Oscillatory markers of cognitive deficits in patients with Alzheimer's disease

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ABSTRACT/PROJECT SUMMARY Aging is typically associated with some limited cognitive decline, although a subgroup of the aging population will experience the rapid and progressive declines that are associated with Alzheimer’s disease (AD) and its common precursor, mild cognitive impairment (MCI). With around 5.1 million Americans living with AD today, there is an immediate need to understand the neurophysiological basis of these mental declines. Attention and working memory (WM) processes are among the earliest and most severely affected cognitive functions in MCI and AD. Attention is defined as the preferential allocation of processing resources towards a specific stimulus or stimuli, whereas WM denotes the on-line temporary storage of information to be used in ongoing cognitive processing. Although neuropsychological testing has shown a clear deficit in these domains in patients with MCI and AD, far less is known about the neural oscillatory activity and computational dynamics that underlie these deficits. The current study aims to partially remedy this knowledge gap by utilizing the spatial precision and exquisite temporal resolution (i.e., millisecond) of magnetoencephalographic (MEG) imaging. Using MEG, we will determine the neurophysiological bases of attentional and WM dysfunction in adults with MCI and AD, as compared to a demographically-matched sample of neurologically-healthy older adults. Briefly, participants will complete two cognitive tasks during MEG recording, one tapping attentional processing and another aimed at WM. Both of these cognitive tasks have been shown to produce robust neural oscillatory activity in healthy controls. The resulting MEG data will be transformed into the time-frequency domain and imaged using an advanced beamforming approach. The output dynamic functional maps of electrical neural activity will be used to examine low frequency (i.e., alpha and theta) oscillatory activity and dynamic functional connectivity among regions serving attention and WM processes. Essentially, we will identify the statistically anomalous neural oscillations and functional connectivity in patients with MCI and AD, and then link these neural data to cognitive performance metrics. Our specific aims are: (1) To identify aberrant theta and alpha oscillatory dynamics in neural regions serving WM and attention processing in patients with MCI and mild AD, and (2) to quantify dynamic functional connectivity during these same cognitive processes in patients with MCI and mild AD. To this end, we will utilize the latest MEG and advanced source reconstruction techniques, neural oscillatory analysis methods, and neuropsychological assessment to delineate the neurophysiological bases of cognitive impairments in patients with MCI and AD. With the world population aging in a highly disproportionate manner, AD prevalence is set to rise in future decades, and the hefty economical and societal burdens associated with the disease will certainly follow. Research aimed at better understanding the disease and providing potential markers for diagnosing and tracking disease progression may ultimately reduce the societal impact, by guiding and informing novel treatment development and reducing the overall financial burden.