Impaired pupillary control in “schizophrenia-like” WISKET rats

Büki, Alexandra; Kalmár, György; Kekesi, Gabriella; Benedek, György; Nyúl, László G.; Horváth, Gyöngyi

doi:10.1016/j.autneu.2018.05.007

Cited by 10 publications

(8 citation statements)

References 48 publications

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“…The detailed discussion of the results of the statistical analysis is found in our recent study [3]. In accordance with these results, the trained decision tree selected almost the same features as predictor variables as the statistical analysis suggested.…”

Section: Classification Resultssupporting

confidence: 66%

“…Clinical studies in schizophrenic patients using pupillometry revealed impaired autonomic regulation [1,2,6,13,22]. PLR was also investigated in rodents [16]; however, only our recent study provided data from animal models of schizophrenia [3].…”

Section: Related Workmentioning

confidence: 96%

“…As it was described recently [3], two series of experiments were performed in sedated (n=54) and anesthetized (n=20) control Wistar, and WISKET rats. After a 10minute long dark adaptation period, the recordings lasted for 15s in sedated, and for 60s in anesthetized animals.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Our recent research investigated the PLR to reveal the schizophrenia-related alterations in the autonomic nervous system of WISKET rats and the related medical results were published in [3]. Contributions in the current work: the explanation of novel pupillogram features; presentation of a decision tree based classifier and the discussion of the results; introduction of a redesigned measurement setup and process; description and evaluation of the new pupil segmentation method.…”

Section: Introductionmentioning

confidence: 99%

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Automating, Analyzing and Improving Pupillometry with Machine Learning Algorithms

et al. 2019

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The investigation of the pupillary light reflex (PLR) is a well-known method to provide information about the functionality of the autonomic nervous system. Pupillometry, a non-invasive technique, was applied to study the PLR alterations in a new, schizophrenia-like rat substrain, named WISKET. The pupil responses to light impulses were recorded with an infrared camera; the videos were automatically processed and features were extracted. Besides the classical statistical analysis (ANOVA), feature selection and classification were applied to reveal the significant differences in the PLR parameters between the control and WISKET animals. Based on these results, the disadvantages of this method were analyzed and the measurement process was redesigned and improved. The automated pupil detection method has also been adapted to the new videos. 2564 images were annotated manually and used to train a fully-convolutional neural network to produce pupil mask images. The method was evaluated on 329 test images and achieved 4% median relative error. With the new setup, the pupil detection became reliable and the new data acquisition offers robustness to the experiments.

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Section: Classification Resultssupporting

confidence: 66%

Section: Related Workmentioning

confidence: 96%

Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automating, Analyzing and Improving Pupillometry with Machine Learning Algorithms

et al. 2019

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“…As a gene-environment interaction model, WISKET rats were reported as a selectively bred line with schizophrenia-like phenotypes (reduced sensorimotor gating, hyperalgesia, and memory deficit) after post-weaning social isolation and chronic ketamine treatment over 15 generations (Büki et al, 2018). The WISKET rats showed increased theta, alpha, and beta-band activities and reduced gamma-band activities in ECoG recordings (Horvath et al, 2016).…”

Section: Animal Models For Schizophreniamentioning

confidence: 99%

Oscillotherapeutics – Time-targeted interventions in epilepsy and beyond

Takeuchi

Berényi

2020

Neuroscience Research

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Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptions of the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, anxiety/traumarelated disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable time-and space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatric disorders as possible targets for regulating their symptoms.

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