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Longitudinal network connectivity measurements in medial temporal lobe subregions discriminate preclinical Alzheimer’s from amyloid-β negative controls

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Alzheimer's & Dementia 2020年第S5期16卷

作者： Pulkit Khandelwal Long Xie Danielle S Bassett Robin de Flores David A Wolk Paul A Yushkevich Sandhitsu R Das University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Inserm UMR-S U1237 Université de Caen-Normandie GIP Cyceron Caen France Penn Memory Center University of Pennsylvania Philadelphia PA USA

Background Finding sensitive outcome measures for disease progression in clinical trials of preclinical Alzheimer’s disease (AD) remains challenging. We hypothesize that longitudinal network connectivity measurements...

Background Finding sensitive outcome measures for disease progression in clinical trials of preclinical Alzheimer’s disease (AD) remains challenging. We hypothesize that longitudinal network connectivity measurements of the medial temporal lobe (MTL) subregions, sites of earliest tau pathology, are more sensitive to progression at early stages than annualized subregional atrophy rates, thus, increasing power to detect a significant effect in a shorter timeframe. Method Longitudinal T1-weighted MRI scans of 229 amyloid-β negative (Aβ-) controls and preclinical AD (Aβ+ controls) from ADNI were included (Table 1). Anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex (PHC) were segmented in baseline MRI (Figure 1, Xie et al . 2019). Deformation-based morphometry was used to obtain follow-up volume measurements, which were subsequently entered in a linear regression to estimate an annualized atrophy rate for each subregion. The network measurements of strength, clustering coefficient, and local efficiency (Soto et al . 2016) were obtained from an adjacency matrix (Figure 1) whose elements represented inter-regional covariance in volume measurements over time. At least three time points were used for each subject. The preclinical AD group was compared to the Aβ- controls with ANCOVA and Bonferroni correction, using age as a nuisance covariate. We compared the discriminative power of the network measurements against the atrophy rates for follow-up MRI scans within 4, 2 and 1.5 years from baseline. Result Discriminative ability of both atrophy rates and network measurements diminishes with decreasing number of follow-up scans. However, at least one network measurement was still significantly stronger, in absolute terms, than atrophy rates for all three follow-up scan time periods (Tables 2,3,4, Figure 2). Even with short follow-up (<1.5 years), a number of network measures were significant while atrophy rate was

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Deep Learning for Ultra High Resolution T2-weighted 7 TeslaEx vivoMagnetic Resonance Imaging Reveals Differential Subcortical Atrophy across Neurodegenerative Diseases

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Alzheimer's & Dementia 2022年第S5期18卷

作者： Pulkit Khandelwal Michael Tran Duong Eunice Chung Shokufeh Sadaghiani Sydney A Lim Sadhana Ravikumar Sanaz Arezoumandan Claire Peterson Madigan L Bedard Noah Capp Ranjit Ittyerah Elyse Migdal Grace Choi Emily Kopp Bridget Loja Patino Eusha Hasan Jiacheng Li Karthik Prabhakaran Gabor Mizsei Marianna Gabrielyan Theresa Schuck John L. Robinson Daniel T Ohm Ilya M. Nasrallah Eddie B Lee John Q Trojanowski Corey T McMillan Murray Grossman David J. Irwin Dylan M Tisdall Sandhitsu R. Das Laura EM Wisse David A. Wolk Paul A. Yushkevich 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 Digital Neuropathology Laboratory Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Translational Neuropathology Research Laboratory Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Philadelphia PA USA Penn Alzheimer’s Disease Research Center Perelman School of Medicine Philadelphia PA USA Perelman School of Medicine University og Pennsylvania Philadelphia PA USA Department of Diagnostic Radiology Clinical Sciences Lund University Lund Sweden Lund University Lund Sweden Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA

Background Ex vivo magnetic resonance imaging (MRI) of the brain provides remarkable advantages over in vivo MRI for linking neuroanatomy and morphometry to underlying pathology (Yushkevich et al. 2021, Ravikumar et a...

