Due to a lack of preventive methods and precise diagnostic tests, only 45% of Alzheimer’s patients are told about their diagnosis. I hypothesized that one can create an accurate diagnostic/prognostic software tool for early detection of Alzheimer's using functional connectivity in resting-state fMRI brain imaging, genetic SNP data, cerebrospinal fluid (CSF) concentrations, demographic information, and psychometric tests.nnUsing R programming language and data from ADNI, an ongoing, longitudinal, global effort tracking clinical/imaging AD biomarkers, I examined 678 4D fMRI scans and 56847 observations of 1722 individuals across three diagnostic groups. ICA on fMRI scans yielded graph structures of connectivity between brain networks. For diagnosis, 4 support vector machines and 6 gradient boosting machines were trained 10 times each for fMRI, genetic, CSF biomarker, and cognitive data. For prognosis, 3 linear regression models predicted cognitive scores 6 to 60 months into the future. Forecasted cognitive scores and demographic information were used for prognosis.nnALZCan had 81.82% diagnostic accuracy. Prognostic accuracy for 6, 12, 18 months in future was 75.4%, 68.3%, 68.6%. AD patients showed significantly lower transitivity and average path length between functional brain networks. I examined relative influence/predictive power of multiple biomarkers, confirming previous findings that gender has higher influence than genetic factors on AD diagnosis. Overall, this study engineered a novel neuroimaging feature selection method by using machine learning and graph-theoretic functional network connectivity properties for diagnosis/prognosis of disease states. This analytical tool is capable of predicting future onset of Alzheimer’s and Mild Cognitive Impairment with significant accuracy.
