General Information

News

2024/04/02 - Take the  NSD / large-scale neuroimaging dataset anonymous survey ! Deadline May 15, 2024.
2023/11/22 - A minor error was discovered in the implemented transformation from anatomical to functional space for each given NSD subject (see  ⁠⁠Technical notes⁠  for details). The error is very minor, and so we have not changed any data files.
2023/08/20 - All data and files associated with the last 3 NSD core scan sessions have now been publicly released.
2021/12/16 - The NSD data paper is now published in Nature Neuroscience.
2021/09/03 - The NSD dataset is now released, and version 1.1 of the NSD Data Manual is now complete. A video walkthrough of NSD data files is also now available (details below).
2021/02/15 - Version 1.0 of the NSD Data Manual is now complete.

Basic information

Welcome to the  Natural Scenes Dataset (NSD) Data Manual . This web site provides a detailed, technical description of all NSD data files that are available. It will be updated as questions and issues arise. The information on this site is also available as a single downloadable PDF  (last snapshot 2023/08/20 - version 1.3)  (this may be convenient for performing "Find" queries).

If you want to browse or download the data, please see  How to get the data⁠ .

The official paper that formally describes the NSD dataset is available as:
 Allen, St-Yves, Wu, Breedlove, Prince, Dowdle, Nau, Caron, Pestilli, Charest, Hutchinson, Naselaris*, & Kay*. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nature Neuroscience (2022). 
We refer to this as the "NSD data paper". The data paper has some associated online resources at the  NSD OSF site . The contents of this data manual assume familiarity with the data paper.

We have created a  video walkthrough that gives an overview of the data files present in the NSD dataset .

Announcements and updates to the NSD dataset will be documented and logged on this page, so check back regularly.

If you have questions about the NSD dataset, please either (1) post your question to the  nsd-users mailing list , (2) open an issue or discussion on the relevant github repository (e.g.  http://github.com/cvnlab/nsdcode/  or  http://github.com/cvnlab/nsddatapaper/ ), (3) send queries directly to  kay@umn.edu , or (4) submit anonymous feedback/suggestions via this  Google form.  Please let us know if there is missing documentation or if something is not clear.

Change history

Substantive changes to NSD data files are documented and logged here:
2023/08/20 - The files related to the last 3 NSD core scan sessions from each subject are now publicly released.
2023/05/27 - The files related to the final memory test (nsdmemory) are now publicly released. See  ⁠⁠Experiments⁠  and  ⁠⁠Behavioral data⁠ .
2022/08/15 - In nsddata/inspections/rois/prf-visualandecc/, a few visualizations were incorrect. Specifically, the files "subj02_prf-eccrois_on_eccentricity.png" and "subj02_prf-visualrois_on_angle.png" have now been corrected.
2022/01/26 - For user convenience, we now provide some additional versions of the nsddata/stimuli/prf stimulus files (description has been updated in  ⁠⁠Experiments⁠ ).
2021/10/20 - Diffusion derivatives are now available (nsddata_diffusion/) and documented in the data manual (see  ⁠⁠Diffusion data⁠ ). Summary b=0 diffusion files (called nsddata/ppdata/subj*/anat/DWI_*.nii.gz) and associated nsddata/inspections/coregistration/*DWI* files have been created to help visualize the quality of the pre-processed diffusion data and their registration to the T1+T2 anatomy. In addition, the "knowndataproblems.txt" file has been slightly updated/modified.
2021/09/03 - Initial public release of the NSD dataset.
2021/09/02 - actually add split-half ncsnr (noise ceiling) files (this was for some reason not completed on the previous iteration on 2021/08/07)
2021/08/07 - add additional files pertaining to BOLDscreen calibration; add information on race to nsddemographics.xlsx; include Phase component of the SWI scans to the raw BIDS data; add split-half ncsnr (noise ceiling) files; add pre-processed eyetracking data and inspection figures
2021/07/23 - design .tsv files for the nsdsynthetic experiment were incorrect; these have been fixed.
2021/05/16 - Added probmap .mgz files (see  ROIs⁠ ) and associated .png surfacevisualizations (see  Data inspections⁠ )
2020/12/20 - Official version 1.0 release of nsd_mapdata (in the  nsdcode  repository).

