A connectome-based neuromarker of the non-verbal number acuity and arithmetic skills

The approximate number system (ANS) is vital for survival and reproduction in animals and crucial in constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, there lacks a neuromarker to characterize an individual's ANS acuity, especially one based on the whole-brain functional connectivity (FC). Here, we identified a distributed brain network (i.e., numerosity network) using a connectome-based predictive modeling (CPM) analysis on the resting-state functional magnetic resonance imaging (rs-fMRI) data based on a large sample size. The summed strength of all FCs within the numerosity network could reliably predict individual differences of the ANS acuity in behavior. Furthermore, in an independent dataset from the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network could also predict individual differences in arithmetic skills. Our findings illustrate that the numerosity network we identified could be an applicable neuromarker of the non-verbal number acuity and might serve as the neural basis underlying the known link between the non-verbal number acuity and mathematical abilities.

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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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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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Neural Network Configuration and Efficiency Underlies Individual Differences in Spatial Orientation Ability

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00491 ◽

2014 ◽

Vol 26 (2) ◽

pp. 380-394 ◽

Cited By ~ 26

Author(s):

Aiden E. G. F. Arnold ◽

Andrea B. Protzner ◽

Signe Bray ◽

Richard M. Levy ◽

Giuseppe Iaria

Keyword(s):

Individual Differences ◽

Spatial Orientation ◽

Primary Motor Cortex ◽

Brain Regions ◽

Network Configuration ◽

Supramarginal Gyrus ◽

Neural Basis ◽

Left Hippocampus ◽

The Right

Spatial orientation is a complex cognitive process requiring the integration of information processed in a distributed system of brain regions. Current models on the neural basis of spatial orientation are based primarily on the functional role of single brain regions, with limited understanding of how interaction among these brain regions relates to behavior. In this study, we investigated two sources of variability in the neural networks that support spatial orientation—network configuration and efficiency—and assessed whether variability in these topological properties relates to individual differences in orientation accuracy. Participants with higher accuracy were shown to express greater activity in the right supramarginal gyrus, the right precentral cortex, and the left hippocampus, over and above a core network engaged by the whole group. Additionally, high-performing individuals had increased levels of global efficiency within a resting-state network composed of brain regions engaged during orientation and increased levels of node centrality in the right supramarginal gyrus, the right primary motor cortex, and the left hippocampus. These results indicate that individual differences in the configuration of task-related networks and their efficiency measured at rest relate to the ability to spatially orient. Our findings advance systems neuroscience models of orientation and navigation by providing insight into the role of functional integration in shaping orientation behavior.

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Behavioural relevance of spontaneous, transient brain network interactions in fMRI

10.1101/779736 ◽

2019 ◽

Cited By ~ 2

Author(s):

D. Vidaurre ◽

A. Llera ◽

S.M. Smith ◽

M.W. Woolrich

Keyword(s):

Functional Connectivity ◽

Network Architecture ◽

Brain Networks ◽

Structural Data ◽

Brain Regions ◽

Brain Network ◽

Time Varying ◽

Human Connectome Project ◽

Brain Data ◽

Structural Brain

AbstractHow spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

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Disrupted neural synchrony mediates the relationship between white matter integrity and cognitive performance in older adults

10.1101/2019.12.29.890228 ◽

2019 ◽

Author(s):

T. Hinault ◽

M. Kraut ◽

A. Bakker ◽

A. Dagher ◽

S.M. Courtney

Keyword(s):

