BackgroundGlobal hippocampal atrophy is a hallmark of Alzheimer’s dementia and has been similarly reported in Parkinson’s disease dementia (PDD). However, there is limited literature on the differential involvement of hippocampal subfields in predicting conversion to PDD. This study is an extension of previous findings on progression to mild cognitive impairment in Parkinson’s disease (PD).MethodsThis cohort study recruited 73 non-demented participants with idiopathic PD (age 65.80±8.17, 75.3% male) from an outpatient neurology clinic. All participants underwent clinical assessment, neuropsychological testing and 3T MRI scans at baseline and 18 months while on prescribed dopaminergic medication. Hippocampal subfield volumes were obtained using automatic segmentation in FreeSurfer V.6.0. Participants who progressed to PDD and those who did not were compared on hippocampal subfield atrophy and cognitive change (episodic memory, attention, executive functions, language, visuospatial abilities). Subfields were further examined for their abilities to predict PDD conversion and distinguish PDD from non-demented PD using receiver operating characteristic analysis.ResultsSmaller baseline global hippocampal volume, cornu ammonis (CA) subfield CA1, subiculum and presubiculum volumes were observed in participants who went on to develop dementia, and predicted PDD conversion. Those who progressed to PDD saw greater decline in global hippocampal volume, granule cell layer of the dentate gyrus, presubiculum, parasubiculum and fimbria. Decline in subiculum and fimbria volume corresponded to cognitive decline in attention and executive functions, respectively.ConclusionsEarly atrophy of CA1, subiculum and presubiculum preceded, and predicted, PDD conversion. Differential patterns of subfield atrophy were also observed among those who progressed to PDD and were associated with impaired executive functions.
Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA-based analysis
