Individual Brain Areas Research Articles

Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings

Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.

Task-based modulation of functional connectivity of dorsal attention network in adult-ADHD

Recent studies have prompted a shift in the understanding of attention deficit hyperactivity disorder (ADHD) from models positing dysfunction of individual brain areas to those that assume alterations in large-scale brain networks. Despite this shift, the underlying neural mechanism of ADHD in the adult population remains uncertain. With functional magnetic resonance imaging (fMRI), this study examined brain connectivity of dorsal and ventral attention networks. Adults with and without ADHD completed a Go/No-Go task inside the scanner and the functional connectivity of attention networks was analysed. The generalized psychophysiological interaction analysis indicated differences involving the dorsal attention network. For the ADHD group, an interaction effect revealed altered dorsal attention-default mode network connectivity modulation, particularly between the right frontal eye field and posterior cingulate gyrus. We conclude that dorsal attention network dysfunction may be involved in sustained attention deficits in adult-ADHD. This study sheds light into network-level alterations contributing to the understanding of adult-ADHD, which may be a potential avenue for future research and clinical interventions.

The effects of musicality on brain network topology in the context of Alzheimer’s disease and memory decline

Abstract Music’s role in modulating brain structure, particularly in neurodegenerative contexts such as Alzheimer’s Disease (AD), has been increasingly recognized. While previous studies have hinted at the potential neuroplastic benefits of musical engagement and training, the mechanisms through which music impacts structural connectivity in neurodegenerative pathways remain underexplored. We aimed to examine the impact of music perception skills, active musical engagement, and musical training on structural connectivity in areas relating to memory, emotion, and learning in individuals with worsening memory impairment, investigating the potential neuroplastic effects of music. Employing diffusion tensor imaging (DTI) based structural connectivity and graph theoretical analysis, we investigated brain topological features in 78 participants aged 42 to 85 with a range of memory impairments. Participants were assessed for musical training, engagement, and perception skills. The study analyzed regional and local network topological metrics to examine the influence of musical activities on graph metrics, while controlling for stages of objective memory impairment (SOMI) and diagnosis, separately. This study aimed to elucidate the effects of musical perception skills, active musical engagement, and musical training on structural connectivity within memory, emotion, and learning-related brain areas in individuals with varying degrees of memory impairment. We found enhanced structural connectivity of the right hippocampus and the right posterior cingulate cortex was associated with stronger local network metrics, such as clustering coefficient and betweenness centrality, with increased music perception skills like melody and beat perception. Musical training specifically impacted the clustering coefficient of the right hippocampus and the node degree of the right mid cingulate gyrus. Active musical engagement influenced the eigenvector centrality of the right hippocampus. Furthermore, musical training was associated with enhanced global metrics, such as global efficiency and characteristic path length. Our study integrates diffusion magnetic resonance imaging (MRI) and graph theoretical analysis to reveal significant effects of musical activities on structural connectivity in key brain regions. The results highlight the potential of musical activities to serve as a non-invasive modulatory tool for cognitive resilience, especially in memory impairment and neurodegeneration contexts. These insights contribute to the understanding of delaying AD onset and aiding early-stage patients through music-based interventions, emphasizing the importance of musical engagement in maintaining cognitive and brain health.

Impact of education on cerebral cortex volume, thickness and Alzheimer’s disease

Introduction and objective: The purpose of our review was to systematize the knowledge regarding the impact of education on cortical volume and thickness along with assessment of the risk and progress of Alzheimer's disease. We considered the problem among children as well as adults, depending on their level of education and particularized the role of cognitive reserve as a protection from brain damage. Furthermore, we investigate the issue of neuronal tolerance in Alzheimer’s disease. Review methods: The article is a review of 22 original papers, cohort studies and meta-analyses concerning the impact of education on cortical volume and thickness along with the risk and progress of Alzheimer’s disease. Abbreviated description of the state of knowledge: Studies have shown that education is associated with an increase in intelligence, which determines the dynamics of brain cortex changes among children. In adults, the thickness of individual brain areas - primarily the temporal and frontal poles - enhances with the increase of their education level. The dynamics of changes in cortical measurements are correlated with education both in healthy adults and in patients with Alzheimer’s disease. A healthy lifestyle, common for those with higher education, has a positive impact on cortical thickness and lowers the risk of developing Alzeihmer’s disease. Moreover, education comes with better neuronal tolerance for accumulated proteins, yet few studies take notice of more dynamic progress of the disease among adults with higher education. Summary: Research has confirmed the impact of education on cortical volume and thickness. The risk and progress of Alzheimer’s disease are also reflected in the patient’s level of education nevertheless the literature is ambiguous regarding this issue.

Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals.

The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues.

All-optical interrogation of millimeter-scale networks and application to developing ferret cortex

BackgroundPerception and behavior require coordinated activity of thousands of neurons operating in networks that span millimeters of brain area. In vivo calcium imaging approaches have proven exceptionally powerful for examining the structure of these networks at large scales, and optogenetics can allow for causal manipulations of large populations of neurons. However, realizing the full potential of these techniques requires the ability to simultaneously measure and manipulate distinct circuit elements on the scale of millimeters. New methodWe describe an opto-macroscope, an artifact-free, all-optical system capable of delivering patterned optogenetic stimulation with high spatial and temporal resolution across millimeters of brain while simultaneously imaging functional neural activity. ResultsWe find that this approach provides direct manipulation of cortical regions ranging from hundreds of microns to several millimeters in area, allowing for the perturbation of individual brain areas or networks of functional domains. Using this system we find that spatially complex endogenous networks in the developing ferret visual cortex can be readily reactivated by precisely designed patterned optogenetic stimuli. Comparison with existing methodsOur opto-macroscope extends current all-optical optogenetic approaches which operate on a cellular scale with multiphoton stimulation, and are poorly suited to investigate the millimeter-scale of many functional networks. It also builds upon other mesoscopic optogenetic techniques that lack simultaneous optical readouts of neural activity. ConclusionsThe large-scale all-optical capabilities of our system make it a powerful new tool for investigating the contribution of cortical domains and brain areas to the functional neural networks that underlie perception and behavior.

Modeling Brain Representations of Words' Concreteness in Context Using GPT-2 and Human Ratings.

The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation ("bat", in a baseball vs. a vampire context), as well as more nuanced differences of meaning-for example, polysemous words such as "book", which can be interpreted in distinct but related senses ("explain a book", information, vs. "open a book", object) whose differences are fine-grained. We study these subtle differences in lexical meaning along the concrete/abstract dimension, as they are triggered by verb-noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine-grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness/abstractness.

In Defense of Modular Thinking.

In this article, Pessoa emphasizes the importance of viewing neural activity from a perspective that functional networks form dynamically in a way that dramatically changes the functional contribution of individual brain areas. In this response, I argue that we should strive toward pluralism in understanding neural activity at both the emergent network and modular levels, on the bases that a purely emergent understanding would be incomplete, and that there are computational advantages to anatomically stable modularity.

The current and future contribution of neuroimaging to the understanding of disorders of consciousness

Patients with disorders of consciousness (DoC) represent a group of severely brain-injured patients with varying capacities for consciousness in terms of both wakefulness and awareness. The current state-of-the-art for assessing these patients is through standardised behavioural examinations, but inaccuracies are commonplace. Neuroimaging and electrophysiological techniques have revealed vast insights into the relationships between neural alterations, andcognitive and behavioural features of consciousness in patients with DoC. This has led to the establishment of neuroimaging paradigms for the clinical assessment of DoC patients. Here, we review selected neuroimaging findings on the DoC population, outlining key findings of the dysfunction underlying DoC and presenting the current clinical utility of neuroimaging tools. We discuss that whilst individual brain areas play instrumental roles in generating and supporting consciousness, activation of these areas alone is not sufficient for conscious experience. Instead, for consciousness to arise, we need preserved thalamo-cortical circuits, in addition to sufficient connectivity between distinctly differentiated brain networks, underlined by connectivity both within, and between such brain networks. Finally, we present recent advances and future perspectives in computational methodologies applied to DoC, supporting the notion that progress in the science of DoC will be driven by a symbiosis of these data-driven analyses, and theory-driven research. Both perspectives will work in tandem to provide mechanistic insights contextualised within theoretical frameworks which ultimately inform the practice of clinical neurology.

