ArticleNeuroImage. Clinical2018
Classification of multi-site MR images in the presence of heterogeneity using multi-task learning.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model.Molecular psychiatry · 2026Article
- Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.Scientific reports · 2024Article
- 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
- Prompt-Based Tuning of Transformer Models for Multi-Center Medical Image Segmentation of Head and Neck Cancer.Bioengineering (Basel, Switzerland) · 2023Article
- Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease.Artificial intelligence in medicine · 2022Article
- Multi-Modal Feature Selection with Feature Correlation and Feature Structure Fusion for MCI and AD Classification.Brain sciences · 2022Article
- Metadata Normalization.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2021Article
- Article
- Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020Article
- The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study.JMIR medical informatics · 2020Article
- Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns.NeuroImage. Clinical · 2020Article
- Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2020Article
- Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2020Article
- A core collection of pan-schizophrenia genes allows building cohort-specific signatures of affected brain.Scientific reports · 2019Article
- An algorithm for learning shape and appearance models without annotations.Medical image analysis · 2019Article
- Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity.Frontiers in human neuroscience · 2019Article
- Group ICA for identifying biomarkers in schizophrenia: 'Adaptive' networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression.NeuroImage. Clinical · 2019Article
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Authors and funding
11 authors.
Funding
Abstract
With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity.
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