Older Adults ◽

Individual Differences ◽

White Matter ◽

Cognitive Functioning ◽

Cognitive Performance ◽

Cognitive Aging ◽

Brain Regions ◽

Individual Variability ◽

White Matter Integrity ◽

Neural Basis

AbstractOur main goal was to determine the influence of white matter integrity on the dynamic coupling between brain regions and the individual variability of cognitive performance in older adults. EEG was recorded while participants performed a task specifically designed to engage working memory and inhibitory processes, and the associations among functional activity, structural integrity, and cognitive performance were assessed. We found that the association between white matter microstructural integrity and cognitive functioning with aging is mediated by time-varying alpha and gamma phase-locking value (PLV). Specifically, older individuals with better preservation of the inferior fronto-occipital fasciculus showed greater task-related modulations of alpha and gamma long-range PLV between the inferior frontal gyrus and occipital lobe, lower local phase-amplitude coupling in occipital lobes, and better cognitive control performance. Our results help delineate the role of individual variability of white matter microstructure in dynamic synchrony and cognitive performance during normal aging, and show that even small reductions in white matter integrity can lead to altered communications between brain regions, which in turn can result in reduced efficiency of cognitive functioning.Significance statementCognitive aging is associated with large individual differences, as some individuals maintain cognitive performance similar to that of young adults while others are significantly impaired. We hypothesized that individual differences in white matter integrity would influence the functional synchrony between frontal and posterior brain regions, and cognitive performance in older adults. We found that the association between reduced tract integrity and worse cognitive performance in older adults was mediated by task-related modulations of coupling synchrony in the alpha and gamma bands. Results offer a mechanistic explanation for the neural basis of the variability of cognitive performance in older adults who do not have any clinically diagnosable neuropathology, and for the association between structural network integrity and cognition in older adults.

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Generalizable predictive modeling of semantic processing ability from functional brain connectivity

10.1101/2021.05.11.443600 ◽

2021 ◽

Author(s):

Danting Meng ◽

Suiping Wang ◽

Patrick Wong ◽

Gangyi Feng

Keyword(s):

Individual Differences ◽

Predictive Modeling ◽

Semantic Processing ◽

Brain Connectivity ◽

Brain Network ◽

Conceptual Information ◽

Human Connectome Project ◽

Unseen Data ◽

Out Of Sample ◽

The Individual

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating meaningful and conceptual information. Neuroimaging studies of SP typically collapse data from many subjects, but both its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural organizations contribute to the individual differences in SP behaviors. Here we aim to identify the neural signatures underlying SP variabilities by analyzing individual functional connectivity (FC) patterns based on a large-sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two-stage predictive modeling approach to build an internally cross-validated model and to test the model's generalizability with unseen data from different HCP sub-populations and task states as well as other out-of-sample datasets that are independent of the HCP. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five semantic tasks. This cross-validated predictive model can be used to predict unseen HCP data. The model generalizability was enhanced with FCs in language tasks than resting state and other task states and was better for females than males. The model constructed from the HCP dataset can be generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out-of-sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education and improve intervention practice for patients with SP and language deficits.

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Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

Computational Intelligence and Neuroscience ◽

10.1155/2018/4835676 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Satoru Hiwa ◽

Shogo Obuchi ◽

Tomoyuki Hiroyasu

Keyword(s):

Working Memory ◽

Functional Connectivity ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Support Vector ◽

Neural Basis ◽

Functional Brain ◽

Functional Brain Network ◽

Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

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Personality influences the neural responses to viewing facial expressions of emotion

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2010.0362 ◽

2011 ◽

Vol 366 (1571) ◽

pp. 1684-1701 ◽

Cited By ~ 61

Author(s):

Andrew J. Calder ◽

Michael Ewbank ◽

Luca Passamonti

Keyword(s):

Individual Differences ◽

Brain Activity ◽

Brain Regions ◽

Neural Basis ◽

System A ◽

Amygdala Response ◽

Correlational Approach ◽

Facial Expressions Of Emotion ◽

And Performance ◽

The Relationship

Cognitive research has long been aware of the relationship between individual differences in personality and performance on behavioural tasks. However, within the field of cognitive neuroscience, the way in which such differences manifest at a neural level has received relatively little attention. We review recent research addressing the relationship between personality traits and the neural response to viewing facial signals of emotion. In one section, we discuss work demonstrating the relationship between anxiety and the amygdala response to facial signals of threat. A second section considers research showing that individual differences in reward drive (behavioural activation system), a trait linked to aggression, influence the neural responsivity and connectivity between brain regions implicated in aggression when viewing facial signals of anger. Finally, we address recent criticisms of the correlational approach to fMRI analyses and conclude that when used appropriately, analyses examining the relationship between personality and brain activity provide a useful tool for understanding the neural basis of facial expression processing and emotion processing in general.

