Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI.

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.

Three-dimensional mapping of neurofibrillary tangle burden in the human medial temporal lobe.

Tau protein neurofibrillary tangles are closely linked to neuronal/synaptic loss and cognitive decline in Alzheimer's disease and related dementias. Our knowledge of the pattern of neurofibrillary tangle progression in the human brain, critical to the development of imaging biomarkers and interpretation of in vivo imaging studies in Alzheimer's disease, is based on conventional two-dimensional histology studies that only sample the brain sparsely. To address this limitation, ex vivo MRI and dense serial histological imaging in 18 human medial temporal lobe specimens (age 75.3 ± 11.4 years, range 45 to 93) were used to construct three-dimensional quantitative maps of neurofibrillary tangle burden in the medial temporal lobe at individual and group levels. Group-level maps were obtained in the space of an in vivo brain template, and neurofibrillary tangles were measured in specific anatomical regions defined in this template. Three-dimensional maps of neurofibrillary tangle burden revealed significant variation along the anterior-posterior axis. While early neurofibrillary tangle pathology is thought to be confined to the transentorhinal region, we found similar levels of burden in this region and other medial temporal lobe subregions, including amygdala, temporopolar cortex, and subiculum/cornu ammonis 1 hippocampal subfields. Overall, the three-dimensional maps of neurofibrillary tangle burden presented here provide more complete information about the distribution of this neurodegenerative pathology in the region of the cortex where it first emerges in Alzheimer's disease, and may help inform the field about the patterns of pathology spread, as well as support development and validation of neuroimaging biomarkers.

Longitudinal atrophy in early Braak regions in preclinical Alzheimer's disease.

A major focus of Alzheimer's disease (AD) research has been finding sensitive outcome measures to disease progression in preclinical AD, as intervention studies begin to target this population. We hypothesize that tailored measures of longitudinal change of the medial temporal lobe (MTL) subregions (the sites of earliest cortical tangle pathology) are more sensitive to disease progression in preclinical AD compared to standard cognitive and plasma NfL measures. Longitudinal T1-weighted MRI of 337 participants were included, divided into amyloid-ß negative (Aß-) controls, cerebral spinal fluid p-tau positive (T+) and negative (T-) preclinical AD (Aß+ controls), and early prodromal AD. Anterior/posterior hippocampus, entorhinal cortex, Brodmann areas (BA) 35 and 36, and parahippocampal cortex were segmented in baseline MRI using a novel pipeline. Unbiased change rates of subregions were estimated using MRI scans within a 2-year-follow-up period. Experimental results showed that longitudinal atrophy rates of all MTL subregions were significantly higher for T+ preclinical AD and early prodromal AD than controls, but not for T- preclinical AD. Posterior hippocampus and BA35 demonstrated the largest group differences among hippocampus and MTL cortex respectively. None of the cross-sectional MTL measures, longitudinal cognitive measures (PACC, ADAS-Cog) and cross-sectional or longitudinal plasma NfL reached significance in preclinical AD. In conclusion, longitudinal atrophy measurements reflect active neurodegeneration and thus are more directly linked to active disease progression than cross-sectional measurements. Moreover, accelerated atrophy in preclinical AD seems to occur only in the presence of concomitant tau pathology. The proposed longitudinal measurements may serve as efficient outcome measures in clinical trials.

Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion.

This paper adapts the joint label fusion (JLF) multi-atlas image segmentation algorithm to the problem of multiple sclerosis (MS) lesion segmentation in multi-modal MRI. Conventionally, JLF requires a set of atlas images to be co-registered to the target image using deformable registration. However, given the variable spatial distribution of lesions in the brain, whole-brain deformable registration is unlikely to line up lesions between atlases and the target image. As a solution, we propose to first pre-segment the target image using an intensity regression based technique, yielding a set of "candidate" lesions. Each "candidate" lesion is then matched to a set of similar lesions in the atlas based on location and size; and deformable registration and JLF are applied at the level of the "candidate" lesion. The approach is evaluated on a dataset of 74 subjects with MS and shown to improve Dice similarity coefficient with reference manual segmentation by 12% over intensity regression technique.