Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders.
Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders.
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DOI：
10.1016/j.nicl.2017.06.014
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Westlye LT
中科院分区：
文献类型：
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作者：
Doan NT;Kaufmann T;Bettella F;Jørgensen KN;Brandt CL;Moberget T;Alnæs D;Douaud G;Duff E;Djurovic S;Melle I;Ueland T;Agartz I;Andreassen OA;Westlye LT
精神分裂症和双相情感障碍的大脑基础是多维的，反映了复杂的病理过程和因果路径，需要多元技术来解开。此外，当结合认知表现和遗传风险的数据时，人们对大脑结构表型的互补临床价值知之甚少。使用皮质厚度、表面积和灰质密度图(GMD)的数据驱动融合，我们发现了六种具有生物学意义的模式，显示出强烈的群体效应，其中四种统计上独立的多模式模式反映了患者同时发生的厚度和GMD的变化，超过了另外两种广泛的厚度和面积减少的独立模式。单独使用认知评分的病例对照分类显示出高准确性，添加成像特征或多基因风险评分可提高性能，这表明它们具有互补的预测价值，认知评分是最敏感的特征。多变量模式分析揭示了精神障碍患者脑形态的不同模式，为脑结构、认知和多基因风险评分在患者分类中的相对重要性提供了洞察力，并证明了多变量方法在研究这些复杂疾病的病理生理基础方面的重要性。连锁ICA表现出对SZ敏感的6种独立的多变量形态模式。机器学习用于比较大脑结构、认知和遗传分数。认知表现出对SZ的最高预测性，这受到大脑结构或遗传学的推动。
The brain underpinnings of schizophrenia and bipolar disorders are multidimensional, reflecting complex pathological processes and causal pathways, requiring multivariate techniques to disentangle. Furthermore, little is known about the complementary clinical value of brain structural phenotypes when combined with data on cognitive performance and genetic risk. Using data-driven fusion of cortical thickness, surface area, and gray matter density maps (GMD), we found six biologically meaningful patterns showing strong group effects, including four statistically independent multimodal patterns reflecting co-occurring alterations in thickness and GMD in patients, over and above two other independent patterns of widespread thickness and area reduction. Case-control classification using cognitive scores alone revealed high accuracy, and adding imaging features or polygenic risk scores increased performance, suggesting their complementary predictive value with cognitive scores being the most sensitive features. Multivariate pattern analyses reveal distinct patterns of brain morphology in mental disorders, provide insights on the relative importance between brain structure, cognitive and polygenetic risk score in classification of patients, and demonstrate the importance of multivariate approaches in studying the pathophysiological substrate of these complex disorders. Linked ICA showed six independent multivariate morphology patterns sensitive to SZ. Machine learning used to compare brain structure, cognitive and genetic scores. Cognition showed highest prediction of SZ, boosted by brain structure or genetics.