The intrinsic spatiotemporal structure of cognitive functions inspires the intervention of brain functions

Yifeng Wang ^* Chi Zhang Qiang Liu Jing Xiujuan ^*

• Sichuan Normal University, Chengdu, China

1IntroductionAll activities of entities occur within specific temporal and spatial scales. These scales are mutually restrictive. The spatiotemporal scales of neural activity are subject to the structural and functional organization of the brain. There is a strong connection between the size of the brain and the frequency of neural activity (Buzsáki et al., 2013), while each brain region or neural circuit has its own spectral characteristics (Keitel & Gross, 2016)(see Fig. 1A). Consequently, cognitive-specific spectral fingerprints should be present if each cognitive function is realized by a distinct neural circuit. This phenomenon is elucidated by the spectral fingerprint hypothesis of cognition (Siegel et al., 2012)(see Fig. 1B). Recent studies have not only uncovered the frequency characteristics of particular brain regions or neural circuits, but also indicated that each cognitive process fluctuates at a preferential frequency (Palva & Palva, 2018).In specificity, if a comprehensive cognitive function is divided into distinct cognitive processing stages, each associated with different neural circuits, then this cognitive function can be characterized by the spatiotemporal structure of nested neural circuits and corresponding spectral profiles. Assessing the level achievable by each cognitive function is contingent upon the resemblance between the spatiotemporal structure of its neural circuit and the optimal spatiotemporal structure. The intrinsic spatiotemporal structure of cognitions is, therefore, crucial for the comprehension of the neural mechanisms governing normal and pathological cognitive processes, as well as for cognitive rehabilitation and interventions in brain function.Insert Fig.1 about hereFigure 1. The intrinsic spatiotemporal structure of cognitions and its implication for the intervention of brain functions. A) The cognitive-specific spatiotemporal brain structure. B) Cognitive fingerprints defined by specific neural circuits and power spectrum. C) Intervening abnormal brain functions through simulated twin spatiotemporal structure of normal brain functions.2Brain stimulation based on the intrinsic spatiotemporal structure of cognitionsBy engaging in a long-term cognitive training, individuals can optimize their cognitive abilities and gradually stabilize the neural circuits associated with those cognitive functions, resulting in the emergence of specific spatiotemporal structures (Yang et al., 2019). The spatiotemporal structure of the neural circuit associated with optimal cognitive functioning can be defined as the intrinsic spatiotemporal structure of the cognitive function. Young adults, prodigies, or individuals with professional training often offer us models that closely approximate the intrinsic spatiotemporal structure (Qiao et al., 2022). Though many cognitive functions may appear to be alike at first glance, they are hard to transfer from one to another due to their exclusive spatiotemporal structure. Different cognitive functions may share many brain regions, networks, and neural oscillations. However, this overlap is insufficient to produce transfer effects across cognitive functions after cognitive training. A specific cognitive function is defined by the complete spatiotemporal structure formed by both these shared and non-shared structures. Due to the difference in intrinsic spatiotemporal structures, each cognitive function is unique, which may be a significant reason for the lack of far transfer effects in cognitive training. We refer to this phenomenon as cognitive isolation. Just like the productive isolation, cognitive isolation impedes the transition between two distinct cognitive functions. This may be an important reason for the absence of the far transfer effect in cognitive trainings (Sala & Gobet, 2019).The intrinsic spatiotemporal structure of cognitive functions may provide advantageous targets for brain stimulations. Current interventions for brain function are limited in their efficacy, as they are restricted to nonspecific neural circuits or frequencies, making it difficult to reach an ideal brain state (Qiao et al., 2022). On the other hand, interventions that target specific spatial or frequency elements of brain function have yielded remarkable outcomes (Cash et al., 2021). Therefore, it is exciting to anticipate the potential outcomes of brain stimulation techniques that are based on intrinsic spatiotemporal structures. In other words, achieving the optimal frequency of activity in each neural circuit, as well as their interactions, may lead to the attainment of optimal cognitive function.With the progression of digital twin technology, it is possible to mirror the twin spatiotemporal structure of every cognitive function in the future. According to the neural entrainment theory, this simulated twin spatiotemporal structure may cause the neural system to resonate in particular patterns (Zhang et al., 2023), thereby optimizing correspondent cognitive functions (see Fig. 1C). It is encouraging that there have been endeavors to combine high-definition transcranial electrical stimulation with broadband or band-removed spectral profiles, resulting in favorable outcomes (Janssens et al., 2022). This has promising implications for brain function rehabilitation and cognitive enhancement.3DiscussionThe implementation of precision intervention is pivotal in improving the efficacy of non-invasive brain stimulation techniques. As each cognitive function is tied to particular neural circuits, stimulating a specific neural circuit at its intrinsic frequency inevitably yields more precise regulation to that cognitive function. However, when multiple cognitive processes or neural circuits are impaired, as commonly observed in mental disorders, the regulation of a single circuit or frequency may not necessarily achieve the desired outcome. Likewise, to enhance holistic cognitive functions, rather than focusing solely on improving a single stage of cognitive processing, it becomes essential to coordinate more circuits at various frequencies. In this instance, the intrinsic spatiotemporal structure of cognitive functions can provide a more optimal solution.4Conflict of InterestThe 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.5FundingThis research was supported by the National Social Science Foundation of China (BBA200030).6ReferenceBuzsáki, G., Logothetis, N., & Singer, W. (2013). Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron, 80(3), 751-764. Cash, R. F. H., Cocchi, L., Lv, J., Fitzgerald, P. B., & Zalesky, A. (2021). Functional magnetic resonance imaging–guided personalization of transcranial magnetic stimulation treatment for depression. JAMA psychiatry, 78(3), 337-339. Janssens, S. E. W., Oever, S. T., Sack, A. T., & Graaf, T. A. d. (2022). “Broadband Alpha Transcranial Alternating Current Stimulation”: Exploring a new biologically calibrated brain stimulation protocol. NeuroImage, 253, 119109. Keitel, A., & Gross, J. (2016). Individual human brain areas can be identified from their characteristic spectral activation fingerprints. PLoS Biology, 14(6), e1002498. Palva, S., & Palva, J. M. (2018). Roles of brain criticality and multiscale oscillations in temporal predictions for sensorimotor processing. Trends in Neurosciences, 41(10), 729-743. Qiao, J., Wang, Y., & Wang, S. (2022). Natural frequencies of neural activities and cognitions may serve as precise targets of rhythmic interventions to the aging brain. Frontiers in aging neuroscience, 14, 988193. Sala, G., & Gobet, F. (2019). Cognitive training does not enhance general cognition. Trends in Cognitive Sciences, 23(1), 9–20. Siegel, M., Donner, T. H., & Engel, A. K. (2012). Spectral fingerprints of large-scale neuronal interactions. Nature Reviews Neuroscience, 13(2), 121-134. Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X.-J. (2019). Task representations in neural networks trained to perform many cognitive tasks. Nature Neuroscience, 22, 297–306. Zhang, S., Qin, Y., Wang, J., Yu, Y., Wu, L., & Zhang, T. (2023). Noninvasive electrical stimulation neuromodulation and digital brain technology: A review. Biomedicines, 11(6), 1513.