Association between structural and functional inter-subject variability of the motor and visual networks
Maxime Chamberland1,2, Gabriel Girard2, Michaël Bernier1, Michael Paquette2, David Fortin3, Maxime Descoteaux2, and Kevin Whittingstall1,4

1Nuclear Medicine and Radiobiology, Université de Sherbrooke, Sherbrooke, QC, Canada, 2Computer science, Université de Sherbrooke, Sherbrooke, QC, Canada, 3Division of Neurosurgery and Neuro-Oncology, Université de Sherbrooke, Sherbrooke, QC, Canada, 4Department of Diagnostic Radiology, Université de Sherbrooke, Sherbrooke, QC, Canada


"Your brain is unique" is an unequivocal sentence that has spanned many research topics in the recent years. For example, functional connectivity (FC) based on resting-state fMRI is highly variable from one subject to the next, yet the source of this variability is unclear. Understanding the source of FC variability is important as it is often used in clinical studies. Here, we explore how this might be explained by variability of white-matter structural connectivity (SC) derived from diffusion MRI tractography connectivity matrices. Our results show that, across multiple brain areas, motor and visual networks show the lowest inter-subject variability. This suggests that, at least in these areas, SC might explain a portion of FC variability.


Part of FC variability of the human brain can be explained by evolutionary cortical expansion, cortical thickness and connection range1. In this abstract, we hypothesise that SC variability may also explain a significant portion of functional variability. A method was proposed by Mueller et al. 2013 to estimate inter-subject functional variance by taking into account intra-subject variability. The result is a variability index which can be mapped to cortical areas and indicates the variance of a specific region. We applied the proposed method on structural connectivity matrices derived from diffusion MRI and compared our results with functional variability maps to determine if those variances are predictive of one another.


Diffusion datasets were obtained from 9 subjects (S) over 3 sessions (T) sessions separated by a 1-month interval. Functional datasets encompassed 1 additional scan per subject within the last session.

Structural connectivity: Diffusion-weighted images were acquired along 64 uniformly distributed directions using a b-value of 1000 s/mm2, a single-shot echo-planar imaging (EPI) sequence on a 1.5 Tesla SIEMENS Magnetom (128 × 128 matrix, 2 mm isotropic resolution, TR/TE 11000/98 ms) and a GRAPPA factor of 2. An anatomical T1-weighted 1 mm isotropic MPRAGE image was also acquired. Fiber Orientation Distribution Functions from spherical deconvolution2 were used for tractography. Partial volume estimation maps from the T1-weighted image were obtained using FSL/Fast3 and used in the tracking process. Streamline tractography was done with particle filtering tractography4, seeding from the white matter (WM) and grey matter (GM) interface (1 × 1 × 1 mm3, ≈200k voxels, 10 seeds/voxel). Freesurfer5 was used to generate a macroscale parcellation of each individual brain into 150 regions6. Structural connectivity (SC) matrices7 (Msc) were finally normalized (i.e. ∑ij M = 1; i, j ∈ lower diagonal)8.

Functional connectivity: Continuous functional recordings were carried out using a standard EPI sequence (eyes closed). For each run, 108 functional volumes consisting of 35 axial slices were obtained with a 64 × 64 matrix, field of view 220 mm, TR/TE 2730/40 ms, for a voxel size of 3.4 × 3.4 × 4.2 mm3. Data was motion-corrected, smoothed, skull-stripped, and filtered using non-local means denoising9, 10. Signals were averaged within each of the previously described 150 regions and correlated between each other. Functional connectivity (FC) matrices (Mfc) were finally group-normalized by applying Fisher’s r-to-z transform.

Inter-subject variability: Variability indices were computed as described by Mueller et al. 2013. The outline of the method is described as follows: the similarity (VTsim) between any 2 connectivity vectors (i.e. rows of M) is given by the correlation (corr) of those 2 vectors across subjects and for each session T. In addition, the intra-subject variability (VSintra) was estimated by first computing corr – 1, (dissimilarity between 2 connectivity vectors across sessions for each subject S). The intra-subject variability was then averaged across all subjects, resulting in a single value (N) for each GM region. Linear regression (VT = [1 - VTsim] - βN - c) allowed the estimation of VTinter, the inter-subject variability for each session. Inter-subject variability (Vinter) was finally obtained for each region by averaging VTinter over the 3 sessions. The Vinter indices were then back-projected to a brain surface for qualitative assessment. The mean variability was also computed for both SC and FC variability maps. Finally, values located in the top 10 percentiles (two-tailed) of Vinter were computed and the intersected between both SC and FC.


Qualitative whole-brain structural and functional variability maps (Vinter) are shown on Fig. 1 (normalized scale). Fig. 2 show quantitative variability index for each of the 150 GM regions. Fig. 3 shows regions where the intersection between SC and FC variability maps was the lowest (a, left and right sulcus central, right post-central sulcus and gyrus, cuneus and calcarine sulcus) and highest (b, right BA47). No significant correlation was found between SC and FC variability at the whole brain level (Fig. 4, r = 0.1).

Discussion and Conclusion

We generated structural and functional variability maps for 9 subjects by taking into account the intra-subject variability. SC and FC were smallest in motor and visual cortex, yet were not correlated at the whole-brain level. Our results are in line with previous studies that compared functional variability with the evolutionary cortex expansion1. We also showed that the inter-subject structural variability is lower than the inter-subject functional variability (0.07 vs 0.25, respectively). Finally, BA47 revealed to be the highest structural and functional variability, a brain structure associated with language and memory processing.


The authors would like to thank David Provencher for useful discussions and acknowledge the funding agencies which have supported this research: NSERC Discovery Grants, QBIN (Quebec Bio-Imaging Network) and the FMSS graduate scholarship program. Maxime Chamberland is supported by the Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program (CGS-D3) from the Natural Sciences and Engineering Research Council of Canada (NSERC). Michaël Bernier, Gabriel Girard and Michael Paquette are supported by the Postgraduate Scholarships-Doctoral Program (PGS D).


1Mueller et al. (2013) Neuron, 2Tournier et al. (2007) NeuroImage, 3Zhang et al. (2001) IEEE Trans. Med. Imaging, 4Girard et al. (2014) NeuroImage, 5Fischl et al. (2004) Cereb. cortex, 6Destrieux et al. (2009) NeuroImage, 7Hagmann et al. (2007) PLoS ONE, 8Girard et al. (2015) ISMRM Toronto, 9Bernier et al. (2014) Frontiers in Human Neuroscience, 10Chamberland et al. (2015) Frontiers in Neuroscience.


Fig 1: a) Structural connectivity (SC) variability. b) Functional connectivity (FC) variability. Values were normalized and backprojected to a brain surface for visualization. Regions of low variability (purple) include the motor and visual networks for both SC and FC. Visualization was done using the FiberNavigator10.

Fig. 2: Average inter-subject variability. a) Variability behind structural connectivity (SC) of 150 GM regions (mean = 0.07). b) Variability behind functional connectivity (FC) of 150 GM regions (mean = 0.25).

Fig. 3: a) Cortical regions where the intersection between SC and FC variability maps was the lowest encompassed the motor and visual networks (lateral and medial views). b) High variability was found in the pars orbitalis (BA47). Visualization was done using the FiberNavigator10.

Fig. 4: Inter-subject structural and functional variability (SC-FC) shows no correlation at the whole brain level (r = 0.1).

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)