publication . Article . 2018

Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database

Christian Ledig; Andreas Schuh; Ricardo Guerrero; Rolf A. Heckemann; Daniel Rueckert;
Open Access English
  • Published: 01 Jul 2018 Journal: Scientific Reports (issn: 2045-2322, Copyright policy)
  • Publisher: Nature Publishing Group
  • Country: United Kingdom
Abstract
Abstract Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach en...
Subjects
free text keywords: Medicine, R, Science, Q, Article, Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, MAGNETIC-RESONANCE IMAGES, HIPPOCAMPAL ATROPHY, MR-IMAGES, SEGMENTATION APPLICATION, CLASSIFICATION, DEMENTIA, PREDICTION, ADNI, TIME, DIAGNOSIS, Multidisciplinary, Atrophy, medicine.disease, Segmentation, Magnetic resonance imaging, medicine.diagnostic_test, Neuroscience, Biomarker Analysis, Biomarker (medicine), Neuroimaging, business.industry, business, Discriminative model, Human brain, medicine.anatomical_structure
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
,
RCUK| Dementia Diagnosis: A tool for healthcare and pharma
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: 101685
  • Funding stream: Innovate UK
,
EC| PREDICTND
Project
PREDICTND
From Patient Data to Clinical Diagnosis in Neurodegenerative Diseases
  • Funder: European Commission (EC)
  • Project Code: 611005
  • Funding stream: FP7 | SP1 | ICT
Communities
Neuroinformatics
84 references, page 1 of 6

Scheltens, P, Fox, NC, Barkhof, F, De Carli, C. Structural magnetic resonance imaging in the practical assessment of dementia: beyond exclusion. The Lancet Neurology. 2002; 1: 13-21 [OpenAIRE] [PubMed] [DOI]

Fennema-Notestine, C. Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Human Brain Mapping. 2009; 30: 3238-3253 [OpenAIRE] [PubMed] [DOI]

Frisoni, GB, Fox, NC, Jack, CR, Scheltens, P, Thompson, PM. The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology. 2010; 6: 67-77 [OpenAIRE] [PubMed] [DOI]

Klöppel, S. Diagnostic neuroimaging across diseases. NeuroImage. 2012; 61: 457-463 [OpenAIRE] [PubMed] [DOI]

Klöppel, S. Accuracy of dementia diagnosis–a direct comparison between radiologists and a computerized method. Brain. 2008; 131: 2969-2974 [OpenAIRE] [PubMed] [DOI]

Heckemann, R. Automatic try on MR brain images can support diagnostic decision making. BMC Medical Imaging. 2008; 8: 9 [OpenAIRE] [PubMed] [DOI]

Falahati, F, Westman, E, Simmon, A. Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. Journal of Alzheimer’s Disease. 2014; 41: 685-708 [OpenAIRE] [DOI]

Sevigny, J. Aducanumab (BIIB037), an anti-amyloid beta monoclonal antibody, in patients with prodromal or mild Alzheimer’s disease: interim results of a randomized, double-blind, placebo-controlled, phase 1b study. Alzheimer’s & Dementia. 2015; 11: P277 [OpenAIRE] [DOI]

Dubois, B. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. The Lancet Neurology. 2007; 6: 734-746 [PubMed] [DOI]

Davatzikos, C, Bhatt, P, Shaw, LM, Batmanghelich, KN, Trojanowski, JQ. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging. 2011; 32: 2322.e19-2322.e27 [OpenAIRE] [DOI]

Colliot, O. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology. 2008; 248: 194-201 [OpenAIRE] [PubMed] [DOI]

Cuingnet, R. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage. 2011; 56: 766-781 [OpenAIRE] [PubMed] [DOI]

Klöppel, S. Automatic classification of MR scans in Alzheimer’s disease. Brain. 2008; 131: 681-689 [OpenAIRE] [PubMed] [DOI]

Sperling, RA. 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. Alzheimer’s & Dementia. 2011; 7: 280-292 [OpenAIRE] [DOI]

