Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features.
10.1007/s12264-017-0190-6
- Author:
Sugai LIANG
1
;
Roberto VEGA
2
;
Xiangzhen KONG
3
;
Wei DENG
1
;
Qiang WANG
1
;
Xiaohong MA
1
;
Mingli LI
1
;
Xun HU
4
;
Andrew J GREENSHAW
5
;
Russell GREINER
2
;
Tao LI
6
Author Information
1. Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China.
2. Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2R7, Canada.
3. Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6525 XD, Nijmegen, The Netherlands.
4. Huaxi Biobank, West China Hospital, Sichuan University, Chengdu, 610041, China.
5. Department of Psychiatry, University of Alberta, Edmonton, AB, T6G 2R7, Canada.
6. Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China. litaohx@scu.edu.cn.
- Publication Type:Clinical Trial
- Keywords:
Graphical LASSO;
Major depressive disorder;
Neurocognition;
Neurocognitive graph;
Schizophrenia
- MeSH:
Adult;
Algorithms;
Depressive Disorder, Major;
classification;
diagnosis;
Endophenotypes;
analysis;
Female;
Humans;
Machine Learning;
Male;
Markov Chains;
Neuropsychological Tests;
Schizophrenia;
classification;
diagnosis;
Young Adult
- From:Neuroscience Bulletin
2018;34(2):312-320
- CountryChina
- Language:English
-
Abstract:
Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia (FES), 125 with MDD, and 237 demographically-matched healthy controls (HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with a one-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD. Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.
