Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power–law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.

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The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

Cerebral Cortex Communications ◽

10.1093/texcom/tgaa088 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Camille Fauchon ◽

David Meunier ◽

Isabelle Faillenot ◽

Florence B Pomares ◽

Hélène Bastuji ◽

...

Keyword(s):

Information Integration ◽

Functional Organization ◽

Brain Connectivity ◽

Brain Networks ◽

Modular Organization ◽

Noxious Heat ◽

Intracranial Eeg ◽

Brain Connectome ◽

Posterior Parietal ◽

The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

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Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive functions

10.1101/2020.07.15.197590 ◽

2020 ◽

Author(s):

Pesoli Matteo ◽

Rucco Rosaria ◽

Liparoti Marianna ◽

Lardone Anna ◽

D’Aurizio Giula ◽

...

Keyword(s):

Sleep Deprivation ◽

Resting State ◽

Large Scale ◽

Brain Networks ◽

Brain Network ◽

Cognitive Tasks ◽

Functional Networks ◽

Long Sleep ◽

Before And After ◽

The Brain

AbstractThe topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

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Emotional states as distinct configurations of functional brain networks

10.1101/2021.07.23.453552 ◽

2021 ◽

Author(s):

Rotem Dan ◽

Marta Weinstock ◽

Gadi Goelman

Keyword(s):

Large Scale ◽

Emotional Experience ◽

Brain Networks ◽

Emotional Valence ◽

Individual Variability ◽

Modular Organization ◽

Emotional States ◽

Functional Brain ◽

Functional Connectome ◽

Functional Brain Networks

The conceptualization of emotional states as patterns of interactions between large-scale brain networks has recently gained support. Yet, few studies have directly examined the brain's network structure during emotional experiences. Here, we investigated the brain's functional network organization during experiences of sadness, amusement, and neutral states elicited by movies, in addition to a resting-state. We tested the effects of the experienced emotion on individual variability in the brain's functional connectome. Next, for each state, we defined a modular organization of the brain and quantified its segregation and integration. Our results show that emotional states increase the similarity between and within individuals in the brain's functional connectome. Second, in the brain's modular organization, sadness, relative to amusement, was associated with higher integration and increased connectivity of cognitive control networks: the salience and fronto-parietal networks. Modular metrics of brain segregation and integration were further associated with the reported emotional valence. Last, in both the functional connectome and emotional report, a higher similarity was found among women. Our results suggest that the experience of emotion is linked to a reconfiguration of whole-brain distributed, not emotion-specific, functional brain networks and that the topological structure carries information about the subjective emotional experience.

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A graph neural network framework for causal inference in brain networks

Scientific Reports ◽

10.1038/s41598-021-87411-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

S. Wein ◽

W. M. Malloni ◽

A. M. Tomé ◽

S. M. Frank ◽

G. -I. Henze ◽

...

Keyword(s):

Neural Network ◽

Large Scale ◽

Diffusion Tensor ◽

Structural Information ◽

Brain Networks ◽

Scale Up ◽

Brain Regions ◽

Neural Activation ◽

Dynamic Interactions ◽

The Brain

AbstractA central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

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The overlapping modular organization of human brain functional networks across the adult lifespan

10.1101/2021.11.12.468371 ◽

2021 ◽

Author(s):

Yue Gu ◽

Liangfang Li ◽

Yining Zhang ◽

Junji Ma ◽

Chenfan Yang ◽

...

Keyword(s):

Cognitive Performance ◽

Mutual Exclusion ◽

Resting State Fmri ◽

Detection Algorithm ◽

Modular Organization ◽

Adult Lifespan ◽

Age Related ◽

Healthy Cohort ◽

Positive Correlations ◽

The Brain

Previous lifespan studies have demonstrated that the brain functional modular organization would change along with the adult lifespan. Yet, they assumed mutual exclusion among functional modules, ignoring convergent evidence for the existence of modular overlap. To reveal how age affects the overlapping functional modular organization, this study applied a detection algorithm requiring no prior knowledge of the resting-state fMRI data of a healthy cohort (N = 570, 18-88 years). Age-related regression analyses found a linear decrease in the overlapping modularity and the similarity of modular structure and overlapping node (i.e., region involved in multiple modules) distribution. The number of overlapping nodes increased with age, but the increment was distributed unevenly. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently exhibited positive correlations with both functional gradient and flexibility. Further, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the perspective of brain functional overlapping modular organization, providing new insight into the adult lifespan change in brain function and its influence on cognitive performance.

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Towards an information theoretical description of communication in brain networks

Network Neuroscience ◽

10.1162/netn_a_00185 ◽

2021 ◽

pp. 1-23

Author(s):

Enrico Amico ◽

Kausar Abbas ◽

Duy Anh Duong-Tran ◽

Uttara Tipnis ◽

Meenusree Rajapandian ◽

...

Keyword(s):

Information Transfer ◽

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Theoretical Description ◽

Structural Topology ◽

Communication Dynamics ◽

The Brain ◽

The Way

Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: Path Processing Score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); Path Broadcasting Strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main “communication regimes” of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; Visual and somato-motor cortices act as multi-channel transducted broadcasters. This work paves the way towards the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.

