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A large part of the success of the ADNI study rests on the consistency of data collection and processing. To guarantee this success, the cores and PIs have created uniform procedures and protocols to be used by researchers participating in the study. ADNI offers detailed information on the research process to ensure that all labs, centers, and researchers participating in the study have the resources they need. To learn more about how ADNI data is collected, expand each section below.

Click each data type for details

The neuropathology data in the ADNI database are derived from the National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease (AD) (1). The neuropathologic data may be considered the ‘gold standard’ against which other clinical, neuropsychological, genetic, neuroimaging and body fluid biomarkers may be compared. Neuropathology data may be used to underpin multimodal studies of the natural history of AD.

Acquisition of Neuropathology Data

Pathological lesions within the brain have been assessed using established neuropathologic diagnostic criteria. The NIA-AA criteria recognize that AD neuropathologic changes may occur in the apparent absence of cognitive impairment. Using the NIA-AA protocol, an “ABC” score for AD neuropathologic change is generated which incorporates histopathologic assessments of amyloid β deposits (A), staging of neurofibrillary tangles (B), and scoring of neuritic plaques (C). In addition, detailed methods for assessing commonly co-morbid conditions such as Lewy body disease, vascular brain injury, hippocampal sclerosis, and TAR DNA binding protein (TDP) immunoreactive inclusions are included (1).

Neuropathology data were captured in the format of the Neuropathology Data Form Version 10 of the National Alzheimer’s Coordinating Center (NACC) established by the National Institute on Aging/NIH (U01 AG016976). For more information see:

Neuropathology Coding Guidebook NACC Version 10
Neuropathology Data Collection Form NACC Version 10
Neuropathology Data Dictionary NACC Version 10

Description of Brain Regions Sampled

The brain areas sampled for microscopic assessment are described in the Neuropathology Core – MICROSCOPY DATABASE FORM 03-01-2018 (see below). These data are included in the Neuropathology Data Set. Brain areas sampled include:

  1. Middle frontal gyrus [L1 MFG].
  2. Precentral gyrus/motor cortex [L21 MX].
  3. Superior and middle temporal gyri [L2 STG].
  4. Anterior cingulate gyrus [L19 A. Cing.].
  5. Amygdala [L23 Amyg.] and entorhinal cortex [L23 Ent. X].
  6. Hippocampus at the level of lateral geniculate nucleus and includes CA1 subfield [L5 CA1], dentate gyrus [L5 DG], and parahippocampal gyrus [L5 PHG].
  7. Inferior parietal lobe (angular gyrus) [L3 IPL].
  8. Occipital lobe [L4 OL].
  9. Caudate nucleus and putamen [L6 Put/C] and olfactory cortex [L6 Olf. X] at level of the nucleus accumbens.
  10. Globus pallidus [L17 GP] and nucleus basalis of Meynert [L17 NBM] at the level of the anterior commissure.
  11. Thalamus [L8 Thal.].
  12. Midbrain [L9 SN].
  13. Pons. Locus caeruleus [L11 LC] and pontine base [L11 Pons].
  14. Medulla oblongata [L12 Med.].
  15. Cerebellum with dentate nucleus [L14 CBM].
  16. Spinal cord [L13 SC].
Dataset Information

This methods document applies to the following dataset(s) available from the ADNI repository:

Dataset Name

Date Submitted

Neuropathology Core – Data Dictionary

01 March 2018

Neuropathology Core – Data Methods

01 March 2018

Neuropathology Core – Neuropathology Data

01 March 2118

Positron Emission Tomography (PET) image analysis was funded by ADNI for the following labs.  Below is a summary of the PET analysis methods. Anyone with an ADNI Data Archive account may view and download the analysis methods and the analyzed data. After logging in, click “Download” and “Study Data” to see all relevant ADNI documents available for download.

Banner Alzheimer’s Institute
Co-investigator: Eric Reiman

The Banner Alzheimer’s Institute (Arizona) of the ADNI PET Core analyzes the FDG-PET data using the computer package SPM5 to examine the progression that correlate with changes in cognition and to evaluate cross-sectional differences among three diagnostic groups: patients with AD, patients with MCI and normal healthy controls. All PET from the post-processed group-4 images were downloaded from the ADNI data archive in NIFTI format.

