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CROSS-SECTIONAL AND LONGITUDINAL MEDIAL TEMPORAL LOBE SUBREGIONAL ATROPHY IN SUSPECTED NON-ALZHEIMER’S PATHOPHYSIOLOGY

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Alzheimer's & Dementia 2019年第7期15卷 P8-P9页

作者： Laura Wisse Long Xie Sandhitsu R. Das de Flores Robin 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 Memory Center University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

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Aging and Alzheimer’s Disease Have Dissociable Effects on Local and Regional Medial Temporal Lobe Connectivity

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Alzheimer's & Dementia 2023年第S10期19卷

作者： Stanislau Hrybouski Sandhitsu R. Das Long Xie Laura EM Wisse Melissa Kelley Jacqueline Lane Monica Sherin Michael DiCalogero Ilya M. Nasrallah John A. Detre 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 Lund University Lund Sweden Department of Radiology University of Pennsylvania Philadelphia PA USA

Background The extent to which pathological processes in aging and Alzheimer’s disease (AD) relate to functional disruption of the medial temporal lobe (MTL)-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity (FC) alterations between anterior and posterior regions of the MTL and in MTL-associated functional communities – the Anterior-Temporal (AT) and Posterior-Medial (PM) networks – in normal agers, individuals with preclinical AD, and patients with Mild Cognitive Impairment or mild dementia due to AD. Method In this cross-sectional study, we analyzed data from 179 individuals from the Aging Brain Cohort study of the penn ADRC. Detailed information about participants is provided in Table 1. For intra-MTL FC comparisons, the MTL subregions were segmented using the automated segmentation of hippocampal subfields-T1 (ASHS-T1) pipeline (Fig. 1a). When modeling the MTL’s interactions with the rest of the cortex, we employed four MTL ROIs (left/right × anterior/posterior) derived from an ex vivo atlas of tau accumulation in the MTL. Our functional datasets were preprocessed using a customized fMRIprep pipeline. Sparse network estimations and modularity-based consensus clustering were used to reconstruct the AT and PM network systems (Fig. 1b). Age effect analyses and group comparisons along the AD continuum were performed using the General Linear Model within the network-based statistical framework. Result The preclinical stage of AD was characterized by increased FC between the perirhinal cortex and other regions of the MTL, as well as between the anterior MTL and its direct neighbors in the AT network (Fig. 1c-d). This effect was not present in symptomatic AD. Instead, symptomatic patients displayed reduced hippocampal and intra-PM connectivity. For normal aging, our results led to three main conclusions (for visuals, see Fig. 2). First, intra-network connectivity of both the AT and PM networks decreases wi

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Epigenetic measurement of biological age associates with tau load in normal brain aging

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Alzheimer's & Dementia 2020年第S5期16卷

作者： Marissa J Maroni David A Wolk Sandhitsu R Das Robin De Flores Laura Wisse Long Xie Paul A Yushkevich Eddie B Lee Corey T McMillan University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA Penn FTD Center University of Pennsylvania Philadelphia PA USA

Background Aging is the most established risk factor for sporadic Alzheimer’s Disease (AD). However, relative risk of AD varies greatly across individuals. One potential explanation for this heterogeneity is that chronological age may not reflect differences in biological aging. One measure of biological age is the “epigenetic clock” which utilizes a weighted statistical model from >350 CpG sites that captures the cumulative work of an individual’s epigenetic maintenance system and has been demonstrated to relate to age, mortality, and survival. In this study, we test the hypothesis that the epigenetic clock will be related to two regional brain aging signatures associated with AD, including neurofibrillary tau tangle (NFTs) load and reduced cortical thickness. Methods We evaluated 40 cognitively normal, amyloid-negative (PET-Aβ <= 1.11) individuals from the Alzheimer’s Disease Neuroimaging Initiative dataset with DNA methylation, 18F-Flortaucipr PET, and volumetric MRI data. 18F-Flortaucipr PET and volumetric MRI data were processed using ANTs Cortical Thickness in 100 neocortical regions of interest (ROI) from the BrainColor/MindBoggle parcellation. 18F-Flortaucipr PET and T1-weighted MRI data was additionally processed using ASHS to derive 12 hippocampal subfield parcellations. Regression analyses related chronological age and Horvath’s “epigenetic clock” measurement of biological age to 18F-Flortaucipr SUVR, mean cortical thickness, and hippocampal subfield volumes. Results In healthy adults, we observed a positive association with increasing epigenetic age related to increased tau SUVR in the entorhinal cortex and the hippocampus (p = 0.029, R = 0.36; p = 0.028, R = 0.397). There was no association of epigenetic age with any of the neocortical tau SUVR ROIs and chronological age was not associated with tau SUVR in any of the hippocampal subfields or neocortical ROIs. In contrast, both epigenetic age and chronological age both trended toward associations of redu

