Dataset Information

The Lifespan Human Connectome Project in Aging: An overview.

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).

SUBMITTER: Bookheimer SY

PROVIDER: S-EPMC6649668 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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The Lifespan Human Connectome Project in Aging: An overview.

Bookheimer Susan Y SY Salat David H DH Terpstra Melissa M Ances Beau M BM Barch Deanna M DM Buckner Randy L RL Burgess Gregory C GC Curtiss Sandra W SW Diaz-Santos Mirella M Elam Jennifer Stine JS Fischl Bruce B Greve Douglas N DN Hagy Hannah A HA Harms Michael P MP Hatch Olivia M OM Hedden Trey T Hodge Cynthia C Japardi Kevin C KC Kuhn Taylor P TP Ly Timothy K TK Smith Stephen M SM Somerville Leah H LH Uğurbil Kâmil K van der Kouwe Andre A Van Essen David D Woods Roger P RP Yacoub Essa E

NeuroImage 20181015

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 + ...[more]

PMID: 30332613

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Project description:Arterial spin labeling (ASL) magnetic resonance imaging (MRI) has become a popular approach for studying cerebral hemodynamics in a range of disorders and has recently been included as part of the Human Connectome Project-Aging (HCP-A). Due to the high spatial resolution and multiple post-labeling delays, ASL data from HCP-A holds promise for localization of hemodynamic signals not only in gray matter but also in white matter. However, gleaning information about white matter hemodynamics with ASL is challenging due in part to longer blood arrival times in white matter compared to gray matter. In this work, we present an analytical approach for deriving measures of cerebral blood flow (CBF) and arterial transit times (ATT) from the ASL data from HCP-A and report on gray and white matter hemodynamics in a large cohort (n = 234) of typically aging adults (age 36-90 years). Pseudo-continuous ASL data were acquired with labeling duration = 1500 ms and five post-labeling delays = 200 ms, 700 ms, 1200, 1700 ms, and 2200 ms. ATT values were first calculated on a voxel-wise basis through normalized cross-correlation analysis of the acquired signal time course in that voxel and an expected time course based on an acquisition-specific Bloch simulation. CBF values were calculated using a two-compartment model and with age-appropriate blood water longitudinal relaxation times. Using this approach, we found that white matter CBF reduces (ρ = 0.39) and white matter ATT elongates (ρ = 0.42) with increasing age (p < 0.001). In addition, CBF is lower and ATTs are longer in white matter compared to gray matter across the adult lifespan (Wilcoxon signed-rank tests; p < 0.001). We also found sex differences with females exhibiting shorter white matter ATTs than males, independently of age (Wilcoxon rank-sum test; p < 0.001). Finally, we have shown that CBF and ATT values are spatially heterogeneous, with significant differences in cortical versus subcortical gray matter and juxtacortical versus periventricular white matter. These results serve as a characterization of normative physiology across the human lifespan against which hemodynamic impairment due to cerebrovascular or neurodegenerative diseases could be compared in future studies.

Project description:Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.

Dataset Information

The Lifespan Human Connectome Project in Aging: An overview.

Publications

The Lifespan Human Connectome Project in Aging: An overview.

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