Study reveals how synapse-to-sequence function may help predict Alzheimers disease risk

University of Houston researcher Ravi Vadivi has shared details of how a brain region known as prefrontal cortex may help predict or detect a persons risk for Alzheimers disease. The work the first to be conducted in humans was published in Alzheimers Dementia: The Journal of the Alzheimers Association a journal of the Alzheimers Association.

Using electroencephalography (EEG) which measures brain waves we confirmed a PET marker related to brain network abnormalities associated with a favorable cognitive outcome and suggested an important role of these biomarkers in prognosis and therapeutic intervention for Alzheimers disease. This was accomplished using neuropsychological assessments as well as cognitive tests said Vadivi a neurologist at UH Cullen and executive director of the Yale Alzheimers Disease Research Center. Our findings add to existing knowledge of the essential role of prefrontal cortical areas in cognitive neuropsychological and functional health and strongly suggest they are useful prognostic markers for Alzheimers which can be used as an aid in prognosis.

The research published in Human Brain Mapping focuses on prefrontal cortex regions (NPAs) in the regions that regulate cognitive functions and memory. These regions have been identified as a gene hub in Alzheimers disease with genetic studies suggesting they may play a pivotal role in the disease.

For this study researchers included 47 people with an average age of 52 on average and 64 healthy controls. Generally participants completed three tests; a version of the Stanford 0-100 composite test (SF-20) a version of the Digit Span test (Population Health Assessment20th run-2a at age 50) and a version of the UCLA MEG asymmetric hippocampal gating test (Avail Atrial fibrillation test ALFT age-matched control). All participants completed another cognitive assessment in which they were asked to complete a computerized vigilance test in a radial maze.

Testing the prefrontal cortex revealed impairments in executive function (the ability to plan and remember actions) and in so-called Domain Specific Cognitive Abilities (mental flexibility and executive functioningdom-based reasoning). Other tasks included memory recall a cardiorespiratory topographic test and the Navigator test for sensorimotor memory which is a measure of the ability to predict movement from navigable self-motion.

In addition to discovering any clues for a predictive diagnosis of Alzheimers these findings also suggest a 10-point intermediate threshold at which accuracy would be high if the analyses were applied to the development of amyloid- plaques associated with neurofibrillary tangles of Alzheimers disease and CTE (cholera toxin form) as well as to age sex brain activity and blood glucose levels (estimated body glucose levels). Furthermore the analysis showed the predictive power of any symptom in the three diagnostic groups as high as or as low including depression irritability income and major depressive disorder.

Future studies will explore whether high precision radiometric brain autoimaging may improve diagnostic accuracy and are open to future refinements incorporating the identification of biomarkers to further clarify their function the researchers wrote.

Jessica Brown M. D. the senior author of the study is the director of Yales ZEUS Longevity and Aging Center and assistant professor of pharmacology at Yale New Haven Hospital in collaboration with Vadivi.

The Yale New Haven Health System affords researchers according to minimally invasive procedures and personal protective equipment. It also supports investigational new drugs which have been life-saving for patients battling terminal illness or aged with damaged brain disorders.

The Yale New Haven Health System also employs an Army research and development facility which is the largest such facility in the nation.

The Yale New Haven Health System is supported by the National Institute on Aging (grant UL3TR001494) and the Yale Foundation for Innovation (grants U24AI148349 and U75AI107489).