Advanced signal processing methods for neural data analysis to support development of brain dynamic biomarkers for research and clinical applications in patients with Alzheimer's and related dementias

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Digital technologies can have enormous impact in the prediction, early detection, and tracking of Alzheimer’s disease progression. In particular, there is a need to develop digital biomarkers that can detect early changes in brain function before the onset of cognitive symptoms and/or brain biomarkers. The EEG is a compelling candidate for an early “digital biomarker” of AD as numerous EEG features are known to be correlated with AD progression and fundamental biomarkers. Unfortunately, there is limited evidence that these same EEG measures, as currently constructed to describe population-level data, can accurately track, or predict AD progression in individuals. One reason for this is that EEG signals have many sources of with- and between- subject variation that are not accounted for in current analysis methods, leading to imprecise markers that only have sufficient statistical power at the population-level. There have been recent advances in neural signal processing that make it possible to account for these sources of error and in turn dramatically improve the precision of EEG-derived measures. Over the past several years our lab has made significant strides to account for these sources of error leading us to develop novel, sophisticated signal processing algorithms that can enhance the precision of EEG derived measures. Through the specific aims of this project, we seek to provide the AD research community with a suite of powerful, accessible signal processing software tools that will dramatically enhance the precision and quality of EEG-derived biomarkers related to AD progression.