Abstracts

Rationale: We report a distinct post-ictal oscillation (PIO) pattern in the hippocampus immediately following temporal lobe seizures in mice. By investigating synchronicity of these oscillations between hippocampal sites, we aim to better understand their network origins and their relationship with ictal resolution.

Methods: We employed the murine model of mesial temporal lobe epilepsy by administering unilateral intrahippocampal kainic acid injections in either the dorsal (n=8) or ventral (n=8) CA1 area of the hippocampus, with recording electrodes placed in ipsilateral (i-) and contralateral (c-) CA1 and DG. Among all seizure EEGs (n=416), PIO phases were identified in 45% of seizures (n=189) as a gamma band oscillation in one or more recording channels, with an average duration of 10.1 to 15.7 seconds. For seizures without PIOs, a time window of average onset and duration was used as comparison. For each seizure, Pearson correlation coefficients were calculated, Fisher Z transformed, and baseline subtracted from a 20-second window prior to seizure onset.

Results: To assess overall variance, PCA was used to visualize dominant factors in the correlation between channel pairs. Clustering indicated significant variance among mice, which was subsequently accounted for to examine the deeper variances in correlation attributed to channel pairings and PIOs.



We first looked at the effect of different pairs of recording electrodes between the four channels in iCA1, cCA1, iDG, and cDG (channel comparison). A factorial ANOVA found statistically significant variance of correlation between channel pairs (F=13.46; p< 0.001). The marginal mean for iCA1 and cCA1 pair was found to be anticorrelated (-0.36 ± 0.05). Any other combination pairing that included either iCA1 or cCA1 had a weaker anticorrelation (iCA1-iDG = -0.14 ± 0.05; iCA1-cDG = -0.12 ± 0.05; cCA1-iDG = -0.20 ± 0.05; cCA1-cDG = -0.17 ± 0.06). The iDG and cDG pair were not statistically different from baseline.



Next, we looked at the effect of having zero, one, or two PIOs for each pair of recording electrodes (PIO Presence). The ANOVA found a statistically significant variance of correlation between PIO Presence (F = 37.99, p < 0.001). When neither channel exhibited a PIO, the marginal mean was positively correlated (0.23 ± 0.06). However, when either channel exhibits a PIO, the EEG signals are weakly anticorrelated (-0.18 ± 0.05). If both channels exhibit a PIO, the two channels are significantly more anticorrelated (-0.52 ± 0.07).


Conclusions: Multiple factors contribute to the variance in synchronicity of PIOs. Ipsilateral and contralateral CA1 channels are anticorrelated, with this anticorrelation increasing as more PIOs are added. Given that hyper-synchronicity is a hallmark of seizure behavior, the desynchronized activity during PIOs may contribute to ictal resolution. These findings also challenge the hypothesis that synchronized spreading cortical depression drives seizure resolution, suggesting instead that there may be two independent sources of spreading depression from both iCA1 and cCA1, indicating a bilateral but asynchronous effort by the brain to resolve ictal hyper-synchronicity.


Funding: N/A