Contributions and challenges for network models in cognitive neuroscience

Modular morals: Mapping the organization of the moral brain

Is morality the product of multiple domain-specific psychological mechanisms, or one domain-general mechanism? Previous research suggests that morality consists of a range of solutions to the problems of cooperation recurrent in human social life. This theory of 'morality as cooperation' suggests that there are (at least) seven specific moral domains: family values, group loyalty, reciprocity, heroism, deference, fairness and property rights. However, it is unclear how these types of morality are implemented at the neuroanatomical level. The possibilities are that morality is (1) the product of multiple distinct domain-specific adaptations for cooperation, (2) the product of a single domain-general adaptation which learns a range of moral rules, or (3) the product of some combination of domain-specific and domain-general adaptations. To distinguish between these possibilities, we first conducted an anatomical likelihood estimation meta-analysis of previous studies investigating the relationship between these seven moral domains and neuroanatomy. This meta-analysis provided evidence for a combination of specific and general adaptations. Next, we investigated the relationship between the seven types of morality - as measured by the Morality as Cooperation Questionnaire (Relevance) - and grey matter volume in a large neuroimaging (n = 607) sample. No associations between moral values and grey matter volume survived whole-brain exploratory testing. We conclude that whatever combination of mechanisms are responsible for morality, either they are not neuroanatomically localised, or else their localisation is not manifested in grey matter volume. Future research should employ phylogenetically informed a priori predictions, as well as alternative measures of morality and of brain function.

Distributed associations among white matter hyperintensities and structural brain networks with fluid cognition in healthy aging

White matter hyperintensities (WMHs) are associated with age-related cognitive impairment and increased risk of Alzheimer's disease. However, the manner by which WMHs contribute to cognitive impairment is unclear. Using a combination of predictive modeling and network neuroscience, we investigated the relationship between structural white matter connectivity and age, fluid cognition, and WMHs in 68 healthy adults (18-78 years). Consistent with previous work, WMHs were increased in older adults and exhibited a strong negative association with fluid cognition. Extending previous work, using predictive modeling, we demonstrated that age, WMHs, and fluid cognition were jointly associated with widespread alterations in structural connectivity. Subcortical-cortical connections between the thalamus/basal ganglia and frontal and parietal regions of the default mode and frontoparietal networks were most prominent. At the network level, both age and WMHs were negatively associated with network density and communicability, and positively associated with modularity. Spatially, WMHs were most prominent in arterial zones served by the middle cerebral artery and associated lenticulostriate branches that supply subcortical regions. Finally, WMHs overlapped with all major white matter tracts, most prominently in tracts that facilitate subcortical-cortical communication and are implicated in fluid cognition, including the anterior thalamic-radiations and forceps minor. Finally, results of mediation analyses suggest that whole-brain WMH load influences age-related decline in fluid cognition. Thus, across multiple levels of analysis, we showed that WMHs were increased in older adults and associated with altered structural white matter connectivity and network topology involving subcortical-cortical pathways critical for fluid cognition.

Neural mechanisms of altruistic decision-making: EEG functional connectivity network analysis

Altruism is an enigmatic form of prosocial behavior, characterized by diverse motivations and significant interindividual differences. Studying neural mechanisms of altruism is crucial to identify objective markers of pro- and antisocial tendencies in behavior. This study was designed to delve into the mechanisms of altruism by analyzing EEG-based functional connectivity patterns within the framework of the network approach. To experimentally induce a situation of altruistic decision-making, we employed the Pain versus Gain (PvsG) task, which implies making choices concerning financial self-benefit and pain of the other. Our results reveal that the behavioral measure of altruism in the experiment correlated with emotional empathy, which is in line with the "empathy-altruism" hypothesis. Applying the network approach to EEG functional connectivity analysis, we discovered that the very process of decision-making in the PvsG is characterized by the synchronous activity of structures in the right hemisphere, which are involved in empathy for pain. The prosociality of decisions was reflected in functional connectivity between the rostral ACC and orbital IFG in the left hemisphere and the overall network centrality of the caudal ACC. This finding additionally points to the distinct functional roles of the ACC subregions in altruistic decision-making. The proposed neural mechanisms of altruism can further be used to identify neurophysiological markers of prosociality in behavior.

Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient's lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient's data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain.

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Structural connectivity changes in unilateral hearing loss

Network connectivity, as mapped by the whole brain connectome, plays a crucial role in regulating auditory function. Auditory deprivation such as unilateral hearing loss might alter structural network connectivity; however, these potential alterations are poorly understood. Thirty-seven acoustic neuroma patients with unilateral hearing loss (19 left-sided and 18 right-sided) and 19 healthy controls underwent diffusion-weighted and T1-weighted imaging to assess edge strength, node strength, and global efficiency of the structural connectome. Edge strength was estimated by pair-wise normalized streamline density from tractography and connectomics. Node strength and global efficiency were calculated through graph theory analysis of the connectome. Pure-tone audiometry and word recognition scores were used to correlate the degree and duration of unilateral hearing loss with node strength and global efficiency. We demonstrate significantly stronger edge strength and node strength through the visual network, weaker edge strength and node strength in the somatomotor network, and stronger global efficiency in the unilateral hearing loss patients. No discernible correlations were observed between the degree and duration of unilateral hearing loss and the measures of node strength or global efficiency. These findings contribute to our understanding of the role of structural connectivity in hearing by facilitating visual network upregulation and somatomotor network downregulation after unilateral hearing loss.

The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro

Modern microtechnology methods are widely used to create neural networks on a chip with a connection architecture demonstrating properties of modularity and hierarchy similar to brain networks. Such in vitro networks serve as a valuable model for studying the interplay of functional architecture within modules, their activity, and the effectiveness of inter-module interaction. In this study, we use a two-chamber microfluidic platform to investigate functional connectivity and global activity in hierarchically connected modular neural networks. We found that the strength of functional connections within the module and the profile of network spontaneous activity determine the effectiveness of inter-modular interaction and integration activity in the network. The direction of intermodular activity propagation configures the different densities of inhibitory synapses in the network. The developed microfluidic platform holds the potential to explore function-structure relationships and efficient information processing in two- or multilayer neural networks, in both healthy and pathological states.