Background Ex vivo magnetic resonance imaging (MRI) of the brain provides remarkable advantages over in vivo MRI for linking neuroanatomy and morphometry to underlying pathology (Yushkevich et al. 2021, Ravikumar et al. 2021). Subcortical structures show atrophy in certain neurodegenerative diseases, especially Frontotemporal Lobar Degeneration with TDP-43 (FTLD-TDP) and four-repeat (4R) tauopathies (i.e., Corticobasal Degeneration, Progressive Supranuclear Palsy) (Miletić et al. 2022), yet few methods exist to measure subcortical atrophy in ex vivo MRI. We present a framework to quantify subcortical morphometry using 7 Tesla ex vivo MRI and distinguish atrophy patterns across neurodegenerative spectrums. Method A deep learning method, nnU-Net (Isensee et al. 2021), was trained on manual segmentations from only 3 brain hemispheres to obtain automated segmentations of 4 subcortical structures (caudate, putamen, globus pallidus, thalamus) across 38 subjects spanning Alzheimer's Disease (AD), Lewy Body Disease (LBD), FTLD-TDP, 4R tauopathies and miscellaneous tauopathies (Figure 1, Table 1). Subcortical volumes were extracted from automated segmentations. Cerebral cortical volume was computed via cortical segmentation method in Khandelwal et al. 2021. Regional volumes were evaluated via likelihood ratio tests (Figure 2), adjusted for covariates (age, sex and intracranial volume from in vivo MRI) and multiple tests. Separately, correlations were computed between subcortical volumes, cortical thicknesses at 18 landmark locations and neuropathological ratings (Khandelwal et al. 2021, Wisse et al. 2021, Figure 3). Result The pipeline validated regional volumetric relationships in neurodegeneration. Global cortex volume did not significantly differ among disease groups (Figure 2). Compared to AD, FTLD-TDP had significantly lower putamen and thalamus volumes while 4R tauopathies had reduced putamen and caudate volumes ( P ’s<0.05, adjusted for covariates/multiple comparisons

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

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Alzheimer's & Dementia 2022年第S4期17卷

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

Background Neuroimaging biomarkers that discriminate between healthy brain aging (BA) and Alzheimer’s disease (AD) are valuable in assessing the heterogeneity of neurodegeneration. Prior work has demonstrated that ma...

Background Neuroimaging biomarkers that discriminate between healthy brain aging (BA) and Alzheimer’s disease (AD) are valuable in assessing the heterogeneity of neurodegeneration. Prior work has demonstrated that machine learning can detect patterns of brain change related to the two processes on an individual level (including the SPARE [Spatial Patterns of Atrophy for REcognition]-AD and SPARE-BA indices investigated herein). However, the substantial overlap between brain regions affected in AD and BA confounds our ability to measure them independently. We present a methodology, and associated results, toward disentangling the two. Method T1-weighted MR images of 4,078 participants (48-95 years) with AD, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. First, a subset of 711 patients with probable AD and 711 age- and sex-matched probable CN adults were selected based purely on clinical diagnosis to train multivariate models of brain age (SPARE-BA1; regression of age using CN) and of an AD index (SPARE-AD1; classification of CN versus AD). Second, analogous groups a+AD and a-CN were selected based on clinical and molecular markers (a+AD: 711 amyloid-positive subjects with AD/MCI, as well as amyloid- and tau-positive CN adults; a-CN: 711 amyloid-negative CN adults). Finally, the combined group of a-CN and a+AD was used to train SPARE-BA3 model, with the notion that it would avoid AD-related changes. Result Several subcortical brain regions became more weighted in the SPARE-BA3 model than in SPARE-BA1 and SPARE-BA2, while regions in the temporal and occipital lobes became more weighted in SPARE-AD2. The correlation between the brain age gap (SPARE-BA minus chronological age) and SPARE-AD in the a-CN group was reduced to insignificant levels ( r =0.29 to -0.06). While both SPARE-AD scores well separated AD from CN (area-under-the-

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Application of histopathologically derived 3D tau burden map as in-vivo region of interest for biomarker analysis

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Alzheimer's & Dementia 2022年第S4期17卷