  • Summary of Statistical Parametric Mapping (SPM) Image Analysis
  • SPM Methods, Results and SPM-based Global Indices
Jagust Lab, UC Berkeley
Co-investigator: William Jagust

Processed FDG Data Methods
A literature search on PubMed was conducted using permutations of six terms relating to FDG-PET, AD, and MCI in order to identify studies that carried out direct whole-brain contrasts of FDG data and reported Z-scores or T-values corresponding to MNI or Talairach coordinates that represented regions in which FDG uptake differed significantly between patients (AD or MCI) and controls. This resulted in a total of 15 studies involving a cross-sectional comparison of AD, MCI, and/or Normal groups and 178 MNI and Talairach coordinates. A spreadsheet of these coordinates, associated t-values or z-scores was created. Talairach coordinates were transformed into MNI space, and t-values were converted to z-scores, which were then mapped to the space of the MNI template brain as intensity values. There were 14 overlapping coordinates, and their z-scores were added. Resulting images were smoothed with a standard 14 mm FWHM kernel. The values of all voxels in the image were then normalized, resulting in an image with values between 0 and 1.

Processed Florbetapir (AV45) Data Methods
The Florbetapir methods description contains an explanation of the Freesurfer-based processing stream, and also a description of the methods used to derive our Florbetapir cutoff for this dataset. These processing methods were developed at Dr. William Jagust’s laboratory of the Helen Wills Neuroscience Institute, UC Berkeley and Lawrence Berkeley National Laboratory.

PET Facility, University of Pittsburgh
Co-investigator: Chester Mathis

An automated template-based method was used to sample multiple regions-of-interest (ROIs) on the ADNI PIB SUVR image. The PIB SUVR was downloaded from the ADNI website along with its corresponding ADNI Processed #3 MR image. The MR image choice was scanner dependent. The #4 PIB SUVR image has been co-registered to the first frame of the raw image file and averaged across frames (for dynamic acquisitions only), reoriented to Talairach space, intensity normalized so that the average of voxels within the mask was exactly 1, and smoothed to achieve a uniform isotropic resolution of 8 mm FWHM. Fourteen ROIs were generated.

 SUVR Re-Normalized to CER

Results were compiled with the following data:

Subject ID, Group, Sex, Age, Study Date, Study UID, Series UID, Image UID, ACG (Anterior Cingulate), FRC (Frontal Cortex), LTC (Lateral Temporal Cortex), PAR (Parietal Cortex), PRC (Precuneus Cortex), MTC (Mesial Temporal Cortex), OCC (Occipital Cortex), OCP (Occipital Pole), PON (Pons), AVS (Anterior Ventral Striatum), CER (Cerebellum), SMC (Sensory Motor Cortex), SWM (Sub-cortical White Matter), THL (Thalmus)

 Delineated ROIs

  • AD Region of Interest PIB PET: Images of the scans with ROIs delineated. Related to Alzheimer’s disease (AD)
  • MCI Region of Interest PIB PET: Images of the scans with ROIs delineated. Related to Mild Cognitive Impairment (MCI)
  • Normal Subjects Region of Interest PIB PET: Images of the scans with ROIs delineated. Related to Normal Subjects (NL)
University of Utah Center for Alzheimer’s Care, Imaging and Research (CACIR)
Co-investigator: Norman L. Foster

The focus of The University of Utah component of the PET Imaging Core is on the individual image analysis and processing of molecular imaging data using 3-dimensional stereotactic surface projections (3D-SSP) computed by Neurostat, developed by Satoshi Minoshima [Minoshima et al., J Nucl Med 1995; 36:1238-48].

