المؤلفون: Harms, MP, Somerville, LH, Ances, BM, Andersson, J, Barch, DM, Bastiani, M, Bookheimer, SY, Brown, TB, Buckner, RL, Burgess, GC, Coalson, TS, Chappell, MA, Dapretto, M, Douaud, GLA, Greve, DN, Hodge, C, Jamison, KW, Jbabdi, S, Kandala, S, Li, X, Mair, RW, Mangia, S, Marcus, D, Mascali, D, Moeller, S, Nichols, TEP, Robinson, EC, Salat, DH, Smith, SM, Sotiropoulos, SN, Terpstra, M, Thomas, KM, Tisdall, MD, Ugurbil, K, Van Der Kouwe, A, Woods, RP, Zöllei, L, Van Essen, DC, Yacoub, E

الوصف: The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100 + years of age. HCP-D is enrolling 1300 + healthy children, adolescents, and young adults (ages 5-21), and HCP-A is enrolling 1200 + healthy adults (ages 36-100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200 + participants, aged 22-35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.

العلاقة: https://ora.ox.ac.uk/objects/uuid:284e55a6-7506-4535-a561-81fe55e4a6baTest; https://doi.org/10.1016/j.neuroimage.2018.09.060Test

الإتاحة: https://doi.org/10.1016/j.neuroimage.2018.09.060Test
https://ora.ox.ac.uk/objects/uuid:284e55a6-7506-4535-a561-81fe55e4a6baTest

المؤلفون: Alfaro-Almagro, F

المساهمون: Jenkinson, M, Smith, SM, Nichols, TEP

مصطلحات موضوعية: Neurosciences

الوصف: UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers six modalities (T1, T2 FLAIR, susceptibility-weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first ∼42,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information, we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this work, we describe the tools and research we have developed in order to generate the data in a way that is readily usable for researchers worldwide. The first half of this thesis will give a brief overview of UK Biobank brain imaging and the acquisition protocol, and then describe the processing pipeline and QC pipeline in detail. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline. The second part of this thesis will show the research we performed to come up with a set of recommendations to deal with confounds in brain imaging studies using UK Biobank. UK Biobank is a powerful resource for studying associations between imaging and non- imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confounding effects, which therefore have to be carefully considered. Here we describe a set of possible confounds (including non-linear effects and interactions) that researchers may wish to consider for their studies using such data. We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=od______1064::2f4d1785987de8aa2aecdaad2af12ed7Test
https://ora.ox.ac.uk/objects/uuid:b38f247d-a727-49dc-a153-ff86f3565379Test