional balance” in surgery for diffuse low-grade glioma: integrating the extent of resection with quality of life. Acta Neurochir. 155, 951–957. doi: 10.1007/s00701-013-1653-9

Crossref Full Text | Google Scholar

Dum, R., and Strick, P. (2002). Motor areas in the frontal lobe of the primate. Physiol. Behav. 77, 677–682. doi: 10.1016/S0031-9384(02)00929-0

Crossref Full Text | Google Scholar

Dum, R. P., and Strick, P. L. (2003). An unfolded map of the cerebellar dentate nucleus and its projections to the cerebral cortex. J. Neurophysiol. 89, 634–639. doi: 10.1152/jn.00626.2002

PubMed Abstract | Crossref Full Text | Google Scholar

Ehrsson, H. H., Fagergren, A., Ehrsson, G. O., and Forssberg, H. (2007). Holding an object: neural activity associated with fingertip force adjustments to external perturbations. J. Neurophysiol. 97, 1342–1352. doi: 10.1152/jn.01253.2005

Crossref Full Text | Google Scholar

Ehrsson, H. H., Fagergren, A., and Forssberg, H. (2001). Differential Fronto-parietal activation depending on force used in a precision grip task: an fMRI study. J. Neurophysiol. 85, 2613–2623. doi: 10.1152/jn.2001.85.6.2613

Crossref Full Text | Google Scholar

Ehrsson, H. H., Fagergren, A., Jonsson, T., Westling, G., Johansson, R. S., and Forssberg, H. (2000). Cortical activity in precision- versus Power-grip tasks: an fMRI study. J. Neurophysiol. 83, 528–536. doi: 10.1152/jn.2000.83.1.528

PubMed Abstract | Crossref Full Text | Google Scholar

Eickhoff, S. B., Amunts, K., Mohlberg, H., Zilles, K. T., Operculum, H. P., and II,. (2006). Stereotaxic maps and correlation with functional imaging results. Cereb. Cortex 16, 268–279. doi: 10.1093/cercor/bhi106

PubMed Abstract | Crossref Full Text | Google Scholar

Eickhoff, S. B., Grefkes, C., Zilles, K., and Fink, G. R. (2007). The Somatotopic Organization of Cytoarchitectonic Areas on the human parietal Operculum. Cereb. Cortex 17, 1800–1811. doi: 10.1093/cercor/bhl090

PubMed Abstract | Crossref Full Text | Google Scholar

Errante, A., and Fogassi, L. (2019). Parieto-frontal mechanisms underlying observation of complex hand-object manipulation. Sci. Rep. 9:348. doi: 10.1038/s41598-018-36640-5

Crossref Full Text | Google Scholar

Errante, A., Ziccarelli, S., Mingolla, G. P., and Fogassi, L. (2021a). Decoding grip type and action goal during the observation of reaching-grasping actions: a multivariate fMRI study. Neuroimage 243:118511. doi: 10.1016/j.neuroimage.2021.118511

PubMed Abstract | Crossref Full Text | Google Scholar

Errante, A., Ziccarelli, S., Mingolla, G., and Fogassi, L. (2021b). Grasping and manipulation: neural bases and anatomical circuitry in humans. Neuroscience 458, 203–212. doi: 10.1016/j.neuroscience.2021.01.028

PubMed Abstract | Crossref Full Text | Google Scholar

Fabbri, S., Strnad, L., Caramazza, A., and Lingnau, A. (2014). Overlapping representations for grip type and reach direction. Neuroimage 94, 138–146. doi: 10.1016/j.neuroimage.2014.03.017

Crossref Full Text | Google Scholar

Fabbri, S., Stubbs, K. M., Cusack, R., and Culham, J. C. (2016). Disentangling representations of object and grasp properties in the human brain. J. Neurosci. 36, 7648–7662. doi: 10.1523/JNEUROSCI.0313-16.2016

PubMed Abstract | Crossref Full Text | Google Scholar

Fagg, A. H., and Arbib, M. A. (1998). Modeling parietal–premotor interactions in primate control of grasping. Neural Netw. 11, 1277–1303. doi: 10.1016/S0893-6080(98)00047-1

Crossref Full Text | Google Scholar

Fattori, P., Breveglieri, R., Raos, V., Bosco, A., and Galletti, C. (2012). Vision for action in the macaque medial posterior parietal cortex. J. Neurosci. 32, 3221–3234. doi: 10.1523/JNEUROSCI.5358-11.2012

PubMed Abstract | Crossref Full Text | Google Scholar

Fattori, P., Raos, V., Breveglieri, R., Bosco, A., Marzocchi, N., and Galletti, C. (2010). The dorsomedial pathway is not just for reaching: grasping neurons in the medial Parieto-occipital cortex of the macaque monkey. J. Neurosci. 30, 342–349. doi: 10.1523/JNEUROSCI.3800-09.2010

PubMed Abstract | Crossref Full Text | Google Scholar

Fiave, P. A., Sharma, S., Jastorff, J., and Nelissen, K. (2018). Investigating common coding of observed and executed actions in the monkey brain using cross-modal multi-variate fMRI classification. Neuroimage 178, 306–317. doi: 10.1016/j.neuroimage.2018.05.043

PubMed Abstract | Crossref Full Text | Google Scholar

Fiehler, K., Bannert, M. M., Bischoff, M., Blecker, C., Stark, R., Vaitl, D., et al. (2011). Working memory maintenance of grasp-target information in the human posterior parietal cortex. Neuroimage 54, 2401–2411. doi: 10.1016/j.neuroimage.2010.09.080

PubMed Abstract | Crossref Full Text | Google Scholar

Filimon, F. (2010). Human cortical control of hand movements: Parietofrontal networks for reaching, grasping, and pointing. Neuroscientist 16, 388–407. doi: 10.1177/1073858410375468

Crossref Full Text | Google Scholar

Fogassi, L. (2001). Cortical mechanism for the visual guidance of hand grasping movements in the monkey: a reversible inactivation study. Brain 124, 571–586. doi: 10.1093/brain/124.3.571

PubMed Abstract | Crossref Full Text | Google Scholar

Fontaine, D., Capelle, L., and Duffau, H. (2002). Somatotopy of the supplementary motor area: evidence from correlation of the extent of surgical resection with the clinical patterns of deficit. Neurosurgery 50, 297–305. doi: 10.1227/00006123-200202000-00011

PubMed Abstract | Crossref Full Text | Google Scholar

Fornia, L., Ferpozzi, V., Montagna, M., Rossi, M., Riva, M., Pessina, F., et al. (2018). Functional characterization of the left ventrolateral premotor cortex in humans: a direct electrophysiological approach. Cereb. Cortex 28, 167–183. doi: 10.1093/cercor/bhw365

PubMed Abstract | Crossref Full Text | Google Scholar

Fornia, L., Leonetti, A., Puglisi, G., Rossi, M., Viganò, L., Della Santa, B., et al. (2023). The parietal architecture binding cognition to sensorimotor integration: a multimodal causal study. Brain 147, 297–310. doi: 10.1093/brain/awad316

PubMed Abstract | Crossref Full Text | Google Scholar

Fornia, L., Puglisi, G., Leonetti, A., Bello, L., Berti, A., Cerri, G., et al. (2020a). Direct electrical stimulation of the premotor cortex shuts down awareness of voluntary actions. Nat. Commun. 11:705. doi: 10.1038/s41467-020-14517-4

PubMed Abstract | Crossref Full Text | Google Scholar

Fornia, L., Rossi, M., Rabuffetti, M., Leonetti, A., Puglisi, G., Viganò, L., et al. (2020b). Direct electrical stimulation of premotor areas: different effects on hand muscle activity during object manipulation. Cereb. Cortex 30, 391–405. doi: 10.1093/cercor/bhz139

Crossref Full Text | Google Scholar

Francois, C., Yelnik, J., Percheron, G., and Fenelon, G. (1994). Topographic distribution of the axonal endings from the sensorimotor and associative striatum in the macaque pallidum and substantia nigra. Exp. Brain Res. 102, 305–318. doi: 10.1007/BF00227517