We have uploaded baseline ADNI1 FDG-PET images warped into Talairach space in dicom format. We have provided a document with 3D-SSP images of AD subjects showing a pattern of glucose hypometabolism that is consistent with frontotemporal dementia. We submit 6 numeric summary values that encapsulate information on the spatial extent and the severity of hypometabolism from FDG-PET and amyloid uptake values from amyloid-PET scans. We compute the averaged uptake values in 18F-FDG, 18F-AV45 and 11C-PIB images, for regions that are typically relevant to Alzheimer’s Disease: the frontal and association cortices. We also compute the differences of subjects compared to a control group by pixel-wise computation of Z-scores in Talairach space. For FDG scans, we submit the number of pixels significantly hypometabolic compared to a group of elderly normal subjects. These are normalized to pons. For amyloid scans, we submit the number of pixels with significant increased uptake compared to a group of amyloid negative subjects. These are normalized to cerebellum and total white matter. Our numeric summary value reporting the spatial extent of significant change counts the significant Z-scores. The numeric summary value that reports the severity of the deviations with spatial extent sums the significant Z-scores. For FDG scans, these values report the extent of hypometabolism, and for the amyloid scans, these values report the increase in tracer uptake.

Center for Brain Health, NYU School of Medicine
Mony J. de Leon

Hippocampal Glucose Metabolism Sampling, the NYU HIPMASK
We developed, validated, and published the HIPMASK technique for measurement of the HIP and other structures. HIPMASK generates a 3-D HIP sampling mask to accurately sample true HIP tissue with approximately 95% anatomical overlap between the HIPMASK and the co-registered MRI in normal elderly, MCI and AD groups.

Quality Control
  • Every Florbetapir and FDG PET scan is reviewed for protocol compliance by the ADNI PET QC team
  • If a correctable problem is identified, the PET QC team contacts the PET technologist directly
  • If a problem with the scan is identified and it is not fixable, the PET QC team provides the PET technologist with protocol guidance to apply to future PET scans

Scans that fail the PET QC and are deemed unusable due to participant motion or non-compliance are documented with the reason as identified by the participant and the technician on the PET Scan Information Form. In these instances, rescans are only scheduled if the participant’s motion is believed to be correctable and is not a result of chronic illness or deteriorated cognitive ability.

Illumina Method

To address DNA strand designation and orientation, Illumina has developed a consistent and simple method to ensure uniformity in the reporting of genotype calls.

Initially, we used Illumina BeadStudio 3.2 software to generate SNP genotypes from bead intensity data. After performing sample verification and quality control bioinformatics, the genotype data for 818 ADNI participants was made available to qualified researchers. Reprocessed array data was made available in 2010 using an updated version of BeadStudio, Illumina GenomeStudio v2009.1, for all 818 samples. Although the 2010 files are highly similar to the initial release, a comparison of calls for all 620,901 markers (from the two versions of Final Reports for each sample) indicated that only nine samples had mismatches in genotypes successfully called in the two versions, and those mismatches comprised <0.01% of all markers. Finally, a new DNA source file is included in the downloads to identify whether peripheral blood (n = 731) or immortalized LCL (n = 87) derived DNA was analyzed for each genotyped sample.

For ADNI GO/ADNI 2, genotyping was performed on 85 genomic DNAs using the Illumina HumanOmniExpress BeadChip, which contains 730,525 SNP markers, according to the manufacturer’s protocols.



The Biomarker Core in ADNI3 is focusing on 4 areas of activity and studies including: biofluid banking (CSF, plasma and serum) management and pre-analytical assessments;  standardization of CSF Ab42, Ab40, t-tau and p-tau181 measurement in ADNI patients using the highly validated Roche Elecsys cobas e 601 fully automated immunoassay platform and reference LC/MSMS methodology for CSF Ab42, Ab40 and Ab38; determination of cut-points for Ab42, t-tau, p-tau181, and ratios using several approaches including ROC analyses using FBP amyloid PET imaging for disease detection and disease independent mixture modeling; collaborative studies on new biomarker development/validation/testing in CSF or plasma by immunoassay or mrm LC/MSMS including NFL, total and phospho-a-synuclein, Vilip-1, sTREM2, progranulin, TDP-43, metabolomics/lipidomic biomarkers and proteomic quantitative assays.

