Optimizing automated MRI measures of atrophy in neurodegenerative disease

DESCRIPTION (provided by applicant): Alzheimer's disease afflicts an estimated 5.1 million people in the United States, and as life expectancies increase it is anticipated that this number will continue to rise (Institute of Medicine, 2008). Besides the personal toll on the patients and their caregivers (who often are family members), the cost of caring for those with dementia is anticipated to grow from the 2011 estimate of 187 billion to 1.1 trillion by the year 2050. Currently the most widely acceptedautomated method for measurement of the hippocampus in Alzheimer's disease is FreeSurfer. At Brain Image Analysis, LLC we have processed the MRI data from the ADNI 1 dataset (gt3500 scans) using our automated pipeline, BRAINS AutoWorkup. In our analysis have found that, in comparisons with both the standard FreeSurfer and the longitudinal stream in FreeSurfer, our methods detect a higher annual atrophy rate with a lower relative standard deviation. This leads to a substantial reduction in the estimates of subjects needed per arm of a clinical trial from 131 or 204 using FreeSurfer (longitudinal and cross-sectional workflows) to 56 using BRAINS ANN methods. This application describes how we intend to implement and test a longitudinal ANN method and test our current hippocampal segmentations against those being prepared by a collaboration of Alzheimer's disease researchers and hippocampus experts. This will provide Brain Image Analysis, LLC with the information it needs to show our methods of subcortical segmentation are the most feasible in the field for commercial image processing in the study of Alzheimer's disease and its treatment. Phase II of this project will encompass implementation of these methods into the newest version of BRAINS and the additional program infrastructure to make our methods available on a larger commercial scale, as well as implementation on a large dataset to explore clinical correlates. Working with us on this project will be renowned neuroscience researchers. These include Dr. Vincent Magnotta and Dr. Nancy Andreasen, long-term leaders of development team for BRAINS, and Dr. Doug Langbehn, serving as our statistician with substantial experience in large imaging studies designed to support clinical trials. PUBLIC HEALTH RELEVANCE: Alzheimer's disease afflicts an estimated 5.1 million people in the United States, and as life expectancies increase it is anticipated that this number will continue to rise (Hebert et al., 2003). Besides the personal toll on the patients and their caregivers (who often are family members), the cost of caring for those with dementia is anticipated to grow from the 2011 estimate of 187 billion to 1.1 trillion by the year 2050 (Institute of Medicine, 2008). The Alzheimer's Disease Neuroimaging Initiative is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic and biochemical biomarkers for the early detection and tracking of Alzheimer's disease. One of the main focuses of work related to the ADNI dataset is to enhance clinical trials through providing sensitive biomarkers, such as hippocampus measures of atrophy rates, with which disease modifying effects may be monitored in clinical trials. At Brain Image Analysis, LLC we have used our current methods to analyze the ADNI MRI dataset and have found our methods to be very sensitive to hippocampal atrophy, requiring less than 1/2 the number of subjects compared to FreeSurfer to detect a 25 percent reduction in atrophy rates in a clinical trial. In this applicatin we propose to further develop and strengthen our methods as commercially available service in monitoring disease course and as biomarkers in clinical trials of agents designed to alter the course of the disease. This Phase I SBIR application proposes the following aims to more completely assess our current methods, expand them into a longitudinal pipeline, and evaluate them against a consensus hippocampal definition being developed in the Alzheimer's research community. AIM 1 - Implement a longitudinal artificial neural network hippocampal segmentation in BRAINS and assess its accuracy and sensitivity in following hippocampal atrophy in Alzheimer's disease, in comparison that the standard ANN used to process the preliminary data. AIM2 - Compare the results ofour current hippocampal segmentation with the consensus methods being developed by Giovanni Frisoni and Clifford Jack. AIM3 - Calculate the ability to detect changes in longitudinal atrophy rates for the methods developed in Aims 1 and 2, and comparewith those of FreeSurfer and other available methods. Working with us on this project will be renowned neuroscience researchers. These include Dr. Vincent Magnotta and Dr. Nancy Andreasen, long-term leaders of development team for BRAINS, and Dr. Doug Langbehn, serving as our statistician with substantial experience in large imaging studies designed to support clinical trials. Should Phase I be successful, Phase II of this project will encompass implementation of these methods into the newest version of BRAINS and the additional program infrastructure to make our methods available on a larger commercial scale, as well as application on the large ADNI dataset to explore clinical correlates relevant to monitoring disease progression and clinical trials.