Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder

11/25/2019
by   Cooper J. Mellema, et al.
0

Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functional MRI (fMRI) characterizes brain connectivity and may be used to inform diagnoses and democratize medicine. However, successful construction of deep learning models from fMRI requires addressing key choices about the model's architecture, including the number of layers and number of neurons per layer. Meanwhile, deriving functional connectivity (FC) features from fMRI requires choosing an atlas with an appropriate level of granularity. Once a model has been built, it is vital to determine which features are predictive of ASD and if similar features are learned across atlas granularity levels. To identify aptly suited architectural configurations, probability distributions of the configurations of high versus low performing models are compared. To determine the effect of atlas granularity, connectivity features are derived from atlases with 3 levels of granularity and important features are ranked with permutation feature importance. Results show the highest performing models use between 2-4 hidden layers and 16-64 neurons per layer, granularity dependent. Connectivity features identified as important across all 3 atlas granularity levels include FC to the supplementary motor gyrus and language association cortex, regions associated with deficits in social and sensory processing in ASD. Importantly, the cerebellum, often not included in functional analyses, is also identified as a region whose abnormal connectivity is highly predictive of ASD. Results of this study identify important regions to include in future studies of ASD, help assist in the selection of network architectures, and help identify appropriate levels of granularity to facilitate the development of accurate diagnostic models of ASD.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 3

07/25/2017

Functional connectivity patterns of autism spectrum disorder identified by deep feature learning

Autism spectrum disorder (ASD) is regarded as a brain disease with globa...
12/07/2021

A deep learning model for data-driven discovery of functional connectivity

Functional connectivity (FC) studies have demonstrated the overarching v...
08/08/2019

Identification of Effective Connectivity Subregions

Standard fMRI connectivity analyses depend on aggregating the time serie...
10/28/2018

Prediction of severity and treatment outcome for ASD from fMRI

Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome....
12/27/2021

MHATC: Autism Spectrum Disorder identification utilizing multi-head attention encoder along with temporal consolidation modules

Resting-state fMRI is commonly used for diagnosing Autism Spectrum Disor...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.