ArticleNeuroImage2014
Bayesian multi-task learning for decoding multi-subject neuroimaging data.
Article in NeuroImage, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Supervised Phenotype Discovery From Multimodal Brain Imaging.IEEE transactions on medical imaging · 2023Article
- Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models.NeuroImage · 2022Article
- Advancing translational research in neuroscience through multi-task learning.Frontiers in psychiatry · 2022Review
- Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning.Human brain mapping · 2019Article
- Comparative Evaluation of Machine Learning Strategies for Analyzing Big Data in Psychiatry.International journal of molecular sciences · 2018Article
- Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging.Scientific reports · 2018Article
- Classification of multi-site MR images in the presence of heterogeneity using multi-task learning.NeuroImage. Clinical · 2018Article
- Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.Neuroinformatics · 2018Article
- Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance.Frontiers in neuroscience · 2018Article
- A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.NeuroImage · 2017Article
- Association Between the Probability of Autism Spectrum Disorder and Normative Sex-Related Phenotypic Diversity in Brain Structure.JAMA psychiatry · 2017Article
- OpenfMRI: Open sharing of task fMRI data.NeuroImage · 2017Article
- Making use of longitudinal information in pattern recognition.Human brain mapping · 2016Article
- Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies.Biological psychiatry · 2016Article
- Toward a multisubject analysis of neural connectivity.Neural computation · 2015Article
- Graph-based inter-subject pattern analysis of FMRI data.PloS one · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Decoding models based on pattern recognition (PR) are becoming increasingly important tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding approaches that use hierarchical models to capture inter-subject variability, inter-subject differences are not typically handled efficiently in PR. In this work, we propose to overcome this problem by recasting the decoding problem in a multi-task learning (MTL) framework. In MTL, a single PR model is used to learn different but related "tasks" simultaneously. The primary advantage of MTL is that it makes more efficient use of the data available and leads to more accurate models by making use of the relationships between tasks. In this work, we construct MTL models where each subject is modelled by a separate task. We use a flexible covariance structure to model the relationships between tasks and induce coupling between them using Gaussian process priors. We present an MTL method for classification problems and demonstrate a novel mapping method suitable for PR models. We apply these MTL approaches to classifying many different contrasts in a publicly available fMRI dataset and show that the proposed MTL methods produce higher decoding accuracy and more consistent discriminative activity patterns than currently used techniques. Our results demonstrate that MTL provides a promising method for multi-subject decoding studies by focusing on the commonalities between a group of subjects rather than the idiosyncratic properties of different subjects.
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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.