Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio

The T1w/T2w ratio is a novel magnetic resonance imaging (MRI) measure that is thought to be sensitive to cortical myelin. Using this novel measure requires developing novel pipelines for the data quality assurance, data analysis, and validation of the findings in order to apply the T1w/T2w ratio for...

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Autores principales: Baranger, David A.A., Halchenko, Yaroslav O., Satz, Skye, Ragozzino, Rachel, Iyengar, Satish, Swartz, Holly A., Manelis, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Protocol Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8720909/
https://www.ncbi.nlm.nih.gov/pubmed/35004227
http://dx.doi.org/10.1016/j.mex.2021.101595
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author Baranger, David A.A.
Halchenko, Yaroslav O.
Satz, Skye
Ragozzino, Rachel
Iyengar, Satish
Swartz, Holly A.
Manelis, Anna
author_facet Baranger, David A.A.
Halchenko, Yaroslav O.
Satz, Skye
Ragozzino, Rachel
Iyengar, Satish
Swartz, Holly A.
Manelis, Anna
author_sort Baranger, David A.A.
collection PubMed
description The T1w/T2w ratio is a novel magnetic resonance imaging (MRI) measure that is thought to be sensitive to cortical myelin. Using this novel measure requires developing novel pipelines for the data quality assurance, data analysis, and validation of the findings in order to apply the T1w/T2w ratio for classification of disorders associated with the changes in the myelin levels. In this article, we provide a detailed description of such a pipeline as well as the reference to the scripts used in our recent report that applied the T1w/T2w ratio and machine learning to classify individuals with depressive disorders from healthy controls.
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spelling pubmed-87209092022-01-07 Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio Baranger, David A.A. Halchenko, Yaroslav O. Satz, Skye Ragozzino, Rachel Iyengar, Satish Swartz, Holly A. Manelis, Anna MethodsX Protocol Article The T1w/T2w ratio is a novel magnetic resonance imaging (MRI) measure that is thought to be sensitive to cortical myelin. Using this novel measure requires developing novel pipelines for the data quality assurance, data analysis, and validation of the findings in order to apply the T1w/T2w ratio for classification of disorders associated with the changes in the myelin levels. In this article, we provide a detailed description of such a pipeline as well as the reference to the scripts used in our recent report that applied the T1w/T2w ratio and machine learning to classify individuals with depressive disorders from healthy controls. Elsevier 2021-12-02 /pmc/articles/PMC8720909/ /pubmed/35004227 http://dx.doi.org/10.1016/j.mex.2021.101595 Text en © 2021 The Authors. Published by Elsevier B.V. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol Article
Baranger, David A.A.
Halchenko, Yaroslav O.
Satz, Skye
Ragozzino, Rachel
Iyengar, Satish
Swartz, Holly A.
Manelis, Anna
Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio
title Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio
title_full Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio
title_fullStr Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio
title_full_unstemmed Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio
title_short Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio
title_sort protocol for a machine learning algorithm predicting depressive disorders using the t1w/t2w ratio
topic Protocol Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8720909/
https://www.ncbi.nlm.nih.gov/pubmed/35004227
http://dx.doi.org/10.1016/j.mex.2021.101595
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