1,289 results on '"Miller, Michael I."'
Search Results
2. Preserving Derivative Information while Transforming Neuronal Curves
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Athey, Thomas L., Tward, Daniel J., Mueller, Ulrich, Younes, Laurent, Vogelstein, Joshua T., and Miller, Michael I.
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Quantitative Biology - Neurons and Cognition ,Mathematics - Numerical Analysis - Abstract
The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between. In this work, we apply the theory of jets to describe how to preserve derivatives of neuron traces up to any order. We provide a framework to compute possible error introduced by standard mapping methods, which involves the Jacobian of the mapping transformation. We show how our first order method improves mapping accuracy in both simulated and real neuron traces under random diffeomorphisms. Our method is freely available in our open-source Python package brainlit.
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- 2023
3. Image Varifolds on Meshes for Mapping Spatial Transcriptomics
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Miller, Michael I, Trouvé, Alain, and Younes, Laurent
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Mathematics - Numerical Analysis - Abstract
Advances in the development of largely automated microscopy methods such as MERFISH for imaging cellular structures in mouse brains are providing spatial detection of micron resolution gene expression. While there has been tremendous progress made in the field Computational Anatomy (CA) to perform diffeomorphic mapping technologies at the tissue scales for advanced neuroinformatic studies in common coordinates, integration of molecular- and cellular-scale populations through statistical averaging via common coordinates remains yet unattained. This paper describes the first set of algorithms for calculating geodesics in the space of diffeomorphisms, what we term Image-Varifold LDDMM,extending the family of large deformation diffeomorphic metric mapping (LDDMM) algorithms to accommodate the "copy and paste" varifold action of particles which extends consistently to the tissue scales. We represent the brain data as geometric measures, termed as {\em image varifolds} supported by a large number of unstructured points, % (i.e., not aligned on a 2D or 3D grid), each point representing a small volume in space % (which may be incompletely described) and carrying a list of densities of {\em features} elements of a high-dimensional feature space. The shape of image varifold brain spaces is measured by transforming them by diffeomorphisms. The metric between image varifolds is obtained after embedding these objects in a linear space equipped with the norm, yielding a so-called "chordal metric."
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- 2022
4. Preserving Derivative Information while Transforming Neuronal Curves
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Athey, Thomas L., Tward, Daniel J., Mueller, Ulrich, Younes, Laurent, Vogelstein, Joshua T., and Miller, Michael I.
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- 2024
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5. A guide to the BRAIN Initiative Cell Census Network data ecosystem
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Hawrylycz, Michael, Martone, Maryann E, Ascoli, Giorgio A, Bjaalie, Jan G, Dong, Hong-Wei, Ghosh, Satrajit S, Gillis, Jesse, Hertzano, Ronna, Haynor, David R, Hof, Patrick R, Kim, Yongsoo, Lein, Ed, Liu, Yufeng, Miller, Jeremy A, Mitra, Partha P, Mukamel, Eran, Ng, Lydia, Osumi-Sutherland, David, Peng, Hanchuan, Ray, Patrick L, Sanchez, Raymond, Regev, Aviv, Ropelewski, Alex, Scheuermann, Richard H, Tan, Shawn Zheng Kai, Thompson, Carol L, Tickle, Timothy, Tilgner, Hagen, Varghese, Merina, Wester, Brock, White, Owen, Zeng, Hongkui, Aevermann, Brian, Allemang, David, Ament, Seth, Athey, Thomas L, Baker, Cody, Baker, Katherine S, Baker, Pamela M, Bandrowski, Anita, Banerjee, Samik, Bishwakarma, Prajal, Carr, Ambrose, Chen, Min, Choudhury, Roni, Cool, Jonah, Creasy, Heather, D’Orazi, Florence, Degatano, Kylee, Dichter, Benjamin, Ding, Song-Lin, Dolbeare, Tim, Ecker, Joseph R, Fang, Rongxin, Fillion-Robin, Jean-Christophe, Fliss, Timothy P, Gee, James, Gillespie, Tom, Gouwens, Nathan, Zhang, Guo-Qiang, Halchenko, Yaroslav O, Harris, Nomi L, Herb, Brian R, Hintiryan, Houri, Hood, Gregory, Horvath, Sam, Huo, Bingxing, Jarecka, Dorota, Jiang, Shengdian, Khajouei, Farzaneh, Kiernan, Elizabeth A, Kir, Huseyin, Kruse, Lauren, Lee, Changkyu, Lelieveldt, Boudewijn, Li, Yang, Liu, Hanqing, Liu, Lijuan, Markuhar, Anup, Mathews, James, Mathews, Kaylee L, Mezias, Chris, Miller, Michael I, Mollenkopf, Tyler, Mufti, Shoaib, Mungall, Christopher J, Orvis, Joshua, Puchades, Maja A, Qu, Lei, Receveur, Joseph P, Ren, Bing, Sjoquist, Nathan, Staats, Brian, Tward, Daniel, van Velthoven, Cindy TJ, Wang, Quanxin, Xie, Fangming, Xu, Hua, Yao, Zizhen, and Yun, Zhixi
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Biological Sciences ,Genetics ,Data Science ,Neurosciences ,Mental Health ,1.1 Normal biological development and functioning ,Neurological ,Animals ,Humans ,Mice ,Brain ,Ecosystem ,Neurons ,Agricultural and Veterinary Sciences ,Medical and Health Sciences ,Developmental Biology ,Agricultural ,veterinary and food sciences ,Biological sciences ,Biomedical and clinical sciences - Abstract
Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.
