Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI
Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI
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DOI：
10.1016/j.neuroimage.2019.116316
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发表时间：
2020-02-01
期刊：
影响因子：
5.7
通讯作者：
Hudetz, Anthony G.
中科院分区：
文献类型：
--
作者：
Campbell, Justin M.;Huang, Zirui;Hudetz, Anthony G.
确定意识障碍（DOC）患者的意识水平仍然具有挑战性。为了应对这一挑战，静息态fMRI（rs-fMRI）已被广泛用于检测DOC患者和健康对照之间的局部，区域和网络活动差异。虽然在这奋进已经取得了实质性的进展，但仍然缺乏基于rs-fMRI的生物标志物来识别意识水平。机器学习的最新发展显示出作为一种工具的希望，可以在临床实践中增强不同意识状态之间的区分。在这里，我们研究了经过训练以在有意识的觉醒和麻醉诱导的无意识之间进行二元区分的机器学习模型是否能够可靠地识别病理诱导的无意识。我们通过提取与44名受试者在清醒，轻度镇静和无反应（深度镇静和全身麻醉）期间的局部活动，区域同质性和区域间功能活动相关的基于rs-fMRI的特征，然后使用这些特征来训练三个不同的候选机器学习分类器：支持向量机，额外树，人工神经网络。首先，我们证明了所有三个分类器都在数据集内实现了可靠的性能（通过嵌套交叉验证），平均接收器工作特征曲线（AUC）下的面积分别为0.95，0.92和0.94。此外，我们观察到相当的交叉数据集性能（对DOC数据进行预测），因为麻醉训练的分类器表现出区分无反应觉醒综合征（UWS/VS）患者和健康对照的一致能力，平均AUC分别为0.99、0.94、0.98。最后，我们探讨了将上述分类器应用于区分意识的中间状态的潜力，特别是轻度麻醉镇静下的受试者和诊断为具有最低意识状态（MCS）的患者。我们的研究结果表明，在来自麻醉下的参与者的rs-fMRI特征上训练的机器学习分类器有可能帮助区分临床患者的病理性无意识程度。
Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differences between DOC patients and healthy controls. Although substantial progress has been made towards this endeavor, the identification of robust rs-fMRI-based biomarkers for level of consciousness is still lacking. Recent developments in machine learning show promise as a tool to augment the discrimination between different states of consciousness in clinical practice. Here, we investigated whether machine learning models trained to make a binary distinction between conscious wakefulness and anesthetic-induced unconsciousness would then be capable of reliably identifying pathologically induced unconsciousness. We did so by extracting rs-fMRI-based features associated with local activity, regional homogeneity, and interregional functional activity in 44 subjects during wakefulness, light sedation, and unresponsiveness (deep sedation and general anesthesia), and subsequently using those features to train three distinct candidate machine learning classifiers: support vector machine, Extra Trees, artificial neural network. First, we show that all three classifiers achieve reliable performance within-dataset (via nested cross-validation), with a mean area under the receiver operating characteristic curve (AUC) of 0.95, 0.92, and 0.94, respectively. Additionally, we observed comparable cross-dataset performance (making predictions on the DOC data) as the anesthesia-trained classifiers demonstrated a consistent ability to discriminate between unresponsive wakefulness syndrome (UWS/VS) patients and healthy controls with mean AUC's of 0.99, 0.94, 0.98, respectively. Lastly, we explored the potential of applying the aforementioned classifiers towards discriminating intermediate states of consciousness, specifically, subjects under light anesthetic sedation and patients diagnosed as having a minimally conscious state (MCS). Our findings demonstrate that machine learning classifiers trained on rs-fMRI features derived from participants under anesthesia have potential to aid the discrimination between degrees of pathological unconsciousness in clinical patients.