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Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages.
JAMA Psychiatry ( IF 25.8 ) Pub Date : 2022-07-01 , DOI: 10.1001/jamapsychiatry.2022.1163
Dominic B Dwyer 1, 2 , Madalina-Octavia Buciuman 1, 3 , Anne Ruef 1 , Joseph Kambeitz 4 , Mark Sen Dong 1 , Caedyn Stinson 5, 6 , Lana Kambeitz-Ilankovic 1, 4 , Franziska Degenhardt 7, 8 , Rachele Sanfelici 1, 9 , Linda A Antonucci 10 , Paris Alexandros Lalousis 11 , Julian Wenzel 4 , Maria Fernanda Urquijo-Castro 1 , David Popovic 1, 3 , Oemer Faruk Oeztuerk 1, 3 , Shalaila S Haas 12 , Johanna Weiske 1 , Daniel Hauke 13, 14, 15 , Susanne Neufang 16 , Christian Schmidt-Kraepelin 16 , Stephan Ruhrmann 4 , Nora Penzel 4 , Theresa Lichtenstein 4 , Marlene Rosen 4 , Katharine Chisholm 11, 17 , Anita Riecher-Rössler 18 , Laura Egloff 13 , André Schmidt 13 , Christina Andreou 13 , Jarmo Hietala 19 , Timo Schirmer 20 , Georg Romer 21 , Chantal Michel 22 , Wulf Rössler 23 , Carlo Maj 24 , Oleg Borisov 24 , Peter M Krawitz 24 , Peter Falkai 1, 9 , Christos Pantelis 25 , Rebekka Lencer 26, 27 , Alessandro Bertolino 28 , Stefan Borgwardt 13, 27 , Markus Noethen 7 , Paolo Brambilla 29, 30 , Frauke Schultze-Lutter 16, 22, 31 , Eva Meisenzahl 32 , Stephen J Wood 2, 33 , Christos Davatzikos 34, 35 , Rachel Upthegrove 11, 14 , Raimo K R Salokangas 19 , Nikolaos Koutsouleris 1, 9, 36 ,
Affiliation  

Importance Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures. Objective To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages. Design, Setting, and Participants A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022. Main Outcomes and Measures A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample. Results There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample. Conclusions and Relevance The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

中文翻译:

早期精神病和情感阶段的临床、大脑和多层次聚类。

需要采用重要方法对处于阳性症状严重程度之外的早期精神病阶段的个体进行分层,以调查与情感和规范变异相关的特异性,并通过病前、纵向和遗传风险措施验证解决方案。目的 使用机器学习技术,利用来自早期精神病和抑郁阶段的临床和脑结构成像数据,对亚组解决方案进行聚类、比较和组合。设计、设置和参与者 一项多中心、自然主义、纵向队列研究(5 个欧洲国家的 10 个中心;包括 9 个月和 18 个月的主要随访间隔),研究对象是精神病临床高风险患者(CHR- P)、新发精神病 (ROP)、新发抑郁症 (ROD) 和健康对照在 2014 年 2 月 1 日之间招募,至 2019 年 7 月 1 日。分析了 2020 年 1 月至 2022 年 1 月之间的数据。主要结果和措施 一种非负矩阵分解技术分别分解临床(287 个变量)和分割的脑结构体积(204 个灰色、白色和脑脊液区域)数据CHR-P、ROP、ROD 和健康对照研究组。稳定性标准使用嵌套交叉验证确定簇数。跨亚组解决方案(病前、纵向和精神分裂症多基因风险评分)比较了验证目标。多类监督机器学习为验证样本生成了一个可转移的解决方案。结果 发现组共有 749 人,验证组有 610 人。个体包括具有 CHR-P (n = 287)、ROP (n = 323)、ROD (n = 285)、和健康对照组 (n = 464),平均 (SD) 年龄为 25.1 (5.9) 岁,702 (51.7%) 为女性。临床 4 维解决方案根据阳性症状、阴性症状、抑郁和功能将个体分开,证明与所有验证目标的关联。大脑聚类揭示了一个亚组,其分布的脑容量减少与阴性症状、表现智商降低和精神分裂症多基因风险评分增加有关。多层次结果区分了正常和疾病相关的大脑差异。亚组结果主要在外部样本中得到验证。结论和相关性 这个纵向队列研究的结果提供了超越跨越疾病阶段和诊断的阳性症状表达的分层。临床结果表明阴性症状、抑郁和功能的重要性。大脑结果表明疾病阶段和规范变异之间存在大量重叠,这可能突出了独立于特定表现的脆弱性特征。病前、纵向和遗传风险验证表明亚组对预防性治疗的临床重要性。
更新日期:2022-05-18
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