Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia
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 a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2019.01165/full |
| _version_ | 1818142538563846144 |
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| author | Philippe Albouy Philippe Albouy Anne Caclin Anne Caclin Sam V. Norman-Haignere Sam V. Norman-Haignere Yohana Lévêque Yohana Lévêque Isabelle Peretz Barbara Tillmann Barbara Tillmann Robert J. Zatorre Robert J. Zatorre |
| author_facet | Philippe Albouy Philippe Albouy Anne Caclin Anne Caclin Sam V. Norman-Haignere Sam V. Norman-Haignere Yohana Lévêque Yohana Lévêque Isabelle Peretz Barbara Tillmann Barbara Tillmann Robert J. Zatorre Robert J. Zatorre |
| author_sort | Philippe Albouy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T11:17:22Z |
| format | Article |
| id | doaj.art-b6f3dc40e47a449588a8fa8337f5b3b5 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-11T11:17:22Z |
| publishDate | 2019-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-b6f3dc40e47a449588a8fa8337f5b3b52022-12-22T01:09:18ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-10-011310.3389/fnins.2019.01165488781Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital AmusiaPhilippe Albouy0Philippe Albouy1Anne Caclin2Anne Caclin3Sam V. Norman-Haignere4Sam V. Norman-Haignere5Yohana Lévêque6Yohana Lévêque7Isabelle Peretz8Barbara Tillmann9Barbara Tillmann10Robert J. Zatorre11Robert J. Zatorre12Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC, CanadaInternational Laboratory for Brain, Music and Sound Research, Montreal, QC, CanadaINSERM, U1028, CNRS, UMR 5292, Lyon Neuroscience Research Center, Brain Dynamics and Cognition Team, Lyon, FranceUniversity Lyon 1, Lyon, FranceZuckerman Institute of Mind, Brain and Behavior, Columbia University, New York, NY, United StatesCNRS, Laboratoire des Sytèmes Perceptifs, Département d’Études Cognitives, ENS, PSL University, Paris, FranceUniversity Lyon 1, Lyon, FranceCNRS, UMR 5292, INSERM, U1028, Lyon Neuroscience Research Center, Auditory Cognition and Psychoacoustics Team, Lyon, FranceInternational Laboratory for Brain, Music and Sound Research, Montreal, QC, CanadaUniversity Lyon 1, Lyon, FranceCNRS, UMR 5292, INSERM, U1028, Lyon Neuroscience Research Center, Auditory Cognition and Psychoacoustics Team, Lyon, FranceCognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC, CanadaInternational Laboratory for Brain, Music and Sound Research, Montreal, QC, CanadaMachine 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.https://www.frontiersin.org/article/10.3389/fnins.2019.01165/fullmultivariate pattern analysis (MVPA)rs-fMRIsMRItask-based fMRItone deafnessdiagnostic |
| spellingShingle | Philippe Albouy Philippe Albouy Anne Caclin Anne Caclin Sam V. Norman-Haignere Sam V. Norman-Haignere Yohana Lévêque Yohana Lévêque Isabelle Peretz Barbara Tillmann Barbara Tillmann Robert J. Zatorre Robert J. Zatorre Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia Frontiers in Neuroscience multivariate pattern analysis (MVPA) rs-fMRI sMRI task-based fMRI tone deafness diagnostic |
| title | Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia |
| title_full | Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia |
| title_fullStr | Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia |
| title_full_unstemmed | Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia |
| title_short | Decoding Task-Related Functional Brain Imaging Data to Identify Developmental Disorders: The Case of Congenital Amusia |
| title_sort | decoding task related functional brain imaging data to identify developmental disorders the case of congenital amusia |
| topic | multivariate pattern analysis (MVPA) rs-fMRI sMRI task-based fMRI tone deafness diagnostic |
| url | https://www.frontiersin.org/article/10.3389/fnins.2019.01165/full |
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