Community-driven content

If you have NSD-related information, tools, resources, tutorials, or links that you would like to share with the community, please contact  kay@umn.edu  and the information can be listed here.
nsdexamples ( http://github.com/kendrickkay/nsdexamples ). These example scripts, written by Kendrick Kay, were created to demonstrate some basic loading, analysis, and visualization of the NSD dataset.
nsd_access ( https://github.com/tknapen/nsd_access ). This toolbox, written by Tomas Knapen, provides a convenient Python-based interface to the NSD dataset. There are also some examples of how to load data and perform basic visualization. The toolbox also enables easy access to COCO image annotation information, including category labels and bounding boxes.   

Papers and pre-prints

Here are links to papers that use NSD data.
 Fractional Ridge Regression: a Fast, Interpretable Reparameterization of Ridge Regression.  Rokem, A. & Kay, K. GigaScience (2020). 
 Extensive sampling for complete models of individual brains.  Naselaris, T., Allen, E., & Kay, K. Current Opinion in Behavioral Sciences (2021). 
 A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.  Allen, St-Yves, Wu, Breedlove, Prince, Dowdle, Nau, Caron, Pestilli, Charest, Hutchinson, Naselaris*, & Kay*.Nature Neuroscience (2022). 
 NeuroGen: activation optimized image synthesis for discovery neuroscience.  Gu, Z., Jamison, K.W., Khosla, M., Allen, E.J., Wu, Y., Naselaris, T., Kay, K., Sabuncu, M.R., Kuceyeski, A.NeuroImage (2022). 
 Non-Neural Factors Influencing BOLD Response Magnitudes within Individual Subjects.Kurzawski, J.W., Gulban, O.F., Jamison, K., Winawer, J.*, Kay, K.* Journal of Neuroscience (2022). 
 Improving the accuracy of single-trial fMRI response estimates using GLMsingle.Prince, J.S., Charest, I., Kurzawski, J.W., Pyles, J.A., Tarr, M.J., Kay, K.N. eLife (2022). 
 Personalized visual encoding model construction with small data.Zijin Gu, Keith Jamison, Mert Sabuncu, and Amy KuceyeskiCommunications Biology (2022). 
 Selectivity for food in human ventral visual cortex.Nidhi Jain, Aria Wang, Margaret M. Henderson, Ruogu Lin, Jacob S. Prince, Michael J. Tarr, and Leila WehbeCommunications Biology (2023). 
 Short-term plasticity in the human visual thalamus.Jan W Kurzawski, Claudia Lunghi, Laura Biagi, Michela Tosetti, Maria Concetta Morrone, Paola BindaeLife (2022). 
 Color-biased regions in the ventral visual pathway are food selective.Pennock, I.M.L., Racey, C., Allen, E.J., Wu, Y., Naselaris, T., Kay, K.N., Franklin, A., Bosten, J.M.Current Biology (2022). 
 Multiple Traces and Altered Signal-to-Noise in Systems Consolidation: Complementary Evidence from the 7T fMRI Natural Scenes Dataset.Vanasse, T.J., Boly, M., Allen, E.J., Wu, Y., Naselaris, T., Kay, K., Cirelli, C., Tononi, G.PNAS (2022). 
 The risk of bias in data denoising methods: examples from neuroimaging.Kay, K.PLoS One (2022). 
 A Highly Selective Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition.Meenakshi Khosla, N. Apurva Ratan Murty, Nancy G KanwisherCurrent Biology (2022). 
See commentary: Visual cortex: Big data analysis uncovers food specificity.Michael M. Bannert and Andreas BartelsCurrent Biology (2022). 
 Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features.Margaret Henderson, Michael J. Tarr, Leila WehbeJournal of Vision (2023). 
 Re-expression of CA1 and entorhinal activity patterns preserves temporal context memory at long timescales.Futing Zou, Wanjia Guo, Emily J. Allen, Yihan Wu, Ian Charest, Thomas Naselaris, Kendrick Kay, Brice A. Kuhl, J. Benjamin Hutchinson, Sarah DuBrowNature Communications (2023). 
 A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex.Margaret M. Henderson, Michael J. Tarr, Leila WehbeJournal of Neuroscience (2023). 
 Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1.Roth, Z.N., Kay, K.*, Merriam, E.P.*Nature Communications (2022). 
 Representations in human primary visual cortex drift over time.Roth, Z.N., Merriam, E.P.Nature Communications (2023). 
 Human brain responses are modulated when exposed to optimized natural images or synthetically generated imagesZijin Gu, Keith Jamison, Mert R. Sabuncu, and Amy KuceyeskiCommunications Biology (2023). 
 Brain-optimized deep neural networks of human visual areas learn non-hierarchical representations.St-Yves, G., Allen, E.J., Wu, Y., Kay, K.*, Naselaris, T.* Nature Communications (2023). 
 Natural scene reconstruction from fMRI signals using generative latent diffusionFurkan Ozcelik and Rufin VanRullen.Scientific Reports (2023). 
 Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset.Wang, A.Y., Kay, K., Naselaris, T., Tarr, M.J., Wehbe, L.Nature Machine Intelligence (2023). 
 Encoding of Visual Objects in the Human Medial Temporal LobeYue Wang, Runnan Cao and Shuo WangJournal of Neuroscience (2024). 
 Mind-bridge: reconstructing visual images based on diffusion model from human brain activityQing Liu, Hongqing Zhu, Ning Chen, Bingcang Huang, Weiping Lu & Ying WangSignal, Image and Video Processing (2024) 
 A unifying framework for functional organization in early and higher ventral visual cortexEshed Margalit, Hyodong Lee, Dawn Finzi, James J. DiCarlo, Kalanit Grill-Spector, Daniel L.K. YaminsNeuron (2024). 

