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Biomarkers of Cognitive Impairment: Brain Cortical Thickness, Volumetrics, and Cerebrospinal Fluid

Ghazi-Saidi, Ladan, PhD*; Walsh, Ryan R., MD, PhD; Shan, Guogen, PhD‡,§; Banks, Sarah J., PhD§ for the Alzheimer’s Disease Neuroimaging Initiative

Alzheimer Disease & Associated Disorders: July-September 2018 - Volume 32 - Issue 3 - p 255–257
doi: 10.1097/WAD.0000000000000226
Brief Reports

*Department of Communication Disorders, College of Education, University of Nebraska at Kearney, Kearney, NE

Muhammad Ali Parkinson Center, Barrow Neurological Institute, Phoenix, AZ

School of Community Health Sciences, University of Nevada Las Vegas

§Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV

Supported by grant funding from the National Institute of General Medical Sciences (grant: P20GM109025). Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( For up-to-date information, see Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech Inc.; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co. Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of Southern California.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:

The authors declare no conflicts of interest.

Reprints: Sarah J. Banks, PhD, Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W Bonneville, Las Vegas, NV 89106 (e-mail:

Received June 6, 2017

Accepted October 2, 2017

Brain changes related to Alzheimer’s disease (AD) exist years before manifestation of any cognitive decline.1 Mild cognitive impairment (MCI) is impairment in cognition with preserved independence and functional abilities. MCI is considered a risk factor for dementia, however, not all MCI conditions are due to AD pathology. Although ultimate diagnosis of AD is made at autopsy,2 more accurate in vivo diagnosis can be achieved by combining biomarkers in combination with clinical tests and history.3 Understanding how combinations of biomarkers improve diagnosis and assessment of the impact of novel therapeutics is important.

Proteins such as τ, Aβ1-42, Pτ181P, which are aggregated as neurofibrillary tangles and amyloid plaques and are also present in the cerebrospinal fluid (CSF), are considered as AD biomarkers.4 Decreased levels of CSF Aβ42 and increased levels of τ and phosphorylated τ (p-τ) are considered biomarkers indicating underlying AD pathology.5 Moreover, AD is associated with changes in brain structure, including changes to cortical thickness (CT) and cortical and subcortical volumes.6 Specifically, reduced volume of hippocampus is accepted as an AD biomarker.5

Although CSF and imaging biomarkers are frequently analyzed in isolation, the current study uses a well-characterized data set to test the utility of biomarkers both in isolation and in combination, in a large population, and a unique statistical approach. The aims are to (1) differentiate groups including AD, MCI, and healthy participants with normal cognitive status (NCS) and (2) assess relationship of imaging (CT and V) and fluid biomarker (CSF) with cognition.

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Participants and Materials

Here we present a cross-sectional study on 822 individuals (mean age=77 years, SD=6.9 years) including healthy controls with NCS and patients diagnosed with MCI or AD. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, ADNI 1 ( The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. Diagnoses were made following ADNI protocol ( NCS participants showed no sign of depression, MCI, or dementia. MCI participants reported a subjective memory concern despite maintaining daily activities, and their performance on Wechsler Memory Scale Logical Memory II. Participants would be labeled as AD if they met the NINCDS/ADRDA criteria for probable AD.7

We used the Mini-mental State Examination (MMSE) data to assess overall cognition. CSF levels of total τ, Aβ1-42, and Pτ181P proteins were analyzed and structural measures of brain regions of interest (ROI) were computed using freesurfer version 4.3, downloaded on April 18, 2016 ( Regions were selected to include currently accepted biomarkers and regions known to be affected early in the AD process.8 These measures included CT of 38 regions (ROIs) as well as total gray matter. ROIs were the left and the right hippocampus, anterior cingulate cortex, insula, inferior parietal cortex, middle frontal cortex, inferior temporal cortex, precentral cortex, postcentral cortex, caudate middle frontal cortex, and frontal poles, inferior frontal gyrus, superior frontal gyrus, inferior parietal gyrus, inferior temporal gyrus, middle temporal gyrus, postcentral cortex, caudate middle frontal cortex, entorhinal cortex, precuneus, and fusiform. Moreover, the volume measures of (V) of 20 ROIs were the right and the left inferior parietal, entorhinal cortex, middle temporal gyrus, temporal lobe, thalamus, hippocampus, middle temporal gyrus, putamen, caudate, and amygdala.

