Dataset Open Access
Informed written consent in accordance with institutional guidelines (protocol approved by the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland, #82/14, #382/11, #26.4.2005) was obtained for all subjects. Data provided are fully anonymized. A total of 70 healthy participants (age 28.8 +- 9.1 years, 27 females) were scanned in a 3-Tesla MRI scanner (Trio, Siemens Medical, Germany) using a 32-channel head-coil. The session protocol was comprised of (1) a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence sensitive to white/gray matter contrast (1-mm in-plane resolution, 1.2-mm slice thickness), (2) a DSI sequence (128 diffusion-weighted volumes and a single b0 volume, maximum b-value 8,000 s/mm2, 2.2x2.2x3.0 mm voxel size), and (3) a gradient echo EPI sequence sensitive to BOLD contrast (3.3-mm in-plane resolution and slice thickness with a 0.3-mm gap, TR 1,920 ms, resulting in 280 images per participant). During the fMRI scan, participants were not engaged in any overt task, and the scan was treated as eyes-open resting-state fMRI (rs-fMRI).
Initial signal processing of all MPRAGE, DSI, and rs-fMRI data was performed using the Connectome Mapper pipeline (Daducci et al., 2012). Gray and white matter were segmented from the MPRAGE volume using freesurfer (Desikan et al., 2006) and parcellated into 83 cortical and subcortical areas. The parcels were then further subdivided into 129, 234, 463 and 1015 approximately equally sized parcels according to the Lausanne anatomical atlas following the method proposed by (Cammoun et al., 2012).
DSI data were reconstructed following the protocol described by (Wedeen et al., 2005), allowing us to estimate multiple diffusion directions per voxel. The diffusion probability density function was reconstructed as the discrete 3D Fourier transform of the signal modulus. The orientation distribution function (ODF) was calculated as the radial summation of the normalized 3D probability distribution function. Thus, the ODF is defined on a discrete sphere and captures the diffusion intensity in every direction.
Structural connectivity matrices were estimated for individual participants using deterministic streamline tractography on reconstructed DSI data, initiating 32 streamline propagations per diffusion direction, per white matter voxel (Wedeen et al., 2008). Within each voxel, the starting points were spatially random. For each starting point, a fiber streamline was grown in two opposite directions with a fixed step of 1 mm. Once the fiber entered a new voxel, the fiber growth continued along the ODF maximum direction that produces the least curvature for the fiber (i.e., was most similar to the trajectory of the fiber to that point). Fibers were stopped if the change in direction was greater than 60 degrees/mm. The process was complete when both ends of the fiber left the white matter mask. Structural connectivity between pairs of regions was measured in terms of fiber density, defined as the number of streamlines between the two regions, normalized by the average length of the streamlines and average surface area of the two regions (Hagmann et al., 2008). The goal of this normalization was to compensate for the bias toward longer fibers inherent in the tractography procedure, as well as differences in region size.
Functional data were pre-processed using routines designed to facilitate subsequent network exploration (Murphy et al., 2009; Power et al., 2012). fMRI volumes were corrected for physiological variables, including regression of white matter, cerebrospinal fluid, as well as motion (three translations and three rotations, estimated by rigid body co-registration). BOLD time series were then subjected to a lowpass filter (temporal Gaussian filter with full width half maximum equal to 1.92 s). The first four time points were excluded from subsequent analysis to allow the time series to stabilize. Motion ‘‘scrubbing’’ was performed as described by (Power et al., 2012). A group-average functional connectivity matrix was constructed from the fMRI BOLD time series by concatenating the regional time series from all participants and estimating a single correlation matrix. To threshold this matrix, we sampled at random 276 points from the concatenated times series and calculated a full correlation matrix from these points. We repeated this analysis 1,000 times. From these bootstrapped samples, we estimated confidence intervals for the correlation magnitude between every pair of brain regions. Pairs whose correlation was consistently positive or negative across the 1,000 samples were retained (along with the sign and weight of the correlation) as putative functional connections.
Cammoun, L., Gigandet, X., Meskaldji, D., Philippe, J., Sporns, O., Do, K.Q., Maeder, P., Meuli, R. & Hagmann, P. (2012) Mapping the human connectome at multiple scales with diffusion spectrum MRI. Journal of Neuroscience Methods, 203, 386-397
Daducci, A., Gerhard, S., Griffa, A., Lemkaddem, A., Cammoun, L., Gigandet, X., Meuli, R., Hagmann, P. & Thiran, J.P. (2012) The connectome mapper: an open-source processing pipeline to map connectomes with MRI. PLoS One, 7, e48121
Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S. & Killiany, R.J. (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31, 968-980
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Wedeen, V.J. & Sporns, O. (2008) Mapping the structural core of human cerebral cortex. PLoS Biol, 6, e159
Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B. & Bandettini, P.A. (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage, 44, 893-905
Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L. & Petersen, S.E. (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59, 2142-2154
Wedeen, V.J., Hagmann, P., Tseng, W.Y., Reese, T.G. & Weisskoff, R.M. (2005) Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med, 54, 1377-1386
Wedeen, V.J., Wang, R.P., Schmahmann, J.D., Benner, T., Tseng, W.Y., Dai, G., Pandya, D.N., Hagmann, P., D'Arceuil, H. & de Crespigny, A.J. (2008) Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage, 41, 1267-1277