Here are links to conference papers and pre-prints that use NSD data.
 What can 5.17 billion regression fits tell us about artificial models of the human visual system?Colin Conwell, Jacob S. Prince, George A. Alvarez, Talia KonkleNeurIPS SVRHM workshop (2021). 
 Large-Scale Benchmarking of Diverse Artificial Vision Models in Prediction of 7T Human Neuroimaging Data.Colin Conwell, Jacob S. Prince, George A. Alvarez, Talia KonklebioRxiv (2022). 
 High-level visual areas act like domain-general filters with strong selectivity and functional specialization.Meenakshi Khosla, Leila WehbebioRxiv (2022). 
 Semantic scene descriptions as an objective of human visionDoerig, A., Kietzmann, T.C., Allen, E., Wu, Y., Naselaris, T., Kay, K., Charest, I.arXiv (2022). 
 Mind Reader: Reconstructing complex images from brain activities.Sikun Lin, Thomas Sprague, Ambuj K Singh.arXiv (2022). 
 High-resolution image reconstruction with latent diffusion models from human brain activity.Takagi, Y., Nishimoto, S.bioRxiv (2022). 
 Decoding natural image stimuli from fMRI data with a surface-based convolutional network.Zijin Gu, Keith Jamison, Amy Kuceyeski, Mert SabuncuarXiv (2022). 
 Sample Reweighting for Label Denoising of Neural Activity DataDongfang Xu, Rong ChenIEEE/EMBS Conference on Neural Engineering (2023) 
 The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes.A.T. Gifford, B. Lahner, S. Saba-Sadiya, M.G. Vilas, A. Lascelles, A. Oliva, K. Kay, G. Roig, R.M. Cichy.arXiv (2023). 
 Neural Selectivity for Real-World Object Size In Natural ImagesAndrew F. Luo, Leila Wehbe, Michael J. Tarr, Margaret M. HendersonbioRxiv (2023) 
 MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural DiffusionYizhuo Lu, Changde Du, Dianpeng Wang, Huiguang HearXiv (2023). 
 The transition from vision to language: distinct patterns of functional connectivity for sub-regions of the visual word form areaMaya Yablonski, Iliana I Karipidis, Emily Kubota, Jason D YeatmanbioRxiv (2023). 
 Reconstructing seen images from human brain activity via guided stochastic searchReese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas NaselarisarXiv (2023). 
 BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus DecodingYulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, Nanning ZhengarXiv (2023). 
 Brain Captioning: Decoding human brain activity into images and textMatteo Ferrante, Furkan Ozcelik, Tommaso Boccato, Rufin VanRullen, Nicola ToschiarXiv (2023). 
 A Unifying Principle for the Functional Organization of Visual CortexEshed Margalit, Hyodong Lee, Dawn Finzi, James J. DiCarlo, Kalanit Grill-Spector, Daniel L. K. YaminsarXiv (2023). 
 Reconstructing the Mind’s Eye: fMRI-to-Image with Contrastive Learning and Diffusion PriorsPaul S. Scotti*, Atmadeep Banerjee*, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Ethan Cohen, Aidan J. Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, Kenneth A. Norman*, and Tanishq Mathew Abraham*arXiv (2023). 
 Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural ImagesGabriel H. Sarch, Michael J. Tarr, Katerina Fragkiadaki, Leila WehbearXiv (2023). 
 Second Sight: Using brain-optimized encoding models to align image distributions with human brain activityReese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas NaselarisarXiv (2023). 
 Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models.Andrew F. Luo, Margaret M. Henderson, Leila Wehbe, Michael J. TarrarXiv (2023).   [NeurIPS 2023 (Oral)]. 
 Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs.Yu Takagi, Shinji NishimotoarXiv (2023).  
 DreamCatcher: Revealing the Language of the Brain with fMRI using GPT EmbeddingSubhrasankar Chatterjee, Debasis SamantaarXiv (2023). 
 What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines?Colin Conwell, Jacob S. Prince, Kendrick N. Kay, George A. Alvarez, Talia KonklebioRxiv (2023) 
 THE ALGONAUTS PROJECT 2023 CHALLENGE: UARK-UALBANY TEAM SOLUTIONXuan Bac Nguyen, Xudong Liu, Xin Li, Khoa LuuarXiv (2023) 
 Memory Encoding ModelHuzheng Yang, James Gee, Jianbo ShiarXiv (2023) 
 Applicability of scaling laws to vision encoding modelsTakuya Matsuyama, Kota S Sasaki, Shinji NishimotoarXiv (2023). 
 A contrastive coding account of category selectivity in the ventral visual streamJacob S. Prince, George A. Alvarez, Talia KonklebioRxiv (2023). 
 Predicting brain activity using TransformersHossein Adeli, Sun Minni, Nikolaus KriegeskortebioRxiv (2023). 
 A Parameter-efficient Multi-subject Model for Predicting fMRI ActivityConnor Lane, Gregory KiararXiv (2023).  
 Expansion of a frontostriatal salience network in individuals with depressionCharles J. Lynch, I. Elbau, Tommy Ng, Aliza Ayaz, Shasha Zhu, Nicola Manfredi, Megan A. Johnson, Daniel L Wolk, Jonathan D. Power, E. Gordon, Kendrick Norris Kay, A. Aloysi, Stefano Moia, C. Caballero-Gaudes, L. Victoria, N. Solomonov, E. Goldwaser, Benjamin Zebley, L. Grosenick, J. Downar, F. Vila-Rodriguez, Z. Daskalakis, D. Blumberger, N. Williams, F. Gunning, C. ListonbioRxiv (2023). 
 UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain ActivityWeijian Mai, Zhijun ZhangarXiv (2023). 
 A Multimodal Visual Encoding Model Aided by Introducing Verbal Semantic InformationMa Shuxiao, Wang Linyuan, Yan BinarXiv (2023). 
 Through their eyes: multi-subject Brain Decoding with simple alignment techniquesMatteo Ferrante, Tommaso Boccato, and Nicola ToschiarXiv (2023). 
 Direct perception of affective valence from visionSaeedeh Sadeghi, Zijin Gu, Eve DeRosa,   Amy Kuceyeski ,  Adam K. AndersonpsyArXiv (2023). 
 Dissociable contributions of the medial parietal cortex to recognition memorySeth R. Koslov, Joseph W. Kable, & Brett L. FosterbioRxiv (2023).  
 UNIDIRECTIONAL BRAIN-COMPUTER INTERFACE: ARTIFICIAL NEURAL NETWORK ENCODING NATURAL IMAGES TO fMRI RESPONSE IN THE VISUAL CORTEXRuixing Liang, Xiangyu Zhang, Qiong Li, Lai Wei, Hexin Liu, Avisha Kumar, Kelley M. Kempski Leadingham, Joshua Punnoose, Leibny Paola Garcia, Amir ManbachiarXiv (2023). 
