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

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

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

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

Time to Switch to Second-line Antiretroviral Therapy in Children With Human Immunodeficiency Virus in Europe and Thailand.

Background: Data on durability of first-line antiretroviral therapy (ART) in children with human immunodeficiency virus (HIV) are limited. We assessed time to switch to second-line therapy in 16 European countries and Thailand. Methods: Children aged <18 years initiating combination ART (≥2 nucleoside reverse transcriptase inhibitors [NRTIs] plus nonnucleoside reverse transcriptase inhibitor [NNRTI] or boosted protease inhibitor [PI]) were included. Switch to second-line was defined as (i) change across drug class (PI to NNRTI or vice versa) or within PI class plus change of ≥1 NRTI; (ii) change from single to dual PI; or (iii) addition of a new drug class. Cumulative incidence of switch was calculated with death and loss to follow-up as competing risks. Results: Of 3668 children included, median age at ART initiation was 6.1 (interquartile range (IQR), 1.7-10.5) years. Initial regimens were 32% PI based, 34% nevirapine (NVP) based, and 33% efavirenz based. Median duration of follow-up was 5.4 (IQR, 2.9-8.3) years. Cumulative incidence of switch at 5 years was 21% (95% confidence interval, 20%-23%), with significant regional variations. Median time to switch was 30 (IQR, 16-58) months; two-thirds of switches were related to treatment failure. In multivariable analysis, older age, severe immunosuppression and higher viral load (VL) at ART start, and NVP-based initial regimens were associated with increased risk of switch. Conclusions: One in 5 children switched to a second-line regimen by 5 years of ART, with two-thirds failure related. Advanced HIV, older age, and NVP-based regimens were associated with increased risk of switch

A Critical Appraisal of the Hippocampal Subfield Segmentation Package in FreeSurfer

In the last decade, the in vivo assessment of hippocampal subfields has received increasing attention because of the differ-ential role of hippocampal subfields in sev-eral neuropsychiatric diseases (Geuze et al., 2005). Several manual segmentation pro-tocols have been developed for 3–7 T MRI (Mueller et al., 2007; Van Leemput et al., 2008; La Joie et al., 2010; Wisse et al., 2012), some of which are automated (Van Leem-put et al., 2008; Yushkevich et al., 2009). One of these automated protocols (Van Leemput et al., 2008, 2009) has recently been implemented in FreeSurfer (Fischl, 2012), a freely available easy-to-use set of automated brain MRI analysis tools. This has made hippocampal subfield segmen-tation available to everyone with 1.5–3 T MRI data and the method is being used in an increasing number of studies (Teiche

Medial Temporal Lobe Networks in Alzheimer's Disease : Structural and Molecular Vulnerabilities

The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them

Perivascular spaces on 7 Tesla brain MRI are related to markers of small vessel disease but not to age or cardiovascular risk factors

Cerebral perivascular spaces (PVS) are small physiological structures around blood vessels in the brain. MRI visible PVS are associated with ageing and cerebral small vessel disease (SVD). 7 Tesla (7T) MRI improves PVS detection. We investigated the association of age, vascular risk factors, and imaging markers of SVD with PVS counts on 7 T MRI, in 50 persons aged ≥ 40. The average PVS count ± SD in the right hemisphere was 17 ± 6 in the basal ganglia and 71 ± 28 in the semioval centre. We observed no relation between age or vascular risk factors and PVS counts. The presence of microbleeds was related to more PVS in the basal ganglia (standardized beta 0.32; p = 0.04) and semioval centre (standardized beta 0.39; p = 0.01), and white matter hyperintensity volume to more PVS in the basal ganglia (standardized beta 0.41; p = 0.02). We conclude that PVS counts on 7T MRI are high and are related SVD markers, but not to age and vascular risk factors. This latter finding may indicate that due to the high sensitivity of 7T MRI, the correlation of PVS counts with age or vascular risk factors may be attenuated by the detection of "normal", non-pathological PVS

Perivascular spaces on 7 Tesla brain MRI are related to markers of small vessel disease but not to age or cardiovascular risk factors

Cerebral perivascular spaces (PVS) are small physiological structures around blood vessels in the brain. MRI visible PVS are associated with ageing and cerebral small vessel disease (SVD). 7 Tesla (7T) MRI improves PVS detection. We investigated the association of age, vascular risk factors, and imaging markers of SVD with PVS counts on 7 T MRI, in 50 persons aged ≥ 40. The average PVS count ± SD in the right hemisphere was 17 ± 6 in the basal ganglia and 71 ± 28 in the semioval centre. We observed no relation between age or vascular risk factors and PVS counts. The presence of microbleeds was related to more PVS in the basal ganglia (standardized beta 0.32; p = 0.04) and semioval centre (standardized beta 0.39; p = 0.01), and white matter hyperintensity volume to more PVS in the basal ganglia (standardized beta 0.41; p = 0.02). We conclude that PVS counts on 7T MRI are high and are related SVD markers, but not to age and vascular risk factors. This latter finding may indicate that due to the high sensitivity of 7T MRI, the correlation of PVS counts with age or vascular risk factors may be attenuated by the detection of "normal", non-pathological PVS

Phospho-tau with subthreshold tau-PET predicts increased tau accumulation rates in amyloid-positive individuals

Different tau biomarkers become abnormal at different stages of Alzheimer's disease, with CSF phospho-tau typically becoming elevated at subthreshold levels of tau-PET binding. To capitalize on the temporal order of tau biomarker-abnormality and capture the earliest changes of tau accumulation, we implemented an observational study design to examine longitudinal changes in Tau-PET, cortical thickness and cognitive decline in amyloid-β-positive (A+) individuals with elevated CSF P-tau levels (P+) but subthreshold Tau-PET retention (T-). To this end, individuals without dementia (i.e., cognitively unimpaired or mild cognitive impairment, N = 231) were selected from the BioFINDER-2 study. Amyloid-β-positive (A+) individuals were categorized into biomarker groups based on cut-offs for abnormal CSF P-tau217 and [18F]RO948 (Tau) PET, yielding groups of tau-concordant-negative (A + P-T-; n = 30), tau-discordant (i.e., A + P+T-; n = 48) and tau-concordant-positive (A + P+T+; n = 18) individuals. In addition, 135 amyloid-β-negative, tau-negative, cognitively unimpaired individuals served as controls. Differences in annual change in regional Tau-PET, cortical thickness and cognition between the groups were assessed using general linear models, adjusted for age, sex, clinical diagnosis and (for cognitive measures only) education. Mean follow-up time was ∼2 years. Longitudinal increase in Tau-PET was faster in the A + P+T- group than in the control and A + P-T- groups across medial temporal and neocortical regions, with the highest accumulation rates in the medial temporal lobe. The A + P+T- group showed a slower rate of increases in tau-PET compared to the A + P+T+ group, primarily in neocortical regions. We did not detect differences in yearly change in cortical thickness or in cognitive decline between the A + P+T- and A + P-T- groups. The A + P+T+ group, however, showed faster cognitive decline compared to all other groups. Altogether, these findings suggest that the A + P+T- biomarker profile in persons without dementia is associated with an isolated effect on increased Tau-PET accumulation rates but not on cortical thinning and cognitive decline. While this suggests that the tau-discordant biomarker profile is not strongly associated with short-term clinical decline, this group does represent an interesting population for monitoring effects of interventions with disease modifying agents on tau accumulation in early Alzheimer's disease, and for examining the emergence of tau aggregates in Alzheimer's disease. Further, we suggest to update the AT(N) criteria for Alzheimer's disease biomarker classification to APT(N)