Predicting seizure freedom is essential for tailoring epilepsy treatment. But accurate prediction remains challenging with traditional methods, especially with diverse patient populations. This study developed a deep learning-based graph neural network (GNN) model to predict seizure freedom from stereo electroencephalography (sEEG) data in patients with refractory epilepsy. We utilized high-quality sEEG data from 15 pediatric patients to train a deep learning model that can accurately predict seizure freedom outcomes and advance understanding of brain connectivity at the seizure onset zone. Our model integrates local and global connectivity using graph convolutions with multi-scale attention mechanisms to capture connections between difficult-to-study regions such as the thalamus and motor regions. The model achieved an accuracy of 92.4% in binary class analysis, 86.6% in patient-wise analysis, and 81.4% in multi-class analysis. Node and edge-level feature analysis highlighted the anterior cingulate and frontal pole regions as key contributors to seizure freedom outcomes. The nodes identified by our model were also more likely to coincide with seizure onset zones. Our findings underscore the potential of new connectivity-based deep learning models such as GNNs for enhancing the prediction of seizure freedom, predicting seizure onset zones, connectivity analysis of the brain during seizure, as well as informing AI-assisted personalized epilepsy treatment planning.

The thalamus is a subcortical gray matter structure that plays a key role in relaying sensory and motor signals within the brain. Its nuclei can atrophy or otherwise be affected by neurological disease and injuries including mild traumatic brain injury. Segmenting both the thalamus and its nuclei is challenging because of the relatively low contrast within and around the thalamus in conventional magnetic resonance (MR) images. This paper explores imaging features to determine key tissue signatures that naturally cluster, from which we can parcellate thalamic nuclei. Tissue contrasts include T1-weighted and T2-weighted images, MR diffusion measurements including FA, mean diffusivity, Knutsson coefficients that represent fiber orientation, and synthetic multi-TI images derived from FGATIR and T1-weighted images. After registration of these contrasts and isolation of the thalamus, we use the uniform manifold approximation and projection (UMAP) method for dimensionality reduction to produce a low-dimensional representation of the data within the thalamus. Manual labeling of the thalamus provides labels for our UMAP embedding from which k nearest neighbors can be used to label new unseen voxels in that same UMAP embedding. N -fold cross-validation of the method reveals comparable performance to state-of-the-art methods for thalamic parcellation.

A powerful symbiotic relationship has blossomed between neuroscience and computer science as of late, with brain systems providing inspiration for prevalent computer algorithms like neural networks and computer-based mathematical models driving important research into the brain's computational methods. Daniel Kahneman's Thinking, Fast and Slow, has popularized the notion that human cognition is divided into distinct hierarchical systems, which Kahneman deems "system 1" and "system 2." Artificial intelligence can handle system 1 tasks, pertaining to fast, nonconscious operations, just as efficiently as humans can. However, it still lags behind when it comes to system 2 tasks, which engage different cognitive pathways that are slower and enlist conscious deliberation. The fact that computers can't compete with humans at deliberate tasks means that computer scientists still have a lot to learn from the brain, which inspired researchers out of the Sorbonne to develop a computational model based on the most recent theories in human learning and cognitive development. They found that processes like synaptic pruning (the elimination of underused synapses), neurogenesis, and energy regulation, and accurate dopamine reinforcement were underrepresented in computational learning models.

Purpose: To develop a fast, accurate, and robust convolutional neural network (CNN) based method for segmentation of thalamic nuclei. Methods: A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on clinical datasets acquired using the white-matter-nulled Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence. A single network was optimized for healthy controls and disease types (multiple sclerosis, essential tremor) and magnetic field strengths (3T and 7T). Another network was developed to use conventional MPRAGE data. Clinical utility was assessed by comparing a cohort of MS patients to healthy subjects. Results: Segmentation of each thalamus into 12 nuclei was achieved in under 4 minutes. For 7T WMn-MPRAGE, the proposed method outperformed current state-of-the-art with statistically significant improvements in Dice ranging from 1.2% to 5.3% for MS and from 2.6% to 38.8% for ET patients. Comparable accuracy (Dice/VSI) was achieved between 7T and 3T data, attesting to the robustness of the method. For conventional MPRAGE, Dice of > 0.7 was achieved for larger nuclei and > 0.6 for the smaller nuclei. Atrophy of five thalamic nuclei and the whole thalamus was observed for MS patients compared to healthy control subjects, after controlling for intracranial volume and age (p<0.004). Conclusion: The proposed segmentation method is fast, accurate, and generalizes across disease types and field strengths and shows great potential for improving our understanding of thalamic nuclei involvement in neurological diseases and healthy aging. KEYWORDS Deep learning, convolutional neural network, transfer learning, thalamic nuclei segmentation