 Cortical and subcortical brain networks predict prevailing heart rateAmy Isabella Sentis, Javier Rasero, Peter J. Gianaros, Timothy D. VerstynenbioRxiv (2023). 
 DREAM: Visual Decoding from REversing HumAn Visual SysteMWeihao Xia, Raoul de Charette, Cengiz Oztireli, Jing-Hao XuearXiv (2023). 
 BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex SelectivityAndrew F. Luo, Margaret M. Henderson, Michael J. Tarr, Leila WehbearXiv (2023). 
 IDENTIFYING INTERPRETABLE VISUAL FEATURES INARTIFICIAL AND BIOLOGICAL NEURAL SYSTEMSDavid Klindt, Sophia Sanborn, Francisco Acosta, Fr ́ed ́eric Poitevin, Nina MiolanearXiv (2023).  
 fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity DecodingXuelin Qian, Yun Wang, Jingyang Huo, Jianfeng Feng, Yanwei FuarXiv (2023). 
 BRAIN DECODING: TOWARD REAL-TIME RECONSTRUCTION OF VISUAL PERCEPTIONYohann Benchetrit, Hubert Banville, Jean-Remi KingarXiv (2023). 
 Soft Matching Distance: A metric on neural representations that captures single-neuron tuningMeenakshi Khosla, Alex H. WilliamsarXiv (2023).  
 Brainformer: Modeling MRI Brain Functions to Machine VisionXuan-Bac Nguyen , Xin Li, Samee U. Khan, Khoa LuuarXiv (2023) 
 Brain Decodes Deep NetsHuzheng Yang, James Gee*, Jianbo Shi*arXiv (2023) 
 OneLLM: One Framework to Align All Modalities with LanguageJiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang,Dahua Lin, Yu Qiao, Peng Gao, Xiangyu YuearXiv (2023). 
 Lite-Mind: Towards Efficient and Versatile Brain Representation NetworkZixuan Gong, Qi Zhang, Duoqian Miao, Guangyin Bao, Liang HuarXiv (2023). 
 Multimodal decoding of human brain activity into images and textMatteo Ferrante, Tommaso Boccato, Furkan Ozcelik, Rufin VanRullen, Nicola ToschiNeurIPS (2023). 
 ALIGNING BRAIN FUNCTIONS BOOSTS THE DECODING OF VISUAL SEMANTICS IN NOVEL SUBJECTSAlexis Thual, Yohann Benchetrit, Felix Geilert, Jeremy Rapin, Iurii Makarov, Hubert Banville, Jean-Remi KingarXiv (2023). 
 Brain-optimized inference improves reconstructions of fMRI brain activityReese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas NaselarisarXiv (2023). 
 Body Cosmos: An Immersive Experience Driven by Real-Time Bio-DataRem RunGu Lin; Yongen Ke; Kang ZhangIEEE VIS Arts Program (VISAP) (2023). 
 MinD-3D: Reconstruct High-quality 3D objects in Human BrainJianxiong Gao, Yuqian Fu, Yun Wang, Xuelin Qian, Jianfeng Feng, Yanwei FuarXiv (2023). 
 A single computational objective drives specialization of streams in visual cortexDawn Finzi, Eshed Margalit, Kendrick Kay, Daniel L. K. Yamins, Kalanit Grill-SpectorbioRxiv (2023). 
 Evaluation of Representational Similarity Scores Across Human Visual CortexFrancisco Acosta, Colin Conwell, Sophia Sanborn, David A. Klindt, Nina MiolaneNeurIPS (2023). 
 A randomized algorithm to solve reduced rank operator regressionGiacomo Turri*, Vladimir Kostic*, Pietro Novelli*, and Massimiliano PontilarXiv (2023). 
