Jeffrey R. Hebert
Christopher M. Filley- 1Physical Performance Laboratory, Marcus Institute for Brain Health, University of Colorado School of Medicine, Aurora, CO, United States
- 2Behavorial Neurology Section, Department of Neurology and Psychiatry, Marcus Institute for Brain Health, University of Colorado School of Medicine, Aurora, CO, United States
The ability to simultaneously process and integrate multiple sensory stimuli is paramount to effective daily function and essential for normal cognition. Multisensory management depends critically on the interplay between bottom-up and top-down processing of sensory information, with white matter (WM) tracts acting as the conduit between cortical and subcortical gray matter (GM) regions. White matter tracts and GM structures operate in concert to manage both multisensory signals and cognition. Altered sensory processing leads to difficulties in reweighting and modulating multisensory input during various routine environmental challenges, and thus contributes to cognitive dysfunction. To examine the specific role of WM in altered sensory processing and cognitive dysfunction, this review focuses on two neurologic disorders with diffuse WM pathology, multiple sclerosis and mild traumatic brain injury, in which persistently altered sensory processing and cognitive impairment are common. In these disorders, cognitive dysfunction in association with altered sensory processing may develop initially from slowed signaling in WM tracts and, in some cases, GM pathology secondary to WM disruption, but also because of interference with cognitive function by the added burden of managing concurrent multimodal primary sensory signals. These insights promise to inform research in the neuroimaging, clinical assessment, and treatment of WM disorders, and the investigation of WM-behavior relationships.
Introduction
In the investigation and conceptualization of human cognition, much attention has been focused on gray matter (GM) structures as the primary regions of interest. Key areas include the neocortices and the hippocampus, and subcortical GM structures including limbic nuclei, the thalamus, and the cerebellar GM (1–9). Gray matter structures important for memory, for example, include the hippocampus, thalamus, and prefrontal cortex [PFC; (10)], and the cerebellum is engaged in the mediation of attention, language, visuospatial processing, and executive function (8). These GM regions, however, do not act independently of each other, and instead, white matter (WM) tracts and GM structures operate in concert.
One of the critical functions of WM in the operations of cognition is the management of multisensory signaling (11). To effectively process sensory signals from the periphery and modulate cognitive behavior, structural interactions between GM regions are vital. White matter tracts comprise the anatomical basis for these interactions (10, 12), serving as a crucial foundation for bottom-up and top-down behavioral integration of multimodal sensory stimuli.
Humans constantly process sensory data from various independent sources, particularly from the visual, auditory, and vestibular end organs. Participation in daily life activities, which requires a continually adapting cognitive repertoire, typically occurs in ever-changing environments, where it is rare that a single sensory stimulus requires processing. Sensory input is thus typically multimodal and requires a complex network of cerebral regions that integrate sensory and cognitive systems. The thalamus, and the thalamo-cortical radiations by which it is connected to the sensory cortices, play key roles in both bottom-up processing that provides central integration of sensory signals from the periphery, and top-down processing that enables higher level behavioral regulation. However, other GM and WM structures are also involved in responses to multimodal sensory stimuli that require centralized weighting and re-weighting of sensory information. For example, when visual and vestibular stimuli are concurrently processed, the middle and inferior frontal gyri (13), the visual and vestibular cortices, the thalamus (14), and the posterior cerebellar vermis (15) are collectively activated and synchronized. The ability to simultaneously process and integrate multiple sensory stimuli is critical for normal cognition and effective daily function across the lifespan (16). The development and lifelong maintenance of cognition is dependent on unaltered multimodal sensory management and processing of convergent signals from several sensory systems, most notably vision, audition, and vestibular (17).
This review delves into the relationship between primary sensory processing and cognitive dysfunction in relation to WM and its pathology, in areas such as impaired attention, an integral function between bottom-up sensory-mediated selection and top-down control of sensory processing. Such relationships are an important focus in behavioral neurology, where much work has recently been done to interrelate attention, sensory integration, and white matter pathology (18, 19). The focus here will be on conceptualizing sensory integration-mediated cognition in terms of WM tract integrity, with an emphasis on three primary sensory modalities: visual, auditory, and vestibular. The contributions of WM pathology and related sensory processing deficits to cognitive impairment will be highlighted using a detailed consideration of these concepts in two common disorders with substantial WM pathology, multiple sclerosis (MS) and mild traumatic brain injury (mTBI). In these disorders, cognitive dysfunction in association with altered sensory processing may develop directly from slowed signaling in WM tracts and, in some cases, GM pathology secondary to WM disruption, or because of sensory overload from managing multisensory stimuli that results in added interference with normal cognitive processing.
Sensory modality processing, networks, fiber tracts, and cognition
Supported by the underpinnings of postsynaptic potentials and synaptic integration models (20), conceptualizations of the interplay between sensory processing and cognitive function rooted in thalamo-cortical circuitry are evolving (11, 21–24). To begin, visual and auditory stimuli are processed within the thalamic lateral and medial geniculate bodies. These sensory nuclei are first-order structures that act as preliminary processers of sensory signals. This initial encoding serves as bottom-up conveyance of ascending sensory input to the primary sensory cortices for higher cortical processing and preliminary behavioral response (25). Concurrently, the thalamic reticular nucleus serves as the coupling mediator between the thalamus and primary sensory cortices through branched connections within bidirectional thalamo-cortical tracts. This arrangement serves to filter out redundant, superfluous stimuli, a process referred to as sensory gating, which leads to greater efficiency of brain response and behavioral modulation (11). Similarly, the pulvinar shares in this process of sensory gating by specifically modulating visual attention (26). Sensory data are further processed through transthalamic signaling streams that involve thalamo-cortical signal transference (27). These thalamo-cortical pathways serve to augment cortical function, forming the basis of top-down processing necessary for the development and modulation of cognitive domains such as attention and memory (22). This processing is necessary because visual and auditory stimuli are pragmatically conflicting, and unresolved competitive visual and auditory signaling leads to a greater load on higher-level top-down processing, which in turn increases demands on selective attention (28).
Multimodal sensory events result in the recruitment and activation of multiple brain regions beyond those required to process unimodal stimuli, leading to the added complexity of simultaneously processing and managing more than one mode of sensory stimulus. For example, when a person performs a visual function task, the addition of auditory signals during task performance requires the activation of thalamo-cortical and transthalamic pathways for completion of the visual task (29). Wolff and colleagues describe how temporal and spatial summation act to integrate separate unimodal inputs (e.g., visual, auditory) into a coalescent modulatory postsynaptic signal in the primary sensory cortices (11). This multimodal signal stacking enhances integration by collectively aggregating the different sensory modal signals to levels that reach the excitatory postsynaptic threshold necessary for cortical activity in structures such as the PFC, thus enhancing cognitive function (11). White matter tracts are the integral connections that tether GM structures together to form cognitive networks (30). Concurrent processing of auditory and visual signals has been linked to activity of the cingulo-opercular region [including the anterior insula, operculum, dorsal anterior cingulate, and thalamus; (31)], an important network for attention, processing speed, memory, sensory perception, speech, and language (32–36). Overlapping with the cingulo-opercular network, the cerebello-frontal network, consisting of other subnetworks including a cerebello-thalamic pathway, is related to cognitive processing speed (37). The cerebello-cortical network is also related to visual attention and working memory (38), and along with cerebello-thalamic and cerebello-pons subnetworks, is instrumental in auditory-mediated attention and memory (39). The fronto-parietal network is also important in that it helps mediate executive function, attention, memory, processing speed, and cognitive flexibility (34, 35, 40–43). In all of these networks, WM provides structural connectivity between GM regions.