Leslie M Shaw and John Q Trojanowski co-lead the ADNI Biomarker Core at the University of Pennsylvania.

Highlights of Biomarker Core activities include:



Results from the analysis of ADNI samples are summarized below. Anyone with an ADNI Data Archive account may view and download the analysis methods and the analyzed data. After logging in, click “Download” and “Study Data” to see all relevant ADNI documents available for download.

ADNI GO and ADNI 2: Analyses of CSF Biomarkers

Leslie M Shaw and John Q Trojanowski
Aliquots of all ADNI GO and ADNI 2 CSF samples were analyzed using the xMAP Luminex platform and Innogenetics/Fujirebio AlzBio3 immunoassay kits. In addition, 28 newly thawed, randomly selected replicate aliquots were tested. The ADNI GO and ADNI 2 CSF Aβ1-42, t-tau and p-tau181 dataset in .csv file format (found in the ADNI data archive) provide details of the analyses and the final set of results. The full ADNI GO and ADNI 2 CSF report can be downloaded from the data archive.

Redox-reactive Antiphospholipid Autoantibodies: Early Stage Alzheimer’s Disease Blood Biomarkers 

John A. McIntyre, Dawn R. Wagenknecht, Curtis J Ramsey, Paul A. Hyslop
The HLA-Vascular Biology Laboratory reports its findings measuring Redox-reactive autoantibodies (R-RAA) aPL in 3 cohorts of individuals: 30 patients with mild cognitive impairment, 30 patients with Alzheimer’s dementia and 30 age-matched volunteer controls, all obtained from the ADNI group. The sera from the MCI population contained significantly elevated R-RAA activity in contrast to AD patient and/or control sera.

Autoantibody biomarkers for detection and diagnosis of Alzheimer’s disease

Robert G. Nagele
We employ our newly developed biomarker discovery strategy and human protein microarrays in an effort to identify autoantibody biomarkers in human serum that can accurately diagnose cases of MCI due to early stages of AD pathology. Microarrays containing 9,486 proteins were probed with individual ADNI serum samples obtained from patients with MCI who later transitioned to the full-blown AD, as well as corresponding controls. Arrays were scanned with a GenePix 4000B Fluorescence Scanner. Data was acquired using GenePix Pro 7, saved as Genepix Results (GPR) files, and can readily be analyzed using ProtoArray® Prospector v5.2 software, a freely available data analysis tool provided by Invitrogen.

  • Completion and upload to LONI the Methods Report, “ADNI3: Batch analyses of Ab42, t-tau, and p-tau181 in ADNI1, GO, 2 CSF samples using the fully automated Roche Elecsys and cobas e immunoassay analyzer system”.  This dataset, uploaded April 2017,  includes a total of 2,401 never-before-thawed aliquots of ADNI1, GO and 2 CSF samples that had been collected between 9/7/2005 and 7/25/2016.  See PPT set #101 for a description of major parts of the method validation for Ab42, and some cut-point estimations and see PPT set # 102 for further analyses, determinations of cut-points and relationships of “abnormal” and “normal” biomarker results to cognitive decline and progression from MCI to AD dementia done so far and working toward:
    • definition of cut-points for Ab1-42, t-tau, p-tau181 and the ratios, Ab1-42/t-tau and Ab1-42/p-tau181 using Mixture Modeling and ROC analyses;
    • an understanding of the predictive performance for cognitive decline and progression of MCI participants to a clinical diagnosis of AD dementia using these cut-points;
  • Assessments of the comparisons between Ab-|tau-, Ab-|tau+, Ab+|tau-, and Ab+|tau+ for predictive performance of each pair [Ab+ is below CSF cut-point value; Ab- is at or above CSF cut-point value; tau- is below and tau+ is at or above the cut-point value for CSF tau; analogous pairs for Ab and p-tau181] for cognitive, memory and functional decline and progression from MCI to a clinical diagnosis of AD.
    • assessments of concordance between CSF Ab1-42, t-tau, p-tau181, the ratios, Ab1-42/t-tau and Ab1-42/p-tau181 and Florbetapir PET imaging-based plaque burden assessments.
    • The inclusion of validated CSF Ab40 to Ab1-42, t-tau, and p-tau181 in ADNI3 will permit evaluation of the Ab1-42/Ab40 ratio for possible improvement over Ab1-42 alone for clinical utility.

