The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

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Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽

10.7554/elife.32696 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 4

Author(s):

Ruedeerat Keerativittayayut ◽

Ryuta Aoki ◽

Mitra Taghizadeh Sarabi ◽

Koji Jimura ◽

Kiyoshi Nakahara

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Large Scale ◽

Brain Regions ◽

Brain Network ◽

Time Varying ◽

Memory Encoding ◽

Graph Metrics ◽

Connectivity Patterns ◽

Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

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Linear Input as a Fundamental Motif of Brain Connectivity Organization

10.1101/549345 ◽

2019 ◽

Author(s):

Jonathan F. O’Rawe ◽

Hoi-Chung Leung

Keyword(s):

Functional Connectivity ◽

Information Transfer ◽

Brain Connectivity ◽

Brain Regions ◽

Fundamental Aspect ◽

Biased Sampling ◽

Whole Brain ◽

3 Dimensional ◽

Face Area ◽

Learning Techniques

AbstractDescribing the pattern of region-to-region functional connectivity is an important step towards understanding information transfer and transformation between brain regions. Although fMRI data are limited in spatial resolution, recent advances in technology afford more precise mapping. Here, we extended previous methods, connective field mapping, to 3 dimensions to provide a more concise estimate of the organization and potential information transformation from one region to another. We first replicated previous work with the 3 dimensional model by showing that the topology of functional connectivity between early visual regions maintained along their eccentricity axis or the anterior-posterior dimension. We then examined higher order visual regions (e,g, fusiform face area) and showed that their pattern of connectivity, the convergence and biased sampling, seem to contribute to some of their core receptive field properties. We further demonstrated that linearity of input is a fundamental aspect of functional connectivity of the whole brain, with higher linearity between regions within a network than across networks; that is, high connective linearity was evident between early visual areas, and between prefrontal areas, but less evident between them. By decomposing the whole brain linearity matrix with manifold learning techniques, we found that the principle mode of the linearity maps onto decompositions in both functional connectivity and genetic expression reported in previous studies. The current work provides evidence supporting that linearity of input is likely a fundamental motif of functional connectivity between regions for information processing across the brain, with high linearity preserving the integrity of information from one region to another within a network.

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Conservative and disruptive modes of adolescent change in brain functional connectivity

10.1101/604843 ◽

2019 ◽

Cited By ~ 4

Author(s):

František Váša ◽

Rafael Romero-Garcia ◽

Manfred G. Kitzbichler ◽

Jakob Seidlitz ◽

Kirstie J. Whitaker ◽

...

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Developmental Change ◽

Aerobic Glycolysis ◽

Brain Regions ◽

Association Cortex ◽

Brain Organization ◽

Healthy Human ◽

Age Related ◽

Strongly Connected

AbstractAdolescent changes in human brain function are not entirely understood. Here we used multi-echo functional magnetic resonance imaging (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in N=298 healthy adolescents. Participants were aged 14-26 years and were scanned on two or more occasions at least 6 months apart. We found two distinct modes of age-related change in FC: “conservative” and “disruptive”. Conservative development was characteristic of primary cortex, which was strongly connected at 14 years and became even more connected in the period 14-26 years. Disruptive development was characteristic of association cortex, hippocampus and amygdala, which were not strongly connected at 14 years but became more strongly connected during adolescence. We defined the maturational index (MI) as the signed coefficient of the linear relationship between baseline FC (at 14 years,FC14) and adolescent change in FC (∆FC14−26). Disruptive systems (with negative MI) were functionally specialised for social cognition and autobiographical memory and were significantly co-located with prior maps of aerobic glycolysis (AG), AG-related gene expression, post-natal expansion of cortical surface area, and adolescent shrinkage of cortical depth. We conclude that human brain organization is disrupted during adolescence by the emergence of strong functional connectivity of subcortical nuclei and association cortical areas, representing metabolically expensive re-modelling of synaptic connectivity between brain regions that were not strongly connected in childhood. We suggest that this re-modelling process may support emergence of social skills and self-awareness during healthy human adolescence.

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Heterogeneity of EEG resting-state brain networks in absolute pitch

10.1101/2020.05.03.063206 ◽

2020 ◽

Author(s):

Marielle Greber ◽

Carina Klein ◽

Simon Leipold ◽

Silvano Sele ◽

Lutz Jäncke

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Resting State ◽

Large Scale ◽

Absolute Pitch ◽

Brain Regions ◽

Brain Network ◽

Higher Order ◽

Neural Basis ◽

Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

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Task-fMRI Group and Functional Connectivity Analysis of the Brain During Faradarmani Consciousness Field Connection

10.20944/preprints202109.0248.v1 ◽

2021 ◽

Author(s):

Mohammad Ali Taheri ◽

Sara Torabi ◽

Noushin Nabavi ◽

Fatemeh Modarresi-Asem ◽

Majid Abbasi Sisara ◽

...

