Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities

Never-Ending Learning for Explainable Brain Computing

Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge-driven forward inference and data-driven reverse inference, bolstered by the pre-trained language modeling techniques and the human-in-the-loop mechanisms. In particular, it incorporates internal evidence learning through multi-task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer-grained understanding of brain activity patterns related to human intelligence.

Abnormal intrinsic brain functional network dynamics in first-episode drug-naïve adolescent major depressive disorder

BACKGROUND

Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls.

RESULTS

Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks.

CONCLUSIONS

Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.

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.

Sleep and physical activity measures are associated with resting-state network segregation in non-demented older adults

Greater physical activity and better sleep are associated with reduced risk of cognitive decline and dementia among older adults, but little is known about their combined associations with measures of brain function and neuropathology. This study investigated potential independent and interactive cross-sectional relationships between actigraphy-estimated total volume of physical activity (TVPA) and sleep patterns [i.e., total sleep time (TST), sleep efficiency (SE)] with resting-state functional magnetic resonance imaging (rs-fMRI) measures of large scale network connectivity and positron emission tomography (PET) measures of amyloid-β. Participants were 135 non-demented older adults from the BIOCARD study (116 cognitively normal and 19 with mild cognitive impairment; mean age = 70.0 years). Using multiple linear regression analyses, we assessed the association between TVPA, TST, and SE with connectivity within the default-mode, salience, and fronto-parietal control networks, and with network modularity, a measure of network segregation. Higher TVPA and SE were independently associated with greater network modularity, although the positive relationship of SE with modularity was only present in amyloid-negative individuals. Additionally, higher TVPA was associated with greater connectivity within the default-mode network, while greater SE was related to greater connectivity within the salience network. In contrast, longer TST was associated with lower network modularity, particularly among amyloid-positive individuals, suggesting a relationship between longer sleep duration and greater network disorganization. Physical activity and sleep measures were not associated with amyloid positivity. These data suggest that greater physical activity levels and more efficient sleep may promote more segregated and potentially resilient functional networks and increase functional connectivity within specific large-scale networks and that the relationship between sleep and functional networks connectivity may depend on amyloid status.

Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.

Brain signaling becomes less integrated and more segregated with age

The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, P int and P seg , from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling

INTRODUCTION

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results.

METHODS

We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls).

RESULTS

Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings.

DISCUSSION

The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.

A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease

Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.

Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep

Consciousness requires a dynamic balance of integration and segregation in functional brain networks. An optimal integration-segregation balance depends on two key aspects of functional connectivity: global efficiency (i.e., integration) and clustering (i.e., segregation). We developed a new fMRI-based measure, termed the integration-segregation difference (ISD), which captures both aspects. We used this metric to quantify changes in brain state from conscious wakefulness to loss of responsiveness induced by the anesthetic propofol. The observed changes in ISD suggest a profound shift to segregation in both whole brain and all brain subnetworks during anesthesia. Moreover, brain networks displayed similar sequences of disintegration and subsequent reintegration during, respectively, loss and return of responsiveness. Random forest machine learning models, trained with the integration and segregation of brain networks, identified the awake vs. unresponsive states and their transitions with accuracy up to 93%. We found that metastability (i.e., the dynamic recurrence of non-equilibrium transient states) is more effectively explained by integration, while complexity (i.e., diversity and intricacy of neural activity) is more closely linked with segregation. The analysis of a sleep dataset revealed similar findings. Our results demonstrate that the integration-segregation balance is a useful index that can differentiate among various conscious and unconscious states.

Functional brain networks in Developmental Topographical Disorientation

Despite a decade-long study on Developmental Topographical Disorientation, the underlying mechanism behind this neurological condition remains unknown. This lifelong selective inability in orientation, which causes these individuals to get lost even in familiar surroundings, is present in the absence of any other neurological disorder or acquired brain damage. Herein, we report an analysis of the functional brain network of individuals with Developmental Topographical Disorientation ($n = 19$) compared against that of healthy controls ($n = 21$), all of whom underwent resting-state functional magnetic resonance imaging, to identify if and how their underlying functional brain network is altered. While the established resting-state networks (RSNs) are confirmed in both groups, there is, on average, a greater connectivity and connectivity strength, in addition to increased global and local efficiency in the overall functional network of the Developmental Topographical Disorientation group. In particular, there is an enhanced connectivity between some RSNs facilitated through indirect functional paths. We identify a handful of nodes that encode part of these differences. Overall, our findings provide strong evidence that the brain networks of individuals suffering from Developmental Topographical Disorientation are modified by compensatory mechanisms, which might open the door for new diagnostic tools.

Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.

Local orchestration of distributed functional patterns supporting loss and restoration of consciousness in the primate brain

A central challenge of neuroscience is to elucidate how brain function supports consciousness. Here, we combine the specificity of focal deep brain stimulation with fMRI coverage of the entire cortex, in awake and anaesthetised non-human primates. During propofol, sevoflurane, or ketamine anaesthesia, and subsequent restoration of responsiveness by electrical stimulation of the central thalamus, we investigate how loss of consciousness impacts distributed patterns of structure-function organisation across scales. We report that distributed brain activity under anaesthesia is increasingly constrained by brain structure across scales, coinciding with anaesthetic-induced collapse of multiple dimensions of hierarchical cortical organisation. These distributed signatures are observed across different anaesthetics, and they are reversed by electrical stimulation of the central thalamus, coinciding with recovery of behavioural markers of arousal. No such effects were observed upon stimulating the ventral lateral thalamus, demonstrating specificity. Overall, we identify consistent distributed signatures of consciousness that are orchestrated by specific thalamic nuclei.

Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder

Thalamocortical circuitry has a substantial impact on emotion and cognition. Previous studies have demonstrated alterations in thalamocortical functional connectivity (FC), characterized by region-dependent hypo- or hyper-connectivity, among individuals with major depressive disorder (MDD). However, the dynamical reconfiguration of the thalamocortical system over time and potential abnormalities in dynamic thalamocortical connectivity associated with MDD remain unclear. Hence, we analyzed dynamic FC (dFC) between ten thalamic subregions and seven cortical subnetworks from resting-state functional magnetic resonance images of 48 patients with MDD and 57 healthy controls (HCs) to investigate time-varying changes in thalamocortical FC in patients with MDD. Moreover, dynamic laterality analysis was conducted to examine the changes in functional lateralization of the thalamocortical system over time. Correlations between the dynamic measures of thalamocortical FC and clinical assessment were also calculated. We identified four dynamic states of thalamocortical circuitry wherein patients with MDD exhibited decreased fractional time and reduced transitions within a negative connectivity state that showed strong correlations with primary cortical networks, compared with the HCs. In addition, MDD patients also exhibited increased fluctuations in functional laterality in the thalamocortical system across the scan duration. The thalamo-subnetwork analysis unveiled abnormal dFC variability involving higher-order cortical networks in the MDD cohort. Significant correlations were found between increased dFC variability with dorsal attention and default mode networks and the severity of symptoms. Our study comprehensively investigated the pattern of alteration of the thalamocortical dFC in MDD patients. The heterogeneous alterations of dFC between the thalamus and both primary and higher-order cortical networks may help characterize the deficits of sensory and cognitive processing in MDD.

Longitudinal monitoring of the mouse brain reveals heterogenous network trajectories during aging

The human aging brain is characterized by changes in network efficiency that are currently best captured through longitudinal resting-state functional MRI (rs-fMRI). These studies however are challenging due to the long human lifespan. Here we show that the mouse animal model with a much shorter lifespan allows us to follow the functional network organization over most of the animal's adult lifetime. We used a longitudinal study of the functional connectivity of different brain regions with rs-fMRI under anesthesia. Our analysis uncovers network modules similar to those reported in younger mice and in humans (i.e., prefrontal/default mode network (DMN), somatomotor and somatosensory networks). Statistical analysis reveals different patterns of network reorganization during aging. Female mice showed a pattern akin to human aging, with de-differentiation of the connectome, mainly due to increases in connectivity of the prefrontal/DMN cortical networks to other modules. Our male cohorts revealed heterogenous aging patterns with only one group confirming the de- differentiation, while the majority showed an increase in connectivity of the somatomotor cortex to the Nucleus accumbens. In summary, in line with human work, our analysis in mice supports the concept of de-differentiation in the aging mammalian brain and reveals additional trajectories in aging mice networks.

Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease

BACKGROUND

Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline.

OBJECTIVES

The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features.

METHODS

Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models.

RESULTS

FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity.

CONCLUSIONS

Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Impaired segregation of the attention deficit hyperactivity disorder related pattern in children

BACKGROUND

Inattention is a key characteristic of attention deficit hyperactivity disorder (ADHD). Specific brain abnormalities associated with this symptom form a discernible pattern related with ADHD in children (i.e., ADHD related pattern) in our earlier research. The developmental processes of segregation and integration may be crucial to ADHD. However, how brains reconfigure these processes of the ADHD related pattern in different subtypes of ADHD and across sexes remain unclear.

METHODS

Nested-spectral partition method was applied to identify effects of subtype and sex on segregation and integration of the ADHD related pattern, using 145 ADHD patients and 135 typically developing controls (TDC) aged 7-14. Relationships between the measures and inattention symptoms were also investigated.

RESULTS

Children with ADHD exhibited lower segregation of the ADHD related pattern (p = 1.17 × 10-8) than TDCs. Only the main effect of subtype was significant (p = 1.14 × 10-5). Both ADHD-C (p = 2.16 × 10-6) and ADHD-I (p = 2.87 × 10-6) patients had lower segregation components relative to the TDC. Moreover, segregation components were negatively correlated with inattention scores.

CONCLUSIONS

This study identified impaired segregation in the ADHD related pattern of children with ADHD and found shared neural bases among different subtypes and sexes.

Associations of resilience, white matter topological organization, and cognitive functions in first-episode, drug-naïve schizophrenia patients: A moderated mediation analysis

BACKGROUND

Cognitive deficits are core symptoms of schizophrenia (SZ) and are associated with impaired resilience to stress. Different cognitive functions appeared to be interrelated, and the mechanism may involve neural alterations. The disrupted topological organization indicated abnormalities in the segregation and integration of brain networks that support various cognitive processes in SZ patients. Therefore, this study aimed to assess the direct and indirect effects of resilience on cognitive functions. We hypothesized that topological properties would moderate these associations.

