Structural MRI biomarkers for preclinical and mild Alzheimer's disease
Noninvasive MRI biomarkers for Alzheimer's disease (AD) may enable earlier clinical diagnosis and the monitoring of therapeutic effectiveness. To assess potential neuroimaging biomarkers, the Alzheimer's Disease Neuroimaging Initiative is following normal controls (NC) and individuals with...
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Published in | Human brain mapping Vol. 30; no. 10; pp. 3238 - 3253 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.10.2009
Wiley-Liss |
Subjects | |
Online Access | Get full text |
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Summary: | Noninvasive MRI biomarkers for Alzheimer's disease (AD) may enable earlier clinical diagnosis and the monitoring of therapeutic effectiveness. To assess potential neuroimaging biomarkers, the Alzheimer's Disease Neuroimaging Initiative is following normal controls (NC) and individuals with mild cognitive impairment (MCI) or AD. We applied high‐throughput image analyses procedures to these data to demonstrate the feasibility of detecting subtle structural changes in prodromal AD. Raw DICOM scans (139 NC, 175 MCI, and 84 AD) were downloaded for analysis. Volumetric segmentation and cortical surface reconstruction produced continuous cortical surface maps and region‐of‐interest (ROI) measures. The MCI cohort was subdivided into single‐ (SMCI) and multiple‐domain MCI (MMCI) based on neuropsychological performance. Repeated measures analyses of covariance were used to examine group and hemispheric effects while controlling for age, sex, and, for volumetric measures, intracranial vault. ROI analyses showed group differences for ventricular, temporal, posterior and rostral anterior cingulate, posterior parietal, and frontal regions. SMCI and NC differed within temporal, rostral posterior cingulate, inferior parietal, precuneus, and caudal midfrontal regions. With MMCI and AD, greater differences were evident in these regions and additional frontal and retrosplenial cortices; evidence for non‐AD pathology in MMCI also was suggested. Mesial temporal right‐dominant asymmetries were evident and did not interact with diagnosis. Our findings demonstrate that high‐throughput methods provide numerous measures to detect subtle effects of prodromal AD, suggesting early and later stages of the preclinical state in this cross‐sectional sample. These methods will enable a more complete longitudinal characterization and allow us to identify changes that are predictive of conversion to AD. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc. |
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Bibliography: | ArticleID:HBM20744 National Center for Research Resources, National Institutes of Health, USA [Morphometry Biomedical Informatics Research Network (BIRN, )] [http://www.nbirn.net] - No. U24 RR021382 istex:7769F80B485D98A2A20B4CBE4C341016270BE7A1 Alzheimer's Disease Neuroimaging Initiative (ADNI; NIH) - No. U01 AG024904 Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu\ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. Complete listing of ADNI investigators available at http://www.loni.ucla.edu/ADNI/Data/ADNI_Authorship_List.pdf. ark:/67375/WNG-L01T19NK-0 http://www.loni.ucla.edu/ADNI/Data/ADNI_Authorship_List.pdf Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. Complete listing of ADNI investigators available at www.loni.ucla.edu\ADNI . ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu\ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. Complete listing of ADNI investigators available at http://www.loni.ucla.edu/ADNI/Data/ADNI_Authorship_List.pdf. |
ISSN: | 1065-9471 1097-0193 1097-0193 |
DOI: | 10.1002/hbm.20744 |