Meeting Banner
Abstract #2878

Complexity and Synchronicity of Resting State FMRI in Normal Aging and Familial Alzheimer's Disease

Collin Liu1, 2, Anitha Priya Krishnan3, Lirong Yan4, Jeffrey R. Alger4, John Ringman5, Danny JJ Wang1, 6

1Ahmanson-Lovelace Brain Mapping Center, UCLA , Los Angeles, CA, United States; 2Neurobehavior Unit, VA Greater LA Healthcare System, Los Angeles, CA, United States; 3Molecular Imaging Center, USC, Los Angeles, CA; 4Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, United States; 5Neurology, UCLA, Los Angeles, CA; 6Neurology, UCLA, Los Angeles , CA

Interpretation of biological signals is essential for diagnosing diseases. Pattern recognition and spectral analyses have commonly been used. More recently a non-linear time-series analysis called approximate entropy has been applied to EEG, ECG, and hormonal levels. Here we applied approximate entropy to resting state BOLD fMRI time-series, to characterize the complexity of the signal in normal aging and familial Alzheimer's disease. Similar calculation can be made between the time-series of a seed voxel and that of all other voxels to provide a measure of synchronicity. This is called cross-approximate entropy. These analyses might provide novel measures of functional connectivity, complementary to cross-correlation.