METHODS

Forty-nine SZ patients and fifty-two healthy controls (HCs) were recruited in this study. The Connor-Davidson Resilience Scale and the MATRICS Consensus Cognitive Battery were used to examine resilience and cognitive functions, respectively, and a graph theory approach was used to assess white matter topological organization.

RESULTS

Compared to HCs, SZ patients showed lower levels of resilience and cognitive functions in multiple domains as well as abnormal global properties and nodal metrics. In addition, shorter characteristic path length was associated with a stronger indirect effect of resilience on working memory through processing speed in SZ patients.

CONCLUSION

Characteristic path length might moderate the mediating effects of processing speed in the relationship between resilience and working memory in schizophrenia patients.

Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory

The human brain, as a complex system, has long captivated multidisciplinary researchers aiming to decode its intricate structure and function. This intricate network has driven scientific pursuits to advance our understanding of cognition, behavior, and neurological disorders by delving into the complex mechanisms underlying brain function and dysfunction. Modelling brain dynamics using machine learning techniques deepens our comprehension of brain dynamics from a computational perspective. These computational models allow researchers to simulate and analyze neural interactions, facilitating the identification of dysfunctions in connectivity or activity patterns. Additionally, the trained dynamical system, serving as a surrogate model, optimizes neurostimulation strategies under the guidelines of control theory. In this chapter, we discuss the recent studies on modelling and controlling brain dynamics at the intersection of machine learning and control theory, providing a framework to understand and improve cognitive function, and treat neurological and psychiatric disorders.

Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development

The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

Altered dynamical integration/segregation balance during anesthesia-induced loss of consciousness

In recent years, brain imaging studies have begun to shed light on the neural correlates of physiologically-reversible altered states of consciousness such as deep sleep, anesthesia, and psychedelic experiences. The emerging consensus is that normal waking consciousness requires the exploration of a dynamical repertoire enabling both global integration i.e., long-distance interactions between brain regions, and segregation, i.e., local processing in functionally specialized clusters. Altered states of consciousness have notably been characterized by a tipping of the integration/segregation balance away from this equilibrium. Historically, functional MRI (fMRI) has been the modality of choice for such investigations. However, fMRI does not enable characterization of the integration/segregation balance at sub-second temporal resolution. Here, we investigated global brain spatiotemporal patterns in electrocorticography (ECoG) data of a monkey (Macaca fuscata) under either ketamine or propofol general anesthesia. We first studied the effects of these anesthetics from the perspective of band-specific synchronization across the entire ECoG array, treating individual channels as oscillators. We further aimed to determine whether synchrony within spatially localized clusters of oscillators was differently affected by the drugs in comparison to synchronization over spatially distributed subsets of ECoG channels, thereby quantifying changes in integration/segregation balance on physiologically-relevant time scales. The findings reflect global brain dynamics characterized by a loss of long-range integration in multiple frequency bands under both ketamine and propofol anesthesia, most pronounced in the beta (13-30 Hz) and low-gamma bands (30-80 Hz), and with strongly preserved local synchrony in all bands.

Cross-module switching diversity of brain network nodes in resting and cognitive states

Large-scale brain network dynamics reflect state change in brain activities and have potential effects on cognition. Such dynamics can be described by node temporal switching between modules; however, there are only a few studies on the influence of brain network node switching on brain cognition. Based on the functional magnetic resonance imaging (fMRI) data of resting and task states, we constructed dynamic functional networks using overlap sliding-time windows and applied multilayer network analysis to study the behaviours of nodes across brain modules. We found that (i) nodes with a high level switching rate in the resting-state mainly come from the default network, while nodes with a low level of switching rate mainly come from the visual network, (ii) nodes with a high switching rate have lower clustering coefficients and shorter characteristic path lengths, which are mainly affected by the somatomotor network and dorsal attention network; and (iii) in task states, there is still a negative correlation between switching rate, clustering coefficient and characteristic path length. However, the main subsystems that affect brain functions are regulated by the tasks. Our findings not only reveal the relevant characteristics of network node switching behaviours but also provide new insights for further understanding the complex functions of the brain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09894-z.

EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task

Reading is a complex cognitive skill that involves visual, attention, and linguistic skills. Because attention is one of the most important cognitive skills for reading and learning, the current study intends to examine the functional brain network connectivity implicated during sustained attention in dyslexic children. 15 dyslexic children (mean age 9.83±1.85 years) and 15 non-dyslexic children (mean age 9.91±1.97 years) were selected for this study. The children were asked to perform a visual continuous performance task (VCPT) while their electroencephalogram (EEG) signals were recorded. In dyslexic children, significant variations in task measurements revealed considerable omission and commission errors. During task performance, the dyslexic group with the absence of a small-world network had a lower clustering coefficient, a longer characteristic pathlength, and lower global and local efficiency than the non-dyslexic group (mainly in theta and alpha bands). When classifying data from the dyslexic and non-dyslexic groups, the current study achieved the maximum classification accuracy of 96.7% using a k-nearest neighbor (KNN) classifier. To summarize, our findings revealed indications of poor functional segregation and disturbed information transfer in dyslexic brain networks during a sustained attention task.

Connectome-based modeling reveals a resting-state functional network that mediates the relationship between social rejection and rumination

Background

Rumination impedes problem solving and is one of the most important factors in the onset and maintenance of multiple psychiatric disorders. The current study aims to investigate the impact of social rejection on rumination and explore the underlying neural mechanisms involved in this process.