Reconstructing brain functional networks through identifiability and deep learning

We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the coparticipation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by deep learning models in supervised classification tasks and therefore requires no a priori assumptions about the nature of such coparticipation. The method is tested on EEG recordings obtained from Alzheimer's and Parkinson's disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting-state conditions, and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity in different frequency bands. Differences are also observed between eyes open and closed conditions, especially for Parkinson's disease patients.

Characterizing the discriminability of visual categorical information in strongly connected voxels

Functional brain responses are strongly influenced by connectivity. Recently, we demonstrated a major example of this: category discriminability within occipitotemporal cortex (OTC) is enhanced for voxel sets that share strong functional connectivity to distal brain areas, relative to those that share lesser connectivity. That is, within OTC regions, sets of 'most-connected' voxels show improved multivoxel pattern discriminability for tool-, face-, and place stimuli relative to voxels with weaker connectivity to the wider brain. However, understanding whether these effects generalize to other domains (e.g. body perception network), and across different levels of the visual processing streams (e.g. dorsal as well as ventral stream areas) is an important extension of this work. Here, we show that this so-called connectivity-guided decoding (CGD) effect broadly generalizes across a wide range of categories (tools, faces, bodies, hands, places). This effect is robust across dorsal stream areas, but less consistent in earlier ventral stream areas. In the latter regions, category discriminability is generally very high, suggesting that extraction of category-relevant visual properties is less reliant on connectivity to downstream areas. Further, CGD effects are primarily expressed in a category-specific manner: For example, within the network of tool regions, discriminability of tool information is greater than non-tool information. The connectivity-guided decoding approach shown here provides a novel demonstration of the crucial relationship between wider brain connectivity and complex local-level functional responses at different levels of the visual processing streams. Further, this approach generates testable new hypotheses about the relationships between connectivity and local selectivity.

Local gradient analysis of human brain function using the Vogt-Bailey Index

In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Bajada et al. (NeuroImage 221:117140, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the 'heavier' the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.

Long-range connections damage in white matter hyperintensities affects information processing speed

White matter hyperintensities, one of the major markers of cerebral small vessel disease, disrupt the integrity of neuronal networks and ultimately contribute to cognitive dysfunction. However, a deeper understanding of how white matter hyperintensities related to the connectivity patterns of brain hubs at the neural network level could provide valuable insights into the relationship between white matter hyperintensities and cognitive dysfunction. A total of 36 patients with moderate to severe white matter hyperintensities (Fazekas score ≥ 3) and 34 healthy controls underwent comprehensive neuropsychological assessments and resting-state functional MRI scans. The voxel-based graph-theory approach-functional connectivity strength was employed to systematically investigate the topological organization of the whole-brain networks. The white matter hyperintensities patients performed significantly worse than the healthy controls in episodic memory, executive function and information processing speed. Additionally, we found that white matter hyperintensities selectively affected highly connected hub regions, predominantly involving the medial and lateral prefrontal, precuneus, inferior parietal lobule, insula and thalamus. Intriguingly, this impairment was connectivity distance-dependent, with the most prominent disruptions observed in long-range connections (e.g. 100-150 mm). Finally, these disruptions of hub connectivity (e.g. the long-range functional connectivity strength in the left dorsolateral prefrontal cortex) positively correlated with the cognitive performance in white matter hyperintensities patients. Our findings emphasize that the disrupted hub connectivity patterns in white matter hyperintensities are dependent on connection distance, especially longer-distance connections, which in turn predispose white matter hyperintensities patients to worse cognitive function.

Magnetoencephalography for Epilepsy Presurgical Evaluation

PURPOSE OF THE REVIEW

Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with subcentimeter accuracy. Its capability to provide high resolution in both of these domains makes it a powerful tool both in basic neuroscience as well as clinical applications. In neurology, it has proven useful in its ability to record and localize epileptiform activity. Epilepsy workup typically begins with scalp electroencephalography (EEG), but in many situations, EEG-based localization of the epileptogenic zone is inadequate. The complementary sensitivity of MEG can be crucial in such cases, and MEG has been adopted at many centers as an important resource in building a surgical hypothesis. In this paper, we review recent work evaluating the extent of MEG influence of presurgical evaluations, novel analyses of MEG data employed in surgical workup, and new MEG instrumentation that will likely affect the field of clinical MEG.

RECENT FINDINGS

MEG consistently contributes to presurgical evaluation and these contributions often change the plan for epilepsy surgery. Extensive work has been done to develop new analytic methods for localizing the source of epileptiform activity with MEG. Systems using optically pumped magnetometry (OPM) have been successfully deployed to record and localize epileptiform activity. MEG remains an important noninvasive tool for epilepsy presurgical evaluation. Continued improvements in analytic methodology will likely increase the diagnostic yield of the test. Novel instrumentation with OPM may contribute to this as well, and may increase accessibility of MEG by decreasing cost.

Traumatic brain injury disrupts state-dependent functional cortical connectivity in a mouse model

Traumatic brain injury (TBI) is the leading cause of death in young people and can cause cognitive and motor dysfunction and disruptions in functional connectivity between brain regions. In human TBI patients and rodent models of TBI, functional connectivity is decreased after injury. Recovery of connectivity after TBI is associated with improved cognition and memory, suggesting an important link between connectivity and functional outcome. We examined widespread alterations in functional connectivity following TBI using simultaneous widefield mesoscale GCaMP7c calcium imaging and electrocorticography (ECoG) in mice injured using the controlled cortical impact (CCI) model of TBI. Combining CCI with widefield cortical imaging provides us with unprecedented access to characterize network connectivity changes throughout the entire injured cortex over time. Our data demonstrate that CCI profoundly disrupts functional connectivity immediately after injury, followed by partial recovery over 3 weeks. Examining discrete periods of locomotion and stillness reveals that CCI alters functional connectivity and reduces theta power only during periods of behavioral stillness. Together, these findings demonstrate that TBI causes dynamic, behavioral state-dependent changes in functional connectivity and ECoG activity across the cortex.

