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Optimized extraction of the medial temporal lobe for postmortem MRI based on custom 3D printed molds

Alzheimer's & Dementia 2020年第S4期16卷

作者： Jade Lasserve Sydney A. Lim Laura Wisse Ranjit Ittyerah Sadhana Ravikumar Madigan Lavery John L. Robinson Theresa Schuck Murray Grossman Eddie B. Lee Paul A. Yushkevich Dylan M. Tisdall Karthik Prabhakaran Gabor Mizsei Emilio Artacho-Perula Maria Mercedes Iniguez de Onzono Martin Maria del Mar Arroyo Jimenez Monica Munoz Francisco Javier Molina Romero Maria del Pilar Marcos Rabal David J. Irwin John Q. Trojanowski David A. Wolk Ricardo Insausti Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Penn FTD Center University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA University of Castilla-La Mancha Albacete Spain Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background Structural magnetic resonance imaging (MRI) biomarkers are important for early detection of Alzheimer’s Disease (AD). However, atrophy measures can be confounded by changes due to aging and comorbid non-AD neurodegenerative pathologies. Linking postmortem MRI of the medial temporal lobe (MTL) to histopathology may identify focal patterns of change associated specifically with early AD. We implemented a pipeline of high-resolution MRI of MTL specimens and serial histopathology imaging [REF]. However, the task of extracting the intact MTL specimen such that it fits into the MRI coil requires anatomical expertise and has proven error prone. Here we present an algorithm to automatically create 3D printed molds guiding MTL extraction. Method INPUTS: 7T MRI scan of a formalin-fixed hemisphere in which the hemisphere, MTL ROI and optional second ROI to be spared during cutting (e.g. frontal lobe) have been segmented using ITK-SNAP semi-automatic segmentation tools. OUTPUTS: Two 3D printed molds with slits that guide cutting. Mold 1 holds the whole hemisphere, guiding four cuts orthogonal to the midsagittal plane (Figure 1). Mold 2 holds the extracted tissue block, guiding three subsequent longitudinal cuts that trim the tissue to fit into a 50mm cylindrical holder (Figure 2). The positioning of the cuts can be specified interactively by the user using ITK-SNAP (translating and rotating 3D images representing cutting planes) or automatically by optimizing (using Powell’s method) energy functions that minimize the volume of the final piece of tissue under multiple constraints (see Figures 3 and 4). Result The algorithm with interactively positioned cut planes was used in four hemispheres; the automated version in one (Figure 5). For each MRI scan, the MTL was intact. By contrast, retrospective review revealed cutting errors in 48% of manually cut specimens. Conclusion Our image-guided approach reduces errors and dependence on anatomical expertise; allows more tiss

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CONTRIBUTION OF TAU, TDP-43, β-AMYLOID AND α-SYNUCLEIN TO MEDIAL TEMPORAL LOBE ATROPHY

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Alzheimer's & Dementia 2019年第7期15卷 P435-P435页

作者： Robin de Flores Laura Wisse Sandhitsu R. Das Long Xie Corey T. McMillan John Q. Trojanowski Murray Grossman Eddie B. Lee David Irwin Paul A. Yushkevich David A. Wolk Department of Neurology University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Penn FTD Center Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA Penn Memory Center University of Pennsylvania Philadelphia PA USA

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P1-441: CONTRIBUTION OF TAU, TDP-43, β-AMYLOID AND α-SYNUCLEIN TO MEDIAL TEMPORAL LOBE ATROPHY

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Alzheimer's & Dementia 2019年第7S_PART_8期15卷

作者： Robin de Flores Laura Wisse Sandhitsu R. Das Long Xie Corey T. McMillan John Q. Trojanowski Murray Grossman Eddie B. Lee David Irwin Paul A. Yushkevich David A. Wolk Department of Neurology University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Penn FTD Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA Penn Memory Center University of Pennsylvania Philadelphia PA USA

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PATHOLOGICAL DRIVERS OF NEURODEGENERATION IN SUSPECTED NON-ALZHEIMER’S PATHOPHYSIOLOGY

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Alzheimer's & Dementia 2019年第7期15卷 P556-P557页

作者： Laura Wisse de Flores Robin Sandhitsu R. Das Corey T. McMillan John Q. Trojanowski Murray Grossman Eddie B. Lee David Irwin Paul A. Yushkevich David A. Wolk University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Penn FTD Center Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Penn FTD Center University of Pennsylvania Philadelphia PA USA The Trustees of the University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA Penn Memory Center University of Pennsylvania Philadelphia PA USA