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Neuroimaging studies exploring the neural basis of social isolation

Epidemiology and Psychiatric Sciences ◽

10.1017/s2045796021000135 ◽

2021 ◽

Vol 30 ◽

Author(s):

Niccolò Zovetti ◽

Maria Gloria Rossetti ◽

Cinzia Perlini ◽

Paolo Brambilla ◽

Marcella Bellani

Keyword(s):

Social Isolation ◽

Brain Regions ◽

Brain Network ◽

Neural Basis ◽

Social Brain ◽

Temporal Lobes ◽

The Social ◽

Neural Underpinnings ◽

Parietal Cortices ◽

Posterior Superior Temporal Sulcus

Abstract According to the social brain hypothesis, the human brain includes a network designed for the processing of social information. This network includes several brain regions that elaborate social cues, interactions and contexts, i.e. prefrontal paracingulate and parietal cortices, amygdala, temporal lobes and the posterior superior temporal sulcus. While current literature suggests the importance of this network from both a psychological and evolutionary perspective, little is known about its neurobiological bases. Specifically, only a paucity of studies explored the neural underpinnings of constructs that are ascribed to the social brain network functioning, i.e. objective social isolation and perceived loneliness. As such, this review aimed to overview neuroimaging studies that investigated social isolation in healthy subjects. Social isolation correlated with both structural and functional alterations within the social brain network and in other regions that seem to support mentalising and social processes (i.e. hippocampus, insula, ventral striatum and cerebellum). However, results are mixed possibly due to the heterogeneity of methods and study design. Future neuroimaging studies with longitudinal designs are needed to measure the effect of social isolation in experimental v. control groups and to explore its relationship with perceived loneliness, ultimately helping to clarify the neural correlates of the social brain.

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Pattern similarity analyses of frontoparietal task coding: Individual variation and genetic influences

10.1101/642397 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joset A. Etzel ◽

Ya’el Courtney ◽

Caitlin E. Carey ◽

Maria Z. Gehred ◽

Arpana Agrawal ◽

...

Keyword(s):

Individual Differences ◽

Neural Coding ◽

Memory Load ◽

Brain Regions ◽

Genetic Influences ◽

Behavioral Performance ◽

Activation Pattern ◽

Human Connectome Project ◽

Pattern Similarity ◽

Perceptual Category

AbstractPattern similarity analyses are increasingly used to characterize coding properties of brain regions, but relatively few have focused on cognitive control processes in FrontoParietal regions. Here, we use the Human Connectome Project (HCP) N-back task fMRI dataset to examine individual differences and genetic influences on the coding of working memory load (0-back, 2-back) and perceptual category (Face, Place). Participants were grouped into 105 MZ (monozygotic) twin, 78 DZ (dizygotic) twin, 99 non-twin sibling, and 100 unrelated pairs. Activation pattern similarity was used to test the hypothesis that FrontoParietal regions would have higher similarity for same load conditions, while Visual regions would have higher similarity in same perceptual category conditions. Results confirmed this highly robust regional double dissociation in neural coding, which also predicted individual differences in behavioral performance. In pair-based analyses, anatomically-selective genetic relatedness effects were observed: relatedness predicted greater activation pattern similarity in FrontoParietal only for load coding, and in Visual only for perceptual coding. Further, in related pairs, the similarity of load coding in FrontoParietal regions was uniquely associated with behavioral performance. Together, these results highlight the power of task fMRI pattern similarity analyses for detecting key coding and heritability features of brain regions.

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