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Publication Detail
Longitudinal changes in structural cortical networks after clinically isolated syndrome
  • Publication Type:
  • Authors:
    Tur C, Eshaghi A, Jenkins TM, Prados F, Clayden JD, Ourselin S, Altmann DR, Wheeler-Kingshott CAM, Miller DH, Thompson AJ, Ciccarelli O, Toosy AT
  • Presented at:
    Organization for Human Brain Mapping
  • Location:
    Geneva, Switzerland
  • Keywords:
    Brain connectivity
  • Addresses:
    Carmen Tur
    UCL Institute of Neurology
    1st floor, Russell Square House 10-12 Russell Square
    WC1B 5EH
    United Kingdom
Introduction Disruption of functional and structural brain networks has been proposed as a possible mechanism underlying irreversible accrual of disability in multiple sclerosis (MS) (1-4) Covariance in grey matter thickness between cortical areas can indicate underlying connections, or imply functional connectivity between areas with similar functions (1). These structural cortical networks (SCN) provide complementary information to other connectivity techniques (functional and structural), which often require MRI sequences not readily available in clinical practice and long acquisitions. So far no one has studied SCNs in early MS (e.g. CIS [clinically isolated syndrome]) or provided longitudinal evidence of SCN disruption in this condition (1,3). Here we 1) estimated SCN parameters and 2) evaluated changes in SCN parameters over 1 year of follow-up in a group of CIS patients with their first episode of optic neuritis. Methods Consecutive patients within 4 weeks of CIS and age-matched healthy controls (HCs) underwent clinical and brain MRI assessments at baseline, 3, 6, and 12 months (1.5 T, axial proton-density [PD, 0.9x0.9x5mm3] and 3DT1-weighted [1.2x1.2x1.2mm3] images among others (5,6)). SCN analysis: 1) Manual segmentation of focal lesions on PD images and “lesion filling” (7) of 3DT1-weighted images (only patients). 2) Estimation of cortical thickness in 68 brain cortical areas (34 left, 34 right) using the FreeSurfer longitudinal stream (8) (Figure 1). 3) Estimation of SCN parameters: only subjects with data at all 4 time points were included in the analysis. For each group and time point, one correlation matrix (using Pearson’s correlation coefficient, PCC) between thicknesses of brain cortical areas was obtained. For each correlation matrix, 6 binarised matrices were obtained, according to 6 PCC thresholds (from +/- 0.3 to +/- 0.8). For each binarised matrix, considered as the numerical representation of a network with 68 nodes (=cortical areas) and edges indicating presence (=1) or absence (=0) of a connection between 2 cortical areas, we obtained: i) network connectivity: proportion of network connections out of total number of possible connections; ii) nodal degree distribution parameters (nodal degree: number of edges emerging from a given node). For each network parameter, permutation tests assessed between-group differences at baseline. 4) Assessment of changes in connectivity and nodal degree parameters over time: we ran logistic and linear regression models, respectively, with the ‘time point’ as the only predictor variable. Results Seventeen patients (14F) and 7 HCs (5F) were included (8 patients became MS during follow up); 8 correlation matrices were obtained: 4 time points x 2 groups (Figure 2). Network connectivity (Figure 3) and mean network degree at baseline (Figure 4) were not significantly different between groups although network distribution in the patient group showed a greater dispersion than that of the control group. In patients, logistic regression methods revealed significant decrease in connectivity over time, for thresholds 0.4 to 0.8 (p<0.001), and the effect of time seemed more marked as the threshold increased (RCs from -0.019 to -0.350·month-1). Regarding degree parameters, although RCs were always negative, suggesting a decrease in mean nodal degree over time for all thresholds, these did not reach statistical significance. In HCs, connectivity or degree parameters did not change over time. Conclusions After CIS, patients show similar SCN characteristics to controls. However, during one year follow up, differences emerge that are compatible with disconnection of SCNs in CIS patients. Future studies will assess whether this is at the expense of those who become MS. We conclude that it is possible to detect subtle effects in early MS that may impact on brain networks even with structural scans and at 1.5T, thus enabling retrospective analyses of larger data sets, which may confirm our results.
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