Versace Amelia, Sharma Vinod, Bertocci Michele A, Bebko Genna, Iyengar Satish, Dwojak Amanda, Bonar Lisa, Perlman Susan B, Schirda Claudiu, Travis Michael, Gill Mary Kay, Diwadkar Vaibhav A, Sunshine Jeffrey L, Holland Scott K, Kowatch Robert A, Birmaher Boris, Axelson David, Frazier Thomas W, Arnold L Eugene, Fristad Mary A, Youngstrom Eric A, Horwitz Sarah M, Findling Robert L, Phillips Mary L
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
PLoS One. 2017 Jul 6;12(7):e0180221. doi: 10.1371/journal.pone.0180221. eCollection 2017.
Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy regulation in youth. Recent studies indicate that machine learning techniques can help elucidate the role of neuroimaging measures in classifying individual subjects by specific symptom trajectory. Cortical thickness measures were extracted in sixty-eight anatomical regions covering the entire brain in 115 participants from the Longitudinal Assessment of Manic Symptoms (LAMS) study and 31 healthy comparison youth (12.5 y/o;-Male/Female = 15/16;-IQ = 104;-Right/Left handedness = 24/5). Using a combination of trajectories analyses, surface reconstruction, and machine learning techniques, the present study aims to identify the extent to which measures of cortical thickness can accurately distinguish youth with higher (n = 18) from those with lower (n = 34) trajectories of manic-like behaviors in a large sample of LAMS youth (n = 115; 13.6 y/o; M/F = 68/47, IQ = 100.1, R/L = 108/7). Machine learning analyses revealed that widespread cortical thickening in portions of the left dorsolateral prefrontal cortex, right inferior and middle temporal gyrus, bilateral precuneus, and bilateral paracentral gyri and cortical thinning in portions of the right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, and right parahippocampal gyrus accurately differentiate (Area Under Curve = 0.89;p = 0.03) youth with different (higher vs lower) trajectories of positive mood and energy dysregulation over a period up to 5years, as measured by the Parent General Behavior Inventory-10 Item Mania Scale. Our findings suggest that specific patterns of cortical thickness may reflect transdiagnostic neural mechanisms associated with different temporal trajectories of positive mood and energy dysregulation in youth. This approach has potential to identify patterns of neural markers of future clinical course.
调节积极情绪和活力存在困难是贯穿不同儿童期精神疾病的一个特征。然而，对于青少年积极情绪和活力调节不同发展轨迹背后的神经机制，我们知之甚少。最近的研究表明，机器学习技术有助于阐明神经影像学测量在按特定症状轨迹对个体受试者进行分类中的作用。在来自躁狂症状纵向评估（LAMS）研究的115名参与者和31名健康对照青少年（12.5岁；男/女=15/16；智商=104；右/左利手=24/5）中，在覆盖整个大脑的68个解剖区域提取了皮质厚度测量值。本研究结合轨迹分析、表面重建和机器学习技术，旨在确定在一大群LAMS青少年（n = 115；13.6岁；男/女=68/47，智商=100.1，右/左=108/7）中，皮质厚度测量能在多大程度上准确区分躁狂样行为轨迹较高（n = 18）和较低（n = 34）的青少年。机器学习分析显示，左侧背外侧前额叶皮质、右侧颞下回和颞中回、双侧楔前叶以及双侧中央旁回部分区域广泛的皮质增厚，以及右侧背外侧前额叶皮质、左侧腹外侧前额叶皮质和右侧海马旁回部分区域的皮质变薄，能够准确区分（曲线下面积=0.89；p = 0.03）在长达5年的时间里积极情绪和活力失调轨迹不同（较高与较低）的青少年，这是通过父母一般行为量表-10项躁狂量表测量的。我们的研究结果表明，皮质厚度的特定模式可能反映了与青少年积极情绪和活力失调不同时间轨迹相关的跨诊断神经机制。这种方法有可能识别未来临床病程的神经标志物模式。
