AUTHOR=Albouy Philippe , Caclin Anne , Norman-Haignere Sam V. , Lévêque Yohana , Peretz Isabelle , Tillmann Barbara , Zatorre Robert J. TITLE=Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia JOURNAL=Frontiers in Neuroscience VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.01165 DOI=10.3389/fnins.2019.01165 ISSN=1662-453X ABSTRACT=

Machine learning classification techniques are frequently applied to structural and resting-state fMRI data to identify brain-based biomarkers for developmental disorders. However, task-related fMRI has rarely been used as a diagnostic tool. Here, we used structural MRI, resting-state connectivity and task-based fMRI data to detect congenital amusia, a pitch-specific developmental disorder. All approaches discriminated amusics from controls in meaningful brain networks at similar levels of accuracy. Interestingly, the classifier outcome was specific to deficit-related neural circuits, as the group classification failed for fMRI data acquired during a verbal task for which amusics were unimpaired. Most importantly, classifier outputs of task-related fMRI data predicted individual behavioral performance on an independent pitch-based task, while this relationship was not observed for structural or resting-state data. These results suggest that task-related imaging data can potentially be used as a powerful diagnostic tool to identify developmental disorders as they allow for the prediction of symptom severity.