Centre for Medical Image Computing, Department of Computer Science, London, United Kingdom; Department of Physiology and Pharmacology, Federal Fluminense University, Niterói, Brazil.
Centre for Medical Image Computing, Department of Computer Science, London, United Kingdom; Department of Physiology and Pharmacology, Federal Fluminense University, Niterói, Brazil.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Aug;4(8):726-733. doi: 10.1016/j.bpsc.2019.04.005. Epub 2019 Apr 17.
The aim of this study was to apply multivariate pattern recognition to predict the severity of behavioral traits and symptoms associated with risk for bipolar spectrum disorder from patterns of whole-brain activation during reward expectancy to facilitate the identification of individual-level neural biomarkers of bipolar disorder risk.
We acquired functional neuroimaging data from two independent samples of transdiagnostically recruited adults (18-25 years of age; n = 56, mean age 21.9 ± 2.2 years, 42 women; n = 36, mean age 21.2 ± 2.2 years, 24 women) during reward expectancy task performance. Pattern recognition model performance in each sample was measured using correlation and mean squared error between actual and whole-brain activation-predicted scores on behavioral traits and symptoms.
In the first sample, the model significantly predicted severity of a specific hypo/mania-related symptom, heightened energy, measured by the energy manic subdomain of the Mood Spectrum Structured Interviews (r = .42, p = .001; mean squared error = 9.93, p = .001). The region with the highest contribution to the model was the left ventrolateral prefrontal cortex. Results were confirmed in the second sample (r = .33, p = .01; mean squared error = 8.61, p = .01), in which the severity of this symptom was predicted using a bilateral ventrolateral prefrontal cortical mask (r = .33, p = .009, mean squared error = 9.37, p = .04).
The severity of a specific hypo/mania-related symptom was predicted from patterns of whole-brain activation in two independent samples. Given that emerging manic symptoms predispose to bipolar disorders, these findings could provide neural biomarkers to aid early identification of individual-level bipolar disorder risk in young adults.
本研究旨在应用多元模式识别技术，从奖赏预期期间的全脑激活模式中预测与双相谱系障碍风险相关的行为特征和症状的严重程度，以促进个体水平的双相障碍风险神经生物标志物的识别。
我们从两个独立的跨诊断招募的成年组（18-25 岁；n=56，平均年龄 21.9±2.2 岁，42 名女性；n=36，平均年龄 21.2±2.2 岁，24 名女性）获得功能神经影像学数据，在进行奖赏预期任务时。在每个样本中，使用实际和全脑激活预测得分之间的相关性和均方误差来衡量模式识别模型在行为特征和症状上的性能。
在第一个样本中，该模型显著预测了一种特定的与轻躁狂/躁狂相关的症状，即精力增强，该症状由心境谱结构化访谈的精力躁狂亚量表（r=0.42，p=0.001；均方误差=9.93，p=0.001）测量。对模型贡献最大的区域是左侧腹外侧前额叶皮层。结果在第二个样本中得到了证实（r=0.33，p=0.01；均方误差=8.61，p=0.01），在第二个样本中，使用双侧腹外侧前额叶皮质掩模预测了该症状的严重程度（r=0.33，p=0.009，均方误差=9.37，p=0.04）。
从两个独立样本的全脑激活模式中预测了一种特定的与轻躁狂/躁狂相关的症状的严重程度。鉴于出现的躁狂症状会导致双相障碍，这些发现可能为个体水平的双相障碍风险提供神经生物标志物，以帮助在年轻人中早期识别。
