论文标题
迈向精神病学中精确的静止状态FMRI生物标志物：转诊研究中的发展，精神病理学的维度模型和规范性神经发育
Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment
论文作者
论文摘要
寻找生物标志物一直是对精神病学领域的主要追求。为此，研究已将候选静止状态生物标志物分类为几乎所有形式的精神障碍。但是，越来越清楚的是，这些生物标志物缺乏特异性，从而限制了它们产生临床影响的能力。我们讨论了克服这一局限性的三个研究途径：（i）采用转诊研究设计，涉及研究并明确比较来自精神病学的不同诊断轴的多种疾病； （ii）绘制全部症状学和跨传统障碍边界的精神病理学的维度模型； （iii）在整个开发过程中对个人的独特功能连接进行建模。我们提供了将这些子字段绑在一起的框架，该框架利用了机器学习和网络科学的工具。
Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.
