Discriminant analysis of multi-channel near-infrared spectroscopy signal data in the prefrontal lobe

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Abstract

BACKGROUND:

Psychiatric disorders such as schizophrenia are largely diagnosed on symptomatology. Recently pattern recognition approaches to the analysis of neuroimaging data such as the classification of patients and healthy controls have attracted people’s interest.

OBJECTIVE:

To apply pattern recognition approaches to distinguish schizophrenia patients from healthy subjects with multi-channel prefrontal near-infrared spectroscopy signals, and to verify its feasibility.

METHODS:

The near-infrared spectroscopy data were measured in the bilateral prefrontal areas of schizophrenia patients and healthy subjects during the Verbal Fluency Test task. After preprocessing, we calculated their mean values for each channel, and ranked the channel features based on the area under curve of the Receiver Operator Characteristic. Then, we trained support vector machine on the combinative features and applied Leave-One-Out-Cross-Validation method to verify the classification ability.

RESULTS AND CONCLUSION:

Our study demonstrated that the combination of the top eight rank channel features could reach the classification accuracy up to 95.24%, and all these channels are located at the right lateral prefrontal cortex. It is inferred that, right lateral prefrontal cortex is the main dominant brain areas in schizophrenia patients; the near-infrared spectroscopy of right lateral prefrontal cortex is a potential means for assistant diagnosis of schizophrenia patients.

Subject headings: schizophrenia; optics and photonics; prefrontal cortex; hemorheology

Funding: the National Natural Science Foundation of China, No. 61201066, 61001159

Liu JZ, Quan WX, Lv B, Xie Y, Dong WT. Discriminant analysis of multi-channel near-infrared spectroscopy signal data in the prefrontal lobe. Zhongguo Zuzhi Gongcheng Yanjiu. 2014;18(20):3190-3195.

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