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Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease*
Mild cognitive impairment can represent a transitional state between normal ageing and Alzheimer's disease. Non-invasive diagnostic methods are needed to identify mild cognitive impairment individuals for early therapeutic interventions. Our objective was to determine whether automated magnetic...
Autores principales: | , , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2714061/ https://www.ncbi.nlm.nih.gov/pubmed/19460794 http://dx.doi.org/10.1093/brain/awp123 |
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author | Desikan, Rahul S. Cabral, Howard J. Hess, Christopher P. Dillon, William P. Glastonbury, Christine M. Weiner, Michael W. Schmansky, Nicholas J. Greve, Douglas N. Salat, David H. Buckner, Randy L. Fischl, Bruce |
author_facet | Desikan, Rahul S. Cabral, Howard J. Hess, Christopher P. Dillon, William P. Glastonbury, Christine M. Weiner, Michael W. Schmansky, Nicholas J. Greve, Douglas N. Salat, David H. Buckner, Randy L. Fischl, Bruce |
author_sort | Desikan, Rahul S. |
collection | PubMed |
description | Mild cognitive impairment can represent a transitional state between normal ageing and Alzheimer's disease. Non-invasive diagnostic methods are needed to identify mild cognitive impairment individuals for early therapeutic interventions. Our objective was to determine whether automated magnetic resonance imaging-based measures could identify mild cognitive impairment individuals with a high degree of accuracy. Baseline volumetric T(1)-weighted magnetic resonance imaging scans of 313 individuals from two independent cohorts were examined using automated software tools to identify the volume and mean thickness of 34 neuroanatomic regions. The first cohort included 49 older controls and 48 individuals with mild cognitive impairment, while the second cohort included 94 older controls and 57 mild cognitive impairment individuals. Sixty-five patients with probable Alzheimer's disease were also included for comparison. For the discrimination of mild cognitive impairment, entorhinal cortex thickness, hippocampal volume and supramarginal gyrus thickness demonstrated an area under the curve of 0.91 (specificity 94%, sensitivity 74%, positive likelihood ratio 12.12, negative likelihood ratio 0.29) for the first cohort and an area under the curve of 0.95 (specificity 91%, sensitivity 90%, positive likelihood ratio 10.0, negative likelihood ratio 0.11) for the second cohort. For the discrimination of Alzheimer's disease, these three measures demonstrated an area under the curve of 1.0. The three magnetic resonance imaging measures demonstrated significant correlations with clinical and neuropsychological assessments as well as with cerebrospinal fluid levels of tau, hyperphosphorylated tau and abeta 42 proteins. These results demonstrate that automated magnetic resonance imaging measures can serve as an in vivo surrogate for disease severity, underlying neuropathology and as a non-invasive diagnostic method for mild cognitive impairment and Alzheimer's disease. |
format | Text |
id | pubmed-2714061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-27140612009-07-23 Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* Desikan, Rahul S. Cabral, Howard J. Hess, Christopher P. Dillon, William P. Glastonbury, Christine M. Weiner, Michael W. Schmansky, Nicholas J. Greve, Douglas N. Salat, David H. Buckner, Randy L. Fischl, Bruce Brain Original Articles Mild cognitive impairment can represent a transitional state between normal ageing and Alzheimer's disease. Non-invasive diagnostic methods are needed to identify mild cognitive impairment individuals for early therapeutic interventions. Our objective was to determine whether automated magnetic resonance imaging-based measures could identify mild cognitive impairment individuals with a high degree of accuracy. Baseline volumetric T(1)-weighted magnetic resonance imaging scans of 313 individuals from two independent cohorts were examined using automated software tools to identify the volume and mean thickness of 34 neuroanatomic regions. The first cohort included 49 older controls and 48 individuals with mild cognitive impairment, while the second cohort included 94 older controls and 57 mild cognitive impairment individuals. Sixty-five patients with probable Alzheimer's disease were also included for comparison. For the discrimination of mild cognitive impairment, entorhinal cortex thickness, hippocampal volume and supramarginal gyrus thickness demonstrated an area under the curve of 0.91 (specificity 94%, sensitivity 74%, positive likelihood ratio 12.12, negative likelihood ratio 0.29) for the first cohort and an area under the curve of 0.95 (specificity 91%, sensitivity 90%, positive likelihood ratio 10.0, negative likelihood ratio 0.11) for the second cohort. For the discrimination of Alzheimer's disease, these three measures demonstrated an area under the curve of 1.0. The three magnetic resonance imaging measures demonstrated significant correlations with clinical and neuropsychological assessments as well as with cerebrospinal fluid levels of tau, hyperphosphorylated tau and abeta 42 proteins. These results demonstrate that automated magnetic resonance imaging measures can serve as an in vivo surrogate for disease severity, underlying neuropathology and as a non-invasive diagnostic method for mild cognitive impairment and Alzheimer's disease. Oxford University Press 2009-08 2009-05-21 /pmc/articles/PMC2714061/ /pubmed/19460794 http://dx.doi.org/10.1093/brain/awp123 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Desikan, Rahul S. Cabral, Howard J. Hess, Christopher P. Dillon, William P. Glastonbury, Christine M. Weiner, Michael W. Schmansky, Nicholas J. Greve, Douglas N. Salat, David H. Buckner, Randy L. Fischl, Bruce Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* |
title | Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* |
title_full | Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* |
title_fullStr | Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* |
title_full_unstemmed | Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* |
title_short | Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* |
title_sort | automated mri measures identify individuals with mild cognitive impairment and alzheimer's disease* |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2714061/ https://www.ncbi.nlm.nih.gov/pubmed/19460794 http://dx.doi.org/10.1093/brain/awp123 |
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