mlsanityData to 14 Oct 2026

Self-Supervised Transformers for fMRI representation

arXiv 2112.05761 ↗First posted 10 Dec 2021eess.IV cs.CV cs.LGcited by 17

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Abstract (arXiv)

We present TFF, which is a Transformer framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data. TFF employs a two-phase training approach. First, self-supervised training is applied to a collection of fMRI scans, where the model is trained to reconstruct 3D volume data. Second, the pre-trained model is fine-tuned on specific tasks, utilizing ground truth labels. Our results show state-of-the-art performance on a variety of fMRI tasks, including age and gender prediction, as well as schizophrenia recognition. Our code for the training, network architecture, and results is attached as supplementary material.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 10 Dec 2021.

Builds on

10 of 36 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,296
  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  7. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  8. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 328
  9. 2020Spatio-Temporal Graph Convolution for Resting-State fMRI Analysiscited by 57
  10. 2017Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networkscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 7; 2026: 1.

17 papers.

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  1. 2023SwiFT: Swin 4D fMRI Transformercited by 36
  2. 2022WOODS: Benchmarks for Out-of-Distribution Generalization in Time Seriescited by 30
  3. 2025Towards a general-purpose foundation model for fMRI analysiscited by 15
  4. 2023DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRIcited by 14
  5. 2024DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networkscited by 7
  6. 2025Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Datacited by 6
  7. 2024See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRIcited by 5
  8. 2025Scaling Vision Transformers for Functional MRI with Flat Mapscited by 5
  9. 2024Self-Supervised Pre-training Tasks for an fMRI Time-series Transformer in Autism Detectioncited by 2
  10. 2025BrainMT: A Hybrid Mamba-Transformer Architecture for Modeling Long-Range Dependencies in Functional MRI Datacited by 1
  11. 2025VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRIcited by 1
  12. 2026Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?cited by 1
5 more
  1. 2023Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Taskscited by 0
  2. 2023Sequential Transfer Learning to Decode Heard and Imagined Timbre from fMRI Datacited by 0
  3. 2024SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysiscited by 0
  4. 2025Atlas-free Brain Network Transformercited by 0
  5. 2025Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representationscited by 0