Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks.

Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging. In this contribution, we used machine learning techniques to classify tasks in a working memory experiment and identify the brain areas involved in processing information. We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. Our findings suggest that from the perspective of considered models, brain signals related to the resting state have a similar degree of complexity to those related to the encoding phase, which does not improve the model's accuracy. However, during the retrieval phase, the signals were easily distinguished from the resting state, indicating their different structure. The study identified brain regions that are crucial for processing information in working memory, as well as the differences in the dynamics of encoding and retrieval processes. Furthermore, our findings indicate spatiotemporal distinctions related to these processes. The analysis confirmed the importance of the basal ganglia in processing information during the retrieval phase. The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.