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Machine learning approaches linking brain function to behavior in the ABCD STOP task

The stop‐signal task (SST) is one of the most common fMRI tasks of response inhibition, and its performance measure, the stop‐signal reaction‐time (SSRT), is broadly used as a measure of cognitive control processes. The neurobiology underlying individual or clinical differences in response inhibitio...

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Autores principales: Yuan, Dekang, Hahn, Sage, Allgaier, Nicholas, Owens, Max M., Chaarani, Bader, Potter, Alexandra, Garavan, Hugh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921227/
https://www.ncbi.nlm.nih.gov/pubmed/36534603
http://dx.doi.org/10.1002/hbm.26172
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author Yuan, Dekang
Hahn, Sage
Allgaier, Nicholas
Owens, Max M.
Chaarani, Bader
Potter, Alexandra
Garavan, Hugh
author_facet Yuan, Dekang
Hahn, Sage
Allgaier, Nicholas
Owens, Max M.
Chaarani, Bader
Potter, Alexandra
Garavan, Hugh
author_sort Yuan, Dekang
collection PubMed
description The stop‐signal task (SST) is one of the most common fMRI tasks of response inhibition, and its performance measure, the stop‐signal reaction‐time (SSRT), is broadly used as a measure of cognitive control processes. The neurobiology underlying individual or clinical differences in response inhibition remain unclear, consistent with the general pattern of quite modest brain–behavior associations that have been recently reported in well‐powered large‐sample studies. Here, we investigated the potential of multivariate, machine learning (ML) methods to improve the estimation of individual differences in SSRT with multimodal structural and functional region of interest‐level neuroimaging data from 9‐ to 11‐year‐olds children in the ABCD Study. Six ML algorithms were assessed across modalities and fMRI tasks. We verified that SST activation performed best in predicting SSRT among multiple modalities including morphological MRI (cortical surface area/thickness), diffusion tensor imaging, and fMRI task activations, and then showed that SST activation explained 12% of the variance in SSRT using cross‐validation and out‐of‐sample lockbox data sets (n = 7298). Brain regions that were more active during the task and that showed more interindividual variation in activation were better at capturing individual differences in performance on the task, but this was only true for activations when successfully inhibiting. Cortical regions outperformed subcortical areas in explaining individual differences but the two hemispheres performed equally well. These results demonstrate that the detection of reproducible links between brain function and performance can be improved with multivariate approaches and give insight into a number of brain systems contributing to individual differences in this fundamental cognitive control process.
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spelling pubmed-99212272023-02-13 Machine learning approaches linking brain function to behavior in the ABCD STOP task Yuan, Dekang Hahn, Sage Allgaier, Nicholas Owens, Max M. Chaarani, Bader Potter, Alexandra Garavan, Hugh Hum Brain Mapp Research Articles The stop‐signal task (SST) is one of the most common fMRI tasks of response inhibition, and its performance measure, the stop‐signal reaction‐time (SSRT), is broadly used as a measure of cognitive control processes. The neurobiology underlying individual or clinical differences in response inhibition remain unclear, consistent with the general pattern of quite modest brain–behavior associations that have been recently reported in well‐powered large‐sample studies. Here, we investigated the potential of multivariate, machine learning (ML) methods to improve the estimation of individual differences in SSRT with multimodal structural and functional region of interest‐level neuroimaging data from 9‐ to 11‐year‐olds children in the ABCD Study. Six ML algorithms were assessed across modalities and fMRI tasks. We verified that SST activation performed best in predicting SSRT among multiple modalities including morphological MRI (cortical surface area/thickness), diffusion tensor imaging, and fMRI task activations, and then showed that SST activation explained 12% of the variance in SSRT using cross‐validation and out‐of‐sample lockbox data sets (n = 7298). Brain regions that were more active during the task and that showed more interindividual variation in activation were better at capturing individual differences in performance on the task, but this was only true for activations when successfully inhibiting. Cortical regions outperformed subcortical areas in explaining individual differences but the two hemispheres performed equally well. These results demonstrate that the detection of reproducible links between brain function and performance can be improved with multivariate approaches and give insight into a number of brain systems contributing to individual differences in this fundamental cognitive control process. John Wiley & Sons, Inc. 2022-12-19 /pmc/articles/PMC9921227/ /pubmed/36534603 http://dx.doi.org/10.1002/hbm.26172 Text en © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Yuan, Dekang
Hahn, Sage
Allgaier, Nicholas
Owens, Max M.
Chaarani, Bader
Potter, Alexandra
Garavan, Hugh
Machine learning approaches linking brain function to behavior in the ABCD STOP task
title Machine learning approaches linking brain function to behavior in the ABCD STOP task
title_full Machine learning approaches linking brain function to behavior in the ABCD STOP task
title_fullStr Machine learning approaches linking brain function to behavior in the ABCD STOP task
title_full_unstemmed Machine learning approaches linking brain function to behavior in the ABCD STOP task
title_short Machine learning approaches linking brain function to behavior in the ABCD STOP task
title_sort machine learning approaches linking brain function to behavior in the abcd stop task
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921227/
https://www.ncbi.nlm.nih.gov/pubmed/36534603
http://dx.doi.org/10.1002/hbm.26172
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