A curated list of fMRI-centric encoding models, decoding frameworks, and representational analyses, covering language/narrative, visual reconstruction, video, audio/music, mental state, and BCI applications.
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- 0. Tag legend
- 1. Surveys / Reviews / Perspectives
- 2. Resources
- 3. Brain->Text / Language / Narrative
- 4. Brain->Image
- 5. Brain->Video / Dynamic Scenes
- 6. Brain->Audio / Music
- 7. Brain->Mental State / Cognition
- 8. Brain->Clinical / Disease
- 9. Cross-cutting Methods
- 10. Toolboxes / Libraries / Related Lists
- 11. Contributing
- [GEN] generative reconstruction or generation
- [ALIGN] representation, latent, semantic, or functional alignment
- [X-SUBJ] explicit cross-subject or subject-agnostic modeling
Tags describe method characteristics only. Domain, application, and code availability are represented by section placement and links, not by tags.
Scope: Global reviews, surveys, tutorials, and perspective pieces on fMRI encoding, decoding, reconstruction, and brain-conditioned generative modeling.
Encoding and decoding in fMRI [NeuroImage 2011] [DOI]
A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli [arXiv 2025]
Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy [IEEE TAI 2025] [Project]
Visual Image Reconstruction from Brain Activity via Latent Representation [Annual Review of Vision Science 2025]
Review of visual neural encoding and decoding methods in fMRI [Journal of Image and Graphics 2023]
Visualizing the mind’s eye: a future perspective on image reconstruction from brain signals [Psychoradiology 2023]
Deep Generative Models in Brain Encoding and Decoding [Engineering 2019]
Machine Learning for Classifying Affective Valence from fMRI: A Systematic Review [Affective Science 2025]
Limits of Decoding Mental States with fMRI [NeuroImage 2022]
Advances in Functional Magnetic Resonance Imaging-Based Brain Decoding and Its Clinical Applications [Psychoradiology 2025]
Language and narrative reviews
Progress, challenges and future of linguistic neural decoding with deep learning [Communications Biology 2025]
Artificial intelligence based multimodal language decoding from brain activity: A review [Brain Research Bulletin 2023]
General brain decoding, BCI, and deep learning perspectives
Non-Invasive Brain-Computer Interfaces: State of the Art and Trends [IEEE Reviews in Biomedical Engineering 2025]
Deep learning approaches for neural decoding across multiple scales [Briefings in Bioinformatics 2021]
A Survey on Brain Encoding and Decoding [IJCAI 2021]
Scope: Public fMRI datasets, benchmarks, and cohort-style resources. Task resources are separated from population, developmental, psychiatric, sensory-loss, and disease cohorts.
Natural Scenes Dataset (NSD) – 7T high-resolution fMRI responses to tens of thousands of natural images. [Nature Neuroscience 2022] [Website] [Data]
Natural Object Dataset (NOD) – large-scale fMRI dataset with 57k naturalistic images (ImageNet / COCO) from 30 participants. [Scientific Data 2023] [OpenNeuro ds004496]
THINGS-data / THINGS-fMRI – multimodal object-vision dataset (fMRI, MEG, behavior) over ~1.8k object concepts. [eLife 2023] [OpenNeuro ds004192] [Collection]
BOLD5000 – slow event-related fMRI dataset for ~5k images drawn from COCO / ImageNet / SUN. [Scientific Data 2019] [Website] [OpenNeuro ds001499]
Deep Image Reconstruction (DIR) dataset – single-subject fMRI for natural images used in the Kamitani deep image reconstruction work. [PLoS Comput Biol 2019] [OpenNeuro ds001506]
Language and narrative datasets
Narratives / Story listening datasets – multi-subject naturalistic spoken-story fMRI. [Scientific Data 2021] [Website] [OpenNeuro ds002345]
Semantic reconstruction of continuous language – dataset used in the Nature Neuroscience 2023 semantic decoding paper. [Nature Neuroscience 2023] [OpenNeuro ds003020]
Natural language fMRI dataset for voxelwise encoding models – five multi-session natural-language listening datasets for voxelwise encoding. [Scientific Data 2023] [GitHub]
