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.

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

2019 ◽  
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 signal 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 decomposition with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture 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.

scholarly journals Graph combinatorics based group-level network inference

2019 ◽  
Author(s):  
Shuo Chen ◽  
Qiong Wu ◽  
L. Elliot Hong
Keyword(s):  
Statistical Inference ◽  
Network Topology ◽  
Network Inference ◽  
Brain Regions ◽  
Superior Performance ◽  
Omics Data ◽  
Group Level ◽  
Data Set ◽  
Joint Inference ◽  
Brain Connectome

AbstractWe consider group-level statistical inference for networks, where outcomes are multivariate edge variables constrained in an adjacency matrix. The graph notation is used to represent a network, where nodes are identical biological units (e.g. brain regions) shared across subjects and edge-variables indicate the strengths of interactive relationships between nodes. Edge-variables vary across subjects and may be associated with covariates of interest. The statistical inference for multivariate edge-variables is challenging because both localized inference on individual edges and the joint inference of a combinatorial of edges (network-level) are desired. Different from conventional multivariate variables (e.g. omics data), the inference of a combinatorial of edges is closely linked with network topology and graph combinatorics. We propose a novel objective function with 𝓁0 norm regularization to robustly capture subgraphs/subnetworks from the whole brain connectome and thus reveal the latent network topology of phenotype-related edges. Our statistical inferential procedure and theories are constructed based on graph combinatorics. We apply the proposed approach to a brain connectome study to identify latent brain functional subnetworks that are associated with schizophrenia and verify the findings using an independent replicate data set. The results demonstrate that the proposed method achieves superior performance with remarkably increased replicability.

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 A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 62
Author(s):  
Zhengwei Liu ◽  
Fukang Zhu
Keyword(s):  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Data Sets ◽  
Binomial Thinning ◽  
Free Case ◽  
Two Parameters ◽  
Conditional Maximum ◽  
Thinning Operator

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.

scholarly journals Heritability and interindividual variability of regional structure-function coupling

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zijin Gu ◽  
Keith Wakefield Jamison ◽  
Mert Rory Sabuncu ◽  
Amy Kuceyeski
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Regional Differences ◽  
Regional Scale ◽  
Brain Regions ◽  
Diffusion Weighted Mri ◽  
Regional Structure ◽  
Human Connectome Project ◽  
Structural Connections ◽  
The Relationship

AbstractWhite matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.

A rigorous approach for testing the constructionist hypotheses of brain function

2012 ◽  
Vol 35 (3) ◽  
pp. 148-149 ◽  
Author(s):  
Gopikrishna Deshpande ◽  
K. Sathian ◽  
Xiaoping Hu ◽  
Joseph A. Buckhalt
Keyword(s):  
Granger Causality ◽  
Strong Evidence ◽  
Bayesian Modeling ◽  
Brain Function ◽  
Brain Regions ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Rigorous Approach ◽  
Target Article ◽  
Co Activation

AbstractAlthough the target article provides strong evidence against the locationist view, evidence for the constructionist view is inconclusive, because co-activation of brain regions does not necessarily imply connectivity between them. We propose a rigorous approach wherein connectivity between co-activated regions is first modeled using exploratory Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.

scholarly journals Alterations of Expression of the Serotonin 5-HT4 Receptor in Brain Disorders

2018 ◽  
Vol 19 (11) ◽  
pp. 3581 ◽  
Author(s):  
Heike Rebholz ◽  
Eitan Friedman ◽  
Julia Castello
Keyword(s):  
Executive Function ◽  
Food Intake ◽  
Serotonin Receptor ◽  
Current Knowledge ◽  
Memory Loss ◽  
Brain Regions ◽  
Brain Disorders ◽  
Protein Levels ◽  
Preclinical Animal Models ◽  
Mood Depression

