We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). 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 (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). We enrolled 461 patients diagnosed with mild cognitive impairments according to criteria (20, 21) which all required data, including plasma p tau 181 measurements and MRI processed, were available for baseline visit (adni.loni.usc.edu).
Plasma p tau 181 measurements
Plasma p tau 181 was analyzed at the University of Gothenburg, Sweden, using the single-molecule array technique. The detailed procedure is described in (adni.loni.usc.edu).
Cortical reconstruction and volumetric segmentation with the FreeSurfer image analysis suite are freely available for download (http://surfer.nmr.mgh.harvard.edu/). Processing of images includes averaging of volumetric T1 weighted images and motion correction (22), using a procedure to remove non-brain tissue (23), automated Talairach transformation, intensity normalization, tessellation of the boundary between gray matter and white matter, automated topology correlation, and optimally placing the border between gray and white matter and gray matter and CSF.
In our study, ADNIGO data available on LONI was used. The image used in ADNI FreeSurfer is a T1 weighted image. An accelerated and non-accelerated T1 weighted images are acquired in ADNIGO for each subject. Images are pre-processed at Mayo Clinic. Processing consisted of three main steps. The first step, autorecon-1, initiates motion correction, non-uniform intensity, Talairach transform computation, and intensity normalization 1skull strip. The Autoreckon-2 performs the creation of the white-matter and pial surfaces and segmentation of the gray and white matter. The autorecon-3 creates the cortical parcellation.
The Mini-Mental State Examination is a 30-points questionnaire used to assess cognitive impairment and thinking ability in medicine for screening dementia (24). The MMSE test includes simple questions in some areas such as repeating lists of words, language use, and comprehension, and basic motor skills (25). Scores more than 24 in MMSE indicate normal cognition but, scores below 9 points( can show severe cognitive impairment. Additional information can be found on the oxford medical education website.
CSF Biomarkers assessments
CSF biomarkers assessed by the electrochemiluminescence immunoassays (ECLIA) Elecsys beta-amyloid (1-42) CSF, phosphorylated Tau (181p) CSF, and Total-Tau CSF on an automated Elecsys cobas e 601 instrument. These immunoassays are available only for investigational uses. Analyses were performed in 36 runs, and each sample runs one time for the CSF biomarkers mentioned above. The analyte was ranging from the lower technical limit to the upper technical limit for each biomarker. Lower and upper limits were 200 to 1700 pg/mL for ABETA, 80 to 1300 pg/mL for Total-Tau, and 8 to 120pg/mL for CSF p Tau, respectively. Resalts with higher values than the upper limit are stated as “>” and results with lower values than the lower limit are stated as “<”.
APOE ε4 genotyping was performed on collected blood samples by ADNI. Subjects with at least one allele considered positive. More details about the procedure are described: http://adni.loni.usc.edu/methods/documents/.
We used SPSS16 for data analysis. First, we implement a partial correlation model for assessing the relation between demographical variables, including age, APOE genotyping, MMSE score, FDG-PET, and sex with each other. In the next, to measure the relation between all biomarker with each other, we used a partial correlation adjusted for age, sex, and APOE genotype. In the last partial correlation, models adjusted for age, sex, and APOE genotype were used to assess the correlation between CSF or plasma biomarkers with brain changes. We add each biomarker and structural values, including thickness, cortical and subcortical volume, and surface area, separately as variables in correlation models. We used the bootstrapping method set at 0.05 for significant results for address type I error due to multiple comparisons.