Emma Christiaen

BACKGROUND: The prognostic value of rapid atrial pacing (RAP)-induced Wenckebach atrioventricular block (W-AVB) as a diagnostic test for predicting permanent pacemaker implantation (PPI) after transcatheter aortic valve replacement (TAVR) remains unclear and requires further validation. The objective of this study was to evaluate the predictive value of RAP-induced W-AVB for PPI and sudden cardiac death within 30 days post-TAVR. METHODS: This prospective, investigator-initiated, multicenter study (PACE-TAVR) included 640 patients undergoing TAVR across 12 centers. RAP was performed before and after TAVR to assess for W-AVB. Using negative predictive values and negative likelihood ratios, the predictive value of W-AVB was evaluated for the primary end point: sudden cardiac death or guideline-based indications for PPI, including complete AVB or alternating bundle branch block (Class I), and preexisting conduction disturbances with new ECG changes, new-onset left bundle branch block, a positive electrophysiology study, or sinus node dysfunction (Class II). RESULTS: RAP was successfully performed post-TAVR in 556 patients, with RAP-induced W-AVB observed in 192 (34.5%). W-AVB was associated with baseline conduction disturbances, amiodarone use, pre-TAVR RAP-induced W-AVB, and anesthesia type. The primary end point was more frequent in patients with W-AVB (15.6% versus 9.3%; odds ratio, 1.80 [95% CI, 1.06–3.04]; P =0.029). However, the absence of W-AVB had a negative predictive value of only 90.7% and a poor negative likelihood ratio (0.79). The test’s utility declined in subgroups at high risk for PPI, including self-expanding valve recipients (odds ratio, 1.47 [95% CI, 0.84–2.58]; negative predictive value, 88.4%; negative likelihood ratio, 0.86) and patients with a baseline or new left bundle branch block (odds ratio, 1.14 [95% CI, 0.54–2.42]; negative predictive value, 81.3%; negative likelihood ratio, 0.95). CONCLUSIONS: RAP-induced W-AVB demonstrates limited utility in predicting post-TAVR PPI or sudden cardiac death, particularly in patients at high risk for PPI. Clinicians should be cautious when using this test for post-TAVR rhythm management. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05278585.

Introduction: Electrophysiological and neuroimaging studies have demonstrated that large-scale brain networks are affected during the development of epilepsy. These networks can be investigated by using diffusion magnetic resonance imaging (dMRI). The most commonly used model to analyze dMRI is diffusion tensor imaging (DTI). However, DTI metrics are not specific to microstructure or pathology and the DTI model does not take into account crossing fibers, which may lead to erroneous results. To overcome these limitations, a more advanced model based on multi-shell multi-tissue constrained spherical deconvolution was used in this study to perform tractography with more precise fiber orientation estimates and to assess changes in intra-axonal volume by using fixel-based analysis. Methods: dMRI images were acquired before and at several time points after induction of status epilepticus in the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy. Tractography was performed, and fixel metrics were calculated in several white matter tracts. The tractogram was analyzed by using the graph theory. Results: Global degree, global and local efficiency were decreased in IPKA animals compared with controls during epileptogenesis. Nodal degree was decreased in the limbic system and default-mode network, mainly during early epileptogenesis. Further, fiber density (FD) and fiber-density-and-cross-section (FDC) were decreased in several white matter tracts. Discussion: These results indicate a decrease in overall structural connectivity, integration, and segregation and decreased structural connectivity in the limbic system and default-mode network. Decreased FD and FDC point to a decrease in intra-axonal volume fraction during epileptogenesis, which may be related to neuronal degeneration and gliosis. Impact statement: To the best of our knowledge, this is the first longitudinal multi-shell diffusion magnetic resonance imaging study that combines whole-brain tractography and fixel-based analysis to investigate changes in structural brain connectivity and white matter integrity during epileptogenesis in a rat model of temporal lobe epilepsy. Our findings present better insights into how the topology of the structural brain network changes during epileptogenesis and how these changes are related to white matter integrity. This could improve the understanding of the basic mechanisms of epilepsy and aid the rational development of imaging biomarkers and epilepsy therapies.