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Frontal Lobe ◽

Critical Role ◽

Group Analysis ◽

Brain Regions ◽

Connectivity Analysis ◽

Functional Connectivity Analysis ◽

Cognitive Activities ◽

The Brain

Task fMRI has played a critical role in recognizing the specific functions of the different regions of human brain during various cognitive activities. This study aimed to investigate group analysis and functional connectivity in the Faradarmangars brain during the Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the Consciousness Filed connection. Clusters that showed significant differences in peak intensity between task and rest group were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of FCF connection task revealed activation of frontal lobe (BA6/BA10/BA11). Moreover, seed based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed an increased connectivity within deactivated clusters and frontal lobe (BA11/BA47) during the FCF connection. Activation clusters as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of the FCF and secondly, indicates a distinctive pattern of connection with this non-material and non-energetic field, in the brain.

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Functional connectivity predicts the dispositional use of expressive suppression but not cognitive reappraisal

10.31234/osf.io/d936f ◽

2019 ◽

Author(s):

Daisy A. Burr ◽

Tracy d'Arbeloff ◽

Maxwell Elliott ◽

Annchen R. Knodt ◽

Bartholomew D. Brigidi ◽

...

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Brain Connectivity ◽

Cognitive Reappraisal ◽

Predictive Performance ◽

Brain Regions ◽

Expressive Suppression ◽

Whole Brain ◽

Functional Connections ◽

Neural Signature

Previous research has identified specific brain regions associated with regulating emotion using common strategies such as expressive suppression and cognitive reappraisal. However, most research focuses on a priori regions and directs participants how to regulate, which may not reflect how people naturally regulate outside the laboratory. Here, we used a data-driven approach to investigate how individual differences in distributed intrinsic functional brain connectivity predict emotion regulation tendency. Specifically, we used connectome-based predictive modeling to extract functional connections in the brain significantly related to the dispositional use of suppression and reappraisal. These edges were then used in a predictive model and cross-validated in novel participants to identify a neural signature that reflects individual differences in the tendency to suppress and reappraise emotion. We found a significant neural signature for the dispositional use of suppression, but not reappraisal. Within this whole-brain signature, the intrinsic connectivity of the default mode network was most informative of suppression tendency. In addition, the predictive performance of this model was significant in males, but not females. These findings help inform how whole-brain networks of functional connectivity characterize how people tend to regulate emotion outside the laboratory.

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Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

10.1101/2020.12.11.422071 ◽

2020 ◽

Author(s):

Behnaz Yousefi ◽

Shella Keilholz

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Spatial Organization ◽

Resting State Fmri ◽

Brain Regions ◽

Intrinsic Activity ◽

Resting State Networks ◽

Whole Brain ◽

Functional Connections ◽

Driving Mechanisms

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as resting-state networks (RSNs) and functional connectivity gradients (FCGs). Dynamic analysis techniques have shown that the time-averaged maps captured by functional connectivity are mere summaries of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain intrinsic activity that cannot be inferred by functional connectivity, RSNs or FCGs. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale FCG, simultaneously across the cortical sheet and in most other brain regions. In some areas, like the thalamus, the propagation suggests previously-undescribed FCGs. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between brain regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between RSNs is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

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Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008310 ◽

2021 ◽

Vol 17 (1) ◽

pp. e1008310

Author(s):

Marco Aqil ◽

Selen Atasoy ◽

Morten L. Kringelbach ◽

Rikkert Hindriks

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Structure Function ◽

Resting State ◽

Brain Activity ◽

Graph Laplacian ◽

Laplacian Operator ◽

Neural Fields ◽

Dynamical Models ◽

Whole Brain

Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed “connectome harmonics”, have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.

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Ultra-high-field imaging reveals increased whole brain connectivity underpins cognitive strategies that attenuate pain

eLife ◽

10.7554/elife.55028 ◽

2020 ◽

Vol 9 ◽

Author(s):

Enrico Schulz ◽

Anne Stankewitz ◽

Anderson M Winkler ◽

Stephanie Irving ◽

Viktor Witkovský ◽

...

Keyword(s):

Functional Connectivity ◽

Diffusion Tensor ◽

Brain Connectivity ◽

Cognitive Strategies ◽

Brain Regions ◽

Imaging Data ◽

Cold Pain ◽

Whole Brain ◽

Safe Place ◽

Strength Change

We investigated how the attenuation of pain with cognitive interventions affects brain connectivity using neuroimaging and a whole brain novel analysis approach. While receiving tonic cold pain, 20 healthy participants performed three different pain attenuation strategies during simultaneous collection of functional imaging data at seven tesla. Participants were asked to rate their pain after each trial. We related the trial-by-trial variability of the attenuation performance to the trial-by-trial functional connectivity strength change of brain data. Across all conditions, we found that a higher performance of pain attenuation was predominantly associated with higher functional connectivity. Of note, we observed an association between low pain and high connectivity for regions that belong to brain regions long associated with pain processing, the insular and cingulate cortices. For one of the cognitive strategies (safe place), the performance of pain attenuation was explained by diffusion tensor imaging metrics of increased white matter integrity.

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