Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects
Introduction
A growing number of large-scale neuroimaging studies have contributed to recent progress in deciphering human brain structure, function, and connectivity. One such endeavor is the Human Connectome Project (HCP), a pair of NIH-funded consortia that refined existing methods, developed new methods, and acquired foundational data to characterize brain networks in healthy young adults aged 22–35 (Glasser et al., 2013, 2016a, 2016b; Setsompop et al., 2013; Smith et al., 2013b; Van Essen et al., 2013; Fan et al., 2016). The success of this ‘Young-Adult’ HCP (HCP-YA), prompted NIH to launch the Lifespan Human Connectome Projects, which include three consortia that collectively target the human postnatal lifespan using “HCP-style” data acquisition, analysis, and sharing. Here, we provide an introduction and overview to the many aspects of the imaging protocols that are common to two of these projects -- the Lifespan Human Connectome Projects in Development (HCP-D) and in Aging (HCP-A) [http://www.humanconnectome.org], which were both funded under the auspices of the NIH Blueprint for Neuroscience Research, starting in the summer of 2016.2 We include analyses of imaging data that help to frame the imaging protocol in the context of HCP-YA and justify the protocol choices. Separate publications address aspects that are unique to the HCP-D project (Somerville et al., 2018) and the HCP-A project (Bookheimer et al., under review).
A major consideration in planning the imaging protocol for HCP-D/A was the challenges particular to scanning younger and older populations. These include an increased tendency for head motion at both younger (Satterthwaite et al., 2012) and older ages (Mowinckel et al., 2012; Geerligs et al., 2017), coupled with a reduced tolerance for long individual scans and long overall scan sessions (e.g., due to boredom and reduced compliance at younger ages, and muscular or joint discomfort or other medical comorbidities at older ages). Key decisions about MRI hardware and protocol design were made with these challenges in mind. Thus, a common theme of this paper is how to manage the challenges of imaging a diverse span of ages in a harmonized but optimal manner.
The HCP-D and HCP-A protocols were strongly influenced by the original HCP Young-Adult (HCP-YA) Project, for which data acquisition was conducted at Washington University (3T customized ‘Connectom’ scanner) and the University of Minnesota (7T scanner) from 2010 to 2016 (Van Essen et al., 2013). Thus, many common data components exist between the HCP-D, HCP-A, and HCP-YA projects. First, both HCP-D and HCP-A include the same imaging modalities as collected in the HCP-YA: structural imaging (T1w and T2w), diffusion imaging (dMRI), resting state functional connectivity (rfMRI), and task-based functional imaging (tfMRI). However, a different scanner platform was used and various changes were made to the imaging protocols in order to customize the HCP-D/A projects to the scientific and pragmatic needs of their specific study populations (see Image Acquisition). The HCP-YA imaging data were acquired over two days in four sessions of ∼1-h each, which exceeds the tolerance of many younger children and older adults. Hence, we shortened the duration for each modality. We modified the task fMRI to make the tasks maximally informative about functional domains of high interest for each lifespan stage. We also maximized the similarity of the HCP-D, HCP-A, and HCP-YA out-of-scanner assessments, although some of the behavioral assessments are only appropriate for limited age ranges (see Somerville et al., 2018; Bookheimer et al., under review for details on the behavioral assessments of each individual project).