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Data Analysis

The Pearson correlations were calculated to measure the association between CSF protein levels, CT of brain ROIs, and Vs of brain ROIs with MMSE scores. To correct for the probability of accepting null hypotheses due to multiple comparisons, family-wise error rate Bonferroni correction was used to adjust P-values. For the whole population, regression models including every individual predictor were used to determine the variables explaining the highest variance of outcome of MMSE. All variables that explained >10% of variance were entered in separate stepwise regression models: specifically, a linear regression model involving the CSF variables, another model involving CT variables and a third model including volumetric variables. Using variable inflation factor (VIF), collinearity was checked among variables. Predictors that showed collinearity were not included in the regression model. Predictors that could significantly contribute in explaining the variance of outcome of MMSE were put in a single stepwise linear regression model. A forward stepwise logistic binary analysis was performed with the variables explaining the variance of outcome of cognitive impairment, to estimate prediction of correct diagnosis (AD, MCI, and NCS).

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Individual regressions showed that CSF τ, Aβ1-42, and pτ181P were significantly associated with MMSE and explained 7% to 11% of its variance. Regression models combining CT and V variables, showed that V of the left hippocampus and both entorhinal cortices and CT of the right middle temporal gyrus, as well as the left entorhinal cortex and the inferior parietal gyrus explained about 20% of variance in MMSE. Stepwise regression of a model including CSF Aβ1-42, as well as the above regions explained 50% of variance in MMSE. Table 1 summarizes the association of CSF proteins, ROI’s V, and CT with MMSE.



CSF Aβ1-42 was also associated with MCI diagnosis. Aβ1-42, volume of the right entorhinal cortex and the CT of the left inferior parietal gyrus, and the entorhinal cortex were associated with AD diagnosis. Normal levels of CSF Aβ1-42 τ combined with no structural changes to the temporal lobes confirmed normal status.

Table 1 summarizes the association of CSF proteins, ROI’s V, and CT with cognitive impairment.

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Combining imaging and CSF results created a more sensitive biomarker associate of cognitive function, validating previous reports.4 The importance of biomarkers for diagnosis was also confirmed. Higher level of CSF Aβ1-42 showed association with MCI (Table 1). It has been argued that a positive CSF Aβ1-42 without any evidence of neuroanatomical injury has an intermediate likelihood that MCI is due to AD.5 This is while lower levels of CSF Aβ1-42 are used to differentiate AD from other dementia.3,5 Consistent with the literature,4 combinations of CSF and magnetic resonance imaging biomarkers offer stronger support for accurate diagnosis, as compared with individual biomarkers. CSF Aβ1-42 levels, combined with CT and V measures of hippocampus and entorhinal cortices were strongly associated with AD diagnosis (Table 1). These results are consistent with previous studies on diagnostic biomarkers studies3: normal levels of CSF Aβ1-42 and τ and normal volume of the left entorhinal and the hippocampus as well as normal thickness of the right middle temporal gyrus significantly were associated with NCS (Table 1).

In line with previous studies,9 in isolation, various CSF levels showed significant but weak associations with MMSE scores, Aβ1-42 showing the strongest association (Table 2). Imaging biomarkers also related to cognition: temporal lobe measures, including the hippocampus and the entorhinal cortex, as well as the parietal gyrus were associated with MMSE (Table 2). Changes to these regions are known to be the early signs of cognitive impairment.10 Given the early course of AD in these regions impacts new memory formation, these results were expected.



This study showed that combined CSF, CT, and V measures offer promise for early AD diagnosis and tracing of cognitive decline in well-defined cohorts. However, their ability to predict outcome needs to be tested prospectively in typical clinical population and with autopsy confirmed AD pathology. Further, CSF measures are invasive. More research is required to reach noninvasive but rapid, inexpensive, and reliable biomarkers.

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1. Jack CR, Knopman DS, Jagust WJ, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12:207–216.
2. Guillozet AL, Weintraub S, Mash DC, et al. Neurofibrillary tangles, amyloid, and memory in aging and mild cognitive impairment. Arch Neurol. 2003;60:729–736.
3. Lista S, Garaci FG, Ewers M, et al. Csf Aβ1-42 combined with neuroimaging biomarkers in the early detection, diagnosis and prediction of alzheimer’s disease. Alzheimers Dement. 2014;10:381–392.
4. Westman E, Muehlboeck JS, Simmons A. Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. Neuroimage. 2012;62:229–238.
5. Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for alzheimer’s disease. Alzheimers Dement. 2011;7:270–279.
6. Weiner MW, Veitch DP, Aisen PS, et al. The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement. 2013;9:e111–e194.
7. McKhann G, Drachman D, Folstein M, et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology. 1984;34:939–944.
8. Pini L, Pievani M, Bocchetta M, et al. Brain atrophy in Alzheimer’s disease and aging. Ageing Res Rev. 2016;30:25–48.
9. Engelborghs S, Maertens K, Vloeberghs E, et al. Neuropsychological and behavioural correlates of CSF biomarkers in dementia. Neurochem Int. 2006;48:286–295.
10. Frisoni GB, Fox NC, Jack CR, et al. The clinical use of structural MRI in alzheimer disease. Nat Rev Neurol. 2010;6:67–77.

Alzheimer’s disease; biomarkers; cortical thickness; volumetric; CSF

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