Joyita Dutta, Ph.D. - US grants

? DESCRIPTION (provided by applicant): The overarching research goal of this Mentored Research Scientist Development Award is to enable improved estimation and quantitative analysis of tau neurofibrillary tangle (NFT) distributions in vivo using [18F]T807 PET. The candidate is a junior faculty member at Harvard Medical School and Massachusetts General Hospital with expertise in signal processing, image analysis, and quantitative molecular imaging who intends to specialize in the imaging of neurodegenerative and aging-related disorders. The training goal of this award is to provide the candidate with instruction in neuroimaging, neuroscience, and neurology, which will enable her to extend her existing skillset to Alzheimer's disease (AD) imaging and pursue independent research in this area. The candidate proposes to perform kinetic modeling and network analysis on dynamic PET data obtained using a novel PET tracer known as [18F]T807 that binds to NFTs, which are a hallmark of AD. The importance of in vivo tau quantitation in AD is supported by autopsy data which indicate that the NFT burden is strongly correlated with neurodegeneration and cognitive deficits. Despite the availability of novel compounds for imaging NFTs, quantitation still remains challenging due to the resolution limits of PET, which are typically much higher than the dimensions of the subcortical structures where NFTs initially appear. This project will address the major challenges in tau quantitation. Accurate tau quantitation would enable early diagnosis of AD, allow accurate monitoring of disease progression, and be a vehicle for the development of disease-modifying therapies (e.g. anti-amyloid and anti-tau treatments). Specifically this application seeks to: (1) perform MR-guided resolution recovery of PET images of tau, (2) perform MR-guided denoising of high-resolution dynamic [18F]T807 images and compute Logan distribution volume ratio images in order to generate tau connectivity networks, (3) study the spatiotemporal characteristics of tau networks and the relationship between tau networks and structural networks obtained from MR tractography. The third and final goal will enable us to test our hypothesis that cell- to-cell propagation of tau has strong links to the structural networ in the brain. The aforementioned steps will be applied to a cohort of subjects comprised of both elderly cognitively normal and impaired individuals. To achieve her research and career goals, the candidate will pursue a rigorous career development plan which will include formal coursework in neuroimaging, neuroscience, and neurology, attendance of seminars covering state-of-the-art topics in neuroimaging and AD, and participation in domestic and international conferences on medical imaging and AD. Throughout the award period, she will receive advice and guidance from a mentoring team comprised of renowned experts in the fields of quantitative molecular imaging, neuroscience, and neurology. Both individual meetings with the mentors and collective meetings with the full mentoring team will be held periodically to monitor the research and career development progress of the candidate.

PROJECT SUMMARY This proposal is responsive to NIH solicitation PA-17-089 for projects involving secondary analysis of pre-existing geriatric datasets. While presently there is no cure for Alzheimer?s disease, existing literature indicates that early diagnosis in the preclinical stage, i.e., before the onset of clinical symptoms, will be key to treatments. There is a pressing need for noninvasive predictors of cognitive decline that can enable early identification of individuals at Alzheimer?s disease risk. A mounting body of scientific evidence suggests that sleep disturbances (including microarchitectural disruptions to non-rapid-eye-motion sleep and decline in sleep quality) might be the earliest observable symptoms of Alzheimer?s disease. On-the-go sleep and activity monitoring could address the need for noninvasive indicators of cognitive decline in subjects who are in the (asymptomatic or mildly symptomatic) preclinical stage of Alzheimer?s disease. Here, we will build on preliminary results that reveal a set of sleep features derived from polysomnography (PSG) that are predictive of cognitive performance. We are proposing to perform secondary analysis of sleep and cognition data from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort using state-of-the-art deep learning tools to enable sleep-based prediction of cognitive impairment for early detection of Alzheimer?s disease. While PSG is the gold standard for sleep measurement, it is not well- suited for routine, day-to-day use. In comparison, wrist-based measurements (e.g. actigraphy, heart rate, ECG, and pulse oximetry) obtained from wearable devices allow ?on-the-go? sleep monitoring. The combination of these on-the-go measures with the latest artificial intelligence tools is a feasible route to early Alzheimer?s diagnostics. We will use attention-guided long short-term memory autoencoders to identify overt and latent characteristics of the raw time-series datasets, which will allow us to more effectively mine the rich MESA data resource. Our deep learning framework will also take into account sociodemographic variables, indicators of health status, and medications. To ensure scientific rigor, secondary validation of the MESA-trained deep learning models will be performed on PSG and actigraphy data from the Harvard Aging Brain Study, which is a longitudinal study designed to further our understanding of what differentiates normal aging from preclinical Alzheimer?s disease. To address any concern about the ?black-box? nature of deep learning models, we will compare the learned feature set with sleep microarchitectural features previously computed using classical statistical techniques. Previous data suggests that a subject?s apolipoprotein ?4 (ApoE4) allele carrier status influences the degree to which their sleep patterns impact their cognitive abilities. We will verify this by incorporating ApoE4 status as an additional input to the deep learning model. Literature shows that over 60% of patients with mild cognitive impairment and Alzheimer?s disease have at least one clinical sleep disorder. The on-the-go prediction paradigm using noninvasive sleep measurements to be validated in this project will have a significant impact on early Alzheimer?s diagnostics and facilitate ongoing clinical trials.

