Modern Statistical Learning Strategies in Imaging Genetics - Volume II

Editorial

06 December 2023

Editorial: Modern statistical learning strategies in imaging genetics, volume II

Chao Huang

Rongjie Liu

Bingxin Zhao

and

Linglong Kong

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Editors

Chao Huang

University of Georgia

Bingxin Zhao

Purdue University

Rongjie Liu

Florida State University

Linglong Kong

University of Alberta

Impact

Single subject with 2D image and no variable selection: from top to bottom, left to right: simulated boundary in red, simulated intensity data, estimated boundary in red and inclusion probability map. (a) Circle (weak). (b) Square (strong). (c) Random (intermediatel).

Methods

18 October 2023

Bayesian nonparametric method for genetic dissection of brain activation region

Zhuxuan Jin

, 1 more and

Tianwei Yu

1,766 views

1 citations

Tensor visualization for 3-dimensional image. Top left panel provides a graphic of a rank-R tensor decomposition for a 3-dimensional tensor X, represented as the sum of tensor products of vectors ar, br, and cr, 1 ≤ r ≤ R. The remaining panels illustrate tensor slices (red) and fibers (blue) corresponding to each of the 3 dimensions of a 3-way tensor cube.

Original Research

23 August 2023

Integrative Bayesian tensor regression for imaging genetics applications

Yajie Liu

, 3 more and

The Alzheimer’s Disease Neuroimaging Initiative

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0 citations

$Schematic overview of functional magnetic resonance imaging (fMRI) data acquisition and processing. MRI, magnetic resonance imaging; fALFF, fractional amplitude of low-frequency fluctuation; FC, functional connectivity.$

Original Research

08 June 2023

Altered regional brain activity and functional connectivity in resting-state brain networks associated with psychological erectile dysfunction

Xue Liu

, 8 more and

Jianhuai Chen

2,191 views

2 citations

Original Research

31 May 2023

The effect of seed location on functional connectivity: evidence from an image-based meta-analysis

Meng-Ting Li

, 5 more and

Jun Ren

Introduction: Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA).

Methods: We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected t maps were obtained from the statistical analyses. The IBMA was performed with the t maps.

Results: We demonstrate that the overlap of meta-analytic maps across different seeds’ ROIs within DMN is relatively low, which cautions us to be cautious with seeds’ selection.

Discussion: Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.

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6 citations