scholarly journals Differences in directed functional brain connectivity related to age, sex and mental health

2019 ◽  
Author(s):  
Martina J. Lund ◽  
Dag Alnæs ◽  
Simon Schwab ◽  
Dennis van der Meer ◽  
Ole A. Andreassen ◽  
...  
Keyword(s):  
Mental Health ◽  
Information Flow ◽  
Psychiatric Symptoms ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Uk Biobank ◽  
Functional Brain ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Insight Into

AbstractObjectiveFunctional interconnections between brain regions define the ‘connectome’ which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the brain connectome and age, sex, intellectual abilities and mental health.MethodsWe applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank.ResultsOur analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health.DiscussionOur findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.

scholarly journals Sex Classification by Resting State Brain Connectivity

2019 ◽  
Vol 30 (2) ◽  
pp. 824-835 ◽  
Author(s):  
Susanne Weis ◽  
Kaustubh R Patil ◽  
Felix Hoffstaedter ◽  
Alessandra Nostro ◽  
B T Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Brain ◽  
Clear Cut ◽  
Human Connectome Project ◽  
Machine Learning Approach ◽  
Imaging Research ◽  
Functional Brain Organization

Abstract A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants’ sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

scholarly journals Sex classification by resting state brain connectivity

2019 ◽  
Author(s):  
Susanne Weis ◽  
Kaustubh Patil ◽  
Felix Hoffstaedter ◽  
Alessandra Nostro ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Brain ◽  
Clear Cut ◽  
Human Connectome Project ◽  
Two Samples ◽  
Imaging Research ◽  
Functional Brain Organization

1AbstractA large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants’ sex can be classified based on spatially specific resting state (RS) brain-connectivity, using two samples from the Human Connectome Project (n1 = 434, n2 = 310) and one fully independent sample from the 1000BRAINS study (n=941). The classifier, which was trained on one sample and tested on the other two, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporo-parietal regions, insula and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

scholarly journals Spatial Patterns for Discriminative Estimation

2019 ◽  
Author(s):  
Alberto Llera ◽  
Roselyne Chauvin ◽  
Peter Mulders ◽  
Jilly Naaijen ◽  
Maarten Mennes ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Brain Connectivity ◽  
Covariance Structure ◽  
Brain Regions ◽  
Linear Filters ◽  
Cognitive Changes ◽  
Experimental Conditions ◽  
Cognitive States ◽  
Human Connectome Project ◽  
Perfect Discrimination

AbstractFunctional connectivity between brain regions is modulated by cognitive states or experimental conditions. A multivariate methodology that can capture fMRI connectivity maps in light of different experimental conditions would be of primary importance to learn about the specific roles of the different brain areas involved in the observed connectivity variations. Here we detail, adapt, optimize and evaluate a supervised dimensionality reduction model to fMRI timeseries. We demonstrate the strength of such an approach for fMRI data using data from the Human Connectome Project to show that the model provides close to perfect discrimination between different fMRI tasks at low dimensionality. The straightforward interpretability and relevance of the model results is demonstrated by the obtained linear filters relating to anatomical areas well known to be involved on each considered task, and its robustness by testing discriminatory generalization and spatial reproducibility with respect to the number of subjects and fMRI time-points acquired. We additionally suggest how such approach can provide a complementary view to traditional task fMRI analyses by looking at changes in the covariance structure as a substitute to changes in the mean signal. We conclude that the presented methodology provides a robust tool to investigate brain connectivity alterations across induced cognitive changes and has the potential to be used in pathological or pharmacological cohort studies. A publicly available toolbox is provided to facilitate the end use and further development of this methodology to extract Spatial Patterns for Discriminative Estimation (SP♠DE).

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

scholarly journals Possible Biological Mechanisms Linking Mental Health and Heat—A Contemplative Review

2018 ◽  
Vol 15 (7) ◽  
pp. 1515 ◽  
Author(s):  
Mare Lõhmus
Keyword(s):  
Mental Health ◽  
Body Temperature ◽  
Mental Disease ◽  
Brain Connectivity ◽  
Biological Mechanisms ◽  
Epidemiological Evidence ◽  
Functional Brain ◽  
Mental Health Patients ◽  
Hot Environments

