Cortical mapping of kinematic parameters during upper limb movement using explainable AI

COEX 

Cortical representation of motor kinematics is crucial for understanding human motor behaviour. Although conventional single-neuron studies have found the existence of a relationship between neuronal activity and motor kinematics such as acceleration, velocity, and position, it is hard to distinguish the neural representations with macroscopic modalities such as electroencephalography (EEG) and magnetoencephalography (MEG) due to their limited spatial resolution.
Deep neural network (DNN) models have shown excellent performance in predicting movement characteristics. This presentation demonstrates that neural features of each kinematic parameter can be identified with a time-series DNN model for decoding with an explainable AI method. It implements integrated gradients between cortical activity and predicted kinematic parameters during reaching movement (Kim et al., 2023, https://doi.org/10.1016/j.neuroimage.2022.119783). We extract the cortical regions strongly contributing to decoding each kinematics from the DNN model.
There are common regions, including bilateral supramarginal gyri and superior parietal lobules, known to be related to the goal of movement and sensory integration. There are also dominant regions for each acceleration, velocity, and position kinematic parameter. In addition, by evaluating differences in cortical contribution values to the movement direction, we found out the global contribution of the brain to move. The movement prediction also required ipsilateral contribution as well as contralateral activity. The explainable AI approach can decompose brain processes into various kinematic components.