In the central integration of multiple sensory stimuli, networks, and cognition, two prominent association tracts, the cingulum and the uncinate fasciculus, merit special attention. The cingulum lies on the medial aspect of the cerebral hemispheres and connects frontal, temporal, parietal, and occipital regions (44). Considered a component of the limbic system (45), the cingulum connects a variety of cortical and subcortical GM regions and forms the matrix of a major exchange route linking sensory processing and cognitive function. Cingulum projections, including thalamo-cingulate and thalamo-cortical fibers and the hippocampal cingulum, serve to augment bottom-up and top-down connections that process sensory stimuli and manage executive function, working memory, and attention (44, 46). Visuospatial function has also been mapped to the cingulum (47), and cingulum disintegrity is correlated with age-related decline in visuospatial performance (48). Abnormal fiber metrics in the cingulum have been found in persons with mild cognitive impairment who have memory, language, and visuospatial dysfunction (49).
The uncinate fasciculus, also a component of the limbic system (45), is a large association tract with a protracted maturation until 30 years of age or greater. The uncinate connects the PFC and the anterior temporal lobe, and thus plays a major role in emotional regulation (50, 51). This fasciculus enables cortico-limbic integration by way of PFC and amygdala connectivity (52), and, together with the cingulum and the thalamo-hippocampal-PFC circuit (53), is instrumental in linking cognition and sensory processing. A recent report from Shiotsu et al. showed that healthy young adults who are more sensitive to auditory stimuli have greater integrity of the uncinate fasciculus (54). This fasciculus is also associated with executive function and memory (55–60), creativity (61), and emotional intelligence (62). Age-related cognitive decline is related to reduced integrity of the uncinate fasciculus (63), and this phenomenon, coupled with altered sensory processing in aging (16), further supports the link between impaired multisensory processing, WM pathology, and cognition.
Although less thoroughly studied than the visual and auditory systems, the vestibular system is also critical for cognition. The physiology of the vestibular end organ is unique in that it delivers multiple distinct sensory inputs (e.g., angular acceleration, linear acceleration), rendering the central processing of vestibular signaling much less linear and more complex to process. Moreover, multiple classes of second-order vestibular neurons of the vestibular nuclei also receive input from visual and somatosensory systems and the cerebellum (64–68). There is no single vestibular-specific thalamic nucleus; instead, vestibular signals are processed by multiple thalamic nuclei, and vestibular-mediated potentials also project to other areas of the brainstem, and cerebellar and cortical regions (69, 70). Compared to visual and auditory modalities, relatively little is known about the central processing of vestibular signaling, including its WM connections and its impact on cognition (71). Nevertheless, with the diversified spread of vestibular signaling throughout the brain comes a variety of neuronal networking and processing demands, and behavioral responses that directly implicate cognitive function. It has been reported, for example, that vestibular signaling leads to responses in WM tracts relevant to cognition (e.g. cingulum) and GM [e.g., frontal cortex, hippocampus; (72)], and processing of vestibular signaling has been associated with multiple cognitive domains such as visuospatial function (e.g., spatial memory, navigation), attention, processing speed, memory, and executive function (73). Animal model and human research provide empirical results that link vestibulopathy and cognitive impairment, showing that advanced levels of vestibulopathy (e.g, bilateral involvement, severe hypofunction, extended duration) are associated with hippocampal atrophy and related cognitive impairment (74, 75). These findings have prompted the hypothesis that vestibulopathy may be a risk factor for the development of dementia including Alzheimer's Disease (75, 76). Moreover, age-related vestibulo-limbic-cortical pathway degeneration (75), may implicate WM pathology as a potential contributor to dementia pathogenesis.
Altered sensory processing, white matter pathology, and cognitive dysfunction
Multiple sclerosis
White matter tract demyelination is the signature neuropathology of MS and is thought to result from autoimmune-mediated inflammation that produces classic demyelinative plaques in the brain and spinal cord (77). When inflammation is more severe, axons are damaged in addition to myelin. Focal MS lesions can also occur in GM, although the primary pathology is demyelination and neuronal injury only develops later. This multifocal lesional pathology produces a wide range of clinical manifestations, including abnormal sensory processing in multiple modalities (78–80) and cognitive impairment (81).
Recent reports abundantly support deficient multisensory processing in MS. Giurgola et al. report that persons in the early stages of MS (e.g., relapsing-remitting disease) demonstrate abnormal multimodal processing involving concurrent visual and auditory signals, which may lead to cognitive impairment (82). Altered unimodal sensory processing and related cognitive impairment is also described in MS. For example, vestibular signal processing is impaired in patients with MS, and is associated with cognitive dysfunction, including slowed processing speed, impaired memory, and visuospatial dysfunction (83). Moreover, cognitive impairment related to modified visual signal processing is detectable early in the disease, and becomes more evident in later stages when impaired visual processing correlates with other deficits including slowed processing speed and poor memory (84).
In recent years, pathologic involvement of the thalamus has been linked with cognitive impairment in MS (85). Thalamic volume loss, for example, has been shown to predict cognitive impairment in the domains of attention, memory, and executive function (86). White matter connections of the thalamus are also damaged, and diffusion tensor imaging (DTI) has disclosed altered thalamic connectivity in association with cognitive loss; one study showed disruption of the thalamo-hippocampal-PFC network in early MS with cognitive impairment (86). Lower integrity of the cingulum and uncinate fasciculus has also been associated with cognitive impairment (87), and conversely, higher integrity of the uncinate has been shown to predict processing speed in MS (86). Recently, Fritz et al. found that lower diffusivity of the superior cerebellar peduncle and reduced volume of the superior and middle cerebellar peduncles related to slower processing speed in persons with MS (88).
The correlations between thalamic and cerebellar volume loss and cognitive impairment in MS raise the question of how a WM disease can so prominently affect GM. Many factors may be involved, but one answer may be that WM tract demyelination appears to precede GM atrophy. An important connection has recently been made between WM and GM pathology in MS by Lie et al., who found that GM degeneration occurs secondarily to WM damage (89). This phenomenon is most prominent in the early stages of the disease, whereas in later stages GM demyelination also contributes to atrophy (89). Lie et al. propose that both retrograde degeneration (backward from the damaged site toward the cell body) and anterograde or Wallerian degeneration (forward from the damaged site toward the axon terminal) contribute to GM atrophy (89). The thalamus appears to be susceptible to this phenomenon (89, 90), helping explain the link between thalamic damage and cognitive impairment. In the context of this review, observations of thalamic atrophy in MS (89) further elaborate the notion of altered sensory processing affecting cognition via WM pathology.
Mild traumatic brain injury
Diffuse axonal injury (DAI), a result of shearing forces that damage multiple WM tracts, is the most important pathology of moderate-to-severe non-penetrating TBI (91), and is likely to be present in mTBI as well (92, 93). Mild TBI-related DAI often leads to widespread and disabling clinical manifestations, and whereas traditional thinking has maintained that post-mTBI symptoms typically resolve within 2–3 months, more recent reports suggest that symptoms can persist far longer in some individuals (94–96).
