A distributed brain network predicts general intelligence from resting-state human neuroimaging data

AbstractIndividual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of longterm life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.

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A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2017.0284 ◽

2018 ◽

Vol 373 (1756) ◽

pp. 20170284 ◽

Cited By ~ 63

Author(s):

Julien Dubois ◽

Paola Galdi ◽

Lynn K. Paul ◽

Ralph Adolphs

Keyword(s):

Individual Differences ◽

Resting State ◽

Brain Volume ◽

Resting State Fmri ◽

Brain Network ◽

Cognitive Tasks ◽

Reliable Estimate ◽

General Intelligence ◽

Total Brain Volume ◽

Human Connectome Project

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N= 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.

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Replication of Resting State-Task Network Correspondence and Novel Findings on Brain Network Activation During Task fMRI in the Human Connectome Project Study

Scientific Reports ◽

10.1038/s41598-018-35209-6 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 4

Author(s):

Lisa D. Nickerson

Keyword(s):

Resting State ◽

Brain Network ◽

Project Study ◽

Network Activation ◽

Human Connectome Project

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Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

10.1101/344473 ◽

2018 ◽

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Resting State ◽

Dynamic Properties ◽

Brain Activity ◽

Temporal Structure ◽

Structural Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Brain Network ◽

Dynamical Analysis ◽

Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

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Rest-task Modulation of fMRI-derived Global Signal Topography is Mediated by Transient Co-activation Patterns

10.1101/798819 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jianfeng Zhang ◽

Zirui Huang ◽

Shankar Tumati ◽

Georg Northoff

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Peak Period ◽

Physiological Noise ◽

Activation Patterns ◽

Global Signal ◽

Co Activation ◽

Human Connectome Project ◽

Partial Dissociation ◽

Signal Correlation

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

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Brain network dynamics correlate with personality traits

10.1101/702266 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aya Kabbara ◽

Veronique Paban ◽

Arnaud Weill ◽

Julien Modolo ◽

Mahmoud Hassan

Keyword(s):

Functional Connectivity ◽

Personality Traits ◽

Resting State ◽

Brain Networks ◽

Neural Substrates ◽

Brain Network ◽

Functional Brain ◽

Human Connectome Project ◽

Functional Brain Networks ◽

The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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Altered brain network organization in romantic love as measured with resting-state fMRI and graph theory

Brain Imaging and Behavior ◽

10.1007/s11682-019-00226-0 ◽

2020 ◽

Vol 14 (6) ◽

pp. 2771-2784 ◽

Cited By ~ 1

Author(s):

Chuan Wang ◽

Sensen Song ◽

Federico d’Oleire Uquillas ◽

Anna Zilverstand ◽

Hongwen Song ◽

...

Keyword(s):

Graph Theory ◽

Resting State ◽

Romantic Love ◽

Resting State Fmri ◽

Brain Network ◽

Network Organization

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Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33012556 ◽

2019 ◽

Vol 33 ◽

pp. 2556-2563 ◽

Cited By ~ 5

Author(s):

Abhay M S Aradhya ◽

Aditya Joglekar ◽

Sundaram Suresh ◽

M. Pratama

Keyword(s):

Resting State ◽

Cognitive Disorders ◽

Feature Space ◽

Resting State Fmri ◽

Transformation Method ◽

Brain Network ◽

Fmri Data ◽

Functional Activities ◽

Temporal Correlations ◽

Spatio Temporal

Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.

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