MacDonald Iii Angus W, Zick Jennifer L, Chafee Matthew V, Netoff Theoden I
Department of Psychology, Translational Research in Cognitive and Affective Mechanisms, University of Minnesota Minneapolis, MN, USA.
Department of Neuroscience, University of Minnesota School of Medicine Minneapolis, MN, USA.
Front Hum Neurosci. 2016 Jan 6;9:698. doi: 10.3389/fnhum.2015.00698. eCollection 2015.
The grand challenges of schizophrenia research are linking the causes of the disorder to its symptoms and finding ways to overcome those symptoms. We argue that the field will be unable to address these challenges within psychiatry's standard neo-Kraepelinian (DSM) perspective. At the same time the current corrective, based in molecular genetics and cognitive neuroscience, is also likely to flounder due to its neglect for psychiatry's syndromal structure. We suggest adopting a new approach long used in reliability engineering, which also serves as a synthesis of these approaches. This approach, known as fault tree analysis, can be combined with extant neuroscientific data collection and computational modeling efforts to uncover the causal structures underlying the cognitive and affective failures in people with schizophrenia as well as other complex psychiatric phenomena. By making explicit how causes combine from basic faults to downstream failures, this approach makes affordances for: (1) causes that are neither necessary nor sufficient in and of themselves; (2) within-diagnosis heterogeneity; and (3) between diagnosis co-morbidity.
精神分裂症研究面临的重大挑战是将该疾病的病因与其症状联系起来,并找到克服这些症状的方法。我们认为,该领域无法在精神病学标准的新克雷佩林主义(DSM)视角内应对这些挑战。与此同时,目前基于分子遗传学和认知神经科学的矫正方法也可能会陷入困境,因为它忽视了精神病学的综合征结构。我们建议采用一种长期用于可靠性工程的新方法,该方法也是这些方法的综合。这种方法称为故障树分析,可以与现有的神经科学数据收集和计算建模工作相结合,以揭示精神分裂症患者以及其他复杂精神现象中认知和情感障碍背后的因果结构。通过明确病因如何从基本故障组合到下游故障,这种方法为以下方面提供了条件:(1)自身既非必要也非充分的病因;(2)诊断内的异质性;(3)诊断间的共病。