fmri

A new fMRI study in iScience finds that dogs process happy human facial expressions in brain regions linked to higher cognition and reward, and can distinguish between negative expressions like anger, sadness, and fear.

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 study demonstrates that contextual semantic relevance, measuring how strongly an incoming word relates to its recent semantic context, reliably predicts fMRI BOLD responses during naturalistic speech comprehension across two datasets, whereas surprisal (local probabilistic expectation) does not. The findings support that slow hemodynamic responses are especially sensitive to contextual semantic integration rather than local prediction.

This paper improves the Huth encoding pipeline for fMRI decoding and introduces fMRIFlamingo, a direct fMRI-to-text framework using Llama 3.2, but finds that decoding success is driven by the language prior rather than neural input.

This paper introduces Augmented Sparse Encoding Models to interpret brain responses to language using sparse features from language models, validated on high-field 7T fMRI data. It recovers known neural tuning properties and discovers a new voxel population tuned to people-related content.

This paper tracks how different learning rules (backprop, feedback alignment, predictive coding, STDP) affect the alignment of CNN representations with human fMRI across training. It finds that backprop destroys V1 alignment in one epoch, while local rules preserve it, suggesting a trade-off between building higher-level representations and retaining early visual features.

CortexMAE is a family of fMRI foundation models trained on 2.1K hours of open fMRI data, accepted to ICML. The release also includes Brainmarks, an open benchmark suite for evaluating such models.

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.

Brain-IT-VQA framework decodes visual content from fMRI signals using transformer architecture, outperforming previous methods. The authors also introduce NSD-VQA, a new dataset with richer annotations for evaluating fMRI-based visual question answering.

Proposes FM-fMRI, an event-conditioned flow matching model that synthesizes task fMRI time series from resting-state fMRI, achieving superior spectral and connectivity agreement over baselines on the Human Connectome Project and an internal autism cohort, and improving downstream autism classification.

This paper investigates brain-LLM alignment across English, Chinese, and French using fMRI data and multiple LLMs, finding that training-language dominance and typological distance, not an inherent English advantage, drive alignment patterns.

Google releases MoGen model, using synthetic neurons to reduce connectome reconstruction error rate by 4.4%, saving 157 person-years of annotation time; meanwhile, AI can already decode thoughts from fMRI, and the proliferation of portable brain sensors raises brain privacy concerns.

BrainCause framework uses generative and brain models to identify causal neural representations in the human brain, demonstrating that activation alone is insufficient for confirming concept representation.

This paper demonstrates that fine-tuning language encoding models on fMRI data improves their ability to predict neural activity from ECoG recordings, despite fMRI's lower temporal resolution. The findings suggest that abundant 'slow' fMRI data can enhance models for 'fast' ECoG data.

This paper investigates whether fMRI representations from different subjects' visual cortices can be aligned using unsupervised geometric methods, finding evidence for approximately isometric structure across individuals, extending the Platonic Representation Hypothesis to human brains.

NeuralSet is a new Python package that provides fast, scalable preprocessing and embedding tools for multimodal neuro-AI data including fMRI, EEG, MEG, ECoG, spikes, plus text, audio, video and images.

This paper investigates whether Brain Score, a metric comparing language model representations to human fMRI activations during reading, is truly capturing human-like language processing or merely structural similarity. The researchers train language models on diverse natural languages and non-linguistic structured data (genome, Python, nested parentheses), finding that models trained on different languages and even non-linguistic sequences achieve similar Brain Score performance, suggesting the metric may not be sensitive enough to distinguish human-specific processing.

This paper introduces a meta-optimized approach for semantic visual decoding from fMRI signals that generalizes to novel subjects without fine-tuning, using in-context learning to infer unique neural encoding patterns from a small set of image-brain activation examples. The method achieves strong cross-subject and cross-scanner generalization without requiring anatomical alignment or stimulus overlap.