Decoding effects of psychoactive drugs in a high-dimensional space of eye movements in monkeys

Author:

Liu Xu1ORCID,Cheng Zhixian2,Lin He3,Tan Jiangxiu4,Chen Wenyao4,Bao Yichuan4,Liu Ying4,Zhong Lei4,Yao Yitian4,Wang Liping45,Wang Jijun16,Gu Yong45ORCID

Affiliation:

1. Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai 200030 , China

2. Lingang Laboratory , Shanghai 200031 , China

3. The Third Research Institute of Ministry of Public Security , Shanghai 200031 , China

4. CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences , Shanghai 200031, China

5. University of Chinese Academy of Sciences , Beijing 100049, China

6. Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University , Shanghai 200030 , China

Abstract

ABSTRACT Oculomotor behavior has been shown to be correlated with mental disorders in clinics, making it promising for disease diagnosis. Here we developed a thorough oculomotor test toolkit, involving saccade, smooth pursuit, and fixation, allowing the examination of multiple oculomotor parameters in monkey models induced by psychoactive drugs. Eye movements were recorded after daily injections of phencyclidine (PCP) (3.0 mg/kg), ketamine (0.8 mg/kg) or controlled saline in two macaque monkeys. Both drugs led to robust reduction in accuracy and increment in reaction time during high cognitive-demanding tasks. Saccades, smooth pursuit, and fixation stability were also significantly impaired. During fixation, the involuntary microsaccades exhibited increased amplitudes and were biased toward the lower visual field. Pupillary response was reduced during cognitive tasks. Both drugs also increased sensitivity to auditory cues as reflected in auditory evoked potentials (AEPs). Thus, our animal model induced by psychoactive drugs produced largely similar abnormalities to that in patients with schizophrenia. Importantly, a classifier based on dimension reduction and machine learning could reliably identify altered states induced by different drugs (PCP, ketamine and saline, accuracy = 93%). The high performance of the classifier was reserved even when data from one monkey were used for training and testing the other subject (averaged classification accuracy = 90%). Thus, despite heterogeneity in baseline oculomotor behavior between the two monkeys, our model allows data transferability across individuals, which could be beneficial for future evaluation of pharmaceutical or physical therapy validity.

Funder

STI

Shanghai Municipal Science and Technology Major Project

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Multidisciplinary

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