neuroimaging

Researchers at the Weizmann Institute have developed an AI tool that reconstructs what a person is looking at from high-resolution fMRI brain scans with remarkable precision, and can also predict brain activity from images. Experts praise its potential to help locked-in patients and understand dreams, while warning it could enable surreptitious extraction of inner thoughts.

A deep-dive explainer on functional ultrasound imaging (fUSI), covering how it captures hemodynamic brain activity at high resolution, its advantages over fMRI, and its potential for neuromodulation, accompanied by new Python tutorials on GitHub since most existing toolkits are locked in Matlab.

This paper conducts a scaling study for fMRI foundation models, revealing that performance depends on the combination of pretraining data size, model size, and training duration, not just compute.

The article announces the 2026 PNPL Competition focused on word classification and efficient cross-subject generalization in the LibriBrain100 dataset, advancing non-invasive speech decoding for brain-computer interfaces.

This paper introduces MEL, a coordinate-preserving EEG tokenization framework for translating EEG to fMRI, addressing representation-interface mismatch and improving prediction over baselines through explicit modeling of hemodynamic latency and spectral-spatial dynamics.

A large neuroimaging study published in Nature found that psilocybin creates hidden order in brain activity, making neural signals clearer during psychedelic experiences, which could enhance psychedelic therapy by emphasizing context and environment.

Proposes MSR-IVA, a state-aware framework for fusing structural MRI and dynamic functional network connectivity, improving matched source coupling by 6.5% and reducing unmatched dependence by 15.7% in an Alzheimer's disease cohort.

This paper introduces Disease Continuum Positioning (DCP), a longitudinal Bayesian learning framework that continuously estimates Alzheimer's disease severity from neuroimaging data, providing an uncertainty-aware score for disease progression prediction.

Brain Researcher is an agentic platform that enhances AI-driven neuroimaging data analysis by enforcing analytic rigor, improving tool selection accuracy, and ensuring reproducibility. The study demonstrates substantial performance gains and integrates methodological judgment into the workflow.

A Zhejiang University study published in NeuroImage finds that watching enjoyable short videos on TikTok and Instagram Reels deactivates key brain regions involved in cognitive control, with implications for addiction and mental health.

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.