Investigators hypothesize that there are specific characteristic of each cognitive and motor condition that can be defined using brains scans.
Specific Aim 1: Determine which features of resting Magnetoencephalography (MEG) brain activity most sensitively discriminate between PD with normal cognition, PD with mild cognitive impairment (MCI), and PD dementia (PDD). Investigators predict that frontal network slowing and connectivity will discriminate between normal cognition and MCI while visuospatial network involvement will distinguish the PDD group. Specific Aim 2: Determine which features of resting MEG brain activity most sensitively discriminate PDD from Alzheimer's Disease. Investigators predict that PDD will be distinguished from Alzheimer's (AD) on the basis of increased network connectivity, particularly in frontal and visuospatial networks. Specific Aim 3 Investigate how resting state MEG activity correlates with task related brain activity. Investigators predict that resting state slowing will be associated with decreased task related brain activity. Specific Aim 4: Determine which features of resting MEG brain activity most sensitively discriminate between motor subtypes of PD and also other relevant clinical populations (essential tremor and Parkinson plus syndromes). Investigators predict that frontal and parietal slowing and connectivity will discriminate PD from related conditions and that patterns of motor cortex connectivity and activity will differentiate among PD motor phenotypes.
Study Type
OBSERVATIONAL
Enrollment
81
University Of Colorado. Denver, MEG Lab
Aurora, Colorado, United States
Focal oscillatory activity
Focal oscillatory activity: Focal band power for delta (0.5-4Hz), theta (4-8 Hz), alpha (9- 13 Hz), low beta (13-20 Hz), high beta (20-30 Hz) and gamma (30-50 Hz) activity will be derived from the autoregressive models.
Time frame: May 2014
Spectral coherence:
2\) Spectral coherence: Coherence is a measure of interdependence between two time series and can be applied to MEG data to determine functional networks.4a-chen Coherence will be derived from the autoregressive models.
Time frame: May 2014
Spectral Granger analysis:
3\) Spectral Granger analysis: Granger analysis is a measure of the directionality of the relationship of two time series and can be applied to sensors or sources found to have significant coherence.
Time frame: May 2014
Reactivity:
Reactivity: Reactivity refers to changes in oscillatory activity between the eye open and eye closed conditions. Prior research in AD has shown significantly reduced reactivity compared to age-matched controls.
Time frame: May 2014
Complex network analysis:
Complex network analysis: Complex network analysis, originally developed in graph theory, is an approach to the study of complex systems such as brain networks. It allows investigators to characterize brain networks with a small number of neurobiologically meaningful and easily computable measures, including transitivity, global efficiency, and betweenness. These measures will be used to reveal the hypothesized connectivity abnormalities in PD and to differentiate different cognitive phenotypes in PD.
Time frame: May 2014
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