Complexity of fractal dimension patterns and machine learning-based classification of altered motor cortical oscillatory activity in rodent models of Parkinson disease - Takeaways - MDSpire

Complexity of fractal dimension patterns and machine learning-based classification of altered motor cortical oscillatory activity in rodent models of Parkinson disease

  • By

  • Mesbah Alam

  • Arif Abdulbaki

  • Adrian Armstrong

  • Kerstin Schwabe

  • Joachim K. Krauss

  • May 20, 2026

  • 0 min

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  • 1

    Enhanced beta frequency activity in cortico-basal ganglia networks is a proposed biomarker for adaptive deep brain stimulation in Parkinson disease.

  • 2

    Fractal dimension analysis revealed higher average values in motor cortex of PD models compared to controls, indicating altered cortical oscillatory patterns.

  • 3

    Apomorphine injection and STN DBS significantly reduced average fractal dimension values in both HALO and 6-OHDA rat models of Parkinson disease.

  • 4

    A support vector machine model achieved 80% classification accuracy and an AUC of 0.86 in predicting neuronal dynamics in the chronic 6-OHDA model.

  • 5

    The study highlights the potential of non-linear fractal dimension analysis and machine learning for classifying altered neural activity in Parkinson disease.

Original Source(s)

Complexity of fractal dimension patterns and machine learning-based classification of altered motor cortical oscillatory activity in rodent models of Parkinson disease

  • by Mesbah Alam, Arif Abdulbaki, Adrian Armstrong, Kerstin Schwabe, Joachim K. Krauss

  • May 20, 2026

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