Pattern of regional cortical thinning associated with cognitive deterioration in Parkinson's disease.

Background: Dementia is a frequent and devastating complication in Parkinson's disease (PD). There is an intensive search for biomarkers that may predict the progression from normal cognition (PD-NC) to dementia (PDD) in PD. Mild cognitive impairment in PD (PD-MCI) seems to represent a transitional state between PD-NC and PDD.

Spectroscopic changes associated with mild cognitive impairment and dementia in Parkinson's disease.

Frontal subcortical cognitive defects are predominant in Parkinson's disease (PD). Temporal lobe dysfunction seems more relevant for progression to dementia. We aimed to study the relative importance of temporal lobe defects versus executive impairment in the progression to dementia in PD by using proton magnetic resonance spectroscopy ((1)H-MRS).

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

Parkinson's disease (PD) has broadly been associated with mild cognitive impairment (PDMCI) and dementia (PDD). Researchers have studied surrogate, neuroanatomic biomarkers provided by magnetic resonance imaging (MRI) that may help in the early diagnosis of this condition. In this article, four classification models (naïve Bayes, multivariate filter-based naïve Bayes, filter selective naïve Bayes and support vector machines, SVM) have been applied to evaluate their capacity to discriminate between cognitively intact patients with Parkinson's disease (PDCI), PDMCI and PDD.

Manual validation of FreeSurfer's automated hippocampal segmentation in normal aging, mild cognitive impairment, and Alzheimer Disease subjects.

Hippocampal volume is reduced in Alzheimer Disease (AD) and has been proposed as a possible surrogate biomarker to aid early diagnosis. Whilst automated methods to segment the hippocampus from magnetic resonance images are available, manual segmentation, in spite of being time-consuming and unsuitable for large samples, is still the standard. In order to study the validity of FreeSurfer's automated method, we compared hippocampal automated measures with manual tracing in a sample composed of healthy elderly (N=41), Mild Cognitive Impairment (MCI) (N=23), and AD (N=25) subjects.