Disynaptic specificity of serial information flow for conditioned fear.

Memory encoding and retrieval rely on specific interactions across multiple brain areas. Although connections between individual brain areas have been extensively studied, the anatomical and functional specificity of neuronal circuit organization underlying information transfer across multiple brain areas remains unclear. Here, we combine transsynaptic viral tracing, optogenetic manipulations, and calcium dynamics recordings to dissect the multisynaptic functional connectivity of the amygdala. We identify a distinct basolateral amygdala (BLA) subpopulation that connects disynaptically to the periaqueductal gray (PAG) via the central amygdala (CeA). This disynaptic pathway serves as a core circuit element necessary for the learning and expression of conditioned fear and exhibits learning-related plasticity. Together, our findings demonstrate the utility of multisynaptic approaches for functional circuit analysis and indicate that disynaptic specificity may be a general feature of neuronal circuit organization.

Functional Connectivity Analysis of Transcutaneous Vagus Nerve Stimulation (tVNS) Using Magnetoencephalography (MEG).

Altered brain functional connectivity has been observed in conditions such as schizophrenia, dementia and depression and may represent a target for treatment. Transcutaneous vagus nerve stimulation (tVNS) is a form of non-invasive brain stimulation that is increasingly used in the treatment of a variety of health conditions. We previously combined tVNS with magnetoencephalography (MEG) and observed that various stimulation frequencies affected different brain areas in healthy individuals. We further investigated whether tVNS had an effect on functional connectivity with a focus on brain regions associated with mood. We compared functional connectivity (whole-head and region of interest) in response to four stimulation frequencies of tVNS using data collected from concurrent MEG and tVNS in 17 healthy participants using Weighted Phase Lag Index (WPLI) to calculate correlation between brain areas. Different frequencies of stimulation lead to changes in functional connectivity across multiple regions, notably areas linked to the default mode network (DMN), salience network (SN) and the central executive network (CEN). It was observed that tVNS delivered at a frequency of 24 Hz was the most effective in increasing functional connectivity between these areas and sub-networks in healthy participants. Our results indicate that tVNS can alter functional connectivity in regions that have been associated with mood and memory disorders. Varying the stimulation frequency led to alterations in different brain areas, which may suggest that personalized stimulation protocols can be developed for the targeted treatment of different medical conditions using tVNS.

Brain Metabolic Correlates of Dopaminergic Denervation in Prodromal and Early Parkinson's Disease.

It remains unclear how brain metabolic activities transform in response to dopamine deficiency in the prodromal and early phases of Parkinson's disease (PD). To investigate the relationship between nigrostriatal dopaminergic denervation and brain glucose metabolism in patients with isolated rapid eye movement sleep behavior disorder (iRBD) and early PD. This cohort study included 28 patients with polysomnography-confirmed iRBD, 24 patients with de novo PD with probable rapid eye movement sleep behavior disorder (denovo PD), and 28 healthy controls (HCs) who underwent two positron emission tomography scans with 18 F-fluorodeoxyglucose (all participants) and 18 F-N-3-fluoropropyl-2β-carboxymethoxy-3β-(4-iodophenyl)-nortropane (except for one denovo PD patient and 15 HCs). We analyzed striatal and voxel-wise whole-brain glucose metabolism in relation to nigrostriatal dopaminergic integrity and comparatively investigated the whole-brain metabolic connectivity among the groups. We also assessed longitudinal metabolic changes against progressive dopaminergic denervation over 4 years in the iRBD group. From HCs to iRBD and finally to the denovo PD, dopaminergic integrity positively correlated with metabolic activity in the caudate, whereas a negative correlation was observed in the posterior putamen. In the iRBD group, there was a metabolic increase in the inferior orbitofrontal cortex against putaminal dopaminergic denervation at baseline, but negative correlations were newly observed in the superior orbitofrontal cortex and superior frontal gyrus at the 4-year follow-up. The denovo PD group showed negative correlations in the cerebellum and fusiform gyrus. Intra- and inter-regional metabolic connectivities in the parieto-occipital cortices were enhanced in the iRBD group compared with the denovo PD and HC groups. In the iRBD group, overall metabolic connectivity was strengthened along with enhanced basal ganglia-frontal connection by advancing dopaminergic denervation. Our findings suggest diverse trajectories of metabolic responses associated with dopaminergic denervation between individual brain areas in the prodromal and early PD stages. © 2022 International Parkinson and Movement Disorder Society.

Increased functional connectivity between limbic brain areas in healthy individuals with high versus low sensitivity to cold pain: A resting state fMRI study.

BackgroundThe representation of variability in sensitivity to pain by differences in neural connectivity patterns and its association with psychological factors needs further investigation. This study assessed differences in resting-state functional connectivity (rsFC) and its association to cognitive-affective aspects of pain in two groups of healthy subjects with low versus high sensitivity to pain (LSP vs. HSP). We hypothesized that HSP will show stronger connectivity in brain regions involved in the affective-motivational processing of pain and that this higher connectivity would be related to negative affective and cognitive evaluations of pain.MethodsForty-eight healthy subjects were allocated to two groups according to their tolerability to cold stimulation (cold pressor test, CPT, 1°C). Group LSP (N = 24) reached the cut-off time of 180±0 sec and group HSP tolerated the CPT for an average of 13±4.8 sec. Heat, cold and mechanical evoked pain were measured, as well as pain-catastrophizing (PCS), depression, anxiety and stress (DASS-21). All subjects underwent resting state fMRI. ROI-to-ROI analysis was performed.ResultsIn comparison to the LSP, the HSP had stronger interhemispheric connectivity of the amygdala (p = 0.01) and between the amygdala and nucleus accumbens (NAc) (p = 0.01). Amygdala connectivity was associated with higher pain catastrophizing in the HSP only (p<0.01).ConclusionsThese findings suggest that high sensitivity to pain may be reflected by neural circuits involved in affective and motivational aspects of pain. To what extent this connectivity within limbic brain structures relates to higher alertness and more profound withdrawal behavior to aversive events needs to be further investigated.

Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain

Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.

Voxel and surface based whole brain analysis shows reading skill associated grey matter abnormalities in dyslexia

Developmental dyslexia (DD) is the most prevalent neurodevelopmental disorder with a substantial negative influence on the individual’s academic achievement and career. Research on its neuroanatomical origins has continued for half a century, yielding, however, inconsistent results, lowered total brain volume being the most consistent finding. We set out to evaluate the grey matter (GM) volume and cortical abnormalities in adult dyslexic individuals, employing a combination of whole-brain voxel- and surface-based morphometry following current recommendations on analysis approaches, coupled with rigorous neuropsychological testing. Whilst controlling for age, sex, total intracranial volume, and performance IQ, we found both decreased GM volume and cortical thickness in the left insula in participants with DD. Moreover, they had decreased GM volume in left superior temporal gyrus, putamen, globus pallidus, and parahippocampal gyrus. Higher GM volumes and cortical thickness in these areas correlated with better reading and phonological skills, deficits of which are pivotal to DD. Crucially, total brain volume did not influence our results, since it did not differ between the groups. Our findings demonstrating abnormalities in brain areas in individuals with DD, which previously were associated with phonological processing, are compatible with the leading hypotheses on the neurocognitive origins of DD.

Temporo-parietal brain regions are involved in higher order object perception

Lesions to posterior temporo-parietal brain regions are associated with deficits in perception of global, hierarchical shapes, but also with impairments in the processing of objects presented under demanding viewing conditions. Evidence from neuroimaging studies and lesion patterns observed in patients with simultanagnosia and agnosia for object orientation suggest similar brain regions to be involved in perception of global shapes and processing of objects in atypical (‘non-canonical’) orientation. In a localizer experiment, we identified individual temporo-parietal brain areas involved in global shape perception and found significantly higher BOLD signals during the processing of non-canonical compared to canonical objects. In a multivariate approach, we demonstrated that posterior temporo-parietal brain areas show distinct voxel patterns for non-canonical and canonical objects and that voxel patterns of global shapes are more similar to those of objects in non-canonical compared to canonical viewing conditions. These results suggest that temporo-parietal brain areas are not only involved in global shape perception but might serve a more general mechanism of complex object perception. Our results challenge a strict attribution of object processing to the ventral visual stream by suggesting specific dorsal contributions in more demanding viewing conditions.

Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy.

ObjectiveOur objective was to identify whether the whole‐brain structural network alterations in patients with temporal lobe epilepsy (TLE) and focal to bilateral tonic–clonic seizures (FBTCS) differ from alterations in patients without FBTCS.MethodsWe dichotomized a cohort of 83 drug‐resistant patients with TLE into those with and without FBTCS and compared each group to 29 healthy controls. For each subject, we used diffusion‐weighted magnetic resonance imaging to construct whole‐brain structural networks. First, we measured the extent of alterations by performing FBTCS‐negative (FBTCS−) versus control and FBTCS‐positive (FBTCS+) versus control comparisons, thereby delineating altered subnetworks of the whole‐brain structural network. Second, by standardizing each patient's networks using control networks, we measured the subject‐specific abnormality at every brain region in the network, thereby quantifying the spatial localization and the amount of abnormality in every patient.ResultsBoth FBTCS+ and FBTCS− patient groups had altered subnetworks with reduced fractional anisotropy and increased mean diffusivity compared to controls. The altered subnetwork in FBTCS+ patients was more widespread than in FBTCS− patients (441 connections altered at t > 3, p < .001 in FBTCS+ compared to 21 connections altered at t > 3, p = .01 in FBTCS−). Significantly greater abnormalities—aggregated over the entire brain network as well as assessed at the resolution of individual brain areas—were present in FBTCS+ patients (p < .001, d = .82, 95% confidence interval = .32–1.3). In contrast, the fewer abnormalities present in FBTCS− patients were mainly localized to the temporal and frontal areas.SignificanceThe whole‐brain structural network is altered to a greater and more widespread extent in patients with TLE and FBTCS. We suggest that these abnormal networks may serve as an underlying structural basis or consequence of the greater seizure spread observed in FBTCS.

Effect-Size Estimation Using Semiparametric Hierarchical Mixture Models in Disease-Association Studies with Neuroimaging Data.

In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or effect sizes for individual brain areas. In this paper, we propose a model-based framework for voxel-based inferences under spatial dependency in neuroimaging data. Specifically, we employ hierarchical mixture models with a hidden Markov random field structure to incorporate the spatial dependency between voxels. A nonparametric specification is proposed for the effect size distribution to flexibly estimate the underlying effect size distribution. Simulation experiments demonstrate that compared with a naive estimation method, the proposed methods can substantially reduce the selection bias in the effect size estimates of the selected voxels with the greatest observed associations. An application to neuroimaging data from an Alzheimer's disease study is provided.

Approaches to inferring multi-regional interactions from simultaneous population recordings: Inferring multi-regional interactions from simultaneous population recordings.

Most past studies of neural representations and dynamics have focused on recordings from single brain areas. However, growing evidence of brain-wide, parallel representations of cognitive variables suggests that analyzing neural representations and dynamics in individual brain areas can benefit from understanding the context of multi-regional interactions that support them. Moreover, perturbation experiments revealed that the manner in which these parallel representations interact with each other can differ dramatically across different pairs of brain areas. Recent advances in recording technology offer a potentially powerful substrate to study how multi-regional interactions coordinate neural representations in individual brain areas and dictate behavior on a single-trial basis through simultaneous recordings of multiple brain areas. We review pragmatic approaches to studying multi-regional interactions and illustrate them in the concrete context of a rodent delayed response task paradigm.

Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals

During the sleep-wake cycle, the brain undergoes profound dynamical changes, which manifest subjectively as transitions between conscious experience and unconsciousness. Yet, neurophysiological signatures that can objectively distinguish different consciousness states based are scarce. Here, we show that differences in the level of brain-wide signals can reliably distinguish different stages of sleep and anesthesia from the awake state in human and monkey fMRI resting state data. Moreover, a whole-brain computational model can faithfully reproduce changes in global synchronization and other metrics such as functional connectivity, structure-function relationship, integration and segregation across vigilance states. We demonstrate that the awake brain is close to a Hopf bifurcation, which naturally coincides with the emergence of globally correlated fMRI signals. Furthermore, simulating lesions of individual brain areas highlights the importance of connectivity hubs in the posterior brain and subcortical nuclei for maintaining the model in the awake state, as predicted by graph-theoretical analyses of structural data.