A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model changes in the brain's functional organization. Here, we present a novel connectome-to-connectome (C2C) state transformation framework that enables us to model the brain's functional reorganization in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-specific connectomes from their task-free connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions. Finally, the C2C model reveals how the connectome reorganizes between cognitive states. Previous studies have reported that task-induced modulation of the brain connectome is domain-specific as well as domain-general, but did not specify how brain systems reconfigure to specific cognitive states. Our observations support the existence of reliable state-specific systems in the brain and indicate that we can quantitatively describe patterns of brain reorganization, common across individuals, in a computational model.

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The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

Scientific Reports ◽

10.1038/s41598-021-94825-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rossana Mastrandrea ◽

Fabrizio Piras ◽

Andrea Gabrielli ◽

Nerisa Banaj ◽

Guido Caldarelli ◽

...

Keyword(s):

Resting State ◽

Functional Organization ◽

Functional Integration ◽

Brain Network ◽

Modular Organization ◽

Node Degree ◽

Imaging Data ◽

Brain Disorder ◽

Functional Connections ◽

The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

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Individual Uniqueness in the Neonatal Functional Connectome

Cerebral Cortex ◽

10.1093/cercor/bhab041 ◽

2021 ◽

Author(s):

Qiushi Wang ◽

Yuehua Xu ◽

Tengda Zhao ◽

Zhilei Xu ◽

Yong He ◽

...

Keyword(s):

Individual Differences ◽

Individual Identification ◽

Imaging Data ◽

Functional Connectome ◽

Middle Range ◽

Human Connectome Project ◽

The Individual ◽

And Behavior ◽

Identification Analysis ◽

The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

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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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Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Frontiers in Neuroscience ◽

10.3389/fnins.2021.782995 ◽

2021 ◽

Vol 15 ◽

Author(s):

Sahin Hanalioglu ◽

Siyar Bahadir ◽

Ilkay Isikay ◽

Pinar Celtikci ◽

Emrah Celtikci ◽

...

Keyword(s):

Human Brain ◽

Diffusion Imaging ◽

Structural Connectivity ◽

Population Level ◽

Similar Distribution ◽

Group Level ◽

Imaging Data ◽

Brain Connectome ◽

Human Connectome Project ◽

Intuitive Method

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

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Functional parcellation of human brain using localized topo-connectivity mapping

10.36227/techrxiv.16550097 ◽

2021 ◽

Author(s):

Yu Zhao ◽

Yurui Gao ◽

Muwei Li ◽

Adam W. Anderson ◽

Zhaohua Ding ◽

...

Keyword(s):

Human Brain ◽

Functional Organization ◽

Statistical Tests ◽

Simulated Data ◽

Fmri Data ◽

Anatomical Structures ◽

Connectivity Mapping ◽

Subcortical Region ◽

Human Connectome Project ◽

The Brain

<p>The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, there has been less progress in the derivation of functional structures from voxel-wise analyses at finer scales. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain using resting-state fMRI data. Here we describe its mathematical background and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data as part of the Human Connectome Project to generate group-average LTM images. Functional structures revealed by this approach agree moderately well with anatomical structures identified by T<sub>1</sub>-weighted images and fractional anisotropy maps derived from diffusion MRI. Moreover, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-informed parcellations are significantly larger than those of random parcellations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.</p>

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Cognitive and Neural State Dynamics of Story Comprehension

10.1101/2020.07.10.194647 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hayoung Song ◽

Bo-yong Park ◽

Hyunjin Park ◽

Won Mok Shim

Keyword(s):

Coherent Structures ◽

Large Scale ◽

Functional Neuroimaging ◽

State Transitions ◽

Cognitive State ◽

Story Comprehension ◽

Time Resolved ◽

Cognitive States ◽

Dominant State ◽

The Brain

AbstractUnderstanding a story involves a constant interplay of the accumulation of narratives and its integration into a coherent structure. This study characterizes cognitive state dynamics during story comprehension and the corresponding network-level reconfiguration of the whole brain. We presented movie clips of temporally scrambled sequences, eliciting fluctuations in subjective feelings of understanding. An understanding occurred when processing events with high causal relations to previous events. Functional neuroimaging results showed that, during moments of understanding, the brain entered into a functionally integrated state with increased activation in the default mode network (DMN). Large-scale neural state transitions were synchronized across individuals who comprehended the same stories, with increasing occurrences of the DMN-dominant state. The time-resolved functional connectivities predicted changing cognitive states, and the predictive model was generalizable when tested on new stories. Taken together, these results suggest that the brain adaptively reconfigures its interactive states as we construct narratives to causally coherent structures.

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Functional parcellation of human brain using localized topo-connectivity mapping

10.36227/techrxiv.16550097.v1 ◽

2021 ◽

Author(s):

Yu Zhao ◽

Yurui Gao ◽

Muwei Li ◽

Adam W. Anderson ◽

Zhaohua Ding ◽

...

