Elsevier

NeuroImage

Volume 80, 15 October 2013, Pages 62-79
NeuroImage

The WU-Minn Human Connectome Project: An overview

https://doi.org/10.1016/j.neuroimage.2013.05.041Get rights and content

Highlights

  • The Human Connectome Project (HCP) will study brain connectivity in healthy adults.

  • Data acquisition: multiple imaging modalities, plus behavioral, and genetic data.

  • Imaging modalities: diffusion MRI, resting-fMRI, task-fMRI, and MEG/EEG.

  • Extensive refinement and optimization efforts are currently underway.

  • Data will be made freely available and will enable flexible data mining.

Abstract

The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.

Introduction

A revolution in noninvasive neuroimaging methods over the past two decades has enabled the analysis and visualization of human brain structure, function, and connectivity in unprecedented detail. These advances make it feasible to systematically explore the human connectome, i.e., to generate maps of brain connectivity that are ‘comprehensive’ down to the spatial resolution of the imaging methods available.

In 2009, the NIH Neuroscience Blueprint Institutes and Centers announced a Request for Applications (RFA) targeted at characterizing the human connectome and its variability using cutting-edge neuroimaging methods. The RFA sought applications that addressed the dual objectives of accelerating advances in key technologies and applying these advances to a large population of healthy adults. In 2010, NIH awarded Human Connectome Project (HCP) grants to two consortia, one led by Washington University, the University of Minnesota, and Oxford University (the “WU-Minn” HCP consortium), and the other led by MGH and UCLA (the MGH-UCLA HCP consortium) (see http://www.neuroscienceblueprint.nih.gov/connectome/).

After summarizing the key objectives of the WU-Minn HCP consortium, this article provides an overview of results from our extensive efforts to refine and optimize the many methods used for data acquisition and analysis. MRI data acquisition protocols for scanning at 3 T were finalized1 in August, 2012, and are now being used to acquire high-quality data from many subjects. In this article we highlight key methodological advances and summarize how these large and complex imaging and behavioral datasets are being acquired, processed, and shared. This sharing includes the release in March 2013 of data from 68 subjects scanned during the first quarter (Q1) of Phase II data collection. This dataset includes unprocessed and ‘minimally preprocessed’ data on all subjects, plus more extensively analyzed group-average data for several modalities.

Additional articles in this special issue go into greater detail in these specific areas and provide a wealth of information about our instrumentation and image acquisition methods (Ugurbil et al., 2013); preprocessing pipelines (Glasser et al., 2013b); diffusion imaging (Sotiropoulos et al., 2013c); resting-state fMRI (Smith et al., 2013); task-fMRI and behavior (Barch et al., 2013); MEG (Larson-Prior et al., 2013); and informatics and quality control processes (Marcus et al., 2013). Other special issue articles describe progress by the MGH-UCLA HCP consortium.

Section snippets

HCP objectives

The WU-Minn HCP consortium aims to characterize human brain connectivity and function in a population of 1200 healthy adults and to enable detailed comparisons between brain circuits, behavior, and genetics at the level of individual subjects. Here, we summarize the overarching objectives and data acquisition plans of the HCP, which have not changed substantially since they were initially reported (Van Essen et al., 2012a).

HCP progress

Here, we summarize progress since funding of the WU-Minn HCP consortium began (September, 2010), beginning with a brief summary of seven broad domains.

  • Subject recruitment, visits, and behavioral testing. Many practical issues have been resolved to allow recruitment and visits to occur at a pace sufficient to study 1200 subjects over 3 years at a single imaging site, as discussed below.

  • 3 T scanning protocol. A two-year effort to develop and refine the scanning protocols for the 3 T Connectome

HCP prospects

At the time this article was submitted, the WU-Minn HCP is at the midway point of the 5-year grant. It is also in a transitional period, with an increasing focus on standardized data acquisition and data sharing, but with important methods refinement efforts are still continuing. The Q1 data release constitutes only ~ 6% of the target number of 1200 subjects. Moreover, the more advanced stages of data analysis which are essential for characterizing structural and functional connectivity are

Acknowledgments

We thank the current and past members of the WU-Minn HCP consortium (Supplemental Table S5) for their dedicated efforts on this project. We especially thank Matthew F. Glasser and Stam Sotiropoulos for their contributions to many of the analyses illustrated herein and Dr. Sandra Curtiss for overall project management as well as comments on the manuscript. The project was supported by an NIH grant 1U54MH091657, funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for

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