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

Abstract

Introduction: Task-based language fMRI is a non-invasive method of identifying brain regions subserving language that is used to plan neurosurgical resections which potentially encroach on eloquent regions. The use of unstructured fMRI paradigms, such as naturalistic fMRI, to map language is of increasing interest. Their analysis necessitates the use of alternative methods such as machine learning (ML) and deep learning (DL) because task regressors may be difficult to define in these paradigms.

Methods: Using task-based language fMRI as a starting point, this study investigates the use of different categories of ML and DL algorithms to identify brain regions subserving language. Data comprising of seven task-based language fMRI paradigms were collected from 26 individuals, and ML and DL models were trained to classify voxel-wise fMRI time series.

Results: The general machine learning and the interval-based methods were the most promising in identifying language areas using fMRI time series classification. The geneal machine learning method achieved a mean whole-brain Area Under the Receiver Operating Characteristic Curve (AUC) of $0.97\pm 0.03$ , mean Dice coefficient of $0.6\pm 0.34$ and mean Euclidean distance of $2.7\pm 2.4$  mm between activation peaks across the evaluated regions of interest. The interval-based method achieved a mean whole-brain AUC of $0.96\pm 0.03$ , mean Dice coefficient of $0.61\pm 0.33$ and mean Euclidean distance of $3.3\pm 2.7$  mm between activation peaks across the evaluated regions of interest.

Discussion: 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.