scholarly journals Graphical Models of Functional MRI Data for Assessing Brain Connectivity

10.5772/24463 ◽  
2012 ◽  
Author(s):  
Junning Li ◽  
Z. JaneWang ◽  
Martin J.
Keyword(s):  
Functional Mri ◽  
Graphical Models ◽  
Brain Connectivity

scholarly journals Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity

Biometrika ◽  
2020 ◽  
Author(s):  
S Na ◽  
M Kolar ◽  
O Koyejo
Keyword(s):  
Graphical Models ◽  
Latent Variable ◽  
Graphical Model ◽  
Brain Connectivity ◽  
Statistical Error ◽  
Real Data ◽  
Low Rank ◽  
Conditional Dependence ◽  
Gradient Descent Algorithm ◽  
The Difference

Abstract Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific investigation. Motivated by modern applications, this manuscript considers an extended setting where each group is generated by a latent variable Gaussian graphical model. Due to the existence of latent factors, the differential network is decomposed into sparse and low-rank components, both of which are symmetric indefinite matrices. We estimate these two components simultaneously using a two-stage procedure: (i) an initialization stage, which computes a simple, consistent estimator, and (ii) a convergence stage, implemented using a projected alternating gradient descent algorithm applied to a nonconvex objective, initialized using the output of the first stage. We prove that given the initialization, the estimator converges linearly with a nontrivial, minimax optimal statistical error. Experiments on synthetic and real data illustrate that the proposed nonconvex procedure outperforms existing methods.

scholarly journals Category representations in the brain are both discretely localized and widely distributed

2018 ◽  
Vol 119 (6) ◽  
pp. 2256-2264 ◽  
Author(s):  
Zarrar Shehzad ◽  
Gregory McCarthy
Keyword(s):  
Functional Mri ◽  
Brain Connectivity ◽  
Strong Support ◽  
Brain Regions ◽  
Distributed Representations ◽  
Domain Specific ◽  
Category Information ◽  
Shared Information ◽  
Unique Domain ◽  
The Brain

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

scholarly journals Dynamic and stable brain connectivity during movie watching as revealed by functional MRI

2021 ◽  
Author(s):  
Xin Di ◽  
Zhiguo Zhang ◽  
Ting Xu ◽  
Bharat B. Biswal
Keyword(s):  
Functional Mri ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Temporoparietal Junction ◽  
Related Information ◽  
Movie Clip ◽  
Brain Changes ◽  
Brain Processing ◽  
Dynamic Connectivity

AbstractSpatially remote brain regions show synchronized activity as typically revealed by correlated functional MRI (fMRI) signals. An emerging line of research has focused on the temporal fluctuations of connectivity, however, its relationships with stable connectivity have not been clearly illustrated. We examined the stable and dynamic connectivity from fMRI data when the participants watched four different movie clips. Using inter-individual correlation, we were able to estimate functionally meaningful dynamic connectivity associated with different movies. Widespread consistent dynamic connectivity was observed for each movie clip as well as their differences between clips. A cartoon movie clip showed higher consistent dynamic connectivity with the posterior cingulate cortex and supramarginal gyrus, while a court drama clip showed higher dynamic connectivity with the auditory cortex and temporoparietal junction, which suggest the involvement of specific brain processing for different movie contents. In contrast, stable connectivity was highly similar among the movie clips, and showed fewer statistical significant differences. The patterns of dynamic connectivity had higher accuracy for classifications of different movie clips than the stable connectivity and regional activity. These results support the functional significance of dynamic connectivity in reflecting functional brain changes, which could provide more functionally related information than stable connectivity.

scholarly journals Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence

2020 ◽  
Vol 39 (2) ◽  
pp. 357-365
Author(s):  
Aiying Zhang ◽  
Biao Cai ◽  
Wenxing Hu ◽  
Bochao Jia ◽  
Faming Liang ◽  
...  
Keyword(s):  
Graphical Models ◽  
Brain Connectivity ◽  
Gaussian Graphical Models

scholarly journals Whole-brain connectivity analysis and classification of spinocerebellar ataxia type 7 by functional MRI

