Data driven dynamic activity/connectivity methods for early detection of Alzheimer’s

Project Summary/Abstract
The development of biomarkers for identifying preclinical or prodromal Alzheimer’s disorder are of great in-
terest. While some initial results based on resting fMRI have been presented, accuracy, robustness, and relia-
bility are still relatively low. One highly promising direction is the development of dynamic functional activity and
functional connectivity approaches. These approaches have been shown to be especially promising most likely
due to the highly dynamic nature of the brain and the unconstrained nature of resting fMRI. Currently, there are
no methods that can provide a full characterization of temporal, spatial, and spatio-temporal dynamics nor can
most existing approaches characterize heterogenous subgroups or complex multiscale relationships. We will
develop new methods that can effectively capture dynamic connectivity and provide summary metrics with a
focus on individualized prediction of Alzheimer’s disease well prior to the onset of the illness. We propose a
novel family of models that builds on the well-structured framework of joint blind source separation to capture a
more complete characterization of (potentially nonlinear) spatio-temporal dynamics. Our models will also pro-
duce a rich set of metrics to characterize the available dynamics and enable in depth comparison with currently
available models. We show evidence that such measures are likely to be considerably more sensitive and more
accurate in classifying individuals. We will extensively validate our approaches in a variety of ways including
simulations, concurrent EEG/fMRI data, and evaluation on a large normative data set. We will apply the devel-
oped methods to several large datasets including a large longitudinal sample of individuals who have been
scanned at Emory University with resting fMRI who also have CSF amyloid and tau PET measures. We will use
the developed markers to predict cognitive decline, amyloid, and tau levels in these data and include both a
discovery data set as well as an independent replication data set. Successful completion of our aims will be an
important first step towards providing an opportunity to develop and evaluate interventions early enough to have
a positive impact on long-term prognosis. We will provide open source tools and release data throughout the
duration of the project via GitHub, a web portal and the NITRC repository, hence enabling other investigators to
compare their own methods with our own as well as to apply them to a large variety of brain disorders. Our tools
also have wide application to the study of the healthy brain as well as many other diseases.
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Grant Number: 5R01AG073949-05
NIH Institute/Center: NIH
Principal Investigator: VINCE CALHOUN