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CROSS-SECTIONAL AND LONGITUDINAL BRAIN NETWORK ATROPHY IN DIFFERENT STAGES OF ALZHEIMER’S DISEASE AND IN RELATION TO COGNITIVE MEASURES

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Alzheimer's & Dementia 2017年第7期13卷 P1055-P1056页

作者： Joyce van Arendonk Laura E.M. Wisse Sandhitsu R. Das Nicholas J. Tustison David A. Wolk University of Pennsylvania Philadelphia PA USA Radboud University Nijmegen Netherlands Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA University of Virginia Charlottesville VA USA Penn Memory Center University of Pennsylvania Philadelphia PA USA

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Agreement between the white matter connectivity based on the tensor-based morphometry and the volumetric white matter parcellations based on diffusion tensor imaging

Agreement between the white matter connectivity based on the...

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IEEE International Symposium on Biomedical Imaging

作者： Seung-Goo Kim Brian B. Avants Hyekyoung Lee James C. Gee Moo K. Chung Richard J Davidson Jamie L. Hanson Seth D. Pollak Department of Brain and Cognitive Sciences Seoul National University South Korea Department of Brain and Cognitive Sciences Department of Nuclear Medicine Institute of Radiation Medicine Medical Research Center Seoul National University South Korea Department of Biostatistics and Medical Informatics Waisman Laboratory for Brain Imaging and Behavior University of Wisconsin Madison WI USA Waisman Laboratory for Brain Imaging and Behavior Department of Psychology University of Wisconsin Madison WI USA Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Psychology University of Wisconsin Madison WI USA Institute of Radiation Medicine Medical Research Center Seoul National University South Korea

We are interested in investigating white matter connectivity using a novel computational framework that does not use diffusion tensor imaging (DTI) but only uses T1-weighted magnetic resonance imaging. The proposed method relies on correlating Jacobian determinants across different voxels based on the tensor-based morphometry (TBM) framework. In this paper, we show agreement between the TBM-based white matter connectivity and the DTI-based white matter atlas. As an application, altered white matter connectivity in a clinical population is determined.

关键词： Correlation Diffusion tensor imaging Biomedical imaging Educational institutions Brain

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Factors associated with the Big 5 personality traits in Alzheimer’s disease and related dementias (ADRD)

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Emma Rhodes Lauren Massimo Dawn Mechanic-Hamilton Corey T McMillan Jeffrey S Phillips Sarah E Burke Quinn Hlava Philip Cook James C. Gee Murray Grossman University of Pennsylvania Frontotemporal Degeneration Center Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn FTD Center Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA University of Pennsylvania Frontotemporal Degeneration Center PHILADELPHIA PA USA Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania Philadelphia PA USA

Background Informant ratings of behavior and personality change are the gold standard of measurement in Alzheimer’s disease and related dementias (ADRD) where insight is often impaired, but informant report can be biased by psychosocial factors such as caregiver burden. This study assessed the impact of caregiver burden on informant ratings of patient Big 5 personality traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness) and associations between degree of discrepancy between informant and patient ratings with cortical volume in regions implicated in loss of insight. Method A sample of 70 ADRD participants with heterogeneous neurodegenerative clinical phenotypes (Figure 1) and their informants completed the NEO Personality Inventory (NEO-PI). Disease severity was measured using the Clinical Dementia Rating Scale (CDR). Caregiver burden was measured using the Zarit Burden Interview (ZBI). Discrepancy scores were computed for all NEO-PI Big 5 scores and a global discrepancy score. Participants were classified as having high/low discrepancy using a median split for each NEO-PI discrepancy score. T1-weighted 3T MRI grey matter volumes were normalized to intracranial volume for all ROIs in the Schaefer100×17 parcellation, and linear regression analyses related global difference score to Bonferroni-corrected atrophy regions. Result Higher levels of caregiver burden were associated with higher informant ratings of patient Neuroticism (ß = 0.02, p = .013) and lower informant ratings of patient Agreeableness (ß = -0.02, p = .0004), Conscientiousness (ß = -0.02, p = .005), and Openness (ß = -0.01, p = .014) independent of disease severity. Patients with high global discrepancy scores showed reduced cortical volumes in left ventral PFC, right medial PFC, and right superior temporal gyrus. Patients with low discrepancy scores showed relatively preserved cortical volumes in bilateral somatomotor cortex, right posterior cingulate, and left supplementary m