Methods

We utilized psychological questionnaire and resting-state brain imaging data from a sample of 560 individuals. The predictive model for rumination scores was constructed using resting-state functional connectivity data through connectome-based predictive modeling. Additionally, a mediation analysis was conducted to investigate the mediating role of the prediction network in the relationship between social rejection and rumination.

Results

A positive correlation between social rejection and rumination was found. We obtained the prediction model of rumination and found that the strongest contributions came from the intra- and internetwork connectivity within the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), and sensorimotor networks (SMN). Analysis of node strength revealed the significance of the supramarginal gyrus (SMG) and angular gyrus (AG) as key nodes in the prediction model. In addition, mediation analysis showed that the strength of the prediction network mediated the relationship between social rejection and rumination.

Conclusion

The findings highlight the crucial role of functional connections among the DMN, DAN, FPCN, and SMN in linking social rejection and rumination, particular in brain regions implicated in social cognition and emotion, namely the SMG and AG regions. These results enhance our understanding of the consequences of social rejection and provide insights for novel intervention strategies targeting rumination.

Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks

Cognitive neuroscience assumes that different mental abilities correspond to at least partly separable brain subnetworks and strives to understand their relationships. However, single-task approaches typically revealed multiple brain subnetworks to be involved in performance. Here, we chose a bottom-up approach of investigating the association between structural and functional brain subnetworks, on the one hand, and domain-specific cognitive abilities, on the other. Structural network was identified using machine-learning graph neural network by clustering anatomical brain properties measured in 838 individuals enroled in the WU-Minn Young Adult Human Connectome Project. Functional network was adapted from seven Resting State Networks (7-RSN). We then analyzed the results of 15 cognitive tasks and estimated five latent abilities: fluid reasoning (Gf), crystallized intelligence (Gc), memory (Mem), executive functions (EF), and processing speed (Gs). In a final step we determined linear associations between these independently identified ability and brain entities. We found no one-to-one mapping between latent abilities and brain subnetworks. Analyses revealed that abilities are associated with properties of particular combinations of brain subnetworks. While some abilities are more strongly associated to within-subnetwork connections, others are related with connections between multiple subnetworks. Importantly, domain-specific abilities commonly rely on node(s) as hub(s) to connect with other subnetworks. To test the robustness of our findings, we ran the analyses through several defensible analytical decisions. Together, the present findings allow a novel perspective on the distinct nature of domain-specific cognitive abilities building upon unique combinations of associated brain subnetworks.

Task-relevant brain dynamics among cognitive subsystems induced by regional stimulation in a whole-brain computational model

Cognition involves the global integration of distributed brain regions that are known to work cohesively as cognitive subsystems during brain functioning. Empirical evidence has suggested that spatiotemporal phase relationships between brain regions, measured as synchronization and metastability, may encode important task-relevant information. However, it remains largely unknown how phase relationships aggregate at the level of cognitive subsystems under different cognitive processing. Here, we probe this question by simulating task-relevant brain dynamics through regional stimulation of a whole-brain dynamical network model operating in the resting-state dynamical regime. The model is constructed with structurally embedded Stuart-Laudon oscillators and then fitted with human resting-state functional magnetic resonance imaging data. Based on this framework, we first demonstrate the plausibility of introducing the cognitive system partition into the modeling analysis framework by showing that the clustering of regions across functional networks is better circumscribed by the predefined partition. At the cognitive subsystem level, we focus on how task-relevant phase dynamics are organized in terms of synchronization and metastability. We found that patterns of cognitive synchronization are more task specific, whereas patterns of cognitive metastability are more consistent across different states, suggesting it may encode a more task-general property during cognitive processing, an inherent property conferred by brain organization. This consistent network architecture in cognitive metastability may be related to the distinct functional responses of realistic cognitive systems. We also provide empirical evidence to partially support our computational results. Our paper may provide insights for the mechanisms underlying task-relevant brain dynamics, and establish a model-based link between brain structure, dynamics, and cognition, a fundamental step for computationally aided brain interventions.

Different patterns of intrinsic functional connectivity at the default mode and attentional networks predict crystalized and fluid abilities in childhood

Crystallized abilities are skills used to solve problems based on experience, while fluid abilities are linked to reasoning without evoke prior knowledge. To what extent crystallized and fluid abilities involve dissociated or overlapping neural systems is debatable. Due to often deployed small sample sizes or different study settings in prior work, the neural basis of crystallized and fluid abilities in childhood remains largely unknown. Here we analyzed within and between network connectivity patterns from resting-state functional MRI of 2707 children between 9 and 10 years from the ABCD study. We hypothesized that differences in functional connectivity at the default mode network (DMN), ventral, and dorsal attentional networks (VAN, DAN) explain differences in fluid and crystallized abilities. We found that stronger between-network connectivity of the DMN and VAN, DMN and DAN, and VAN and DAN predicted crystallized abilities. Within-network connectivity of the DAN predicted both crystallized and fluid abilities. Our findings reveal that crystallized abilities rely on the functional coupling between attentional networks and the DMN, whereas fluid abilities are associated with a focal connectivity configuration at the DAN. Our study provides new evidence into the neural basis of child intelligence and calls for future comparative research in adulthood during neuropsychiatric diseases.

Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort

Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.

Altered brain functional connectivity in vegetative state and minimally conscious state

Objectives

The pathological mechanism for a disorder of consciousness (DoC) is still not fully understood. Based on traditional behavioral scales, there is a high rate of misdiagnosis for subtypes of DoC. We aimed to explore whether topological characterization may explain the pathological mechanisms of DoC and be effective in diagnosing the subtypes of DoC.

Methods

Using resting-state functional magnetic resonance imaging data, the weighted brain functional networks for normal control subjects and patients with vegetative state (VS) and minimally conscious state (MCS) were constructed. Global and local network characteristics of each group were analyzed. A support vector machine was employed to identify MCS and VS patients.

Results

The average connection strength was reduced in DoC patients and roughly equivalent in MCS and VS groups. Global efficiency, local efficiency, and clustering coefficients were reduced, and characteristic path length was increased in DoC patients (p < 0.05). For patients of both groups, global network measures were not significantly different (p > 0.05). Nodal efficiency, nodal local efficiency, and nodal clustering coefficient were reduced in frontoparietal brain areas, limbic structures, and occipital and temporal brain areas (p < 0.05). The comparison of nodal centrality suggested that DoC causes reorganization of the network structure on a large scale, especially the thalamus. Lobal network measures emphasized that the differences between the two groups of patients mainly involved frontoparietal brain areas. The accuracy, sensitivity, and specificity of the classifier for identifying MCS and VS patients were 89.83, 78.95, and 95%, respectively.

Conclusion

There is an association between altered network structures and clinical symptoms of DoC. With the help of network metrics, it is feasible to differentiate MCS and VS patients.

[Effect of electroconvulsive therapy on brain functional network in major depressive disorder]

Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.

Acupuncture Alters Brain's Dynamic Functional Network Connectivity in Stroke Patients with Motor Dysfunction: A Randomised Controlled Neuroimaging Trial

Objectives

Neuroimaging studies have confirmed that acupuncture can promote static functional reorganization in poststroke patients with motor dysfunction. But its effect on dynamic brain networks remains unclear. This study is aimed at investigating how acupuncture affected the brain's dynamic functional network connectivity (dFNC) after ischemic stroke.

Methods

We conducted a single-center, randomised controlled neuroimaging study in ischemic stroke patients. A total of 53 patients were randomly divided into the true acupoint treatment group (TATG) and the sham acupoint treatment group (SATG) at a ratio of 2 : 1. Clinical assessments and magnetic resonance imaging (MRI) scans were performed on subjects before and after treatment. We used dFNC analysis to estimate distinct dynamic connectivity states. Then, the temporal properties and strength of functional connectivity (FC) matrix were compared within and between the two groups. The correlation analysis between dynamic characteristics and clinical scales was also calculated.

Results

All functional network connectivity (FNC) matrices were clustered into 3 connectivity states. After treatment, the TATG group showed a reduced mean dwell time and found attenuated FC between the sensorimotor network (SMN) and the frontoparietal network (FPN) in state 3, which was a sparsely connected state. The FC between the dorsal attention network (DAN) and the default mode network (DMN) was higher after treatment in the TATG group in state 1, which was a relative segregated state. The SATG group preferred to increase the mean dwell time and FC within FPN in state 2, which displayed a local tightly connected state. In addition, we found that the FC value increased between DAN and right frontoparietal network (RFPN) in state 1 in the TATG group after treatment compared to the SATG group. Correlation analyses before treatment showed that the Fugl-Meyer Assessment (FMA) lower score was negatively correlated with the mean dwell time in state 3. FMA score showed positive correlation with FC in RFPN-SMN in state 3. FMA-lower score was positively correlated with FC in DAN-DMN and DAN-RFPN in state 1.

Conclusions

Acupuncture has the potential to modulate abnormal temporal properties and promote the balance of separation and integration of brain function. True acupoint stimulation may have a more positive effect on regulating the brain's dynamic function. Clinical Trial Registration. This trial is registered with Chinese Clinical Trials Registry (ChiCTR1800016263).

Nonlinear changes in delayed functional network topology in Alzheimer's disease: relationship with amyloid and tau pathology

BACKGROUND

Alzheimer's disease is a neurodegenerative disorder associated with the abnormal deposition of pathological processes, such as amyloid-ß and tau, which produces nonlinear changes in the functional connectivity patterns between different brain regions across the Alzheimer's disease continuum. However, the mechanisms underlying these nonlinear changes remain largely unknown. Here, we address this question using a novel method based on temporal or delayed correlations and calculate new whole-brain functional networks to tackle these mechanisms.

METHODS

To assess our method, we evaluated 166 individuals from the ADNI database, including amyloid-beta negative and positive cognitively normal subjects, patients with mild cognitive impairment, and patients with Alzheimer's disease dementia. We used the clustering coefficient and the global efficiency to measure the functional network topology and assessed their relationship with amyloid and tau pathology measured by positron emission tomography, as well as cognitive performance using tests measuring memory, executive function, attention, and global cognition.

