Citations 2
Overview of ADNI MRI
Jack, Clifford R. , Arani, Arvin , Borowski, Bret, Crawford, Karen , DeCarli, Charles , Fletcher, Evan, Fox, Nick C. , Gunter, Jeffrey L. , Ittyerah, Ranjit, Harvey, Danielle , Jahanshad, Neda , Maillard, Pauline , Malone, Ian B., Reyes, Denise A., Thomas, David L. , Thompson, Paul M., Tosun, Duygu , Ward, Chadwick P., Weiner, Michael W., Initiative, Alzheimer's Disease Neuroimaging, DeCarli, Charles , Malone, Ian B. , Borowski, Bret J, Jack, Clifford R , Arani, Arvin , Fox, Nick C , Harvey, Danielle J , Tosun, Duygu , Maillard, Pauline , Cash, David M. , Das, Sandhitsu R. , Nir, Talia M. , Reid, Robert I. , Schwarz, Christopher G. , Senjem, Matthew L. , Thompson, Paul M. , Yushkevich, Paul A. , Initiative, Alzheimer's Disease Neuroimaging
Alzheimer's & Dementia | September 11, 2024 | Volume: 20 | Issue: 10 | Pages: 7350-7360
Abstract The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi‐platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1‐weighted and fluid‐attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. Highlights The MRI Core provides multi‐platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF‐fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM‐SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF‐fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.
Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities
Armstrong, Nicola J. , Mather, Karen A. , Sargurupremraj, Muralidharan , Knol, Maria J. , Malik, Rainer, Satizábal, Claudia L. , Yanek, Lisa R., Wen, Wei , Guðnason, Vilmundur , Dueker, Nicole , Elliott, Lloyd T. , Hofer, Edith, Bis, Joshua C. , Jahanshad, Neda , Li, Shuo , Logue, Mark, Scholz, Markus , Smith, Albert V. , Trompet, Stella , Alfaro‐Almagro, Fidel , Ames, David, Amin, Najaf , Beiser, Alexa S., Brodaty, Henry , Fennema‐Notestine, Christine , Gampawar, Piyush , Gottesman, Rebecca, Griffanti, Ludovica , Kral, Brian G. , Lampe, Leonie, Liewald, David C. , Maillard, Pauline , Marchini, Jonathan, Bastin, Mark E., Pirpamer, Lukas , Roshchupkin, Gennady V. , Stott, David J., Thalamuthu, Anbupalam , Tzourio, Christophe , Grond, Jeroen van der , Vernooij, Meike W. , Witte, A. Veronica , Wright, Margaret J. , Yang, Qiong, Siggurdsson, Siggi, Psaty, Bruce M., Villringer, Arno , Schmidt, Helena, Duijn, Cornelia M. van , Jukema, J. Wouter , Dichgans, Martin , Sacco, Ralph L., Kremen, William S., Becker, Lewis C. , Thompson, Paul M., Mosley, Thomas H. , Wardlaw, Joanna M. , Ikram, M. Arfan , Adams, Hieab H.H. , Seshadri, Sudha , Sachdev, Perminder S. , Smith, Stephen M. , Launer, L. J., Longstreth, W.T. , DeCarli, Charles , Schmidt, Reinhold, Fornage, Myriam , Debette, Stéphanie , DeCarli, Charles , Dichgans, Martin , Wardlaw, Joanna Marguerite , Thalamuthu, Anbupalam , Wen, Wei , Adams, Hieab H H , Satizabal, Claudia L , Seshadri, Sudha , Brodaty, Henry , Sachdev, Perminder S , Wright, Margaret J , Tzourio, Christophe , van Duijn, Cornelia M , Debette, Stephanie , Smith, Albert V , Maillard, Pauline , Hofer, Edith , Luciano, Michelle , Vojinović, Dina , Xia, Rui , Amouyel, Philippe , Deary, Ian J. , Jack, Clifford R. , Jenkinson, Mark , Jiang, Jiyang , Kwok, John B. , Marchini, Jonathan , Mazoyer, Bernard , Romero, José R. , Schofield, Peter R. , Schroeter, Matthias L , Trollor, Julian N , Morris, Zoë , Psaty, Bruce M. , Håberg, Asta K , Wright, Clinton B. , Thompson, Paul M. , Nyquist, Paul
Stroke | June 10, 2020 | Volume: 51 | Issue: 7 | Pages: 2111-2121
Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.