Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

11/18/2016
by   Alexandre Abraham, et al.
0

Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines that extract the most predictive biomarkers from the data. These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas. Connectomes are then compared across participants to learn patterns of connectivity that differentiate typical controls from individuals with autism. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Good choices of methods for the various steps of the pipeline lead to 67 ABIDE data, which is significantly better than previously reported results. We perform extensive validation on multiple subsets of the data defined by different inclusion criteria. These enables detailed analysis of the factors contributing to successful connectome-based prediction. First, prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available. Second, the definition of functional brain areas is of paramount importance for biomarker discovery: brain areas extracted from large R-fMRI datasets outperform reference atlases in the classification tasks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 9

page 14

page 17

page 18

07/12/2016

Statistical power and prediction accuracy in multisite resting-state fMRI connectivity

Connectivity studies using resting-state functional magnetic resonance i...
10/24/2020

Shared Space Transfer Learning for analyzing multi-site fMRI data

Multi-voxel pattern analysis (MVPA) learns predictive models from task-b...
03/23/2021

Embracing the Disharmony in Heterogeneous Medical Data

Heterogeneity in medical imaging data is often tackled, in the context o...
09/19/2021

Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning

Autism Spectrum Disorder(ASD) is a set of neurodevelopmental conditions ...
10/10/2019

Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

This is an empirical study to investigate the impact of scanner effects ...
05/25/2020

Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data

Clinical neuroimaging has recently witnessed explosive growth in data av...
02/22/2022

Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study

Many supervised machine learning frameworks have been proposed for disea...
This week in AI

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