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The Human Connectome Project's neuroimaging approach

Nature Neuroscience volume 19, pages 1175–1187 (2016)Cite this article

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Abstract

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

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Figure 1: Combined representation of cortical surface vertices and subcortical voxels in the CIFTI grayordinates standard space.
Figure 2: Improved intersubject registration using information based on areal features in addition to cortical folding.
Figure 3: The HCP_MMP1.0.
Figure 4: Example parcellated analyses run using HCP data and the HCP_MMP1.0 cortical parcellation.
Figure 5: Current and future projects that use HCP-style data acquisition, analysis and sharing.
Figure 6: Fidelity of localizing area MT+ (architectonic area hOc5) when mapped to the cortical surface by different methods.

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Acknowledgements

We thank the other investigators and staff members of the Human Connectome Project consortium for invaluable contributions to data acquisition, analysis and sharing. Additionally, we thank the many colleagues outside the HCP upon whose methodological contributions the paradigm espoused in this paper are also based. We thank S. Danker for assistance in manuscript preparation. Supported in part by the Human Connectome Project, WU-Minn-Ox Consortium (1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; the McDonnell Center for Systems Neuroscience at Washington University; and NIH F30 MH097312 (M.F.G.), RO1 MH-60974 (D.C.V.E.), P41 EB015894 (NIBIB; K.U.), Wellcome Trust 098369/Z/12/Z (S.M.S., J.L.R.A., T.E.J.B., M.J., E.C.R., S.N.S.), 5R01EB009352 (D.S.M.), 5P30NS048056 (D.S.M.) and 5R24MH108315 (D.S.M.).

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Authors and Affiliations

  1. Department of Neuroscience, Washington University Medical School, St. Louis, Missouri, USA

    Matthew F Glasser, Timothy S Coalson & David C Van Essen

  2. Nuffield Department of Clinical Neurosciences, FMRIB Centre, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Stephen M Smith, Jesper L R Andersson, Timothy E J Behrens, Mark Jenkinson & Stamatios N Sotiropoulos

  3. Department of Radiology, Washington University Medical School, St. Louis, Missouri, USA

    Daniel S Marcus

  4. Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, Minnesota, USA

    Edward J Auerbach, Steen Moeller, Essa Yacoub & Kamil Ugurbil

  5. Department of Psychiatry, Washington University Medical School, St. Louis, Missouri, USA

    Michael P Harms

  6. Department of Computing, Imperial College London, London, UK

    Emma C Robinson

  7. 7Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Junqian Xu

Authors
  1. Matthew F Glasser
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  2. Stephen M Smith
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  3. Daniel S Marcus
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  4. Jesper L R Andersson
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  5. Edward J Auerbach
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  6. Timothy E J Behrens
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  7. Timothy S Coalson
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  9. Mark Jenkinson
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  10. Steen Moeller
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  11. Emma C Robinson
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  12. Stamatios N Sotiropoulos
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  16. David C Van Essen
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Contributions

M.F.G., S.M.S., D.S.M., K.U. and D.C.V.E. framed the issues and generated the initial draft. M.F.G., S.M.S., D.S.M., J.L.R.A., E.J.A., T.E.J.B., T.S.C., M.P.H., M.J., S.M., E.C.R., S.N.S., J.X., E.Y., K.U. and D.C.V.E. contributed novel methods or analyses. M.F.G., S.M.S., D.S.M., T.E.J.B., T.S.C., M.P.H., E.C.R., S.N.S., J.X., E.Y., K.U. and D.C.V.E. wrote the manuscript.

Corresponding authors

Correspondence to Matthew F Glasser or David C Van Essen.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Average cortical thickness map of 210 HCP subjects at each left hemisphere vertex and the associated colorized histogram.

The mean cortical thickness is around 2.6 mm, and this roughly divides fMRI data into high resolution (less than the mean cortical thickness) and low resolution (more than the mean cortical thickness). The HCP’s 3T and 7T chosen resolutions are also plotted. For the data in this figure and other Supplementary Figures, subject recruitment procedures and informed consent forms, including consent to share de-identified data, were approved by the Washington University institutional review board.

Supplementary Figure 2 dMRI data for 3T and 7T scans of the same subject (HCP Subject 158035).

Top row: Fractional anisotropy (FA) maps (axial, coronal and sagittal views). Notice that B1 inhomogeneities at 7T lead to poor SNR and noisy FA estimates in the inferior temporal regions (evident in the coronal views), but efforts have been taken to minimize them. Bottom row: DTI principal fiber orientations (coronal zoomed view of the area delineated by the yellow box). The orientations are RGB color-coded (Red: Left–Right, Green: Anterior–Posterior, Blue: Superior–Inferior) and superimposed on the structural T1w image. The pial surface and the WM/GM boundary surface are also shown. Reproduced, with permission, from Ref. 78.

Supplementary Figure 3 Patterns of cortico-striatal connectivity revealed by tractography.

