AIBullisharXiv – CS AI · Jun 57/10
🧠Researchers demonstrate that synthetic fMRI data generated by TRIBE v2, a large pretrained encoding model, can significantly improve brain-to-image decoding performance in low-data scenarios, achieving up to 68% improvement in accuracy. The findings suggest that foundation models trained on extensive neural data can enhance data efficiency for brain decoding tasks and enable zero-shot capabilities.
AINeutralarXiv – CS AI · May 287/10
🧠Researchers using fMRI and MEG data found that while backpropagated gradients in deep neural networks can predict brain activity in higher visual cortex, their spatial and temporal organization fundamentally diverges from how the human brain processes visual information. This suggests that although artificial and biological neural networks may learn similar representations, they employ distinctly different learning mechanisms.
AINeutralarXiv – CS AI · Apr 77/10
🧠Researchers found that large language models align with human brain activity during creative thinking tasks, with alignment increasing based on model size and idea originality. Different post-training approaches selectively reshape how LLMs align with creative versus analytical neural patterns in humans.
🧠 Llama
AIBullisharXiv – CS AI · Mar 37/103
🧠Researchers developed Brain-IT, a new AI system using Brain Interaction Transformer technology to reconstruct images from fMRI brain recordings with significantly improved accuracy. The method requires only 1 hour of data versus 40 hours needed by current approaches while surpassing state-of-the-art results.
AINeutralarXiv – CS AI · Jun 46/10
🧠Researchers demonstrate that brain foundation models (BFMs)—billion-parameter Transformers trained on fMRI data—paradoxically predict cognitive performance worse than simple linear regression on functional connectivity matrices. The study identifies a variance allocation problem where BFM pretraining captures dominant fMRI variance but destroys higher-order statistical structures (third-order co-skewness) that actually predict cognition, solved through a lightweight linear pipeline requiring no pretraining.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers have developed MindDiffuser, a two-stage framework that reconstructs visual images from brain activity recordings with improved accuracy across multiple neuroimaging modalities (fMRI, EEG, MEG). The system combines semantic guidance from text-to-image models with structural refinement using visual features, advancing brain-computer interface technology and neural decoding capabilities.
🧠 Stable Diffusion
AINeutralarXiv – CS AI · Jun 16/10
🧠Researchers propose Dual-Spectral Flow Matching (DSFM), a generative AI framework that synthesizes functional MRI brain imaging data by combining wavelet and cosine transforms with spectral flow matching. The approach addresses limitations in replicating complex BOLD signal dynamics for improved brain disorder identification and analysis.
AINeutralarXiv – CS AI · May 296/10
🧠Researchers have developed Brain-IT-VQA, a framework that decodes visual question answers directly from fMRI brain signals with significantly improved accuracy over previous methods. The team also introduced NSD-VQA, a new benchmark dataset with 20 controlled question categories per image, enabling more reliable evaluation of how visual information is represented in the brain.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers have developed NeuroGAN-3D, a generative AI model that enhances the spatial resolution of functional brain imaging maps derived from resting-state fMRI scans. The technology leverages adversarial neural networks to improve the precision of neuroimaging data, enabling better detection of brain connectivity patterns and potential biomarkers for neurological conditions.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers propose MADCLE, a machine learning framework that learns consistent brain disorder representations across multiple brain atlases by disentangling disease-related features from atlas-dependent and covariate factors. The approach demonstrates competitive performance on neurological disorder datasets (ADNI and ADHD-200) while addressing the fundamental problem that different brain parcellation schemes produce heterogeneous and sometimes contradictory functional connectivity representations.
AINeutralarXiv – CS AI · May 115/10
🧠Researchers introduce INCAMA, a novel method for inferring causal brain networks from indirect neuroimaging data like fMRI. The approach addresses the fundamental challenge that brain imaging signals are distorted by physics of hemodynamics and volume conduction, making direct causal inference impossible without accounting for these measurement artifacts.
AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers used computational lesions on multilingual large language models to identify how the brain processes language across different languages. By selectively disabling parameters, they found that a shared computational core handles 60% of multilingual processing, while language-specific components fine-tune predictions for individual languages, providing new insights into how multilingual AI aligns with human neurobiology.
AIBullisharXiv – CS AI · Mar 176/10
🧠Researchers developed LUMINA, a new Graph Convolutional Network architecture that improves AI-driven diagnosis of neurodevelopmental disorders using fMRI brain data. The system achieved 84.66% accuracy for ADHD and 88.41% for autism spectrum disorder detection by addressing traditional GCN limitations in capturing neural connection dynamics.
AIBullisharXiv – CS AI · Mar 36/108
🧠Researchers have developed FCN-LLM, a framework that enables Large Language Models to understand brain functional connectivity networks from fMRI scans through multi-task instruction tuning. The system uses a multi-scale encoder to capture brain features and demonstrates strong zero-shot generalization across unseen datasets, outperforming conventional supervised models.
AIBullisharXiv – CS AI · Mar 27/1013
🧠Researchers have developed Brain-OF, the first omnifunctional brain foundation model that can process fMRI, EEG, and MEG data simultaneously within a unified framework. The model introduces novel techniques like Any-Resolution Neural Signal Sampler and Masked Temporal-Frequency Modeling, trained on 40 datasets to achieve superior performance across diverse neuroscience tasks.
AIBullisharXiv – CS AI · Mar 27/1017
🧠Researchers introduced SemVideo, a breakthrough AI framework that can reconstruct videos from brain activity using fMRI scans. The system uses hierarchical semantic guidance to overcome previous limitations in visual consistency and temporal coherence, achieving state-of-the-art results in brain-to-video reconstruction.
$RNDR
AIBullisharXiv – CS AI · Feb 276/107
🧠Researchers have developed an atlas-free Brain Network Transformer (BNT) that uses individualized brain parcellations from subject-specific fMRI data instead of standardized brain atlases. The approach outperformed existing methods in sex classification and brain age prediction tasks, offering improved precision and robustness for neuroimaging biomarkers and clinical diagnostics.
AINeutralarXiv – CS AI · Mar 54/10
🧠Researchers introduce NEURONA, a neuro-symbolic framework that combines AI symbolic reasoning with fMRI brain data to decode neural activity patterns. The system demonstrates improved accuracy in understanding how the brain processes visual concepts by incorporating structural priors and compositional reasoning.