The missing third dimension-Functional correlations of BOLD signals incorporating white matter

Correlations between magnetic resonance imaging (MRI) blood oxygenation level-dependent (BOLD) signals from pairs of gray matter areas are used to infer their functional connectivity, but they are unable to describe how white matter is engaged in brain networks. Recently, evidence that BOLD signals in white matter are robustly detectable and are modulated by neural activities has accumulated. We introduce a three-way correlation between BOLD signals from pairs of gray matter volumes (nodes) and white matter bundles (edges) to define the communication connectivity through each white matter bundle. Using MRI images from publicly available databases, we show, for example, that the three-way connectivity is influenced by age. By integrating functional MRI signals from white matter as a third component in network analyses, more comprehensive descriptions of brain function may be obtained.

Measuring excitation-inhibition balance through spectral components of local field potentials

The balance between excitation and inhibition is critical to brain functioning, and dysregulation of this balance is a hallmark of numerous psychiatric conditions. Measuring this excitation-inhibition (E:I) balance in vivo has remained difficult, but theoretical models have proposed that characteristics of local field potentials (LFP) may provide an accurate proxy. To establish a conclusive link between LFP and E:I balance, we recorded single units and LFP from the prefrontal cortex (mPFC) of rats during decision making. Dynamic measures of synaptic coupling strength facilitated direct quantification of E:I balance and revealed a strong inverse relationship to broadband spectral power of LFP. These results provide a critical link between LFP and underlying network properties, opening the door for non-invasive recordings to measure E:I balance in clinical settings.

Analysis of Cortico-Muscular Coupling and Functional Brain Network under Different Standing Balance Paradigms

Maintaining standing balance is essential for people to engage in productive activities in daily life. However, the process of interaction between the cortex and the muscles during balance regulation is understudied. Four balance paradigms of different difficulty were designed by closing eyes and laying sponge pad under feet. Ten healthy subjects were recruited to stand for ten 15 s trials in each paradigm. This study used simultaneously acquired electroencephalography (EEG) and electromyography (EMG) to investigate changes in the human cortico-muscular coupling relationship and functional brain network characteristics during balance control. The coherence and causality of EEG and EMG signals were calculated by magnitude-squared coherence (MSC) and transfer entropy (TE). It was found that changes in balance strategies may lead to a shift in cortico-muscular coherence (CMC) from the beta band to the gamma band when the difficulty of balance increased. As subjects performed the four standing balance paradigms, the causality of the beta band and the gamma band was stronger in the descending neural pathway than that in the ascending neural pathway. A multi-rhythmic functional brain network with 19 EEG channels was constructed and analyzed based on graph theory, showing that its topology also changed with changes in balance difficulty. These results show an active adjustment of the sensorimotor system under different balance paradigms and provide new insights into the endogenous physiological mechanisms underlying the control of standing balance.

Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer's disease

Cognitive functions produced by large-scale neural integrations are the most representative 'emergence phenomena' in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer's disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.

Virtual neural network-guided optimization of non-invasive brain stimulation in Alzheimer's disease

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique with potential for counteracting disrupted brain network activity in Alzheimer's disease (AD) to improve cognition. However, the results of tDCS studies in AD have been variable due to different methodological choices such as electrode placement. To address this, a virtual brain network model of AD was used to explore tDCS optimization. We compared a large, representative set of virtual tDCS intervention setups, to identify the theoretically optimized tDCS electrode positions for restoring functional network features disrupted in AD. We simulated 20 tDCS setups using a computational dynamic network model of 78 neural masses coupled according to human structural topology. AD network damage was simulated using an activity-dependent degeneration algorithm. Current flow modeling was used to estimate tDCS-targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density (alpha bands, 8-10 Hz and 10-13 Hz), total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC). Virtual tDCS performance varied, with optimized strategies improving all outcome measures, while others caused further deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of stimulated regions and tDCS success was not observed. This modeling-informed approach can guide and perhaps accelerate tDCS therapy development and enhance our understanding of tDCS effects. Follow-up studies will compare the general predictions to personalized virtual models and validate them with tDCS-magnetoencephalography (MEG) in a clinical AD patient cohort.

Temporal differences and commonalities between hand and tool neural processing

Object recognition is a complex cognitive process that relies on how the brain organizes object-related information. While spatial principles have been extensively studied, less studied temporal dynamics may also offer valuable insights into this process, particularly when neural processing overlaps for different categories, as it is the case of the categories of hands and tools. Here we focus on the differences and/or similarities between the time-courses of hand and tool processing under electroencephalography (EEG). Using multivariate pattern analysis, we compared, for different time points, classification accuracy for images of hands or tools when compared to images of animals. We show that for particular time intervals (~ 136-156 ms and ~ 252-328 ms), classification accuracy for hands and for tools differs. Furthermore, we show that classifiers trained to differentiate between tools and animals generalize their learning to classification of hand stimuli between ~ 260-320 ms and ~ 376-500 ms after stimulus onset. Classifiers trained to distinguish between hands and animals, on the other hand, were able to extend their learning to the classification of tools at ~ 150 ms. These findings suggest variations in semantic features and domain-specific differences between the two categories, with later-stage similarities potentially related to shared action processing for hands and tools.

Structural and functional connectivity alteration patterns of the cingulate gyrus in Type 2 diabetes

OBJECTIVE

We aimed to reveal the role of structural and functional alterations of cingulate gyrus in early cognitive impairment in Type 2 diabetes mellitus (T2DM) patients.

