Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism

authored by

Ali Abdul Nabi Ali, Mesbah Alam, Simon Klein, Nicolai Behmann, Joachim K. Krauss, Theodor Doll, Holger Blume, Kerstin Schwabe

Abstract

In neurological and neuropsychiatric disorders neuronal oscillatory activity between basal ganglia and cortical circuits are altered, which may be useful as biomarker for adaptive deep brain stimulation. We investigated whether changes in the spectral power of oscillatory activity in the motor cortex (MCtx) and the sensorimotor cortex (SMCtx) of rats after injection of the dopamine (DA) receptor antagonist haloperidol (HALO) would be similar to those observed in Parkinson disease. Thereafter, we tested whether a convolutional neural network (CNN) model would identify brain signal alterations in this acute model of parkinsonism. A sixteen channel surface micro-electrocorticogram (ECoG) recording array was placed under the dura above the MCtx and SMCtx areas of one hemisphere under general anaesthesia in rats. Seven days after surgery, micro ECoG was recorded in individual free moving rats in three conditions: (1) basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) with additional injection of apomorphine (APO) (1 mg/kg). Furthermore, a CNN-based classification consisting of 23,530 parameters was applied on the raw data. HALO injection decreased oscillatory theta band activity (4–8 Hz) and enhanced beta (12–30 Hz) and gamma (30–100 Hz) in MCtx and SMCtx, which was compensated after APO injection (P ¡ 0.001). Evaluation of classification performance of the CNN model provided accuracy of 92%, sensitivity of 90% and specificity of 93% on one-dimensional signals. The CNN proposed model requires a minimum of sensory hardware and may be integrated into future research on therapeutic devices for Parkinson disease, such as adaptive closed loop stimulation, thus contributing to more efficient way of treatment.

Organisation(s)

Architectures and Systems Section

External Organisation(s)

Hannover Medical School (MHH)

Type

Article

Journal

NEURAL NETWORKS

Volume

146

Pages

334-340

No. of pages

7

ISSN

0893-6080

Publication date

02.2022

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Cognitive Neuroscience, Artificial Intelligence

Electronic version(s)

https://doi.org/10.1016/j.neunet.2021.11.025 (Access: Closed)