Volume-Wise Task fMRI Decoding with Deep Learning:Enhancing Temporal Resolution and Cognitive Function Analysis
This work addresses the problem of detailed cognitive function analysis for neuroscience researchers by enabling higher temporal resolution in fMRI decoding, though it is incremental in applying deep learning to a known bottleneck.
The study tackled the limitation of low temporal resolution in task fMRI decoding by proposing a deep neural network for volume-wise identification, achieving mean accuracies of 94.0% and 79.6% on motor and gambling datasets, respectively.
In recent years,the application of deep learning in task functional Magnetic Resonance Imaging (tfMRI) decoding has led to significant advancements. However,most studies remain constrained by assumption of temporal stationarity in neural activity,resulting in predominantly block-wise analysis with limited temporal resolution on the order of tens of seconds. This limitation restricts the ability to decode cognitive functions in detail. To address these limitations, this study proposes a deep neural network designed for volume-wise identification of task states within tfMRI data,thereby overcoming the constraints of conventional methods. Evaluated on Human Connectome Project (HCP) motor and gambling tfMRI datasets,the model achieved impressive mean accuracy rates of 94.0% and 79.6%,respectively. These results demonstrate a substantial enhancement in temporal resolution,enabling more detailed exploration of cognitive processes. The study further employs visualization algorithms to investigate dynamic brain mappings during different tasks,marking a significant step forward in deep learning-based frame-level tfMRI decoding. This approach offers new methodologies and tools for examining dynamic changes in brain activities and understanding the underlying cognitive mechanisms.