In particular, post-mTBI cognitive symptoms are more persistent than previously recognized (97). The reasons for this persistence are multifactorial, but a growing body of evidence is implicating DAI, and potentially chronic inflammation and later degenerative pathology, as important determinants of lasting symptomatology (98). Among many WM tracts vulnerable to mTBI, the cingulum and the uncinate fasciculus have both been shown to be damaged by DAI, and this form of injury predicts cognitive impairment (99, 100).
With respect to sensory function, DAI of prominent WM tracts is likely to further exacerbate post-traumatic cognitive sequelae by decoupling the interplay between sensory processing and cognitive function (101). Dysfunctional unimodal sensory processing of visual (102, 103), auditory (104, 105), and vestibular (106) stimuli are found in persons with prior mTBI. In addition, altered central sensory processing of vestibular (107, 108), visual, (101, 107, 108), and auditory (109) signals has been proposed as underlying persistent mTBI-related symptoms, including those indicating cognitive dysfunction. Moreover, multimodal sensory management requirements incur a greater processing demand for persons with prior mTBI (110–112). Compromised multimodal sensory management of visual-vestibular stimuli is commonly found in persons with mTBI (110–113). The added processing challenges of multimodal signals often leads to impaired cognition well past the onset of mTBI. For example, dual visual-auditory processing deficits have been reported more than 2 years after mTBI, and results in greater deficits in cognition including executive dysfunction (114).
As in MS, thalamic pathology occurs in mTBI, and further exacerbates impaired sensory processing and cognitive dysfunction. Reductions in thalamic and hippocampal volume have been reported in mTBI 1 month after injury, and at 6 months post-injury, volume loss persists in the thalamus only (115). Moreover, it was found that the 6-month thalamic volume loss correlated with persistent vestibular and cognitive symptoms (115). Reduced processing speed and working memory were found in persons with mTBI who had symptoms lasting 32 days post-injury, and impaired working memory correlated with decreased thalamic functional connectivity (116).
Also similar to the pathology of MS, damage to WM connections of the thalamus is important in mTBI. Bai et al. recently showed that impaired processing speed and working memory are persistent problems up to 6–12 months post-injury, and relate to WM disruption most notable in the thalamo-cortical radiations and the anterior corpus callosum (117). Consistent with recent findings in MS (89), it has been proposed in mTBI that WM tract damage may lead to progressive GM degeneration in the thalamus, further perpetuating chronic post-mTBI cognitive impairment (118, 119).
Taken together, a growing body of evidence from studies of MS and mTBI underscores the burgeoning link between disrupted primary sensory processing and cognitive dysfunction. White matter damage plays a central role in this relationship, either through overt injury or more subtle loss of WM integrity. In these disorders, cognitive dysfunction may initially be related to slowed signaling in relevant WM tracts, and, in some cases, GM pathology secondary to WM disruption in networks responsible for sensory processing and cognition. Added to this problem is the challenge of concurrently managing multimodal sensory stimuli, which can lead to processing demand overload and interference with normal cognitive processing. This sensory overload, or sensory processing flooding effect, can be found in cognitively unimpaired persons (120) and in diseases such as schizophrenia (121), but is magnified in disorders such as MS and mTBI that feature pathological sensory processing and cognitive impairment resulting from damaged WM.
Discussion
By virtue of living in the natural world, humans are constantly exposed to environments with various levels of concurrent external stimuli. Sensory input from the visual, auditory, and vestibular systems is required for the brain to process critical information, which then enables a variety of psychological and behavioral responses. Successful processing of multimodal primary sensory signals thus serves as a necessary condition for normal cognitive function. These signals provide a foundation for human behavior, which then manifests as a complex amalgam of cognitive and emotional domains that requires bi-directional data exchange within distributed neural networks for long-term development and modulation.
This review highlights the role of primary sensory input in cognition by considering the visual, auditory, and vestibular modalities. Although the thalamus is a key sensory data hub in these processes, numerous WM tracts such as the thalamic radiations, cingulum, and uncinate fasciculus are crucial for the relay of initially processed sensory signals to cortical regions for higher-level coding that enables both bottom-up and top-down processing. In addition, the WM tracts of the middle and superior cerebellar peduncles play critical roles in sensory signal processing and the facilitation of cognitive function. White matter tracts make up the mediating circuitry between GM structures throughout the brain, and the distributed neural networks that include GM and WM comprise the infrastructure for multimodal sensory processing and cognition.
As is observable in disorders such as MS and mTBI, pathology of the thalamus and its associated WM tracts results in cognitive impairment that furthermore can be associated with altered unimodal and multimodal sensory processing. These observations suggest that further research is warranted to better define the causative relationship between disrupted sensory processing and cognitive impairment in these and other disorders, with a special focus on WM pathology. Studies are also needed that investigate differentiating the impact of the initial effects of slowed signaling in tracts and, in some cases, GM pathology secondary to WM disruption, as compared to the flooding effect of sensory processing on cognition. Critical to investigations of this kind will be the application of advanced neuroimaging that will allow for the detailed depiction and analysis of healthy and pathologic WM tracts. To help elucidate subtle findings in WM tracts implicated in the sensory-cognitive connection, more precise imaging of WM microstructure will be necessary. Therefore, in addition to conventional MRI and DTI, future studies will benefit from the deployment of emerging WM imaging techniques and approaches, such as neurite orientation dispersion and density imaging (NODDI) to provide a more detailed and accurate assessment of microstructure than standard metrics such as fractional anisotropy [FA; (122, 123)], and from the application of multiple MR modalities that are sensitive to different neurobiological features such as magnetization transfer saturation, and R1 and R2* metrics that add triangular compliments to routine tractography metrics such as FA to enhance WM specificity (124).
In the clinical setting, standardized neuropsychological assessment of cognition is important and useful, but it has some limitations when multisensory dysfunction is considered. Current cognitive assessment measures are effective for detecting common and overt cognitive impairments; however, they are less useful for identifying covert dysfunction, and determining functional, contextual factors that implicate real-life, ecological considerations such as sensory processing demands that contribute to cognitive impairment (125). This point is illustrated by the directive that, for diseases such as MS and mTBI, the standard administration of cognitive assessments is to be conducted in “quiet”, “sterile” environments “without distractions” (126, 127).
Recent reviews of neuropsychological testing have suggested a transition from a reductionistic approach (e.g., focusing on strict association between localized brain pathology and cognition, and assessing within a decontextualized environments) to a more inclusive, real-world approach (128, 129) that accounts for naturalistic multimodal sensory processing (130, 131). Such strategies would enhance the early detection of both the initial pathological alternations in sensory processing, and the additional multimodal sensory flooding effect on cognition, and are likely to disclose more subtle cognitive loss, especially in early disease stages when prompt treatment may lead to better outcomes (132).
Evidence gathered from studies discussed above will better inform researchers and clinicians so that more precise therapies can be investigated for their effectiveness. Attention should be devoted to interdisciplinary care or integrated practice units, where cognition can be considered a shared outcome so that therapies informed by the WM contributions to cognitive impairment discussed in this review can be implemented. Among the various disciplines offering care (e.g., rehabilitation, psychology), the use of experiential therapy approaches, that require managing enriched environments and novel multimodal sensory signals, can aid in providing the appropriate stimuli required for augmenting experience-dependent brain activity, neuromodulation, plasticity, and ultimately cognitive function (133–135). The application of such therapies would be intended to exploit the potential for dual targeting of cognitive loss in WM disorders, from pathology in WM tracts subserving cognition, and from sensory overload resulting from altered multimodal processing that further interferes with cognition.