PubMed Abstract | Crossref Full Text | Google Scholar

Fransson, P., and Marrelec, G. (2008). The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis. Neuroimage 42, 1178–1184. doi: 10.1016/j.neuroimage.2008.05.059

PubMed Abstract | Crossref Full Text | Google Scholar

Frey, S. H. (2008). Tool use, communicative gesture and cerebral asymmetries in the modern human brain. Philos. Trans. R. Soc. B Biol. Sci. 363, 1951–1957. doi: 10.1098/rstb.2008.0008

PubMed Abstract | Crossref Full Text | Google Scholar

Frey, S. H., Hansen, M., and Marchal, N. (2015). Grasping with the Press of a Button: grasp-selective responses in the human anterior intraparietal sulcus depend on nonarbitrary causal relationships between hand movements and end-effector actions. J. Cogn. Neurosci. 27, 1146–1160. doi: 10.1162/jocn_a_00766

PubMed Abstract | Crossref Full Text | Google Scholar

Frey, S. H., Vinton, D., Norlund, R., and Grafton, S. T. (2005). Cortical topography of human anterior intraparietal cortex active during visually guided grasping. Cogn. Brain Res. 23, 397–405. doi: 10.1016/j.cogbrainres.2004.11.010

PubMed Abstract | Crossref Full Text | Google Scholar

Galletti, C., Fattori, P., Kutz, D. F., and Gamberini, M. (1999). Brain location and visual topography of cortical area V6A in the macaque monkey. Eur. J. Neurosci. 11, 575–582. doi: 10.1046/j.1460-9568.1999.00467.x

PubMed Abstract | Crossref Full Text | Google Scholar

Gallivan, J. P., Cavina-Pratesi, C., and Culham, J. C. (2009). Is that within reach? fMRI reveals that the human superior Parieto-occipital cortex encodes objects reachable by the hand. J. Neurosci. 29, 4381–4391. doi: 10.1523/JNEUROSCI.0377-09.2009

PubMed Abstract | Crossref Full Text | Google Scholar

Gallivan, J. P., Chapman, C. S., Mclean, D. A., Flanagan, J. R., and Culham, J. C. (2013). Activity patterns in the category-selective occipitotemporal cortex predict upcoming motor actions. Eur. J. Neurosci. 38, 2408–2424. doi: 10.1111/ejn.12215

PubMed Abstract | Crossref Full Text | Google Scholar

Gallivan, J. P., McLean, A., and Culham, J. C. (2011a). Neuroimaging reveals enhanced activation in a reach-selective brain area for objects located within participants’ typical hand workspaces. Neuropsychologia 49, 3710–3721. doi: 10.1016/j.neuropsychologia.2011.09.027

Crossref Full Text | Google Scholar

Gallivan, J. P., McLean, D. A., Valyear, K. F., Pettypiece, C. E., and Culham, J. C. (2011b). Decoding action intentions from preparatory brain activity in human Parieto-frontal networks. J. Neurosci. 31, 9599–9610. doi: 10.1523/JNEUROSCI.0080-11.2011

PubMed Abstract | Crossref Full Text | Google Scholar

Gamberini, M., Passarelli, L., Fattori, P., Zucchelli, M., Bakola, S., Luppino, G., et al. (2009). Cortical connections of the visuomotor parietooccipital area V6Ad of the macaque monkey. J. Comp. Neurol. 513, 622–642. doi: 10.1002/cne.21980

PubMed Abstract | Crossref Full Text | Google Scholar

Garr, E. (2019). Contributions of the basal ganglia to action sequence learning and performance. Neurosci. Biobehav. Rev. 107, 279–295. doi: 10.1016/j.neubiorev.2019.09.017

PubMed Abstract | Crossref Full Text | Google Scholar

Gatti, R., Rocca, M. A., Fumagalli, S., Cattrysse, E., Kerckhofs, E., Falini, A., et al. (2017). The effect of action observation/execution on mirror neuron system recruitment: an fMRI study in healthy individuals. Brain Imaging Behav. 11, 565–576. doi: 10.1007/s11682-016-9536-3

Crossref Full Text | Google Scholar

Gatti, R., Rocca, M. A., Fumagalli, S., Cattrysse, E., Kerckhofs, E., and Filippi, M. (2015). Mirror neuron system recruitment during observation and simultaneous observation and execution of tasks with different characteristics. Physiotherapy 101:e445. doi: 10.1016/j.physio.2015.03.3227

Crossref Full Text | Google Scholar

Gerbella, M., Belmalih, A., Borra, E., Rozzi, S., and Luppino, G. (2011). Cortical connections of the anterior (F5a) subdivision of the macaque ventral premotor area F5. Brain Struct. Funct. 216, 43–65. doi: 10.1007/s00429-010-0293-6

PubMed Abstract | Crossref Full Text | Google Scholar

Gerbella, M., Borra, E., Tonelli, S., Rozzi, S., and Luppino, G. (2013). Connectional heterogeneity of the ventral part of the macaque area 46. Cereb. Cortex 23, 967–987. doi: 10.1093/cercor/bhs096

PubMed Abstract | Crossref Full Text | Google Scholar

Geschwind, N. (1975). The apraxias: neural mechanisms of disorders of learned movement. Am. Sci. 63, 188–195.

PubMed Abstract | Google Scholar

Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178. doi: 10.1038/nature18933

PubMed Abstract | Crossref Full Text | Google Scholar

Glover, S., Wall, M. B., and Smith, A. T. (2012). Distinct cortical networks support the planning and online control of reaching-to-grasp in humans. Eur. J. Neurosci. 35, 909–915. doi: 10.1111/j.1460-9568.2012.08018.x

PubMed Abstract | Crossref Full Text | Google Scholar

Goldenberg, G. (2009). Apraxia and the parietal lobes. Neuropsychologia 47, 1449–1459. doi: 10.1016/j.neuropsychologia.2008.07.014

Crossref Full Text | Google Scholar

Goldman-Rakic, P. S. (1987). “Circuitry of primate prefrontal cortex and regulation of behavior by representational memory” in Comprehensive physiology (Wiley), 373–417.

Google Scholar

Gordon, E. M., Chauvin, R. J., van, A. N., Rajesh, A., Nielsen, A., Newbold, D. J., et al. (2023). A somato-cognitive action network alternates with effector regions in motor cortex. Nature 617, 351–359. doi: 10.1038/s41586-023-05964-2

PubMed Abstract | Crossref Full Text | Google Scholar

Grafton, S. T. (2010). The cognitive neuroscience of prehension: recent developments. Exp. Brain Res. 204, 475–491. doi: 10.1007/s00221-010-2315-2

PubMed Abstract | Crossref Full Text | Google Scholar

Grafton, S. T., Fagg, A. H., Woods, R. P., and Arbib, M. A. (1996). Functional anatomy of pointing and grasping in humans. Cereb. Cortex 6, 226–237. doi: 10.1093/cercor/6.2.226

Crossref Full Text | Google Scholar

Grafton, S. T., and Tunik, E. (2011). Human basal ganglia and the dynamic control of force during on-line corrections. J. Neurosci. 31, 1600–1605. doi: 10.1523/JNEUROSCI.3301-10.2011

PubMed Abstract | Crossref Full Text | Google Scholar

Grèzes, J., Armony, J., Rowe, J., and Passingham, R. (2003). Activations related to “mirror” and “canonical” neurones in the human brain: an fMRI study. Neuroimage 18, 928–937. doi: 10.1016/S1053-8119(03)00042-9

PubMed Abstract | Crossref Full Text | Google Scholar

Grodd, W., Hülsmann, E., Lotze, M., Wildgruber, D., and Erb, M. (2001). Sensorimotor mapping of the human cerebellum: fMRI evidence of somatotopic organization. Hum. Brain Mapp. 13, 55–73. doi: 10.1002/hbm.1025

PubMed Abstract | Crossref Full Text | Google Scholar

Grol, M. J., Majdandz̆ić, J., Stephan, K. E., Verhagen, L., Dijkerman, H. C., Bekkering, H., et al. (2007). Parieto-frontal connectivity during visually guided grasping. J. Neurosci. 27, 11877–11887. doi: 10.1523/JNEUROSCI.3923-07.2007

PubMed Abstract | Crossref Full Text | Google Scholar

Gutteling, T. P., Petridou, N., Dumoulin, S. O., Harvey, B. M., Aarnoutse, E. J., Kenemans, J. L., et al. (2015). Action preparation shapes processing in early visual cortex. J. Neurosci. 35, 6472–6480. doi: 10.1523/JNEUROSCI.1358-14.2015

PubMed Abstract | Crossref Full Text | Google Scholar

Haber, S. N. (2003). The primate basal ganglia: parallel and integrative networks. J. Chem. Neuroanat. 26, 317–330. doi: 10.1016/j.jchemneu.2003.10.003

Crossref Full Text | Google Scholar

Hamzei, F., Vry, M. S., Saur, D., Glauche, V., Hoeren, M., Mader, I., et al. (2016). The dual-loop model and the human Mirror neuron system: an exploratory combined fMRI and DTI study of the inferior frontal gyrus. Cereb. Cortex 26, 2215–2224. doi: 10.1093/cercor/bhv066

PubMed Abstract | Crossref Full Text | Google Scholar

Hecht, E. E., Murphy, L. E., Gutman, D. A., Votaw, J. R., Schuster, D. M., Preuss, T. M., et al. (2013). Differences in neural activation for object-directed grasping in chimpanzees and humans. J. Neurosci. 33, 14117–14134. doi: 10.1523/JNEUROSCI.2172-13.2013

PubMed Abstract | Crossref Full Text | Google Scholar

Herbet, G., and Duffau, H. (2020). Revisiting the functional anatomy of the human brain: toward a Meta-networking theory of cerebral functions. Physiol. Rev. 100, 1181–1228. doi: 10.1152/physrev.00033.2019

PubMed Abstract | Crossref Full Text | Google Scholar

Hihara, S., Taoka, M., Tanaka, M., and Iriki, A. (2015). Visual responsiveness of neurons in the secondary somatosensory area and its surrounding parietal Operculum regions in awake macaque monkeys. Cereb. Cortex 25, 4535–4550. doi: 10.1093/cercor/bhv095

PubMed Abstract | Crossref Full Text | Google Scholar

Hillman, E. M. C. (2014). Coupling mechanism and significance of the BOLD signal: a status report. Annu. Rev. Neurosci. 37, 161–181. doi: 10.1146/annurev-neuro-071013-014111

PubMed Abstract | Crossref Full Text | Google Scholar

Hinkley, L. B. N., Krubitzer, L. A., Padberg, J., and Disbrow, E. A. (2009). Visual-manual exploration and posterior parietal cortex in humans. J. Neurophysiol. 102, 3433–3446. doi: 10.1152/jn.90785.2008

PubMed Abstract | Crossref Full Text | Google Scholar

Holmström, L., de Manzano, Ö., Vollmer, B., Forsman, L., Valero-Cuevas, F. J., Ullén, F., et al. (2011). Dissociation of brain areas associated with force production and stabilization during manipulation of unstable objects. Exp. Brain Res. 215, 359–367. doi: 10.1007/s00221-011-2903-9

PubMed Abstract | Crossref Full Text | Google Scholar

Hong, J. H., and Jang, S. (2011). Neural network related to hand movement: a combined study of diffusion tensor Tractography and functional MRI. J. Phys. Ther.Sci. 23, 97–101. doi: 10.1589/jpts.23.97

Crossref Full Text | Google Scholar

Hoover, J. E., and Strick, P. L. (1993). Multiple output channels in the basal ganglia. Science 259, 819–821.

Google Scholar

Hopkins, W. D., Taglialatela, J. P., Russell, J. L., Nir, T. M., and Schaeffer, J. (2010). Cortical representation of lateralized grasping in chimpanzees (Pan troglodytes): a combined MRI and PET study. PLoS One 5:e13383. doi: 10.1371/journal.pone.0013383

PubMed Abstract | Crossref Full Text | Google Scholar

Hoshi, E., Tremblay, L., Féger, J., Carras, P. L., and Strick, P. L. (2005). The cerebellum communicates with the basal ganglia. Nat. Neurosci. 8, 1491–1493. doi: 10.1038/nn1544

Crossref Full Text | Google Scholar

Howells, H., Simone, L., Borra, E., Fornia, L., Cerri, G., and Luppino, G. (2020). Reproducing macaque lateral grasping and oculomotor networks using resting state functional connectivity and diffusion tractography. Brain Struct. Funct. 225, 2533–2551. doi: 10.1007/s00429-020-02142-2

PubMed Abstract | Crossref Full Text | Google Scholar

Hyva¨rinen, J. (1981). Regional distribution of functions in parietal association area 7 of the monkey. Brain Res. 206, 287–303. doi: 10.1016/0006-8993(81)90533-3

Crossref Full Text | Google Scholar

Jancke, L. (2001). The role of the inferior parietal cortex in linking the tactile perception and manual construction of object shapes. Cereb. Cortex 11, 114–121. doi: 10.1093/cercor/11.2.114

PubMed Abstract | Crossref Full Text | Google Scholar

Jeannerod, M., Arbib, M. A., Rizzolatti, G., and Sakata, H. (1995). Grasping objects: the cortical mechanisms of visuomotor transformation. Trends Neurosci. 18, 314–320. doi: 10.1016/0166-2236(95)93921-J

PubMed Abstract | Crossref Full Text | Google Scholar

Jellema, T., Baker, C. I., Wicker, B., and Perrett, D. I. (2000). Neural representation for the perception of the intentionality of actions. Brain Cogn. 44, 280–302. doi: 10.1006/brcg.2000.1231

PubMed Abstract | Crossref Full Text | Google Scholar

Johansen-Berg, H., Behrens, T. E. J., Sillery, E., Ciccarelli, O., Thompson, A. J., Smith, S. M., et al. (2005). Functional–anatomical validation and individual variation of diffusion Tractography-based segmentation of the human thalamus. Cereb. Cortex 15, 31–39. doi: 10.1093/cercor/bhh105

PubMed Abstract | Crossref Full Text | Google Scholar

Johnson, P. B., Ferraina, S., Bianchi, L., and Caminiti, R. (1996). Cortical networks for visual reaching: physiological and anatomical Organization of Frontal and Parietal Lobe arm Regions. Cereb. Cortex 6, 102–119. doi: 10.1093/cercor/6.2.102

Crossref Full Text | Google Scholar

Kelly, R. M., and Strick, P. L. (2003). Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J. Neurosci. 23, 8432–8444. doi: 10.1523/JNEUROSCI.23-23-08432.2003

PubMed Abstract | Crossref Full Text | Google Scholar

Kim, M. J., Hong, J. H., and Jang, S. H. (2011). The cortical effect of clapping in the human brain: a functional MRI study. NeuroRehabilitation 28, 75–79. doi: 10.3233/NRE-2011-0634

PubMed Abstract | Crossref Full Text | Google Scholar

Kim, J.-H., Kim, J., Yeon, J., Park, J. Y., Chung, D., and Kim, S. P. (2021). Neural correlates of tactile hardness intensity perception during active grasping. PeerJ 9:e11760. doi: 10.7717/peerj.11760

PubMed Abstract | Crossref Full Text | Google Scholar

Knights, E., Mansfield, C., Tonin, D., Saada, J., Smith, F. W., and Rossit, S. (2021). Hand-selective visual regions represent how to grasp 3D tools: brain decoding during real actions. J. Neurosci. 41, 5263–5273. doi: 10.1523/JNEUROSCI.0083-21.2021

