Early amygdala and ERC atrophy linked to 3D reconstruction of rostral neurofibrillary tau tangle pathology in Alzheimer’s disease

[1]  K. Suma,et al.  Deep Learning for Alzheimer's Disease Detection using Multimodal MRI-PET Fusion , 2022, 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C).

[2]  Kaitlin M. Stouffer,et al.  Projective LDDMM: Mapping Molecular Digital Pathology with Tissue MRI , 2022, bioRxiv.

[3]  K. Blennow,et al.  Biomarker modeling of Alzheimer’s disease using PET-based Braak staging , 2022, Nature Aging.

[4]  D. Greve,et al.  Entorhinal Subfield Vulnerability to Neurofibrillary Tangles in Aging and the Preclinical Stage of Alzheimer’s Disease , 2022, Journal of Alzheimer's disease : JAD.

[5]  L. Grinberg,et al.  Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation , 2021, NeuroImage.

[6]  John L. Robinson,et al.  Ex vivo MRI atlas of the human medial temporal lobe: characterizing neurodegeneration due to tau pathology , 2021, Acta Neuropathologica Communications.

[7]  John L. Robinson,et al.  Ex vivo MRI atlas of the human medial temporal lobe: characterizing neurodegeneration due to tau pathology , 2021, Acta Neuropathologica Communications.

[8]  Ling Yu Hung,et al.  Three-dimensional mapping of neurofibrillary tangle burden in the human medial temporal lobe. , 2021, Brain : a journal of neurology.

[9]  Shi Zhou,et al.  Diagnostic value of amygdala volume on structural magnetic resonance imaging in Alzheimer’s disease , 2021, World journal of clinical cases.

[10]  David W. Nauen,et al.  Amyloid‐beta is present in human lymph nodes and greatly enriched in those of the cervical region , 2021, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[11]  J. Laczó,et al.  Mild Behavioral Impairment Is Associated With Atrophy of Entorhinal Cortex and Hippocampus in a Memory Clinic Cohort , 2021, Frontiers in Aging Neuroscience.

[12]  A. Trouvé,et al.  Hierarchical Computational Anatomy: Unifying the Molecular to Tissue Continuum Via Measure Representations of the Brain , 2021, bioRxiv.

[13]  Philip S. Insel,et al.  Early stages of tau pathology and its associations with functional connectivity, atrophy and memory , 2021, Brain : a journal of neurology.

[14]  Anonymous,et al.  2021 Alzheimer's disease facts and figures , 2021, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[15]  Philip S. Insel,et al.  Mild behavioral impairment and its relation to tau pathology in preclinical Alzheimer’s disease , 2021, Translational Psychiatry.

[16]  González,et al.  Clinical correlates for immune checkpoint therapy: significance for CNS malignancies. , 2020, Neuro-oncology advances.

[17]  Andrew J. Holbrook,et al.  Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer's disease , 2020, Alzheimer's & dementia.

[18]  L. Younes,et al.  Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease , 2020, Frontiers in Neuroscience.

[19]  Nick C Fox,et al.  Imaging biomarkers in neurodegeneration: current and future practices , 2020, Alzheimer's Research & Therapy.

[20]  H. Zetterberg,et al.  Biomarkers for Alzheimer’s disease—preparing for a new era of disease-modifying therapies , 2020, Molecular Psychiatry.

[21]  Richard Beare,et al.  Accuracy of automated amygdala MRI segmentation approaches in Huntington's disease in the IMAGE‐HD cohort , 2020, Human brain mapping.

[22]  Robert Fisk,et al.  Borders , 1998, The Frontier in British India.

[23]  Andrew J. Holbrook,et al.  Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer’s disease , 2019 .

[24]  O. Hansson,et al.  Staging β-Amyloid Pathology With Amyloid Positron Emission Tomography. , 2019, JAMA neurology.

[25]  Daniela Ushizima,et al.  Deep Learning for Alzheimer’s Disease: Mapping Large-scale Histological Tau Protein for Neuroimaging Biomarker Validation , 2019 .

[26]  Michael I. Miller,et al.  Identifying Changepoints in Biomarkers During the Preclinical Phase of Alzheimer’s Disease , 2019, Front. Aging Neurosci..