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Deep Learning Registration with Scan-temporal Consistency May Improve Detection of Alzheimer’s Disease Progression

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Alzheimer's & Dementia 2022年第S5期18卷

作者： Mengjin Dong Long Xie Jiancong Wang 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 Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center Perelman School of Medicine Philadelphia PA USA

Background Structural changes in Alzheimer’s disease (AD) can be detected as atrophy in the medial temporal lobe (MTL), which is normally studied by applying volumetric or deformation-based morphometry (DBM) to longitudinal scans. However, conventional DMB methods are computationally intensive and may not estimate the atrophy accurately due to the noise in the longitudinal images, e.g., head motion or MRI artifacts. We hypothesize that deep learning models can be trained to estimate atrophy from longitudinal MRI scans and are more sensitive to biological changes rather than artifactual. Method Two models were trained on longitudinal T1 MRI from 155 ADNI participants and tested on 326 participants. First, a deep learning-based registration model Voxelmorph (Balakrishnan et al., 2019) was applied to generate a deformation field to calculate MTL atrophy rate for each pair of images. Hypothesizing that noise is independent of time while neurodegeneration is time dependent, we then extended Voxelmorph to incorporate a loss that encourages the model to correctly infer the input scan temporal order (STO). In addition, in order to preserve the deformable registration nature of Voxelmorph, it’s weights were held fixed during network training, and instead a U-Net reconstruction network was introduced, to synthesize inputs to Voxelmorph. Models were compared with a DBM method ALOHA (Das et al., 2012). Result Voxelmorph generated atrophy measurements comparable to ALOHA in terms of differentiating AD stages (Figure 2) and STO consistency (Table 2). Voxelmorph + STO model has increased STO consistency and can more significantly differentiate disease stages (in absolute terms), especially between the normal controls and preclinical AD. Conclusion Deep learning-based registration method Voxelmorph achieves comparable performance in longitudinal analysis of subtle changes as in DBM method ALOHA. With the introduction of a scan temporal order loss and reconstruction network, the mod

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Medial temporal lobe gray matter microstructure alterations provide improved sensitivity to detect neurodegeneration associated with Alzheimer’s disease biomarkers

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Alzheimer's & Dementia 2023年第S16期19卷

作者： Christopher Brown Sandhitsu R. Das Long Xie Ilya M. Nasrallah John A. Detre Corey T McMillan Paul A. Yushkevich David A. Wolk University of Pennsylvania Philadelphia PA 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 Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA Penn FTD Center University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center Perelman School of Medicine Philadelphia PA USA Institute on Aging University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA

Background Despite the strengths conventional structural imaging measures to detect atrophy, cross-sectional relationships between neurodegeneration and Alzheimer’s disease (AD) biomarkers in early stages is limited. Neurite orientation dispersion and density imaging (NODDI) measures microstructural alterations that are unique from conventional structural metrics. Alterations in microstructure have been detected in individuals with AD compared to cognitively normal individuals, but there is sparse data regarding the relationship between these metrics and AD biomarkers. Here we sought to characterize how NODDI metrics in the medial temporal lobe (MTL) are associated with plasma AD biomarkers. Method One-hundred and sixty-seven cognitively normal (CN) individuals, 47 individuals with mild cognitive impairment, and 16 individuals with AD underwent diffusion and T1w imaging. T1w images were segmented using the ASHS-T1 protocol to form anterior and posterior MTL ROIs. Multi-shell DTI data were collected in 113 non-collinear directions with 1.5mm isotropic voxels, were pre-processed using a custom pipeline, and fit with voxel-wise NODDI models. ANTs was used to linearly register NODDI and T1w images to allow transformation of MTL ROIs to diffusion space. Stepwise-regression models examined relationships between anterior and posterior MTL NODDI metrics with plasma biomarkers in CN and impaired (MCI/AD) groups after accounting for MTL volume. Result NODDI metrics of intracellular volume fraction (icVf) and orientation dispersion index (ODI) were decreased, and isometric volume fraction (isoVf) was increased in cognitively impaired compared to CN. Amyloid positivity was associated with decreased ODI and increased isoVf in cognitively impaired individuals. While posterior MTL volume predicted plasma p-Tau-181, inclusion of NODDI metrics subsumed this effect (F-change>7.54, p <0.0067), and reduced ODI was the best predictor of plasma pTau-181 within cognitively impaired indivi