 A manuscript describing the concordance performance of ADNIGO/2 Roche Elecsys CSF Ab42, t-tau and p-tau181 biomarker data and that from the Swedish BioFINDER study with either Florbetapir PET or Flutemetamol amyloid PET imaging, respectively, in the respective study cohorts, has been accepted for publication (Hansson et al, 2018b).

Provided support for the development of new immunoassays for CSF Ab42, t-tau, p-tau181 by providing residual CSF aliquot samples to 3 vendor laboratories.

Figure 2. Timing for the onset and progression of AD

The figure below illustrates the timing for the onset and progression of the AD in the upper right panel with examples of mixed pathologies found in AD brains in the upper left panel while the lower panel summarizes the timing of pathology deposition and neuron death as well as current considerations for the treatment of AD. This figure is from a recently published update of earlier ADNI reviews from Kang et al, 2015, that provides our current understanding of the hypothetical timeline for the onset and progression of Alzheimer’s Disease neurodegeneration and cognitive impairments progressing from normal to mild cognitive impairment and then to Alzheimer’s disease dementia.


  • Implementation of a fully validated reference LC/MSMS method for CSF Ab42 and further validated for CSF Ab40 and Ab (see Korecka etal, 2014, Panee etal, 2016; Kuhlman, etal 2016) for measurements in all ADNIGO/2 BASELINE and LONGITUDINAL CSF samples. The Methods document and dataset for these analyses will be uploaded to LONI ADNI website, March, 2018.  The methods document includes frequency plots for each analyte and for the Ab42/Ab40 ratio and mixture modeling to determine cut-points using disease-independent statistical methodology.
  • Analyses of CSF for Aβ, t-tau and p-tau181 moved from the Research-Use-Only Fujirebio AlzBio3 xMAP bead-based immunoassay to the fully automated Roche Elecsys platform following extensive validation studies, and for Ab42, comparisons with validated reference method LC/MSMS using the primary reference standard preparation of Ab42, provided by the Institute for Reference Materials and Measurements (IRMM) following finalization of replicate amino acid analyses(Kuhlman etal, 2017, Certification Report).

Continue to receive, aliquot, store and curate all biofluid samples (CSF, plasma, serum) collected from subjects enrolled in ADNI3, including all who “carry over” from ADNIGO/2 and all newly enrolled individuals, with 24/7 surveillance in the ADNI freezers that are housed in secure, dedicated space at UPENN. The updated (as of Feb 28, 2018) list of pristine aliquots of CSF, plasma and serum samples collected from ADNI subjects, “ALIQUOTS_LIST.csv” can be found on the ADNI LONI website under BIOSPECIMENS, but below in Table 1 is a brief summary of these samples.

Table 1. Summary of ADNI CSF, plasma and serum samples received and aliquots prepared as of 3/2/2018.



Continue to monitor details involved in the preparation of Biofluid samples at study sites including time from obtaining each sample to the time of freezing on dry ice (a summary of this is provided in Figure 1 below). We continue to review details involved in the pre-analytical steps involved in biofluid samples. In the figure below a focus on sample preparation time shows that for CSF the mean, 95%CI and median values across 1,318 samples collected from ADNIGO/2 phase participants is: 44.8 min, (41.7-47.8 min) and 28 min respectively. For 3,908 plasma samples the respective values are: 71.7 min, (70.0-73.4 min) and 55 min. This information is available for each ADNI sample. The handling at each ADNI site of these biofluid samples is very important to assure the quality of each sample. Avoidance of hemolysis and time-efficient sample preparation are essential to the goal of sample quality. For plasma, the recommended time from collection to freezing is no longer than 120 min; for CSF the recommended time is 60 min or less in order to minimize risk for biomarker degradation due to metabolic processes.