PubMed Abstract | Crossref Full Text | Google Scholar

Knights, E., Smith, F. W., and Rossit, S. (2022). The role of the anterior temporal cortex in action: evidence from fMRI multivariate searchlight analysis during real object grasping. Sci. Rep. 12:9042. doi: 10.1038/s41598-022-12174-9

PubMed Abstract | Crossref Full Text | Google Scholar

Koziol, L. F., Budding, D., Andreasen, N., D’Arrigo, S., Bulgheroni, S., Imamizu, H., et al. (2014). Consensus paper: the Cerebellum’s role in movement and cognition. Cerebellum 13, 151–177. doi: 10.1007/s12311-013-0511-x

PubMed Abstract | Crossref Full Text | Google Scholar

Krainik, A., Duffau, H., Capelle, L., Cornu, P., Boch, A. L., Mangin, J. F., et al. (2004). Role of the healthy hemisphere in recovery after resection of the supplementary motor area. Neurology 62, 1323–1332. doi: 10.1212/01.WNL.0000120547.83482.B1

PubMed Abstract | Crossref Full Text | Google Scholar

Króliczak, G., Cavina-Pratesi, C., Goodman, D. A., and Culham, J. C. (2007). What does the brain do when you fake it? An fMRI study of pantomimed and real grasping. J. Neurophysiol. 97, 2410–2422. doi: 10.1152/jn.00778.2006

PubMed Abstract | Crossref Full Text | Google Scholar

Króliczak, G., and Frey, S. H. (2009). A common network in the left cerebral hemisphere represents planning of tool use pantomimes and familiar intransitive gestures at the hand-independent level. Cereb. Cortex 19, 2396–2410. doi: 10.1093/cercor/bhn261

PubMed Abstract | Crossref Full Text | Google Scholar

Kuhtz-Buschbeck, J. P., Ehrsson, H. H., and Forssberg, H. (2001). Human brain activity in the control of fine static precision grip forces: an fMRI study. Eur. J. Neurosci. 14, 382–390. doi: 10.1046/j.0953-816x.2001.01639.x

PubMed Abstract | Crossref Full Text | Google Scholar

Kurniawan, I. T., Seymour, B., Talmi, D., Yoshida, W., Chater, N., and Dolan, R. J. (2010). Choosing to make an effort: the role of striatum in signaling physical effort of a chosen action. J. Neurophysiol. 104, 313–321. doi: 10.1152/jn.00027.2010

PubMed Abstract | Crossref Full Text | Google Scholar

Lehéricy, S., Bardinet, E., Tremblay, L., van de Moortele, P. F., Pochon, J. B., Dormont, D., et al. (2006). Motor control in basal ganglia circuits using fMRI and brain atlas approaches. Cereb. Cortex 16, 149–161. doi: 10.1093/cercor/bhi089

PubMed Abstract | Crossref Full Text | Google Scholar

Lehéricy, S., Benali, H., van de Moortele, P. F., Pélégrini-Issac, M., Waechter, T., Ugurbil, K., et al. (2005). Distinct basal ganglia territories are engaged in early and advanced motor sequence learning. Proc. Natl. Acad. Sci. 102, 12566–12571. doi: 10.1073/pnas.0502762102

PubMed Abstract | Crossref Full Text | Google Scholar

Lehmann, S. J., and Scherberger, H. (2013). Reach and gaze representations in macaque parietal and premotor grasp areas. J. Neurosci. 33, 7038–7049. doi: 10.1523/JNEUROSCI.5568-12.2013

PubMed Abstract | Crossref Full Text | Google Scholar

Leo, A., Handjaras, G., Bianchi, M., Marino, H., Gabiccini, M., Guidi, A., et al. (2016). A synergy-based hand control is encoded in human motor cortical areas. Elife 5:13420. doi: 10.7554/eLife.13420

PubMed Abstract | Crossref Full Text | Google Scholar

Livne, T., Kim, D., Metcalf, N. V., Zhang, L., Pini, L., Shulman, G. L., et al. (2022). Spontaneous activity patterns in human motor cortex replay evoked activity patterns for hand movements. Sci. Rep. 12, 1–11. doi: 10.1038/s41598-022-20866-5

Crossref Full Text | Google Scholar

Luders, H., Lesser, R. P., and Morris, H. H. (1987). “Negative motor responses elicited by stimulation of the human cortex” in Epileptology. eds. P. Wolf, M. Dam, and J. D. Adv (New York: Raven Press), 229–231.

Google Scholar

Maeda, K., Ishida, H., Nakajima, K., Inase, M., and Murata, A. (2015). Functional properties of parietal hand manipulation–related neurons and Mirror neurons responding to vision of own hand action. J. Cogn. Neurosci. 27, 560–572. doi: 10.1162/jocn_a_00742

PubMed Abstract | Crossref Full Text | Google Scholar

Makris, N., Zhu, A., Papadimitriou, G. M., Mouradian, P., Ng, I., Scaccianoce, E., et al. (2017). Mapping temporo-parietal and temporo-occipital cortico-cortical connections of the human middle longitudinal fascicle in subject-specific, probabilistic, and stereotaxic Talairach spaces. Brain Imaging Behav. 11, 1258–1277. doi: 10.1007/s11682-016-9589-3

PubMed Abstract | Crossref Full Text | Google Scholar

Makuuchi, M., Someya, Y., Ogawa, S., and Takayama, Y. (2012). Hand shape selection in pantomimed grasping: interaction between the dorsal and the ventral visual streams and convergence on the ventral premotor area. Hum. Brain Mapp. 33, 1821–1833. doi: 10.1002/hbm.21323

PubMed Abstract | Crossref Full Text | Google Scholar

Malach, R., Reppas, J. B., Benson, R. R., Kwong, K. K., Jiang, H., Kennedy, W. A., et al. (1995). Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. Proc. Natl. Acad. Sci. 92, 8135–8139. doi: 10.1073/pnas.92.18.8135

PubMed Abstract | Crossref Full Text | Google Scholar

Mantini, D., Gerits, A., Nelissen, K., Durand, J. B., Joly, O., Simone, L., et al. (2011). Default mode of brain function in monkeys. J. Neurosci. 31, 12954–12962. doi: 10.1523/JNEUROSCI.2318-11.2011

PubMed Abstract | Crossref Full Text | Google Scholar

Maranesi, M., Rodà, F., Bonini, L., Rozzi, S., Ferrari, P. F., Fogassi, L., et al. (2012). Anatomo-functional organization of the ventral primary motor and premotor cortex in the macaque monkey. Eur. J. Neurosci. 36, 3376–3387. doi: 10.1111/j.1460-9568.2012.08252.x

PubMed Abstract | Crossref Full Text | Google Scholar

Marangon, M., Kubiak, A., and Króliczak, G. (2016). Haptically guided grasping. fMRI shows right-hemisphere parietal stimulus encoding, and bilateral Dorso-ventral parietal gradients of object- and action-related processing during grasp execution. Front. Hum. Neurosci. 9:691. doi: 10.3389/fnhum.2015.00691

PubMed Abstract | Crossref Full Text | Google Scholar

Marneweck, M., and Grafton, S. T. (2020). Representational neural mapping of dexterous grasping before lifting in humans. J. Neurosci. 40, 2708–2716. doi: 10.1523/JNEUROSCI.2791-19.2020

PubMed Abstract | Crossref Full Text | Google Scholar

Martin, K., Jacobs, S., and Frey, S. H. (2011). Handedness-dependent and -independent cerebral asymmetries in the anterior intraparietal sulcus and ventral premotor cortex during grasp planning. Neuroimage 57, 502–512. doi: 10.1016/j.neuroimage.2011.04.036

PubMed Abstract | Crossref Full Text | Google Scholar

Martino, J., da Silva-Freitas, R., Caballero, H., Marco de Lucas, E., García-Porrero, J. A., and Vázquez-Barquero, A. (2013). Fiber dissection and diffusion tensor imaging Tractography study of the Temporoparietal Fiber intersection area. Oper. Neurosurg. 72:ons-87. doi: 10.1227/NEU.0b013e318274294b

PubMed Abstract | Crossref Full Text | Google Scholar

Mason, C. R., Miller, L. E., Baker, J. F., and Houk, J. C. (1998). Organization of Reaching and Grasping Movements in the primate cerebellar nuclei as revealed by focal Muscimol Inactivations. J. Neurophysiol. 79, 537–554. doi: 10.1152/jn.1998.79.2.537

PubMed Abstract | Crossref Full Text | Google Scholar

Matelli, M., and Luppino, G. (1996). Thalamic input to mesial and superior area 6 in the macaque monkey. J. Comp. Neurol. 372, 59–87. doi: 10.1002/(SICI)1096-9861(19960812)372:1<59::AID-CNE6>3.0.CO;2-L

PubMed Abstract | Crossref Full Text | Google Scholar

Matsuda, T., Watanabe, S., Kuruma, H., Murakami, Y., Watanabe, R., and Senou, A. (2009). A comparison of three bimanual coordinations: an fMRI study. J. Phys. Ther. Sci. 21, 85–92. doi: 10.1589/jpts.21.85

Crossref Full Text | Google Scholar

Matsumura, M., Kawashima, R., Naito, E., Satoh, K., Takahashi, T., Yanagisawa, T., et al. (1996). Changes in rCBF during grasping in humans examined by PET. Neuroreport 7, 749–752. doi: 10.1097/00001756-199602290-00017

PubMed Abstract | Crossref Full Text | Google Scholar

Michalowski, B., Buchwald, M., Klichowski, M., Ras, M., and Kroliczak, G. (2022). Action goals and the praxis network: an fMRI study. Brain Struct. Funct. 227, 2261–2284. doi: 10.1007/s00429-022-02520-y

PubMed Abstract | Crossref Full Text | Google Scholar

Middleton, F. (2000). Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Res. Rev. 31, 236–250. doi: 10.1016/S0165-0173(99)00040-5

PubMed Abstract | Crossref Full Text | Google Scholar

Milner, T. E., Franklin, D. W., Imamizu, H., and Kawato, M. (2007). Central control of grasp: manipulation of objects with complex and simple dynamics. Neuroimage 36, 388–395. doi: 10.1016/j.neuroimage.2007.01.057

PubMed Abstract | Crossref Full Text | Google Scholar

Monaco, S., Cavina-Pratesi, C., Sedda, A., Fattori, P., Galletti, C., and Culham, J. C. (2011). Functional magnetic resonance adaptation reveals the involvement of the dorsomedial stream in hand orientation for grasping. J. Neurophysiol. 106, 2248–2263. doi: 10.1152/jn.01069.2010

PubMed Abstract | Crossref Full Text | Google Scholar

Monaco, S., Chen, Y., Medendorp, W. P., Crawford, J. D., Fiehler, K., and Henriques, D. Y. P. (2014). Functional magnetic resonance imaging adaptation reveals the cortical networks for processing grasp-relevant object properties. Cereb. Cortex 24, 1540–1554. doi: 10.1093/cercor/bht006

PubMed Abstract | Crossref Full Text | Google Scholar

Monaco, S., Gallivan, J. P., Figley, T. D., Singhal, A., and Culham, J. C. (2017). Recruitment of foveal Retinotopic cortex during haptic exploration of shapes and actions in the dark. J. Neurosci. 37, 11572–11591. doi: 10.1523/JNEUROSCI.2428-16.2017

PubMed Abstract | Crossref Full Text | Google Scholar

Monaco, S., Sedda, A., Cavina-Pratesi, C., and Culham, J. C. (2015). Neural correlates of object size and object location during grasping actions. Eur. J. Neurosci. 41, 454–465. doi: 10.1111/ejn.12786

PubMed Abstract | Crossref Full Text | Google Scholar

Monticelli, M., Zeppa, P., Altieri, R., Veneziani Santonio, F., Cofano, F., Melcarne, A., et al. (2020). Exploring the anatomy of negative motor areas (NMAs): findings in awake surgery. J. Clin. Neurosci. 73, 219–223. doi: 10.1016/j.jocn.2020.01.049

PubMed Abstract | Crossref Full Text | Google Scholar

Mostofsky, S. H., Schafer, J. G. B., Abrams, M. T., Goldberg, M. C., Flower, A. A., Boyce, A., et al. (2003). fMRI evidence that the neural basis of response inhibition is task-dependent. Cogn. Brain Res. 17, 419–430. doi: 10.1016/S0926-6410(03)00144-7

PubMed Abstract | Crossref Full Text | Google Scholar

Murata, A., Fadiga, L., Fogassi, L., Gallese, V., Raos, V., and Rizzolatti, G. (1997). Object representation in the ventral premotor cortex (area F5) of the monkey. J. Neurophysiol. 78, 2226–2230. doi: 10.1152/jn.1997.78.4.2226

PubMed Abstract | Crossref Full Text | Google Scholar

Murata, A., Gallese, V., Luppino, G., Kaseda, M., and Sakata, H. (2000). Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area AIP. J. Neurophysiol. 83, 2580–2601. doi: 10.1152/jn.2000.83.5.2580

PubMed Abstract | Crossref Full Text | Google Scholar

Nachev, P., Kennard, C., and Husain, M. (2008). Functional role of the supplementary and pre-supplementary motor areas. Nat. Rev. Neurosci. 9, 856–869. doi: 10.1038/nrn2478

Crossref Full Text | Google Scholar

Nakamura, K., Sakai, K., and Hikosaka, O. (1998). Neuronal activity in medial frontal cortex during learning of sequential procedures. J. Neurophysiol. 80, 2671–2687. doi: 10.1152/jn.1998.80.5.2671

PubMed Abstract | Crossref Full Text | Google Scholar

Nathan, D. E., Guastello, S. J., Prost, R. W., and Jeutter, D. C. (2012a). Understanding neuromotor strategy during functional upper extremity tasks using symbolic dynamics. Nonlinear Dynamics Psychol. Life Sci. 16, 37–59.

PubMed Abstract | Google Scholar

Nathan, D. E., Prost, R. W., Guastello, S. J., Jeutter And, D. C., and Reynolds, N. C. (2012b). Investigating the neural correlates of goal-oriented upper extremity movements. NeuroRehabilitation 31, 421–428. doi: 10.3233/NRE-2012-00812

PubMed Abstract | Crossref Full Text | Google Scholar

Neely, K. A., Coombes, S. A., Planetta, P. J., and Vaillancourt, D. E. (2011). Segregated and overlapping neural circuits exist for the production of static and dynamic precision grip force. Hum. Brain Mapp. 34, 698–712. doi: 10.1002/hbm.21467

PubMed Abstract | Crossref Full Text | Google Scholar

Nelissen, K., Fiave, P. A., and Vanduffel, W. (2018). Decoding grasping movements from the parieto-frontal reaching circuit in the nonhuman primate. Cereb. Cortex 28, 1245–1259. doi: 10.1093/cercor/bhx037

PubMed Abstract | Crossref Full Text | Google Scholar

Nelissen, K., and Vanduffel, W. (2011). Grasping-related functional magnetic resonance imaging brain responses in the macaque monkey. J. Neurosci. 31, 8220–8229. doi: 10.1523/JNEUROSCI.0623-11.2011

PubMed Abstract | Crossref Full Text | Google Scholar

Nishimura, Y., Onoe, H., Morichika, Y., Tsukada, H., and Isa, T. (2007). Activation of parieto-frontal stream during reaching and grasping studied by positron emission tomography in monkeys. Neurosci. Res. 59, 243–250. doi: 10.1016/j.neures.2007.07.003

PubMed Abstract | Crossref Full Text | Google Scholar

Nitschke, M., Kleinschmidt, A., Wessel, K., and Frahm, J. (1996). Somatotopic motor representation in the human anterior cerebellum. Brain 119, 1023–1029. doi: 10.1093/brain/119.3.1023

PubMed Abstract | Crossref Full Text | Google Scholar

Orban, G. A. (2016). Functional definitions of parietal areas in human and non-human primates. Proc. R. Soc. B Biol. Sci. 283:20160118. doi: 10.1098/rspb.2016.0118

