Kuan Elaine, Vegh Viktor, Phamnguyen John, O'Brien Kieran, Hammond Amanda, Reutens David
Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia.
ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD, Australia.
Front Radiol. 2024 Nov 27;4:1495181. doi: 10.3389/fradi.2024.1495181. eCollection 2024.
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.
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.
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 , mean Dice coefficient of and mean Euclidean distance of mm between activation peaks across the evaluated regions of interest. The interval-based method achieved a mean whole-brain AUC of , mean Dice coefficient of and mean Euclidean distance of mm between activation peaks across the evaluated regions of interest.
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.
基于任务的语言功能磁共振成像（fMRI）是一种用于识别支持语言的脑区的非侵入性方法，可用于规划可能侵犯明确语言区的神经外科切除术。使用非结构化fMRI范式（如自然主义fMRI）来绘制语言区域的兴趣日益增加。由于在这些范式中可能难以定义任务回归变量，因此对它们的分析需要使用机器学习（ML）和深度学习（DL）等替代方法。
本研究以基于任务的语言fMRI为起点，研究使用不同类别的ML和DL算法来识别支持语言的脑区。从26名个体收集了包含七种基于任务的语言fMRI范式的数据，并训练ML和DL模型以对体素级fMRI时间序列进行分类。
在使用fMRI时间序列分类识别语言区域方面，通用机器学习和基于区间的方法最有前景。通用机器学习方法在评估的感兴趣区域的激活峰值之间实现了平均全脑受试者操作特征曲线下面积（AUC）为 ，平均骰子系数为 ，平均欧几里得距离为 毫米。基于区间的方法在评估的感兴趣区域的激活峰值之间实现了平均全脑AUC为 ，平均骰子系数为 ，平均欧几里得距离为 毫米。
本研究证明了不同的ML和DL方法在对基于任务的语言fMRI时间序列进行分类中的效用。这些方法的一个潜在应用是从非结构化范式中识别语言激活。