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Disentangling Alzheimer’s disease neurodegeneration from typical brain aging using machine learning

arXiv

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arXiv 2021年

作者： Hwang, Gyujoon Abdulkadir, Ahmed Erus, Guray Habes, Mohamad Pomponio, Raymond Shou, Haochang Doshi, Jimit Mamourian, Elizabeth Rashid, Tanweer Bilgel, Murat Fan, Yong Sotiras, Aristeidis Srinivasan, Dhivya Morris, John C. Marcus, Daniel Albert, Marilyn S. Bryan, Nick R. Resnick, Susan M. Nasrallah, Ilya M. Davatzikos, Christos Wolk, David A. Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia United States Department of Radiology University of Pennsylvania Philadelphia United States Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio United States Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia United States Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore United States Department of Radiology Washington University in St. Louis St. Louis United States Department of Neurology Washington University in St. Louis St. Louis United States Department of Neurology Johns Hopkins University School of Medicine United States Department of Diagnostic Medicine University of Texas AustinAustin United States Department of Neurology Penn Memory Center University of Pennsylvania Philadelphia United States

Neuroimaging biomarkers that distinguish between changes due to typical brain aging and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multi-variate patterns of brain change related to the two processes (including the SPARE-AD [Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease] and SPARE-BA [of Brain Aging] scores investigated herein). However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, toward disentangling the two. T1-weighted MRI images of 4,054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment, or cognitively normal diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically- or molecularly-defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched cognitively normal adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using cognitively normal individuals) and SPARE-AD1 (classification of cognitively normal versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718;consisting of amyloid-positive Alzheimer’s disease, amyloid-positive mild cognitive impairment, and amyloid- and tau-positive cognitively normal individuals) and amyloid-negative cognitively normal group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative cognitively normal individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regar

关键词： Magnetic resonance imaging

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ALZHEIMER’S DISEASE AND THE HIPPOCAMPUS: NOVEL INSIGHTS FROM AN EX VIVO COMPUTATIONAL ATLAS COMBINING MRI AND HISTOLOGY

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Alzheimer's & Dementia 2017年第7期13卷 P1534-P1535页

作者： Laura E.M. Wisse Daniel Adler Ranjit Ittyerah John B. Pluta Song-Lin Ding Long Xie Jiancong Wang Salmon Kadivar John L. Robinson Theresa Schuck John Q. Trojanowski Murray Grossman John A. Detre Mark A. Elliott Jon B. Toledo Weixia Liu Stephen Pickup Sandhitsu R. Das David A. Wolk Paul A. Yushkevich Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Allen Brain Institute Seattle WA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn FTD Center University of Pennsylvania Philadelphia PA USA Institute on Aging / University of Pennsylvania Philadelphia PA USA Houston Methodist Hospital Houston TX USA Penn Memory Center University of Pennsylvania Philadelphia PA USA

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High gamma and beta band oscillations in left ventral posterior parietal cortex are regionally dissociated during verbal episodic encoding and recall

arXiv

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arXiv 2019年

作者： Rubinstein, D. Camarillo-Rodriguez, L. Waldman, Z.J. Orosz, I. Stein, J. Das, S. Gorniak, R. Sharan, A.D. Gross, R. Lega, B.C. Zaghloul, K. Jobst, B.C. Davis, K.A. Wanda, P.A. Worrell, G. Sperling, M.R. Weiss, S.A. Depts. of Neurology and Neuroscience Dept. of Neurology Dept. of Radiology Dept. of Neurosurgery Thomas Jefferson University PhiladelphiaPA19107 United States Dept. of Neurology David Geffen School of Medicine at UCLA Los AngelesCA90095 United States Penn Image Computing and Science Laboratory Department of Radiology Department of Psychology Department of Neurology University of Pennsylvania PhiladelphiaPA19104 United States Department of Neurosurgery Emory University AtlantaGA United States University of Texas Southwestern Medical Center Department of Neurosurgery DallasTX75390 United States Surgical Neurology Branch NINDS NIH BethesdaMD20892 United States Dartmouth-Hitchcock Medical Center Department of Neurology LebanonNH United States Depts. of Neurology Physiology & biomedical Engineering Mayo Clinic RochesterMN55905

