期刊
NEUROIMAGE-CLINICAL
卷 26, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2019.102108
关键词
Clinical high risk; Prediction; Cross-validation; Resting-state functional connectivity; Connectome
类别
资金
- United States National Institute of Mental Health [R21 MH 093294, R01 MH 101052, R01 MH 111448, R01 MH 108574, R01 MH 64023]
- Ministry of Science and Technology of China [2016 YFC 1306803]
- European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant [749201]
- VA Merit Awards from the US Department of Veterans Affairs
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH108574] Funding Source: NIH RePORTER
The first episode of psychosis is typically preceded by a prodromal phase with subthreshold symptoms and functional decline. Improved outcome prediction in this stage is needed to allow targeted early intervention. This study assesses a combined clinical and resting-state fMRI prediction model in 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome at one-year follow-up, participants were separated into three outcome categories including good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Validated clinical predictors from the psychosis-risk calculator were combined with measures of resting-state functional connectivity. Using multinomial logistic regression analysis and leave-one-out cross-validation, a clinical-only prediction model did not achieve a significant level of outcome prediction (F-1 = 0.32, p = .154). An imaging-only model yielded a significant prediction model (F-1 = 0.41, p = .016), but a combined model including both clinical and connectivity measures showed the best performance (F-1 = 0.46, p < .001). Influential predictors in this model included functional decline, verbal learning performance, a family history of psychosis, default-mode and frontoparietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, sensorimotor, and cerebellar networks. These findings suggest that brain changes reflected by alterations in functional connectivity may be useful for outcome prediction in the prodromal stage.
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