ArticleNeuroinformatics2018
Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.
Article in Neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Behavioral profile predicts ethanol preference in adolescent mice, but not in adults: A machine learning approach.Alcohol, clinical & experimental research · 2026Article
- Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.Brain and behavior · 2025Article
- Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods.Medical & biological engineering & computing · 2024Article
- Abnormalities of regional brain activity and executive function in patients with temporal lobe epilepsy: A cross-sectional and longitudinal resting-state functional MRI study.Neuroradiology · 2024Article
- Article
- Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.Neuroradiology · 2022Article
- Individual prediction and classification of cognitive impairment in patients with white matter lesions based on gray matter volume.Annals of translational medicine · 2022Article
- Treatment Response Prediction for Major Depressive Disorder PatientsFrontiers in computational neuroscience · 2022Article
- Differential mechanisms of posterior cingulate cortex downregulation and symptom decreases in posttraumatic stress disorder and healthy individuals using real-time fMRI neurofeedback.Brain and behavior · 2022Article
- Structural Features Predict Sexual Trauma and Interpersonal Problems in Borderline Personality Disorder but Not in Controls: A Multi-Voxel Pattern Analysis.Frontiers in human neuroscience · 2022Article
- Structural Features Related to Affective Instability Correctly Classify Patients With Borderline Personality Disorder. A Supervised Machine Learning Approach.Frontiers in psychiatry · 2022Article
- Article
- Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features.BMC psychiatry · 2021Article
- Brain activation during the observation of real soccer game situations predicts creative goal scoring.Social cognitive and affective neuroscience · 2021Article
- Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach.Frontiers in psychiatry · 2021Article
- Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning.Frontiers in neuroscience · 2021Article
- How do you perceive threat? It's all in your pattern of brain activity.Brain imaging and behavior · 2020Article
- All roads lead to the default-mode network-global source of DMN abnormalities in major depressive disorder.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2020Article
- Investigating the effect of changing parameters when building prediction models for post-stroke aphasia.Nature human behaviour · 2020Article
- Atlas-Based Classification Algorithms for Identification of Informative Brain Regions in fMRI Data.Neuroinformatics · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo).
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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.