Hydrocephalus is often treated with a cerebrospinal fluid shunt (CFS) for excessive amounts of cerebrospinal fluid in the brain. However, it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes, such as brain atrophy after brain damage and surgery. The non-trivial evaluation of the consciousness level, along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made. We studied 32 secondary mild hydrocephalus patients with different consciousness levels, who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage. We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages. Then, we built a regression model to regress the JFK Coma Recovery Scale-Revised (CRS-R) scores to quantify the level of consciousness. The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients. The regression model has high potential for the evaluation of consciousness in clinical practice.

中文翻译：

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腰椎引流术前后继发性可疑脑积水患者的神经影像意识评估。

脑积水通常用脑脊液分流术 (CFS) 治疗，以应对大脑中过量的脑脊液。然而，很难区分脑室扩大是由于脑积水还是其他原因，例如脑损伤和手术后的脑萎缩。因此，在做出 CFS 决定之前，对意识水平的重要评估以及腰池的连续引流试验在临床上很重要。我们研究了 32 名不同意识水平的继发性轻度脑积水患者，他们在腰脑脊液引流前后接受了 T1 和弥散张量成像磁共振扫描。我们应用了一种新的机器学习方法来从多模态神经图像中找到最具辨别力的特征。然后，我们建立了一个回归模型来回归 JFK 昏迷恢复量表修订版 (CRS-R) 分数以量化意识水平。实验结果表明，我们的方法不仅近似了 CRS-R 评分，而且还跟踪了个体患者的时间变化。回归模型在临床实践中评估意识的潜力很大。