RESULTS

Our study found nonlinear changes in the global efficiency, but not in the clustering coefficient, showing that the nonlinear changes in functional connectivity are due to an altered ability of brain regions to communicate with each other through direct paths. These changes in global efficiency were most prominent in early disease stages. However, later stages of Alzheimer's disease were associated with widespread network disruptions characterized by changes in both network measures. The temporal delays required for the detection of these changes varied across the Alzheimer's disease continuum, with shorter delays necessary to detect changes in early stages and longer delays necessary to detect changes in late stages. Both global efficiency and clustering coefficient showed quadratic associations with pathological amyloid and tau burden as well as cognitive decline.

CONCLUSIONS

This study suggests that global efficiency is a more sensitive indicator of network changes in Alzheimer's disease when compared to clustering coefficient. Both network properties were associated with pathology and cognitive performance, demonstrating their relevance in clinical settings. Our findings provide an insight into the mechanisms underlying nonlinear changes in functional network organization in Alzheimer's disease, suggesting that it is the lack of direct connections that drives these functional changes.

A systematic approach to brain dynamics: cognitive evolution theory of consciousness

The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' level respectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.

Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures

We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks' global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm. This approach revealed that the groups are indistinguishable in the univariate feature space but become distinguishable in a three-dimensional feature space formed by the identified most important features: mean node strength, clustering coefficient, and the number of edges. LDA achieves the highest accuracy when considering the network with all connections or only the strongest ones. Our approach allowed us to analyze the separability of classes in the multidimensional feature space, which is critical for interpreting the results of machine learning models. We demonstrated that the parametric planes of the control and MDD groups rotate in the feature space with increasing the thresholding parameter and that their intersection increases with approaching the threshold of 0.45, for which classification accuracy is minimal. Overall, the combined approach for feature selection provides an effective and interpretable scenario for discriminating between MDD patients and healthy controls using measures of functional connectivity networks. This approach can be applied to other machine learning tasks to achieve high accuracy while ensuring the interpretability of the results.

Dynamic segregation and integration of brain functional networks associated with emotional arousal

The organization of brain functional networks dynamically changes with emotional stimuli, but its relationship to emotional behaviors is still unclear. In the DEAP dataset, we used the nested-spectral partition approach to identify the hierarchical segregation and integration of functional networks and investigated the dynamic transitions between connectivity states under different arousal conditions. The frontal and right posterior parietal regions were dominant for network integration whereas the bilateral temporal, left posterior parietal, and occipital regions were responsible for segregation and functional flexibility. High emotional arousal behavior was associated with stronger network integration and more stable state transitions. Crucially, the connectivity states of frontal, central, and right parietal regions were closely related to arousal ratings in individuals. Besides, we predicted the individual emotional performance based on functional connectivity activities. Our results demonstrate that brain connectivity states are closely associated with emotional behaviors and could be reliable and robust indicators for emotional arousal.

Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification

The idea that brain networks have a hierarchical modular organization is pervasive. Increasing evidence suggests that brain modules overlap. However, little is known about the hierarchical overlapping modular structure in the brain. In this study, we developed a framework to uncover brain hierarchical overlapping modular structures based on a nested-spectral partition algorithm and an edge-centric network model. Overlap degree between brain modules is symmetrical across hemispheres, with highest overlap observed in the control and salience/ventral attention networks. Furthermore, brain edges are clustered into two groups: intrasystem and intersystem edges, to form hierarchical overlapping modules. At different levels, modules are self-similar in the degree of overlap. Additionally, the brain's hierarchical structure contains more individual identifiable information than a single-level structure, particularly in the control and salience/ventral attention networks. Our results offer pathways for future studies aimed at relating the organization of hierarchical overlapping modules to brain cognitive behavior and disorders.

Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE).

METHODS

In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration-segregation axis," by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction.

RESULTS

Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE.

SIGNIFICANCE

Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation.

Brain connectivity in frailty: Insights from The Irish Longitudinal Study on Ageing (TILDA)

Frailty in older adults is associated with greater risk of cognitive decline. Brain connectivity insights could help understand the association, but studies are lacking. We applied connectome-based predictive modeling to a 32-item self-reported Frailty Index (FI) using resting state functional MRI data from The Irish Longitudinal Study on Ageing. A total of 347 participants were included (48.9% male, mean age 68.2 years). From connectome-based predictive modeling, we obtained 204 edges that positively correlated with the FI and composed the "frailty network" characterised by connectivity of the visual network (right); and 188 edges that negatively correlated with the FI and formed the "robustness network" characterized by connectivity in the basal ganglia. Both networks' highest degree node was the caudate but with different patterns: from caudate to visual network in the frailty network; and to default mode network in the robustness network. The FI was correlated with walking speed but not with metrics of global cognition, reinforcing the matching between the FI and the brain connectivity pattern found (main predicted connectivity in basal ganglia).

Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level

In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don't use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band ( 0.5 - 45 H z ) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.