[27]  R. Insausti,et al.  Cytoarchitectonic Areas of the Gyrus ambiens in the Human Brain , 2019, Front. Neuroanat..

[28]  A. Nordberg,et al.  Tau PET imaging in neurodegenerative tauopathies—still a challenge , 2019, Molecular Psychiatry.

[29]  Michael I. Miller,et al.  Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease , 2018, bioRxiv.

[30]  Kwame S. Kutten,et al.  3D Normal Coordinate Systems for Cortical Areas , 2018, Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore.

[31]  Alzheimer's Disease Neuroimaging Initiative,et al.  Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment , 2018, NeuroImage: Clinical.

[32]  Daniel S. Marcus,et al.  OASIS-3: LONGITUDINAL NEUROIMAGING, CLINICAL, AND COGNITIVE DATASET FOR NORMAL AGING AND ALZHEIMER’S DISEASE , 2018, Alzheimer's & Dementia.

[33]  Ling Yue,et al.  Asymmetry of Hippocampus and Amygdala Defect in Subjective Cognitive Decline Among the Community Dwelling Chinese , 2018, Front. Psychiatry.

[34]  C. Jack,et al.  NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease , 2018, Alzheimer's & Dementia.

[35]  C. Rowe,et al.  Imaging tau and amyloid-β proteinopathies in Alzheimer disease and other conditions , 2018, Nature Reviews Neurology.

[36]  Bela G Nelson,et al.  The Amygdala as a Locus of Pathologic Misfolding in Neurodegenerative Diseases , 2018, Journal of neuropathology and experimental neurology.

[37]  A. Schleicher,et al.  Receptor-driven, multimodal mapping of the human amygdala , 2017, Brain Structure and Function.

[38]  Michael Miller,et al.  Unbiased Diffeomorphic Mapping of Longitudinal Data with Simultaneous Subject Specific Template Estimation , 2017, GRAIL/MFCA/MICGen@MICCAI.

[39]  B Fischl,et al.  High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas , 2017, NeuroImage.

[40]  E. Düzel,et al.  A protocol for manual segmentation of medial temporal lobe subregions in 7 Tesla MRI , 2017, NeuroImage: Clinical.

[41]  Z. M. Saygina,et al.  High-resolution magnetic resonance imaging reveals nuclei of the human amygdala : manual segmentation to automatic atlas , 2017 .

[42]  B. Platt,et al.  Soluble pre-fibrillar tau and β-amyloid species emerge in early human Alzheimer’s disease and track disease progression and cognitive decline , 2016, Acta Neuropathologica.

[43]  C. Sorg,et al.  Progressively Disrupted Intrinsic Functional Connectivity of Basolateral Amygdala in Very Early Alzheimer’s Disease , 2016, Front. Neurol..

[44]  Matthew F. Glasser,et al.  The Human Connectome Project: Progress and Prospects , 2016, Cerebrum: the Dana Forum on Brain Science.

[45]  G. V. Van Hoesen,et al.  Organization and detailed parcellation of human hippocampal head and body regions based on a combined analysis of Cyto‐ and chemoarchitecture , 2015, The Journal of comparative neurology.

[46]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[47]  Michael I. Miller,et al.  Network Neurodegeneration in Alzheimer’s Disease via MRI Based Shape Diffeomorphometry and High-Field Atlasing , 2015, Front. Bioeng. Biotechnol..

[48]  L. Younes,et al.  Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer's disease , 2014, NeuroImage: Clinical.

[49]  Jane S. Paulsen,et al.  Regionally selective atrophy of subcortical structures in prodromal HD as revealed by statistical shape analysis , 2012, Human brain mapping.

[50]  Song-Lin Ding,et al.  Comparative anatomy of the prosubiculum, subiculum, presubiculum, postsubiculum, and parasubiculum in human, monkey, and rodent , 2013, The Journal of comparative neurology.

[51]  David S. Lee,et al.  The diffeomorphometry of temporal lobe structures in preclinical Alzheimer's disease☆ , 2013, NeuroImage: Clinical.

[52]  Mert R. Sabuncu,et al.  Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models , 2013, NeuroImage.

