Early detection of Alzheimer's disease using MRI hippocampal texture

Cognitive impairment in patients with Alzheimer's disease (AD) is associated with reduction in hippocampal volume in magnetic resonance imaging (MRI). However, it is unknown whether hippocampal texture changes in persons with mild cognitive impairment (MCI) that does not have a change in hippoc...

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Bibliographic Details
Published in:Human brain mapping Vol. 37; no. 3; pp. 1148 - 1161
Main Authors: Sørensen, Lauge, Igel, Christian, Liv Hansen, Naja, Osler, Merete, Lauritzen, Martin, Rostrup, Egill, Nielsen, Mads
Format: Journal Article
Language:English
Published: United States Blackwell Publishing Ltd 01.03.2016
John Wiley & Sons, Inc
John Wiley and Sons Inc
Subjects:
Aged
Alzheimer Disease - diagnosis
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - metabolism
Alzheimer Disease - pathology
Area Under Curve
biomarker
classification
Cognition
Cohort Studies
Databases, Factual
Early Diagnosis
Female
Glucose - metabolism
hippocampus
Hippocampus - diagnostic imaging
Hippocampus - metabolism
Hippocampus - pathology
Humans
image analysis
Image Interpretation, Computer-Assisted - methods
machine learning
magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Middle Aged
mild cognitive impairment
Organ Size
Positron-Emission Tomography
Prognosis
early diagnosis
magnetic resonance imaging
hippocampus
mild cognitive impairment
biomarker
classification
machine learning
image analysis
ISSN:1065-9471, 1097-0193
Online Access:Get full text
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Summary:Cognitive impairment in patients with Alzheimer's disease (AD) is associated with reduction in hippocampal volume in magnetic resonance imaging (MRI). However, it is unknown whether hippocampal texture changes in persons with mild cognitive impairment (MCI) that does not have a change in hippocampal volume. We tested the hypothesis that hippocampal texture has association to early cognitive loss beyond that of volumetric changes. The texture marker was trained and evaluated using T1‐weighted MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and subsequently applied to score independent data sets from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) and the Metropolit 1953 Danish Male Birth Cohort (Metropolit). Hippocampal texture was superior to volume reduction as predictor of MCI‐to‐AD conversion in ADNI (area under the receiver operating characteristic curve [AUC] 0.74 vs 0.67; DeLong test, p = 0.005), and provided even better prognostic results in AIBL (AUC 0.83). Hippocampal texture, but not volume, correlated with Addenbrooke's cognitive examination score (Pearson correlation, r = −0.25, p < 0.001) in the Metropolit cohort. The hippocampal texture marker correlated with hippocampal glucose metabolism as indicated by fluorodeoxyglucose‐positron emission tomography (Pearson correlation, r = −0.57, p < 0.001). Texture statistics remained significant after adjustment for volume in all cases, and the combination of texture and volume did not improve diagnostic or prognostic AUCs significantly. Our study highlights the presence of hippocampal texture abnormalities in MCI, and the possibility that texture may serve as a prognostic neuroimaging biomarker of early cognitive impairment. Hum Brain Mapp 37:1148–1161, 2016. © 2015 Wiley Periodicals, Inc.
Bibliography:istex:E010330D191A8F853CF7E026194B1D189B6BCE71
ArticleID:HBM23091
ark:/67375/WNG-R7DG5XM8-R
The Danish National Advanced Technology Foundation, Eurostars, and NORDEA Foundation/Center for Healthy Aging
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. A complete listing of ADNI investigators can be found at
http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
www.loni.usc.edu/ADNI
www.aibl.csiro.au
The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at
.
Data used in preparation of this article were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) and from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database
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Data used in preparation of this article were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) and from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (http://www.loni.usc.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. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at http://www.aibl.csiro.au.
ISSN:1065-9471
1097-0193
DOI:10.1002/hbm.23091