Evaluation of Task fMRI Decoding With Deep Learning on a Small Sample Dataset.

ABSTRACT: Recently, several deep learning methods have been applied to decoding in task-related fMRI, and their advantages have been exploited in a variety of ways. However, this paradigm is sometimes problematic, due to the difficulty of applying deep learning to high-dimensional data and small sample size conditions. The difficulties in gathering a large amount of data to develop predictive machine learning models with multiple layers from fMRI experiments with complicated designs and tasks are well-recognized. Group-level, multi-voxel pattern analysis with small sample sizes results in low statistical power and large accuracy evaluation errors; failure in such instances is ascribed to the individual variability that risks information leakage, a particular issue when dealing with a limited number

SUBMITTER: Yotsutsuji S 

PROVIDER: S-EPMC7928289 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature