Employing Transformer Encoders for Enhanced Functional Connectivity Mapping

Functional magnetic resonance imaging (fMRI) provides a way to spatially and temporally map brain activity, making it a crucial tool in many advanced psychology and neuroscience studies. A variety of techniques are suggested to analyze the four-dimensional data produced by fMRI scans. When it comes to classification tasks, the most prevalent method involves examining functional connectivity. This process involves dividing the brain volume into separate regions and determining the correlation between the series of events occurring over time in these regions. While deep graph models and deep convolutional models are frequently employed to process functional connectivity, these methods can sometimes overcomplicate the procedure. In contrast, we present a straightforward approach that utilizes transformer encoders to map functional connectivity to labels. Our method demonstrates superior performance in gender classification tasks when compared to existing deep graph and convolution models. We've validated this on two publicly accessible datasets.