PubMed Abstract | Crossref Full Text | Google Scholar

Orban, G. A., and Caruana, F. (2014). The neural basis of human tool use. Front. Psychol. 5:310. doi: 10.3389/fpsyg.2014.00310

PubMed Abstract | Crossref Full Text | Google Scholar

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. doi: 10.1136/bmj.n71

Crossref Full Text | Google Scholar

Park, J. W., Kwon, Y. H., Lee, M. Y., Bai, D., Nam, K. S., Cho, Y. W., et al. (2008). Brain activation pattern according to exercise complexity: a functional MRI study. NeuroRehabilitation 23, 283–288. doi: 10.3233/NRE-2008-23311

PubMed Abstract | Crossref Full Text | Google Scholar

Pavlova, E., Hedberg, Å., Ponten, E., Gantelius, S., Valero-Cuevas, F. J., and Forssberg, H. (2015). Activity in the brain network for dynamic manipulation of unstable objects is robust to acute tactile nerve block: an fMRI study. Brain Res. 1620, 98–106. doi: 10.1016/j.brainres.2015.05.016

PubMed Abstract | Crossref Full Text | Google Scholar

Penfield, W. (1954). Mechanisms of Voluntary Movement. Brain 77, 1–17. doi: 10.1093/brain/77.1.1

Crossref Full Text | Google Scholar

Penfield, W., and Boldrey, E. (1937). Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 60, 389–443. doi: 10.1093/brain/60.4.389

Crossref Full Text | Google Scholar

Percheron, G., François, C., Talbi, B., Yelnik, J., and Fénelon, G. (1996). The primate motor thalamus. Brain Res. Brain Res. Rev. 22, 93–181. doi: 10.1016/0165-0173(96)00003-3

Crossref Full Text | Google Scholar

Pitzalis, S., Sereno, M. I., Committeri, G., Fattori, P., Galati, G., Tosoni, A., et al. (2013). The human homologue of macaque area V6A. Neuroimage 82, 517–530. doi: 10.1016/j.neuroimage.2013.06.026

PubMed Abstract | Crossref Full Text | Google Scholar

Plata, J. B., Modroño, C., Marcano, F., and González-Mora, J. L. (2014). The mirror neuron system and motor dexterity: what happens? Neuroscience 275, 285–295. doi: 10.1016/j.neuroscience.2014.06.010

PubMed Abstract | Crossref Full Text | Google Scholar

Pope, P., Wing, A. M., Praamstra, P., and Miall, R. C. (2005). Force related activations in rhythmic sequence production. Neuroimage 27, 909–918. doi: 10.1016/j.neuroimage.2005.05.010

Crossref Full Text | Google Scholar

Prado, J., Clavagnier, S., Otzenberger, H., Scheiber, C., Kennedy, H., and Perenin, M. T. (2005). Two cortical Systems for Reaching in central and peripheral vision. Neuron 48, 849–858. doi: 10.1016/j.neuron.2005.10.010

PubMed Abstract | Crossref Full Text | Google Scholar

Przybylski, Ł., and Króliczak, G. (2017). Planning functional grasps of simple tools invokes the hand-independent praxis representation network: an fMRI study. J. Int. Neuropsychol. Soc. 23, 108–120. doi: 10.1017/S1355617716001120

PubMed Abstract | Crossref Full Text | Google Scholar

Rand, M. K., Shimansky, Y., Stelmach, G. E., Bracha, V., and Bloedel, J. R. (2000). Effects of accuracy constraints on reach-to-grasp movements in cerebellar patients. Exp. Brain Res. 135, 179–188. doi: 10.1007/s002210000528

PubMed Abstract | Crossref Full Text | Google Scholar

Raos, V., Umiltá, M.-A., Murata, A., Fogassi, L., and Gallese, V. (2006). Functional properties of grasping-related neurons in the ventral premotor area F5 of the macaque monkey. J. Neurophysiol. 95, 709–729. doi: 10.1152/jn.00463.2005

PubMed Abstract | Crossref Full Text | Google Scholar

Ras, M., Wyrwa, M., Stachowiak, J., Buchwald, M., Nowik, A. M., and Kroliczak, G. (2022). Complex tools and motor-to-mechanical transformations. Sci. Rep. 12, 1–15. doi: 10.1038/s41598-022-12142-3

Crossref Full Text | Google Scholar

Rathelot, J.-A., and Strick, P. L. (2009). Subdivisions of primary motor cortex based on cortico-motoneuronal cells. Proc. Natl. Acad. Sci. 106, 918–923. doi: 10.1073/pnas.0808362106

PubMed Abstract | Crossref Full Text | Google Scholar

Rech, F., Duffau, H., Pinelli, C., Masson, A., Roublot, P., Billy-Jacques, A., et al. (2017). Intraoperative identification of the negative motor network during awake surgery to prevent deficit following brain resection in premotor regions. Neurochirurgie 63, 235–242. doi: 10.1016/j.neuchi.2016.08.006

PubMed Abstract | Crossref Full Text | Google Scholar

Rech, F., Herbet, G., Gaudeau, Y., Mézières, S., Moureau, J. M., Moritz-Gasser, S., et al. (2019). A probabilistic map of negative motor areas of the upper limb and face: a brain stimulation study. Brain 142, 952–965. doi: 10.1093/brain/awz021

PubMed Abstract | Crossref Full Text | Google Scholar

Rech, F., Herbet, G., Moritz-Gasser, S., and Duffau, H. (2014). Disruption of bimanual movement by unilateral subcortical electrostimulation. Hum. Brain Mapp. 35, 3439–3445. doi: 10.1002/hbm.22413

PubMed Abstract | Crossref Full Text | Google Scholar

Rech, F., Herbet, G., Moritz-Gasser, S., and Duffau, H. (2016). Somatotopic organization of the white matter tracts underpinning motor control in humans: an electrical stimulation study. Brain Struct. Funct. 221, 3743–3753. doi: 10.1007/s00429-015-1129-1

PubMed Abstract | Crossref Full Text | Google Scholar

Rech, F., Wassermann, D., and Duffau, H. (2020). New insights into the neural foundations mediating movement/language interactions gained from intrasurgical direct electrostimulations. Brain Cogn. 142:105583. doi: 10.1016/j.bandc.2020.105583

PubMed Abstract | Crossref Full Text | Google Scholar

Renzi, C., Ricciardi, E., Bonino, D., Handjaras, G., Vecchi, T., and Pietrini, P. (2013). The effects of visual control and distance in modulating Peripersonal spatial representation. PLoS One 8:e59460. doi: 10.1371/journal.pone.0059460

PubMed Abstract | Crossref Full Text | Google Scholar

Rilling, J. K., Glasser, M. F., Preuss, T. M., Ma, X., Zhao, T., Hu, X., et al. (2008). The evolution of the arcuate fasciculus revealed with comparative DTI. Nat. Neurosci. 11, 426–428. doi: 10.1038/nn2072

Crossref Full Text | Google Scholar

Rizzolatti, G., Cattaneo, L., Fabbri-Destro, M., and Rozzi, S. (2014). Cortical mechanisms underlying the Organization of Goal-Directed Actions and Mirror Neuron-Based Action Understanding. Physiol. Rev. 94, 655–706. doi: 10.1152/physrev.00009.2013

Crossref Full Text | Google Scholar

Rizzolatti, G., and Craighero, L. (2004). The Mirror-Neuron System. Annu. Rev. Neurosci. 27, 169–192. doi: 10.1146/annurev.neuro.27.070203.144230

Crossref Full Text | Google Scholar

Rizzolatti, G., Luppino, G., and Matelli, M. (1998). The organization of the cortical motor system: new concepts. Electroencephalogr. Clin. Neurophysiol. 106, 283–296. doi: 10.1016/S0013-4694(98)00022-4