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- 2023
6. Prospective Learning: Principled Extrapolation to the Future
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De Silva, Ashwin, Ramesh, Rahul, Ungar, Lyle, Shuler, Marshall Hussain, Cowan, Noah J., Platt, Michael, Li, Chen, Isik, Leyla, Roh, Seung-Eon, Charles, Adam, Venkataraman, Archana, Caffo, Brian, How, Javier J., Kebschull, Justus M, Krakauer, John W., Bichuch, Maxim, Kinfu, Kaleab Alemayehu, Yezerets, Eva, Jayaraman, Dinesh, Shin, Jong M., Villar, Soledad, Phillips, Ian, Priebe, Carey E., Hartung, Thomas, Miller, Michael I., Dey, Jayanta, Ningyuan, Huang, Eaton, Eric, Etienne-Cummings, Ralph, Ogburn, Elizabeth L., Burns, Randal, Osuagwu, Onyema, Mensh, Brett, Muotri, Alysson R., Brown, Julia, White, Chris, Yang, Weiwei, Rusu, Andrei A., Verstynen, Timothy, Kording, Konrad P., Chaudhari, Pratik, and Vogelstein, Joshua T.
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the assumption that the future will be identical to the past in distribution or change adversarially. But these assumptions can be either too optimistic or pessimistic for many problems in the real world. Real world scenarios evolve over multiple spatiotemporal scales with partially predictable dynamics. Here we reformulate the learning problem to one that centers around this idea of dynamic futures that are partially learnable. We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning. We argue that prospective learning more accurately characterizes many real world problems that (1) currently stymie existing artificial intelligence solutions and/or (2) lack adequate explanations for how natural intelligences solve them. Thus, studying prospective learning will lead to deeper insights and solutions to currently vexing challenges in both natural and artificial intelligences., Comment: Accepted at the 2nd Conference on Lifelong Learning Agents (CoLLAs), 2023
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- 2022
7. STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
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Clifton, Kalen, Anant, Manjari, Aihara, Gohta, Atta, Lyla, Aimiuwu, Osagie K., Kebschull, Justus M., Miller, Michael I., Tward, Daniel, and Fan, Jean
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- 2023
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8. Automatic comprehensive radiological reports for clinical acute stroke MRIs
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Liu, Chin-Fu, Zhao, Yi, Yedavalli, Vivek, Leigh, Richard, Falcao, Vitor, Miller, Michael I., Hillis, Argye E., and Faria, Andreia V.
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- 2023
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9. Digital 3D Brain MRI Arterial Territories Atlas
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Liu, Chin-Fu, Hsu, Johnny, Xu, Xin, Kim, Ganghyun, Sheppard, Shannon M., Meier, Erin L., Miller, Michael I., Hillis, Argye E., and Faria, Andreia V.
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- 2023
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10. Automatic comprehensive aspects reports in clinical acute stroke MRIs
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Liu, Chin-Fu, Li, Jintong, Kim, Ganghyun, Miller, Michael I., Hillis, Argye E., and Faria, Andreia V.
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- 2023
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11. Hidden Markov Modeling for Maximum Likelihood Neuron Reconstruction
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Athey, Thomas L., Tward, Daniel J., Mueller, Ulrich, Vogelstein, Joshua T., and Miller, Michael I.
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Recent advances in brain clearing and imaging have made it possible to image entire mammalian brains at sub-micron resolution. These images offer the potential to assemble brain-wide atlases of neuron morphology, but manual neuron reconstruction remains a bottleneck. Several automatic reconstruction algorithms exist, but most focus on single neuron images. In this paper, we present a probabilistic reconstruction method, ViterBrain, which combines a hidden Markov state process that encodes neuron geometry with a random field appearance model of neuron fluorescence. Our method utilizes dynamic programming to compute the global maximizers of what we call the "most probable" neuron path. Our most probable estimation method models the task of reconstructing neuronal processes in the presence of other neurons, and thus is applicable in images with several neurons. Our method operates on image segmentations in order to leverage cutting edge computer vision technology. We applied our algorithm to imperfect image segmentations where false negatives severed neuronal processes, and showed that it can follow axons in the presence of noise or nearby neurons. Additionally, it creates a framework where users can intervene to, for example, fit start and endpoints. The code used in this work is available in our open-source Python package brainlit.
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- 2021
12. BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes
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Athey, Thomas L., Wright, Matthew A., Pavlovic, Marija, Chandrashekhar, Vikram, Deisseroth, Karl, Miller, Michael I., and Vogelstein, Joshua T.
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- 2023
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13. Space-feature measures on meshes for mapping spatial transcriptomics
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Miller, Michael I., Trouvé, Alain, and Younes, Laurent
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- 2024
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14. Regularized regression on compositional trees with application to MRI analysis
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Wang, Bingkai, Caffo, Brian S., Luo, Xi, Liu, Chin-Fu, Faria, Andreia V., Miller, Michael I., and Zhao, Yi
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Statistics - Methodology ,Statistics - Applications - Abstract
A compositional tree refers to a tree structure on a set of random variables where each random variable is a node and composition occurs at each non-leaf node of the tree. As a generalization of compositional data, compositional trees handle more complex relationships among random variables and appear in many disciplines, such as brain imaging, genomics and finance. We consider the problem of sparse regression on data that are associated with a compositional tree and propose a transformation-free tree-based regularized regression method for component selection. The regularization penalty is designed based on the tree structure and encourages a sparse tree representation. We prove that our proposed estimator for regression coefficients is both consistent and model selection consistent. In the simulation study, our method shows higher accuracy than competing methods under different scenarios. By analyzing a brain imaging data set from studies of Alzheimer's disease, our method identifies meaningful associations between memory declination and volume of brain regions that are consistent with current understanding.
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- 2021
15. Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometry
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Athey, Thomas L., Teneggi, Jacopo, Vogelstein, Joshua T., Tward, Daniel, Mueller, Ulrich, and Miller, Michael I.
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Quantitative Biology - Neurons and Cognition ,Computer Science - Mathematical Software ,Mathematics - Differential Geometry - Abstract
Neuromorphology is crucial to identifying neuronal subtypes and understanding learning. It is also implicated in neurological disease. However, standard morphological analysis focuses on macroscopic features such as branching frequency and connectivity between regions, and often neglects the internal geometry of neurons. In this work, we treat neuron trace points as a sampling of differentiable curves and fit them with a set of branching B-splines. We designed our representation with the Frenet-Serret formulas from differential geometry in mind. The Frenet-Serret formulas completely characterize smooth curves, and involve two parameters, curvature and torsion. Our representation makes it possible to compute these parameters from neuron traces in closed form. These parameters are defined continuously along the curve, in contrast to other parameters like tortuosity which depend on start and end points. We applied our method to a dataset of cortical projection neurons traced in two mouse brains, and found that the parameters are distributed differently between primary, collateral, and terminal axon branches, thus quantifying geometric differences between different components of an axonal arbor. The results agreed in both brains, further validating our representation. The code used in this work can be readily applied to neuron traces in SWC format and is available in our open-source Python package brainlit: http://brainlit.neurodata.io/.
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- 2021
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16. Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors
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Zhao, Yi, Wang, Bingkai, Liu, Chin-Fu, Faria, Andreia V., Miller, Michael I., Caffo, Brian S., and Luo, Xi
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Statistics - Applications - Abstract
Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an L1-type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in L2-norm and the model selection is also consistent. By applying to a brain structural magnetic resonance imaging dataset acquired from the Alzheimer's Disease Neuroimaging Initiative, the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions but at various levels of brain segmentation.
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- 2021
17. Shape Diffeomorphometry of Brain Structures in Neurodegeneration and Neurodevelopment
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Ratnanather, J. Tilak, Liu, Chin-Fu, Miller, Michael I., and Thakor, Nitish V., editor
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- 2023
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18. The Brain Chart of Aging: Machine‐learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans
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Habes, Mohamad, Pomponio, Raymond, Shou, Haochang, Doshi, Jimit, Mamourian, Elizabeth, Erus, Guray, Nasrallah, Ilya, Launer, Lenore J, Rashid, Tanweer, Bilgel, Murat, Fan, Yong, Toledo, Jon B, Yaffe, Kristine, Sotiras, Aristeidis, Srinivasan, Dhivya, Espeland, Mark, Masters, Colin, Maruff, Paul, Fripp, Jurgen, Völzk, Henry, Johnson, Sterling C, Morris, John C, Albert, Marilyn S, Miller, Michael I, Bryan, R Nick, Grabe, Hans J, Resnick, Susan M, Wolk, David A, Davatzikos, Christos, and for the iSTAGING consortium, the Preclinical AD consortium
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Biological Psychology ,Psychology ,Biomedical Imaging ,Aging ,Neurosciences ,Alzheimer's Disease ,Dementia ,Clinical Research ,Acquired Cognitive Impairment ,Brain Disorders ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Neurodegenerative ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Neurological ,Adult ,Aged ,Aged ,80 and over ,Amyloid beta-Peptides ,Atrophy ,Biomarkers ,Brain ,Cerebral Small Vessel Diseases ,Cognitive Dysfunction ,Disease Progression ,Female ,Humans ,Image Processing ,Computer-Assisted ,Machine Learning ,Magnetic Resonance Imaging ,Male ,Middle Aged ,Neuropsychological Tests ,White Matter ,Young Adult ,Alzheimer's disease pathology ,beta-amyloid ,brain aging ,brain signatures ,cognitive testing ,harmonized neuroimaging cohorts ,MRI ,Neuroimaging ,PET ,preclinical Alzheimer's disease ,small vessel ischemic disease ,tau ,iSTAGING consortium ,the Preclinical AD consortium ,the ADNI ,and the CARDIA studies ,Clinical Sciences ,Geriatrics ,Clinical sciences ,Biological psychology - Abstract
IntroductionRelationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).MethodsThree brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.ResultsWMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.DiscussionA Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.
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- 2021
19. Cognitive reserve and rate of change in Alzheimer's and cerebrovascular disease biomarkers among cognitively normal individuals
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Pettigrew, Corinne, Soldan, Anja, Zhu, Yuxin, Cai, Qing, Wang, Mei-Cheng, Moghekar, Abhay, Miller, Michael I, Singh, Baljeet, Martinez, Oliver, Fletcher, Evan, DeCarli, Charles, and Albert, Marilyn
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Biological Psychology ,Psychology ,Prevention ,Behavioral and Social Science ,Aging ,Alzheimer's Disease ,Acquired Cognitive Impairment ,Neurodegenerative ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Brain Disorders ,Clinical Research ,Neurosciences ,Dementia ,Neurological ,Aged ,Alzheimer Disease ,Amyloidogenic Proteins ,Biomarkers ,Cerebrovascular Disorders ,Cognitive Reserve ,Female ,Humans ,Magnetic Resonance Imaging ,Male ,Middle Aged ,Neuroimaging ,Neuropsychological Tests ,White Matter ,tau Proteins ,Cognitive reserve ,Alzheimer's disease ,Cerebrovascular disease ,Amyloid ,Tau ,Clinical Sciences ,Neurology & Neurosurgery ,Biological psychology - Abstract
We examined whether cognitive reserve (CR) impacts level of, or rate of change in, biomarkers of Alzheimer's disease (AD) and small-vessel cerebrovascular disease in >250 individuals who were cognitively normal and middle-aged and older at the baseline. The four primary biomarker categories commonly examined in studies of AD were measured longitudinally: cerebrospinal fluid measures of amyloid (A) and tau (T); cerebrospinal fluid and neuroimaging measures of neuronal injury (N); and neuroimaging measures of white matter hyperintensities (WMHs) to assess cerebrovascular pathology (V). CR was indexed by a composite score including years of education, reading, and vocabulary test performance. Higher CR was associated with lower levels of WMHs, particularly among those who subsequently progressed from normal cognition to MCI. CR was not associated with WMH trajectories. In addition, CR was not associated with either levels of, or rate of change in, A/T/N biomarkers. This may suggest that higher CR is associated with lifestyle factors that reduce levels of cerebrovascular disease, allowing individuals with higher CR to better tolerate other types of pathology.
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- 2020
20. A Model for Elastic Evolution on Foliated Shapes
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Hsieh, Dai-Ni, Arguillère, Sylvain, Charon, Nicolas, Miller, Michael I., and Younes, Laurent
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Mathematics - Optimization and Control ,49Q10, 49N45 - Abstract
We study a shape evolution framework in which the deformation of shapes from time t to t + dt is governed by a regularized anisotropic elasticity model. More precisely, we assume that at each time shapes are infinitesimally deformed from a stress-free state to an elastic equilibrium as a result of the application of a small force. The configuration of equilibrium then becomes the new resting state for subsequent evolution. The primary motivation of this work is the modeling of slow changes in biological shapes like atrophy, where a body force applied to the volume represents the location and impact of the disease. Our model uses an optimal control viewpoint with the time derivative of force interpreted as a control, deforming a shape gradually from its observed initial state to an observed final state. Furthermore, inspired by the layered organization of cortical volumes, we consider a special case of our model in which shapes can be decomposed into a family of layers (forming a "foliation"). Preliminary experiments on synthetic layered shapes in two and three dimensions are presented to demonstrate the effect of elasticity., Comment: 12 pages, 6 figures
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- 2018
21. Early amygdala and ERC atrophy linked to 3D reconstruction of rostral neurofibrillary tau tangle pathology in Alzheimer’s disease
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Stouffer, Kaitlin M., Chen, Claire, Kulason, Sue, Xu, Eileen, Witter, Menno P., Ceritoglu, Can, Albert, Marilyn S., Mori, Susumu, Troncoso, Juan, Tward, Daniel J., and Miller, Michael I.
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- 2023
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22. Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease
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Hu, Beini, Younes, Laurent, Bu, Xuan, Liu, Chin-Fu, Ratnanather, J. Tilak, Paulsen, Jane, Georgiou-Karistianis, Nellie, Miller, Michael I., Ross, Christopher, and Faria, Andreia V.
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- 2023
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23. Estimating Diffeomorphic Mappings between Templates and Noisy Data: Variance Bounds on the Estimated Canonical Volume Form
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Tward, Daniel J., Mitra, Partha, and Miller, Michael I.
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Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Anatomy is undergoing a renaissance driven by availability of large digital data sets generated by light microscopy. A central computational task is to map individual data volumes to standardized templates. This is accomplished by regularized estimation of a diffeomorphic transformation between the coordinate systems of the individual data and the template, building the transformation incrementally by integrating a smooth flow field. The canonical volume form of this transformation is used to quantify local growth, atrophy, or cell density. While multiple implementations exist for this estimation, less attention has been paid to the variance of the estimated diffeomorphism for noisy data. Notably, there is an infinite dimensional un-observable space defined by those diffeomorphisms which leave the template invariant. These form the stabilizer subgroup of the diffeomorphic group acting on the template. The corresponding flat directions in the energy landscape are expected to lead to increased estimation variance. Here we show that a least-action principle used to generate geodesics in the space of diffeomorphisms connecting the subject brain to the template removes the stabilizer. This provides reduced-variance estimates of the volume form. Using simulations we demonstrate that the asymmetric large deformation diffeomorphic mapping methods (LDDMM), which explicitly incorporate the asymmetry between idealized template images and noisy empirical images, provide lower variance estimators than their symmetrized counterparts (cf. ANTs). We derive Cramer-Rao bounds for the variances in the limit of small deformations. Analytical results are shown for the Jacobian in terms of perturbations of the vector fields and divergence of the vector field.
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- 2018
24. Multimodal Cross-registration and Quantification of Metric Distortions in Whole Brain Histology of Marmoset using Diffeomorphic Mappings
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Lee, Brian C., Lin, Meng Kuan, Fu, Yan, Hata, Junichi, Miller, Michael I., and Mitra, Partha P.
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Quantitative Biology - Neurons and Cognition - Abstract
Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified the distortions in brain geometry from in-vivo to ex-vivo brains due to the tissue processing, which will be important when computing properties such as local cell and process densities at the voxel level in creating reference brain maps. Further, existing approaches focus on registering uni-modal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research, it is necessary to cross-register multi-modal data sets including MRIs and multiple histological series that can help address individual variations in brain architecture. Here we present a computational approach for same-subject multimodal MRI guided reconstruction of a histological series, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during the different stages of histological processing of the brains using the Jacobian determinant of the diffeomorphic transformations involved. There are two major steps in the histology process with associated scale distortions (a) brain perfusion (b) histological sectioning and reassembly. By mapping the final image stacks to the ex-vivo post fixation MRI, we show that tape-transfer histology can be reassembled accurately into 3D volumes with a local scale change of 2.0 $\pm$ 0.4% per axis dimension. In contrast, the perfusion step, as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs, shows a larger local scale change of 6.9 $\pm$ 2.1% per axis dimension. This is the first systematic quantification of the local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species.
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- 2018
25. A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
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Burns, Randal, Perlman, Eric, Baden, Alex, Roncal, William Gray, Falk, Ben, Chandrashekhar, Vikram, Collman, Forrest, Seshamani, Sharmishtaa, Patsolic, Jesse, Lillaney, Kunal, Kazhdan, Michael, Hider Jr., Robert, Pryor, Derek, Matelsky, Jordan, Gion, Timothy, Manavalan, Priya, Wester, Brock, Chevillet, Mark, Trautman, Eric T., Khairy, Khaled, Bridgeford, Eric, Kleissas, Dean M., Tward, Daniel J., Crow, Ailey K., Wright, Matthew A., Miller, Michael I., Smith, Stephen J, Vogelstein, R. Jacob, Deisseroth, Karl, and Vogelstein, Joshua T.
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Quantitative Biology - Neurons and Cognition ,Quantitative Biology - Quantitative Methods - Abstract
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar spanning 20+ publications and 100+ terabytes including nanoscale ultrastructure, microscale synaptogenetic diversity, and mesoscale whole brain connectivity, making NeuroData the largest and most diverse open repository of brain data.
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- 2018
26. NeuroStorm: Accelerating Brain Science Discovery in the Cloud
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Kiar, Gregory, Anderson, Robert J., Baden, Alex, Badea, Alexandra, Bridgeford, Eric W., Champion, Andrew, Chandrashekhar, Vikram, Collman, Forrest, Duderstadt, Brandon, Evans, Alan C., Engert, Florian, Falk, Benjamin, Glatard, Tristan, Roncal, William R. Gray, Kennedy, David N., Maitin-Shepard, Jeremy, Marren, Ryan A., Nnaemeka, Onyeka, Perlman, Eric, Seshamani, Sharmishtaas, Trautman, Eric T., Tward, Daniel J., Valdés-Sosa, Pedro Antonio, Wang, Qing, Miller, Michael I., Burns, Randal, and Vogelstein, Joshua T.
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Quantitative Biology - Other Quantitative Biology - Abstract
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capacities is hampered by technical challenges. The key barriers to accelerating scientific discovery correspond to the FAIR data principles: findability, global access to data, software interoperability, and reproducibility/re-usability. We conducted a hackathon dedicated to making strides in those steps. This manuscript is a technical report summarizing these achievements, and we hope serves as an example of the effectiveness of focused, deliberate hackathons towards the advancement of our quickly-evolving field., Comment: 10 pages, 4 figures, hackathon report
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- 2018
27. On variational solutions for whole brain serial-section histology using the computational anatomy random orbit model
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Lee, Brian C., Tward, Daniel J., Mitra, Partha P., and Miller, Michael I.
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Electrical Engineering and Systems Science - Image and Video Processing - Abstract
This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 um meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.
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- 2018
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28. Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility
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Li, Xu, Chen, Lin, Kutten, Kwame, Ceritoglu, Can, Li, Yue, Kang, Ningdong, Hsu, John T, Qiao, Ye, Wei, Hongjiang, Liu, Chunlei, Miller, Michael I, Mori, Susumu, Yousem, David M, van Zijl, Peter CM, and Faria, Andreia V
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Bioengineering ,Neurosciences ,Aging ,Biomedical Imaging ,Adult ,Aged ,Atlases as Topic ,Brain ,Brain Mapping ,Datasets as Topic ,Female ,Gray Matter ,Humans ,Image Processing ,Computer-Assisted ,Male ,Middle Aged ,QSM ,SWI ,Atlas ,Automated segmentation ,Susceptibility quantification ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Neurology & Neurosurgery - Abstract
Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0.77 ± 0.09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0.79 ± 0.07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0.79 ± 0.08 to manual labels and 0.89 ± 0.04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0.97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www.mricloud.org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future.
- Published
- 2019
29. Magnetic resonance imaging of mouse brain networks plasticity following motor learning
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Badea, Alexandra, Ng, Kwan L, Anderson, Robert J, Zhang, Jiangyang, Miller, Michael I, and O’Brien, Richard J
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Biological Psychology ,Biomedical and Clinical Sciences ,Neurosciences ,Psychology ,Bioengineering ,Biomedical Imaging ,Rehabilitation ,Neurological ,Animals ,Brain ,Brain Mapping ,Learning ,Magnetic Resonance Imaging ,Male ,Mice ,Mice ,Inbred C57BL ,Motor Skills ,Nerve Net ,Neuronal Plasticity ,General Science & Technology - Abstract
We do not have a full understanding of the mechanisms underlying plasticity in the human brain. Mouse models have well controlled environments and genetics, and provide tools to help dissect the mechanisms underlying the observed responses to therapies devised for humans recovering from injury of ischemic nature or trauma. We aimed to detect plasticity following learning of a unilateral reaching movement, and relied on MRI performed with a rapid structural protocol suitable for in vivo brain imaging, and a longer diffusion tensor imaging (DTI) protocol executed ex vivo. In vivo MRI detected contralateral volume increases in trained animals (reachers), in circuits involved in motor control, sensory processing, and importantly, learning and memory. The temporal association area, parafascicular and mediodorsal thalamic nuclei were also enlarged. In vivo MRI allowed us to detect longitudinal effects over the ~25 days training period. The interaction between time and group (trained versus not trained) supported a role for the contralateral, but also the ipsilateral hemisphere. While ex vivo imaging was affected by shrinkage due to the fixation, it allowed for superior resolution and improved contrast to noise ratios, especially for subcortical structures. We examined microstructural changes based on DTI, and identified increased fractional anisotropy and decreased apparent diffusion coefficient, predominantly in the cerebellum and its connections. Cortical thickness differences did not survive multiple corrections, but uncorrected statistics supported the contralateral effects seen with voxel based volumetric analysis, showing thickening in the somatosensory, motor and visual cortices. In vivo and ex vivo analyses identified plasticity in circuits relevant to selecting actions in a sensory-motor context, through exploitation of learned association and decision making. By mapping a connectivity atlas into our ex vivo template we revealed that changes due to skilled motor learning occurred in a network of 35 regions, including the primary and secondary motor (M1, M2) and sensory cortices (S1, S2), the caudate putamen (CPu), visual (V1) and temporal association cortex. The significant clusters intersected tractography based networks seeded in M1, M2, S1, V1 and CPu at levels > 80%. We found that 89% of the significant cluster belonged to a network seeded in the contralateral M1, and 85% to one seeded in the contralateral M2. Moreover, 40% of the M1 and S1 cluster by network intersections were in the top 80th percentile of the tract densities for their respective networks. Our investigation may be relevant to studies of rehabilitation and recovery, and points to widespread network changes that accompany motor learning that may have potential applications to designing recovery strategies following brain injury.
- Published
- 2019
30. Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment
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Kulason, Sue, Tward, Daniel J, Brown, Timothy, Sicat, Chelsea S, Liu, Chin-Fu, Ratnanather, J Tilak, Younes, Laurent, Bakker, Arnold, Gallagher, Michela, Albert, Marilyn, Miller, Michael I, and Initiative, for the Alzheimer's Disease Neuroimaging
- Subjects
Biological Psychology ,Psychology ,Alzheimer's Disease ,Brain Disorders ,Acquired Cognitive Impairment ,Dementia ,Neurosciences ,Neurodegenerative ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Aging ,Aetiology ,2.1 Biological and endogenous factors ,Neurological ,Aged ,Amygdala ,Atrophy ,Brain ,Cognitive Dysfunction ,Entorhinal Cortex ,Female ,Hippocampus ,Humans ,Image Processing ,Computer-Assisted ,Magnetic Resonance Imaging ,Male ,Entorhinal cortex ,Transentorhinal cortex ,Mild cognitive impairment ,Braak staging ,Cortical thickness ,Shape analysis ,Longitudinal analysis ,Alzheimer's Disease Neuroimaging Initiative ,Biological psychology ,Clinical and health psychology - Abstract
This study examines the atrophy rates of subjects with mild cognitive impairment (MCI) compared to controls in four regions within the medial temporal lobe: the transentorhinal cortex (TEC), entorhinal cortex (ERC), hippocampus, and amygdala. These regions were manually segmented and then corrected for undesirable longitudinal variability via Large Deformation Diffeomorphic Metric Mapping (LDDMM) based longitudinal diffeomorphometry. Diffeomorphometry techniques were used to compare thickness measurements in the TEC with the ERC. There were more significant changes in thickness atrophy rate in the TEC than medial regions of the entorhinal cortex. Volume measures were also calculated for all four regions. Classifiers were constructed using linear discriminant analysis to demonstrate that average thickness and atrophy rate of TEC together was the most discriminating measure compared to the thickness and volume measures in the areas examined, in differentiating MCI from controls. These findings are consistent with autopsy findings demonstrating that initial neuronal changes are found in TEC before spreading more medially in the ERC and to other regions in the medial temporal lobe. These findings suggest that the TEC thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease.
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- 2019
31. Computerized paired associate learning performance and imaging biomarkers in older adults without dementia
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Pettigrew, Corinne, Soldan, Anja, Brichko, Rostislav, Zhu, Yuxin, Wang, Mei-Cheng, Kutten, Kwame, Bilgel, Murat, Mori, Susumu, Miller, Michael I., and Albert, Marilyn
- Published
- 2022
- Full Text
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32. Hidden Markov modeling for maximum probability neuron reconstruction
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Athey, Thomas L., Tward, Daniel J., Mueller, Ulrich, Vogelstein, Joshua T., and Miller, Michael I.
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- 2022
- Full Text
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33. Projective diffeomorphic mapping of molecular digital pathology with tissue MRI
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Stouffer, Kaitlin M., Witter, Menno P., Tward, Daniel J., and Miller, Michael I.
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- 2022
- Full Text
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34. A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information
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Kutten, Kwame S., Charon, Nicolas, Miller, Michael I., Ratnanather, J. T., Matelsky, Jordan, Baden, Alexander D., Lillaney, Kunal, Deisseroth, Karl, Ye, Li, and Vogelstein, Joshua T.
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
CLARITY is a method for converting biological tissues into translucent and porous hydrogel-tissue hybrids. This facilitates interrogation with light sheet microscopy and penetration of molecular probes while avoiding physical slicing. In this work, we develop a pipeline for registering CLARIfied mouse brains to an annotated brain atlas. Due to the novelty of this microscopy technique it is impractical to use absolute intensity values to align these images to existing standard atlases. Thus we adopt a large deformation diffeomorphic approach for registering images via mutual information matching. Furthermore we show how a cascaded multi-resolution approach can improve registration quality while reducing algorithm run time. As acquired image volumes were over a terabyte in size, they were far too large for work on personal computers. Therefore the NeuroData computational infrastructure was deployed for multi-resolution storage and visualization of these images and aligned annotations on the web.
- Published
- 2016
35. From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data
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Stouffer, Kaitlin M., Wang, Zhenzhen, Xu, Eileen, Lee, Karl, Lee, Paige, Miller, Michael I., Tward, Daniel J., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Syeda-Mahmood, Tanveer, editor, Li, Xiang, editor, Madabhushi, Anant, editor, Greenspan, Hayit, editor, Li, Quanzheng, editor, Leahy, Richard, editor, Dong, Bin, editor, and Wang, Hongzhi, editor
- Published
- 2021
- Full Text
- View/download PDF
36. Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM
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Kutten, Kwame S., Vogelstein, Joshua T., Charon, Nicolas, Ye, Li, Deisseroth, Karl, and Miller, Michael I.
- Subjects
Quantitative Biology - Quantitative Methods ,Computer Science - Computer Vision and Pattern Recognition - Abstract
The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically find the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images. Our code is available as open source software at http://NeuroData.io.
