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

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

Organisationseinheiten

Externe Organisationen

  • Medizinische Hochschule Hannover (MHH)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)334-340
Seitenumfang7
FachzeitschriftNEURAL NETWORKS
Jahrgang146
Frühes Online-Datum1 Dez. 2021
PublikationsstatusVeröffentlicht - Feb. 2022

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.

ASJC Scopus Sachgebiete

Zitieren

Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism. / Ali, Ali Abdul Nabi; Alam, Mesbah; Klein, Simon et al.
in: NEURAL NETWORKS, Jahrgang 146, 02.2022, S. 334-340.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Ali AAN, Alam M, Klein S, Behmann N, Krauss JK, Doll T et al. Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism. NEURAL NETWORKS. 2022 Feb;146:334-340. Epub 2021 Dez 1. doi: 10.1016/j.neunet.2021.11.025
Download
@article{f57b2d05997b4e2885328f01721b9fb9,
title = "Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism",
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.",
keywords = "Acute rat model, Convolutional Neural Network, Deep learning, Electrocorticogram, Electroencephalogram, Parkinson's disease",
author = "Ali, {Ali Abdul Nabi} and Mesbah Alam and Simon Klein and Nicolai Behmann and Krauss, {Joachim K.} and Theodor Doll and Holger Blume and Kerstin Schwabe",
note = "Funding Information: The authors gratefully acknowledge the partial funding of this work by German BMBF , grant number 13GW0050B .",
year = "2022",
month = feb,
doi = "10.1016/j.neunet.2021.11.025",
language = "English",
volume = "146",
pages = "334--340",
journal = "NEURAL NETWORKS",
issn = "0893-6080",
publisher = "Elsevier Ltd.",

}

Download

TY - JOUR

T1 - Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism

AU - Ali, Ali Abdul Nabi

AU - Alam, Mesbah

AU - Klein, Simon

AU - Behmann, Nicolai

AU - Krauss, Joachim K.

AU - Doll, Theodor

AU - Blume, Holger

AU - Schwabe, Kerstin

N1 - Funding Information: The authors gratefully acknowledge the partial funding of this work by German BMBF , grant number 13GW0050B .

PY - 2022/2

Y1 - 2022/2

N2 - 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.

AB - 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.

KW - Acute rat model

KW - Convolutional Neural Network

KW - Deep learning

KW - Electrocorticogram

KW - Electroencephalogram

KW - Parkinson's disease

UR - http://www.scopus.com/inward/record.url?scp=85121244921&partnerID=8YFLogxK

U2 - 10.1016/j.neunet.2021.11.025

DO - 10.1016/j.neunet.2021.11.025

M3 - Article

VL - 146

SP - 334

EP - 340

JO - NEURAL NETWORKS

JF - NEURAL NETWORKS

SN - 0893-6080

ER -

Von denselben Autoren