PubMed Abstract | Crossref Full Text | Google Scholar

Rohlfing, T., Kroenke, C. D., Sullivan, E. V., Dubach, M. F., Bowden, D. M., Grant, K. A., et al. (2012). The INIA19 template and NeuroMaps atlas for primate brain image Parcellation and spatial normalization. Front. Neuroinform. 6:27. doi: 10.3389/fninf.2012.00027

PubMed Abstract | Crossref Full Text | Google Scholar

Rojkova, K., Volle, E., Urbanski, M., Humbert, F., Dell’Acqua, F., and Thiebaut de Schotten, M. (2016). Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study. Brain Struct. Funct. 221, 1751–1766. doi: 10.1007/s00429-015-1001-3

PubMed Abstract | Crossref Full Text | Google Scholar

Rolland, A., Herbet, G., and Duffau, H. (2018). Awake surgery for gliomas within the right inferior parietal lobule: new insights into the functional connectivity gained from stimulation mapping and surgical implications. World Neurosurg. 112, e393–e406. doi: 10.1016/j.wneu.2018.01.053

Crossref Full Text | Google Scholar

Rosenberg, K., Nossek, E., Liebling, R., Fried, I., Shapira-Lichter, I., Hendler, T., et al. (2010). Prediction of neurological deficits and recovery after surgery in the supplementary motor area: a prospective study in 26 patients. J. Neurosurg. 113, 1152–1163. doi: 10.3171/2010.6.JNS1090

PubMed Abstract | Crossref Full Text | Google Scholar

Rosenzopf, H., Wiesen, D., Basilakos, A., Yourganov, G., Bonilha, L., Rorden, C., et al. (2022). Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia of gesture production. Brain Commun. 4:fcac004. doi: 10.1093/braincomms/fcac004

PubMed Abstract | Crossref Full Text | Google Scholar

Rossi, M., Fornia, L., Puglisi, G., Leonetti, A., Zuccon, G., Fava, E., et al. (2018). Assessment of the praxis circuit in glioma surgery to reduce the incidence of postoperative and long-term apraxia: a new intraoperative test. J. Neurosurg. 130, 17–27. doi: 10.3171/2017.7.JNS17357

PubMed Abstract | Crossref Full Text | Google Scholar

Rossi, M., Sciortino, T., Conti Nibali, M., Gay, L., Viganò, L., Puglisi, G., et al. (2021). Clinical pearls and methods for intraoperative motor mapping. Neurosurgery 88, 457–467. doi: 10.1093/neuros/nyaa359

PubMed Abstract | Crossref Full Text | Google Scholar

Rossit, S., McAdam, T., Mclean, D. A., Goodale, M. A., and Culham, J. C. (2013). FMRI reveals a lower visual field preference for hand actions in human superior parieto-occipital cortex (SPOC) and precuneus. Cortex 49, 2525–2541. doi: 10.1016/j.cortex.2012.12.014

Crossref Full Text | Google Scholar

Roux, F.-E., Djidjeli, I., and Durand, J.-B. (2018). Functional architecture of the somatosensory homunculus detected by electrostimulation. J. Physiol. 596, 941–956. doi: 10.1113/JP275243

PubMed Abstract | Crossref Full Text | Google Scholar

Roux, F., Niare, M., Charni, S., Giussani, C., and Durand, J. (2020). Functional architecture of the motor homunculus detected by electrostimulation. J. Physiol. 598, 5487–5504. doi: 10.1113/JP280156

PubMed Abstract | Crossref Full Text | Google Scholar

Rozzi, S., Calzavara, R., Belmalih, A., Borra, E., Gregoriou, G. G., Matelli, M., et al. (2006). Cortical connections of the inferior parietal cortical convexity of the macaque monkey. Cereb. Cortex 16, 1389–1417. doi: 10.1093/cercor/bhj076

PubMed Abstract | Crossref Full Text | Google Scholar

Rozzi, S., Ferrari, P. F., Bonini, L., Rizzolatti, G., and Fogassi, L. (2008). Functional organization of inferior parietal lobule convexity in the macaque monkey: electrophysiological characterization of motor, sensory and mirror responses and their correlation with cytoarchitectonic areas. Eur. J. Neurosci. 28, 1569–1588. doi: 10.1111/j.1460-9568.2008.06395.x

PubMed Abstract | Crossref Full Text | Google Scholar

Sakata, H., Taira, M., Kusunoki, M., Murata, A., and Tanaka, Y. (1997). The TINS lecture the parietal association cortex in depth perception and visual control of hand action. Trends Neurosci. 20, 350–357. doi: 10.1016/S0166-2236(97)01067-9

PubMed Abstract | Crossref Full Text | Google Scholar

Saleem, K. S., Miller, B., and Price, J. L. (2014). Subdivisions and connectional networks of the lateral prefrontal cortex in the macaque monkey. J. Comp. Neurol. 522, 1641–1690. doi: 10.1002/cne.23498

PubMed Abstract | Crossref Full Text | Google Scholar

Saleem, K. S., Pauls, J. M., Augath, M., Trinath, T., Prause, B. A., Hashikawa, T., et al. (2002). Magnetic resonance imaging of neuronal connections in the macaque monkey. Neuron 34, 685–700. doi: 10.1016/S0896-6273(02)00718-3

Crossref Full Text | Google Scholar

Sartin, S., Ranzini, M., Scarpazza, C., and Monaco, S. (2022). Cortical areas involved in grasping and reaching actions with and without visual information: an ALE meta-analysis of neuroimaging studies. bioRxiv. Available at: https://unimi.primo.exlibrisgroup.com/openurl/39UMI_INST/39UMI_INST:VU1?sid=EMBASE&issn=26928205&id=doi:10.1101%2F2022.06.01.494343&atitle=Cortical+areas+involved+in+grasping+and+reaching+actions+with+and+without+visual+information%3A+an+ALE+meta-analysis+of+neuroimaging+studies&stitle=bioRxiv&title=bioRxiv&volume=&issue=&spage=&epage=&aulast=Sartin&aufirst=Samantha&auinit=S.&aufull=Sartin+S.&coden=&isbn=&pages=-&date=2022&auinit1=S&auinitm=.

Google Scholar

Schucht, P., Moritz-Gasser, S., Herbet, G., Raabe, A., and Duffau, H. (2013). Subcortical electrostimulation to identify network subserving motor control. Hum. Brain Mapp. 34, 3023–3030. doi: 10.1002/hbm.22122

PubMed Abstract | Crossref Full Text | Google Scholar

Sherman, S. M., and Guillery, R. W. (1996). Functional organization of thalamocortical relays. J. Neurophysiol. 76, 1367–1395. doi: 10.1152/jn.1996.76.3.1367

Crossref Full Text | Google Scholar

Sherrington, C. S. (1906). The integrative action of the nervous system : New York C Scribner Sons.

Google Scholar

Shimizu, T., Hosaki, A., Hino, T., Sato, M., Komori, T., Hirai, S., et al. (2002). Motor cortical disinhibition in the unaffected hemisphere after unilateral cortical stroke. Brain 125, 1896–1907. doi: 10.1093/brain/awf183

PubMed Abstract | Crossref Full Text | Google Scholar

Shinoda, Y., Sugiuchi, Y., Futami, T., and Izawa, R. (1992). Axon collaterals of mossy fibers from the pontine nucleus in the cerebellar dentate nucleus. J. Neurophysiol. 67, 547–560. doi: 10.1152/jn.1992.67.3.547

PubMed Abstract | Crossref Full Text | Google Scholar

Shmuelof, L., and Zohary, E. (2006). A mirror representation of others’ actions in the human anterior parietal cortex. J. Neurosci. 26, 9736–9742. doi: 10.1523/JNEUROSCI.1836-06.2006

Crossref Full Text | Google Scholar

Spraker, M. B., Corcos, D. M., and Vaillancourt, D. E. (2009). Cortical and subcortical mechanisms for precisely controlled force generation and force relaxation. Cereb. Cortex 19, 2640–2650. doi: 10.1093/cercor/bhp015

