Elsevier

NeuroImage

Volume 80, 15 October 2013, Pages 144-168
NeuroImage

Resting-state fMRI in the Human Connectome Project

https://doi.org/10.1016/j.neuroimage.2013.05.039Get rights and content

Highlights

  • The Human Connectome Project is mapping brain connectivity in vivo in detail.

  • Resting-state fMRI (rfMRI) is a major modality in the Human Connectome Project.

  • We describe rfMRI acquisition and analysis protocols for the HCP.

Abstract

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1 h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2 × 2 × 2 mm and a temporal resolution of 0.7 s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

Introduction

The term “connectome” (Sporns et al., 2005) refers to the mapping of connectivity throughout the brain using such imaging modalities as resting-state functional magnetic resonance imaging (rfMRI) and diffusion MRI. rfMRI is used to study connectivity in the brain by acquiring fMRI data from a subject lying “at rest” in the scanner, and utilising the fact that the spontaneous timeseries from functionally related brain regions are correlated (Biswal et al., 1995, De Luca et al., 2005, Fox and Raichle, 2007, Fox et al., 2005, Greicius et al., 2003). Given sufficient quantity and quality of rfMRI data, one is able over time to generate maps of all major functional networks in the brain, as each spontaneously fluctuates in its activation levels (Smith et al., 2009). The simplest analysis methods, based on strength of correlation between the timecourses of any two brain regions, allow one to infer whether the regions are functionally “connected”, although such simple measures are not quantitative.1 More complex (and, importantly, multivariate) analysis methods such as independent component analysis (ICA — McKeown et al., 1998, Kiviniemi et al., 2003) allow, from a single data-driven analysis, the simultaneous estimation of multiple distinct components, with control over the level of spatial granularity (level of component sub-splitting). However, none of these methods reveal whether connectivity is direct or indirect (Marrelec et al., 2006); indeed, a major problem for rfMRI-based network modelling (and graph theory) occurs if inferences are made that rely on the assumption that correlation between two nodes' timecourses is unambiguously indicative of a direct connection.

Emerging from the background of general connectivity estimation techniques such as seed-based correlation and ICA, “connectome” mapping often includes two stages: first the identification of a set of “nodes” (through a parcellation of the brain's grey matter), and secondly, estimation of the set of connections or “edges” between these nodes, based on the fMRI timeseries associated with the nodes. In some approaches, the directionality of these connections is estimated, in an attempt to infer how information flows through the network (see detailed discussion and refs in Smith, 2012).

Mapping the connectome is often assumed to begin with the parcellation of grey matter into (often non-overlapping) regions, for example, on the basis of the rfMRI data itself (Cohen et al., 2008, Craddock et al., 2011, Flandin et al., 2002). Ideally, the regions are functionally specialised parcels, within each of which connectivities are relatively homogeneous — all locations within a parcel are assumed to have a similar general pattern of connectivity to locations in the brain outside the parcel. While acknowledging that there can be variations in connectivity across a parcel (de Reus and van den Heuvel, 2013, van den Heuvel and Hulshoff Pol, 2010), one would hope that such variations are smaller than the differences in connection patterns between different parcels, thus rendering the parcellation (and the functional borders implied) meaningful and reproducible. Although researchers contributing to the WU-Minn Human Connectome Project (in this paper referred to simply as “HCP”) accept that any given parcellation of the brain is an oversimplification, it is still a useful tool by which to reduce the data. As a result, brain connectivity can be represented by the manageable “parcellated connectome” (a parcels × parcels matrix), as opposed to the much larger original “dense connectome” (for example, the voxels × voxels matrix). The HCP will make both forms of the estimated connectome available to the research community (along with various versions of the timeseries data, from different stages of our processing pipeline), but we anticipate that it may be the parcellated connectome that will be of most use to neuroscientists, at least until more sophisticated representations of connectivity are developed by the community. It is likely that the HCP will produce more than one parcellation, as we investigate a range of techniques using data from different combinations of imaging modalities.

