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Machine learning identifies unaffected first‐degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients
- Source :
- Hum Brain Mapp
- Publication Year :
- 2019
- Publisher :
- John Wiley & Sons, Inc., 2019.
-
Abstract
- Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large-scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs-the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus-were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave-one-out cross-validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.
- Subjects :
- Adult
Male
medicine.medical_specialty
Multivariate statistics
Audiology
Sensitivity and Specificity
050105 experimental psychology
Pattern Recognition, Automated
Functional networks
Machine Learning
03 medical and health sciences
Young Adult
0302 clinical medicine
Cerebellum
Connectome
Medicine
Humans
0501 psychology and cognitive sciences
Radiology, Nuclear Medicine and imaging
Cognitive Dysfunction
Family
First-degree relatives
Cognitive impairment
Default mode network
Research Articles
Cerebral Cortex
Radiological and Ultrasound Technology
Receiver operating characteristic
business.industry
05 social sciences
Cognition
medicine.disease
Magnetic Resonance Imaging
Neurology
Schizophrenia
Female
Neurology (clinical)
Anatomy
Nerve Net
business
030217 neurology & neurosurgery
Biomarkers
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Hum Brain Mapp
- Accession number :
- edsair.doi.dedup.....4aa06d7ee25f2ab8674ba71fe115c294