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Parkinson Disease ll: Pathophysiology01:24

Parkinson Disease ll: Pathophysiology

Parkinson disease (PD) is a progressive neurodegenerative disorder primarily affecting movement, with additional non-motor features. Its pathophysiology involves complex interactions among genetic susceptibility, environmental exposures, and cellular dysfunction, including dopaminergic neuron loss, protein aggregation, and mitochondrial impairment.Selective NeurodegenerationA key feature is the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to reduced...
Parkinson Disease l: Introduction01:24

Parkinson Disease l: Introduction

Parkinson’s disease is a chronic, progressive neurodegenerative disorder that primarily affects movement. It is characterized by motor symptoms such as resting tremors, muscle rigidity, bradykinesia (slowness of movement), and postural instability. Patients may notice hand tremors at rest, stiffness during movement, or a shuffling gait. In addition to motor features, non-motor symptoms include sleep disturbances, mood and behavioral changes, constipation, and cognitive impairment, all of which...
Dementia l: Introduction01:22

Dementia l: Introduction

Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

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 to...
Alzheimer Disease ll: Pathophysiology01:23

Alzheimer Disease ll: Pathophysiology

Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and microglia. Abnormal...

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Related Experiment Video

Updated: May 16, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Predicting dementia development in Parkinson's disease using Bayesian network classifiers.

Dinora A Morales1, Yolanda Vives-Gilabert, Beatriz Gómez-Ansón

  • 1Computational Intelligence Group, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660 Boadilla del Monte, Madrid 28660, Spain. dinora.morales@fi.upm.es

Psychiatry Research
|November 15, 2012
PubMed
Summary

Magnetic resonance imaging (MRI) can help diagnose cognitive impairment in Parkinson's disease (PD). A multivariate filter-based naïve Bayes model accurately distinguished between cognitively intact PD, mild cognitive impairment, and dementia stages.

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Related Experiment Videos

Last Updated: May 16, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
07:26

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Neuroimaging
  • Neurology
  • Machine Learning

Background:

  • Parkinson's disease (PD) is frequently associated with cognitive impairment, ranging from mild cognitive impairment (PDMCI) to dementia (PDD).
  • Neuroanatomic biomarkers from magnetic resonance imaging (MRI) show promise for early PD diagnosis.
  • Differentiating between cognitively intact PD (PDCI), PDMCI, and PDD is crucial for patient management.

Purpose of the Study:

  • To evaluate the efficacy of four classification models in discriminating between PDCI, PDMCI, and PDD using MRI data.
  • To identify key neuroanatomic features associated with cognitive decline in Parkinson's disease.

Main Methods:

  • Utilized MRI scans from 45 subjects (16 PDCI, 15 PDMCI, 14 PDD).
  • Applied Freesurfer for post-processing to extract 112 variables (subcortical volumes, cortical thickness).
  • Compared four machine learning models: naïve Bayes, multivariate filter-based naïve Bayes, filter selective naïve Bayes, and support vector machines (SVM).

Main Results:

  • The multivariate filter-based naïve Bayes model demonstrated the highest performance in classification.
  • This model achieved superior cross-validated sensitivity, specificity, and accuracy in distinguishing cognitive states.
  • Key predictors for dementia in PD included white matter volume, lateral ventricles, and hippocampal volumes.

Conclusions:

  • Machine learning models, particularly multivariate filter-based naïve Bayes, can effectively differentiate cognitive impairment levels in Parkinson's disease using MRI data.
  • Specific neuroanatomic features like white matter, lateral ventricles, and hippocampus volumes are significant indicators of cognitive decline in PD.
  • These findings support the potential of MRI-based biomarkers for early diagnosis and monitoring of cognitive changes in Parkinson's disease.