Article
Reference
Identifying motor functional neurological disorder using resting-state functional connectivity
WEGRZYK, Jennifer, et al.
Abstract
Motor functional neurological disorder (mFND) is a clinical diagnosis with reliable features;
however, patients are reluctant to accept the diagnosis and physicians themselves bear doubts on potential misdiagnoses. The identification of a positive biomarker could help limiting unnecessary costs of multiple referrals and investigations, thus promoting early diagnosis and allowing early engagement in appropriate therapy.
WEGRZYK, Jennifer, et al. Identifying motor functional neurological disorder using resting-state functional connectivity. NeuroImage: Clinical, 2018, vol. 17, p. 163-168
DOI : 10.1016/j.nicl.2017.10.012 PMID : 29071210
Available at:
http://archive-ouverte.unige.ch/unige:123840
Disclaimer: layout of this document may differ from the published version.
Supplemental Data
Appendix 1:
Motion-related artefacts
As motion-related artefacts have previously been shown to influence FC, we calculated the mean frame displacement (using the 6 motion parameters extracted during the realignment procedure), as computed with Power’s displacement (1) and Van Dijk’s RMS translation (2). We first checked whether any subject showed excessive values, and then computed Mann-Whitney U tests on each measure to assess whether there were any significant differences in motion between controls and patients. After exclusion of 5 subjects that showed excessive movement, no significant differences in motion parameters were found between the patient and control group (see Table S1).
Appendix 2:
Atlases and functional connectivity matrix
For the AAL atlas, we used 88 regions (whole atlas without the cerebellum and pallidum), all 83 regions from the Hammers atlas, and all 90 regions from the Shirer atlas. The functional connectivity matrix was used as the adjacency matrix of a connectivity graph, where each atlas region corresponded to a vertex and the strength of FC (the correlation coefficient) between two regions was encoded in the edge weight. We used direct graph embedding, in which the upper triangular part of
Our leave-one-out cross validation approach was the following: in each fold, all subjects but one were used for training the classifier and the remaining subject was used for testing. This was repeated until all of the subjects had been tested, i.e., the number of folds was equal to the number of subjects. The classification accuracy was expressed as the average performance across all folds.
Appendix 4:
Post-hoc seed connectivity analyses
First, an additional preprocessing step had to be performed: functional images were spatially smoothed with a 6mm full-width-at-half-maximum Gaussian filter. Then, seed-based FC was computed between the averaged time series of the seed and the rest of the brain, which was constrained to a binarized DARTEL group template comprising all voxels with a probability ≥0.3 of being grey matter. Correlation coefficients were then Fisher-Z transformed, and correlation maps were normalized to the MNI template.
Group comparisons were performed using a two-sample t-test, and the statistical threshold was set at p < 0.005 uncorrected at the voxel level (minimum cluster size = 10 voxels), and p < 0.05 FDR-corrected at the cluster level.
Legend of Supplemental Tables and Figures
Table S1. Mean frame displacement (FD) computed with Power’s displacement and Van Dijk’s RMS translation
Table S2. Logistic regression models testing the effect of anxiety (STAI-S), depression (BDI) and medication in all subjects (A) and on patients only (B) on classification performance.
Table S3. Mean functional connectivity in controls and patients between pairs of regions showing discriminative functional connectivity (Z-scores).
Figure S1. Discriminative connections based on the Shirer (A) and Hammers (B) atlas.
Figure S2. ROC curves of the individual classification performance of all three atlases, indicating the trade-off between sensitivity and specificity.
Table S1. Mean frame displacement (FD) computed with Power’s displacement (1) and Van Dijk’s RMS translation (2).
controls patients z-value p-value mean FD Van Dijk 0.04 ± 0.02 0.04 ± 0.03 0.06 0.950
mean FD Power 0.15 ± 0.04 0.16 ± 0.07 -0.48 0.634
Table S2. Logistic regression models testing the effect of anxiety (STAI-S), depression (BDI) and medication in all subjects (A) and on patients only (B) on classification performance.
A) B)
Beta coefficients p-value Beta coefficients p-value
intercept 1.31 <0.001 intercept 1.07 0.039
STAI-S -0.02 0.024* STAI-S -1.01 0.356
BDI 0.02 0.192 BDI 0.01 0.756
medication 0.00 0.994 medication 0.01 0.661
intercept 1.25 <0.001 intercept 1.13 0.010
STAI-S -0.02 0.049* STAI-S -0.01 0.289
intercept 0.63 <0.001 intercept 0.70 0.001
BDI 0.01 0.562 BDI -0.00 0.898
intercept 0.61 0.091 intercept 0.22 0.739
medication 0.69 0.351 medication 1.08 0.250
STAI-S: Anxiety State value, BDI: Beck Depression Index, CGI: Clinical Global Impression. * significant beta coefficient
Table S3. Mean functional connectivity in controls and patients between pairs of regions showing discriminative functional connectivity (Z-scores).
HC mFND
Caudate R Amygdala L 0.17 0.57
Postcentral L 0.06 0.61
Postcentral R 0.12 0.56
Paracentral R Front Mid Orb L 0.04 0.48
Front Mid Orb R 0.04 0.46
Parietal Inf R Front Sub Orb R 0.72 0.53
Angular R Cuneus R -0.01 0.46
Transv temp (Heschl) L Transv temp (Heschl) R 0.55 0.87
Cuneus L Lingual L 1.18 0.82
Lingual R 1.11 0.89
Supramarginal L Front Inf L 0.66 0.52
Figure S1. Discriminative connections based on the Shirer (A) and Hammers (B) atlas.
Thicker lines correspond to connections that have higher weights in classification performance. Colors of spheres correspond to different networks (for Shirer atlas:
light yellow/green=posterior salience, yellow= primary visual, orange= right executive control, red=visuospatial, dark blue=dorsal default mode, turquoise=sensorimotor, or different lobes (for Hammers atlas: light yellow/green=frontal, yellow=parietal, light blue=insula/cingulate, dark blue=temporal, orange=central, red=ventricular,
turquoise=occipital).
A B
Figure S2. ROC curves of the individual classification performance of all three atlases, indicating the trade-off between sensitivity and specificity.
References Supplemental Data
1. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 2012; 59(3): 2142-54.
2. Van Dijk KR, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 2012; 59(1): 431-8.
