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Depositordc.contributorValdés Hernández, Maria
Funderdc.contributor.otherUniversity of Edinburghen_UK
Data Creatordc.creatorValdés Hernández, Maria del C
Date Accessioneddc.date.accessioned2016-11-16T15:44:01Z
Date Availabledc.date.available2016-11-16T15:44:01Z
Citationdc.identifier.citationValdés Hernández, Maria del C. (for the Alzheimer’s Disease Neuroimaging Initiative). (2016). Reference segmentations of white matter hyperintensities from a subset of 20 subjects scanned three consecutive years, 2010-2014 [dataset]. University of Edinburgh. Centre for Clinical Brain Sciences. Neuroimaging Department. https://doi.org/10.7488/ds/1555. Data used in preparation of this work 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. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdfen
Persistent Identifierdc.identifier.urihttps://hdl.handle.net/10283/2186
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/1555
Dataset Description (abstract)dc.description.abstractSUPERSEDED - THIS ITEM HAS BEEN REPLACED BY http://datashare.is.ed.ac.uk/handle/10283/2214. This dataset contains structural magnetic resonance imaging (MRI)-derived data from 20 participants enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. These data are likelihood maps of cerebrospinal fluid, grey matter and normal-appearing white matter, and binary masks of white matter hyperintensities (WMH), all obtained from MRI acquired at three consecutive study visits spaced 12 months apart. ## Acknowledgements ## Investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or processing which produced the derived data presented here. 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 . Data collection and sharing for ADNI 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 (www.fnih.org). 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 Neuro Imaging at the University of Southern California.en_UK
Dataset Description (TOC)dc.description.tableofcontents### Data structure ### Each folder’s name has the following format: ADNI contributor centre identification (ID) number – S – Patient’s ID number – Year the scan was acquired For example: 002_S_0413_2012 means that these are the data from patient 413 scanned on the Centre 2 in 2012. Each folder has the following files inside: 1) FLAIR.nii  FLAIR image acquired at the year specified on the folder’s name, linearly registered to the T2*-weighted scan acquired on the same visit, in NIfTI-1 format. 2) CSF.nii, GM.nii and NAWM.nii  Likelihood maps of cerebrospinal fluid, grey matter and normal-appearing white matter respectively, all in NIfTI-1 format. 3) WMH.obj  Object map of the white matter hyperintensities (WMH). This is in the object map format of AnalyzeTM 11.0. For cases that have a stroke lesion as well, this file has the name Lesions.obj instead. 4) WMH.hdr/img  WMH binary mask in Analyze 7.5 format, obtained semi-automatically using the region-growing algorithm from the ROI tool in Analyze. Some folders have the files WMH_2.obj and WMH_2.hdr/img that correspond to a second WMH segmentation, obtained in the same way as the first one and by the same image analyst, blind to the first result, used for intra-observer reliability analysis. ### Methods for obtaining the data ### The FLAIR images provided were the result of rigidly aligning the T2-FLAIR sequence acquired at each scanning session to the T2-star sequence acquired at the same scanning session, using FSL FLIRT [1], a linear registration tool from the FMRIB Software Library (v 5.0). The likelihood maps of cerebrospinal fluid, grey matter and normal-appearing white matter were generated from the MPRAGE sequence, after this being rigidly aligned to the T2-star sequence (as per above), using FSL-FAST [2]. The voxel values of these maps represent the probability of each belonging to one of these three classes. WMH object maps were created semi-automatically by thresholding the FLAIR images using the region-growing algorithm in the Object Extractor tool of AnalyzeTM software (ver 11.0), simultaneously guided by the co-registered T1- and T2-weighted sequences, all acquired at the same scanning session. Each brain scan was processed independently, blind to any clinical, cognitive or demographic information and to the results of the WMH segmentations from the same individual at different time points. ### Intra-observer reliability ### A second WMH segmentation mask was obtained from 10/60 images randomly chosen. The graphs below show the results of the Bland-Altman plots. The mean difference between the measurements is 0.7 (SD 1.8) ml and the mean volume of the average measurements is 4.9 (SD 4.5) ml. The mean Dice coefficient is 0.6 (SD 0.2).en_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. Centre for Clinical Brain Sciences. Neuroimaging Departmenten_UK
Superseded Bydc.relation.isreplacedbyhttp://datashare.is.ed.ac.uk/handle/10283/2214
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Sourcedc.sourceData used in preparation of this dataset were obtained 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).en_UK
Titledc.titleSUPERSEDED - Reference segmentations of white matter hyperintensities from a subset of 20 subjects scanned three consecutive yearsen_UK
Alternative Titledc.title.alternativeReference segmentations of white matter hyperintensities and likelihood maps of main brain tissues from 60 MRI scans from ADNI Databaseen_UK
Typedc.typedataseten_UK
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