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. 2019 Jul 1;142(7):1955-1972.
doi: 10.1093/brain/awz125.

Characterizing the role of the structural connectome in seizure dynamics

Preya Shah  1   2 , Arian Ashourvan  1   2 , Fadi Mikhail  2   3 , Adam Pines  4 , Lohith Kini  1   2 , Kelly Oechsel  2   3 , Sandhitsu R Das  3 , Joel M Stein  5 , Russell T Shinohara  6 , Danielle S Bassett  1   2   3   7   8 , Brian Litt  1   2   3 , Kathryn A Davis  2   3
Affiliations

Characterizing the role of the structural connectome in seizure dynamics

Preya Shah et al. Brain. .

Abstract

How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.

Keywords: epilepsy; functional connectivity; high-angular resolution diffusion imaging; intracranial EEG; structural connectivity.

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Figures

Figure 1
Figure 1
Summary of patient-level SC-FC analysis pipeline. (A) HARDI preprocessing and whole-brain tractography was carried out. (B) iEEG data were preprocessed and seizures were annotated, with each seizure event consisting of an ictal period and an associated pre-ictal period of equivalent duration. (C) Regions of interest (ROIs) were selected via a one-to-one spatial correspondence between electrode centroids and atlas regions. (D) The structural connectivity (SC) network was generated using log-normalized streamline counts between atlas regions of interest associated with each electrode location. (E) Time-varying broadband functional connectivity (FC) networks were generated for each 1 s time window by computing correlation between iEEG signals across electrode pairs. Frequency-specific functional connectivity networks were similarly computed using coherence between iEEG signals across electrode pairs. (F) SC-FC relationships were quantified across time, frequency, and space (see ‘Materials and methods’ section for details).
Figure 2
Figure 2
SC-FC analysis using broadband functional connectivity. (A) Temporal dynamics of SC-FC correlation as measured by Fisher’s z for one example seizure in one patient, along with permutation-based null distribution of z values (mean ± standard deviation). (B) Per-seizure z-values during interictal, pre-ictal, and ictal periods reveal SC-FC correlations significantly greater than chance across all periods (P < 0.05). (C) Temporal dynamics of SC-FC correlation across all subjects (mean ± standard deviation across seizures in each subject). For visualization purposes only, time courses were normalized to span 200 evenly spaced time windows (100 pre-ictal and 100 ictal) and smoothed with a 5-window moving average filter. (D) Per-seizure paired differences in mean z-values reveal significantly greater SC-FC correlation during ictal periods than pre-ictal periods (P = 0.023). This effect holds when substituting pre-ictal periods with interictal periods (P = 0.021), with no significant difference between pre-ictal and interictal period SC-FC correlation values (P = 0.70). *P < 0.05.
Figure 3
Figure 3
Frequency-specific SC-FC analysis. (A) Temporal dynamics of SC-FC correlation as measured by Fisher’s z in α/θ, β, low-γ, and high-γ frequency bands (mean ± standard deviation across seizures in each subject, following interpolation to normalize ictal and pre-ictal durations). (B) Per-seizure z-values during interictal, pre-ictal, and ictal periods (mean ± SD) are significantly greater than chance (P < 0.05, permutation-based testing). (C) The increase in SC-FC correlation between pre-ictal and ictal periods is further illustrated using paired differences for each individual seizure (P < 0.05, linear mixed effects analysis with subject as random effect). (D) Seizures within subjects evolve similarly, as evidenced by higher between-patient Euclidean distances between SC-FC correlation time courses compared to within-patient distances (P < 0.001, R2 = 0.50, PERMANOVA). *P < 0.05.
Figure 4
Figure 4
Assessment of SC-FC coupling in postictal periods. (A) Illustration of broadband SC-FC coupling across pre-ictal, ictal, and postictal periods, in three example seizures. Each represents one of three observed distinct patterns of post-ictal SC-FC coupling: (top) SC-FC coupling persists, but does not increase, in the immediate postictal period and subsequently decreases, (middle) SC-FC coupling increases into the immediate postictal period and later decreases, and (bottom) SC-FC coupling decreases sharply at or prior to the start of the postical period. (B) Per-seizure paired differences in mean z-values reveal significant decreases in SC-FC coupling between ictal periods and later postictal (Minutes 1–5) periods across all tested frequency bands, with significant differences between SC-FC coupling between ictal periods and immediate postictal (Minutes 0–1) periods occurring only in α/θ and β frequency bands. (P < 0.05, linear mixed effects analysis with subject as random effect). Pre-ictal period bars included for reference. *P < 0.05.
Figure 5
Figure 5
Assessment of SC-FC coupling in focal to bilateral tonic-clonic seizures. (A) Illustration of SC-FC coupling in two focal to bilateral tonic-clonic seizures, one from Subject 1 and one from Subject 3, reveals decrease in SC-FC coupling following bilateral tonic-clonic (BTC) onset (bilateral tonic-clonic onset indicated by dotted red line). For comparison, SC-FC coupling time course from a focal impaired awareness seizure in Subject 3 (without bilateral tonic-clonic) does not illustrate the same decrease. (B) In all bilateral tonic-clonic seizures, per-seizure paired differences in mean z-values reveal significantly greater SC-FC correlation during pre-bilateral tonic-clonic ictal periods than pre-ictal periods (P < 0.05), as well as significantly greater SC-FC correlation during pre-bilateral tonic-clonic ictal periods than post-bilateral tonic-clonic ictal periods (P < 0.05). *P < 0.05.
Figure 6
Figure 6
Subject-specific virtual edge resection approach to determine the contribution, σ(i), of each structural edge i on the increase in SC-FC correlation during seizures. Results are shown for an example seizure in a patient with left temporal lobe epilepsy. Only ‘contributor’ edges [σ(i) > 0 and Δzictal(i)>0] are included to highlight edges that are associated with the SC-FC increase, with edge thickness and colour used to representing magnitude of σ(i) .
Figure 7
Figure 7
Relationship between edge contribution and edge length. Findings reveal that contributor edges are shorter-range in terms of both (A) Euclidean distance and (C) streamline length (P < 0.05, two-tailed paired t-test). Furthermore, there is a trend that edges with higher contribution are shorter-range, in terms of both (B) Euclidean distance and (D) streamline length, with significant differences between low and medium contribution edges (P < 0.05, two-tailed paired t-test), and low and high contribution edges (P < 0.05, two-tailed paired t-test). *P < 0.05.
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