作者： Sandhitsu R. Das Long Xie Robin de Flores Monica Munoz Maria Mercedes Iniguez de Onzono Martin Ranjit Ittyerah Sydney A Lim Sadhana Ravikumar Madigan Lavery Stephen R Pickup Weixia Liu Jiancong Wang Ling Yu Hung Jade Lasserve Nicolas Vergnet Mengjin Dong Salena Cui Lauren McCollum John Robinson Theresa Schuck Murray Grossman Dylan M Tisdall Karthik Prabhakaran Gabor Mizsei Emilio Artacho-Perula Maria del Mar Arroyo Jimenez Maria del Pilar Marcos Rabal Francisco Javier Molina Romero Sandra Cebada Sanchez José Carlos Delgado González Carlos de la Rosa Prieto Marta Córcoles Parada Eddie B Lee John Q Trojanowski Daniel T Ohm Ilya M. Nasrallah Melissa Kelley Jacqueline Lane Michael Dicalogero Laura Wisse David J. Irwin Ricardo Insausti Paul Yushkevich David A. Wolk University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Inserm UMR-S U1237 Université de Caen-Normandie GIP Cyceron Caen France University of Castilla-La Mancha Albacete Spain Thomas Jefferson University Philadelphia PA USA Penn FTD Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Castilla-La Mancha - Albacete Campus Albacete Spain Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Frontotemporal Degeneration Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Memory Center University of Pennsylvania Philadelphia PA USA Department of Diagnostic Radiology Clinical Sciences Lund University Lund Sweden Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Memory Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background Quantitative three-dimensional maps of tau neurofibrillary tangles (NFT) burden derived from dense serial histology have potential application for in-vivo biomarker studies. We constructed a group-level NFT...

Background Quantitative three-dimensional maps of tau neurofibrillary tangles (NFT) burden derived from dense serial histology have potential application for in-vivo biomarker studies. We constructed a group-level NFT burden map from 15 medial temporal lobe (MTL) specimens, majority with P rimary A ge- R elated T auopathy (PART) or low-level Alzheimer’s disease neuropathologic change, and showed relatively greater NFT burden in the anterior vs. posterior MTL. We investigated whether in-vivo MRI and PET measures in ROIs derived from this map show meaningful biological relationships. Method Multimodal in-vivo imaging data from 292 participants in the A ging B rain C ohort were used. The group-level NFT burden map was mapped to each participant’s MRI to define an ROI mask, further divided into anterior (aMTL) and posterior (pMTL) ROIs. Cortical thickness (N=292) and 18 F-Flortaucipir SUVR maps (N=86) were computed. Participants’ age was correlated with average thickness in the aMTL, pMTL, and anatomically defined MTL ROIs. 18 F-Flortaucipir uptake was compared between aMTL and pMTL. The analyses were repeated in subsets of cognitive normal participants, and those with negative amyloid PET scans. Result Cortical thickness in the aMTL ROI showed stronger correlation with age than pMTL and anatomically defined MTL subregional thickness. A polynomial fit provided the best age regression, with older participants showing a parabolic decline in thickness around age 60, when substantial NFT accumulation begins in MTL. Further, tau tracer uptake in aMTL was slightly but significantly higher than in pMTL (SUVR 1.19 vs. 1.18, p=0.02). Conclusion We demonstrate the potential use of ex-vivo NFT burden maps for in-vivo image analysis. NFT deposition is particularly prominent in the anterior MTL in cases without or with low amyloid burden and at early Braak stage. Prior work suggests that even in the absence of amyloid, this tangle pathology may drive neurodegeneration in the aging p

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NOVEL MEASURES OF LONGITUDINAL CHANGE IN MEDIAL TEMPORAL LOBE SUBREGIONS DISCRIMINATE PRECLINICAL ALZHEIMER’S DISEASE PATIENTS FROM AMYLOID-β NEGATIVE CONTROLS

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Alzheimer's & Dementia 2019年第7期15卷 P397-P398页

作者： Long Xie Laura Wisse Sandhitsu R. Das Ranjit Ittyerah Robin de Flores Paul A. Yushkevich David A. Wolk Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

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P1-374: NOVEL MEASURES OF LONGITUDINAL CHANGE IN MEDIAL TEMPORAL LOBE SUBREGIONS DISCRIMINATE PRECLINICAL ALZHEIMER'S DISEASE PATIENTS FROM AMYLOID-β NEGATIVE CONTROLS