The goal of the HCP is to generate the most detailed in vivo mapping of functional connectivities in the healthy adult human brain achieved to date in a large cohort (over 1000 subjects, drawn from families with twins and non-twin siblings; Van Essen et al., 2013). We are acquiring a total of 1 h of 3 T rfMRI data for each subject, with an isometric spatial resolution of 2 mm and a temporal resolution of 0.7 s, relying on recent developments in multiband accelerated echo-planar imaging. In the following sections we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines. We briefly describe the spatial pre-processing pipelines (see Glasser et al., 2013 for full details) and then discuss temporal pre-processing in more depth. We present some initial example results showing data quality and example functional connectivity analyses, and end by discussing important outstanding issues. Most of the results shown in this paper use data acquired from 20 of the earliest subjects (all unrelated to each other) scanned during the first quarter (Q1) after the HCP scanning protocol was finalised (“HCP Phase 2”, where “Phase 1” refers to the methods optimisation and piloting efforts).

Section snippets

Acquisition protocol for multiband-accelerated rfMRI data

In this section we review and attempt to explain the major decisions taken in setting up the acquisition protocols for the HCP rfMRI data. For more detail on the pulse sequences, see (Ugurbil et al., 2013). The majority of the HCP data is being acquired at 3 T, which we considered to be the field strength currently most suitable for acquiring high quality data reliably from a large cohort of subjects. A subset of 200 HCP subjects will also be scanned at higher field strength. Acquisitions are

Data pre-processing and dissemination — general strategy

HCP rfMRI data will be made publicly available in several forms. The raw timeseries data will be made available (along with associated images, such as those needed to carry out B0 distortion correction), as some researchers may prefer to apply their own pre-processing. However, we also carry out optimised spatial pre-processing of the raw data, which corrects for various distortions and head motion, and aligns the timeseries data to the structural data and into standard space. The outputs from

Spatial pre-processing

The goal of spatial (or “minimal”) pre-processing is to remove spatial artefacts from the data without removing other potentially useful information. Briefly, the functional data are: corrected for spatial distortions caused by gradient nonlinearity; corrected for head motion by registration to the single band reference image; corrected for B0 distortion; and registered to the T1w structural image. All of the preceding transforms are concatenated, together with the structural-to-MNI nonlinear

Temporal pre-processing and artefact removal

A variety of “temporal” pre-processing steps could be applied after the above approaches for spatial pre-processing. These options include: slice timing correction; simple filtering out of low and/or high temporal frequencies; removal/regression of global mean timeseries (averaged over whole-brain, or grey-matter only, or a combination of white matter and cerebro-spinal fluid); removal of spatio-temporal artefacts such as residual motion artefacts, scanner artefacts (including potential

Example connectivity results

Fig. 8 shows an example RSN (parts of the DMN — the default mode network) identified by ICA applied to a single 15-minute run from a single subject. ICA was run on the volumetric data, and this non-artefactual component is shown at the top, overlaid onto the single-band reference scan. The ICA timecourses were then regressed into the grayordinate timeseries version of the same dataset, resulting in corresponding spatial maps in grayordinate space; the map matching this component is shown,

Ongoing issues and discussion

To date, the majority of the effort in the HCP has gone into developing and optimising the data acquisition methods and protocols, and in the development of optimised robust data analysis pre-processing pipelines. That acquisition and analysis work, described above and in the other HCP papers in this special issue, was essential as a prelude to beginning systematic Phase 2 data acquisitions on the 1200 subjects, and to start to publicly disseminate the timeseries data — all of which is now well

Conclusions

We have described the efforts to date by the HCP to create a rich, large, resource for functional connectivity mapping, as part of the wider HCP goal of structural and functional connectome mapping in the adult human brain. We are hopeful that in terms of data quality, resolution and quantity, this will be an extremely valuable resource that investigators from many fields will find useful for many years to come.

Acknowledgments

We are very grateful: to Natalie Voets, Sonia Bishop, David Cole, Nicola Filippini, Alejo Nevado and Chris Summerfield (Oxford) and Deanna Barch and Nick Bloom (WashU) for help with the FMRIB multiband motion piloting; to Erin Reid and Donna Dierker (WashU), for helping with the FIX training (hand-labelling of ICA components); and to David Flitney (Oxford), for creating the Melview ICA component viewing and labelling tool. We are grateful for funding via the following NIH grants: 1U54MH091657-01

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