PHILADELPHIA – A research group in the Perelman School of Medicine at the University of Pennsylvania
led by Christos Davatzikos, PhD
, a professor of Radiology, has received two grants from the National Institutes on Aging, National Institute of Mental Health, and general office of the Director of the National Institutes of Health, of $6.5 million, and a high-end instrumentation grant of $2 million. The funding will support two large multi-site international neuroimaging consortia, one on brain aging and Alzheimer’s Disease (AD), and one on psychosis, as well as a broader computing infrastructure for imaging analytics and machine learning at Penn Medicine. Using machine learning methods and large databases of imaging and clinical data, the researchers will unravel brain changes associated with aging and psychosis, and will develop a “Brain Chart” allowing researchers and clinicians to compare a patient’s brain MRI with population-derived data, using pattern analysis and machine learning methods. The two studies bring together MRI data from 23 international studies of approximately 18,000 individuals.
Alzheimer’s Disease (AD), and early stages of cognitive decline, pose a significant burden to patients and those caring for them. Last year, an estimated 5.5 million adults were estimated to be living with Alzheimer’s dementia, and estimates suggest that number could soar to 16 million by 2050. This year, the direct cost to American society of care for those suffering from Alzheimer's and other dementias will total an estimated $259 billion, according to the Alzheimer’s Association.
“The scientific goal is to understand the diversity of brain aging trajectories, and relate it to observed types of cognitive decline, as well as to presence of pathologies such as amyloid and cerebrovascular lesions,” said Davatzikos. “This is one of few brain imaging studies of this magnitude in the history of the field, bringing together existing data into this meta-analysis, with participating groups from the US, Europe, Australia, China, and Latin America.”
Looking at structural MRIs, resting state functional MRIs, and amyloid – proteins that become folded into a form that allows numerous copies of that protein to attach together – imaging, the aging consortium will study large databases of imaging and clinical data patterns to better understand co-existing processes occurring with typical and abnormal aging, as well as early stages of AD, and to develop personalized predictors of cognitive decline and dementia.
In a similar effort of smaller magnitude, the psychosis consortium will study structural MRIs to understand imaging signatures of psychosis, to develop personalized predictors of first episode individuals likely to progress clinically and develop schizophrenia. “Personalized disease treatment calls for fine quantification of heterogeneity and for more precise placement of each individual patient into a multi-dimensional spectrum of neuroanatomical alterations found in neuropsychiatric disorders. We have that opportunity in our hands to leverage large databases and advanced imaging analytics methods to achieve this goal,” said Davatzikos.
The third NIH grant, totaling $2 million, funds a high-end computing cluster to facilitate imaging analytics, pattern analysis, and machine learning techniques previously developed in the team’s center to measure the diversity of complex patterns and abnormalities in various disorders.
Before achieving these goals, the international team with strengthen imaging standards and minimize conflicting standards to ensure data across studies and scanners can be used constructively for further discoveries in the field.
Penn faculty on the international aging consortium include David Wolk (Neurology), Mohamad Habes, Nick Bryan, and Ilya Nasrallah (Radiology), and Haochang Shou (Biostatistics). Penn faculty on the psychosis consortium include Ted Satterthwaite, Dan Wolf, Raquel Gur (Psychiatry), and Taki Shinohara (Biostatistics). They are all also affiliated with the Center for Biomedical Image Computing and Analytics.
Funding for this study comes from the National Institutes on Aging (RF1 AG054409), National Institute of Mental Health (R01 MH112070), and general office of the National Institutes of Health Director (S10 OD023495).
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