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Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Connectivity Changes in Parkinson's Disease.

Antonio Cerasa1, Fabiana Novellino1, Aldo Quattrone2,3

  • 1IBFM, National Research Council, Germaneto, CZ, Italy.

Current Neurology and Neuroscience Reports
|August 29, 2016
PubMed
Summary
This summary is machine-generated.

Neuroimaging reveals altered brain connectivity in Parkinson's disease (PD), impacting motor and non-motor symptoms. Future intelligent neuroimaging aims for personalized PD predictions from functional connectivity data.

Keywords:
Functional magnetic resonance imagingMachine learningParkinson’s diseaseResting-state functional connectivitySeed-based approach

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Neurology

Background:

  • Parkinson's disease (PD) is a progressive neurological disorder affecting both motor and non-motor functions.
  • Functional connectivity neuroimaging has advanced understanding of PD pathophysiology but struggles with individual variability.
  • Group-based neuroimaging analyses offer insights but generalization to individual PD phenotypes remains a challenge.

Purpose of the Study:

  • To review the generalizability of group-based neuroimaging findings in Parkinson's disease.
  • To explore neural correlates of motor/non-motor symptoms and treatment effects using resting-state functional connectivity.
  • To introduce the potential of intelligent neuroimaging for personalized PD predictions.

Main Methods:

  • Review of literature on resting-state functional connectivity studies in Parkinson's disease.
  • Analysis of seed-based, network-based, and graph theory approaches.
  • Exploration of neuroimaging data for motor and non-motor symptom correlates and treatment effects (levodopa, deep brain stimulation).

Main Results:

  • Consistent evidence links altered striato-frontal pathway communication to motor degeneration in PD.
  • Neuroimaging reveals complex, yet incomplete, neural correlates for non-motor symptoms in Parkinson's disease.
  • Functional connectivity studies provide insights into the neural mechanisms of PD treatments like levodopa and deep brain stimulation.

Conclusions:

  • Group-level neuroimaging provides a foundation for understanding PD, particularly motor symptoms.
  • Intelligent neuroimaging using automated multivariate analysis promises personalized predictions for Parkinson's disease.
  • A new era of clinical neuroimaging for Parkinson's disease is emerging, focusing on individual patient data.