(199) The posterior parietal cortex (PPC) has a unique role in memory retrieval: fMRI1–4 and electrocorticography5 studies suggest that within the ventral PPC (VPC) specifically, there is an anterior-posterior functional divergence between externally-oriented and internally-oriented attention to memory (AtoM)6–9. However, the role of VPC during verbal episodic encoding, and the relationship between encoding- and retrieval-related activity, is less understood. Here we show that activation within a subregion of VPC is doubly dissociated between its anterior and posterior parts, during encoding compared to recall in a free recall task. We found that regional activation defined by increased high gamma power and decreased beta power oscillations during encoding and recall correlated with recall success. During word encoding, iEEG sites that showed this correlation were located anterior to those that showed deactivation. Conversely, during word recall, sites that showed stronger correlations between activity and number of words recalled were located more posteriorly. Our results demonstrate the significance of high gamma and beta oscillations suggesting a push-pull relationship between attention to external stimuli and internal memories within left ventral PPC. Knowledge of this divergence of function along the anterior-posterior axis within left ventral PPC may prove useful for guiding brain stimulation strategies. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal encoding

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Tau-Neurodegeneration mismatch reveals vulnerability and resilience in Alzheimer’s continuum and Non-Alzheimer’s pathophysiology

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Alzheimer's & Dementia 2022年第S5期18卷

作者： Xueying Lyu Michael Tran Duong Long Xie Hayley Richardson Robin de Flores Michael DiCalogero Corey T McMillan John Robinson John Q Trojanowski Murray Grossman Eddie B Lee David J. Irwin Brad C. Dickerson Sharon X Xie Ilya M. Nasrallah Paul A. Yushkevich David A. Wolk Sandhitsu R. Das University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn FTD Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA Harvard Medical School Boston MA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA

Background In previous work (Das et al., 2021), we exploited the non-specific nature of neurodegeneration by investigating the amount and spatial pattern of deviation from the level of neurodegeneration (N) expected for a given level of tau (T). Using a data-driven clustering approach based on T-N mismatch , we found phenotypes that had greater N than expected for T (“vulnerable”) and phenotypes with less N than expected for T (“resilient”) in specific patterns. Here we extend this work to include amyloid positive (A+) and negative (A-) participants. The logic of inclusion of A- individuals is that areas of mismatch in A+ individuals is likely driven by factors outside of AD pathology, which can be present in the absence of AD pathology. Method We employed cortical thickness to represent N and 18 F-flortaucipir tracer uptake as T for 104 bilateral cortical regions of interest (ROI) in 343 symptomatic individuals including both A+ and A- from ADNI. We established T-N robust regression and partitioned participants based on thresholded residuals. Longitudinal trajectories of cognitive scores were assessed with linear mixed-effects model effect models using CDRSB as the dependent variable. Result We replicated prior work and obtained clusters with distinct T-N mismatch patterns. We defined the largest group as “canonical”, as it displayed typical T-N relationships (N∼T, low residuals). The limbic vulnerable cluster displayed N>T (negative residuals) in limbic regions, while diffuse vulnerable group displayed N>T throughout the cortex. Alternatively, the resilient (N

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Unfolding the medial temporal lobe to characterize neurodegeneration due to Alzheimer's disease pathology

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Alzheimer's & Dementia 2022年第S4期17卷

作者： Sadhana Ravikumar Laura Wisse Sydney A. Lim Ranjit Ittyerah Long Xie Madigan L. Bedard Sandhitsu R. Das David J. Irwin Eddie B. Lee Dylan M. Tisdall Karthik Prabhakaran John A. Detre John Q. Trojanowski John Robinson Theresa Schuck Murray Grossman Gabor Mizsei Emilio Artacho-Perula Maria Mercedes Iniguez de Onzono Martin Maria del Mar Arroyo Jimenez Monica Munoz Francisco Javier Molina Romero Maria del Pilar Marcos Rabal Sandra Cebada Sanchez Jose Carlos Delgado Gonzalez Carlos de la Rosa Prieto Marta Corcoles Parada David A. Wolk Ricardo Insausti Paul A. Yushkevich Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Lund University Lund Sweden Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Castilla-La Mancha Albacete Spain

