Holmes Scott, Mar'i Joud, Simons Laura E, Zurakowski David, LeBel Alyssa Ann, O'Brien Michael, Borsook David
Pediatric Pain Pathway Lab, Department of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital - Harvard Medical School, Boston, MA, United States.
Pain and Affective Neuroscience Center, Boston Children's Hospital, Boston, MA, United States.
Front Pain Res (Lausanne). 2022 May 17;3:859881. doi: 10.3389/fpain.2022.859881. eCollection 2022.
Post-traumatic headache (PTH) is a challenging clinical condition to identify and treat as it integrates multiple subjectively defined symptoms with underlying physiological processes. The precise mechanisms underlying PTH are unclear, and it remains to be understood how to integrate the patient experience with underlying biology when attempting to classify persons with PTH, particularly in the pediatric setting where patient self-report may be highly variable. The objective of this investigation was to evaluate the use of different machine learning (ML) classifiers to differentiate pediatric and young adult subjects with PTH from healthy controls using behavioral data from self-report questionnaires that reflect concussion symptoms, mental health, pain experience of the participants, and structural brain imaging from cortical and sub-cortical locations. Behavioral data, alongside brain imaging, survived data reduction methods and both contributed toward final models. Behavioral data that contributed towards the final model included both the child and parent perspective of the pain-experience. Brain imaging features produced two unique clusters that reflect regions that were previously found in mild traumatic brain injury (mTBI) and PTH. Affinity-based propagation analysis demonstrated that behavioral data remained independent relative to neuroimaging data that suggest there is a role for both behavioral and brain imaging data when attempting to classify children with PTH.
创伤后头痛（PTH）是一种难以识别和治疗的临床病症，因为它将多种主观定义的症状与潜在的生理过程结合在一起。PTH背后的确切机制尚不清楚，在试图对PTH患者进行分类时，尤其是在儿科环境中，患者自我报告可能差异很大，如何将患者体验与潜在生物学结合起来仍有待了解。本研究的目的是评估使用不同的机器学习（ML）分类器，通过自我报告问卷中的行为数据（反映脑震荡症状、心理健康、参与者的疼痛体验）以及来自皮质和皮质下位置的结构脑成像，将患有PTH的儿科和年轻成人受试者与健康对照区分开来。行为数据与脑成像一起，通过数据缩减方法得以保留，并都对最终模型做出了贡献。对最终模型有贡献的行为数据包括儿童和家长对疼痛体验的看法。脑成像特征产生了两个独特的聚类，反映了先前在轻度创伤性脑损伤（mTBI）和PTH中发现的区域。基于亲和度的传播分析表明，行为数据相对于神经成像数据保持独立，这表明在试图对患有PTH的儿童进行分类时，行为数据和脑成像数据都有作用。
