Frontiers in neurology · 2026
Complexity of fractal dimension patterns and machine learning-based classification of altered motor cortical oscillatory activity in rodent models of Parkinson disease.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Alam Mesbah, Abdulbaki Arif, Armstrong Adrian, Schwabe Kerstin, Krauss Joachim K
Аннотация
BACKGROUND: In Parkinson disease (PD), enhanced beta frequency band activity in cortico-basal ganglia networks has been proposed as a possible biomarker for adaptive deep brain stimulation (DBS). Previous studies demonstrated enhanced beta frequency peaks both in the acute Haloperidol (HALO) and the chronic 6-hydroxydopamine (6-OHDA) rat models of parkinsonism. Beta peaks decreased after apomorphine (APO) injection or with DBS of the subthalamic nucleus (STN).
OBJECTIVE: We investigate changes in motor cortical oscillatory activity using fractal dimension (FD) in the HALO and the 6-OHDA rat models of PD. Additionally, we test a support vector machine (SVM) model to predict neuronal dynamics in the 6-OHDA PD model which has been used earlier in an acute rat model of PD.
METHODS: In the HALO model, electrocorticogram (ECoG) was recorded from the motor cortex (MCtx) (1) during basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) after subsequent APO injection (1 mg/kg). For the chronic 6-OHDA model, MCtx-ECoG recordings were obtained (1) during basal activity, and (2) during STN DBS. Higuchi's FD algorithm and SVM-based classification were utilized for analysis.
RESULTS: Average FD values in the MCtx were higher in both PD models compared to controls (P < 0.001). APO injection (P < 0.001) and STN DBS (P < 0.05) reduced average FD values in both models. The SVM model achieved 80% classification accuracy and an AUC of 0.86 in the 6-OHDA rat model.
CONCLUSION: The non-linear analysis of FD reveals changes in cortical oscillatory patterns in rodent models of PD. SVM-based predictions demonstrate potential for classifying altered neural activity, which may offer future strategies for adaptive DBS.
OBJECTIVE: We investigate changes in motor cortical oscillatory activity using fractal dimension (FD) in the HALO and the 6-OHDA rat models of PD. Additionally, we test a support vector machine (SVM) model to predict neuronal dynamics in the 6-OHDA PD model which has been used earlier in an acute rat model of PD.
METHODS: In the HALO model, electrocorticogram (ECoG) was recorded from the motor cortex (MCtx) (1) during basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) after subsequent APO injection (1 mg/kg). For the chronic 6-OHDA model, MCtx-ECoG recordings were obtained (1) during basal activity, and (2) during STN DBS. Higuchi's FD algorithm and SVM-based classification were utilized for analysis.
RESULTS: Average FD values in the MCtx were higher in both PD models compared to controls (P < 0.001). APO injection (P < 0.001) and STN DBS (P < 0.05) reduced average FD values in both models. The SVM model achieved 80% classification accuracy and an AUC of 0.86 in the 6-OHDA rat model.
CONCLUSION: The non-linear analysis of FD reveals changes in cortical oscillatory patterns in rodent models of PD. SVM-based predictions demonstrate potential for classifying altered neural activity, which may offer future strategies for adaptive DBS.