Keyword(s):

Human Brain ◽

Functional Organization ◽

Statistical Tests ◽

Simulated Data ◽

Fmri Data ◽

Anatomical Structures ◽

Connectivity Mapping ◽

Subcortical Region ◽

Human Connectome Project ◽

The Brain

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Dynamic updating of hippocampal object representations reflects new conceptual knowledge

10.1101/071118 ◽

2016 ◽

Author(s):

Michael L. Mack ◽

Bradley C. Love ◽

Alison R. Preston

Keyword(s):

Conceptual Knowledge ◽

Relevant Information ◽

Object Representations ◽

Imaging Data ◽

Neural Representations ◽

New Information ◽

Stimulus Features ◽

Task Goals ◽

Dynamic Updating ◽

The Brain

AbstractConcepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal.Significance StatementA cosmopolitan couple looking for a home may focus on trendy features. But, with news of a baby on the way, they must quickly learn which features make for a child-friendly home to conceptually reorganize their set of potential homes. Here, we investigate how conceptual knowledge is updated in the brain when goals change and attention shifts to new information. By combining fMRI with computational modeling, we find that object representations in the human hippocampus are dynamically updated with concept-relevant information during learning. We also demonstrate that when concept updating is most consequential, the hippocampus is functionally coupled with neocortex. Our findings suggest that the brain reorganizes when concepts change and provide support for a neurocomputational theory of concept formation.

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Renormalization of the brain connectome: Duality of particle and wave

10.1101/2020.12.02.408518 ◽

2020 ◽

Author(s):

S. Petkoski ◽

V.K. Jirsa

Keyword(s):

Graph Theory ◽

Information Processing ◽

Human Subjects ◽

Brain Rhythms ◽

Brain Connectome ◽

Human Connectome Project ◽

Wide Range ◽

Common Framework ◽

Network Properties ◽

The Brain

AbstractNetworks in neuroscience determine how brain function unfolds. Perturbations of the network lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual particle-wave-perspective, integrate time delays due to finite transmission speeds and derive a renormalization of the connectome. When applied to the data base of the Human Connectome project, we explain the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N=100) and are a fundamental network property within the wave picture. The renormalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms.One Sentence SummarySpatiotemporal and topological network properties are unified within a novel common framework, the renormalized connectome, that explains the organization of fundamental frequency-specific network cores.

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Decoding task-specific cognitive states with slow, directed functional networks in the human brain

10.1101/681544 ◽

2019 ◽

Author(s):

Devarajan Sridharan ◽

Shagun Ajmera ◽

Hritik Jain ◽

Mali Sundaresan

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Human Brain ◽

Brain Networks ◽

Fmri Data ◽

Invasive Technique ◽

Functional Interactions ◽

Cognitive States ◽

Human Connectome Project ◽

The Brain

AbstractFlexible functional interactions among brain regions mediate critical cognitive functions. Such interactions can be measured from functional magnetic resonance imaging (fMRI) data with either instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity; only the latter approach permits inferring directed functional interactions. Yet, the fMRI hemodynamic response is slow, and sampled at a timescale (seconds) several orders of magnitude slower than the underlying neural dynamics (milliseconds). It is, therefore, widely held that lag-based fMRI functional connectivity, measured with approaches like as Granger-Geweke causality (GC), provides spurious and unreliable estimates of underlying neural interactions. Experimental verification of this claim has proven challenging because neural ground truth connectivity is often unavailable concurrently with fMRI recordings. We address this challenge by combining machine learning with GC functional connectivity estimation. We estimated instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants, drawn from the Human Connectome Project database. A linear classifier, trained on either instantaneous or lag-based GC, reliably discriminated among seven different task and resting brain states, with over 80% cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With human fMRI data, instantaneous and lag-based GC identified distinct, cognitive core networks. Finally, variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our findings show that instantaneous and lag-based methods reveal complementary aspects of functional connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, provide robust markers of behaviorally relevant cognitive states.Author SummaryFunctional MRI (fMRI) is a leading, non-invasive technique for mapping networks in the human brain. Yet, fMRI signals are noisy and sluggish, and fMRI scans are acquired at a timescale of seconds, considerably slower than the timescale of neural spiking (milliseconds). Can fMRI, then, be used to infer dynamic processes in the brain such as the direction of information flow among brain networks? We sought to answer this question by applying machine learning to fMRI scans acquired from 1000 participants in the Human Connectome Project (HCP) database. We show that directed brain networks, estimated with a technique known as Granger-Geweke Causality (GC), accurately predicts individual subjects’ task-specific cognitive states inside the scanner, and also explains variations in a variety of behavioral scores across individuals. We propose that directed functional connectivity, as estimated with fMRI-GC, is relevant for understanding human cognitive function.

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