METHODS

Fifty-six T2DM patients and 60 healthy controls (HCs) underwent a neuropsychological assessment and sagittal three-dimensional T1-weighted and resting-state functional MRI. Differences in the cortical thickness of the cingulate cortex and the functional connectivity (FC) of the nine subregions of the cingulate gyrus and the whole brain were compared between T2DM patients and HCs. Correlation analysis was performed between cortex thickness and FC and the participants' clinical/cognitive variables.

RESULTS

The cortical thickness of the cingulate gyrus was not significantly different between T2DM patients and HCs. However, the T2DM patients showed significantly lower FC between the pregenual ACC (pACC) and the bilateral hippocampus, significantly higher FC between the pACC and bilateral lateral prefrontal cortex (LPFC) and left precentral gyrus, and significantly lower FC between the retrosplenial cortex (RSC) and right cerebellar Crus I. The FC between the pACC and the left hippocampus was negatively correlated with the FC between the pACC and LPFC (r = -0.306, p = 0.022).

INTERPRETATION

The pACC and the RSC show dysfunctional connectivity before the appearance of structural abnormalities in T2DM patients. Abnormal FC of the pACC with the bilateral hippocampus and LPFC may imply a neural compensatory mechanism for memory function. These findings provide valuable information and new directions for possible interventions for the T2DM-related cognitive impairment.

Socioeconomic resources in youth are linked to divergent patterns of network integration and segregation across the brain's transmodal axis

Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully influence intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neurobiological meaning of these widespread alterations remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage the resources of graph theory to precisely characterize multivariate and univariate associations between household SER and the functional integration and segregation (i.e., participation coefficient, within-module degree) of brain regions across major cognitive, affective, and sensorimotor systems during the resting state in 5,821 youth (ages 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are related to greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that the effects of SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread effects of SER on brain organization, indicating that SER levels differentially configure the intrinsic functional architecture of developing unimodal and transmodal systems. This study highlights both sensorimotor and higher-order networks that may serve as neural markers of environmental stress and opportunity, and which may guide efforts to scaffold healthy neurobehavioral development among disadvantaged communities of youth.

Lateralization of delta band power in magnetoencephalography (MEG) in patients with unilateral focal epilepsy and its relation to verbal fluency

INTRODUCTION

Delta power is a clinically established biomarker for abnormal brain processes. However, in patients with unilateral focal epilepsy (FE) it is still not well understood, how it relates to the epileptogenic zone and to neurocognitive functioning. The aim of the present study was thus to assess how delta power relates to the affected hemisphere, whether lateralization strength differs between the patients, and how changes in delta power correlate with cognitive functioning.

METHOD

We retrospectively studied patients with left (LFE) and right FE (RFE) who had undergone a resting-state magnetoencephalography measurement. We computed global and hemispheric delta power and lateralization indices and examined whether delta power correlates with semantic and letter verbal fluency (former being a marker for language and verbal memory, latter for executive functions) in 26 FE patients (15 LFE, 11 RFE) and 10 healthy controls.

RESULTS

Delta power was increased in FE patients compared to healthy controls. However, the increase across hemispheres was related to the site of the epileptic focus: On group level, LFE patients showed higher delta power in both hemispheres, whereas RFE patients primarily exhibited higher delta power in the ipsilateral right hemisphere. Both groups showed co-fluctuations of delta power between the hemispheres. Besides, delta power correlated negatively only with letter verbal fluency.

CONCLUSION

The findings confirm and provide further evidence that delta power is a marker of pathological activity and abnormal brain processes in FE. Delta power dynamics differ between patient groups, indicating that delta power could offer additional diagnostic value. The negative association of delta power and letter verbal fluency suggests that executive dysfunctions are related to low frequency abnormalities.

Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex

Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.

Stimulus-dependent functional network topology in mouse visual cortex

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Effect of transcranial direct current stimulation on the functionality of 40 Hz auditory steady state response brain network: graph theory approach

Introduction

Measuring whole-brain networks of the 40 Hz auditory steady state response (ASSR) is a promising approach to describe the after-effects of transcranial direct current stimulation (tDCS). The main objective of this study was to evaluate the effect of tDCS on the brain network of 40 Hz ASSR in healthy adult males using graph theory. The second objective was to identify a population in which tDCS effectively modulates the brain network of 40 Hz ASSR.

Methods

This study used a randomized, sham-controlled, double-blinded crossover approach. Twenty-five adult males (20-24 years old) completed two sessions at least 1 month apart. The participants underwent cathodal or sham tDCS of the dorsolateral prefrontal cortex, after which 40 Hz ASSR was measured using magnetoencephalography. After the signal sources were mapped onto the Desikan-Killiany brain atlas, the statistical relationships between localized activities were evaluated in terms of the debiased weighted phase lag index (dbWPLI). Weighted and undirected graphs were constructed for the tDCS and sham conditions based on the dbWPLI. Weighted characteristic path lengths and clustering coefficients were then measured and compared between the tDCS and sham conditions using mixed linear models.

Results

The characteristic path length was significantly lower post-tDCS simulation (p = 0.04) than after sham stimulation. This indicates that after tDCS simulation, the whole-brain networks of 40 Hz ASSR show a significant functional integration. Simple linear regression showed a higher characteristic path length at baseline, which was associated with a larger reduction in characteristic path length after tDCS. Hence, a pronounced effect of tDCS is expected for those who have a less functionally integrated network of 40 Hz ASSR.

Discussion

Given that the healthy brain is functionally integrated, we conclude that tDCS could effectively normalize less functionally integrated brain networks rather than enhance functional integration.

Bifurcation in space: Emergence of function modularity in the neocortex

How does functional modularity emerge in a multiregional cortex made with repeats of a canonical local circuit architecture? We investigated this question by focusing on neural coding of working memory, a core cognitive function. Here we report a mechanism dubbed "bifurcation in space", and show that its salient signature is spatially localized "critical slowing down" leading to an inverted V-shaped profile of neuronal time constants along the cortical hierarchy during working memory. The phenomenon is confirmed in connectome-based large-scale models of mouse and monkey cortices, offering an experimentally testable prediction to assess whether working memory representation is modular. Many bifurcations in space could explain the emergence of different activity patterns potentially deployed for distinct cognitive functions, This work demonstrates that a distributed mental representation is compatible with functional specificity as a consequence of macroscopic gradients of neurobiological properties across the cortex, suggesting a general principle for understanding brain's modular organization.