The serotonin 4 receptor, 5-HT4R, represents one of seven different serotonin receptor families and is implicated in a variety of physiological functions and their pathophysiological variants, such as mood and depression or anxiety, food intake and obesity or anorexia, or memory and memory loss in Alzheimer’s disease. Its central nervous system expression pattern in the forebrain, in particular in caudate putamen, the hippocampus and to lesser extent in the cortex, predispose it for a role in executive function and reward-related actions. In rodents, regional overexpression or knockdown in the prefrontal cortex or the nucleus accumbens of 5-HT4R was shown to impact mood and depression-like phenotypes, food intake and hypophagia; however, whether expression changes are causally involved in the etiology of such disorders is not clear. In this context, more data are emerging, especially based on PET technology and the use of ligand tracers that demonstrate altered 5-HT4R expression in brain disorders in humans, confirming data stemming from post-mortem tissue and preclinical animal models. In this review, we would like to present the current knowledge of 5-HT4R expression in brain regions relevant to mood/depression, reward and executive function with a focus on 5-HT4R expression changes in brain disorders or caused by drug treatment, at both the transcript and protein levels.

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

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.

scholarly journals A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Feng Zhao ◽  
Zhiyuan Chen ◽  
Islem Rekik ◽  
Peiqiang Liu ◽  
Ning Mao ◽  
...  
Keyword(s):  
Feature Selection ◽  
Functional Connectivity ◽  
Evaluation Method ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Superior Performance ◽  
Selection Strategy ◽  
Dynamic Functional Connectivity ◽  
Discriminative Feature ◽  
Fingerprint Feature

The sliding-window-based dynamic functional connectivity networks (SW-D-FCN) derive from resting-state functional Magnetic Resonance Imaging has become an increasingly useful tool in the diagnosis of various neurodegenerative diseases. However, it is still challenging to learn how to extract and select the most discriminative features from SW-D-FCN. Conventionally, existing methods opt to select a single discriminative feature set or concatenate a few more from the SW-D-FCN. However, such reductionist strategies may fail to fully capture the personalized discriminative characteristics contained in each functional connectivity (FC) sequence of the SW-D-FCN. To address this issue, we propose a unit-based personalized fingerprint feature selection (UPFFS) strategy to better capture the most discriminative feature associated with a target disease for each unit. Specifically, we regard the FC sequence between any pair of brain regions of interest (ROIs) is regarded as a unit. For each unit, the most discriminative feature is identified by a specific feature evaluation method and all the most discriminative features are then concatenated together as a feature set for the subsequent classification task. In such a way, the personalized fingerprint feature derived from each FC sequence can be fully mined and utilized in classification decision. To illustrate the effectiveness of the proposed strategy, we conduct experiments to distinguish subjects diagnosed with autism spectrum disorder from normal controls. Experimental results show that the proposed strategy can select relevant discriminative features and achieve superior performance to benchmark methods.

scholarly journals Slow Cortical Waves via Cyclicity

2021 ◽  
Author(s):  
Ivan Abraham ◽  
Bahar Shahsavarani ◽  
Ben Zimmerman ◽  
Fatima Husain ◽  
yuliy baryshnikov
Keyword(s):  
Time Series ◽  
Dynamic Structure ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fine Grained ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
The Brain ◽  
Cortical Waves ◽  
Temporal Ordering

Fine-grained information about dynamic structure of cortical networks is crucial in unpacking brain function. Here,we introduced a novel analytical method to characterize the dynamic interaction between distant brain regions,based on cyclicity analysis, and applied it to data from the Human Connectome Project. Resting-state fMRI time series are aperiodic and, hence, lack a base frequency. Cyclicity analysis, which is time-reparametrization invariant, is effective in recovering dynamic temporal ordering of such time series along a circular trajectory without assuming any time scale. Our analysis detected the propagation of slow cortical waves across thebrain with consistent shifts in lead-lag relationships between specific brain regions. We also observed short bursts of strong temporal ordering that dominated overall lead-lag relationships between pairs of regions in the brain, which were modulated by tasks. Our results suggest the possible role played by slow waves of ordered information between brain regions that underlie emergent cognitive function.

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