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. Few studies have explored the structural changes that differentiate PD-NC from PD-MCI and PDD patients. OBJECTIVES AND METHODS: We aimed to analyze changes in cortical thickness on 3.0T Magnetic Resonance Imaging (MRI) across stages of cognitive decline in a prospective sample of PD-NC (n = 26), PD-MCI (n = 26) and PDD (n = 20) patients, compared to a group of healthy subjects (HC) (n = 18). Cortical thickness measurements were made using the automatic software Freesurfer. RESULTS: In a sample of 72 PD patients, a pattern of linear and progressive cortical thinning was observed between cognitive groups in cortical areas functionally specialized in declarative memory (entorhinal cortex, anterior temporal pole), semantic knowledge (parahippocampus, fusiform gyrus), and visuoperceptive integration (banks of the superior temporal sulcus, lingual gyrus, cuneus and precuneus). Positive correlation was observed between confrontation naming and thinning in the fusiform gyrus, parahippocampal gyrus and anterior temporal pole; clock copy with thinning of the precuneus, parahippocampal and lingual gyrus; and delayed memory with thinning of the bilateral anteromedial temporal cortex. CONCLUSIONS: The pattern of regional decreased cortical thickness that relates to cognitive deterioration is present in PD-MCI patients, involving areas that play a central role in the storage of prior experiences, integration of external perceptions, and semantic processing.

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. For this purpose, the MRI studies of 45 subjects (16 PDCI, 15 PDMCI and 14 PDD) were acquired and post-processed with Freesurfer, obtaining 112 variables (volumes of subcortical structures and thickness of cortical parcels) per subject. A multivariate filter-based naïve Bayes model was found to be the best classifier, having the highest cross-validated sensitivity, specificity and accuracy. Additionally, the most relevant variables related to dementia in PD, as predicted by our classifiers, were cerebral white matter, and volumes of the lateral ventricles and hippocampi.

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). The (1)H-MRS features of PD patients with intact cognition (PD-CgInt; n = 16), mild cognitive impairment (MCI; n = 15) and dementia (PDD; n = 15) were compared, to delineate the metabolic alterations correlating with cognitive status. Metabolite concentrations were acquired from voxels localized to the hippocampus and dorsolateral prefrontal cortex (DL-PFC). Cognitive status was established following the Movement Disorder Society PDD criteria, administering the Clinical Dementia Rating Scale and Mattis Dementia Rating Scale. The Parkinson's Disease Cognitive Rating Scale (PD-CRS) was used to correlate (1)H-MRS with neuropsychology. N-acetylaspartate (NAA) concentrations in the right DL-PFC were decreased in PD-MCI compared with PD-CgInt patients (p = 0.002), and correlated with frontal subcortical tasks. Decreased NAA concentrations in the left hippocampus in PDD compared to PD-MCI (p = 0.03) correlated with confrontation naming. The present findings support that executive impairment is related to dorsolateral prefrontal dysfunction from the early stages, while progression to dementia is linked to the additional impairment of temporal lobe structures. The PD-CRS was able to capture the differential impairment of prefrontal versus temporal cortical areas.

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. Percent volume overlap, percent volume difference, correlations, and Bland-Altman plots were studied. Automated measures were slightly larger than hand tracing ones (mean difference 10%). Percent volume overlap showed good results, but was far from perfect (78%). Manual and automated volume correlations were approximately 0.84 and the Bland-Altman analysis showed acceptable interchangeability of methods. Within-group analysis demonstrated that patient samples obtained smaller values in validity indexes than controls. Globally, FreeSurfer's automated hippocampal volumetry showed adequate validity when compared to manual tracing, with a tendency to overestimation. Nevertheless, the greater difference between automated and manual segmentation in atrophic brains suggests that studies in AD based on this software could be more likely to produce false negatives.