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Avalanche criticality in individuals, fluid intelligence and working memory

10.1101/2020.08.24.260588 ◽

2020 ◽

Author(s):

Longzhou Xu ◽

Lianchun Yu ◽

Jianfeng Feng

Keyword(s):

Working Memory ◽

Cognitive Performance ◽

Large Scale ◽

Fluid Intelligence ◽

Phase Synchronization ◽

Brain Networks ◽

Regional Analysis ◽

Neural Dynamics ◽

Transition Diagram ◽

Disorder Phase Transition

AbstractThe critical brain hypothesis suggests that efficient neural computation can be realized by neural dynamics characterized by a scale-free avalanche activity. However, the relation between human cognitive performance and the avalanche criticality in large-scale brain networks remains unclear. In this study, we used the phase synchronization analysis to determine the location of individual brains in the order-disorder phase transition diagram. We then performed avalanche analysis to identify subjects whose brain dynamics are close to the criticality. We showed that complexity in functional connectivity, as well as structure-function coupling, is maximized around criticality, inconsistent with theory predictions. Finally, we showed evidence that the neural dynamics of human participants with higher fluid intelligence and working memory scores are closer to criticality. The regional analysis showed it is the prefrontal cortex and inferior parietal cortex whose critical dynamics exhibit significant positive correlations with human cognitive performance. Our findings suggested that large-scale brain networks operate around a critical point to optimize human cognitive performance.

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Hierarchical Brain Systems Support Multiple Representations of Valence and Mixed Affect

Emotion Review ◽

10.1177/1754073916667237 ◽

2017 ◽

Vol 9 (2) ◽

pp. 124-132 ◽

Cited By ~ 13

Author(s):

Vincent Man ◽

Hannah U. Nohlen ◽

Hans Melo ◽

William A. Cunningham

Keyword(s):

Large Scale ◽

Brain Networks ◽

Multiple Representations ◽

Levels Of Analysis ◽

Psychological Literature ◽

Single Dimension ◽

Theoretical Perspectives ◽

Mental Experience ◽

Affective Processes ◽

The Brain

We review the psychological literature on the organization of valence, discussing theoretical perspectives that favor a single dimension of valence, multiple valence dimensions, and positivity and negativity as dynamic and flexible properties of mental experience that are contingent upon context. Turning to the neuroscience literature that spans three levels of analysis, we discuss how positivity and negativity can be represented in the brain. We show that the evidence points toward both separable and overlapping brain systems that support affective processes depending on the level of resolution studied. We move from large-scale brain networks that underlie generalized processing, to functionally specific subcircuits, finally to intraregional neuronal distributions, where the organization and interaction across levels allow for multiple types of valence and mixed evaluations.

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Structural differences between healthy subjects and patients with schizophrenia and schizoaffective disorder: A graph and control theoretical perspective

10.1101/2021.02.17.21251624 ◽

2021 ◽

Author(s):

Cristiana Dimulescu ◽

Serdar Gareayaghi ◽

Fabian Kamp ◽

Sophie Fromm ◽

Klaus Obermayer ◽

...

Keyword(s):

Schizoaffective Disorder ◽

Large Scale ◽

Brain Activity ◽

Theoretical Perspective ◽

Structural Mri ◽

Input Energy ◽

Strong Reduction ◽

Patient Groups ◽

And Control ◽

The Brain

AbstractThe coordinated, dynamical interactions of large-scale networks give rise to cognitive function. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts a fundamental constraint on the dynamical landscape of the brain. Consequently, changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Specifically, evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia. To investigate potential differences in graph and control theoretic measures between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we use structural MRI data. More specifically, we first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability identifies brain areas that can guide brain activity into different, easily reachable states with little input energy. Modal controllability on the other hand, characterizes regions that can push the brain into difficult-to-reach states, i.e. states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. Specifically, we found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

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Learning function from structure in neuromorphic networks

10.1101/2020.11.10.350876 ◽

2020 ◽

Author(s):

Laura E. Suárez ◽

Blake A. Richards ◽

Guillaume Lajoie ◽

Bratislav Misic

Keyword(s):

Network Architecture ◽

Large Scale ◽

Cognitive Task ◽

Cognitive Capacity ◽

Human Cognition ◽

Modular Organization ◽

Wide Range ◽

Macro Scale ◽

The Brain

AbstractThe connection patterns of neural circuits in the brain form a complex network. Collective signaling within the network manifests as patterned neural activity, and is thought to support human cognition and adaptive behavior. Recent technological advances permit macro-scale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging, and use reservoir computing to implement these connectomes as artificial neural networks. We then train these neuromorphic networks to learn a cognitive task. We show that biologically realistic neural architectures perform optimally when they display critical dynamics. We find that performance is driven by network topology, and that the modular organization of large-scale functional systems is computationally relevant. Throughout, we observe a prominent interaction between network structure and dynamics, such that the same underlying architecture can support a wide range of learning capacities across dynamical regimes. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity, conceptually bridging neuroscience and artificial intelligence.

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