A Supervised Machine Learning Approach to Classify Brain Morphology of Professional Visual Artists versus Non-Artists

This study aimed to investigate whether there are structural differences in the brains of professional artists who received formal training in the visual arts and non-artists who did not have any formal training or professional experience in the visual arts, and whether these differences can be used to accurately classify individuals as being an artist or not. Previous research using functional MRI has suggested that general creativity involves a balance between the default mode network and the executive control network. However, it is not known whether there are structural differences between the brains of artists and non-artists. In this study, a machine learning method called Multi-Kernel Learning (MKL) was applied to gray matter images of 12 artists and 12 non-artists matched for age and gender. The results showed that the predictive model was able to correctly classify artists from non-artists with an accuracy of 79.17% (AUC 88%), and had the ability to predict new cases with an accuracy of 81.82%. The brain regions most important for this classification were the Heschl area, amygdala, cingulate, thalamus, and parts of the parietal and occipital lobes as well as the temporal pole. These regions may be related to the enhanced emotional and visuospatial abilities that professional artists possess compared to non-artists. Additionally, the reliability of this circuit was assessed using two different classifiers, which confirmed the findings. There was also a trend towards significance between the circuit and a measure of vividness of imagery, further supporting the idea that these brain regions may be related to the imagery abilities involved in the artistic process.

Neuronal growth on high-aspect-ratio diamond nanopillar arrays for biosensing applications

Monitoring neuronal activity with simultaneously high spatial and temporal resolution in living cell cultures is crucial to advance understanding of the development and functioning of our brain, and to gain further insights in the origin of brain disorders. While it has been demonstrated that the quantum sensing capabilities of nitrogen-vacancy (NV) centers in diamond allow real time detection of action potentials from large neurons in marine invertebrates, quantum monitoring of mammalian neurons (presenting much smaller dimensions and thus producing much lower signal and requiring higher spatial resolution) has hitherto remained elusive. In this context, diamond nanostructuring can offer the opportunity to boost the diamond platform sensitivity to the required level. However, a comprehensive analysis of the impact of a nanostructured diamond surface on the neuronal viability and growth was lacking. Here, we pattern a single crystal diamond surface with large-scale nanopillar arrays and we successfully demonstrate growth of a network of living and functional primary mouse hippocampal neurons on it. Our study on geometrical parameters reveals preferential growth along the nanopillar grid axes with excellent physical contact between cell membrane and nanopillar apex. Our results suggest that neuron growth can be tailored on diamond nanopillars to realize a nanophotonic quantum sensing platform for wide-field and label-free neuronal activity recording with sub-cellular resolution.

Altered brain structural and functional connectivity in cannabis users

Cannabis is one of the most used and commodified illicit substances worldwide, especially among young adults. The neurobiology mechanism of cannabis is yet to be identified particularly in youth. The purpose of this study was to concurrently measure alterations in brain structural and functional connectivity in cannabis users using resting-state functional magnetic resonance images (rs-fMRI) and diffusion-weighted images (DWI) from a group of 73 cannabis users (age 22-36, 19 female) in comparison with 73 healthy controls (age 22-36, 14 female) from Human Connectome Project (HCP). Several significant differences were observed in local structural/functional network measures (e.g. degree and clustering coefficient), being prominent in the insular and frontal opercular cortex and lateral/medial temporal cortex. The rich-club organization of structural networks revealed a normal trend, distributed within bilateral frontal, temporal and occipital regions. However, minor differences were found between the two groups in the superior and inferior temporal gyri. Functional rich-club nodes were mostly located within parietal and posterior areas, with minor differences between the groups found mainly in the centro-temporal and parietal regions. Regional network measures of structural/functional networks were associated with times used cannabis (TUC) in several regions. Although the structural/functional network in both groups showed small-world property, no differences between cannabis users and healthy controls were found regarding the global network measures, showing no association with cannabis use. After FDR correction, all of the significant associations between network measures and TUC were found to be insignificant, except for the association between degree and TUC within the presubiculum region. To recap, our findings revealed alterations in local topological properties of structural and functional networks in cannabis users, although their global brain network organization remained intact.

Whole-brain dynamics of human sensorimotor adaptation

Humans vary greatly in their motor learning abilities, yet little is known about the neural processes that underlie this variability. We identified distinct profiles of human sensorimotor adaptation that emerged across 2 days of learning, linking these profiles to the dynamics of whole-brain functional networks early on the first day when cognitive strategies toward sensorimotor adaptation are believed to be most prominent. During early learning, greater recruitment of a network of higher-order brain regions, involving prefrontal and anterior temporal cortex, was associated with faster learning. At the same time, greater integration of this "cognitive network" with a sensorimotor network was associated with slower learning, consistent with the notion that cognitive strategies toward adaptation operate in parallel with implicit learning processes of the sensorimotor system. On the second day, greater recruitment of a network that included the hippocampus was associated with faster learning, consistent with the notion that declarative memory systems are involved with fast relearning of sensorimotor mappings. Together, these findings provide novel evidence for the role of higher-order brain systems in driving variability in adaptation.

Efficacy and safety of oxiracetam in patients with vascular cognitive impairment: A multicenter, randomized, double-blinded, placebo-controlled, phase IV clinical trial

BACKGROUND

Oxiracetam may have a modest effect on preventing cognitive decline. Exercise can also enhance cognitive function. This trial aims to investigate the effect of oxiracetam on post-stroke cognitive impairment and explore whether this effect is modified by exercise. Furthermore, the mechanisms that mediate this effect will be investigated through a neural network analysis.

