Language task-based fMRI analysis using machine learning and deep learning

This study demonstrates the utility of different ML and DL methods in classifying task-based language fMRI time series. A potential application of these methods is the identification of language activation from unstructured paradigms.

Authors

David C. Reutens (ORCID: https://orcid.org/0000-0002-1652-9369)
Kieran O’Brien (ORCID: https://orcid.org/0000-0003-1996-3563)
Viktor Vegh (ORCID: https://orcid.org/0000-0002-4310-1421)
John Phamnguyen
Elaine Kuan
Amanda Hammond

Institutions

The University of Queensland (AU)
Royal Brisbane and Women's Hospital (AU)
Siemens (Australia) (AU)

Publication Details

Journal: Frontiers in Radiology
Published: 2024-11-27
DOI: https://doi.org/10.3389/fradi.2024.1495181
Citations: 1
Primary Topic: Time Series Analysis and Forecasting
Type: article
Field-Weighted Citation Impact: 0.35

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article

Language task-based fMRI analysis using machine learning and deep learning

David C. Reutens, Kieran O’Brien, Viktor Vegh, John Phamnguyen et al.

Nov 27, 2024

1 citations

Frontiers in Radiology

Time Series Analysis and Forecasting

0.35

article

Language task-based fMRI analysis using machine learning and deep learning

David C. Reutens, Kieran O’Brien, Viktor Vegh, John Phamnguyen, Elaine Kuan, Amanda Hammond

November 27, 2024article en

1 citations

Abstract

This study demonstrates the utility of different ML and DL methods in classifying task-based language fMRI time series. A potential application of these methods is the identification of language activation from unstructured paradigms.

Frontiers in RadiologyVol. 4

The University of Queensland (AU), Royal Brisbane and Women's Hospital (AU), Siemens (Australia) (AU)

Quality Education

Openalex Percentile: Top 41%

Time Series Analysis and Forecasting

0.35

DOI Open Access PDF OpenAlex

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