Altered structural connectome in non-lesional newly diagnosed focal epilepsy: Relation to pharmacoresistance

Abstract

Despite an expanding literature on brain alterations in patients with longstanding epilepsy, few neuroimaging studies investigate patients with newly diagnosed focal epilepsy (NDfE). Understanding brain network impairments at diagnosis is necessary to elucidate whether or not brain abnormalities are principally due to the chronicity of the disorder and to develop prognostic markers of treatment outcome. Most adults with NDfE do not have MRI-identifiable lesions and the reasons for seizure onset and refractoriness are unknown. We applied structural connectomics to T1-weighted and multi-shell diffusion MRI data with generalized q-sampling image reconstruction using Network Based Statistics (NBS). We scanned 27 patients within an average of 3.7 (SD = 2.9) months of diagnosis and anti-epileptic drug treatment outcomes were collected 24 months after diagnosis. Seven patients were excluded due to lesional NDfE and outcome data was available in 17 patients. Compared to 29 healthy controls, patients with non-lesional NDfE had connectomes with significantly decreased quantitative anisotropy in edges connecting right temporal, frontal and thalamic nodes and increased diffusivity in edges between bilateral temporal, frontal, occipital and parietal nodes. Compared to controls, patients with persistent seizures showed the largest effect size (|d|>=1) for decreased anisotropy in right parietal edges and increased diffusivity in edges between left thalamus and left parietal nodes. Compared to controls, patients who were rendered seizure-free showed the largest effect size for decreased anisotropy in the edge connecting the left thalamus and right temporal nodes and increased diffusivity in edges connecting right frontal nodes. As demonstrated by large effect sizes, connectomes with decreased anisotropy (edge between right frontal and left insular nodes) and increased diffusivity (edge between right thalamus and left parietal nodes) were found in patients with persistent seizures compared to patients who became seizure-free. Patients who had persistent seizures showed larger effect sizes in all network metrics than patients who became seizure-free when compared to each other and compared to controls. Furthermore, patients with focal-to-bilateral tonic-clonic seizures (FBTCS, N = 11) had decreased quantitative anisotropy in a bilateral network involving edges between temporal, parietal and frontal nodes with greater effect sizes than those of patients without FBTCS (N = 9). NBS findings between patients and controls indicated that structural network changes are not necessarily a consequence of longstanding refractory epilepsy and instead are present at the time of diagnosis. Computed effect sizes suggest that there may be structural network MRI-markers of future pharmacoresistance and seizure severity in patients with a new diagnosis of focal epilepsy.

Keywords: Anti-epileptic drug outcome; Diffusion MRI; Network based statistics.

Fig. 1

Processing steps shown on an example participant. Data was processed with FreeSurfer (T1w), FSL (dMRI), DSI-studio (combined nodes and edges), MATLAB (connectivity matrix) and NBS (structural network). As seen from left to right and top to bottom. T1w = T1-weighted MRI; dMRI = diffusion MRI; NBS = network-based statistics.

Fig. 2

Significant NBS networks in patients vs controls across different T-score thresholds. Networks were visualized with NBSview. Inset shows complete node set used for analysis. NBS identified several edges for different T-scores and significant networks are displayed for selected exemplary T-scores for visualization purposes. Edges with the highest T-scores are denoted by highlighting the affected node in red along with the connected node and T-score in the subplot titles. L = left; R = right; A = anterior; P = posterior; rh = right hemisphere; lh = left hemisphere; QA = quantitative anisotropy; MD/AD/RD = mean/axial/radial diffusivity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Significant mean (A) and radial diffusivity (B) connectograms (|T|>=2.7) with scatterplots. All diffusivity metrics are in units of 10⁻³ mm²/s. Networks were visualized with BrainNet Viewer (Xia et al., 2013). Node size represents number of edges. Edge thickness and color represent magnitude of T-statistic. Inset shows complete node set (N = 82) with edges used for analysis. L = left; R = right; A = anterior; P = posterior; MD/RD = mean/radial diffusivity.

Fig. 4

NBS networks with small-sample-size corrected Cohen's Dmagnitudes > 1.0 in outcome subgroup comparisons. Networks were visualized with BrainNet Viewer using the red-white-blue colormap (He, 2020). Edge thickness and color represent magnitude of Cohen's D. Inset shows complete node set with edges used for analysis. SF = seizure-free; PS = persistent seizures; L = left; R = right; A = anterior; P = posterior; QA/FA = quantitative/fractional anisotropy; MD/AD/RD = mean/axial/radial diffusivity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

NBS networks with small-sample-size corrected Cohen's Dmagnitudes> 1.0 in FBTCS subgroup comparisons. Networks were visualized with BrainNet Viewer using the red-white-blue colormap. Edge thickness and color represent magnitude of Cohen's D. Inset shows complete node set with edges used for analysis. SF = seizure-free; PS = persistent seizures; L = left; R = right; A = anterior; P = posterior; QA/FA = quantitative/fractional anisotropy; MD/AD/RD = mean/axial/radial diffusivity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)