Magnetic resonance imaging (MRI) is frequently used for preclinical treatment monitoring in glioblastoma (GB). Discriminating between tumors and tumor-associated changes is challenging on in vivo MRI. In this study, we compared in vivo MRI scans with ex vivo MRI and histology to estimate more precisely the abnormal mass on in vivo MRI. Epileptic seizures are a common symptom in GB. Therefore, we used a recently developed GB-associated epilepsy model from our group with the aim of further characterizing the model and making it useful for dedicated epilepsy research. Ten days after GB inoculation in rat entorhinal cortices, in vivo MRI (T2w and mean diffusivity (MD)), ex vivo MRI (T2w) and histology were performed, and tumor volumes were determined on the different modalities. The estimated abnormal mass on ex vivo T2w images was significantly smaller compared to in vivo T2w images, but was more comparable to histological tumor volumes, and might be used to estimate end-stage tumor volumes. In vivo MD images displayed tumors as an outer rim of hyperintense signal with a core of hypointense signal, probably reflecting peritumoral edema and tumor mass, respectively, and might be used in the future to distinguish the tumor mass from peritumoral edema—associated with reactive astrocytes and activated microglia, as indicated by an increased expression of immunohistochemical markers—in preclinical models. In conclusion, this study shows that combining imaging techniques using different structural scales can improve our understanding of the pathophysiology in GB.

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 comorbidities1. 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 level2. 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. Additionally we investigate whether these changes are related to the severity of epilepsy, quantified with electroencephalography (EEG). Subjects 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. (1998)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: 1, 3, 6, 10 and 16 weeks after SE. At each time point an anatomical TurboRARE T2 image and three resting-state blood-oxygen level dependent (BOLD) fMRI images (TR=2s, TE=20ms, 300 repetitions) were acquired on a 7T system (Bruker PharmaScan). During image acquisition animals were anesthetized with medetomidine. The fMRI images were corrected for slice timing and motion, normalized to a template, smoothed with a Gaussian kernel (FWHM=0.8 mm), and band-pass filtered (0.01-0.1 Hz) using SPM12. The mean time series of 38 predefined regions of interest (ROIs) were extracted from the preprocessed images and the Pearson correlation coefficient between each pair of ROIs was calculated and stored in a correlation matrix using a graph theoretical network analysis toolbox (GRETNA)4. Different thresholds were applied to the correlation matrix to remove the weakest connections, resulting in 31 correlation matrices with a density ranging from 20% to 50%. Each of these matrices was visualized as a graph in which the nodes represent the ROIs and the edges the correlation coefficients between the time series of the ROIs. Several network measures were calculated at each time point, including clustering coefficient and local efficiency (measures of segregation), characteristic path length and global efficiency (measures of integration), and small-world coefficient. The mean value was calculated over the range of densities and plotted as a function of time to visualize how the properties of the functional networks change during the development of epilepsy. After the acquisition of the fMRI images, the animals were implanted with electrodes in the left and right dorsal hippocampus. EEG was measured during 8 consecutive days and several parameters, including seizure frequency and mean seizure duration, were calculated. The relation between these parameters and the network measures was assessed using the Pearson correlation coefficient. Results and Discussion In Fig. 1 the distribution of the correlation coefficients is shown at different time points during the development of epilepsy in the IPKA rat model and in control animals. The correlation coefficients shift to smaller values during epileptogenesis. This indicates that network connections progressively become weaker during the development of epilepsy. In Fig. 2A and 2B clustering coefficient and local efficiency are shown. Both decrease during epileptogenesis, indicating a decrease in segregation or local interconnectivity in the functional brain network. Fig. 2C and 2D show that characteristic path length increases and global efficiency decreases during epileptogenesis. This indicates that the integration in the brain network decreases, so there is a decrease in overall communication efficiency. The correlation between seizure frequency and 2 global network measures 16 weeks after SE is visualized in Fig. 3. Seizure frequency is positively correlated with clustering coefficient and negatively with characteristic path length at this time point. This indicates that a larger decrease in functional segregation and integration during epileptogenesis is associated with a lower seizure frequency. Conclusion The results of this study show that functional brain network connections progressively become weaker and that segregation and integration of the network are decreased during epileptogenesis. The decrease in segregation and integration 16 weeks after SE is negatively correlated with seizure frequency. Further research is necessary to obtain more insight into the biological mechanisms underlying these findings. Summary Abnormal functional brain networks could be involved in the development of temporal lobe epilepsy (TLE). In this study, changes in these networks during the development of epilepsy were investigated in a rat model for TLE. Resting-state functional magnetic resonance images were acquired at several time points during epileptogenesis to identify functional networks. Our results suggest that network connections in the functional brain network progressively become weaker during the development of epilepsy. We also find a decrease in local interconnectivity and overall communication efficiency in the network and this decrease is inversely proportional to the severity of epilepsy.