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Alzheimer's & Dementia 2019年第7S_PART_7期15卷

作者： Long Xie Laura Wisse Sandhitsu R. Das Ranjit Ittyerah Robin de Flores Paul A. Yushkevich David A. Wolk Alzheimer's Disease Neuroimaging Initiative Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

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Novel ex vivo MRI atlas of the medial temporal lobe can be used to characterize structural changes due to Alzheimer’s disease pathology

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Alzheimer's & Dementia 2020年第S4期16卷

作者： Sadhana Ravikumar Laura Wisse Long Xie Ranjit Ittyerah Sandhitsu R. Das John A. Detre Murray Grossman Madigan Lavery Sydney A. Lim David J. Irwin Theresa Schuck John Q. Trojanowski Emilio Artacho-Perula Maria Mercedes Iniguez de Onzono Martin Eddie B. Lee Gabor Mizsei Dylan M. Tisdall Maria del Mar Arroyo Jimenez Monica Munoz Jose Carlos Delgado Gonzalez Marta Corcoles Parada Carlos de la Rosa Prieto Sandra Cebada Sanchez Karthik Prabhakaran Maria del Pilar Marcos Rabal Francisco Javier Molina Romero John L. Robinson David A. Wolk Ricardo Insausti Paul A. Yushkevich 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 Penn Memory Center University of Pennsylvania Philadelphia PA USA

Background Finding effective biomarkers that can support early-stage clinical trials is a major challenge in Alzheimer’s Disease (AD). MRI measures of structural change in the medial temporal lobe (MTL) have proven t...

Background Finding effective biomarkers that can support early-stage clinical trials is a major challenge in Alzheimer’s Disease (AD). MRI measures of structural change in the medial temporal lobe (MTL) have proven to be sensitive to change in the early stages of AD. However, other frequently comorbid non-AD factors (e.g. cerebrovascular disease, TDP-43 pathology) also cause changes in the MTL. For MRI biomarkers to detect changes specifically linked to AD pathology , patterns of structural change must be linked to the underlying neuropathology. To provide this linkage, we use ex vivo imaging with pathologically derived ratings of AD and non-AD pathology to study the localized effects of the disease on MTL structure. 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 MTL specimens from 24 donors were scanned at 0.2x0.2x0.2mm 3 on 9.4T MRI. The specimens contain varying degrees of pathology spanning the spectrum of AD and common co-morbid non-AD pathologies. For 21 specimens, a pathologist provided semi-quantitative ratings of tau and TDP-43 severity (Table 1). Extending on techniques developed in our prior work, a computational atlas of the MTL was built via groupwise registration of all specimens’ MRI scans (Adler et al, 2018; Ravikumar et al, 2020). The atlas was used to investigate the correlation between tau pathology and cortical thickness. Result The developed atlas achieves excellent groupwise alignment and captures anatomical variability in the MTL despite its complex geometry (Figure 1). Correlation analysis between tau pathology and atrophy (correcting for age and TDP-43) reveals significant clusters near the transentorhinal region and subiculum (Figure 2). Conclusion These correlation patterns are consistent with the early Braak stages and resemble results from a previous study which looked

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High-resolution postmortem MRI reveals TDP-43 association with medial temporal lobe subregional atrophy

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Alzheimer's & Dementia 2020年第S4期16卷

作者： Laura Wisse Sadhana Ravikumar Ranjit Ittyerah Sydney A. Lim Jacqueline Lane Madigan Lavery Long Xie John L. Robinson Theresa Schuck Murray Grossman Eddie B. Lee Dylan M. Tisdall Karthik Prabhakaran John A. Detre Sandhitsu R. Das 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 J. Irwin John Q. Trojanowski 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 Penn Memory Center 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

Background The medial temporal lobe (MTL) is a hotspot of different neurodegenerative pathologies, and recent studies have shown associations of severity of pathology with atrophy measured on antemortem MRI. However, ...