2014 ◽  
Vol 1 (1) ◽  
pp. 2 ◽  
Author(s):  
Carlos R Hernandez-Castillo ◽  
Víctor Galvez ◽  
Consuelo Morgado-Valle ◽  
Juan Fernandez-Ruiz
Keyword(s):  
Functional Mri ◽  
Spinocerebellar Ataxia ◽  
Brain Connectivity ◽  
Spinocerebellar Ataxia Type ◽  
Connectivity Analysis ◽  
Whole Brain ◽  
Spinocerebellar Ataxia Type 7

scholarly journals GGMnonreg: Non-Regularized Gaussian Graphical Models in R

2021 ◽  
Author(s):  
Donald Ray Williams
Keyword(s):  
Graphical Models ◽  
Brain Connectivity ◽  
Dependence Structure ◽  
Gaussian Graphical Models ◽  
Graphical Modeling ◽  
Conditional Dependence ◽  
Common Task ◽  
The Social ◽  
Cortical Regions ◽  
Low Dimensional

Studying complex relations in multivariate datasets is a common task across the sciences. Cognitive neuroscientists model brain connectivity with the goal of unearthing functional and structural associations betweencortical regions. In clinical psychology, researchers wish to better understand the intri-cate web of symptom interrelations that underlie mental health disorders. To this end, graphical modeling has emerged as an oft-used tool in the chest of scientific inquiry. Thebasic idea is to characterize multivariate relations by learning the conditional dependence structure. The cortical regions or symptoms are nodes and the featured connections linking nodes are edges that graphically represent the conditional dependence structure. Graphical modeling is quite common in fields with wide data, that is, when there are more variables (p) thanobservations (n). Accordingly, many regularization-based approaches have been developed for those kinds of data. More recently, graphical modeling has emerged in psychology, where the data is typically long or low-dimensional. The primary purpose of GGMnonreg is to provide methods that were specifically designed for low-dimensional data (e.g., those common in the social-behavioral sciences), for which there is a dearth of methodology.

Estimating Brain Connectivity Using Copula Gaussian Graphical Models

2019 ◽  
Author(s):  
Xu Gao ◽  
Weining Shen ◽  
Chee-Ming Ting ◽  
Steven C. Cramer ◽  
Ramesh Srinivasan ◽  
...  
Keyword(s):  
Graphical Models ◽  
Brain Connectivity ◽  
Gaussian Graphical Models

scholarly journals Individual structural features constrain the mouse functional connectome

2019 ◽  
Vol 116 (52) ◽  
pp. 26961-26969 ◽  
Author(s):  
Francesca Melozzi ◽  
Eyal Bergmann ◽  
Julie A. Harris ◽  
Itamar Kahn ◽  
Viktor Jirsa ◽  
...  
Keyword(s):  
Functional Mri ◽  
Mouse Brain ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Structural Data ◽  
Structural Features ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Functional Connectome ◽  
The Individual

Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure–function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.

scholarly journals A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control,A PrioriKnowledge, and Group Inference

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Aiping Liu ◽  
Junning Li ◽  
Z. Jane Wang ◽  
Martin J. McKeown
Keyword(s):  
False Discovery Rate ◽  
Graphical Models ◽  
Error Control ◽  
Functional Neuroimaging ◽  
Brain Connectivity ◽  
Practical Method ◽  
Fmri Data ◽  
Single Subject ◽  
Computationally Efficient ◽  
False Discovery

Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity. Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges. The PCfdralgorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically. The original PCfdralgorithm was unable to accommodatea prioriinformation about connectivity and was designed to infer connectivity from a single subject rather than a group of subjects. Here we extend the original PCfdralgorithm and propose a multisubject, error-rate-controlled brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity. In simulations, we show that the two proposed extensions can still control the FDR around or below a specified threshold. When the proposed approach is applied to fMRI data in a Parkinson’s disease study, we find robust group evidence of the disease-related changes, the compensatory changes, and the normalizing effect of L-dopa medication. The proposed method provides a robust, accurate, and practical method for the assessment of brain connectivity patterns from functional neuroimaging data.

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