This review provides examples of possible biological mechanisms that could, at least partly, explain the existing epidemiological evidence of heatwave-related exacerbation of mental disease morbidity. The author reviews the complicated central processes involved in the challenge of maintaining a stable body temperature in hot environments, and the maladaptive effects of certain psychiatric medicines on thermoregulation. In addition, the author discusses some alternative mechanisms, such as interrupted functional brain connectivity and the effect of disrupted sleep, which may further increase the vulnerability of mental health patients during heatwaves.

scholarly journals A covariate-constraint method to map brain feature space into lower dimensional manifolds

2020 ◽  
pp. 1-30
Author(s):  
Félix Renard ◽  
Christian Heinrich ◽  
Marine Bouthillon ◽  
Maleka Schenck ◽  
Francis Schneider ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Feature Space ◽  
Graph Metrics ◽  
Brain Connectome ◽  
Biological Phenomena ◽  
Number Of Patients ◽  
Population Comparison ◽  
Constraint Method ◽  
The Brain ◽  
Insight Into

Human brain connectome studies aim at both exploring healthy brains, and extracting and analyzing relevant features associated to pathologies of interest. Usually this consists in modeling the brain connectome as a graph and in using graph metrics as features. A fine brain description requires graph metrics computation at the node level. Given the relatively reduced number of patients in standard cohorts, such data analysis problems fall in the high-dimension low sample size framework. In this context, our goal is to provide a machine learning technique that exhibits flexibility, gives the investigator grip on the features and covariates, allows visualization and exploration, and yields insight into the data and the biological phenomena at stake. The retained approach is dimension reduction in a manifold1 learning methodology, the originality lying in that one (or several) reduced variables be chosen by the investigator. The proposed method is illustrated on two studies, the first one addressing comatose patients, the second one addressing young versus elderly population comparison. The method sheds light on the differences between brain connectivity graphs using graph metrics and potential clinical interpretations of theses differences.

scholarly journals Unique mapping of structural and functional connectivity on cognition

2018 ◽  
Author(s):  
J. Zimmermann ◽  
J.G. Griffiths ◽  
A.R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Unique Mapping ◽  
Human Connectome Project ◽  
Behavioural Patterns ◽  
Wide Range ◽  
Psychological Tasks ◽  
New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

scholarly journals The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

2020 ◽  
pp. 1-15
Author(s):  
Tommy Boshkovski ◽  
Ljupco Kocarev ◽  
Julien Cohen-Adad ◽  
Bratislav Mišić ◽  
Stéphane Lehéricy ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Added Value ◽  
Modular Organization ◽  
Sensitive Measure ◽  
Higher Order Functions ◽  
Insight Into

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

scholarly journals Genetic and environmental influence on the human functional connectome

2018 ◽  
Author(s):  
Andrew E. Reineberg ◽  
Alexander S. Hatoum ◽  
John K. Hewitt ◽  
Marie T. Banich ◽  
Naomi P. Friedman
Keyword(s):  
Cognitive Abilities ◽  
Environmental Influence ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Genetic Influence ◽  
Functional Connections ◽  
Functional Connectome ◽  
Intermediate Phenotypes ◽  
Human Connectome Project ◽  
Shared Environmental Influence

AbstractDetailed mapping of genetic and environmental influences on the functional connectome is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyze resting-state data from two, adult twin samples - 390 twins from the Colorado Longitudinal Twin Sample and 422 twins from the Human Connectome Project - to examine genetic and environmental influence on all pairwise functional connections between 264 brain regions (~35,000 functional connections). Non-shared environmental influence was high, genetic influence was moderate, and shared environmental influence was weak-to-moderate across the connectome. The brain’s genetic organization is diverse and not as one would expect based solely on structure evident in non-genetically informative data or lower-resolution data. As follow-up, we make novel classifications of functional connections and examine highly-localized connections with particularly strong genetic influence. This high-resolution genetic taxonomy of brain connectivity will be useful in understanding genetic influences on brain disorders.

scholarly journals A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 629
Author(s):  
Andrea Duggento ◽  
Gaetano Valenza ◽  
Luca Passamonti ◽  
Salvatore Nigro ◽  
Maria Giovanna Bianco ◽  
...  
Keyword(s):  
Granger Causality ◽  
Current Knowledge ◽  
Laguerre Polynomials ◽  
Brain Regions ◽  
Parameters Estimation ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Global Parameter ◽  
Brain Connectome ◽  
Human Connectome Project

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener–Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.

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