Global brain network dynamics predict therapeutic responsiveness to cannabidiol treatment for refractory epilepsy.

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. 2020 Aug 31;2(2):fcaa140.

doi: 10.1093/braincomms/fcaa140. eCollection 2020.

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David E Anderson et al. Brain Commun. .

Abstract

Refractory epilepsy is a chronic brain network disorder characterized by unresponsiveness to multiple (>2) anti-epileptic drugs. Cannabidiol, a non-psychotropic neuroactive substance, is an emerging anti-epileptic treatment that was recently approved by the US Food and Drug Administration for the treatment of refractory epilepsy, especially Lennox Gastaut syndrome and Dravet syndrome. Here, we evaluated associations between global brain network dynamics and related changes and responsiveness to cannabidiol therapy using a combination of electroencephalography phase coherence and graph theoretical analyses. Refractory epilepsy patients with Lennox Gastaut syndrome or Dravet syndrome underwent serial electroencephalography testing prior to and during cannabidiol treatment. Patients showing greater than 70% seizure frequency reduction were classified as treatment responders for the purposes of this study. We calculated inter-electrode electroencephalography phase coherence in delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz) and beta (13-30 Hz) frequency bands. Graph theoretical analysis of brain network dynamics was extracted from phase coherence to evaluate measures of network integration (i.e. characteristic path length, global efficiency and degree) and segregation (i.e. modularity and transitivity). We found that responders, relative to non-responders, showed increased network integration-as indexed by relatively higher global efficiency and lower degree-and increased network segregation-as indexed by relatively higher modularity-exclusively in the beta-frequency band. We also found that larger cannabidiol dosages were associated with increased network integration-as indexed by higher global efficiency with increasing dose-and increased network segregation-as indexed by lower transitivity with increasing dose-in the delta, theta and alpha frequency bands. In summary, we demonstrate novel effects of cannabidiol on brain network dynamics with important implications for the treatment of refractory epilepsy and, possibly, across broader research applications in the future.

Keywords: EEG; anti-epileptic drugs; cannabidiol; graph theory; refractory epilepsy.

Figures

Graphical Abstract

Graphical Abstract
Figure 1

Figure 1

Reliability of phase coherence estimates. Each subplot shows split-half reliability coefficients for each frequency band. EEG recordings were divided into two equal segments and phase coherence analyses were performed separately on each half. Split-half reliability was then estimated on phase coherence estimates from each segment using the Spearman–Brown prophesy formula. Percentiles are marked in grey lines. Reliability for the delta frequency band (1–3 Hz) was greater than 0.86, 0.90 and 0.94 for the 25th, 50th and 75th quartile, respectively. Reliability for the theta frequency band (4–7 Hz) was greater than 0.85, 0.91 and 0.95 for the 25th, 50th and 75th quartile, respectively. Reliability for the alpha frequency band (8–12 Hz) was greater than 0.94, 0.96 and 0.97 for the 25th, 50th and 75th quartile, respectively. Reliability for the beta frequency band (13–30 Hz) was greater than 0.90, 0.93 and 0.95 for the 25th, 50th and 75th quartile, respectively.

Figure 2

Figure 2

Evaluating effects of treatment dosage on CBD-related changes in phase coherence. Topographic maps of electrode pairs showing an increase (red lines) or decrease (blue line) in phase coherence for each frequency band between T1 and T2 (A), T2 and T3 (B) and T1 and T3 (C) measurements. Patients were separated into high (left) and low (middle) CBD dosage groups; topographic maps of between-group differences were constructed (right). (A) Between T1 and T2, relatively greater reductions in phase coherence were observed in the low dosage relative to the high dosage groups across all frequency bands. (B) Between T2 and T3, relatively greater increases in phase coherence were observed in the high dosage relative to the low dosage groups in the delta, theta and alpha frequency bands. (C) Between T1 and T3, relatively greater increases in phase coherence were observed in the high dose relative to the low dose group in the delta, theta and alpha frequency bands.

Figure 3

Figure 3

Evaluating differences in CBD-related changes in phase coherence between responders and non-responders. Topographic maps of electrode pairs showing an increase (red lines) or decrease (blue line) in phase coherence for each frequency band between T1 and T2 (A), T2 and T3 (B) and T1 and T3 (C) measurements. Patients were separated into responder (left) and non-responder (middle) groups; topographic maps of between-group differences were constructed (right). (A) Between T1 and T2, relatively greater reductions in phase coherence were observed across all frequency bands in the responder relative non-responder group. (B) Between T2 and T3, relative greater increases in phase coherence observed across all frequency bands in both groups. (C) Between T1 and T3, relative greater increases in phase coherence were observed in the non-responder group, whereas the responder group showed greater reductions in phase coherence.

Figure 4

Figure 4

Comparing graph theoretical measures of global brain network dynamics in CBD responder and non-responder groups. Between-group differences in graph theoretical measures of global brain network dynamics were observed only in the beta frequency band. (A) Global efficiency, a measure of network integration, showed a trending group-by-visit interaction (P = 0.074; Cohen’s d = 0.74). Higher global efficiency was observed in the responder relative to non-responder group (P = 0.013). Greater reductions in global efficiency were observed between visits 1 and 2 in the non-responder (P = 0.005) but not responder group (P = 0.80). (B) Degree, a measure of network connectivity, was lower in the responder relative to non-responder group (P = 0.030; Cohen’s d = 1.33). (C) Modularity, a measure of network segregation, showed a trending effect of group (P = 0.093; Cohen’s d = 1.01), where higher modularity was observed in the responder relative to non-responder group.

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References

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