ArticleCell reports methods2022
Fast construction of interpretable whole-brain decoders.
Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Model-based semantic distance reveals adaptive coordination of distinct cognitive systems in flexible knowledge retrieval.Nature communications · 2026Article
- Machine-Learning Decomposition Identifies a Big Two Structure in Human Personality with Distinct Neurocognitive Profiles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference.bioRxiv : the preprint server for biology · 2024Article
- A neural signature of the vividness of prospective thought is modulated by temporal proximity during intertemporal decision making.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Researchers often seek to decode mental states from brain activity measured with functional MRI. Rigorous decoding requires the use of formal neural prediction models, which are likely to be the most accurate if they use the whole brain. However, the computational burden and lack of interpretability of off-the-shelf statistical methods can make whole-brain decoding challenging. Here, we propose a method to build whole-brain neural decoders that are both interpretable and computationally efficient. We extend the partial least squares algorithm to build a regularized model with variable selection that offers a unique "fit once, tune later" approach: users need to fit the model only once and can choose the best tuning parameters post hoc. We show in real data that our method scales well with increasing data size and yields interpretable predictors. The algorithm is publicly available in multiple languages in the hope that interpretable whole-brain predictors can be implemented more widely in neuroimaging research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.