Conclusion
The contributions to cognitive impairment made by altered multimodal processing of primary sensory stimuli represents an important perspective in behavioral neurology. Whereas, agnosias and related syndromes have long been appreciated in association with pathology in higher sensory regions of the brain (25), primary sensory dysfunction and its relationship to cognitive impairment has been more recently recognized and investigated. In this review, two disorders with prominent WM pathology have been discussed to point out that impaired cognition can occur not only because of involvement of cognitively relevant regions, but also because of simultaneous processing of primary visual, auditory, and vestibular stimuli that can overwhelm the capacity for normal engagement of those regions. The interference with normal cognition that can result from the burden of multimodal sensory processing illustrates how altered WM connectivity of primary sensory systems plays a key role in cognitive dysfunction. This insight adds more nuanced detail to the behavioral neurology of WM (136, 137), and suggests that a deeper understanding of the concept of WM dementia (138) may be achieved by considering tracts not typically considered relevant to cognition. Supporting this approach, innovative spatially unbiased region and network-based methods such as the Network Modification (NeMo) Tool developed by Kuceyeski et al., a novel topology software pipeline, should be considered to augment the ability to predict GM connectivity outcomes based on changes in WM in diseases such as MS (139). The cognitive impact of abnormal multimodal sensory integration from WM pathology offers a host of intriguing opportunities for further research to investigate fundamental WM-behavior relationships, develop more sensitive clinical testing instruments, and seek improved treatments for a wide range of WM disorders.
Author contributions
Both authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
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.
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.
References
1. Freedman DJ, Ibos G. An integrative framework for sensory, motor, and cognitive functions of the posterior parietal cortex. Neuron. (2018) 97:1219–34. doi: 10.1016/j.neuron.2018.01.044
2. Rolls ET, Grabenhorst F. The orbitofrontal cortex and beyond: from affect to decision-making. Prog Neurobiol. (2008) 86:216–44. doi: 10.1016/j.pneurobio.2008.09.001
3. Miller EK. The prefrontal cortex and cognitive control. Nat Rev Neurosci. (2000) 1:59–65. doi: 10.1038/35036228
4. Antonucci LA, Penzel N, Pigoni A, Dominke C, Kambeitz J, Pergola G. Flexible and specific contributions of thalamic subdivisions to human cognition. Neurosci Biobehav Rev. (2021) 124:35–53. doi: 10.1016/j.neubiorev.2021.01.014
5. Lisman J, Buzsáki G, Eichenbaum H, Nadel L, Ranganath C, Redish AD. Viewpoints: how the hippocampus contributes to memory, navigation and cognition [published correction appears in Nat Neurosci. 2017 Dec 20]. Nat Neurosci. (2017) 20:1434–47. doi: 10.1038/nn.4661
6. Lin F, Ren P, Mapstone M, Meyers SP, Porsteinsson A, Baran TM, et al. The cingulate cortex of older adults with excellent memory capacity. Cortex. (2017) 86:83–92. doi: 10.1016/j.cortex.2016.11.009
7. Callen DJ, Black SE, Gao F, Caldwell CB, Szalai JP. Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD. Neurology. (2001) 57:1669–74. doi: 10.1212/WNL.57.9.1669
8. Schmahmann JD. The cerebellum and cognition. Neurosci Lett. (2019) 688:62–75. doi: 10.1016/j.neulet.2018.07.005
9. Jacobi H, Faber J, Timmann D, Klockgether T. Update cerebellum and cognition. J Neurol. (2021) 268:3921–5. doi: 10.1007/s00415-021-10486-w
10. Varela C, Kumar S, Yang JY, Wilson MA. Anatomical substrates for direct interactions between hippocampus, medial prefrontal cortex, and the thalamic nucleus reuniens. Brain Struct Funct. (2014) 219:911–29. doi: 10.1007/s00429-013-0543-5
11. Wolff M, Morceau S, Folkard R, Martin-Cortecero J, Groh A. A thalamic bridge from sensory perception to cognition. Neurosci Biobehav Rev. (2021) 120:222–35. doi: 10.1016/j.neubiorev.2020.11.013
12. Deng F, Wang Y, Huang H, Niu M, Zhong S, Zhao L, et al. Abnormal segments of right uncinate fasciculus and left anterior thalamic radiation in major and bipolar depression. Prog Neuropsychopharmacol Biol Psychiatry. (2018) 81:340–9. doi: 10.1016/j.pnpbp.2017.09.006
13. Brandt T, Strupp M, Dieterich M. Towards a concept of disorders of “higher vestibular function”. Front Integr Neurosci. (2014) 8:47. doi: 10.3389/fnint.2014.00047
14. Della-Justina HM, Gamba HR, Lukasova K, Nucci-da-Silva MP, Winkler AM, Amaro E Jr. Interaction of brain areas of visual and vestibular simultaneous activity with fMRI. Exp Brain Res. (2015) 233:237–52. doi: 10.1007/s00221-014-4107-6
15. Beylergil SB, Shaikh AG. Visual perception of heading in the syndrome of oculopalatal tremor. Cerebellum. (2021) 20:788–95. doi: 10.1007/s12311-020-01176-w
16. Humes LE. Age-related changes in cognitive and sensory processing: focus on middle-aged adults. Am J Audiol. (2015) 24:94–7. doi: 10.1044/2015_AJA-14-0063
17. Jorquera-Cabrera S, Romero-Ayuso D, Rodriguez-Gil G, Triviño-Juárez JM. Assessment of sensory processing characteristics in children between 3 and 11 years old: a systematic review [published correction appears in Front Pediatr. 2017 Dec 12;5:266]. Front Pediatr. (2017) 5:57. doi: 10.3389/fped.2017.00057
18. Ghajar J, Ivry RB. Cognitive and Neurobiological Research Consortium. The predictive brain state: timing deficiency in traumatic brain injury? Neurorehabil Neural Repair. (2008) 22:217–27. doi: 10.1177/1545968308315600
19. Maruta J, Lee SW, Jacobs EF, Ghajar J, A. unified science of concussion. Ann N Y Acad Sci. (2010) 1208:58–66. doi: 10.1111/j.1749-6632.2010.05695.x
20. Byrne JH. Postsynaptic potentials and synaptic Integration. In:Byrne JH, Heidelberger R, Waxham N, , editors. From Molecules to Networks (Third Edition) An Introduction to Cellular and Molecular Neuroscience. London; Waltham; San Diego, CA: Academic Press (2014). p. 489–507. doi: 10.1016/B978-0-12-397179-1.00016-6
21. Zajzon B, Morales-Gregorio A. Trans-thalamic pathways: strong candidates for supporting communication between functionally distinct cortical areas. J Neurosci. (2019) 39:7034–6. doi: 10.1523/JNEUROSCI.0656-19.2019