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Reduced longitudinal change in18F-flortaucipir PET is associated with clinical phenotype in atypical Alzheimer’s disease

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Jeffrey S Phillips Frederick J. Nitchie IV Fulvio Da Re Christopher A Olm Philip Cook Corey T McMillan David J. Irwin James C. Gee Jacob G. Dubroff Murray Grossman Ilya M. Nasrallah Penn FTD Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Neurology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA School of Medicine and Surgery Milan Center for Neuroscience (NeuroMI) University of Milano-Bicocca Milan Italy Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Digital Neuropathology Laboratory Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background Longitudinal positron emission tomography (PET) studies of tau accumulation in Alzheimer’s disease (AD) have noted reduced increases or frank decreases in tau signal. It is unclear whether these reductions reflect measurement/processing error or biological changes in advanced neurodegeneration. Method We assessed regional and interindividual variation in rates of longitudinal change on 18 F-flortaucipir PET imaging in 24 amyloid-β+ patients with atypical, early-onset amnestic or non-amnestic AD and 117 amyloid-β+ participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Result In atypical AD, 18 F-flortaucipir uptake appeared to slow or decline over time in areas with high baseline signal and older individuals with greater cognitive impairment. In secondary analyses, these effects were not explained by regression to the mean or atrophy. Among ADNI participants, longitudinal change was reduced in early Braak stage regions relative to late-stage neocortical areas, demonstrating partial generalization of findings from atypical AD. Conclusion Results suggested that radioligand uptake may plateau or decline in advanced neurodegeneration. Further research is warranted to investigate whether results generalize to other tau-binding radioligands and to typical, later-onset AD.

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Subtle differences in the appearance of hippocampal layers differentiate preclinical AD from healthy aging

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Alzheimer's & Dementia 2020年第S4期16卷

作者： Long Xie Sandhitsu R. Das Laura Wisse Ranjit Ittyerah Nicolas Vergnet Robin de Flores David A. Wolk Paul A. Yushkevich Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Lund University Lund Sweden Inserm UMR-S U1237 Université de Caen-Normandie GIP Cyceron Caen France Penn Memory Center University of Pennsylvania Philadelphia PA USA

Background Degeneration of the stratum radiatum lacunosum moleculare (SRLM), located between the cornu ammonis (CA)/subiculum and dentate gyrus (DG) subfields of the hippocampus (Figure 1) and containing apical dendrites of CA pyramidal cells, may be an early feature of AD and can be observed in postmortem studies (Adler et al, 2018). The pathological change in SRLM may produce subtle alteration of image signal on high-resolution in vivo MRI. We hypothesize that convolutional neural networks (CNNs) can detect such alterations in preclinical AD, yielding a more sensitive biomarker than conventional morphological measurements. Methods Cross-sectional 0.4x0.4x2.0mm 3 T2-weighted MRI of 285 cognitively normal controls from ADNI were included. The dataset was dichotomized according to amyloid-ß (Aß) status (+ or -) based on PET and divided into training (48%), validation (12%) and test sets (40%, Table 1). Volumes of medial temporal lobe subregions (CA1-3, DG, subiculum, entorhinal cortex, Brodmann areas 35/36, parahippocampal cortex) were extracted automatically using ASHS (Yushkevich et al., 2015). Small image patches (∼6x6x6 mm 3 ) centered on the SRLM were extracted. A CNN was trained to discriminate patches as Aß+ or Aß-. The output of the last CNN layer was averaged over all SRLM patches to generate a CNN-based score for each subject in the test set. ANCOVA with age, sex, education and intracranial volume as covariates was used to test for significance. Results We observed a significant difference in CNN score between Ab+ and Ab- controls in the test set (Table 2). None of the conventional volume measures show statistical significance except a trend level effect in parahippcampal cortex opposite the expected result (greater atrophy in Aß+). Conclusions In this proof-of-concept study, we demonstrated that our novel CNN-based score is more sensitive to Aß status than standard medial temporal lobe volume measurements. Since the image patches only cover a small area ar

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