Seed locations were in different cortical regions, including vmPFC (ventromedial prefrontal cortex), OFC (orbito-frontal cortex), dACC (dorsal anterior cingulate cortex), dPFC (dorsal prefrontal cortex) and Premotor cortex. Path probabilities (yellow: high, red: intermediate, black: low) are obtained using probabilistic tractography (FSL’s probtracx2, http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide) and the Matrix 3 (bidirectional white matter voxel to gray-matter terminations) algorithm to compute dense connectomes. N=150 HCP subjects were analyzed and averaged. Note the strong similarity to patterns of tracer-based connectivity reported in the macaque monkey, shown in the sketch on the right (reproduced with permission from Ref. 100).

Supplementary Figure 4 Multi-band imaging schematic and exemplar results.

Left: A schematic representation of the array coil elements over a human head image and a multi-band (MB) excitation (8 slices, in red). Each coil detects a linear combination of signals from each slice weighted by the sensitivity profile of that coil. Right. Four slices from a whole brain data set with standard acquisition (upper left, MB1) and with SMS/MB acquisition with MB=8, MB=12, and MB=16, all obtained using a 32-channel coil on a 3T scanner. Visual inspection of the M=16 image reveals discernible artifacts (e.g., ghosting), whereas the MB=12 and MB=8 images appear much cleaner. Quantitative estimates of cross slice contamination and reconstruction noise can be made. The HCP used MB=8 for fMRI and MB=3 for dMRI (now MB=4 or more for HCP Short).

Supplementary Figure 5 Beta map of the mean fMRI timeseries.

The timecourses were averaged over the whole brain (including gray matter, white matter, and CSF), regressed into the data of each subject, and then averaged across (n = 210 HCP subjects). One particularly striking characteristic of this map is how tissue-specific the global signal is (after ICA+FIX data cleanup), being generally most positive in grey matter, close to zero in white matter, and slightly negative in the ventricles. The tissue specificity of this signal argues against a non-physiologic, non-BOLD contrast-based cause of the signal (e.g., direct biophysical effects of subject head motion). This map by itself does not tell us to what extent the global signal is physiological noise vs. neural signal. Although the data are averaged in the volume across subjects, they still appear relatively sharp because they are not smoothed. Data at http://balsa.wustl.edu/0L1m.

Supplementary Figure 6 Visualization of the mean grey signal.

The mean grey timeseries beta map (top rows) and the ratio of the variance of the mean grey timeseries to the total BOLD variance (i.e., variance classified as signal by ICA+FIX; bottom rows). The absolute magnitude of the mean grey signal is highest in sensory regions including visual cortex, early somatosensory cortex (particularly of the face), early auditory cortex, and several thalamic nuclei including the LGN/MGN. Visual, somatosensory, auditory, and likely vestibular cortical areas are highlighted with black outlines. Data based on averaging across 210 HCP subjects, aligned using MSMAll for the cortical surface. When the global signal is strong in individual subjects, it closely matches the group average pattern, however when it is weaker, it may or may not match the group pattern. In these cases, the global signal often looks like one or another widespread RSN (e.g., the task positive or task negative (default mode) network). This is further evidence that removing the global signal as a preprocessing step may distort resting state functional connectivity and hence that we need a more selective way to clean global noise out of the data. The bottom row is a relative measure of how much the fMRI timeseries will be altered by regressing out the mean grey timeseries (on average across subjects), as it is a measure of the proportion of the total BOLD signal represented by the mean grey timeseries. Regressing out the mean grey signal will also tend to cause resting state gradient boundaries to move somewhat in the regions where this map has sharp gradients. Data at http://balsa.wustl.edu/2VnN.

Supplementary Figure 7 Effects of the Wishart rolloff on dense functional connectivity maps of both an individual subject and group data (210 HCP subjects; MSMAll surface registration).

Top rows show an individual subject, before (column 1) and after (column 2) Wishart rolloff for a seed location in lateral parietal cortex (white dot in upper left panels). The correlation increases dramatically as unstructured spatio-temporal noise is reduced, however the map is not substantially “smoothed” as it would be with typical smoothing algorithms. Bottom rows show a group dataset before and after Wishart rolloff for a seed location in the posterior cingulate sulcus (white dot in lower left panels). The dataset has been created using the MIGP algorithm to generate a group PCA series (d=4500) that represents the group concatenated timeseries. Because of the hard cutoff at PCA component number 4500, there is a ‘ringing” pattern resulting from spatial autocorrelation in the spatio-temporal noise that is represented by the PCA components with the lowest eigenvalues. If a Gaussian filter had been applied, this pattern of “local connectivity” would be a blob instead of rings. The Wishart rolloff eliminates these rings and again dramatically increases the SNR of the data. Data at http://balsa.wustl.edu/rrpl.

Supplementary Figure 8 The HCP language task (story vs baseline) beta maps and their spatial gradients.

