ArticleDevelopmental science2016
Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI.
Article in Developmental science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI.NeuroImage. Clinical · 2023Pooled it
- Using U-Nets to Predict the Effects of Head Motion on Simulated Specific Absorption Rate for Ultra-High Field Magnetic Resonance Imaging With Parallel Transmission.Magnetic resonance in medicine · 2026Article
- Altered neurodevelopmental trajectories of brain structure in Tourette syndrome and Chronic Tic Disorders.medRxiv : the preprint server for health sciences · 2026Article
- Multimodal brain imaging neurotransmitter marker study of TD: a review of TCM effect mechanisms and precise diagnosis and treatment combining traditional Chinese and western medicine.American journal of translational research · 2026Review
- Wearable fNIRS platform for dense sampling and precision functional neuroimaging.NPJ digital medicine · 2025Article
- Identifying texture features from structural magnetic resonance imaging scans associated with Tourette's syndrome using machine learning.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Disrupted multi-scale topological organization of directed functional brain networks in patients with disorders of consciousness.Brain communications · 2023Article
- Real-time motion monitoring improves functional MRI data quality in infants.Developmental cognitive neuroscience · 2022Article
- Classification of tic disorders based on functional MRI by machine learning: a study protocol.BMJ open · 2022Article
- Rigid motion-resolvedMagnetic resonance in medicine · 2022Article
- Enhancing neuroimaging genetics through meta-analysis for Tourette syndrome (ENIGMA-TS): A worldwide platform for collaboration.Frontiers in psychiatry · 2022Article
- Standardizing human brain parcellations.Scientific data · 2021Article
- Discrimination of Tourette Syndrome Based on the Spatial Patterns of the Resting-State EEG Network.Brain topography · 2021Article
- Specific absorption rate implications of within-scan patient head motion for ultra-high field MRI.Magnetic resonance in medicine · 2020Article
- Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Review
- Article
- Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry.Biological psychiatry · 2020Review
- A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum.NeuroImage · 2020Article
- Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity.iScience · 2020Article
- Atypical Functional Connectivity in Tourette Syndrome Differs Between Children and Adults.Biological psychiatry · 2020Article
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Authors and funding
9 authors.
Funding
Abstract
Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method - support vector machine (SVM) classification - to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8-15 yrs) and 42 unaffected controls (age, IQ, in-scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS.
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