METHODS

This is a multicenter, randomized, double-blind, placebo-controlled phase IV trial. Patients who complained of cognitive decline 3 months after stroke and had a high risk of cognitive decline were eligible. Patients were randomly assigned to receive either 800 mg of oxiracetam or placebo twice daily for 36 weeks. After randomization, a predetermined exercise protocol was provided to each participant, and the degree of physical activity was assessed using wrist actigraphy at 4, 12, 24, and 36 weeks. Resting-state functional MRI was obtained in baseline and 36-week follow-up. Co-primary endpoints are changes in the Mini-Mental State Examination and Clinical Dementia Rating-Sum of Boxes. Secondary endpoints include changes in the NINDS-CSN VCIHS-Neuropsychology Protocol, Euro QoL, patient's global assessment, and functional network connectivity. If there is a significant difference in physical activity between the two groups, the interaction effect between physical activity and the treatment group will be examined. A total of 500 patients were enrolled from February 2018, and the last patient's final follow-up was completed in September 2022.

CONCLUSION

This trial is meaningful not only to prove the efficacy of oxiracetam, but also evaluate whether exercise can modify the effects of medication and how cognitive function can be restored. Trial registrationhttp://cris.nih.go.kr (KCT0005137).

Saccades and presaccadic stimulus repetition alter cortical network topology and dynamics: evidence from EEG and graph theoretical analysis

Parietal and frontal cortex are involved in saccade generation, and their output signals modify visual signals throughout cortex. Local signals associated with these interactions are well described, but their large-scale progression and network dynamics are unknown. Here, we combined source localized electroencephalography (EEG) and graph theory analysis (GTA) to understand how saccades and presaccadic visual stimuli interactively alter cortical network dynamics in humans. Twenty-one participants viewed 1-3 vertical/horizontal grids, followed by grid with the opposite orientation just before a horizontal saccade or continued fixation. EEG signals from the presaccadic interval (or equivalent fixation period) were used for analysis. Source localization-through-time revealed a rapid frontoparietal progression of presaccadic motor signals and stimulus-motor interactions, with additional band-specific modulations in several frontoparietal regions. GTA analysis revealed a saccade-specific functional network with major hubs in inferior parietal cortex (alpha) and the frontal eye fields (beta), and major saccade-repetition interactions in left prefrontal (theta) and supramarginal gyrus (gamma). This network showed enhanced segregation, integration, synchronization, and complexity (compared with fixation), whereas stimulus repetition interactions reduced synchronization and complexity. These cortical results demonstrate a widespread influence of saccades on both regional and network dynamics, likely responsible for both the motor and perceptual aspects of saccades.

Analysis of functional connectivity in depression based on a weighted hyper-network method

Objective. Brain connectivity network is a vital tool to reveal the interaction between different brain regions. Currently, most functional connectivity methods can only capture pairs of information to construct brain networks which ignored the high-order correlations between brain regions.Approach. Therefore, this study proposed a weighted connectivity hyper-network based on resting-state EEG data, and then applied to depression identification and analysis. The hyper-network model was build based on least absolute shrinkage and selection operator sparse regression method to effectively represent the higher-order relationships of brain regions. On this basis, by integrating the correlation-based weighted hyper-edge information, the weighted hyper-network is constructed, and the topological features of the network are extracted for classification.Main results. The experimental results obtained an optimal accuracy compared to the traditional coupling methods. The statistical results on network metrics proved that there were significant differences between depressive patients and normal controls. In addition, some brain regions and electrodes were found and discussed to highly correlate with depression by analyzing of the critical nodes and hyper-edges.Significance. These may help discover disease-related biomarkers important for depression diagnosis.

Brain diffusion tensor imaging reveals altered connections and networks in epilepsy patients

Introduction

Accumulating evidence shows that epilepsy is a disease caused by brain network dysfunction. This study explored changes in brain network structure in epilepsy patients based on graph analysis of diffusion tensor imaging data.

Methods

The brain structure networks of 42 healthy control individuals and 26 epilepsy patients were constructed. Using graph theory analysis, global and local network topology parameters of the brain structure network were calculated, and changes in global and local characteristics of the brain network in epilepsy patients were quantitatively analyzed.

Results

Compared with the healthy control group, the epilepsy patient group showed lower global efficiency, local efficiency, clustering coefficient, and a longer shortest path length. Both healthy control individuals and epilepsy patients showed small-world attributes, with no significant difference between groups. The epilepsy patient group showed lower nodal local efficiency and nodal clustering coefficient in the right olfactory cortex and right rectus and lower nodal degree centrality in the right olfactory cortex and the left paracentral lobular compared with the healthy control group. In addition, the epilepsy patient group showed a smaller fiber number of edges in specific regions of the frontal lobe, temporal lobe, and default mode network, indicating reduced connection strength.

Discussion

Epilepsy patients exhibited lower global and local brain network properties as well as reduced white matter fiber connectivity in key brain regions. These findings further support the idea that epilepsy is a brain network disorder.

Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

Altered global modular organization of intrinsic functional connectivity in autism arises from atypical node-level processing

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by restricted interests and repetitive behaviors as well as social-communication deficits. These traits are associated with atypicality of functional brain networks. Modular organization in the brain plays a crucial role in network stability and adaptability for neurodevelopment. Previous neuroimaging research demonstrates discrepancies in studies of functional brain modular organization in ASD. These discrepancies result from the examination of mixed age groups. Furthermore, recent findings suggest that while much attention has been given to deriving atlases and measuring the connections between nodes, within node information may also be crucial in determining altered modular organization in ASD compared with typical development (TD). However, altered modular organization originating from systematic nodal changes are yet to be explored in younger children with ASD. Here, we used graph-theoretical measures to fill this knowledge gap. To this end, we utilized multicenter resting-state fMRI data collected from 5 to 10-year-old children-34 ASD and 40 TD obtained from the Autism Brain Image Data Exchange (ABIDE) I and II. We demonstrate that alterations in topological roles and modular cohesiveness are the two key properties of brain regions anchored in default mode, sensorimotor, and salience networks, and primarily relate to social and sensory deficits in children with ASD. These results demonstrate that atypical global network organization in children with ASD arises from nodal role changes, and contribute to the growing body of literature suggesting that there is interesting information within nodes providing critical markers of functional brain networks in autistic children.

