A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol.
Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing t...
| Publicado en: | Brain Imaging & Behavior Vol. 13; no. 5; pp. 1453 - 1468 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138504737&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138504737 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Oct2019 vid: 13 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138504737 138504737 144192139 NLM30191514 138504737 10.1007/s11682-018-9941-x NLM30191514 138504737 ppf: 1453 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol. aug: au: Adhikari, Bhim M. Jahanshad, Neda Shukla, Dinesh Turner, Jessica Grotegerd, Dominik Dannlowski, Udo Kugel, Harald Engelen, Jennifer Dietsche, Bruno Krug, Axel Kircher, Tilo Fieremans, Els Veraart, Jelle Novikov, Dmitry S. Boedhoe, Premika S. W. van der Werf, Ysbrand D. van den Heuvel, Odile A. Ipser, Jonathan Uhlmann, Anne Stein, Dan J. affil: Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA sug: subj: Image Processing, Computer Assisted Brain Brain Mapping Methods Magnetic Resonance Imaging Methods Artifacts Female Aged Sensitivity and Specificity Young Adult Diagnosis, Neurologic Human Middle Age Adult Male Meta Analysis Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools Ferrans and Powers Quality of Life Index Scales Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Female Male ab: Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations. pubtype: Academic Journal doctype: meta analysis research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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