22. Ward LM. The thalamus: gateway to the mind. Wiley Interdiscip Rev Cogn Sci. (2013) 4:609–22. doi: 10.1002/wcs.1256
23. Mitchell AS, Sherman SM, Sommer MA, Mair RG, Vertes RP, Chudasama Y. Advances in understanding mechanisms of thalamic relays in cognition and behavior. J Neurosci. (2014) 34:15340–6. doi: 10.1523/JNEUROSCI.3289-14.2014
24. Steiner L, Federspiel A, Slavova N, Wiest R, Grunt S, Steinlin M, et al. Functional topography of the thalamo-cortical system during development and its relation to cognition. Neuroimage. (2020) 223:117361. doi: 10.1016/j.neuroimage.2020.117361
25. Mesulam MM. From sensation to cognition. Brain. (1998) 121:1013–52. doi: 10.1093/brain/121.6.1013
26. Bridge H, Leopold DA, Bourne JA. Adaptive pulvinar circuitry supports visual cognition. Trends Cogn Sci. (2016) 20:146–57. doi: 10.1016/j.tics.2015.10.003
27. Saalmann YB, Kastner S. Cognitive and perceptual functions of the visual thalamus. Neuron. (2011) 71:209–23. doi: 10.1016/j.neuron.2011.06.027
28. McMains S, Kastner S. Interactions of top-down and bottom-up mechanisms in human visual cortex. J Neurosci. (2011) 31:587–97. doi: 10.1523/JNEUROSCI.3766-10.2011
29. van den Brink RL, Cohen MX, van der Burg E, Talsma D, Vissers ME, Slagter HA. Subcortical, modality-specific pathways contribute to multisensory processing in humans. Cereb Cortex. (2014) 24:2169–77. doi: 10.1093/cercor/bht069
30. McDonough IM, Siegel JT. The relation between white matter microstructure and network complexity: implications for processing efficiency. Front Integr Neurosci. (2018) 12:43. doi: 10.3389/fnint.2018.00043
31. Eckert MA, Menon V, Walczak A, Ahlstrom J, Denslow S, Horwitz A, et al. At the heart of the ventral attention system: the right anterior insula. Hum Brain Mapp. (2009) 30:2530–41. doi: 10.1002/hbm.20688
32. Vaden KI Jr, Kuchinsky SE, Cute SL, Ahlstrom JB, Dubno JR, Eckert MA. The cingulo-opercular network provides word-recognition benefit. J Neurosci. (2013) 33:18979–86. doi: 10.1523/JNEUROSCI.1417-13.2013
33. Sadaghiani S, D'Esposito M. Functional characterization of the cingulo-opercular network in the maintenance of tonic alertness. Cereb Cortex. (2015) 25:2763–73. doi: 10.1093/cercor/bhu072
34. Sheffield JM, Repovs G, Harms MP, Carter CS, Gold JM, MacDonald III AW, et al. Fronto-parietal and cingulo-opercular network integrity and cognition in health and schizophrenia. Neuropsychologia. (2015) 73:82–93. doi: 10.1016/j.neuropsychologia.2015.05.006
35. Wallis G, Stokes M, Cousijn H, Woolrich M, Nobre AC. Frontoparietal and cingulo-opercular networks play dissociable roles in control of working memory. J Cogn Neurosci. (2015) 27:2019–34. doi: 10.1162/jocn_a_00838
36. Cohen N, Ben-Yakov A, Weber J, Edelson MG, Paz R, Dudai Y. Prestimulus activity in the cingulo-opercular network predicts memory for naturalistic episodic experience. Cereb Cortex. (2020) 30:1902–13. doi: 10.1093/cercor/bhz212
37. Küchenhoff S, Sorg C, Schneider SC, Kohl O, Müller HJ, Napiórkowski N, et al. Fronto-cerebellar connectivity mediating cognitive processing speed. Neuroimage. (2021) 226:117556. doi: 10.1016/j.neuroimage.2020.117556
38. Brissenden JA, Somers DC. Cortico-cerebellar networks for visual attention and working memory. Curr Opin Psychol. (2019) 29:239–47. doi: 10.1016/j.copsyc.2019.05.003
39. McLachlan NM, Wilson SJ. The contribution of brainstem and cerebellar pathways to auditory recognition. Front Psychol. (2017) 8:265. doi: 10.3389/fpsyg.2017.00265
40. Reineberg AE, Banich MT. Functional connectivity at rest is sensitive to individual differences in executive function: a network analysis. Hum Brain Mapp. (2016) 37:2959–75. doi: 10.1002/hbm.23219
41. Parks EL, Madden DJ. Brain connectivity and visual attention. Brain Connect. (2013) 3:317–38. doi: 10.1089/brain.2012.0139
42. Qiao L, Xu M, Luo X, Zhang L, Li H, Chen A. Flexible adjustment of the effective connectivity between the fronto-parietal and visual regions supports cognitive flexibility. Neuroimage. (2020) 220:117158. doi: 10.1016/j.neuroimage.2020.117158
43. Küchenhoff S, Sorg C, Schneider SC, Kohl O, Müller HJ, Napiórkowski N, et al. Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks. Eur J Neurosci. (2021) 53:3362–77. doi: 10.1111/ejn.15206
44. Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: anatomy, function, and dysfunction. Neurosci Biobehav Rev. (2018) 92:104–27. doi: 10.1016/j.neubiorev.2018.05.008
45. Pascalau R, Popa Stănilă R, Sfrângeu S, Szabo B. Anatomy of the limbic white matter tracts as revealed by fiber dissection and tractography. World Neurosurg. (2018) 113:e672–89. doi: 10.1016/j.wneu.2018.02.121
46. Maldonado IL, Parente de. Matos V, Castro Cuesta TA, Herbet G, Destrieux C. The human cingulum: From the limbic tract to the connectionist paradigm. Neuropsychologia. (2020) 144:107487. doi: 10.1016/j.neuropsychologia.2020.107487
47. Landers MJF, Sitskoorn MM, Rutten GM, Mandonnet E, De Baene W. A systematic review of the use of subcortical intraoperative electrical stimulation mapping for monitoring of executive deficits and neglect: what is the evidence so far? Acta Neurochir. (2022) 164:177–91. doi: 10.1007/s00701-021-05012-w
48. Fan YT, Fang YW, Chen YP, Leshikar ED, Lin CP, Tzeng OJ, et al. Aging, cognition, and the brain: effects of age-related variation in white matter integrity on neuropsychological function. Aging Ment Health. (2019) 23:831–9. doi: 10.1080/13607863.2018.1455804
49. Park CH, Kim SH, Jung HY. Characteristics of the uncinate fasciculus and cingulum in patients with mild cognitive impairment: diffusion tensor tractography study. Brain Sci. (2019) 9:377. doi: 10.3390/brainsci9120377
50. Bhatia K, Henderson L, Yim M, Hsu E, Dhaliwal R. Diffusion tensor imaging investigation of uncinate fasciculus anatomy in healthy controls: description of a subgenual stem. Neuropsychobiology. (2017) 75:132–40. doi: 10.1159/000485111
51. Wycoco V, Shroff M, Sudhakar S, Lee W. White matter anatomy: what the radiologist needs to know. Neuroimaging Clin N Am. (2013) 23:197–216. doi: 10.1016/j.nic.2012.12.002
