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RELATIVE SPARING OF MEDIAL TEMPORAL SUBREGION VOLUMES IN NON-AMNESTIC ALZHEIMER’S DISEASE

Alzheimer's & Dementia 2019年第7期15卷 P116-P117页

作者： Jeffrey S. Phillips Long Xie Laura Wisse James C. Gee Paul A. Yushkevich Murray Grossman David Irwin Penn FTD Center 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

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P4-068: AMYLOID-POSITIVE COGNITIVE IMPAIRMENT IS LESS “ALZHEIMER'S-LIKE” IN THE ABSENCE OF TAU

引用

Alzheimer's & Dementia 2019年第7S_PART_25期15卷

作者： Lauren McCollum Sandhitsu R. Das Long Xie Salena Cui Robin de Flores Laura Wisse 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 Department of Neurology University of Pennsylvania Philadelphia PA USA

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AMYLOID-POSITIVE COGNITIVE IMPAIRMENT IS LESS “ALZHEIMER’S-LIKE” IN THE ABSENCE OF TAU

引用

Alzheimer's & Dementia 2019年第7期15卷 P1299-P1299页

作者： Lauren McCollum Sandhitsu R. Das Long Xie Salena Cui Robin de Flores Laura Wisse 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 Department of Neurology University of Pennsylvania Philadelphia PA USA

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SEMANTIC RICHNESS IN PEOPLE WITH PRECLINICAL ALZHEIMER’S DISEASE

引用

Alzheimer's & Dementia 2019年第7期15卷 P793-P794页

作者： Nathaniel Klooster Arun Pilania Laura Wisse Sandhitsu R. Das Long Xie David A. Wolk Anjan Chatterjee University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

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P2-462: SEMANTIC RICHNESS IN PEOPLE WITH PRECLINICAL ALZHEIMER'S DISEASE

引用

Alzheimer's & Dementia 2019年第7S_PART_15期15卷

作者： Nathaniel Klooster Arun Pilania Laura Wisse Sandhitsu R. Das Long Xie David A. Wolk Anjan Chatterjee University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

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IC-04-05: CROSS-SECTIONAL AND LONGITUDINAL MEDIAL TEMPORAL LOBE SUBREGIONAL ATROPHY IN SUSPECTED NON-ALZHEIMER'S PATHOPHYSIOLOGY

引用

Alzheimer's & Dementia 2019年第7S_PART_1期15卷

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

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

引用

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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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

引用

arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing... 详细信息

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

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MEDIAL TEMPORAL LOBE CORRELATES OF ANOSAGNOSIA IN PRODROMAL AD

引用

Alzheimer's & Dementia 2019年第7期15卷 P732-P733页

作者： Laura Wisse Trevor Glenn Sandhitsu R. Das de Flores Robin Long Xie 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 Johns Hopkins University Baltimore MD USA Penn Memory Center University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

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Multidimensional representations in late-life depression: Convergence in neuroimaging, cognition, clinical symptomatology and genetics

arXiv

引用

arXiv 2021年

作者： Wen, Junhao Fu, Cynthia H.Y. Tosun, Duygu Veturi, Yogasudha Yang, Zhijian Abdulkadir, Ahmed Mamourian, Elizabeth Srinivasan, Dhivya Bao, Jingxuan Erus, Guray Shou, Haochang Habes, Mohamad Doshi, Jimit Varol, Erdem Mackin, Scott R. Sotiras, Aristeidis Fan, Yong Saykin, Andrew J. Sheline, Yvette I. Shen, Li Ritchie, Marylyn D. Wolk, David A. Albert, Marilyn Resnick, Susan M. Davatzikos, Christos Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia United States University of East London School of Psychology London United Kingdom Centre for Affective Disorders Institute of Psychiatry Psychology and Neuroscience King’s College London London United Kingdom Department of Radiology and Biomedical Imaging University of California San FranciscoCA United States Department of Genetics Institute for Biomedical Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine PhiladelphiaPA19104 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 Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio United States Department of Statistics Center for Theoretical Neuroscience Zuckerman Institute Columbia University New York United States Department of Psychiatry University of California San FranciscoCA United States Department of Radiology Institute for Informatics Washington University School of Medicine St. Louis United States Radiology and Imaging Sciences Center for Neuroimaging Department of Radiology and Imaging Sciences Indiana Alzheimer’s Disease Research Center Melvin and Bren Simon Cancer Center Indiana University School of Medicine Indianapolis United States Center for Neuromodulation in Depression and Stress Department of Psychiatry University of Pennsylvania Perelman School of Medicine PhiladelphiaPA United States Department of Neurology Penn Memory Center University of Pennsylvania Philadelphia United States Department of Neurology Johns Hopkins University School of Medicine United States Laboratory o

Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms and pave the road to preci... 详细信息

Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms and pave the road to precision and individualized medicine. Objective: We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles. Design & setting: The iSTAGING study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with over 35,000 participants, including a subset of individuals with major depressive disorders. Participants: Multimodal data from a multicentre sample (N=996), including neuroimaging, neurocognitive assessments, and genetics: 501 LLD participants (332 women, mean age 67.39 ± 5.56 years) and 495 healthy controls (333 women, mean age 66.53 ± 5.16 years) were analyzed. A semi-supervised clustering method (HYDRA) was applied to regional grey matter (GM) brain volumes to derive dimensional representations. Exposure: None Main outcome and Measure: Two dimensions were identified, which accounted for the LLD-related heterogeneity in voxel-wise GM maps, white matter (WM) fractional anisotropy (FA), neurocognitive functioning, clinical phenotype, and genetics. Results: Dimension one (Dim1) demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy controls. In contrast, dimension two (Dim2) showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, one de novo independent genetic variant (rs13120336) was significantly associated with Dim 1 but not with Dim 2. Notably, the two dimensions demonstrated significant SNP-based heritability of 18-27% within the general population (N=12,518 in UKBB). Lastly, in a subset of individuals having longitudinal measurements, Dim2 demon

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