PROJECT SUMMARY AND ABSTRACT In Alzheimer’s Disease (AD) studies, longitudinal within-subject imaging and analysis of the human brain gives us valuable insight into the temporal dynamics of the early disease process in individual subjects and allows to assess therapeutic efficacy. However, longitudinal imaging tools have not yet been optimized for clinical studies or for use on nonharmonized scans. Challenges include reduction of noise across serial magnetic resonance imaging (MRI) scans while weighting each time point equally to avoid biases; and appropriately accounting for atrophy all in the presence of varying image intensity, contrasts, MR distortions and subject motion across time. Many general tools exist for detecting longitudinal change in carefully curated research data (such as ADNI) in which the scan protocol has been harmonized across acquisition sites so as to minimize differential distortion and gradient nonlinearities removed prior to data release. Unfortunately, these tools do not work accurately for unharmonized MRI scans that comprise the bulk of the research data available, and on clinical data, where the practical need for clinicians to schedule a subject on different scanners leads to additional differences in scans acquired across multiple scan sessions. For retrospective analysis of past scans or clinical use, it is thus critical to develop imaging tools that are agnostic to global scanner-induced differences in images but very sensitive to subtle neuroanatomical change, such as atrophy in AD, that is highly predictive of the early disease process. To address the above issues, we propose to design, implement and validate a deep learning (DL) AD image analysis framework for detecting neuroanatomical change in the presence of large image differences due to the acquisition process itself, including the field strength, receive coil, sequence parameters, gradient nonlinearities and B0 distortions, scanner manufacturer, and subject motion in the images across time. We leverage the fact that, within a subject, there is a physical deformation that relates the brain scans acquired across time unlike the cross-subject case. Focusing exclusively on longitudinal within-subject studies allows us to craft ultra-sensitive registration and change detection tools that drastically outperform general purpose ones used in cross-subject studies, where registration is intended only to find approximate anatomical correspondences. Our longitudinal imaging framework is thus able to learn to disentangle true neuroanatomical change from irrelevant distortions. Since the applicant has a computational background, the proposed training program at Harvard, MIT and MGH will focus on neuroscience and neurology during the K99 phase to develop the skills needed to transition to independence in the R00 phase. The applicant aims to become an expert in clinical imaging of AD and push the limits of what is currently possible in AD research, fundamentally enhancing the quality of healthcare. We believe that the proposed project is a first step in this direction and the tools developed will further pave the way for clinical imaging and analysis of AD and neurodegenerative disease processes in general.

PROJECT NARRATIVE Certain change in the human brain is strongly predictive of early disease processes (such as atrophy in the case of Alzheimer’s Disease—AD). However, using conventional image processing to detect this anatomical change in clinical MR scans taken across time is difficult due to various MR effects and subject motion, which can mask the anatomical change of interest relevant to AD diagnosis and research. The aims of the proposed project are to leverage the recent advances in deep learning to design, develop and evaluate an AD imaging and analysis framework that can resolve anatomical change with high accuracy, aiding in the early diagnosis and intervention before the onset of dysfunction.