Altered whole-brain gray matter volume in form-deprivation myopia rats based on voxel-based morphometry: A pilot study

Background

Myopia is one of the major public health problems worldwide. However, the exact pathogenesis of myopia remains unclear. This study proposes using voxel-based morphometry (VBM) to investigate potential morphological alterations in gray matter volume (GMV) in form-deprivation myopia (FDM) rats.

Methods

A total of 14 rats with FDM (FDM group) and 15 normal controls (NC group) underwent high-resolution magnetic resonance imaging (MRI). Original T2 brain images were analyzed using VBM method to identify group differences in GMV. Following MRI examination, all rats were perfused with formalin, and immunohistochemical analysis of NeuN and c-fos levels was performed on the visual cortex.

Results

In the FDM group, compared to the NC group, significantly decreased GMVs were found in the left primary visual cortex, left secondary visual cortex, right subiculum, right cornu ammonis, right entorhinal cortex and bilateral molecular layer of the cerebellum. Additionally, significantly increased GMVs were found in the right dentate gyrus, parasubiculum, and olfactory bulb.

Conclusions

Our study revealed a positive correlation between mGMV and the expression of c-fos and NeuN in the visual cortex, suggesting a molecular relationship between cortical activity and macroscopic measurement of visual cortex structural plasticity. These findings may help elucidate the potential neural pathogenesis of FDM and its relationship to changes in specific brain regions.

The rostral zona incerta: a subcortical integrative hub and potential DBS target for OCD

BACKGROUND

The zona incerta (ZI) is involved in mediating survival behaviors and is connected to a wide range of cortical and subcortical structures, including key basal ganglia nuclei. Based on these connections and their links to behavioral modulation, we propose that the ZI is a connectional hub for mediating between top-down and bottom-up control and a possible target for deep brain stimulation for obsessive-compulsive disorder.

METHODS

We analyzed the trajectory of cortical fibers to the ZI in nonhuman and human primates based on tracer injections in monkeys and high-resolution diffusion magnetic resonance imaging in humans. The organization of cortical and subcortical connections within the ZI were identified in the nonhuman primate studies.

RESULTS

Monkey anatomical data and human diffusion magnetic resonance imaging data showed a similar trajectory of fibers/streamlines to the ZI. Prefrontal cortex/anterior cingulate cortex terminals all converged within the rostral ZI, with dorsal and lateral areas being most prominent. Motor areas terminated caudally. Dense subcortical reciprocal connections included the thalamus, medial hypothalamus, substantia nigra/ventral tegmental area, reticular formation, and pedunculopontine nucleus and a dense nonreciprocal projection to the lateral habenula. Additional connections included the amygdala, dorsal raphe nucleus, and periaqueductal gray.

CONCLUSIONS

Dense connections with dorsal and lateral prefrontal cortex/anterior cingulate cortex cognitive control areas and the lateral habenula and the substantia nigra/ventral tegmental area, coupled with inputs from the amygdala, hypothalamus, and brainstem, suggest that the rostral ZI is a subcortical hub positioned to modulate between top-down and bottom-up control. A deep brain stimulation electrode placed in the rostral ZI would not only involve connections common to other deep brain stimulation sites but also capture several critically distinctive connections.

Socioeconomic resources are associated with distributed alterations of the brain's intrinsic functional architecture in youth

Little is known about how exposure to limited socioeconomic resources (SER) in childhood gets "under the skin" to shape brain development, especially using rigorous whole-brain multivariate methods in large, adequately powered samples. The present study examined resting state functional connectivity patterns from 5821 youth in the Adolescent Brain Cognitive Development (ABCD) study, employing multivariate methods across three levels: whole-brain, network-wise, and connection-wise. Across all three levels, SER was associated with widespread alterations across the connectome. However, critically, we found that parental education was the primary driver of neural associations with SER. These parental education associations with the developing connectome exhibited notable concentrations in somatosensory and subcortical regions, and they were partially accounted for by home enrichment activities, child's cognitive abilities, and child's grades, indicating interwoven links between parental education, child stimulation, and child cognitive performance. These results add a new data-driven, multivariate perspective on links between household SER and the child's developing functional connectome.

Disentangling hand and tool processing: Distal effects of neuromodulation

Neural processing within a local brain region that responds to more than one object category (e.g., hands and tools) nonetheless have different functional connectivity patterns with other distal brain areas, which suggests that local processing can affect and/or be affected by processing in distal areas, in a category-specific way. Here we wanted to test whether administering either a hand- or tool-related training task in tandem with transcranial direct current stimulation (tDCS) to a region that responds both to hands and tools (posterior middle temporal gyrus; pMTG), modulated local and distal neural processing more for the trained than the untrained category in a subsequent fMRI task. After each combined tDCS/training session, participants viewed images of tools, hands, and animals, in an fMRI scanner. Using multivoxel pattern analysis, we found that tDCS stimulation to pMTG indeed improved the classification accuracy between tools vs. animals, but only when combined with a tool and not a hand training task. Surprisingly, tDCS stimulation to pMTG also improved classification accuracy between hands vs. animals when combined with a tool but not a hand training task. Our findings suggest that overlapping but functionally-specific networks may be engaged separately by using a category-specific training task together with tDCS - a strategy that can be applied more broadly to other cognitive domains using tDCS. By hypothesis, these effects on local processing are a direct result of within-domain connectivity constraints from domain-specific networks that are at play in the processing and organization of object representations.