PROJECT SUMMARY Alzheimer?s disease, the most common cause of dementia in the elderly, is characterized by a cognitively asymptomatic preclinical stage which is identified and monitored via longitudinal tracking of pathophysiological biomarkers, e.g., tau and amyloid. Since the aggregation of tau protein tangles in the medial temporal lobe is a key driver of memory impairment, accurate image-based longitudinal prediction of tau burden could fill a critical gap in biomarker development for preclinical Alzheimer?s disease. Tau tangles exhibit stereotypical neuroanatomical patterns of spatiotemporal spread that correlate strongly with the progression of neurodegeneration. Studies in animal models have suggested that the characteristic patterns of tau spread associated with Alzheimer?s progression are determined by neural connectivity rather than physical proximity between different brain regions. Graph-theoretic methods that utilize macroscale structural connectivity mapping in humans to predict future tau burden could lead to valuable prognostic tools for Alzheimer?s disease. The overarching research goal of this R01 Research Project Grant is to develop an interpretable machine learning model that uses individual structural connectomics to make personalized predictions of differential measures of tau from multimodal baseline data. Our approach relies on longitudinal 18F-Flortaucipir positron emission tomography (PET) for the imaging of tau tangles, 11C-Pittsburgh Compound B (PiB) for the imaging of amyloid plaques, and high-angular-resolution diffusion magnetic resonance (MR) imaging for individualized structural connectomics in human subjects. We will develop a physics-informed and interpretable graph neural network to predict the annual rate of change of the regional tau burden from multimodal inputs, including baseline tau, A?, and an array of structural connectivity metrics. We will also develop novel physics-based analytic models for tau progression, which will be used to effectively guide the machine learning framework. Finally, we will apply the machine learning model to investigate the earliest cortical site of tau aggregation, to examine the connectomic basis of early tau spread, and to leverage our model?s interpretability to discover and validate novel connectomic biomarkers to characterize preclinical Alzheimer?s disease. To validate the machine learning model, we will use serial tau PET data at two and three timepoints from the Harvard Aging Brain Study, one of the largest longitudinal imaging resources for preclinical Alzheimer?s disease. To ensure scientific rigor, secondary validation of the models will be performed using data from the Alzheimer?s Disease Neuroimaging Initiative database. The proposed personalized predictive model could significantly impact preclinical Alzheimer?s prognosis, facilitate ongoing clinical trials, and shed light on the neuroconnectomic and biological underpinnings of Alzheimer?s disease.

PROJECT SUMMARY Preclinical Alzheimer?s disease (the presymptomatic phase of Alzheimer?s disease) is characterized by pathophysiological changes without measurable cognitive decline and begins decades before the onset of cognitive symptoms. Preclinical Alzheimer?s disease research is in pressing need of new biomarker endpoints to enable disease monitoring before traditional cognitive endpoints are measurable. The overarching research objectives of this R03 Small Project Grant are to develop a super-resolution (SR) positron emission tomography (PET) imaging framework for tau (a pathophysiological hallmark of Alzheimer?s disease) and to assess the clinical utility of localized outcome measures obtained from SR PET images. Studies show that tau pathology in the medial temporal lobe is an important marker of cognitive decline in Alzheimer?s disease. Cohorts focused on preclinical Alzheimer?s now incorporate serialized 18F-flortaucipir PET scans for longitudinal tracking of tau accumulation in key anatomical regions-of-interest (ROIs). The quantitative accuracy of tau PET, however, is degraded by the limited spatial resolution capabilities of PET, which lead to inter-ROI spillover and partial volume effects. The problem is further compounded in studies spanning several decades, many of which were commenced on legacy scanners with even lower resolution capabilities than the current state of the art. Additionally, many longitudinal studies began on older scanners and later transitioned to newer models posing a multi-scanner data harmonization challenge. The proposed SR framework will perform a mapping from a low- resolution scanner?s image domain to a high-resolution scanner?s image domain and enable PET resolution recovery and data harmonization. Underlying the proposed framework is a neural network model that can be adversarially trained in self-supervised mode without requiring paired input/output image samples for training. This critical feature ensures practical clinical utility of the method as the need for paired low-resolution and high- resolution datasets from the same subject with similar tracer dose and scan settings is a major barrier for the clinical translatability of simpler supervised alternatives for SR. The proposed network, although trained using unpaired clinical data, receives guidance from an ancillary neural network separately pretrained using paired simulation datasets. For this purpose, we will synthesize paired low- and high-resolution images from a series of digital tau phantoms that will be created for this project. Training and validation of the self-supervised SR framework will be performed via secondary use of de-identified 18F-flortaucipir PET scans from the Harvard Aging Brain Study, a longitudinal cohort focused on preclinical Alzheimer?s disease. We will evaluate SR performance using a variety of image quality metrics. To assess the clinical utility of localized super-resolution measures, we will perform cross-sectional statistical power analyses that estimate sample sizes per arm needed to power clinical trials. Accurate localized measures of tau generated by this project could enable early diagnosis of Alzheimer?s disease and facilitate ongoing clinical trials by reducing sample sizes required for a given effect size.