[53]  Tamal K. Dey,et al.  Delaunay Mesh Generation , 2012, Chapman and Hall / CRC computer and information science series.

[54]  P. Fletcher,et al.  Geodesic Regression and the Theory of Least Squares on Riemannian Manifolds , 2013, International Journal of Computer Vision.

[55]  Hugo J. Kuijf,et al.  Subfields of the hippocampal formation at 7T MRI: In vivo volumetric assessment , 2012, NeuroImage.

[56]  J. Schneider,et al.  National Institute on Aging–Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease , 2012, Alzheimer's & Dementia.

[57]  J. Morris,et al.  Amygdala atrophy is prominent in early Alzheimer's disease and relates to symptom severity , 2011, Psychiatry Research: Neuroimaging.

[58]  Denise C. Park,et al.  Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[59]  Stéphane Mallat,et al.  Group Invariant Scattering , 2011, ArXiv.

[60]  G. V. Van Hoesen,et al.  Borders, extent, and topography of human perirhinal cortex as revealed using multiple modern neuroanatomical and pathological markers , 2010, Human brain mapping.

[61]  Peter J Hellyer,et al.  Human brain mapping , 2012, Nature Methods.

[62]  Brian B. Avants,et al.  A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T , 2009, NeuroImage.

[63]  Alain Trouvé,et al.  Bayesian template estimation in computational anatomy , 2008, NeuroImage.

[64]  Isidro Ferrer,et al.  Argyrophilic grain disease. , 2008, Brain : a journal of neurology.

[65]  Michael I. Miller,et al.  Smooth functional and structural maps on the neocortex via orthonormal bases of the Laplace-Beltrami operator , 2006, IEEE Transactions on Medical Imaging.

[66]  J. Becker,et al.  Lewy bodies in the amygdala increase risk for major depression in subjects with Alzheimer disease , 2006, Neurology.

[67]  H. Braak,et al.  Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry , 2006, Acta Neuropathologica.

[68]  Alain Trouvé,et al.  Geodesic Shooting for Computational Anatomy , 2006, Journal of Mathematical Imaging and Vision.

[69]  K. Amunts,et al.  Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps , 2005, Anatomy and Embryology.

[70]  M. Lierz,et al.  Diagnostic Value of , 2005 .

[71]  David Wechsler,et al.  Wechsler Memory scale. , 2005 .

[72]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[73]  John Q Trojanowski,et al.  Lewy bodies in the amygdala: increase of alpha-synuclein aggregates in neurodegenerative diseases with tau-based inclusions. , 2004, Archives of neurology.

[74]  H. Braak,et al.  Neuropathological stageing of Alzheimer-related changes , 2004, Acta Neuropathologica.

[75]  Thomas E. Nichols,et al.  Controlling the familywise error rate in functional neuroimaging: a comparative review , 2003, Statistical methods in medical research.

[76]  G. Halliday,et al.  Clinical correlates of selective pathology in the amygdala of patients with Parkinson's disease. , 2002, Brain : a journal of neurology.

[77]  H. Braak,et al.  Phases of Aβ-deposition in the human brain and its relevance for the development of AD , 2002, Neurology.

[78]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[79]  U. Grenander,et al.  Computational anatomy: an emerging discipline , 1998 .

[80]  Yvette I. Sheline,et al.  Amygdala core nuclei volumes are decreased in recurrent major depression , 1998, Neuroreport.

[81]  H. Soininen,et al.  MR volumetric analysis of the human entorhinal, perirhinal, and temporopolar cortices. , 1998, AJNR. American journal of neuroradiology.

[82]  C. Jack,et al.  Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease , 1997, Neurology.

[83]  R. Insausti,et al.  The human entorhinal cortex: A cytoarchitectonic analysis , 1995, The Journal of comparative neurology.

[84]  S. M. Sumi,et al.  The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) , 1991, Neurology.

[85]  G. V. Van Hoesen,et al.  The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease. , 1991, Cerebral cortex.

[86]  C. P. Hughes,et al.  A New Clinical Scale for the Staging of Dementia , 1982, British Journal of Psychiatry.

[87]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[88]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[89]  Denis Dooley,et al.  Atlas of the Human Brain. , 1971 .