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Differentiating Between Healthy Control Participants and Those with Mild Cognitive Impairment Using Volumetric MRI Data

Published online by Cambridge University Press:  27 May 2019

Renée DeVivo Open the ORCID record for Renée DeVivo [Opens in a new window] ,
Lauren Zajac ,
Asim Mian ,
Anna Cervantes-Arslanian ,
Eric Steinberg ,
Michael L. Alosco ,
Jesse Mez ,
Robert Stern ,
Ronald Killany  and
for the Alzheimer’s Disease Neuroimaging Initiative
Renée DeVivo
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Center for Biomedical Imaging, Boston University School of Medicine, Boston, Massachusetts, USA
Lauren Zajac
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Center for Biomedical Imaging, Boston University School of Medicine, Boston, Massachusetts, USA
Asim Mian
Affiliation:
Department of Radiology, Boston Medical Center, Boston, Massachusetts, USA
Anna Cervantes-Arslanian
Affiliation:
Department of Neurosurgery, Boston University School of Medicine, Boston, Massachusetts, USA
Eric Steinberg
Affiliation:
Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA
Michael L. Alosco
Affiliation:
Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
Jesse Mez
Affiliation:
Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
Robert Stern
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Department of Neurosurgery, Boston University School of Medicine, Boston, Massachusetts, USA Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
Ronald Killany*
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Center for Biomedical Imaging, Boston University School of Medicine, Boston, Massachusetts, USA Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Boston University School of Public Health, Boston, Massachusetts, USA
for the Alzheimer’s Disease Neuroimaging Initiative
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Center for Biomedical Imaging, Boston University School of Medicine, Boston, Massachusetts, USA Department of Radiology, Boston Medical Center, Boston, Massachusetts, USA Department of Neurosurgery, Boston University School of Medicine, Boston, Massachusetts, USA Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA Boston University School of Public Health, Boston, Massachusetts, USA
*
Correspondence and reprint requests to: Ronald Killiany, Boston University School of Medicine, Center for Biomedical Imaging, 700 Albany Street, W701, Boston, MA 02118, USA. E-mail: killiany@bu.edu
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Abstract

Objective: To determine whether volumetric measures of the hippocampus, entorhinal cortex, and other cortical measures can differentiate between cognitively normal individuals and subjects with mild cognitive impairment (MCI). Method: Magnetic resonance imaging (MRI) data from 46 cognitively normal subjects and 50 subjects with MCI as part of the Boston University Alzheimer’s Disease Center research registry and the Alzheimer’s Disease Neuroimaging Initiative were used in this cross-sectional study. Cortical, subcortical, and hippocampal subfield volumes were generated from each subject’s MRI data using FreeSurfer v6.0. Nominal logistic regression models containing these variables were used to identify subjects as control or MCI. Results: A model containing regions of interest (superior temporal cortex, caudal anterior cingulate, pars opercularis, subiculum, precentral cortex, caudal middle frontal cortex, rostral middle frontal cortex, pars orbitalis, middle temporal cortex, insula, banks of the superior temporal sulcus, parasubiculum, paracentral lobule) fit the data best (R2 = .7310, whole model test chi-square = 97.16, p < .0001). Conclusions: MRI data correctly classified most subjects using measures of selected medial temporal lobe structures in combination with those from other cortical areas, yielding an overall classification accuracy of 93.75%. These findings support the notion that, while volumes of medial temporal lobe regions differ between cognitively normal and MCI subjects, differences that can be used to distinguish between these two populations are present elsewhere in the brain.

Keywords

NeuroimagingHealthy agingHippocampusEntorhinal cortexAtrophyLogistic models
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 25 , Issue 8 , September 2019 , pp. 800 - 810
Copyright
Copyright © INS. Published by Cambridge University Press, 2019. 

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Footnotes

*

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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