neuroimaging

This paper proposes MPP-GNN, a Meta Probabilistic Pooling GNN that adaptively discovers subject-specific brain modules for fMRI-based Alzheimer's disease classification, achieving state-of-the-art AUC on two public datasets and aligning with canonical brain network organization.

This paper presents an interpretable MEG-to-audio retrieval model for perceived speech, redesigned with spherical-harmonics spatial attention and source mapping, achieving 39.75% Top-1 accuracy with far fewer decoder parameters while revealing which speech features drive retrieval.

This paper proposes a lightweight CNN classifier that uses Fourier-Bessel Series Expansion with Euclidean Distance (FBSE-ED) to represent EEG signals as images, achieving 93.60% accuracy in predicting the outcome of rTMS depression therapy, outperforming both EEG-specific and pretrained deep learning models.

This paper introduces iLENS, an interpretable LLM-guided mixture-of-experts framework for survival prediction and patient subtyping in Alzheimer's disease using neuroimaging data. The approach provides transparent, biologically grounded rationales for its routing decisions, bridging high-performance survival analysis with interpretable clinical decision support.

CALM is a framework for learning interpretable associations between brain regions and genetic pathways from completely unpaired datasets, enabling biomarker discovery for neuropsychiatric disorders like autism without requiring paired multimodal data.

Aleph Neuro announces the first 3D image of a living human brain using ultrasound localization microscopy through the skull, achieving 100 times greater volumetric resolution than CT. The company open-sources its imaging pipeline and dataset.

This paper introduces Neuro-JEPA, a foundation model that uses a latent predictive objective and Mixture-of-Experts architecture to encode brain MRI scans across T1w, T2w, and FLAIR sequences, pretrained on a large dataset of 1.55 million scans.

This paper introduces BrainSimSiam, a lightweight self-supervised framework using siamese networks to learn robust fMRI representations from positive-only pairs, achieving strong performance on downstream tasks even with limited data.

This paper uses EEG recordings to study neural dynamics when humans process AI-generated hallucinated content, revealing distinct cognitive patterns and differences between misjudged and correctly judged hallucinations.

This blog post explores the intersection of machine learning and neuroscience, specifically focusing on using multivariate classification techniques on neuroimaging data to understand brain function and behavior.