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.Comment: Submitted to NeuroImage for revie

Childhood socioeconomic status and childhood maltreatment: Distinct associations with brain structure

The present study examined the relationship between childhood socioeconomic status (SES), childhood maltreatment, and the volumes of the hippocampus and amygdala between the ages of 25 and 36 years. Previous work has linked both low SES and maltreatment with reduced hippocampal volume in childhood, an effect attributed to childhood stress. In 46 adult subjects, only childhood maltreatment, and not childhood SES, predicted hippocampal volume in regression analyses, with greater maltreatment associated with lower volume. Neither factor was related to amygdala volume. When current SES and recent interpersonal stressful events were also considered, recent interpersonal stressful events predicted smaller hippocampal volumes over and above childhood maltreatment. Finally, exploratory analyses revealed a significant sex by childhood SES interaction, with women’s childhood SES showing a significantly more positive relation (less negative) with hippocampus volume than men’s. The overall effect of childhood maltreatment but not SES, and the sex-specific effect of childhood SES, indicate that different forms of stressful childhood adversity affect brain development differently

Hippocampal T2 hyperintensities on 7Tesla MRI

AbstractHippocampal focal T2 hyperintensities (HT2Hs), also referred to as hippocampal sulcal cavities, are a common finding on Magnetic Resonance (MR) images. There is uncertainty about their etiology and clinical significance. In this study we aimed to describe these HT2Hs in more detail using high resolution 7Tesla MR imaging, addressing 1) the MR signal characteristics of HT2Hs, 2) their occurrence frequency, 3) their location within the hippocampus, and 4) their relation with age. We also performed an explorative post-mortem study to examine the histology of HT2Hs.Fifty-eight persons without a history of invalidating neurological or psychiatric disease (mean age 64±8years; range 43–78years), recruited through their general practitioners, were included in this study. They all underwent 7Tesla MRI, including a T1, T2, and FLAIR image. MR signal characteristics of the HT2Hs were assessed on these images by two raters. Also, the location and number of the HT2Hs were assessed. In addition, four formalin-fixed brain slices from two subjects were scanned overnight. HT2Hs identified in these slices were subjected to histopathological analysis.HT2Hs were present in 97% of the subjects (median number per person 10; range 0–20). All HT2Hs detected on the T2 sequence were hypointense on T1 weighted images. Of all HT2Hs, 94% was hypointense and 6% hyperintense on FLAIR. FLAIR hypointense HT2Hs were all located in the vestigial sulcus of the hippocampus, FLAIR hyperintense HT2Hs in the hippocampal sulcus or the gray matter. Post-mortem MRI and histopathological analysis suggested that the hypointense HT2Hs on FLAIR were cavities filled with cerebrospinal fluid. A hyperintense HT2H on FLAIR proved to be a microinfarct upon microscopy.In conclusion, hippocampal T2Hs are extremely common and unrelated to age. They can be divided into two types (hypo- and hyperintense on FLAIR), probably with different etiology

Sharing brain imaging data in the Open Science era:how and why?

The sharing of human neuroimaging data has great potential to accelerate the development of imaging biomarkers in neurological and psychiatric disorders; however, major obstacles remain in terms of how and why to share data in the Open Science context. In this Health Policy by the European Cluster for Imaging Biomarkers, we outline the current main opportunities and challenges based on the results of an online survey disseminated among senior scientists in the field. Although the scientific community fully recognises the importance of data sharing, technical, legal, and motivational aspects often prevent active adoption. Therefore, we provide practical advice on how to overcome the technical barriers. We also call for a harmonised application of the General Data Protection Regulation across EU countries. Finally, we suggest the development of a system that makes data count by recognising the generation and sharing of data as a highly valuable contribution to the community.</p

Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website: https://pulkit-khandelwal.github.io/exvivo-brain-upen

Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories

The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the entorhinal and parahippocampal cortices as well as Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 μm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized slices spaced 5 mm apart (pixel size 0.4 μm at 20× magnification). Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while the definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed less saliently. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed neuroimaging research on the human MTL cortex

Eigenvalue asymptotics for weighted Laplace equations on rough Riemannian manifolds with boundary

