Biologically inspired hybrid model for Alzheimer’s disease classification using structural MRI in the ADNI dataset - Slimi, Houmem; Cherif, Imen; Abid, Sabeur; Sayadi, Mounir (Frontiers in Artificial Intelligence, 2025)
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and structural brain alterations such as cortical atrophy and hippocampal degeneration. Early diagnosis remains challenging due to subtle neuroanatomical changes in early stages. This study proposes a hybrid convolutional neural network-spiking neural network (CNN-SNN) architecture to classify AD stages using structural MRI (sMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The model synergizes CNNs for hierarchical spatial feature extraction and SNNs for biologically inspired temporal dynamics processing. The CNN component processes image slices through convolutional layers, batch normalization, and dropout, while the SNN employs leaky integrate-and-fire (LIF) neurons across 25 time steps to simulate temporal progression of neurodegeneration—even with static sMRI inputs. Trained on a three-class task [AD, mild cognitive impairment (MCI), and cognitively normal (CN) subjects], the hybrid network optimizes mean squared error (MSE) loss with L2 regularization and Adam, incorporating early stopping to enhance generalization. Evaluation on ADNI data demonstrates robust performance, with training/validation accuracy and loss tracked over 30 epochs. Classification metrics (precision, recall, F1-score) highlight the model’s ability to disentangle complex spatiotemporal patterns in neurodegeneration. Visualization of learning curves further validates stability during training. An ablation study demonstrates the SNN’s critical role, with its removal reducing accuracy from 99.58 to 75.67%, underscoring the temporal module’s importance. The SNN introduces architectural sparsity via spike-based computation, reducing overfitting and enhancing generalization while aligning with neuromorphic principles for energy-efficient deployment. By bridging deep learning with neuromorphic principles, this work advances AD diagnostic frameworks, offering a computationally efficient and biologically plausible approach for clinical neuroimaging. The results underscore the potential of hybrid CNN-SNN architectures to improve early detection and stratification of neurodegenerative diseases, paving the way for future applications in neuromorphic healthcare systems.