The Study of depression and its effects on the brain is essential since this common mental health disorder affects millions. In addition to disturbing emotional and cognitive processes, depression also disrupts activity in discrete brain regions. Identifying these distortions is important for expanding the diagnosis and treatment plans. A recently introduced spiking neural network framework called NeuCube has successfully demonstrated its effectiveness in modeling dynamic brain activity using EEG signals by capturing the temporal and spatial patterns of neural activity. In this study, we introduce a new model to interpret brain region contributions in depression by integrating NeuCube architecture with a dictionary learning method. NeuCube artificially replicates neural events in multiple brain regions and those output spike trains are then combined with dictionary learning to recognize depression-related patterns. This hybrid solution allows a high level of interpretability with respect to the contribution from biology and connectivity while maintaining strong separation power for depression diagnosis. Significant contributions were observed in neuromarkers of brain regions, such as frontal and temporal lobes for eyes-closed, and eye-open states. Through the analysis of sparse codes, we reveal which brain region interactions are affected by depression, providing an understanding of the neural underpinnings behind this disorder. In addition, our model achieves an accuracy of 91% for both these conditions, stronger than traditional methods. Notably, we reach this level of performance without the use of windowing techniques emphasizing the resilience and practicality of our approach in depression diagnosis.

Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brainâ\euro™s underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional braintemplate structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.