PubMed Abstract | Crossref Full Text | Google Scholar

Stark, A., and Zohary, E. (2008). Parietal mapping of visuomotor transformations during human tool grasping. Cereb. Cortex 18, 2358–2368. doi: 10.1093/cercor/bhm260

PubMed Abstract | Crossref Full Text | Google Scholar

Stoodley, C. J., Desmond, J. E., Guell, X., and Schmahmann, J. D. (2021). “Functional topography of the human cerebellum revealed by functional neuroimaging studies” in Handbook of the cerebellum and cerebellar disorders 1–37 (Springer International Publishing)

Google Scholar

Strick, P. L., Dum, R. P., and Fiez, J. A. (2009). Cerebellum and nonmotor function. Annu. Rev. Neurosci. 32, 413–434. doi: 10.1146/annurev.neuro.31.060407.125606

Crossref Full Text | Google Scholar

Styrkowiec, P. P., Nowik, A. M., and Króliczak, G. (2019). The neural underpinnings of haptically guided functional grasping of tools: an fMRI study. Neuroimage 194, 149–162. doi: 10.1016/j.neuroimage.2019.03.043

PubMed Abstract | Crossref Full Text | Google Scholar

Sulpizio, V., Neri, A., Fattori, P., Galletti, C., Pitzalis, S., and Galati, G. (2020). Real and imagined grasping movements differently activate the human dorsomedial parietal cortex. Neuroscience 434, 22–34. doi: 10.1016/j.neuroscience.2020.03.019

PubMed Abstract | Crossref Full Text | Google Scholar

SurfIce. (2015). Available at: https://www.nitrc.org/projects/surfice/

Google Scholar

Tabu, H., Mima, T., Aso, T., Takahashi, R., and Fukuyama, H. (2012). Common inhibitory prefrontal activation during inhibition of hand and foot responses. Neuroimage 59, 3373–3378. doi: 10.1016/j.neuroimage.2011.10.092

PubMed Abstract | Crossref Full Text | Google Scholar

Taira, M., Mine, S., Georgopoulos, A. P., Murata, A., and Sakata, H. (1990). Parietal cortex neurons of the monkey related to the visual guidance of hand movement. Exp. Brain Res. 83, 29–36. doi: 10.1007/BF00232190

PubMed Abstract | Crossref Full Text | Google Scholar

Talati, A., Valero-Cuevas, F. J., and Hirsch, J. (2005). Visual and tactile guidance of dexterous manipulation tasks: an fMRI study. Percept. Mot. Skills 101, 317–334. doi: 10.2466/pms.101.1.317-334

PubMed Abstract | Crossref Full Text | Google Scholar

Tanaka, Y., Yoshida, A., Kawahata, N., Hashimoto, R., Obayashi, T. D., and dyspraxia,. (1996). Clinical characteristics, responsible lesion and possible underlying mechanism. Brain 119, 859–874. doi: 10.1093/brain/119.3.859

PubMed Abstract | Crossref Full Text | Google Scholar

Thompson, K. G., Hanes, D. P., Bichot, N. P., and Schall, J. D. (1996). Perceptual and motor processing stages identified in the activity of macaque frontal eye field neurons during visual search. J. Neurophysiol. 76, 4040–4055. doi: 10.1152/jn.1996.76.6.4040

PubMed Abstract | Crossref Full Text | Google Scholar

Tomasino, B., Guarracino, I., Ius, T., Budai, R., and Skrap, M. (2022). Real-time neuropsychological testing of sensorimotor cognition during awake surgery in precentral and Postsomatosensory areas. World Neurosurg. 164, e599–e610. doi: 10.1016/j.wneu.2022.05.018

PubMed Abstract | Crossref Full Text | Google Scholar

Tunik, E., Ortigue, S., Adamovich, S. V., and Grafton, S. T. (2008). Differential recruitment of anterior intraparietal sulcus and superior parietal lobule during visually guided grasping revealed by electrical neuroimaging. J. Neurosci. 28, 13615–13620. doi: 10.1523/JNEUROSCI.3303-08.2008

PubMed Abstract | Crossref Full Text | Google Scholar

Turella, L., Rumiati, R., and Lingnau, A. (2020). Hierarchical action encoding within the human brain. Cereb. Cortex 30, 2924–2938. doi: 10.1093/cercor/bhz284

PubMed Abstract | Crossref Full Text | Google Scholar

Vaillancourt, D. E., Mayka, M. A., Thulborn, K. R., and Corcos, D. M. (2004). Subthalamic nucleus and internal globus pallidus scale with the rate of change of force production in humans. Neuroimage 23, 175–186. doi: 10.1016/j.neuroimage.2004.04.040

PubMed Abstract | Crossref Full Text | Google Scholar

Vaillancourt, D. E., Yu, H., Mayka, M. A., and Corcos, D. M. (2007). Role of the basal ganglia and frontal cortex in selecting and producing internally guided force pulses. Neuroimage 36, 793–803. doi: 10.1016/j.neuroimage.2007.03.002

PubMed Abstract | Crossref Full Text | Google Scholar

Van Essen, D. C., Donahue, C., Dierker, D. L., and Glasser, M. F. (2016). “Parcellations and connectivity patterns in human and macaque cerebral cortex” in Micro-, Meso- and macro-Connectomics of the brain. Cham (CH) (Springer), 89–106.

Google Scholar

Verhagen, L., Dijkerman, H. C., Grol, M. J., and Toni, I. (2008). Perceptuo-motor interactions during prehension movements. J. Neurosci. 28, 4726–4735. doi: 10.1523/JNEUROSCI.0057-08.2008

PubMed Abstract | Crossref Full Text | Google Scholar

Viganò, L., Fornia, L., Rossi, M., Howells, H., Leonetti, A., Puglisi, G., et al. (2019). Anatomo-functional characterisation of the human “hand-knob”: a direct electrophysiological study. Cortex 113, 239–254. doi: 10.1016/j.cortex.2018.12.011

PubMed Abstract | Crossref Full Text | Google Scholar

Viganò, L., Howells, H., Rossi, M., Rabuffetti, M., Puglisi, G., Leonetti, A., et al. (2022). Stimulation of frontal pathways disrupts hand muscle control during object manipulation. Brain 145, 1535–1550. doi: 10.1093/brain/awab379

PubMed Abstract | Crossref Full Text | Google Scholar

Vingerhoets, G., Acke, F., Alderweireldt, A. S., Nys, J., Vandemaele, P., and Achten, E. (2012a). Cerebral lateralization of praxis in right- and left-handedness: same pattern, different strength. Hum. Brain Mapp. 33, 763–777. doi: 10.1002/hbm.21247

PubMed Abstract | Crossref Full Text | Google Scholar

Vingerhoets, G., Stevens, L., Meesdom, M., Honoré, P., Vandemaele, P., and Achten, E. (2012b). Influence of perspective on the neural correlates of motor resonance during natural action observation. Neuropsychol. Rehabil. 22, 752–767. doi: 10.1080/09602011.2012.686885

Crossref Full Text | Google Scholar

Ward, N. S., and Frackowiak, R. S. J. (2003). Age-related changes in the neural correlates of motor performance. Brain 126, 873–888. doi: 10.1093/brain/awg071

PubMed Abstract | Crossref Full Text | Google Scholar

Weeks, A. C. W., Ivanco, T. L., Leboutillier, J. C., Racine, R. J., and Petit, T. L. (2000). Sequential changes in the synaptic structural profile following long-term potentiation in the rat dentate gyrus. II. Induction / early maintenance phase. Synapse 36, 286–296. doi: 10.1002/(SICI)1098-2396(20000615)36:4<286::AID-SYN5>3.0.CO;2-T

Crossref Full Text | Google Scholar

Zackowski, K. M., Thach, W. T., and Bastian, A. J. (2002). Cerebellar subjects show impaired coupling of reach and grasp movements. Exp. Brain Res. 146, 511–522. doi: 10.1007/s00221-002-1191-9

PubMed Abstract | Crossref Full Text | Google Scholar