Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.

ABSTRACT:

Introduction

Dynamic functional connectivity (dFC), which can capture the abnormality of brain activity over time in resting-state functional magnetic resonance imaging (rs-fMRI) data, has a natural advantage in revealing the abnormal mechanism of brain activity in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Several deep learning methods have been proposed to learn dynamic changes from rs-fMRI for FC analysis, and achieved superior performance than those using static FC. However, most existing methods only consider dependencies of two adjacent timestamps, which is limited when the change is related to the course of many timestamps.

Methods

In this paper, we propose a novel Temporal Dependence neural Network (TDNet) for FC representation learning and tempor

SUBMITTER: Wang M 

PROVIDER: S-EPMC10750397 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature