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Jack CR, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, Dale AM, Carmichael O, Ching C, DeCarli C, Desikan RS, Fennema-Notestine C, Fjell AM, Fletcher E, Fox NC, Gunter J, Gutman BA, Holland D, Hua X, Insel P, Kantarci K, Killiany RJ, Krueger G, Leung KK, Mackin S, Maillard P, Malone IB, Mattsson N, McEvoy L, Modat M, Mueller S, Nosheny R, Ourselin S, Schuff N, Senjem ML, Simonson A, Thompson PM, Rettmann D, Vemuri P, Walhovd K, Zhao Y, Zuk S, Weiner M. Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2. Alzheimers Dement 2016; 11:740-56. [PMID: 26194310 DOI: 10.1016/j.jalz.2015.05.002] [Citation(s) in RCA: 133] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 04/28/2015] [Accepted: 05/05/2015] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. METHODS We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. RESULTS Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.
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Affiliation(s)
| | - Josephine Barnes
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | | | | | - James Brewer
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Shona Clegg
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anders M Dale
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Owen Carmichael
- Department of Neurology, University of California at Davis, Davis, CA, USA
| | - Christopher Ching
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Rahul S Desikan
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California at San Diego, La Jolla, CA, USA
| | - Anders M Fjell
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Evan Fletcher
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Nick C Fox
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jeff Gunter
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Boris A Gutman
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dominic Holland
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Xue Hua
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Philip Insel
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Ron J Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | | | - Kelvin K Leung
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Scott Mackin
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Ian B Malone
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Niklas Mattsson
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
| | - Linda McEvoy
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Marc Modat
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Susanne Mueller
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Rachel Nosheny
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Sebastien Ourselin
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Norbert Schuff
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | | | - Alix Simonson
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dan Rettmann
- MR Applications and Workflow, GE Healthcare, Rochester, MN, USA
| | | | | | | | - Samantha Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Weiner
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA; Department of Medicine, University of California at San Francisco, San Francisco, CA, USA; Department of Neurology, University of California at San Francisco, San Francisco, CA, USA
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Prasad G, Joshi SH, Nir TM, Toga AW, Thompson PM. Brain connectivity and novel network measures for Alzheimer's disease classification. Neurobiol Aging 2015; 36 Suppl 1:S121-31. [PMID: 25264345 PMCID: PMC4276322 DOI: 10.1016/j.neurobiolaging.2014.04.037] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 04/18/2014] [Accepted: 04/18/2014] [Indexed: 11/19/2022]
Abstract
We compare a variety of different anatomic connectivity measures, including several novel ones, that may help in distinguishing Alzheimer's disease (AD) patients from controls. We studied diffusion-weighted magnetic resonance imaging from 200 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We first evaluated measures derived from connectivity matrices based on whole-brain tractography; next, we studied additional network measures based on a novel flow-based measure of brain connectivity, computed on a dense 3-dimensional lattice. Based on these 2 kinds of connectivity matrices, we computed a variety of network measures. We evaluated the measures' ability to discriminate disease with a repeated, stratified 10-fold cross-validated classifier, using support vector machines, a supervised learning algorithm. We tested the relative importance of different combinations of features based on the accuracy, sensitivity, specificity, and feature ranking of the classification of 200 people into normal healthy controls and people with early or late mild cognitive impairment or AD.
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Affiliation(s)
- Gautam Prasad
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Shantanu H Joshi
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of California, Los Angeles, School of Medicine, Los Angeles, CA, USA.
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Prasad G, Joshi SH, Jahanshad N, Villalon-Reina J, Aganj I, Lenglet C, Sapiro G, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Toga AW, Thompson PM. Automatic clustering and population analysis of white matter tracts using maximum density paths. Neuroimage 2014; 97:284-95. [PMID: 24747738 DOI: 10.1016/j.neuroimage.2014.04.033] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Revised: 03/24/2014] [Accepted: 04/08/2014] [Indexed: 10/25/2022] Open
Abstract
We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.
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Affiliation(s)
- Gautam Prasad
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Shantanu H Joshi
- Department of Neurology, University of California Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Julio Villalon-Reina
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Iman Aganj
- Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Guillermo Sapiro
- Dept. of Electrical and Computer Engineering, Computer Science, Duke University, NC, USA; Dept. of Biomedical Engineering, Duke University, NC, USA
| | - Katie L McMahon
- Center for Advanced Imaging, University of Queensland, Brisbane, Australia
| | | | | | - Margaret J Wright
- School of Psychology, University of Queensland, Brisbane, Australia; QIMR Berghofer Medical Research Institute, Herston, Australia
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Dept. of Neurology, Psychiatry, Engineering, Radiology, University of Southern California, Los Angeles, CA, USA; Dept. of Ophthalmology, University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of California Los Angeles, CA, USA; Dept. of Neurology, Psychiatry, Engineering, Radiology, University of Southern California, Los Angeles, CA, USA; Dept. of Ophthalmology, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, University of Southern California, Los Angeles, CA, USA.
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