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Abstract #1909

Analyzing Task-based fMRI Time Series using Machine Learning

Elaine Yuen Fong Kuan1,2, Viktor Vegh1,2, Kieran O'Brien3, Amanda Hammond3, Javier Urriola Yaksic1, and David Reutens1,2
1Centre of Advanced Imaging, The University of Queensland, Brisbane, Australia, 2ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia, 3Siemens Healthcare Pty Ltd, Brisbane, Australia


Common approaches for analyzing task-based fMRI data rely upon the use of regressors, which in some experimental paradigms are difficult to define. A machine learning method is proposed to overcome this challenge. Three machine learning methods with established utility for time series classification were used to classify areas of activation and non-activation in a language fMRI study. Machine learning methods were able to identify the activation regions identified by analyses using the General Linear Model (GLM). Machine learning may be useful for fMRI time series analysis, particularly when regressors required for GLM-based analyses are difficult to define.

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