52. Arbeloff TC, Kim MJ, Knodt AR, Radtke SR, Brigidi BD, Hariri AR. Microstructural integrity of a pathway connecting the prefrontal cortex and amygdala moderates the association between cognitive reappraisal and negative emotions. Emotion. (2018) 18:912–5. doi: 10.1037/emo0000447
53. Charlton RA, Barrick TR, Lawes IN, Markus HS, Morris RG. White matter pathways associated with working memory in normal aging. Cortex. (2010) 46:474–89. doi: 10.1016/j.cortex.2009.07.005
54. Shiotsu D, Jung M, Habata K, Kamiya T, Omori IM, Okazawa H, et al. Elucidation of the relationship between sensory processing and white matter using diffusion tensor imaging tractography in young adults. Sci Rep. (2021) 11:12088. doi: 10.1038/s41598-021-91569-6
55. Sasson E, Doniger GM, Pasternak O, Assaf Y. Structural correlates of memory performance with diffusion tensor imaging. Neuroimage. (2010) 50:1231–42. doi: 10.1016/j.neuroimage.2009.12.079
56. Sasson E, Doniger GM, Pasternak O, Tarrasch R, Assaf Y. White matter correlates of cognitive domains in normal aging with diffusion tensor imaging. Front Neurosci. (2013) 7:32. doi: 10.3389/fnins.2013.00032
57. Davis SW, Szymanski A, Boms H, Fink T, Cabeza R. Cooperative contributions of structural and functional connectivity to successful memory in aging. Netw Neurosci. (2018) 3:173–94. doi: 10.1162/netn_a_00064
58. Krogsrud SK, Fjell AM, Tamnes CK, Grydeland H, Due-Tønnessen P, Bjørnerud A, et al. Development of white matter microstructure in relation to verbal and visuospatial working memory-A longitudinal study. PLoS ONE. (2018) 13:e0195540. doi: 10.1371/journal.pone.0195540
59. Diao L, Yu H, Zheng J, Chen Z, Huang D, Yu L. Abnormalities of the uncinate fasciculus correlate with executive dysfunction in patients with left temporal lobe epilepsy. Magn Reson Imaging. (2015) 33:544–50. doi: 10.1016/j.mri.2015.02.011
60. Sato T, Maruyama N, Hoshida T, Minato K. Correlation between uncinate fasciculus and memory tasks in healthy individual using diffusion tensor tractography. Annu Int Conf IEEE Eng Med Biol Soc. (2012) 2012:424–7. doi: 10.1109/EMBC.2012.6345958
61. Wertz CJ, Chohan MO, Ramey SJ, Flores RA, Jung RE. White matter correlates of creative cognition in a normal cohort. Neuroimage. (2020) 208:116293. doi: 10.1016/j.neuroimage.2019.116293
62. Pisner DA, Smith R, Alkozei A, Klimova A, Killgore WD. Highways of the emotional intellect: white matter microstructural correlates of an ability-based measure of emotional intelligence. Soc Neurosci. (2017) 12:253–67. doi: 10.1080/17470919.2016.1176600
63. Korthauer LE, Nowak NT, Moffat SD, An Y, Rowland LM, Barker PB, et al. Correlates of virtual navigation performance in older adults. Neurobiol Aging. (2016) 39:118–27. doi: 10.1016/j.neurobiolaging.2015.12.003
64. Waespe W, Henn V. Vestibular nuclei activity during optokinetic after-nystagmus (OKAN) in the alert monkey. Exp Brain Res. (1977) 30:323–30. doi: 10.1007/BF00237259
65. Waespe W, Henn V. Neuronal activity in the vestibular nuclei of the alert monkey during vestibular and optokinetic stimulation. Exp Brain Res. (1977) 27:523–38. doi: 10.1007/BF00239041
66. Scudder CA, Fuchs AF. Physiological and behavioral identification of vestibular nucleus neurons mediating the horizontal vestibuloocular reflex in trained rhesus monkeys. J Neurophysiol. (1992) 68:244–64. doi: 10.1152/jn.1992.68.1.244
67. Meng H, Blázquez PM, Dickman JD, Angelaki DE. Diversity of vestibular nuclei neurons targeted by cerebellar nodulus inhibition. J Physiol. (2014) 592:171–88. doi: 10.1113/jphysiol.2013.259614
68. Cullen KE. Vestibular processing during natural self-motion: implications for perception and action. Nat Rev Neurosci. (2019) 20:346–63. doi: 10.1038/s41583-019-0153-1
69. Wijesinghe R, Protti DA, Camp AJ. Vestibular interactions in the thalamus. Front Neural Circuits. (2015) 9:79. doi: 10.3389/fncir.2015.00079
70. Gamba P. Vestibular-limbic relationships: brain mapping. Insights Depress Anxiety. (2018) 2:007–13. doi: 10.29328/journal.ida.1001006
71. Hitier M, Besnard S, Smith PF. Vestibular pathways involved in cognition. Front Integr Neurosci. (2014) 8:59. doi: 10.3389/fnint.2014.00059
72. Lopez C, Blanke O. The thalamocortical vestibular system in animals and humans. Brain Res Rev. (2011) 67:119–46. doi: 10.1016/j.brainresrev.2010.12.002
73. Bigelow RT, Agrawal Y. Vestibular involvement in cognition: visuospatial ability, attention, executive function, and memory. J Vestib Res. (2015) 25:73–89. doi: 10.3233/VES-150544
74. Smith PF. The vestibular system and cognition. Curr Opin Neurol. (2017) 30:84–9. doi: 10.1097/WCO.0000000000000403
75. Previc FH. Vestibular loss as a contributor to Alzheimer's disease. Med Hypotheses. (2013) 80:360–7. doi: 10.1016/j.mehy.2012.12.023
76. Harun A, Oh ES, Bigelow RT, Studenski S, Agrawal Y. Vestibular impairment in dementia. Otol Neurotol. (2016) 37:1137–42. doi: 10.1097/MAO.0000000000001157
77. Lassmann H. Multiple sclerosis pathology. Cold Spring Harb Perspect Med. (2018) 8:a028936. doi: 10.1101/cshperspect.a028936
78. Engel-Yeger B, DeLuca J, Hake P, Goverover Y. The role of sensory processing difficulties, cognitive impairment, and disease severity in predicting functional behavior among patients with multiple sclerosis. Disabil Rehabil. (2021) 43:1129–36. doi: 10.1080/09638288.2019.1653998
79. Hebert JR, Corboy JR, Vollmer T, Forster JE, Schenkman M. Efficacy of balance and eye-movement exercises for persons with multiple sclerosis (BEEMS). Neurology. (2018) 90:e797–807. doi: 10.1212/WNL.0000000000005013
80. Hebert JR, Manago MM. Reliability and validity of the computerized dynamic posturography sensory organization test in people with multiple sclerosis. Int J MS Care. (2017) 19:151–7. doi: 10.7224/1537-2073.2016-027
81. Gromisch ES, DeLuca J, Benedict RHB, Foley FW. Managing cognitive dysfunction in multiple sclerosis: a snapshot of changes in screening, assessment, and treatment practices. Int J MS Care. (2022) 24:104–9. doi: 10.7224/1537-2073.2020-139
82. Giurgola S, Casati C, Stampatori C, Perucca L, Mattioli F, Vallar G, et al. Abnormal multisensory integration in relapsing-remitting multiple sclerosis. Exp Brain Res. (2022) 240:953–68. doi: 10.1007/s00221-022-06310-0
83. Cochrane GD, Christy JB, Sandroff BM, Motl RW. Cognitive and central vestibular functions correlate in people with multiple sclerosis. Neurorehabil Neural Repair. (2021) 35:1030–8. doi: 10.1177/15459683211046268