Background The medial temporal lobe (MTL) is a hotspot of different neurodegenerative pathologies, and recent studies have shown associations of severity of pathology with atrophy measured on antemortem MRI. However, these studies are limited by the interval between time of scan and death and the relatively low resolution of the commonly acquired in vivo MRI scans, limiting the granularity of the regions being measured. We investigate the association of different neurodegenerative pathologies, particularly TDP-43, and the thickness of different MTL subregions measured on high-resolution postmortem MRI. As prior work suggests an anterior-to-posterior gradient of atrophy for TDP-43 in frontotemporal dementia spectrum disorders and Limbic-predominant Age-related TDP-43 Encephalopathy (LATE), we also examined differences along the long axis of the MTL. Method Tau, TDP-43, β-amyloid and α-synuclein pathology were rated (0-absent – 3-frequent) in the hippocampus and entorhinal cortex (ERC) of 35 individuals with and without neurodegenerative diseases (Table 1). Thickness measurements were obtained from 0.2x0.2x0.2 mm 3 post-mortem MRI scans of excised MTL specimens from the contralateral hemisphere (Figure 1) in the ERC, Brodmann Area 35 and 36, parahippocampal cortex, subiculum, cornu ammonis (CA)1 and the stratum radiatum lacunosum moleculare. For each region, thickness was measured at an anterior and posterior location using a semi-automated approach (Figure 2). Spearman’s rank correlations were performed, correcting for age, sex and hemisphere, including all four proteinopathies in the model. Result We find strong negative associations of TDP-43 with thickness in all cortical MTL regions (Table 2, Figure 3) and with CA1, averaged over the two locations. We repeated the analyses for the thickness measurements separately in the anterior and posterior locations, but did not find a clear difference along the longitudinal axis. This is potentially due to the severity of th

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Variability in tau-atrophy relationships reveals underlying phenotypes in individuals on the Alzheimer’s disease continuum

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Alzheimer's & Dementia 2020年第S5期16卷

作者： Sandhitsu R Das Long Xie Lauren McCollum Robin de Flores David J Irwin Ilya M Nasrallah Brad C Dickerson Paul Yushkevich David A Wolk Alzheimer's Disease Neuroimaging Initiative University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Inserm UMR-S U1237 Université de Caen-Normandie GIP Cyceron Caen France Penn FTD Center University of Pennsylvania Philadelphia PA USA Massachusetts General Hospital Boston MA USA Penn Memory Center University of Pennsylvania Philadelphia PA USA

Background Tau neurofibrillary tangles (T) are thought to be the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in AD. However, there is substantial variability in the T-N relat...

Background Tau neurofibrillary tangles (T) are thought to be the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in AD. However, there is substantial variability in the T-N relationship – manifested in higher or lower atrophy than expected for the level of tau in a given brain region. We quantitatively describe and categorize source(s) of variability using region-based measures of T and N to gain insight into underlying modulatory factors, including polypathology. Method Regional cortical thickness and 18 F-Flortaucipir SUVR were computed in 108 gray matter ROIs from cognitively-impaired, amyloid-positive individuals (n=137) in ADNI. ROI-specific residuals from a robust linear fit between SUVR and cortical thickness were computed. Using a threshold of +/- 1.5 standard deviation (S.D.), ROIs were categorized into “positive”, “negative”, or “neutral” residual (Fig 1). The residual signs for all ROIs were used in a hierarchical clustering framework to partition the dataset. Result Six groups appear to represent different phenotypes summarized in Table 1. Overall, the groups differed in age, burden of white matter hyperintensity (WMH), clinical status, and MMSE. However, they did not differ in tau burden in key brain ROIs (e.g. inferotemporal cortex, precuneus, angular gyrus). Group 1 is largest, consisting of individuals with low residuals described as having a canonical “T-N” relationship. Group 2 has significantly higher temporolimbic atrophy relative to tau with many cases having a pattern of atrophy suggestive of TDP-43 copathology. Group 3 appears to be a resilient phenotype with less atrophy relative to tau in lateral cortical regions. Group 4 is similar to Group 2 with high temporal atrophy, but also more diffuse atrophy. Group 5 also displays resilience, particularly in the temporal lobe. Group 6 shows widespread higher atrophy relative to tau, has the lowest MMSE and highest WMH. Conclusion We demonstrate that a measure o

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

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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...

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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