BrainScope Company, Chevy Chase, Maryland.
Institute of Data Science and Artificial Intelligence, Universidad de Navarra, Pamplona, Spain.
JAMA Netw Open. 2024 Feb 5;7(2):e2355910. doi: 10.1001/jamanetworkopen.2023.55910.
The identification of brain activity-based concussion subtypes at time of injury has the potential to advance the understanding of concussion pathophysiology and to optimize treatment planning and outcomes.
To investigate the presence of intrinsic brain activity-based concussion subtypes, defined as distinct resting state quantitative electroencephalography (qEEG) profiles, at the time of injury.
DESIGN, SETTING, AND PARTICIPANTS: In this retrospective, multicenter (9 US universities and high schools and 4 US clinical sites) cohort study, participants aged 13 to 70 years with mild head injuries were included in longitudinal cohort studies from 2017 to 2022. Patients had a clinical diagnosis of concussion and were restrained from activity by site guidelines for more than 5 days, with an initial Glasgow Coma Scale score of 14 to 15. Participants were excluded for known neurological disease or history of traumatic brain injury within the last year. Patients were assessed with 2 minutes of artifact-free EEG acquired from frontal and frontotemporal regions within 120 hours of head injury. Data analysis was performed from July 2021 to June 2023.
Quantitative features characterizing the EEG signal were extracted from a 1- to 2-minute artifact-free EEG data for each participant, within 120 hours of injury. Symptom inventories and days to return to activity were also acquired.
From the 771 participants (mean [SD] age, 20.16 [5.75] years; 432 male [56.03%]), 600 were randomly selected for cluster analysis according to 471 qEEG features. Participants and features were simultaneously grouped into 5 disjoint subtypes by a bootstrapped coclustering algorithm with an overall agreement of 98.87% over 100 restarts. Subtypes were characterized by distinctive profiles of qEEG measure sets, including power, connectivity, and complexity, and were validated in the independent test set. Subtype membership showed a statistically significant association with time to return to activity.
In this cohort study, distinct subtypes based on resting state qEEG activity were identified within the concussed population at the time of injury. The existence of such physiological subtypes supports different underlying pathophysiology and could aid in personalized prognosis and optimization of care path.
在受伤时识别基于大脑活动的脑震荡亚型有可能促进对脑震荡病理生理学的理解,并优化治疗计划和结果。
研究在受伤时是否存在基于内在大脑活动的脑震荡亚型,这些亚型定义为不同的静息状态定量脑电图(qEEG)图谱。
设计、地点和参与者:在这项回顾性的、多中心(9 所美国大学和高中以及 4 个美国临床站点)队列研究中,纳入了 2017 年至 2022 年期间参加纵向队列研究的年龄在 13 至 70 岁之间的轻度头部受伤患者。患者有临床脑震荡诊断,根据现场指南限制活动超过 5 天,初始格拉斯哥昏迷量表评分为 14 至 15。排除已知神经疾病或过去一年有创伤性脑损伤史的患者。在受伤后 120 小时内,使用来自额部和额颞部的 2 分钟无伪迹脑电图评估患者。数据于 2021 年 7 月至 2023 年 6 月进行分析。
从每个参与者在受伤后 120 小时内的 1 至 2 分钟无伪迹脑电图数据中提取了描述 EEG 信号的定量特征。还获得了症状清单和恢复活动的天数。
在 771 名参与者(平均[SD]年龄,20.16[5.75]岁;432 名男性[56.03%])中,根据 471 个 qEEG 特征,随机选取 600 名参与者进行聚类分析。通过自举 coclustering 算法,同时将参与者和特征分组为 5 个不相交的亚型,100 次重新启动的总体一致性为 98.87%。亚型的特征是 qEEG 测量集的独特特征,包括功率、连通性和复杂性,并在独立测试集中得到验证。亚型成员资格与恢复活动的时间有统计学显著关联。
在这项队列研究中,在受伤时从脑震荡人群中确定了基于静息状态 qEEG 活动的不同亚型。这种生理亚型的存在支持不同的潜在病理生理学机制,并可能有助于个性化预后和优化护理路径。