Our topological setting is a smooth compact manifold of dimension two or higher with smooth boundary. Although this underlying topological structure is smooth, the Riemannian metric tensor is only assumed to be bounded and measurable. This is known as a rough Riemannian manifold. For a large class of boundary conditions we demonstrate a Weyl law for the asymptotics of the eigenvalues of the Laplacian associated to a rough metric. Moreover, we obtain eigenvalue asymptotics for weighted Laplace equations associated to a rough metric. Of particular novelty is that the weight function is not assumed to be of fixed sign, and thus the eigenvalues may be both positive and negative. Key ingredients in the proofs were demonstrated by Birman and Solomjak nearly fifty years ago in their seminal work on eigenvalue asymptotics. In addition to determining the eigenvalue asymptotics in the rough Riemannian manifold setting for weighted Laplace equations, we also wish to promote their achievements which may have further applications to modern problems

Ellipro scores of donor epitope specific HLA antibodies are not associated with kidney graft survival

In kidney transplantation, donor HLA antibodies are a risk factor for graft loss. Accessibility of donor eplets for HLA antibodies is predicted by the ElliPro score. The clinical usefulness of those scores in relation to transplant outcome is unknown. In a large Dutch kidney transplant cohort, Ellipro scores of pretransplant donor antibodies that can be assigned to known eplets (donor epitope specific HLA antibodies [DESAs]) were compared between early graft failure and long surviving deceased donor transplants. We did not observe a significant Ellipro score difference between the two cohorts, nor significant differences in graft survival between transplants with DESAs having high versus low total Ellipro scores. We conclude that Ellipro scores cannot be used to identify DESAs associated with early versus late kidney graft loss in deceased donor transplants.</p

Determination of the clinical relevance of donor epitope-specific HLA-antibodies in kidney transplantation

In kidney transplantation, survival rates are still partly impaired due to the deleterious effects of donor specific HLA antibodies (DSA). However, not all luminex-defined DSA appear to be clinically relevant. Further analysis of DSA recognizing polymorphic amino acid configurations, called eplets or functional epitopes, might improve the discrimination between clinically relevant vs. irrelevant HLA antibodies. To evaluate which donor epitope-specific HLA antibodies (DESAs) are clinically important in kidney graft survival, relevant and irrelevant DESAs were discerned in a Dutch cohort of 4690 patients using Kaplan–Meier analysis and tested in a cox proportional hazard (CPH) model including nonimmunological variables. Pre-transplant DESAs were detected in 439 patients (9.4%). The presence of certain clinically relevant DESAs was significantly associated with increased risk on graft loss in deceased donor transplantations (p &lt; 0.0001). The antibodies recognized six epitopes of HLA Class I, 3 of HLA-DR, and 1 of HLA-DQ, and most antibodies were directed to HLA-B (47%). Fifty-three patients (69.7%) had DESA against one donor epitope (range 1–5). Long-term graft survival rate in patients with clinically relevant DESA was 32%, rendering DESA a superior parameter to classical DSA (60%). In the CPH model, the hazard ratio (95% CI) of clinically relevant DESAs was 2.45 (1.84–3.25) in deceased donation, and 2.22 (1.25–3.95) in living donation. In conclusion, the developed model shows the deleterious effect of clinically relevant DESAs on graft outcome which outperformed traditional DSA-based risk analysis on antigen level.</p

Ellipro scores of donor epitope specific HLA antibodies are not associated with kidney graft survival

In kidney transplantation, donor HLA antibodies are a risk factor for graft loss. Accessibility of donor eplets for HLA antibodies is predicted by the ElliPro score. The clinical usefulness of those scores in relation to transplant outcome is unknown. In a large Dutch kidney transplant cohort, Ellipro scores of pretransplant donor antibodies that can be assigned to known eplets (donor epitope specific HLA antibodies [DESAs]) were compared between early graft failure and long surviving deceased donor transplants. We did not observe a significant Ellipro score difference between the two cohorts, nor significant differences in graft survival between transplants with DESAs having high versus low total Ellipro scores. We conclude that Ellipro scores cannot be used to identify DESAs associated with early versus late kidney graft loss in deceased donor transplants