Olfactory dysfunction is an early and common symptom in Parkinson's disease (PD). In an effort to determine whether otherwise unexplained (idiopathic) olfactory dysfunction is associated with an increased risk of developing PD, we designed a prospective study in a cohort of 361 asymptomatic relatives (parents, siblings, or children) of PD patients. A combination of olfactory detection, identification, and discrimination tasks was used to select groups of hyposmic (n ‫؍‬ 40) and normosmic (n ‫؍‬ 38) individuals for a 2-year clinical follow-up evaluation and sequential single-photon emission computed tomography (SPECT), using [123 I]␤-CIT as a dopamine transporter ligand, to assess nigrostriatal dopaminergic function at baseline and 2 years from baseline. A validated questionnaire, sensitive to the presence of parkinsonism, was used in the follow-up of the remaining 283 relatives. Two years from baseline, 10% of the individuals with idiopathic hyposmia, who also had strongly reduced [123 I]␤-CIT binding at baseline, had developed clinical PD as opposed to none of the other relatives in the cohort. In the remaining nonparkinsonian hyposmic relatives, the average rate of decline in dopamine transporter binding was significantly higher than in the normosmic relatives. These results indicate that idiopathic olfactory dysfunction is associated with an increased risk of developing PD of at least 10%.

Extensive changes in resting-state oscillatory brain activity have recently been demonstrated using magnetoencephalography (MEG) in moderately advanced, non-demented Parkinson's disease patients relative to age-matched controls. The aim of the present study was to determine the onset and evolution of these changes over the disease course and their relationship with clinical parameters. In addition, we evaluated the effects of dopaminomimetics on resting-state oscillatory brain activity in levodopa-treated patients. MEG background oscillatory activity was studied in a group of 70 Parkinson's disease patients with varying disease duration and severity (including 18 de novo patients) as well as in 21 controls that were age-matched to the de novo patients. Whole head 151-channel MEG recordings were obtained in an eyes-closed resting-state condition. Levodopa-treated patients (N = 37) were examined both in a practically defined 'OFF' as well as in the 'ON' state. Relative spectral power was calculated for delta, theta, low alpha, high alpha, beta and gamma frequency bands and averaged for 10 cortical regions of interest (ROIs). Additionally, extensive clinical and neuropsychological testing was performed in all subjects. De novo Parkinson's disease patients showed widespread slowing of background MEG activity relative to controls. Changes included a widespread increase in theta and low alpha power, as well as a loss of beta power over all but the frontal ROIs and a loss of gamma power over all but the right occipital ROI. Neuropsychological assessment revealed abnormal perseveration in de novo patients, which was associated with increased low alpha power in centroparietal ROIs. In the whole group of Parkinson's disease patients, longer disease duration was associated with reduced low alpha power in the right temporal and right occipital ROI, but not with any other spectral power measure. No association was found between spectral power and disease stage, disease severity or dose of dopaminomimetics. In patients on levodopa therapy, a change from the 'OFF' to the 'ON' state was associated with decreases in right frontal theta, left occipital beta and left temporal gamma power and an increase in right parietal gamma power. Widespread slowing of oscillatory brain activity is a characteristic of non-demented Parkinson's disease patients from the earliest clinical stages onwards that is (largely) independent of disease duration, stage and severity and hardly influenced by dopaminomimetic treatment. Some early cognitive deficits in Parkinson's disease appear to be associated with increased low alpha power. We postulate a role for hypofunctional non-dopaminergic ascending neurotransmitter systems in spectral power changes in non-demented Parkinson's disease patients.

Although alterations in resting-state functional connectivity between brain regions have previously been reported in Parkinson's disease, the spatial organization of these changes remains largely unknown. Here, we longitudinally studied brain network topology in Parkinson's disease in relation to clinical measures of disease progression, using magnetoencephalography and concepts from graph theory. We characterized whole-brain functional networks by means of a standard graph analysis approach, measuring clustering coefficient and shortest path length, as well as the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks. We observed that brain networks in early stage untreated patients displayed lower local clustering with preserved path length in the delta frequency band in comparison to controls. Longitudinal analysis over a 4-year period in a larger group of patients showed a progressive decrease in local clustering in multiple frequency bands together with a decrease in path length in the alpha2 frequency band. In addition, minimum spanning tree analysis revealed a decentralized and less integrated network configuration in early stage, untreated Parkinson's disease that also progressed over time. Moreover, the longitudinal changes in network topology identified with both techniques were associated with deteriorating motor function and cognitive performance. Our results indicate that impaired local efficiency and network decentralization are very early features of Parkinson's disease that continue to progress over time, together with reductions in global efficiency. As these network changes appear to reflect clinically relevant phenomena, they hold promise as markers of disease progression.

Resting-state neuroimaging is a dominant paradigm for studying brain function in health and disease. It is attractive for clinical research because of its simplicity for patients, straightforward standardization, and sensitivity to brain disorders. Importantly, non-sensory experiences like mind wandering may arise from ongoing brain activity. However, little is known about the link between ongoing brain activity and cognition, as phenotypes of resting-state cognition—and tools to quantify them—have been lacking. To facilitate rapid and structured measurements of resting-state cognition we developed a 50-item self-report survey, the Amsterdam Resting-State Questionnaire (ARSQ). Based on ARSQ data from 813 participants assessed after 5 min eyes-closed rest in their home, we identified seven dimensions of resting-state cognition using factor analysis: Discontinuity of Mind, Theory of Mind, Self, Planning, Sleepiness, Comfort, and Somatic Awareness. Further, we showed that the structure of cognition was similar during resting-state fMRI and EEG, and that the test-retest correlations were remarkably high for all dimensions. To explore whether inter-individual variation of resting-state cognition is related to health status, we correlated ARSQ-derived factor scores with psychometric scales measuring depression, anxiety, and sleep quality. Mental health correlated positively with Comfort and negatively with Discontinuity of Mind. Finally, we show that sleepiness may partially explain a resting-state EEG profile previously associated with Alzheimer's disease. These findings indicate that the ARSQ readily provides information about cognitive phenotypes and that it is a promising tool for research on the neural correlates of resting-state cognition in health and disease.

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