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- 2016
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37. Shape Diffeomorphometry of Brain Structures in Neurodegeneration and Neurodevelopment
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Ratnanather, J. Tilak, primary, Liu, Chin-Fu, additional, and Miller, Michael I., additional
- Published
- 2021
- Full Text
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38. A Model for Elastic Evolution on Foliated Shapes
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Hsieh, Dai-Ni, Arguillère, Sylvain, Charon, Nicolas, Miller, Michael I., Younes, Laurent, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Chung, Albert C. S., editor, Gee, James C., editor, Yushkevich, Paul A., editor, and Bao, Siqi, editor
- Published
- 2019
- Full Text
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39. Association of peripheral inflammatory markers with connectivity in large-scale functional brain networks of non-demented older adults
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Walker, Keenan A., Gross, Alden L., Moghekar, Abhay R., Soldan, Anja, Pettigrew, Corinne, Hou, Xirui, Lu, Hanzhang, Alfini, Alfonso J., Bilgel, Murat, Miller, Michael I., Albert, Marilyn S., and Walston, Jeremy
- Published
- 2020
- Full Text
- View/download PDF
40. Grand Challenges at the Interface of Engineering and Medicine
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Subramaniam, Shankar, primary, Akay, Metin, additional, Anastasio, Mark A., additional, Bailey, Vasudev, additional, Boas, David, additional, Bonato, Paolo, additional, Chilkoti, Ashutosh, additional, Cochran, Jennifer R., additional, Colvin, Vicki, additional, Desai, Tejal A., additional, Duncan, James S., additional, Epstein, Frederick H., additional, Fraley, Stephanie, additional, Giachelli, Cecilia, additional, Grande-Allen, K. Jane, additional, Green, Jordan, additional, Guo, X. Edward, additional, Hilton, Isaac B., additional, Humphrey, Jay D., additional, Johnson, Chris R, additional, Karniadakis, George, additional, King, Michael R., additional, Kirsch, Robert F., additional, Kumar, Sanjay, additional, Laurencin, Cato T., additional, Li, Song, additional, Lieber, Richard L., additional, Lovell, Nigel, additional, Mali, Prashant, additional, Margulies, Susan S., additional, Meaney, David F., additional, Ogle, Brenda, additional, Palsson, Bernhard, additional, A. Peppas, Nicholas, additional, Perreault, Eric J., additional, Rabbitt, Rick, additional, Setton, Lori A., additional, Shea, Lonnie D., additional, Shroff, Sanjeev G., additional, Shung, Kirk, additional, Tolias, Andreas S., additional, van der Meulen, Marjolein C.H., additional, Varghese, Shyni, additional, Vunjak-Novakovic, Gordana, additional, White, John A., additional, Winslow, Raimond, additional, Zhang, Jianyi, additional, Zhang, Kun, additional, Zukoski, Charles, additional, and Miller, Michael I., additional
- Published
- 2024
- Full Text
- View/download PDF
41. Amidst an amygdala renaissance in Alzheimer’s disease
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Stouffer, Kaitlin M, primary, Grande, Xenia, additional, Düzel, Emrah, additional, Johansson, Maurits, additional, Creese, Byron, additional, Witter, Menno P, additional, Miller, Michael I, additional, Wisse, Laura E M, additional, and Berron, David, additional
- Published
- 2023
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42. Space-feature measures on meshes for mapping spatial transcriptomics
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Miller, Michael I., primary, Trouvé, Alain, additional, and Younes, Laurent, additional
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- 2023
- Full Text
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43. Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke
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Liu, Chin-Fu, Hsu, Johnny, Xu, Xin, Ramachandran, Sandhya, Wang, Victor, Miller, Michael I., Hillis, Argye E., and Faria, Andreia V.
- Published
- 2021
- Full Text
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44. Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment
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Liu, Chin-Fu, Padhy, Shreyas, Ramachandran, Sandhya, Wang, Victor X., Efimov, Andrew, Bernal, Alonso, Shi, Linyuan, Vaillant, Marc, Ratnanather, J. Tilak, Faria, Andreia V., Caffo, Brian, Albert, Marilyn, and Miller, Michael I.
- Published
- 2019
- Full Text
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45. Core Competencies for Undergraduates in Bioengineering and Biomedical Engineering: Findings, Consequences, and Recommendations
- Author
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White, John A., Gaver, Donald P., Butera, Jr., Robert J., Choi, Bernard, Dunlop, Mary J., Grande-Allen, K. Jane, Grosberg, Anna, Hitchcock, Robert W., Huang-Saad, Aileen Y., Kotche, Miiri, Kyle, Aaron M., Lerner, Amy L., Linehan, John H., Linsenmeier, Robert A., Miller, Michael I., Papin, Jason A., Setton, Lori, Sgro, Allyson, Smith, Michael L., Zaman, Muhammad, and Lee, Abraham P.
- Published
- 2020
- Full Text
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46. The diffeomorphometry of regional shape change rates and its relevance to cognitive deterioration in mild cognitive impairment and Alzheimer's disease.
- Author
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Tang, Xiaoying, Holland, Dominic, Dale, Anders M, Younes, Laurent, Miller, Michael I, and Alzheimer's Disease Neuroimaging Initiative
- Subjects
Alzheimer's Disease Neuroimaging Initiative ,Cerebral Ventricles ,Amygdala ,Hippocampus ,Humans ,Alzheimer Disease ,Atrophy ,Magnetic Resonance Imaging ,Organ Size ,Follow-Up Studies ,Psychiatric Status Rating Scales ,Image Processing ,Computer-Assisted ,Aged ,Aged ,80 and over ,Middle Aged ,Female ,Male ,Functional Laterality ,Mild Cognitive Impairment ,Alzheimer's disease ,diffeomorphometry ,longitudinal analysis ,medial temporal lobe ,mild cognitive impairment ,regional shape change rates ,Cognitive Dysfunction ,Experimental Psychology ,Neurosciences ,Cognitive Sciences - Abstract
We proposed a diffeomorphometry-based statistical pipeline to study the regional shape change rates of the bilateral hippocampus, amygdala, and ventricle in mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared with healthy controls (HC), using sequential magnetic resonance imaging (MRI) scans of 713 subjects (3,123 scans in total). The subgroup shape atrophy rates of the bilateral hippocampus and amygdala, as well as the expansion rates of the bilateral ventricles, for a majority of vertices were found to follow the order of AD>MCI>HC. The bilateral hippocampus and the left amygdala were subsegmented into multiple functionally meaningful subregions with the help of high-field MRI scans. The largest group differences in localized shape atrophy rates on the hippocampus were found to occur in CA1, followed by subiculum, CA2, and finally CA3/dentate gyrus, which is consistent with the neurofibrillary tangle accumulation trajectory. Highly nonuniform group differences were detected on the amygdala; vertices on the core amygdala (basolateral and lateral nucleus) revealed much larger atrophy rates, whereas those on the noncore amygdala (mainly centromedial) displayed similar or even smaller atrophy rates in AD relative to HC. The temporal horns of the ventricles were observed to have the largest localized ventricular expansion rate differences; with the AD group showing larger localized expansion rates on the anterior horn and the body part of the ventricles as well. Significant correlations were observed between the localized shape change rates of each of these six structures and the cognitive deterioration rates as quantified by the Alzheimer's Disease Assessment Scale-Cognitive Behavior Section increase rate and the Mini Mental State Examination decrease rate.
