Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress.
Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress.
复制标题
从事创伤后应激的退伍军人的多潜电功能性MRI数据准确分类的自我报告应力的框架。
DOI：
10.1177/24705470231203655
复制
发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
Ress, David
中科院分区：
文献类型：
--
作者：
Goel, Rahul;Tse, Teresa;Smith, Lia J;Floren, Andrew;Naylor, Bruce;Williams, M Wright;Salas, Ramiro;Rizzo, Albert S;Ress, David
背景：创伤后应激障碍（PTSD）是从伊拉克和阿富汗战争返回的退伍军人的一个重大负担。虽然经验支持的治疗已经证明减少PTSD的发病率，但仍然需要提高治疗效果。功能性磁共振成像（fMRI）神经反馈已成为一种可能的治疗，以改善创伤后应激障碍症状的严重程度。虚拟现实（VR）方法在提高治疗依从性和结果方面也显示出了希望。为了促进功能磁共振成像神经反馈相关的治疗，这将是有利的，以准确地分类内部大脑压力水平，而退伍军人暴露于创伤相关的VR图像。研究方法：在2个会议上，我们使用功能磁共振成像收集创伤相关的VR样刺激的男性战斗退伍军人与PTSD症状（N = 8）的神经反应。退伍军人报告他们的自我感知的压力水平的规模从1到8每15秒在整个功能磁共振成像会议。 在我们提出的框架中，我们精确地对皮层灰质的fMRI数据进行采样，使数据沿着灰质流形模糊，以减少噪声和维数，同时保留最大的神经信息。然后，我们独立地将3种机器学习（ML）算法应用于在2个会话中收集的fMRI数据，分别针对每个退伍军人，以构建个性化的ML模型，预测他们的内部大脑状态（自我报告的压力反应）。结果如下：我们使用最佳ML方法对所有退伍军人的8类自我报告的压力反应进行了准确分类，平均（±标准误差）均方根误差为0.6（± 0.1）。结论：研究结果表明，ML算法的预测能力适用于在个别退伍军人会议期间收集的全脑皮层功能磁共振成像数据。我们开发的用于预处理全脑皮层fMRI数据和跨会话训练ML模型的框架将提供一个有价值的工具，以在PTSD的VR样暴露治疗期间实现个性化的实时fMRI神经反馈。
Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically supported treatments have demonstrated reductions in PTSD symptomatology, there remains a need to improve treatment effectiveness. Functional magnetic resonance imaging (fMRI) neurofeedback has emerged as a possible treatment to ameliorate PTSD symptom severity. Virtual reality (VR) approaches have also shown promise in increasing treatment compliance and outcomes. To facilitate fMRI neurofeedback-associated therapies, it would be advantageous to accurately classify internal brain stress levels while Veterans are exposed to trauma-associated VR imagery. Methods: Across 2 sessions, we used fMRI to collect neural responses to trauma-associated VR-like stimuli among male combat Veterans with PTSD symptoms (N = 8). Veterans reported their self-perceived stress level on a scale from 1 to 8 every 15 s throughout the fMRI sessions. In our proposed framework, we precisely sample the fMRI data on cortical gray matter, blurring the data along the gray-matter manifold to reduce noise and dimensionality while preserving maximum neural information. Then, we independently applied 3 machine learning (ML) algorithms to this fMRI data collected across 2 sessions, separately for each Veteran, to build individualized ML models that predicted their internal brain states (self-reported stress responses). Results: We accurately classified the 8-class self-reported stress responses with a mean (± standard error) root mean square error of 0.6 (± 0.1) across all Veterans using the best ML approach. Conclusions: The findings demonstrate the predictive ability of ML algorithms applied to whole-brain cortical fMRI data collected during individual Veteran sessions. The framework we have developed to preprocess whole-brain cortical fMRI data and train ML models across sessions would provide a valuable tool to enable individualized real-time fMRI neurofeedback during VR-like exposure therapy for PTSD.