 Aligned with LLM: a new multi-modal training paradigm for encoding fMRI activity in visual cortexShuxiao Ma, Linyuan Wang, Senbao Hou, Bin YanarXiv (2024). 
 Recent statistics shift object representations in parahippocampal cortexSolomon, S.H., Kay, K., & Schapiro, A.C.bioRxiv (2024). 
 Parsing Brain Network Specialization: A Replication and Expansion of Wang et al. (2014)Madeline Peterson, Dorothea L. Floris, and Jared A. NielsenbioRxiv (2024). 
 CLIP-MUSED: CLIP-GUIDED MULTI-SUBJECT VISUAL NEURAL INFORMATION SEMANTIC DECODINGQiongyi Zhou, Changde Du, Shengpei Wang and Huiguang HeICLR (2024). 
 NeuralDiffuser: Controllable fMRI Reconstruction with Primary Visual Feature Guided DiffusionHaoyu Li, Hao Wu, Badong ChenarXiv (2024). 
 Visual Image Reconstruction from Human Brain Activity using Linear Image Decoders plus Nonlinear Noise SuppressionQiang LibioRxiv (2024).  
 See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI Yulong Liu, Yongqiang Ma, Guibo Zhu, Haodong Jing, and Nanning ZhengarXiv (2024). 
 Inter-individual and inter-site neural code conversion and image reconstruction without shared stimuliHaibao Wang, Jun Kai Ho, Fan L. Cheng, Shuntaro C. Aoki, Yusuke Muraki, Misato Tanaka, Yukiyasu KamitaniarXiv (2024).  
 NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level ModulationJingyang Huo, Yikai Wang, Xuelin Qian, Yun Wang, Chong Li, Jianfeng Feng, Yanwei FuarXiv (2024). 
 Psychometry: An Omnifit Model for Image Reconstruction from Human Brain ActivityRuijie Quan, Wenguan Wang, Zhibo Tian, Fan Ma, Yi YangarXiv (2024). 
 Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised LearningYujian Xiong, Wenhui Zhu, Zhong-Lin Lu, Yalin WangarXiv (2024). 
 Unified Multimodal Decoding of Brain SignalsWeihao Xia, Raoul de Charette, Cengiz Oztireli, Jing-Hao XuearXiv (2024). 
 MindBridge: A Cross-Subject Brain Decoding FrameworkShizun Wang, Songhua Liu, Zhenxiong Tan, Xinchao WangarXiv (2024).  
 Functional Brain-to-Brain Transformation with No Shared DataNavve Wasserman, Roman Beliy, Roy Urbach, and Michal IraniarXiv (2024).  
 MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic CorrectionZixuan Gong, Qi Zhang, Guangyin Bao, Lei Zhu, Ke Liu, Liang Hu, Duoqian MiaoarXiv (2024). 
 Wills Aligner: A Robust Multi-Subject Brain Representation LearnerGuangyin Bao, Zixuan Gong, Qi Zhang, Jialei Zhou, Wei Fan, Kun Yi, Usman Naseem, Liang Hu, Duoqian MiaoarXiv (2024). 
 Integrated Gradient Correlation: a Dataset-wise Attribution MethodPierre Lelièvre, Chien-Chung ChenarXiv (2024). 
D isentangling signal and noise in neural responses through generative modelingKay, K., Prince, J.S., Gebhart, T., Tuckute, G., Zhou, J., Naselaris, T., Schutt, H.bioRxiv (2024). 
 Neuro-Vision to Language: Image Reconstruction and Language enabled Interaction via Brain RecordingsGuobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang and Yi ZengarXiv (2024).