Learning Personal Representations from fMRI by Predicting Neurofeedback Performance
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Osin et al. and Hashimoto demonstrated that training a self-supervised framework could be beneficial to generate representative latent features of brain fMRIs before fine-tuning on image-level class labels.
“In this case both Osin et al. and Hashimoto demonstrated that training a self-supervised framework could be beneficial to generate representative latent features of brain fMRIs before fine-tuning on image-level class labels57,90.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [57]
A particularly interesting medical task explored was classification of psychiatric diseases or psychiatric traits using fMRI.
“A particularly interesting medical task that was explored was classification of psychiatric diseases or psychiatric traits using fMRI57,62,90.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [57]
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Self-citationProcess-based framework for precise neuromodulation
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Self-citationAmygdala Regulation Following fMRI-Neurofeedback without Instructed Strategies
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Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
- Authors
- Osin, J., Wolf, L., Gurevitch, G., Keynan, J. N., Fruchtman-Steinbok, T., Or-Borichev, A., & Hendler, T.
- Affiliations
- Tel Aviv University (IL)
- Corresponding author
- Wolf, Lior
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 469-478
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3089995275
- Cited by
- 3
- References
- 13
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1
- FWCI
- 0.65
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- personal representation, neurofeedback, amygdala activity, self-regulation, self-supervised learning, recurrent neural networks, psychiatric traits, personality prediction, individual differences
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