Beta maps (rows 1 and 3) and gradient maps (rows 2 and 4) are from two independent groups of 210 HCP subjects, “210P” (rows 1 and 2) and “210V” (rows 3 and 4). Because of the large number of high quality HCP subjects, the beta maps are very similar across the two groups, and the strong gradients in the beta maps are also very similar. Also shown are white contours of a Bonferroni corrected significance threshold across all 91282 grayordinates (z+/- ~5). Two things are apparent: 1) Because of the large amount of high quality data, most of the brain is either significantly activated or deactivated (an issue that has been discussed before, see Ref. 60). Thus the statistical threshold is not particularly biologically meaningful (a point about statistical thresholds that generally applies). At the same time, the statistical threshold is also not strongly reproducible, in spite of the large amount of high quality data (highlighted ellipses show large differences in the area of activation classified as “significant” that are not particularly impressive when viewing the unthresholded beta maps). In contrast, the strong gradients in the beta maps are much more reproducible, are likely also more biologically meaningful, and hence provide a better substrate for defining regions of activation or comparing across studies. Data at http://balsa.wustl.edu/PrmK.

Supplementary Figure 9 Effects on average brain volume of registering 196 HCP brains to MNI space.

The total brain volume (minimal preprocessing pipelines’ whole brain mask) measured in the subject’s native physical space is around ~1350 cc; however, after registration to MNI space it is ~1800 cc, though the variability in brain volumes goes down as indicated by the narrower standard deviation error bars. This reduction in variability is the intended effect of registration, but the increase in brain volume is group average registration drift that was built into MNI standard space during the non-rigid registrations of the template generation process.

Supplementary Figure 10 A comparison between the HCP data and published retinotopic parcellation data.

Data from Ref. 64 and from 120 HCP subjects (from Q1-2) were registered using MSM areal-feature-based registration and group average registration drift was removed from both. Because of this, a contour in functional connectivity in the MT+ region distinguishing strong connectivity to the heavily myelinated IPS hotspot (LIPv, column 1) and to the STS (column 3) lines up with the border between MT+V4t (orange and yellow) and MST+FST (red and maroon, middle column). This illustrates the kind of precise cross-modal, cross-study boundary comparisons possible using the HCP-Style paradigm. Data at http://balsa.wustl.edu/x2Lz.

Supplementary Figure 11 Classification of area 55b in individual subjects by the areal classifier.

The typical location of area 55b is shown in black or white outlines on the inflated left hemisphere surface. The top two rows show a subject having an area 55b in the typical location found in the population. Rows 1 and 2 are entirely separate ‘test/retest’ runs of this subject through the full HCP MRI acquisition and analysis protocol. Column 1 is the subject’s individual curvature map, column 2 is the subject’s myelin map, column 3 is a d=40 RSN map that shows strong connectivity between 55b and other areas of the language network, column 4 is the raw probabilistic output of the areal classifier, column 5 is the final output of the areal classifier. Rows 3 and 4 show a different subject whose 55b is atypically split (heavy myelination running through the population average location of 55b and a concomitant lack of resting state connectivity). In both runs through the protocol, this subject shows a split 55b that is accurately detected by the areal classifier, showing that the classifier can accurately classify atypical subjects and that these atypical patterns are stable across time. Rows 5 and 6 show a third subject whose 55b is atypically shifted relative to its neighbors. Again the pattern is stable across time and the areal classifier is able to accurately detect the area. Reprinted from Ref. 34. Data at http://balsa.wustl.edu/WPPn.

Supplementary Figure 12 Effects of averaging surface coordinates and folding maps after areal feature-based registration (MSMAll).

The first row shows the group average midthickness surface (left), and the group average curvature map also displayed on inflated and flat surfaces (center and right). The group consisted of 210 subjects. The second row shows an individual subject’s midthickness surface (left), and the individual’s curvature map displayed also on inflated and flat surfaces (center and right). Note how much less detailed the group average surfaces and curvature maps are in most regions of cortex. However, in regions with consistent folding patterns across subjects and consistent relationships between cortical areas and folds, the group average patterns remain sharp (e.g. the central and calcarine sulci and the insula). The third row shows the group average T1w volume (after FNIRT nonlinear volume registration to MNI152 space of each subject in the group) together with the group average white (green) and pial (blue) surface contours. The fourth row shows the same individual subject’s T1w volume together with the individual’s white and pial surfaces (after aligning both the T1w volume and the surfaces to the group average in MNI space using FNIRT nonlinear volume registration). Despite the high precision of the white and pial surfaces in following the grey matter ribbon in the individual, the group average surfaces do not follow the group average volume particularly well, except in the regions where there are consistent folding patterns across subjects and consistent relationships between cortical areas and folds (as mentioned above). These issues also occur with folding-based surface registration (not shown), though they are less severe, because for folding-based registration the dominant factor is inconsistency in folding patterns across subjects (as no attempt is made to directly align cortical areas). The midthickness surfaces are the average of the white and pial surfaces (this average is performed on each individual, and the group midthickness surface is the average of the individual midthickness surfaces). Data at http://balsa.wustl.edu/7qP3.

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Glasser, M., Smith, S., Marcus, D. et al. The Human Connectome Project's neuroimaging approach. Nat Neurosci 19, 1175–1187 (2016). https://doi.org/10.1038/nn.4361

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