Video, affective, and multimodal naturalistic datasets
BOLD Moments Dataset (BMD) – video fMRI responses to ~1k short naturalistic clips with rich object / action / text metadata. [Nature Communications 2024] [OpenNeuro ds005165] [Code]
Spacetop – multimodal fMRI dataset with >100 participants, combining movie viewing with a broad battery of cognitive / affective tasks and physiology. [Scientific Data 2025] [OpenNeuro ds005256]
Emo-FilM – film-based fMRI with dense emotion annotations and concurrent physiological recordings. [Scientific Data 2025] [OpenNeuro ds004892]
An fMRI dataset in response to large-scale short natural dynamic facial expression videos [Scientific Data 2024] [DOI]
A naturalistic fMRI dataset in response to public speaking [Scientific Data 2025] [DOI]
Human Connectome Project (HCP, Young Adult S1200) – multimodal MRI for ~1.2k healthy young adults (3T structural, resting-state and task fMRI, diffusion MRI), with a subset scanned at 7T and some MEG; widely used for connectivity, representation learning, and population-based decoding. [NeuroImage 2013] [S1200 Data Releases] [S1200 Reference Manual (PDF)]
UK Biobank Imaging – very large population cohort (>100k participants targeted) with structural MRI, resting-state and task fMRI, and other imaging (cardiac, abdominal, DXA, carotid ultrasound); primarily designed for population health and genetics, but increasingly used for large-scale brain encoder pretraining and brain-phenotype prediction. [NeuroImage 2016] [Nature Communications 2020 imaging enhancement] [Imaging project overview]
ABCD Study (Adolescent Brain Cognitive Development) – longitudinal cohort (~10k+ children/adolescents) with structural MRI, diffusion, resting-state and task fMRI (e.g., MID, SST, n-back), plus rich behavioral, cognitive, and environmental measures; useful for developmental decoding and pretraining. [Dev Cogn Neurosci 2018] [ABCD Study website] [Imaging documentation]
Clinical, psychiatric, sensory-loss, and developmental cohorts
PPMI (Parkinson’s Progression Markers Initiative) – longitudinal, multi-center cohort with extensive clinical, multi-modal imaging (structural MRI, DaTscan, and additional MRI sequences at some sites), biospecimens and genetics for Parkinson’s disease and at-risk individuals; standard benchmark for PD progression modeling and biomarker discovery. [Prog Neurobiol 2018] [Study website] [Data access]
ADNI (Alzheimer’s Disease Neuroimaging Initiative) – multi-center longitudinal study with structural MRI, PET, some resting-state fMRI derivatives, cognitive assessments, genetics and CSF/blood biomarkers for MCI / Alzheimer’s and controls; widely used for neurodegenerative disease prediction and progression modeling. [ADNI neuroimaging overview] [ADNI MRI component] [ADNI Data portal]
ABIDE I / II (Autism Brain Imaging Data Exchange) – multi-site repositories aggregating structural MRI and resting-state fMRI for individuals with autism spectrum disorder and controls; standard benchmark for ASD classification, connectome-based decoding, and cross-site generalization. [ABIDE overview (NeuroImage 2014)] [ABIDE portal] [ABIDE II] [ABIDE Preprocessed]
ADHD-200 - multi-site structural MRI and resting-state fMRI dataset for ADHD and typically-developing controls, originally released for the ADHD-200 Global Competition; extensively used as a benchmark for rs-fMRI-based psychiatric diagnosis and generalization across scanners/sites. [ADHD-200 portal] [Global Competition summary] [ADHD-200 Preprocessed]
Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation [Scientific Data 2024] [DOI]
An fMRI dataset for appetite neural correlates in people living with Motor Neuron Disease [Scientific Data 2025] [DOI]
101 Dalmatians: a multimodal naturalistic fMRI dataset in typical development and congenital sensory loss [Scientific Data 2025] [DOI]
Scope: fMRI-to-text, language reconstruction, narrative decoding, and language-oriented alignment or generation. Method variants are indicated with [GEN], [ALIGN], and [X-SUBJ] rather than separate sub-buckets.
Toward a universal decoder of linguistic meaning from brain activation [Nature Communications 2018] [OSF project]
Semantic reconstruction of continuous language from non-invasive brain recordings [Nature Neuroscience 2023] [Code] [Dataset]
How Many Bytes Can You Take Out Of Brain-To-Text Decoding? (Information-theoretic evaluation and augmentation of fMRI→text decoders) [arXiv 2024]
Mind captioning: Evolving descriptive text of mental content from human brain activity [Science Advances 2025] [Code] [OpenNeuro ds005191]
Representation-alignment and embedding-space decoders
Decoding naturalistic experiences from human brain activity via distributed representations of words [NeuroImage 2018]
Towards Sentence-Level Brain Decoding with Distributed Representations [AAAI 2019]
Fine-grained Neural Decoding with Distributed Word Representations [Information Sciences 2020]
Neural Encoding and Decoding With Distributed Sentence Representations [IEEE TNNLS 2021]
MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities [NAACL 2024]
High-level visual representations in the human brain are aligned with large language models [Nature Machine Intelligence 2025] [arXiv] [Code]
Generative and LLM-based decoders
Towards Brain-to-Text Generation: Neural Decoding with Pre-trained Encoder-Decoder Models [NeurIPS 2021 (AI4Science Workshop)]
[GEN] UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language [ACL 2023]
[X-SUBJ] Decoding Continuous Character-based Language from Non-invasive Brain Recordings [bioRxiv 2024] [arXiv] [Dataset]
[GEN] BrainDEC: A Multimodal LLM for the Non-Invasive Decoding of Text from Brain Recordings [Information Fusion 2025] [Code]
Brain-Inspired fMRI-to-Text Decoding via Incremental and Wrap-Up Language Modeling (CogReader) [NeurIPS 2025 Spotlight] [Code]
[GEN] [X-SUBJ] MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding [ICML 2025 (poster)] [arXiv 2025] [Code]
MindGPT: Interpreting What You See With Non-Invasive Brain Recordings [IEEE TIP 2025] [Code]
Open-vocabulary Auditory Neural Decoding Using fMRI-prompted LLM (Brain Prompt GPT / BP-GPT) [ICASSP 2025 preprint] [Code]
[GEN] Generative language reconstruction from brain recordings (BrainLLM) [Communications Biology 2025] [Code]
[X-SUBJ] fMRI-LM: Towards a Universal Foundation Model for Multi-Task Brain Decoding [arXiv]
Brain-language fusion enables interactive neural readout and in-silico experimentation (CorText / CorText-AMA) [arXiv]
Bridge entries pending review
[X-SUBJ] Decoding speech perception from non-invasive brain recordings (MEG/EEG contrastive decoding of perceived speech, strong reference for non-invasive language decoding) [Nature Machine Intelligence 2023] [Code]
[X-SUBJ] Towards decoding individual words from non-invasive brain recordings (EEG/MEG – non-fMRI but highly influential for non-invasive brain-to-text) [Nature Communications 2025]
[X-SUBJ] Brain-to-Text Decoding: A Non-invasive Approach via Typing (Brain2Qwerty) (sentence-level typing decoded from EEG/MEG) [arXiv 2025] [Project page]
Scope: Static visual reconstruction, visual semantic decoding, cross-subject visual decoders, and concept-level visual analysis from fMRI. Generative, alignment, and cross-subject variants are tagged rather than split into separate method sections.
Early approaches that do not rely on modern deep generative image models, often based on hand-crafted features or simpler encoding/decoding pipelines.
Visual image reconstruction from human brain activity using a combination of multiscale local image decoders [Neuron 2008]
Reconstructing Natural Scenes from fMRI Patterns using Hierarchical Visual Features [NeuroImage 2011]
Deep generative reconstruction with learned image priors
fMRI→image reconstruction that uses deep generative models as image priors (GAN, latent diffusion, Stable Diffusion variants, etc.).
