Deep Collaborative Learning With Application to the Study of Multimodal Brain Development.

ABSTRACT:

Objective

Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aims to develop a neural network framework to extract phenotype related cross-data relationships and use it to study the brain development.

Methods

We propose a novel method, deep collaborative learning (DCL), to address the limitation of existing methods. DCL first uses a deep network to represent original data and then seeks their correlations, while also linking the data representation with phenotypical information.

Results

We studied the difference of functional connectivity (FCs) between different age groups and also use

SUBMITTER: Hu W 

PROVIDER: S-EPMC8177041 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature