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      A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity

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          Abstract

          Alzheimer's disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, “AD signature” measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12.

          Highlights

          • Evaluate thickness- and volume-based quantitative measures of AD severity

          • Volume- and thickness-based measures perform similarly for separating by diagnosis.

          • Volume-based measures are correlated with head size; thickness-based mostly aren't.

          • We recommend an AD signature measure formed from cortical thickness measures.

          • We recommend thicknesses using ANTs software with input segmentations from SPM12.

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          Most cited references43

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          Distinct genetic influences on cortical surface area and cortical thickness.

          Neuroimaging studies examining the effects of aging and neuropsychiatric disorders on the cerebral cortex have largely been based on measures of cortical volume. Given that cortical volume is a product of thickness and surface area, it is plausible that measures of volume capture at least 2 distinct sets of genetic influences. The present study aims to examine the genetic relationships between measures of cortical surface area and thickness. Participants were men in the Vietnam Era Twin Study of Aging (110 monozygotic pairs and 92 dizygotic pairs). Mean age was 55.8 years (range: 51-59). Bivariate twin analyses were utilized in order to estimate the heritability of cortical surface area and thickness, as well as their degree of genetic overlap. Total cortical surface area and average cortical thickness were both highly heritable (0.89 and 0.81, respectively) but were essentially unrelated genetically (genetic correlation = 0.08). This pattern was similar at the lobar and regional levels of analysis. These results demonstrate that cortical volume measures combine at least 2 distinct sources of genetic influences. We conclude that using volume in a genetically informative study, or as an endophenotype for a disorder, may confound the underlying genetic architecture of brain structure.
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            Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies.

            Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies. Copyright 2009 Elsevier Inc. All rights reserved.
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              The Mayo Clinic Study of Aging: Design and Sampling, Participation, Baseline Measures and Sample Characteristics

              Background: The objective of this study was to establish a prospective population-based cohort to investigate the prevalence, incidence and risk factors for mild cognitive impairment (MCI) and dementia. Methods: The Olmsted County, Minn., population, aged 70–89 years on October 1, 2004, was enumerated using the Rochester Epidemiology Project. Eligible subjects were randomly selected and invited to participate. Participants underwent a comprehensive in-person evaluation including the Clinical Dementia Rating Scale, a neurological evaluation and neuropsychological testing. A consensus diagnosis of normal cognition, MCI or dementia was made by a panel using previously published criteria. A subsample of subjects was studied via telephone interview. Results: Four hundred and two subjects with dementia were identified from a detailed review of their medical records but were not contacted. At baseline, we successfully evaluated 703 women aged 70–79 years, 769 women aged 80–89 years, 730 men aged 70–79 years and 517 men aged 80–89 years (total n = 2,719). Among the participants, 2,050 subjects were evaluated in person and 669 via telephone. Conclusions: Strengths of the study are that the subjects were randomly selected from a defined population, the majority of the subjects were examined in person, and MCI was defined using published criteria. Here, we report the design and sampling, participation, baseline measures and sample characteristics.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                30 May 2016
                2016
                30 May 2016
                : 11
                : 802-812
                Affiliations
                [a ]Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
                [b ]Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
                [c ]Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
                [d ]Department of Neuroscience (Neuropathology), Mayo Clinic and Foundation, Jacksonville, FL, USA
                [e ]Department of Laboratory Medicine, Mayo Clinic and Foundation, Rochester, MN, USA
                [f ]Veterans Affairs, University of California, San Francisco, CA, USA
                [g ]Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
                Author notes
                [* ]Corresponding author at: Mayo Clinic, Diagnostic Radiology, 200 First Street SW, Rochester, MN, 55905, USA.Mayo ClinicDiagnostic Radiology200 First Street SWRochesterMN55905USA schwarz.christopher@ 123456mayo.edu
                [1]

                A portion of data used in 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 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.

                Article
                S2213-1582(16)30093-6
                10.1016/j.nicl.2016.05.017
                5187496
                28050342
                d0fc0689-0b9e-4e60-9b5a-3c13ce09ed93
                © 2016 Commonwealth Scientific and Industrial Research Organisation

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 11 December 2015
                : 29 April 2016
                : 27 May 2016
                Categories
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