Braak, H, Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica. 1991; 82: 239-259 [PubMed] [DOI]

84 references, page 1 of 6
Abstract
Abstract Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach en...
Subjects
free text keywords: Medicine, R, Science, Q, Article, Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, MAGNETIC-RESONANCE IMAGES, HIPPOCAMPAL ATROPHY, MR-IMAGES, SEGMENTATION APPLICATION, CLASSIFICATION, DEMENTIA, PREDICTION, ADNI, TIME, DIAGNOSIS, Multidisciplinary, Atrophy, medicine.disease, Segmentation, Magnetic resonance imaging, medicine.diagnostic_test, Neuroscience, Biomarker Analysis, Biomarker (medicine), Neuroimaging, business.industry, business, Discriminative model, Human brain, medicine.anatomical_structure
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
,
RCUK| Dementia Diagnosis: A tool for healthcare and pharma
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: 101685
  • Funding stream: Innovate UK
,
EC| PREDICTND
Project
PREDICTND
From Patient Data to Clinical Diagnosis in Neurodegenerative Diseases
  • Funder: European Commission (EC)
  • Project Code: 611005
  • Funding stream: FP7 | SP1 | ICT
Communities
Neuroinformatics
84 references, page 1 of 6

Scheltens, P, Fox, NC, Barkhof, F, De Carli, C. Structural magnetic resonance imaging in the practical assessment of dementia: beyond exclusion. The Lancet Neurology. 2002; 1: 13-21 [OpenAIRE] [PubMed] [DOI]

Fennema-Notestine, C. Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Human Brain Mapping. 2009; 30: 3238-3253 [OpenAIRE] [PubMed] [DOI]

Frisoni, GB, Fox, NC, Jack, CR, Scheltens, P, Thompson, PM. The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology. 2010; 6: 67-77 [OpenAIRE] [PubMed] [DOI]

Klöppel, S. Diagnostic neuroimaging across diseases. NeuroImage. 2012; 61: 457-463 [OpenAIRE] [PubMed] [DOI]

Klöppel, S. Accuracy of dementia diagnosis–a direct comparison between radiologists and a computerized method. Brain. 2008; 131: 2969-2974 [OpenAIRE] [PubMed] [DOI]

Heckemann, R. Automatic try on MR brain images can support diagnostic decision making. BMC Medical Imaging. 2008; 8: 9 [OpenAIRE] [PubMed] [DOI]

Falahati, F, Westman, E, Simmon, A. Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. Journal of Alzheimer’s Disease. 2014; 41: 685-708 [OpenAIRE] [DOI]

Sevigny, J. Aducanumab (BIIB037), an anti-amyloid beta monoclonal antibody, in patients with prodromal or mild Alzheimer’s disease: interim results of a randomized, double-blind, placebo-controlled, phase 1b study. Alzheimer’s & Dementia. 2015; 11: P277 [OpenAIRE] [DOI]

Dubois, B. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. The Lancet Neurology. 2007; 6: 734-746 [PubMed] [DOI]

Davatzikos, C, Bhatt, P, Shaw, LM, Batmanghelich, KN, Trojanowski, JQ. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging. 2011; 32: 2322.e19-2322.e27 [OpenAIRE] [DOI]

Colliot, O. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology. 2008; 248: 194-201 [OpenAIRE] [PubMed] [DOI]

Cuingnet, R. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage. 2011; 56: 766-781 [OpenAIRE] [PubMed] [DOI]

Klöppel, S. Automatic classification of MR scans in Alzheimer’s disease. Brain. 2008; 131: 681-689 [OpenAIRE] [PubMed] [DOI]

Sperling, RA. 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. Alzheimer’s & Dementia. 2011; 7: 280-292 [OpenAIRE] [DOI]

Braak, H, Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica. 1991; 82: 239-259 [PubMed] [DOI]

84 references, page 1 of 6
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