Deep image reconstruction from human brain activity [PLoS Comput Biol 2019] [Code] [Dataset]
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI [NeurIPS 2019] [Code]
High-resolution image reconstruction with latent diffusion models from human brain activity [GEN] [CVPR 2023] [Project] [Code]
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding (MinD-Vis) [CVPR 2023] [Project]
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors (MindEye) [GEN] [ALIGN] [NeurIPS 2023] [Project] [Code]
MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion [ACM MM 2023] [arXiv] [Code]
Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI (DBDM) [Bioengineering 2023]
Mental image reconstruction from human brain activity [Neural Networks 2024]
NeuralDiffuser: Neuroscience-Inspired Diffusion Guidance for fMRI Visual Reconstruction [IEEE TIP 2025] [arXiv]
Balancing Semantic and Structural Decoding for fMRI-to-Image Reconstruction [Expert Systems with Applications 2025]
Cross-subject and universal visual decoders / encoders
Brain->image decoders and image→fMRI encoders that explicitly target cross-subject / cross-site generalization, few-shot adaptation, or universal representations. Tagged with [X-SUBJ] when cross-subject generalization is a core focus, [ALIGN] when explicit representation or functional alignment is central, and [GEN] only when the method performs generative reconstruction or generation. Some of these also relate to generative image reconstruction or cross-cutting foundation methods; we keep them here when the primary contribution is image-first visual decoding or reconstruction.
MindEye2 [GEN] [ALIGN] [X-SUBJ]: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data [ICML 2024] [arXiv] [Project] [Code]
MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data [ALIGN] [X-SUBJ] [ICML 2025]
[X-SUBJ] ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding [NeurIPS 2025] [Code]
Psychometry [GEN] [X-SUBJ]: An Omnifit Model for Image Reconstruction from Human Brain Activity [CVPR 2024] [arXiv]
[X-SUBJ] NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [ECCV 2024] [arXiv] [Project]
[X-SUBJ] Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding [AAAI 2025] [arXiv]
[X-SUBJ] BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities [AAAI 2025 (Oral)] [arXiv] [Project]
[X-SUBJ] MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding [NeurIPS 2025] [arXiv] [Code]
[X-SUBJ] Inter-individual and inter-site neural code conversion without shared stimuli (General-purpose cross-subject / cross-site alignment that can support various decoding tasks beyond visual reconstruction.) [Nature Computational Science 2025]
Self-Supervised Natural Image Reconstruction and Large-Scale Semantic Classification from Brain Activity [NeuroImage 2022]
[X-SUBJ] The Wisdom of a Crowd of Brains: A Universal Brain Encoder [arXiv 2024]
SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning [arXiv 2025] [NeurIPS 2025]
Interpretability and concept-level decoding
Brain->image pipelines that explicitly emphasize interpretability, concept-level representations, or analysis of how much information generative priors actually extract from the brain (e.g., concept bottlenecks, probing, attribution analyses).
MindReader: Reconstructing complex images from brain activities [NeurIPS 2022] [Code]
Bridging Brains and Concepts: Interpretable Visual Decoding from fMRI with Semantic Bottlenecks [NeurIPS 2025 Poster] [PDF]
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using? [NeurIPS 2024] [arXiv] [Code]
Scope: Decoding and reconstruction of movies, dynamic natural vision, and video-like scene sequences from fMRI. Task-specific video work remains here even when it uses generative or multimodal components.
Encoding-model and decoding pipelines for natural movies, often predicting voxel responses from visual features and then decoding semantic content or categories.
Reconstructing visual experiences from brain activity evoked by natural movies [Current Biology 2011]
Neural encoding and decoding with deep learning for dynamic natural vision [Cerebral Cortex 2018]
The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motion (Benchmark challenge for predicting fMRI responses to >1k short everyday videos.) [arXiv 2021] [Challenge]
Representation-alignment and retrieval-based video decoders
Approaches that map fMRI into a shared embedding space (e.g., clip-level or text-level representations) and then perform video retrieval or matching, often with the help of multimodal large models.