Background Measurements of medial temporal lobe (MTL) neurodegeneration derived using MRI have been shown to be sensitive to changes during the early stages of AD. The specificity of these measurements to tau neurofibrillary tangle (NFT) pathology is limited by other frequently comorbid non-AD factors which also cause structural changes in the MTL. Here, we directly link changes in MTL structure to underlying NFT pathology by combining ex vivo MRI with ratings of NFT severity derived from serial histology using a dataset of 18 human MTL specimens. We hypothesize that such an analysis can be used to define MTL “hotspots” where in vivo measures will be more sensitive to disease progression in preclinical AD than current state of the art biomarkers. Method Ex vivo specimens from 18 donors were scanned at 0.2x0.2x0.2mm 3 on 9.4T MRI. Following MRI scanning, the specimens underwent histological processing with staining for cytoarchitecture and in 15 specimens, immunohistochemistry (IHC) with the anti-tau AT8 antibody. Using a topological unfolding method (DeKraker et al. 2018), we created 2D representations of the extra-hippocampal cortex which implicitly align cortical folding patterns across specimens (Fig. 1). An average MTL subregion segmentation was generated in unfolded space using manual segmentations completed in 11 specimens on the basis of cytoarchitecture. Additionally, heat maps quantifying NFT burden in each of the specimens with anti-tau IHC sections were generated using a deep learning algorithm (Yushkevich et al. 2021). Using the heatmaps and the average subregion segmentation, we investigated the relationship between NFT severity and cortical thickness. Result Correlation analysis between NFT measures and thickness (correcting for age) reveals strong associations in the entorhinal cortex and the border of Brodmann Area 35, consistent with the early Braak regions, and parts of Brodmann Area 36 (Fig. 2). Conclusion We present an unfolding framework applied

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A multi-site study of Alzheimer’s disease neuroimaging and cognitive biomarkers in 2819 cognitively normal individuals

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Alzheimer's & Dementia 2023年第S14期19卷

作者： Long Xie Sandhitsu R. Das Laura EM Wisse Xueying Lyu Emily McGrew Robin de Flores Eli Gibson Paul A. Yushkevich David A. Wolk Alzheimer’s Disease Neuroimaging Initiative Siemens Healthineers Princeton NJ USA Correspondece Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Department of Diagnostic Radiology Clinical Sciences Lund University Lund Sweden University of Pennsylvania Philadelphia PA USA Normandie Univ UNICAEN INSERM U1237 PhIND “Physiopathology and Imaging of Neurological Disorders” NeuroPresage Team GIP Cyceron Caen France Siemens Heathineers Princeton NJ USA Institute on Aging University of Pennsylvania Philadelphia PA USA

Background Developing effective Alzheimer’s disease (AD) biomarkers in cognitively normal (CN) individuals is crucial. Prior studies have identified neuroimaging and cognitive measures that are sensitive to preclinical AD. However, the generalizability of these biomarkers in large, multi-site cohorts and their power to predict disease progression remain unclear. Structural MRI, tau PET and standardized cognitive tests of 2819 CN adults were pooled from three studies to investigate the power of these measures in discriminating β-amyloid positivity (A+/-) and predicting disease progression. Methods T1-weighted MRI and cognitive data of 2819 CN (A-/A+: 1180/1540, Table 2) individuals from ADNI, HABS and A4 were included. Baseline medial temporal lobe structural measures were extracted from MRI (complete list in Table 1). For participants with prospective longitudinal MRI or cognitive measures (4.5 years), annualized change rate of each measurement was estimated. ANCOVA and receiver operating characteristic analyses were used to test biomarker differences between A+/A-. In 563 CN with cross-sectional tau PET available, stepwise linear mixed effect modeling was performed to identify the subset of baseline cross-sectional biomarkers (tau, MRI and cognition) that yields the optimal model (smallest Akaike information criterion) in predicting longitudinal atrophy and cognitive decline. The area under the curve (AUC) of each model in discriminating the first (fast) and last (slow progressors) tertiles of each longitudinal measurement was reported. Results Significant differences between A+/A- (Table 2) were observed in cross-sectional tau (most significant), cross-sectional and longitudinal cognitive, as well as longitudinal structural MRI measures. When predicting disease progression with baseline measures (Figure 1-a), tau (selected in all the models) and cognitive biomarkers were included in the most predictive models while MRI biomarkers did not provide additional informa

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