Dynamic functional connectivity and graph theory metrics in a rat model of temporal lobe epilepsy

Introduction Epilepsy is a neurological disorder characterized by recurrent epileptic seizures. The involvement of abnormal functional brain networks in the development of epilepsy and its comorbidities has been demonstrated by electrophysiological and neuroimaging studies in epilepsy patients1. In this longitudinal resting state functional MRI (rsfMRI) study, changes in dynamic functional connectivity (dFC) and network topology during epileptogenesis were investigated using the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy (TLE). Subjects and Methods 24 adult male Sprague-Dawley rats were used. 17 were i.p. injected with kainic acid according to the protocol of Hellier et al. (1998)2, resulting in status epilepticus (SE), and 7 with saline (controls). Before and 5 times post-SE, rsfMRI scans were acquired on a 7T system. Correlation between fMRI timeseries was calculated within a sliding window of 50s with a 2s step length. The resulting correlation matrices were classified into 6 states using k-means clustering. For all time-varying correlation matrices, graph theoretical metrics were calculated and classified into 6 states of network topology. Percentage dwell time in and number of transitions between each state were calculated and their correlation with seizure frequency, based on hippocampal EEG recordings, was assessed. Results and Discussion The 6 states of FC were sorted from highest to lowest mean value. Percentage dwell time in State 1, 2 and 3 was significantly lower in the IPKA group compared to controls, while dwell time in State 5 and 6 was significantly higher. A significant effect of time post-SE could be found in the IPKA group, where a significant decrease in dwell time in State 1, 2, 3 and 4 and increase in State 5 and 6 could be observed during epileptogenesis (Fig. 1A). The number of transitions was significantly lower in the IPKA group compared to controls and decreased significantly during epileptogenesis in the IPKA group (Fig. 1B). Seizure frequency was positively correlated with dwell time in State 2 one week post-SE and in State 4 16 weeks post-SE, and negatively with dwell time in State 5 one week post-SE and State 6 16 weeks post-SE (Fig. 2). The number of transitions 16 weeks post-SE was positively correlated with seizure frequency. Similar results were obtained for the states of network topology. Conclusion States with a lower mean FC occurred more often in IPKA animals compared to controls. FC became less variable during epileptogenesis, which might be related to cognitive problems. Seizure frequency was positively correlated with dwell time in states with high FC and number of transitions between states, indicating that dwelling in states of higher FC and more switching between states seem to increase the probability that seizures are generated

Functional connectivity changes during epileptogenesis: a longitudinal rs-fMRI study

1. INTRODUCTION Temporal lobe epilepsy (TLE) is the most common form of epilepsy in adults. Research has shown that abnormal functional brain networks could be involved in the development of epilepsy and its comorbidities [1]. Gaining more insight into these networks can be useful for the development of new therapies. Resting-state functional magnetic resonance imaging (rs-fMRI) can visualize changes in functional networks on a whole-brain level [2]. In this study, we aim to map changes in functional networks during epileptogenesis in the intraperitoneal kainic acid (IPKA) rat model for TLE using longitudinal resting-state fMRI and graph theory. 2. MATERIALS AND METHODS Twenty-four adult male Sprague-Dawley rats (276 ± 15 g body weight) were used in this study. Seventeen animals were intraperitoneally injected with kainic acid (KA) according to the protocol of Hellier et al. [3] resulting in status epilepticus (SE). The other 7 animals were injected with saline and used as a control group. Rs-fMRI images were acquired before the KA injections and at 5 time points during the development of epilepsy. At each time point an anatomical image and three resting-state fMRI images were acquired on a 7T system, while the animals were anesthetized with medetomidine. The fMRI images were corrected for slice timing and motion, normalized, smoothed and band-pass filtered using SPM12. The Pearson correlation coefficient was calculated between the fMRI time series of 38 regions of interest (ROIs) and stored in a correlation matrix. After applying different thresholds to remove the weakest connections, several network measures were calculated using a graph theoretical network analysis toolbox (GRETNA), and plotted as a function of time to visualize how the properties of the functional networks change during epileptogenesis. 3. RESULTS AND DISCUSSION In the IPKA rat model the correlation coefficients shift to smaller values during epileptogenesis, indicating that network connections progressively become weaker. Clustering coefficient and local efficiency decrease during epileptogenesis, indicating a decrease in segregation or local interconnectivity in the functional brain network. Characteristic path length increases and global efficiency decreases during epileptogenesis, indicating a decrease in integration or overall communication efficiency. In the next phase of this study, EEG monitoring will be used to characterize the severity of epilepsy in these rats to investigate how changes in functional brain networks during epileptogenesis correlate with epilepsy severity