84. Fischer M, Köhler W, Faiss JH, Hoffmann F, Kunkel A, Sailer M, et al. A smart peek: processing of rapid visual displays is disturbed in newly diagnosed, cognitively intact MS patients and refers to cognitive performance and disease progression in late stages. J Neurol Sci. (2019) 401:118–24. doi: 10.1016/j.jns.2019.04.031
85. Minagar A, Barnett MH, Benedict RH, Pelletier D, Pirko I, Sahraian MA, et al. The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neurology. (2013) 80:210–9. doi: 10.1212/WNL.0b013e31827b910b
86. Kern KC, Gold SM, Lee B, Montag M, Horsfall J, O'Connor MF, et al. Thalamic-hippocampal-prefrontal disruption in relapsing-remitting multiple sclerosis. Neuroimage Clin. (2014) 8:440–7. doi: 10.1016/j.nicl.2014.12.015
87. Hulst HE, Steenwijk MD, Versteeg A, Pouwels PJ, Vrenken H, Uitdehaag BM, et al. Cognitive impairment in MS: impact of white matter integrity, gray matter volume, and lesions. Neurology. (2013) 80:1025–32. doi: 10.1212/WNL.0b013e31828726cc
88. Fritz NE, Edwards EM, Ye C, Prince J, Yang Z, Gressett T, et al. Cerebellar contributions to motor and cognitive control in multiple sclerosis⋆⋆⋆. Arch Phys Med Rehabil. (2022) 103:1592–9. doi: 10.1016/j.apmr.2021.12.010
89. Lie IA, Weeda MM, Mattiesing RM, Mol MA, Pouwels PJ, Barkhof F, et al. Relationship between white matter lesions and gray matter atrophy in multiple sclerosis: a systematic review. Neurology. (2022) 98:e1562–73. doi: 10.1212/WNL.0000000000200006
90. Klaver R, De Vries HE, Schenk GJ, Geurts JJ. Grey matter damage in multiple sclerosis: a pathology perspective. Prion. (2013) 7:66–75. doi: 10.4161/pri.23499
91. Adams JH, Doyle D, Ford I, Gennarelli TA, Graham DI, McLellan DR. Diffuse axonal injury in head injury: definition, diagnosis and grading. Histopathology. (1989) 15:49–59. doi: 10.1111/j.1365-2559.1989.tb03040.x
92. Smith DH. Neuromechanics and pathophysiology of diffuse axonal injury in concussion. Bridge (Wash D C). (2016) 46:79–84.
93. Kirov II, Tal A, Babb JS, Lui YW, Grossman RI, Gonen O. Diffuse axonal injury in mild traumatic brain injury: a 3D multivoxel proton MR spectroscopy study. J Neurol. (2013) 260:242–52. doi: 10.1007/s00415-012-6626-z
94. Van Gils A, Stone J, Welch K, Davidson LR, Kerslake D, Caesar D, et al. Management of mild traumatic brain injury. Pract Neurol. (2020) 20:213–21. doi: 10.1136/practneurol-2018-002087
95. Hocke LM, Duszynski CC, Debert CT, Dleikan D, Dunn JF. Reduced functional connectivity in adults with persistent post-concussion symptoms: a functional near-infrared spectroscopy study. J Neurotrauma. (2018) 35:1224–32. doi: 10.1089/neu.2017.5365
96. Hebert JR, Forster JE, Stearns-Yoder KA, Penzenik ME, Brenner LA. Persistent symptoms and objectively measured balance performance among OEF/OIF veterans with remote mild traumatic brain injury. J Head Trauma Rehabil. (2018) 33:403–11. doi: 10.1097/HTR.0000000000000385
97. Rabinowitz AR, Smith DH. Traumatic brain injury and rationale for a neuropsychological diagnosis of diffuse axonal injury. In:Lazarov O, Tesco G, , editors. Genes, Environment and Alzheimer's Disease. London; San Diego, CA; Cambridge; Oxford: Academic Press (2016). p. 267–93. doi: 10.1016/B978-0-12-802851-3.00009-7
98. Michel BF, Sambuchi N, Vogt BA. Impact of mild traumatic brain injury on cingulate functions. In:Vogt BA, , editor. Handbook of Clinical Neurology. Amsterdam; Oxford; Cambridge: Elsevier (2019). p. 151–62. doi: 10.1016/B978-0-444-64196-0.00010-8
99. Jang SH, Kim SH, Lee HD. Traumatic axonal injury of the cingulum in patients with mild traumatic brain injury: a diffusion tensor tractography study. Neural Regen Res. (2019) 14:1556–61. doi: 10.4103/1673-5374.255977
100. Wang JY, Bakhadirov K, Abdi H, Devous MD, De La Plata CM, Moore C, et al. Longitudinal changes of structural connectivity in traumatic axonal injury. Neurology. (2011) 77:818–26. doi: 10.1212/WNL.0b013e31822c61d7
101. Moore RD, Broglio SP, Hillman CH. Sport-related concussion and sensory function in young adults. J Athl Train. (2014) 49:36–41. doi: 10.4085/1062-6050-49.1.02
102. Prak RF, Marsman JC, Renken R, van der Naalt J, Zijdewind I. Fatigue following mild traumatic brain injury relates to visual processing and effort perception in the context of motor performance. Neuroimage Clin. (2021) 32:102783. doi: 10.1016/j.nicl.2021.102783
103. Alnawmasi MM, Chakraborty A, Dalton K, Quaid P, Dunkley BT, Thompson B. The effect of mild traumatic brain injury on the visual processing of global form and motion. Brain Inj. (2019) 33:1354–63. doi: 10.1080/02699052.2019.1641842
104. Vander Werff KR, Rieger B. Impaired auditory processing and neural representation of speech in noise among symptomatic post-concussion adults. Brain Inj. (2019) 33:1320–31. doi: 10.1080/02699052.2019.1641624
105. Białuńska A, Salvatore AP. The auditory comprehension changes over time after sport-related concussion can indicate multisensory processing dysfunctions. Brain Behav. (2017) 7:e00874. doi: 10.1002/brb3.874
106. Akin FW, Murnane OD, Hall CD, Riska KM. Vestibular consequences of mild traumatic brain injury and blast exposure: a review. Brain Inj. (2017) 31:1188–94. doi: 10.1080/02699052.2017.1288928
107. Kaae C, Cadigan K, Lai K, Theis J. Vestibulo-ocular dysfunction in mTBI: Utility of the VOMS for evaluation and management - A review. NeuroRehabilitation. (2022) 50:279–96. doi: 10.3233/NRE-228012
108. Campbell KR, Parrington L, Peterka RJ, Martini DN, Hullar TE, Horak FB, et al. Exploring persistent complaints of imbalance after mTBI: oculomotor, peripheral vestibular and central sensory integration function. J Vestib Res. (2021) 31:519–30. doi: 10.3233/VES-201590
109. Franke LM, Perera RA, Aygemang AA, Marquardt CA, Teich C, Sponheim SR, et al. Auditory evoked brain potentials as markers of chronic effects of mild traumatic brain injury in mid-life. Clin Neurophysiol. (2021) 132:2979–88. doi: 10.1016/j.clinph.2021.09.007
110. Wright WG, Handy JD, Haskell A, Servatius L, Servatius RJ. History of mild traumatic brain injury affects static balance under complex multisensory manipulations. J Neurotrauma. (2022) 39:821–8. doi: 10.1089/neu.2020.7600