Mind2Word: Towards Generalized Visual Neural Representations for High-Quality Video Reconstruction – Maps fMRI into a sequence of pseudo-words in a text embedding space, and then uses a video generator for high-quality reconstruction. [Expert Systems with Applications 2025]
Decoding the Moving Mind: Multi-Subject fMRI-to-Video Retrieval with MLLM Semantic Grounding – Multi-subject fMRI-to-video retrieval using multimodal large language models to ground semantic similarity between brain activity and candidate clips. [bioRxiv 2025]
Deep generative fMRI-to-video reconstruction
Models that aim to reconstruct full video sequences (or high-frame-rate approximations) from fMRI, typically using deep video generators or diffusion models conditioned on brain activity.
Reconstructing rapid natural vision with fMRI-conditional video generative adversarial network [Cerebral Cortex 2022]
A Penny for Your (visual) Thoughts: Self-Supervised Reconstruction of Natural Movies from Brain Activity [arXiv 2022]
Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity (Mind-Video) [GEN] [ALIGN] [NeurIPS 2023] [Project]
Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity (Mind-Animator) [GEN] [ICLR 2025] [arXiv] [Project]
NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction [GEN] [NeurIPS 2024] [Code]
Scope: fMRI-centric decoding approaches where the output is sound, music, or audio features. Non-fMRI audio bridge entries are retained only as review-needed references until the bridge policy is finalized.
Capturing the musical brain with Lasso: Dynamic decoding of musical features from fMRI data [NeuroImage 2014] [DOI]
Brain2Music: Reconstructing Music from Human Brain Activity [arXiv 2023] [Project]
Reconstructing Music Perception from Brain Activity Using a Prior-Guided Diffusion Model [Scientific Reports 2025]
R&B - Rhythm and Brain: Cross-Subject Music Decoding from fMRI via Prior-Guided Diffusion Model [Preprint 2025]
Identifying musical pieces from fMRI data using encoding and decoding models [Scientific Reports 2018]
Bridge entries pending review
Music Can Be Reconstructed from Human Auditory Cortex Activity Using Nonlinear Decoding Models (iEEG) [PLOS Biology 2023]
Neural Decoding of Music from the EEG (EEG combined with fMRI-informed source localisation) [Scientific Reports 2023]
Decoding Reveals the Neural Representation of Perceived and Imagined Musical Sounds (MEG) [PLOS Biology 2024]
Scope: fMRI-based decoding of affect, attention, cognition, mental imagery, spontaneous thought, subjective content, and related mental-state constructs when the target is not disease diagnosis or a clinical biomarker.
Brain Decoding of Spontaneous Thought: Predictive Modeling of Self-Relevance and Valence Using Personal Narratives [PNAS 2024]
Spatial representation of multidimensional information in emotional faces revealed by fMRI [NeuroImage 2024] [DOI]
BrainCodec: Neural fMRI Codec for the Decoding of Cognitive Brain States [arXiv 2024] [Code]
Brain Decoding of the Human Connectome Project Tasks in a Dense Individual fMRI Dataset [NeuroImage 2023]
Probabilistic Cognitive State Modeling (PCSM): Decoding dynamic brain states to derive emergent cognitive processing properties from task fMRI [NeuroImage 2026] [DOI]
Real-time decoding of covert attention in higher-order visual areas [NeuroImage 2018] [DOI]
Decoding the visual and subjective contents of the human brain [Nature Neuroscience 2005] [DOI]
Neural decoding of autobiographical mental image features with a general semantic model [ALIGN] [Nature Communications 2025] [DOI]
Explainable Deep-Learning Framework: Decoding Brain Task and Predicting Individual Performance in False-Belief Tasks at Early Childhood Stage [Preprint 2024]
Scaling Vision Transformers for Functional MRI with Flat Maps [NeurIPS 2025 Workshop] [arXiv] [Code]
Benchmarking Explanation Methods for Mental State Decoding with Deep Learning Models [NeuroImage 2023] [Code]
Scope: Disease, biomarker, psychiatric, diagnosis, risk, or progression-oriented fMRI decoding and clinically targeted brain-network modeling.
Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis [NeuroImage 2025] [DOI]
Decoding dynamic brain networks in Parkinson's disease with temporal attention [Scientific Reports 2025] [DOI]
GraSTI-ACL: Graph spatial-temporal infomax with adversarial contrastive learning for brain disorders diagnosis based on resting-state fMRI [Medical Image Analysis 2026] [DOI]
(See also Section 2.2 for large-scale clinical, psychiatric, developmental, and disease cohorts used as downstream benchmarks.)
Scope: Method-first work with clear multi-task, multi-modal, cross-dataset, foundation-model, or cross-subject scope. Task-specific text, image, video, audio, cognition, and clinical papers remain in their primary task section and use tags when needed.
Across-subject ensemble-learning alleviates the need for large samples for fMRI decoding [X-SUBJ] [MICCAI 2024] [DOI] [Code]
Spatio-temporal Pre-trained Foundation Model for Neural Decoding with Fine-grained Optimization [MICCAI 2025] [DOI]
Towards neural foundation models for vision: Aligning EEG, MEG, and fMRI representations for decoding, encoding, and modality conversion [ALIGN] [Information Fusion 2026] [DOI]
UMBRAE: Unified Multimodal Brain Decoding [ALIGN] [X-SUBJ] [ECCV 2024] [Project] [Code]
BrainCLIP: Bridging Brain and Visual-Linguistic Representation via CLIP for Generic Natural Visual Stimulus Decoding [ALIGN] [arXiv 2023] [Code]
Modality-Agnostic fMRI Decoding of Vision and Language [ALIGN] [ICLR 2024 Workshop] [arXiv]
Brain Harmony: A Multimodal Foundation Model Unifying Morphology and Function into 1D Tokens [X-SUBJ] [NeurIPS 2025] [Code]
Scope: General-purpose codebases for brain decoding and fMRI analysis, preprocessing pipelines, and other curated awesome lists relevant to fMRI-based brain decoding.
DeepImageReconstruction codebase [GitHub]
End-to-end Deep Image Reconstruction [GitHub]
Inter-individual Deep Image Reconstruction [GitHub]
semantic-decoding (language reconstruction) [GitHub]
MindEye (fMRI-to-image with contrastive + diffusion priors) [GitHub]
MindEye2 implementation (shared-subject fMRI-to-image) [GitHub]
Brain-Diffuser implementation [GitHub]
UMBRAE implementation (unified multimodal brain decoding) [GitHub]
BrainCLIP implementation [GitHub]
Brain2Music implementation [GitHub]
BrainCodec: neural fMRI codec for cognitive-state decoding [GitHub]
Preprocessing, analysis, and utility libraries
fMRIPrep – robust preprocessing pipeline for task / resting-state fMRI [GitHub] [Docs]
XCP-D – post-processing (denoising, connectivity, QA) for fMRIPrep outputs [GitHub]
NiBabies – fMRIPrep-style preprocessing for infant / neonatal MRI [GitHub]
Nilearn – machine learning & decoding tools for neuroimaging in Python [GitHub] [Docs]
BrainIAK – Brain Imaging Analysis Kit (advanced fMRI analyses) [GitHub] [Docs] [Tutorials]
fmralign – functional alignment and inter-subject mapping [GitHub]
bdpy – Brain Decoder Toolbox in Python [GitHub]
BrainStat: A toolbox for brain-wide statistics and multimodal feature associations [NeuroImage 2023] [Docs] [Code]
RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI [NeuroImage 2022] [Docs] [Code]
NextBrain: A probabilistic histological atlas of the human brain for MRI segmentation [Nature 2025]
awesome-brain-decoding (general, multi-modality) [GitHub]
Awesome Brain Encoding & Decoding [GitHub]
Awesome Brain Graph Learning with GNNs [GitHub]
Awesome Neuroimaging in Python (nibabel, nilearn, MNE, etc.) [GitHub]
Contributions are welcome! 🎉
Recommended entry format:
Paper Title
[[Venue Year](paper_link)] [[Code](code_link)] [[Project](project_link)] [[Dataset](dataset_link)]