A NOVEL FMRI BIOMARKER OF INCIPIENT ALZHEIMER?S DISEASE

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Alzheimer?s Disease (AD) is the most common cause of dementia affecting as many as 1 in 10 people over age 65 and 1 in 3 people over age 85. Epidemiological studies estimate the prevalence of AD in the United States to be approximately 4 million people currently and this number is expected to reach 13 million by the year 2050. Currently approved therapy only provides modest symptomatic benefit, but several drugs designed to prevent the onset or progression of disease are undergoing clinical trials. With the potential for preventive or curative therapy on the horizon and an understanding that such treatments will be most effective when begun at the earliest stages of the illness, research has turned to the early, and ideally pre-clinical, detection of AD. Despite intensive efforts to develop an in vivo diagnostic assay for AD, researchers have been unable to identify biomarkers in either serum or cerebrospinal fluid analyses with sufficient sensitivity and specificity to merit their use as a clinical diagnostic test. A number of groups have examined the potential role of MRI, both functional and structural, in detecting early AD and predicting which at-risk patients will develop AD. To date, however, these imaging approaches have also lacked sufficient sensitivity and specificity at the individual patient level. We have modified a novel functional MRI technique?functional connectivity MRI (fcMRI)?to detect a specific resting-state network (RSN) that incorporates several brain regions affected in the earliest stages of AD. The method includes assigning a quantitative score to individual subjects reflecting the degree to which their RSN matches a standard template of the RSN. We have developed this method with the goal of making it both clinically relevant and broadly applicable (i.e beyond university teaching hospitals to community hospitals). To this end, it is automated, does not require projection of stimuli or recording of behavioral responses, and does not require a high field MRI scanner. Our preliminary data suggests that this approach has the potential to distinguish healthy elderly controls from individual AD patients at the earliest stage of the disease.