- Published
- 2015
47. Baseline shape diffeomorphometry patterns of subcortical and ventricular structures in predicting conversion of mild cognitive impairment to Alzheimer's disease.
- Author
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Tang, Xiaoying, Holland, Dominic, Dale, Anders M, Younes, Laurent, and Miller, Michael I
- Subjects
Brain ,Humans ,Alzheimer Disease ,Disease Progression ,Image Interpretation ,Computer-Assisted ,Magnetic Resonance Imaging ,Prognosis ,Discriminant Analysis ,Linear Models ,Neuropsychological Tests ,Databases ,Factual ,Aged ,Female ,Male ,Cognitive Dysfunction ,Alzheimer's disease ,lateral ventricles ,linear discriminant analysis ,mild cognitive impairment ,prediction ,principal component analysis ,shape diffeomorphometry ,subcortical structures ,Image Interpretation ,Computer-Assisted ,Databases ,Factual ,Neurology & Neurosurgery ,Clinical Sciences ,Cognitive Sciences ,Neurosciences - Abstract
In this paper, we propose a novel predictor for the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). This predictor is based on the shape diffeomorphometry patterns of subcortical and ventricular structures (left and right amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricle) of 607 baseline scans from the Alzheimer's Disease Neuroimaging Initiative database, including a total of 210 healthy control subjects, 222 MCI subjects, and 175 AD subjects. The optimal predictor is obtained via a feature selection procedure applied to all of the 14 sets of shape features via linear discriminant analysis, resulting in a combination of the shape diffeomorphometry patterns of the left hippocampus, the left lateral ventricle, the right thalamus, the right caudate, and the bilateral putamen. Via 10-fold cross-validation, we substantiate our method by successfully differentiating 77.04% (104/135) of the MCI subjects who converted to AD within 36 months and 71.26% (62/87) of the non-converters. To be specific, for the MCI-converters, we are capable of correctly predicting 82.35% (14/17) of subjects converting in 6 months, 77.5% (31/40) of subjects converting in 12 months, 74.07% (20/27) of subjects converting in 18 months, 78.13% (25/32) of subjects converting in 24 months, and 73.68% (14/19) of subject converting in 36 months. Statistically significant correlation maps were observed between the shape diffeomorphometry features of each of the 14 structures, especially the bilateral amygdala, hippocampus, lateral ventricle, and two neuropsychological test scores--the Alzheimer's Disease Assessment Scale-Cognitive Behavior Section and the Mini-Mental State Examination.
- Published
- 2015
48. APOE Affects the Volume and Shape of the Amygdala and the Hippocampus in Mild Cognitive Impairment and Alzheimer's Disease: Age Matters.
- Author
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Tang, Xiaoying, Holland, Dominic, Dale, Anders M, Miller, Michael I, and Alzheimer's Disease Neuroimaging Initiative
- Subjects
Alzheimer's Disease Neuroimaging Initiative ,Amygdala ,Hippocampus ,Humans ,Alzheimer Disease ,Disease Progression ,Magnetic Resonance Imaging ,Organ Size ,Cohort Studies ,Follow-Up Studies ,Mental Status Schedule ,Aging ,Heterozygote ,Aged ,Aged ,80 and over ,Middle Aged ,Female ,Male ,Functional Laterality ,Apolipoprotein E4 ,Cognitive Dysfunction ,Age intervention ,Alzheimer’s disease ,amygdala ,apolipoprotein E ,conversion ,hippocampus ,mild cognitive impairment ,shape morphometrics ,Alzheimer's disease ,and over ,Neurology & Neurosurgery ,Clinical Sciences ,Cognitive Sciences ,Neurosciences - Abstract
This paper examines how age intervenes in the effects of APOE ɛ4 allele on the volume and shape morphometrics of the hippocampus and the amygdala in mild cognitive impairment (MCI) and Alzheimer's disease. We evaluate the structural morphological differences between ɛ4 carriers and non-carriers in two age-dependent subgroups; younger than 75 years (Young-Old) and older than 80 years (Very-Old). While we show that the four structures of interest atrophy significantly in the ɛ4 carriers, relative to the non-carriers, of the Young-Old group, this effect is not observed in their Very-Old counterparts. The structures in the right hemisphere are found to be more affected by the APOE genotype than those in the left hemisphere and we identify the relevant regions in which significant atrophy occurs to be parts of the basolateral, centromedial, and lateral nucleus subregions of the amygdala and the CA1 and subiculum subregions of the hippocampus. We also observe that the APOE genotype only affects MCI patients that deteriorated to dementia within 3 years while leaving their "non-converting" counterparts unaffected.
- Published
- 2015
49. From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data
- Author
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Stouffer, Kaitlin M., primary, Wang, Zhenzhen, additional, Xu, Eileen, additional, Lee, Karl, additional, Lee, Paige, additional, Miller, Michael I., additional, and Tward, Daniel J., additional
- Published
- 2021
- Full Text
- View/download PDF
50. Extended multimodal whole-brain anatomical covariance analysis: detection of disrupted correlation networks related to amyloid deposition
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Ye, Chenfei, Albert, Marilyn, Brown, Timothy, Bilgel, Murat, Hsu, Johnny, Ma, Ting, Caffo, Brian, Miller, Michael I., Mori, Susumu, and Oishi, Kenichi
- Published
- 2019
- Full Text
- View/download PDF
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