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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