The Importance of Anti-correlations in Graph Theory Based Classification of Autism Spectrum Disorder.

ABSTRACT: With the release of the multi-site Autism Brain Imaging Data Exchange, many researchers have applied machine learning methods to distinguish between healthy subjects and autistic individuals by using features extracted from resting state functional MRI data. An important part of applying machine learning to this problem is extracting these features. Specifically, whether to include negative correlations between brain region activities as relevant features and how best to define these features. For the second question, the graph theoretical properties of the brain network may provide a reasonable answer. In this study, we investigated the first issue by comparing three different approaches. These included using the positive correlation matrix (comprising only the positive values of the orig

SUBMITTER: Kazeminejad A 

PROVIDER: S-EPMC7426475 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature