Neuroimage-Based Consciousness Evaluation of Patients with Secondary Doubtful Hydrocephalus Before and After Lumbar Drainage

Jiayu Huo1 • Zengxin Qi 2,3,4 • Sen Chen1 • Qian Wang1 • Xuehai Wu 2,3,4 • Di Zang 2,3,4 • Tanikawa Hiromi 2,3,4 • Jiaxing Tan 2,3,4 • Lichi Zhang1 • Weijun Tang 5 • Dinggang Shen 6,7

1 Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai 200030, China

2 Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200030, China

3 Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200030, China

4 State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200030, China

5 Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200030, China

6 Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

7 Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea


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.


Hydrocephalus; Disorder of consciousness; Structural imaging; Feature selection; Regression


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