Joint-embeddings reveal functional differences in default-mode network architecture between marmosets and humans

The default-mode network (DMN) is a distributed functional brain system integral for social and higher-order cognition in humans with implications in a myriad of neuropsychological disorders. In this study, we compared the functional architecture of the DMN between humans and marmosets to assess their similarities and differences using joint gradients. This approach permits simultaneous large-scale mapping of functional systems across the cortex of humans and marmosets, revealing evidence of putative homologies between them. In doing so, we find that the DMN architecture of the marmoset exhibits differences along its anterolateral-posterior axis. Specifically, the anterolateral node of the DMN (dorsolateral prefrontal cortex) displayed weak connections and inconsistent connection topographies as compared to its posterior DMN-nodes (posterior cingulate and posterior parietal cortices). We also present evidence that the marmoset medial prefrontal cortex and temporal lobe areas correspond to other macroscopical distributed functional systems that are not part of the DMN. Given the importance of the marmoset as a pre-clinical primate model for higher-order cognitive functioning and the DMN's relevance to cognition, our results suggest that the marmoset may lack the capacity to integrate neural information to subserve cortical dynamics that is necessary for supporting diverse cognitive demands.

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Schizophrenia is a severe psychotic disorder characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional networks in schizophrenia patients and healthy controls, and we studied dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Hallucinations were associated with higher integration in attention systems, and avolition was related to a more variable segregating process in default-mode network (DMN) and control systems. In a machine-learning model, NSP-based features outperformed graph measures at predicting positive and negative symptoms. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.

Altered white matter functional network in nicotine addiction

Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.

Brain networks for temporal adaptation, anticipation, and sensory-motor integration in rhythmic human behavior

Human interaction often requires the precise yet flexible interpersonal coordination of rhythmic behavior, as in group music making. The present fMRI study investigates the functional brain networks that may facilitate such behavior by enabling temporal adaptation (error correction), prediction, and the monitoring and integration of information about 'self' and the external environment. Participants were required to synchronize finger taps with computer-controlled auditory sequences that were presented either at a globally steady tempo with local adaptations to the participants' tap timing (Virtual Partner task) or with gradual tempo accelerations and decelerations but without adaptation (Tempo Change task). Connectome-based predictive modelling was used to examine patterns of brain functional connectivity related to individual differences in behavioral performance and parameter estimates from the adaptation and anticipation model (ADAM) of sensorimotor synchronization for these two tasks under conditions of varying cognitive load. Results revealed distinct but overlapping brain networks associated with ADAM-derived estimates of temporal adaptation, anticipation, and the integration of self-controlled and externally controlled processes across task conditions. The partial overlap between ADAM networks suggests common hub regions that modulate functional connectivity within and between the brain's resting-state networks and additional sensory-motor regions and subcortical structures in a manner reflecting coordination skill. Such network reconfiguration might facilitate sensorimotor synchronization by enabling shifts in focus on internal and external information, and, in social contexts requiring interpersonal coordination, variations in the degree of simultaneous integration and segregation of these information sources in internal models that support self, other, and joint action planning and prediction.

Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach

Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.

Altered global signal topography in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease associated with widespread disruptions in intrinsic local specialization and global integration in the functional system of the brain. These changes in integration may further disrupt the global signal (GS) distribution, which might represent the local relative contribution to global activity in functional magnetic resonance imaging (fMRI).

METHODS

fMRI scans from a discovery dataset (n = 809) and a validated dataset (n = 542) were used in the analysis. We investigated the alteration of GS topography using the GS correlation (GSCORR) in patients with mild cognitive impairment (MCI) and AD. The association between GS alterations and functional network properties was also investigated based on network theory. The underlying mechanism of GSCORR alterations was elucidated using imaging-transcriptomics.

FINDINGS

Significantly increased GS topography in the frontal lobe and decreased GS topography in the hippocampus, cingulate gyrus, caudate, and middle temporal gyrus were observed in patients with AD (Padj < 0.05). Notably, topographical GS changes in these regions correlated with cognitive ability (P < 0.05). The changes in GS topography also correlated with the changes in functional network segregation (ρ = 0.5). Moreover, the genes identified based on GS topographical changes were enriched in pathways associated with AD and neurodegenerative diseases.

INTERPRETATION

Our findings revealed significant changes in GS topography and its molecular basis, confirming the informative role of GS in AD and further contributing to the understanding of the relationship between global and local neuronal activities in patients with AD.

FUNDING

Beijing Natural Science Funds for Distinguished Young Scholars, China; Fundamental Research Funds for the Central Universities, China; National Natural Science Foundation, China.

Brain network integration, segregation and quasi-periodic activation and deactivation during tasks and rest

Previous studies have shown that a re-organization of the brain's functional connectome expressed in terms of network integration and segregation may play a pivotal role for brain function. However, it has been proven difficult to fully capture both processes independently in a single methodological framework. In this study, by starting from pair-wise assessments of instantaneous phase synchronization and community membership, we assemble spatiotemporally flexible networks that reflect changes in integration/segregation that occur at a spectrum of spatial as well as temporal scales. This is achieved by iteratively assembling smaller networks into larger units under the constraint that the smaller units should be internally integrated, i.e. belong to the same community. The assembled subnetworks can be partly overlapping and differ in size across time. Our results show that subnetwork integration and segregation occur simultaneously in the brain. During task performance, global changes in synchronization between networks arise that are tied to the underlying temporal design of the experiment. We show that a hallmark property of the dynamics of the brain's functional connectome is a presence of quasi-periodic patterns of network activation and deactivation, which during task performance becomes intertwined with the underlying temporal structure of the experimental paradigm. Additionally, we show that the degree of network integration throughout a n-back working memory task is correlated to performance.

