Apathy, depression, and motor symptoms have distinct and separable resting activity patterns in idiopathic Parkinson disease.

Apathy and depression are heterogeneous syndromes with symptoms that overlap clinically. This clinical overlap leads to problems with classification and diagnosis in clinical populations. No functional imaging study has attempted to separate brain regions altered in apathy from those altered in depression in a clinical population. Parkinson disease (PD) is a disorder in which apathy and depression co-exist in a single population. We evaluate the relationship between apathy, depression, and motor severity of disease in PD, focusing on the relationship between these factors and the amplitude of the low frequency fluctuation (ALFF) in the resting state. We first evaluated if the resting ALFF signal is a reliable measure for our clinical question. For this, we develop and introduce a cross validation approach we term the "Regional Mapping of Reliable Differences" (RMRD) method to evaluate reliability of regions of interest deemed "significant" by standard voxel-wise techniques. Using this approach, we show that the apathy score in this sample is best predicted by ALFF signal in the left supplementary motor cortex, the right orbitofrontal cortex, and the right middle frontal cortex, whereas depression score is best predicted by ALFF signal in the right subgenual cingulate. Disease severity was best predicted by ALFF signal in the right putamen. A number of additional regions are also statistically (but not reliably) correlated with our neuropsychological measures and disease severity. Our results support the use of resting fMRI as a means to evaluate neuropsychiatric states and motor disease progression in Parkinson disease, and the clinical and epidemiologic observation that apathy and depression are distinct pathological entities. Our finding that "significance" and "reliability" are dissociated properties of regions of interest identified as significant using standard voxel-wise techniques suggests that including reliability analyses may add useful scientific information in neurobehavioral research.

Reliability analysis of the resting state can sensitively and specifically identify the presence of Parkinson disease.

Parkinson disease (PD) is characterized by a number of motor and behavioral abnormalities that could be considered deficits of a "no task" or "resting" state, including resting motor findings and defects in emerging from a resting state (e.g., resting tremor, elevated resting tone, abulia, akinesia, apathy). PET imaging, and recently, the MRI technique of continuous arterial spin labeling (CASL) have shown evidence of changes in metabolic patterns in individuals with PD. The purpose of this study was to learn if the presence of PD could be "predicted" based on resting fluctuations of the BOLD signal. Participants were 15 healthy controls, 14 subjects with PD, and 1 subject who presented as a control but later developed PD. The amplitude of the low frequency fluctuation (ALFF) was used as an index of brain activity level in the resting state. Participants with PD using this index showed a reliable decrease in activity in a number of regions, including the supplementary motor cortex, the mesial prefrontal cortex, the right middle frontal gyrus, and the left cerebellum (lobule VII/VIII) as well as increased activity in the right cerebellum (lobule IV/V). Using a cross validation approach we term "Reliability Mapping of Regional Differences" (RMRD) to analyze our sample, we were able to reliably distinguish participants with PD from controls with 92% sensitivity and 87% specificity. Our "pre-diagnostic" subject segregated in our analysis with the PD group. These results suggest that resting fMRI should be considered for development as a biomarker and analytical tool for evaluation of PD.

Connectivity brain networks based on wavelet correlation analysis in Parkinson fMRI data.

Recent studies have shown that aging, psychiatric and neurologic diseases, and dopaminergic blockade all result in altered brain network efficiency. We investigated the efficiency of human brain functional networks as measured by fMRI in individuals with idiopathic Parkinson's disease (N=14) compared to healthy age-matched controls (N=15). Functional connectivity between 116 cortical and subcortical regions was estimated by wavelet correlation analysis in the frequency interval of 0.06-0.12 Hz. Efficiency of the associated network was analyzed, comparing PD to healthy controls. We found that individuals with Parkinson's disease had a marked decrease in nodal and global efficiency compared to healthy age-matched controls. Our results suggest that algorithmic approach and graph metrics might be used to identify and track neurodegenerative diseases, however more studies will be needed to evaluate utility of this type of analysis for different disease states.