Figure 1. Sample collection to freezing time for ADNI GO/2 plasma and CSF primary samples



Regularly communicate with Clinical Core staff regarding biofluid collections and any issues concerning sample quality, labeling discrepancies, and provision of updated samples-received summaries.

Continue our collaborative studies on identifying and controlling pre-analytical factors that can contribute to variability in CSF or plasma biomarker measurements especially Aβ1-42 (Vanderstichele, etal, 2011; O’Bryant, etal, 2015; Hansson, etal, 2018). An example of this is a world-wide collaborative effort under the auspices of the Alz Association Global Biomarker Standardization Consortium(GBSC) whose members from industry and academic centers are defining a unified protocol for CSF sample collection for use in routine clinical practice and another for large multicenter studies such as ADNI. A new effort is the Biomarkers Consortium NSC – Plasma Aβ Working Group that is just being organized to pursue various aspects of plasma Aβ measurement. Although there have been mixed results for plasma Aβ42/Aβ40 for accurate detection of AD using a number of immunoassay approaches, it is fervently hoped that by identifying and controlling pre-analytcal factors, improved analytical techniques, and controlling for concomitant disease factors(Rissman, etal, 2012) that progress can be made on improving the diagnostic utility of these measurements (Ovod V, etal, 2017). We will provide updates of these developments at the annual ADNI Steering Committee meetings.

RARC-approved studies using ADNI Biofluids

Prepare and ship biofluid samples(CSF, plasma or serum) to all investigators whose biomarker study proposals have been approved by the RARC (Resource Allocation Review Committee, appointed by the NIA) and following final review by NIA (see Table 2 for an up to date brief summary of these studies and status of results upload). Once completed the data from these studies, performed blinded, are uploaded on the LONI/ADNI website together with a Methods document that describes the methodology involved and quality control performance. Biomarker Core faculty, Drs Trojanowski and Shaw are happy to provide input on any study although this is not required but often we are asked. The procedure for making an application to the Resource Allocation Review Board(RARC) for ADNI biofluid aliquot samples can be found on the ADNI web site.

A study that builds upon the 2013-2014 proteomic study that used LC/MSMS mrm mass spectrometry methodology will be conducted in 2018 to determine accurate concentration values for 5 candidate biomarkers that showed promise in the earlier semi-quantitative-based study(see Table 2 for a brief synopsis of all of these studies). An important characteristic of this study is its emphasis on measurements in longitudinal CSF samples across time from entry into the ADNI study (BASELINE) to at least 4 years later in order to assess these biomarker trajectories. Such data can be informative to clinical trials that are seeking to use biomarkers as indices of drug engagement and drug effect.

We continue to collaborate with biomarker scientists at UPenn and elsewhere (Irwin et al, 2017, 2018; Hu, 2010, 2015; Toledo et al, 2013, 2018; Mattsson, etal, 2013, 2016; Zetterberg and Blennow, 2016). New CSF biomarker-neuropathology correlations done with UPenn collaborators resulted in studies involving CSF tau and histochemical tau in FTLD and CSF tau and Aβ42 and synucleinopathy in autopsied Lewy Body disorders (Irwin etal, 2017, 2018). Such studies take advantage of the large set of CSF collected at UPenn from individuals prior to death and for whom an autopsy diagnosis provides accurate detection of not only AD neuropathology but concomitant pathologies such as Lewy Bodies, TDP-43 deposits, hippocampal sclerosis. A list of the publications describing the results of these studies thus far is included in References. Figure 2 illustrates the timing for onset and progression of AD, trajectories for amyloid and plaque biomarkers and some highlight characteristics of the mixed pathologies such as synucleinopathy and TDP-43 deposits that likely impact the timeline for clinical decline in individual patients.

Biomarker Consortium Project

Biomarker tools for early diagnosis and disease progression in Alzheimer’s disease (AD) remain key issues in AD drug development. Identification and validation of cost-effective, non-invasive methods to identify early AD and to monitor treatment effects in mild-moderate AD patients could revolutionize current clinical trial practice. Treatment prior to the onset of dementia may also ensure intervention occurs before irreversible neuropathology.