The NIH Funding Opportunity Announcements for HCP-D and HCP-A excluded the 22–35 years age range under the rationale that the young adult age range was already well sampled by the HCP-YA project. This poses challenges for bridging the full lifespan across the HCP-D, HCP-A, and HCP-YA projects, since the 3T data for HCP-YA were collected on a customized ‘Connectom’ scanner using a longer scan protocol and modestly different scan parameters. In contrast, scanning for HCP-D and HCP-A is conducted on standard Siemens 3T Prisma scanners. Thus, work is needed to determine the degree to which the data can be merged across projects, as well as with other large-scale projects. To aid in addressing this issue, we have acquired data on the HCP-D/A and HCP-YA platforms in the same 17 participants (see MRI Scanner). These scans will be analyzed and made publicly available to enable systematic analysis of the impact of hardware and protocol differences, and to enable users to investigate approaches for maximally harmonizing and jointly analyzing HCP-D and HCP-A with the extensive young-adult data collected by the HCP-YA study. Additionally, data from more than 100 healthy subjects in the 22–35 age range, scanned using protocols very similar to ours, will be made available via the NIH-funded Connectomes Related to Human Disease projects. We will also facilitate harmonization with the brain imaging component of the UK Biobank prospective epidemiological study (Miller et al., 2016) [http://imaging.ukbiobank.ac.uk], which has an imaging target of 100,000 participants, by scanning 20 participants from the HCP-A project through the Biobank imaging protocol on a standard Siemens 3T Skyra scanner at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (which is the same model scanner being used for the Biobank project).
To meet the HCP-D/A recruitment and diversity goals, data are currently being acquired at four different institutions for each project.3 All scanning is conducted on a common platform across sites running the same software version (E11C) using an electronically distributed protocol. One volunteer served as a “human phantom” who was scanned at each site twice (5–7 months apart); these data will be analyzed for site effects within a single individual. Additionally, using ongoing QC processes (Marcus et al., 2013; Hodge et al., 2016) we monitor for possible site differences in imaging measures and check for drifting or abrupt changes in measures as a function of time. Initial analyses of QC data indicate that less than 10% of variance is explained by site on QC measures derived from resting-state data such as temporal SNR, relative (frame-to-frame) motion, and image smoothness. Thus, even though there may ultimately be some statistically significant differences across sites (due to the power to detect even small differences with a large number of subjects), we anticipate that variability will be mainly attributable to non-site-specific factors. To ensure procedures are followed consistently and accurately across sites, we instituted a set of “standard operating procedures” (SOPs), training sessions, frequent cross-site communication, and evaluation visits to each site by the lead coordinator.
Section snippets
MRI hardware
Scanning at all sites uses a Siemens 3T Prisma, a whole-body scanner with 80 mT/m gradients capable of a slew rate of 200 T/m/s. These gradients are the product variant of the 100 mT/m research gradients used in the customized 3T ‘Connectom’ (HCP-YA) scanner. High gradient strength is especially valuable for diffusion MRI (dMRI), as it achieves high diffusion weighting with high sensitivity (Sotiropoulos et al., 2013), made possible by the much shorter echo times that can be achieved compared
Image acquisition
To promote consistently high data quality, to ensure participant satisfaction across the lifespan, and to accomplish that in the context of a large-scale study, it was important to reduce the total scanning from the 4 sessions of HCP-YA to 2 sessions for HCP-D/A. When designing the protocol, we targeted approximately 45 min of total scan time per session, so that most participants would be “in the bore” less than 60 min. The reduction of the alloted scanning duration by over 50% relative to
Informatics and public release
The HCP-D and HCP-A are intended to serve as multipurpose datasets that scientists can use for a wide variety of analytical purposes. We will continue to release ‘unprocessed’ data for users that desire access to the imaging data at its most basic level.10
Conclusion
The Human Connectome Projects in Development and Aging are large scale extensions to nearly the full lifespan (ages 5–100+ years) of the original HCP in Young Adults. The data being acquired will be openly released to the scientific community, providing a rich resource for exploration of cross-sectional differences and longitudinal changes across the lifespan, as well as a reference for exploration of atypical development and aging. The data include a variety of MRI modalities that will allow
Acknowledgements
Research reported in this publication was supported by grants U01MH109589, U01MH109589-S1, U01AG052564, and U01AG052564-S1 and by the 14 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, by the McDonnell Center for Systems Neuroscience at Washington University, by the Office of the Provost at Washington University, and by the University of Minnesota Medical School. We thank the HCP-D/A Project Coordinator, Sandra Curtiss, and the staff at each site for all
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