scholarly journals Assessing dynamic functional connectivity in heterogeneous samples

2017 ◽  
Author(s):  
B. C. L. Lehmann ◽  
S. R. White ◽  
R. N. Henson ◽  
Cam-CAN ◽  
L. Geerligs
Keyword(s):  
Functional Connectivity ◽  
Functional Organization ◽  
Measurement Noise ◽  
Fmri Data ◽  
Group Differences ◽  
Dynamic Features ◽  
Dynamic Functional Connectivity ◽  
Heterogeneous Samples ◽  
Alternative Sources ◽  
Connectivity Strength

AbstractSeveral methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding-window analysis, which aims to capture how the brain’s functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non-dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k-means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC.

scholarly journals Model testing for distinctive functional connectivity gradients with resting-state fMRI data

2018 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Jaime S. Ide ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Organization ◽  
Region Of Interest ◽  
Resting State Fmri ◽  
Model Testing ◽  
Fmri Data ◽  
Extensive Literature ◽  
Topographic Organization ◽  
Lateral Organization

AbstractIn accordance with the concept of topographic organization of neuroanatomical structures, there is an increased interest in estimating and delineating continuous changes in the functional connectivity patterns across neighboring voxels within a region of interest using resting-state fMRI data. Fundamental to this functional connectivity gradient analysis is the assumption that the functional organization is stable and uniform across the region of interest. To evaluate this assumption, we developed a model testing procedure to arbitrate between overlapping, shifted, or different topographic connectivity gradients across subdivisions of a structure. We tested the procedure using the striatum, a subcortical structure consisting of the caudate nucleus and putamen, in which an extensive literature, primarily from rodents and non-human primates, suggest to have a shared topographic organization of a single diagonal gradient. We found, across multiple resting state fMRI data samples of different spatial resolutions in humans, and one macaque resting state fMRI data sample, that the models with different functional connectivity gradients across the caudate and putamen was the preferred model. The model selection procedure was validated in control conditions of checkerboard subdivisions, demonstrating the expected overlapping gradient. More specifically, while we replicated the diagonal organization of the functional connectivity gradients in both the caudate and putamen, our analysis also revealed a medial-lateral organization within the caudate. Not surprisingly, performing the same analysis assuming a unitary gradient obfuscates the medial-lateral organization of the caudate, producing only a diagonal gradient. These findings demonstrate the importance of testing basic assumptions and evaluating interpretations across species. The significance of differential topographic gradients across the putamen and caudate and the medial-lateral gradient of the caudate in humans should be tested in future studies.

scholarly journals A Longitudinal Model for Functional Connectivity Networks Using Resting-State fMRI

2017 ◽  
Author(s):  
Brian Hart ◽  
Ivor Cribben ◽  
Mark Fiecas ◽  
Keyword(s):  
Functional Connectivity ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Resting State Fmri ◽  
Testing Procedure ◽  
Fmri Data ◽  
Series Data ◽  
Group Differences ◽  
Permutation Testing ◽  
Novel Method

AbstractMany neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current network modeling literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC network model using a variance components approach. First, for all subjects’ visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the FC network, and 3) the FC network’s longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in baseline FC and change in FC over time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer’s Disease Neuroimaging Initiative database.

scholarly journals Beyond linear dynamic functional connectivity: a vine copula change point model

2021 ◽  
Author(s):  
Xin Xiong ◽  
Ivor Cribben
Keyword(s):  
Functional Connectivity ◽  
Change Point ◽  
Sliding Window ◽  
Brain Regions ◽  
Change Points ◽  
Fmri Data ◽  
Data Sets ◽  
Dynamic Functional Connectivity ◽  
Vine Copula ◽  
Vine Copulas

To estimate dynamic functional connectivity for functional magnetic resonance imaging (fMRI) data, two approaches have dominated: sliding window and change point methods. While computationally feasible, the sliding window approach has several limitations. In addition, the existing change point methods assume a Gaussian distribution for and linear dependencies between the fMRI time series. In this work, we introduce a new methodology called Vine Copula Change Point (VCCP) to estimate change points in the functional connectivity network structure between brain regions. It uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between brain regions including tail, symmetric and asymmetric dependence, which has not been explored before in the analysis of neuroimaging data. We apply VCCP to various simulation data sets and to two fMRI data sets: a reading task and an anxiety inducing experiment. In particular, for the former data set, we illustrate the complexity of textual changes during the reading of Chapter 9 in Harry Potter and the Sorcerer's Stone and find that change points across subjects are related to changes in more than one type of textual attributes. Further, the graphs created by the vine copulas indicate the importance of working beyond Gaussianity and linear dependence. Finally, the R package vccp implementing the methodology from the paper is available from CRAN.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

2021 ◽  
Author(s):  
Yue Cheng ◽  
Gaoyan Zhang ◽  
Xiaodong Zhang ◽  
Yuexuan Li ◽  
Jingli Li ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Functional Connectivity ◽  
Time Window ◽  
Brain Connectivity ◽  
Minimal Hepatic Encephalopathy ◽  
Classification Performance ◽  
Support Vector ◽  
Dynamic Features ◽  
Dynamic Functional Connectivity ◽  
Window Approach

Abstract To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n=30; noHE, n=32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary , DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

scholarly journals Graph convolutional network for fMRI analysis based on connectivity neighborhood

2020 ◽  
pp. 1-13
Author(s):  
Lebo Wang ◽  
Kaiming Li ◽  
Xiaoping P. Hu
Keyword(s):  
Functional Connectivity ◽  
Functional Organization ◽  
Resting State Fmri ◽  
Superior Performance ◽  
Individual Identification ◽  
Fmri Data ◽  
Convolutional Network ◽  
Spatial Features ◽  
Fmri Analysis

There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.

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