For more information on outlining Proteomic Analysis of ADNI Data, download the study strategies below.

ADNI has funded the following MRI core analysis labs to generate numeric summaries from the high-quality MRI data available in the data archive. Below is a summary of the MRI analysis methods. Anyone with an ADNI Data Archive account may view and download the analysis methods and the analyzed data. After logging in, click “Download” and “Study Data” to see all relevant ADNI documents available for download.


Laboratory of Neuro Imaging, USC

Co-investigator: Paul Thompson
Diffusion Tensor Imaging Summary Statistics of White Matter Regions of Interest
Diffusion tensor imaging (DTI) allows for the study of microstructural properties of white matter tracts. Regional summary measures were calculated from DTI to include measures of diffusion and anisotropy of various fiber tracts within the brain. A standard DTI template, with a corresponding white matter tract atlas, was registered to each individual subject. Registrations were subsequently applied to the segmented atlas. Visual inspection of the images ensured adequate registration. The mean of all voxels from each of the regions of interest from the atlas were obtained from maps of fraction (FA) and mean diffusivity (MD).
Tensor-Based Morphometry Protocol
Tensor Based Morphometry (TBM) is applied to cross-sectional MRI data for local volumetric comparisons between two or more groups of subjects, based on nonlinearly registering individual brain scans to a common anatomical template. Moreover, when TBM is applied to a longitudinal MRI study, the derived Jacobian maps reflect the percentage of tissue change over time.

Center for Imaging of Neurodegenerative Diseases, UCSF

Co-investigator: Norbert Schuff
FreeSurfer Overview and Quality Control
Cortical reconstruction and volumetric segmentation is performed with the FreeSurfer image analysis suite. FreeSurfer analysis was completed using Version 4.3 for ADNI1 cross-sectional data[UCSFFSX], Version 4.4 for ADNI1 longitudinal data[UCSFFSL], and Version 5.1 for ADNI GO and 2 data[UCSFFSX51].
ASL Perfusion Processing Methods
The Center for Imaging of Neurodegenerative Diseases (CIND) processing pipeline for Arterial Spin Label (ASL) imaging, prepares perfusion-weighted images (PWI) and computes a quantitative map of cerebral blood flow (CBF) and a regional analysis.
Hippocampal Voluming Analysis
Semi-automated hippocampal volumetry was carried out using a commercially available high-dimensional brain mapping tool: Medtronic Surgical Navigation Technologies (SNT). This method of hippocampal voluming has a documented reliability of an intraclass coefficient better than .94.

Alzheimer’s Disease Center, UC Davis

Co-investigator: Charles S. DeCarli
Total Cranial Vault Segmentation: Method and Grading Rubric
The quality of the total cranial vault segmentation using DSE T2 weighted MRI brain scans has been verified and individually graded.
4-Tissue Segmentation Methods for ADNI MR Scans
This document describes the 4-Tissue segmentation methods used for ADNI scans to produce segmentations of each image into four tissue types: White Matter, Gray Matter, Cerebrospinal Fluid, and White Matter Hyperintensity.

Institute of Neurology, University College London

Co-investigator: Nick Fox
Brain and Ventricular Boundary Shift Integral
We describe the processing methods in the brain and ventricular boundary shift integral (BSI). The brain and ventricles were first semi-automatically delineated from the T1-weighted MRI scans. The repeat scans were then registered to the baseline scans using 9-degree-of-freedom registration. The intensity inhomogeneity between the baseline and registered repeat scans was corrected using the differential bias correction. Finally, BSI was calculated over the boundaries of the brain and ventricles respectively using the registered and corrected scans.

Biomedical Image Analysis, University of Pennsylvania

Co-investigator: Christos Davatzikos
Spatial Patter of Abnormalities for Recognition of Early AD
The SPARE-AD score was calculated for each individual, using a specific pattern classification method. This score indicates the presence of an AD-like spatial pattern of brain atrophy, if positive, and otherwise if negative.


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