The Lifespan Human Connectome Project in Aging: An overview

Susan Y. Bookheimer, David H. Salat, Melissa Terpstra, Beau M. Ances, Deanna M. Barch, Randy L. Buckner, Gregory C. Burgess, Sandra W. Curtiss, Mirella Diaz-Santos, Jennifer Stine Elam, Bruce Fischl, Douglas N. Greve, Hannah A. Hagy, Michael P. Harms, Olivia M. Hatch, Trey Hedden, Cynthia Hodge, Kevin C. Japardi, Taylor P. Kuhn, Timothy K. LyStephen M. Smith, Leah H. Somerville, Kâmil Uğurbil, Andre van der Kouwe, David Van Essen, Roger P. Woods, Essa Yacoub

Research output: Contribution to journalArticlepeer-review

179 Scopus citations

Abstract

The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D, Somerville et al., 2018), and the image acquisition protocol common to both studies (Harms et al., 2018).

Original languageEnglish
Pages (from-to)335-348
Number of pages14
JournalNeuroImage
Volume185
DOIs
StatePublished - Jan 15 2019

Keywords

  • Brain
  • Connectivity
  • Connectomics
  • Diffusion imaging
  • Functional connectivity
  • MRI
  • Morphometry
  • Neuroimaging
  • fMRI

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