Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia; National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, Maryland.
Department of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, Connecticut.
Biol Psychiatry. 2024 Apr 1;95(7):699-708. doi: 10.1016/j.biopsych.2023.09.017. Epub 2023 Sep 26.
Accurate psychiatric risk assessment requires biomarkers that are both stable and adaptable to development. Functional network connectivity (FNC), which steadily reconfigures over time, potentially contains abundant information to assess psychiatric risks. However, the absence of suitable analytical methodologies has constrained this area of investigation.
We investigated the brainwide risk score (BRS), a novel FNC-based metric that contrasts the relative distances of an individual's FNC to that of psychiatric disorders versus healthy control references. To generate group-level disorder and healthy control references, we utilized a large brain imaging dataset containing 5231 total individuals diagnosed with schizophrenia, autism spectrum disorder, major depressive disorder, and bipolar disorder and their corresponding healthy control individuals. The BRS metric was employed to assess the psychiatric risk in 2 new datasets: Adolescent Brain Cognitive Development (ABCD) Study (n = 8191) and Human Connectome Project Early Psychosis (n = 170).
The BRS revealed a clear, reproducible gradient of FNC patterns from low to high risk for each psychiatric disorder in unaffected adolescents. We found that low-risk ABCD Study adolescent FNC patterns for each disorder were strongly present in over 25% of the ABCD Study participants and homogeneous, whereas high-risk patterns of each psychiatric disorder were strongly present in about 1% of ABCD Study participants and heterogeneous. The BRS also showed its effectiveness in predicting psychosis scores and distinguishing individuals with early psychosis from healthy control individuals.
The BRS could be a new image-based tool for assessing psychiatric vulnerability over time and in unaffected individuals, and it could also serve as a potential biomarker, facilitating early screening and monitoring interventions.
准确的精神科风险评估需要既稳定又能适应发展的生物标志物。功能网络连接（FNC）随着时间的推移而稳定地重新配置，可能包含大量信息来评估精神科风险。然而，缺乏合适的分析方法限制了这一研究领域。
我们研究了脑宽风险评分（BRS），这是一种新的基于 FNC 的度量标准，它对比了个体的 FNC 与精神障碍和健康对照组的相对距离。为了生成组水平的障碍和健康对照组参考，我们利用了一个包含 5231 名个体的大型脑成像数据集，这些个体被诊断为精神分裂症、自闭症谱系障碍、重度抑郁症和双相情感障碍及其相应的健康对照组。BRS 度量标准用于评估 2 个新数据集的精神科风险：青少年大脑认知发育研究（ABCD）研究（n=8191）和人类连接组计划早期精神病研究（n=170）。
BRS 揭示了一种清晰、可重复的 FNC 模式梯度，从低风险到高风险，适用于每个未受影响的青少年的精神障碍。我们发现，每个障碍的低风险 ABCD 研究青少年 FNC 模式在超过 25%的 ABCD 研究参与者中强烈存在且同质，而每种精神障碍的高风险模式在大约 1%的 ABCD 研究参与者中强烈存在且异质。BRS 还显示出其在预测精神病评分和区分早期精神病患者与健康对照组方面的有效性。
BRS 可能是一种新的基于图像的工具，用于随时间和在未受影响的个体中评估精神脆弱性，它也可以作为一种潜在的生物标志物，促进早期筛查和监测干预。
