YNIMG-10480; No. of pages: 18; 4C: 7, 8, 9, 10, 12, 13, 14, 15 NeuroImage xxx (2013) xxx–xxx Contents lists available at SciVerse ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg 1 Review 2 The WU-Minn Human Connectome Project: An overview Q13 David C. Van Essen a,⁎, Stephen M. Smith b, Deanna M. Barch c, Timothy E.J. Behrens b, Essa Yacoub d, 4 Kamil Ugurbil dfor the WU-Minn HCP Consortium Q55 a Department of Anatomy & Neurobiology, Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110, USA 6 b University of Oxford, Functional MRI of Brain, Oxford OX3 9DU, UK 7 c Psychology Department, Washington University, St. Louis, MO 63105, USA 8 d Center for Magnetic Resonance Imaging, University of Minnesota, Minneapolis, MN 55455, USA 9 article info abstract 10 11 Article history: The Human Connectome Project consortium led by Washington University, University of Minnesota, and 18 12 Accepted 6 May 2013 Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their 19 13 Available online xxxx relationship to behavior in a large population of healthy adults. This overview article focuses on progress 20 161415 made during the first half of the 5-year project in refining the methods for data acquisition and analysis. 21 17 Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the 22 exceptionally high quality of the data from each modality. The first quarterly release of imaging and 23 behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets free- 24 ly accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and asso- 25 ciated methods and software will become increasingly valuable resources for characterizing human brain 26 connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings. 27 © 2013 Published by Elsevier Inc. 28 31 2930 3233 Contents 34 Introduction ................................................................ 0 35 HCP objectives ............................................................... 0 36 Subjects ................................................................ 0 37 Imaging data .............................................................. 0 38 Behavior ................................................................ 0 39 Genetic data .............................................................. 0 40 Data sharing .............................................................. 0 41 HCP progress ................................................................ 0 42 Subject recruitment, visits, and behavioral testing ............................................. 0 43 Inclusion and exclusion criteria .................................................. 0 44 Screening interviews .......................................................... 0 45 Two-day subject visits ......................................................... 0 46 3 T connectome scanner — hardware, pulse sequences, and scanning protocols . ............................. 0 47 3 T hardware .......................................................... 0 48 Pulse sequences ......................................................... 0 49 Head motion and physiological monitoring ............................................ 0 50 Image reconstructionUNCORRECTED and conversion to unprocessed NIFTI data ................................... PROOF 0 51 7 T hardware and pulse sequences .................................................... 0 52 Data processing and preliminary analyses ................................................ 0 53 Structural MRI and cortical shape analyses ............................................. 0 54 Resting-state fMRI ........................................................ 0 55 Temporal filtering and de-noising ................................................. 0 56 Diffusion MRI analyses ......................................................... 0 57 Task-fMRI (tfMRI) analyses ....................................................... 0 ⁎ Corresponding author. Fax: +1 314 747 3436. E-mail address: [email protected] (D.C. Van Essen). 1053-8119/$ – see front matter © 2013 Published by Elsevier Inc. http://dx.doi.org/10.1016/j.neuroimage.2013.05.041 Please cite this article as: Van Essen, D.C., et al., The WU-Minn Human Connectome Project: An overview, NeuroImage (2013), http://dx.doi.org/ 10.1016/j.neuroimage.2013.05.041 2 D.C. Van Essen et al. / NeuroImage xxx (2013) xxx–xxx 58 Cross-modal comparisons ........................................................ 0 59 Subcortical signals ............................................................ 0 60 MEG acquisition and analysis ...................................................... 0 61 Informatics and data sharing ....................................................... 0 62 Open access and restricted access datasets ................................................ 0 63 Some lessons learned ....................................................... 0 64 Teams and working groups .................................................... 0 65 HCP prospects ................................................................ 0 Q4266 Uncited references .............................................................. 0 67 Acknowledgments .............................................................. 0 68 References ................................................................. 0 69 70 Introduction to enable detailed comparisons between brain circuits, behavior, and 114 genetics at the level of individual subjects. Here, we summarize the 115 71 A revolution in noninvasive neuroimaging methods over the past overarching objectives and data acquisition plans of the HCP, which 116 72 two decades has enabled the analysis and visualization of human have not changed substantially since they were initially reported (Van 117 73 brain structure, function, and connectivity in unprecedented detail. Essen et al., 2012a). 118 74 These advances make it feasible to systematically explore the human 75 connectome, i.e., to generate maps of brain connectivity that are ‘com- 76 prehensive’ down to the spatial resolution of the imaging methods Subjects 119 77 available. 78 In 2009, the NIH Neuroscience Blueprint Institutes and Centers an- HCP subjects are drawn from a population of adult twins and their 120 79 nounced a Request for Applications (RFA) targeted at characterizing non-twin siblings, in the age range of 22–35 years. Studying sibships 121 80 the human connectome and its variability using cutting-edge neuroim- that include twins offers multiple advantages. Most obviously, it en- 122 81 aging methods. The RFA sought applications that addressed the dual ables a systematic assessment of the heritability of neural circuits. 123 82 objectives of accelerating advances in key technologies and applying Monozygotic (MZ) twins should have the greatest similarity because 124 83 these advances to a large population of healthy adults. In 2010, NIH they are genetically nearly identical. Dizygotic (DZ) twins are no more 125 84 awarded Human Connectome Project (HCP) grants to two consortia, related genetically than ordinary full siblings, but they share childhood 126 85 one led by Washington University, the University of Minnesota, and environment, including in utero environment, to a greater degree. 127 86 Oxford University (the “WU-Minn” HCP consortium), and the other Combined analyses of MZ and DZ pairs will allow estimation of the 128 87 led by MGH and UCLA (the MGH-UCLA HCP consortium) (see http:// extent to which genotype, shared environment, and non-shared 129 88 www.neuroscienceblueprint.nih.gov/connectome/). influences each contribute to variation in traits. Including additional 130 89 After summarizing the key objectives of the WU-Minn HCP consor- (non-twin) siblings provides a further increase in statistical power for 131 90 tium, this article provides an overview of results from our extensive analyzing heritability, distinguishing between genetic and environmen- 132 91 efforts to refine and optimize the many methods used for data acquisi- tal influences (Posthuma and Boomsma, 2000; Van Essen et al., 2012a) 133 Q8 92 tion and analysis. MRI data acquisition protocols for scanning at 3 T and relating genotype to phenotype. 134 93 were finalized1 in August, 2012, and are now being used to acquire Many aspects of brain circuitry and its relation to behavior are 135 94 high-quality data from many subjects. In this article we highlight key likely to involve small contributions from many genes, rather than 136 95 methodological advances and summarize how these large and complex dominant contributions from one or a few genes. Consequently, a 137 96 imaging and behavioral datasets are being acquired, processed, and large number of subjects will be needed in order to identify relation- 138 97 shared. This sharing includes the release in March 2013 of data from ships between brain circuit phenotype and genotype. For practical 139 98 68 subjects scanned during the first quarter (Q1) of Phase II data collec- reasons, our target number for the HCP is limited to 1200 subjects. 140 99 tion. This dataset includes unprocessed and ‘minimally preprocessed’ This target reflects not only budget considerations but also logistical 141 100 data on all subjects, plus more extensively analyzed group-average constraints associated with the number of scans feasible to carry out 142 101 data for several modalities.