Introduction Temporal lobe epilepsy (TLE) is the most common form of epilepsy in adults. Research has shown that abnormal functional brain networks are involved in the development of epilepsy and in the generation and spread of seizures. Gaining more insight into these networks can be useful for the development of new epilepsy therapies. Resting-state functional magnetic resonance imaging (rs-fMRI) can visualize changes in neural activity on a whole-brain level and can be used to identify functionally connected brain regions and functional networks. In this study, we aim to map changes in functional networks during epileptogenesis in the systemic kainic acid rat model of temporal lobe epilepsy using resting-state fMRI and graph theory. Methods Three adult male Sprague-Dawley rats (426 ± 23 g body weight) were included in a pilot study. The animals were intraperitoneally injected with kainic acid (KA) according to the protocol of Hellier et al. (1998) resulting in status epilepticus (SE). Rs-fMRI images were acquired before the KA injections and at 5 time points during the development of epilepsy: 1, 3, 9, 12 and 17 weeks after the KA injections. At each time point an anatomical TurboRARE T2 image and two resting-state blood-oxygen level dependent (BOLD) fMRI images (TR=2s, TE=20ms, 300 repetitions) were acquired on a 7T system (Bruker PharmaScan). During image acquisition the animals were anesthetized with medetomidine. The fMRI images were corrected for slice timing and motion, normalized to a template, smoothed with a Gaussian kernel (FWHM=0.8 mm), and band-pass filtered (0.01-0.1 Hz) using SPM12. The mean time series of 40 predefined regions of interest (ROIs) were extracted from the preprocessed images and the Pearson correlation coefficient between each pair of ROIs was calculated and stored in a correlation matrix using a graph theoretical network analysis toolbox (GRETNA). Different thresholds were applied to the correlation matrix to remove the weakest connections, resulting in 21 correlation matrices with a density ranging from 20% to 40%. Each of these matrices was visualized as a graph in which the nodes represent the ROIs and the edges the correlation coefficients between the time series of the ROIs. Five network measures were calculated: the correlation coefficient and the local efficiency (measures of segregation), the characteristic path length and the global efficiency (measures of integration), and the small-world coefficient. The network measures were calculated for each correlation matrix and the z-scores of the measures were plotted as a function of network density. The mean value was calculated over this range of densities and plotted as a function of time to visualize how the properties of the functional networks change during the development of epilepsy. Results Visualization of the network measures as a function of time shows that during the first three weeks after the KA injections, the clustering coefficient and the local efficiency increase, indicating an increased segregation of the functional network. The characteristic path length increases and the global efficiency decreases, indicating a decreased integration, and the small-word coefficient increases, indicating an increase in small-worldness. After these first weeks, there is a decrease in segregation and small-worldness and an increase in integration, and 17 weeks after the KA injections, when the latency period is over, the network measures have returned to their baseline values. Conclusion The results of this pilot study suggest that there is an increased segregation, a decreased integration and an increased small-worldness in the functional brain network during the first three weeks after KA injections in the KA rat model for TLE. After three weeks, the network measures start to return to their baseline values.