111. Mayer AR, Hanlon FM, Dodd AB, Ling JM, Klimaj SD, Meier TB, et al. functional magnetic resonance imaging study of cognitive control and neurosensory deficits in mild traumatic brain injury. Hum Brain Mapp. (2015) 36:4394–406. doi: 10.1002/hbm.22930
112. Pogoda TK, Hendricks AM, Iverson KM, Stolzmann KL, Krengel MH, Baker E, et al. Multisensory impairment reported by veterans with and without mild traumatic brain injury history. J Rehabil Res Dev. (2012) 49:971–84. doi: 10.1682/JRRD.2011.06.0099
113. Allen JW, Trofimova A, Ahluwalia V, Smith JL, Abidi SA, Peters MA, et al. Altered processing of complex visual stimuli in patients with postconcussive visual motion sensitivity. Am J Neuroradiol. (2021) 42:930–7. doi: 10.3174/ajnr.A7007
114. Tapper A, Gonzalez D, Roy E, Niechwiej-Szwedo E. Executive function deficits in team sport athletes with a history of concussion revealed by a visual-auditory dual task paradigm. J Sports Sci. (2017) 35:231–40. doi: 10.1080/02640414.2016.1161214
115. Zhuo J, Jiang L, Sours Rhodes C, Roys S, Shanmuganathan K, Chen H, et al. Early stage longitudinal subcortical volumetric changes following mild traumatic brain injury. Brain Inj. (2021) 35:725–33. doi: 10.1080/02699052.2021.1906445
116. Xiong KL, Zhang JN, Zhang YL, Zhang Y, Chen H, Qiu MG. Brain functional connectivity and cognition in mild traumatic brain injury. Neuroradiology. (2016) 58:733–9. doi: 10.1007/s00234-016-1675-0
117. Bai L, Bai G, Wang S, Yang X, Gan S, Jia X, et al. Strategic white matter injury associated with long-term information processing speed deficits in mild traumatic brain injury. Hum Brain Mapp. (2020) 41:4431–41. doi: 10.1002/hbm.25135
118. Grossman EJ, Inglese M. The role of thalamic damage in mild traumatic brain injury. J Neurotrauma. (2016) 33:163–7. doi: 10.1089/neu.2015.3965
119. Grossman EJ, Ge Y, Jensen JH, Babb JS, Miles L, Reaume J, et al. Thalamus and cognitive impairment in mild traumatic brain injury: a diffusional kurtosis imaging study. J Neurotrauma. (2012) 29:2318–27. doi: 10.1089/neu.2011.1763
120. Armougum A, Gaston-Bellegarde A, Joie-La Marle C, Piolino P. Physiological investigation of cognitive load in real-life train travelers during information processing. Appl Ergon. (2020) 89:103180. doi: 10.1016/j.apergo.2020.103180
121. Smucny J, Rojas DC, Eichman LC, Tregellas JR. Neural effects of auditory distraction on visual attention in schizophrenia. PLoS ONE. (2013) 8:e60606. doi: 10.1371/journal.pone.0060606
122. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODD practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. (2012) 61:1000–16. doi: 10.1016/j.neuroimage.2012.03.072
123. Palacios EM, Owen JP, Yuh EL, Wang MB, Vassar MJ, Ferguson AR, et al. The evolution of white matter microstructural changes after mild traumatic brain injury: a longitudinal DTI and NODDI study. Sci Adv. (2020) 6:eaaz6892. doi: 10.1126/sciadv.aaz6892
124. Lazari A, Salvan P, Cottaar M, Papp D, van der Werf OJ, Johnstone A, et al. Reassessing associations between white matter and behaviour with multimodal microstructural imaging. Cortex. (2021) 145:187–200. doi: 10.1016/j.cortex.2021.08.017
125. Casaletto KB, Heaton RK. Neuropsychological assessment: past and future. J Int Neuropsychol Soc. (2017) 23:778–90. doi: 10.1017/S1355617717001060
126. Meca-Lallana V, Gascón-Giménez F, Ginestal-López RC, Higueras Y, Téllez-Lara N, Carreres-Polo J, et al. Cognitive impairment in multiple sclerosis: diagnosis and monitoring. Neurol Sci. (2021) 42:5183–93. doi: 10.1007/s10072-021-05165-7
127. Prince C, Bruhns ME. Evaluation and treatment of mild traumatic brain injury: the role of neuropsychology. Brain Sci. (2017) 7:105. doi: 10.3390/brainsci7080105
128. Piau A, Wild K, Mattek N, Kaye J. Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res. (2019) 21:e12785. doi: 10.2196/12785
129. Ferguson CE. Network neuropsychology: the map and the territory. Neurosci Biobehav Rev. (2022) 132:638–47. doi: 10.1016/j.neubiorev.2021.11.024
130. Shamay-Tsoory SG, Mendelsohn A. Real-life neuroscience: an ecological approach to brain and behavior research. Perspect Psychol Sci. (2019) 14:841–59. doi: 10.1177/1745691619856350
131. Gioia GA, Kenworthy L, Isquith PK. Executive function in the real world: BRIEF lessons from Mark Ylvisaker. J Head Trauma Rehabil. (2010) 25:433–9. doi: 10.1097/HTR.0b013e3181fbc272
132. Johnson LW, Lundgren K, Henrich V, Phillips S. Factors influencing recovery from mild traumatic brain injury. Brain Inj. (2020) 34:1202–12. doi: 10.1080/02699052.2020.1795719
133. Krivanek TJ, Gale SA, McFeeley BM, Nicastri CM, Daffner KR. Promoting successful cognitive aging: a ten-year update. J Alzheimers Dis. (2021) 81:871–920. doi: 10.3233/JAD-201462
134. Schomaker J, Meeter M. Short- and long-lasting consequences of novelty, deviance and surprise on brain and cognition. Neurosci Biobehav Rev. (2015) 55:268–79. doi: 10.1016/j.neubiorev.2015.05.002
135. Hannan AJ. Environmental enrichment and brain repair: harnessing the therapeutic effects of cognitive stimulation and physical activity to enhance experience-dependent plasticity. Neuropathol Appl Neurobiol. (2014) 40:13–25. doi: 10.1111/nan.12102
136. Filley CM. The behavioral neurology of cerebral white matter. Neurology. (1998) 50:1535–40. doi: 10.1212/WNL.50.6.1535
137. Filley CM, Fields RD. White matter and cognition: making the connection. J Neurophysiol. (2016) 116:2093–104. doi: 10.1152/jn.00221.2016
138. Filley CM, Franklin GM, Heaton RK, Rosenberg NL. White matter dementia: clinical disorders and implications. Neuropsychiatry Neuropsychol Behav Neurol. (1988) 1:239–54.
Keywords: sensory integration, sensory processing, white matter, cognition, multiple sclerosis, mild traumatic brain injury
Citation: Hebert JR and Filley CM (2022) Multisensory integration and white matter pathology: Contributions to cognitive dysfunction. Front. Neurol. 13:1051538. doi: 10.3389/fneur.2022.1051538
Received: 22 September 2022; Accepted: 18 October 2022;
Published: 02 November 2022.
Edited by:
Ching-Kuan Liu, Kaohsiung Medical University Hospital, TaiwanReviewed by:
Jun Maruta, Icahn School of Medicine at Mount Sinai, United StatesCopyright © 2022 Hebert and Filley. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jeffrey R. Hebert, jeffrey.hebert@cuanschutz.edu