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Background Subjective cognitive decline (SCD) may occur very early in the course of Parkinson’s disease (PD) before the onset of objective cognitive decline. Data on neural correlates and determinants of SCD in PD are rare. Objective The aim of the present study was to identify neural correlates as well as sociodemographic, clinical, and neuropsychological predictors of SCD in patients with PD. Methods We retrospectively analyzed 30 patients with PD without cognitive impairment (23% female, 66.90 ± 7.20 years, UPDRS-III: 19.83 ± 9.29), of which n = 12 patients were classified as having no SCD (control group, PD-CG) and n = 18 as having SCD (PD-SCD). Neuropsychological testing and 18-fluoro-2-deoxyglucose positron emission tomography (FDG-PET) were conducted. SCD was assessed using a questionnaire covering multiple cognitive domains. Results SCD subscores differed significantly between PD-CG and PD-SCD and correlated significantly with other scales measuring related concepts. FDG-PET whole-brain voxel-wise regression analysis revealed hypometabolism in middle frontal, middle temporal, and occipital areas, and the angular gyrus as neural correlates of SCD in PD. Next to this hypometabolism, depressive symptoms were an independent significant determinant of SCD in a stepwise regression analysis (adjusted R2 = 50.3%). Conclusion This study strengthens the hypothesis of SCD being an early manifestation of future cognitive decline in PD and, more generally, early pathological changes in PD. The early identification of the vulnerability for future cognitive decline constitutes the basis for successful prevention and delay of this non-motor symptom.

Due to the expanding older population and increasing prevalence of dementia and currently lacking curative therapy but potentially conceivable availability of molecular-specific treatment to delay the progression of cognitive decline, the early diagnosis of cognitive deficits and their etiological differential diagnosis becomes increasingly more important. The advances in nuclear medicine diagnostics in the field of neurodegenerative diseases within the last few years have been substantial. In this article the relevance of these technologies in the diagnostic process of dementia is described.

Cognitive reserve (CR) shows protective effects in Alzheimer’s disease (AD) and reduces the risk of dementia. Despite the clinical significance of CR, a clinically useful diagnostic biomarker of brain changes underlying CR in AD is not available yet. Our aim was to develop a fully-automated approach applied to fMRI to produce a biomarker associated with CR in subjects at increased risk of AD. We computed resting-state global functional connectivity (GFC), i.e. the average connectivity strength, for each voxel within the cognitive control network, which may sustain CR due to its central role in higher cognitive function. In a training sample including 43 mild cognitive impairment (MCI) subjects and 24 healthy controls (HC), we found that MCI subjects with high CR (> median of years of education, CR+) showed increased frequency of high GFC values compared to MCI-CR- and HC. A summary index capturing such a surplus frequency of high GFC was computed (called GFC reserve (GFC-R) index). GFC-R discriminated MCI-CR+ vs. MCI-CR-, with the area under the ROC = 0.84. Cross-validation in an independently recruited test sample of 23 MCI subjects showed that higher levels of the GFC-R index predicted higher years of education and an alternative questionnaire-based proxy of CR, controlled for memory performance, gray matter of the cognitive control network, white matter hyperintensities, age, and gender. In conclusion, the GFC-R index that captures GFC changes within the cognitive control network provides a biomarker candidate of functional brain changes of CR in patients at increased risk of AD.

Huntington's disease (HD) is an autosomal dominant neurodegenerative condition characterized by a triad of movement disorder, neuropsychiatric symptoms and cognitive deficits. The striatum is particularly vulnerable to the effects of mutant huntingtin, and cell loss can already be found in presymptomatic stages. Since the striatum is well known for its role in reinforcement learning, we hypothesized to find altered behavioral and neural responses in HD patients in a probabilistic reinforcement learning task performed during functional magnetic resonance imaging. We studied 24 HD patients without central nervous system (CNS)-active medication and 25 healthy controls. Twenty HD patients and 24 healthy controls were able to complete the task. Computational modeling was used to calculate prediction error values and estimate individual parameters. We observed that gray matter density and prediction error signals during the learning task were related to disease stage. HD patients in advanced disease stages appear to use a less complex strategy in the reversal learning task. In contrast, HD patients in early disease stages show intact encoding of learning signals in the degenerating left ventral striatum. This effect appears to be lost with disease progression.