Disturbed functional connectivity and topological properties of the frontal lobe in minimally conscious state based on resting-state fNIRS

Background

Patients in minimally conscious state (MCS) exist measurable evidence of consciousness. The frontal lobe is a crucial part of the brain that encodes abstract information and is closely related to the conscious state. We hypothesized that the disturbance of the frontal functional network exists in MCS patients.

Methods

We collected the resting-state functional near-infrared spectroscopy (fNIRS) data of fifteen MCS patients and sixteen age- and gender-matched healthy controls (HC). The Coma Recovery Scale-Revised (CRS-R) scale of MCS patients was also composed. The topology of the frontal functional network was analyzed in two groups.

Results

Compared with HC, the MCS patients showed widely disrupted functional connectivity in the frontal lobe, especially in the frontopolar area and right dorsolateral prefrontal cortex. Moreover, the MCS patients displayed lower clustering coefficient, global efficiency, local efficiency, and higher characteristic path length. In addition, the nodal clustering coefficient and nodal local efficiency in the left frontopolar area and right dorsolateral prefrontal cortex were significantly reduced in MCS patients. Furthermore, the nodal clustering coefficient and nodal local efficiency in the right dorsolateral prefrontal cortex were positively correlated to auditory subscale scores.

Conclusion

This study reveals that MCS patients' frontal functional network is synergistically dysfunctional. And the balance between information separation and integration in the frontal lobe is broken, especially the local information transmission in the prefrontal cortex. These findings help us to understand the pathological mechanism of MCS patients better.

Activity flow mapping over probabilistic functional connectivity

Emerging evidence indicates that activity flow over resting-state network topology allows the prediction of task activations. However, previous studies have mainly adopted static, linear functional connectivity (FC) estimates as activity flow routes. It is unclear whether an intrinsic network topology that captures the dynamic nature of FC can be a better representation of activity flow routes. Moreover, the effects of between- versus within-network connections and tight versus loose (using rest baseline) task contrasts on the prediction of task-evoked activity across brain systems remain largely unknown. In this study, we first propose a probabilistic FC estimation derived from a dynamic framework as a new activity flow route. Subsequently, activity flow mapping was tested using between- and within-network connections separately for each region as well as using a set of tight task contrasts. Our results showed that probabilistic FC routes substantially improved individual-level activity flow prediction. Although it provided better group-level prediction, the multiple regression approach was more dependent on the length of data points at the individual-level prediction. Regardless of FC type, we consistently observed that between-network connections showed a relatively higher prediction performance in higher-order cognitive control than in primary sensorimotor systems. Furthermore, cognitive control systems exhibit a remarkable increase in prediction accuracy with tight task contrasts and a decrease in sensorimotor systems. This work demonstrates that probabilistic FC estimates are promising routes for activity flow mapping and also uncovers divergent influences of connectional topology and task contrasts on activity flow prediction across brain systems with different functional hierarchies.

Putative cause of seizure-induced cognitive alterations: The oscillatory reconfiguration of seizure network

Introduction

The dynamic reconfiguration of network oscillations is connected with cognitive processes. Changes in how neural networks and signaling pathways work are crucial to how epilepsy and related conditions develop. Specifically, there is evidence that prolonged or recurrent seizures may induce or exacerbate cognitive impairment. However, it still needs to be determined how the seizure brain configures its functional structure to shape the battle of strong local oscillations vs. slow global oscillations in the network to impair cognitive function.

Methods

In this paper, we aim to deduce the network mechanisms underlying seizure-induced cognitive impairment by comparing the evolution of strong local oscillations with slow global oscillations and their link to the resting state of healthy controls. Here, we construct a dynamically efficient network of pathological seizures by calculating the synchrony and directionality of information flow between nine patients' SEEG signals. Then, using a pattern-based method, we found hierarchical modules in the brain's functional network and measured the functional balance between the network's local strong and slow global oscillations.

Results and discussion

According to the findings, a tremendous rise in strong local oscillations during seizures and an increase in slow global oscillations after seizures corresponded to the initiation and recovery of cognitive impairment. Specifically, during the interictal period, local strong and slow global oscillations are in metastable balance, which is the same as a normal cognitive process and can be switched easily. During the pre-ictal period, the two show a bimodal pattern of separate peaks that cannot be easily switched, and some flexibility is lost. During the seizure period, a single-peak pattern with negative peaks is showcased, and the network eventually transitions to a very intense strong local oscillation state. These results shed light on the mechanism behind network oscillations in epilepsy-induced cognitive impairment. On the other hand, the differential (similarity) of oscillatory reorganization between the local (non) epileptogenic network and the global network may be an emergency protective mechanism of the brain, preventing the spread of pathological information flow to more healthy brain regions.

Dynamical interactions reconfigure the gradient of cortical timescales

The functional organization of the brain is usually presented with a back-to-front gradient of timescales, reflecting regional specialization with sensory areas (back) processing information faster than associative areas (front), which perform information integration. However, cognitive processes require not only local information processing but also coordinated activity across regions. Using magnetoencephalography recordings, we find that the functional connectivity at the edge level (between two regions) is also characterized by a back-to-front gradient of timescales following that of the regional gradient. Unexpectedly, we demonstrate a reverse front-to-back gradient when nonlocal interactions are prominent. Thus, the timescales are dynamic and can switch between back-to-front and front-to-back patterns.