Allison Jack1. 1. Department of Pharmacology and Physiology, Autism and Neurodevelopmental Disorders Institute, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA.
Abstract
PURPOSE OF REVIEW: Resting-state fMRI assessment of instrinsic functional brain connectivity (rs-fcMRI) in autism spectrum disorders (ASD) allows assessment of participants with a wide range of functioning levels, and collection of multisite databases that facilitate large-scale analysis. These heterogeneous multisite data present both promise and methodological challenge. Herein, we provide an overview of recent (1 October 2016-1 November 2017) empirical research on ASD rs-fcMRI, focusing on work that helps clarify how best to leverage the power of these data. RECENT FINDINGS: Recent research indicates that larger samples, careful atlas selection, and attention to eye status of participants will improve the sensitivity and power of resting-state fMRI analyses conducted using multisite data. Use of bandpass filters that extend into a slightly higher frequency range than typical defaults may prevent loss of disease-relevant information. Connectivity-based parcellation as an approach to region of interest analyses may allow for improved understanding of functional connectivity disruptions in ASD. Treatment approaches using rs-fcMRI to determine target engagement, predict treatment, or facilitate neurofeedback demonstrate promise. SUMMARY: Rs-fcMRI data have great promise for biomarker identification and treatment development in ASD; however, ongoing methodological development and evaluation is crucial for progress.
PURPOSE OF REVIEW: Resting-state fMRI assessment of instrinsic functional brain connectivity (rs-fcMRI) in autism spectrum disorders (ASD) allows assessment of participants with a wide range of functioning levels, and collection of multisite databases that facilitate large-scale analysis. These heterogeneous multisite data present both promise and methodological challenge. Herein, we provide an overview of recent (1 October 2016-1 November 2017) empirical research on ASD rs-fcMRI, focusing on work that helps clarify how best to leverage the power of these data. RECENT FINDINGS: Recent research indicates that larger samples, careful atlas selection, and attention to eye status of participants will improve the sensitivity and power of resting-state fMRI analyses conducted using multisite data. Use of bandpass filters that extend into a slightly higher frequency range than typical defaults may prevent loss of disease-relevant information. Connectivity-based parcellation as an approach to region of interest analyses may allow for improved understanding of functional connectivity disruptions in ASD. Treatment approaches using rs-fcMRI to determine target engagement, predict treatment, or facilitate neurofeedback demonstrate promise. SUMMARY: Rs-fcMRI data have great promise for biomarker identification and treatment development in ASD; however, ongoing methodological development and evaluation is crucial for progress.
Authors: Cynthia M Solek; Nasr A I Farooqi; Niklas Brake; Philip Kesner; Anne Schohl; Jack P Antel; Edward S Ruthazer Journal: J Neurosci Date: 2021-06-08 Impact factor: 6.167