Title: Discovery of Neurocognitive Phenotypes of Autism By Analyzing Functional Connectivity in the Default Mode Network and Dorsolateral Prefrontal Cortex

Author: Amith Vasantha
Advisor: Dr. Judith Ford

Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually presenting as reduced social interaction, lessened verbal communication, and repetitive behavior. Diagnosing ASD is extremely difficult because of its wide variety of symptoms, so it can only be diagnosed through behavioral tests and analysis of developmental history. Resting-state fMRI can help researchers discover a neural substrate for ASD to diagnose it earlier. One prominent fMRI database for ASD research is the Autism Brain Imaging Data Exchange, a large-scale collection of anonymized functional MRI scans subdivided by age, gender, handedness, and scores on behavioral assessments.


This analysis focused on two brain networks: the default mode network (DMN), which is active when minds wander, and the executive network, which is active during the performance of tasks. The medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and angular gyrus are nodes of the DMN, and the dorsolateral prefrontal cortex (DLPFC) is the main node in the executive network. Both networks are affected by ASD.

This research used preprocessed resting-state fMRI data to establish neurocognitive phenotypes for ASD. Bivariate correlation was used to compare connectivity in the DMN and DLPFC between ASD and control fMRI scans, and these differences were analyzed for correlations with each patient’s assessment scores. After the Benjamini-Hochberg procedure was applied to reduce the false discovery rate, analysis of these metrics revealed that in ASD patients there was underconnectivity between the right PCC and the right mPFC, while in control patients there was overconnectivity between the right angular gyrus and left DLPFC. ASD is extremely heritable, so phenotypic research is absolutely necessary for discovering more about the genetic causes of ASD, which will speed up ASD diagnosis and help researchers develop more targeted treatments for ASD.