Evaluating the Alterations Induced by Virtual Reality in Cerebral Small-World Networks Using Graph Theory Analysis with Electroencephalography

Virtual reality (VR), a rapidly evolving technology that simulates three-dimensional virtual environments for users, has been proven to activate brain functions. However, the continuous alteration pattern of the functional small-world network in response to comprehensive three-dimensional stimulation rather than realistic two-dimensional media stimuli requires further exploration. Here, we aimed to validate the effect of VR on the pathways and network parameters of a small-world organization and interpret its mechanism of action. Fourteen healthy volunteers were selected to complete missions in an immersive VR game. The changes in the functional network in six different frequency categories were analyzed using graph theory with electroencephalography data measured during the pre-, VR, and post-VR stages. The mutual information matrix revealed that interactions between the frontal and posterior areas and those within the frontal and occipital lobes were strengthened. Subsequently, the betweenness centrality (BC) analysis indicated more robust and extensive pathways among hubs. Furthermore, a specific lateralized channel (O1 or O2) increment in the BC was observed. Moreover, the network parameters improved simultaneously in local segregation, global segregation, and global integration. The overall topological improvements of small-world organizations were in high-frequency bands and exhibited some degree of sustainability.

Atlas-based data integration for mapping the connections and architecture of the brain

Detailed knowledge about the neural connections among regions of the brain is key for advancing our understanding of normal brain function and changes that occur with aging and disease. Researchers use a range of experimental techniques to map connections at different levels of granularity in rodent animal models, but the results are often challenging to compare and integrate. Three-dimensional reference atlases of the brain provide new opportunities for cumulating, integrating, and reinterpreting research findings across studies. Here, we review approaches for integrating data describing neural connections and other modalities in rodent brain atlases and discuss how atlas-based workflows can facilitate brainwide analyses of neural network organization in relation to other facets of neuroarchitecture.

Brain connectivity changes associated with episodic recollection decline in aging: A review of fMRI studies

With advancing age, individuals experience a gradual decline in recollection, the ability to retrieve personal experiences accompanied by details, such as temporal and spatial contextual information. Numerous studies have identified several brain regions that exhibit age-related activation differences during recollection tasks. More recently, an increasing number of studies have provided evidence regarding how brain connectivity among the regions supporting recollection contributes to the explanation of recollection deficits in aging. However, brain connectivity evidence has not been examined jointly to provide an integrative view of how these new findings have improved our knowledge of the neurofunctional changes underlying the recollection deficits associated with aging. Therefore, the aim of the present study was to examine functional magnetic resonance imaging (fMRI) studies that employed one of the numerous methods available for analyzing brain connectivity in older adults. Only studies that applied connectivity analysis to data recorded during episodic recollection tasks, either during encoding or retrieval, were assessed. First, the different brain connectivity analysis methods and the information conveyed were briefly described. Then, the brain connectivity findings from the different studies were described and discussed to provide an integrative point of view of how these findings explain the decline in recollection associated with aging. The studies reviewed provide evidence that the hippocampus consistently decreased its connectivity with the parahippocampal gyrus and the posterior cingulate cortex, essential regions of the recollection network, in older adults relative to young adults. In addition, older adults exhibited increased connectivity between the hippocampus and several widespread regions compared to young adults. The increased connectivity was interpreted as brain intensification recourse to overcome recollection decay. Additionally, suggestions for future research in the field are outlined.

NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis

Identifying the directed connectivity that underlie networked activity between different cortical areas is critical for understanding the neural mechanisms behind sensory processing. Granger causality (GC) is widely used for this purpose in functional magnetic resonance imaging analysis, but there the temporal resolution is low, making it difficult to capture the millisecond-scale interactions underlying sensory processing. Magnetoencephalography (MEG) has millisecond resolution, but only provides low-dimensional sensor-level linear mixtures of neural sources, which makes GC inference challenging. Conventional methods proceed in two stages: First, cortical sources are estimated from MEG using a source localization technique, followed by GC inference among the estimated sources. However, the spatiotemporal biases in estimating sources propagate into the subsequent GC analysis stage, may result in both false alarms and missing true GC links. Here, we introduce the Network Localized Granger Causality (NLGC) inference paradigm, which models the source dynamics as latent sparse multivariate autoregressive processes and estimates their parameters directly from the MEG measurements, integrated with source localization, and employs the resulting parameter estimates to produce a precise statistical characterization of the detected GC links. We offer several theoretical and algorithmic innovations within NLGC and further examine its utility via comprehensive simulations and application to MEG data from an auditory task involving tone processing from both younger and older participants. Our simulation studies reveal that NLGC is markedly robust with respect to model mismatch, network size, and low signal-to-noise ratio, whereas the conventional two-stage methods result in high false alarms and mis-detections. We also demonstrate the advantages of NLGC in revealing the cortical network-level characterization of neural activity during tone processing and resting state by delineating task- and age-related connectivity changes.

Cognitive theories of autism based on the interactions between brain functional networks

Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5-14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism.

Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping

The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain from resting-state fMRI data. Here we describe its mathematical formulation and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data acquired as part of the Human Connectome Project to generate group-average LTM images. Generally, most of the functional structures revealed by LTM images agree in the boundaries with anatomical structures identified by T1-weighted images and fractional anisotropy maps derived from diffusion MRI. In addition, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-derived functional parcels are significantly larger than those with geometric perturbations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.

Time-sequential graph adversarial learning for brain modularity community detection

Brain community detection is an efficient method to represent the communities of brain networks. However, time-variable functions of the brain and the intricate brain community structure impose a great challenge on it. In this paper, a time-sequential graph adversarial learning (TGAL) framework is proposed to detect brain communities and characterize the structure of communities from brain networks. In the framework, a novel time-sequential graph neural network is designed as an encoder to extract efficient graph representations by spatio-temporal attention mechanism. Since it is difficult to capture the community structure, the measurable modularity loss is used to optimize by maximizing the modularity of the community. In addition, the framework employs an adversarial scheme to guide the learning of representation. The effectiveness of our model is shown through experiments on the real-world brain network datasets, and the great performance of brain community detection demonstrates the advantage of the proposed framework.