In examining interactive effects of the 2 main seizure factors, seizure frequency and lateralization, on intratemporal effective connectivity as well as on verbal memory and language impairment, we had 3 specific aims. First, we wanted not only to confirm the effect of seizure lateralization on the impairment of verbal memory and language, but also to assess whether a nonadditive effect of the 2 seizure factors exists. Second, we sought to examine whether alterations in intratemporal effective connectivity are affected by one or more seizure factors. Third, we asked whether the association between effective connectivity changes and cognitive impairment is influenced by both seizure factors.

Although hippocampal sclerosis is the primary underlying pathology of TLE, 15 progressive atrophy in the MTL is not limited to the hippocampus, but affects surrounding regions. 16 , 17 We thus included the hippocampus (Hipp), the parahippocampal gyrus (PHG), and the amygdala (Amyg) as the MTL regions of interest for effective connectivity modeling. The 3 regions are included in the initiating circuit of epileptic seizures in TLE, 18 and moreover they serve as the anatomical components of the MTL memory system, 19 playing an important role in memory.

Classical or deterministic DCM, 12 the first DCM framework that was developed, relies on a deterministic model of neuronal activity explaining observed hemodynamic responses. Applying the DCM to rsfMRI was made possible by the development of stochastic DCM (sDCM), 13 which employs an extended model accounting for stochastic fluctuations in neuronal activity. As the stochastic model in sDCM entails excessive computational complexity, spectral DCM (spDCM), 14 which employs a deterministic model under a stationarity assumption, was later suggested as a simple and efficient way of estimating effective connectivity in rsfMRI. In this study, we used spDCM to estimate effective connectivity in rsfMRI from TLE patients due to its computational efficiency.

Clinical relevance of functional connectivity in resting‐state functional magnetic resonance imaging (rsfMRI) has been suggested to relate to seizure focus and surgical outcome. 10 , 11 Functional connectivity, however, has an inherent limitation in identifying causal interactions between brain regions. In this study, we investigated intratemporal effective connectivity by employing dynamic causal modeling (DCM). This framework is characterized by an explicit generative model that explains measured data via a biophysically plausible model of neuronal activity in a distributed neuronal network.

Although both seizure frequency and lateralization have influences on neuroimaging features as well as cognitive function, interactions between the 2 seizure factors have not yet been thoroughly evaluated. Knowledge about such an interactive effect of the 2 seizure factors remains lacking despite its potentially crucial clinical implications. Despite concurrence of the 2 seizure factors, changes in neuroimaging features and cognitive function have been mostly viewed with respect to each seizure factor, but not both. For a more complete understanding of the influence of the 2 seizure factors, interaction between them should be considered. Here, interaction means a 2‐way effect such that the effect of 2 factors is not simply additive and the effect of 1 seizure factor may be dependent on the status of the other seizure factor.

Effects of the seizure factors on cognitive function have been reported as well. In particular, due to progressive damage to the medial temporal lobe (MTL) in TLE and the material‐specific lateralization of the MTL for preferential involvement in verbal and nonverbal mnemonic processing, 5 , 6 seizure lateralization has been related to memory and language impairment. More severe deficiency of verbal memory is usually observed in TLE patients with the seizure focus in the left MTL. 7 Decline in language performance also occurs in those with the seizure focus in the left MTL, 8 , 9 specifically in association with typical left language dominance.

The progression of temporal lobe epilepsy (TLE) is related to epileptic seizure characteristics, typically seizure frequency and lateralization. 1 , 2 Effects of the seizure‐related factors on neuroimaging features have been observed. Although there is some debate, a relationship between seizure frequency and the progressive temporal and extratemporal atrophy 1 or functional connectivity 2 has been observed. In addition, a relationship between seizure lateralization and hippocampal volume 3 or structural connectivity 4 has been reported.

In addition, the relationship between connection strengths and cognitive test scores was analyzed for TLE patients, using Spearman rank correlation. To assess whether the relationship was affected by both seizure factors, we tested whether an association for 1 seizure factor was present irrespective of the status of the other seizure factor. For instance, if the significant association was shown in the GSC group as a whole, we also tested whether the association was significant in both or just one of its subgroups (GSC/LTLE or GSC/RTLE).

Having selected the best effective connectivity model, connection strengths were compared for each intrinsic and extrinsic connection between the healthy control group and each TLE subgroup, using 2‐sample t tests. Two‐way ANOVA was used to test for the interaction between the 2 seizure factors in TLE patients. Statistical significance was identified for P < 0.05 primarily when it passed FDR correction 25 for multiple comparisons and secondarily when P < 0.05 but it failed to pass multiple comparison correction.

In searching for the best model among the 64 models at the group level, we assumed that the best model was a random effects (RFX) model of the population, and thus we employed an RFX Bayesian model selection by taking the heterogeneity of the effective connectivity structure across the participants into consideration. The best model was selected separately for the healthy controls and TLE patients in terms of the exceedance probability 28 that quantifies belief about the likelihood of a particular model relative to other models.

In modeling intratemporal effective connectivity, with assumptions of intrinsic connections (self‐connections) for the 3 temporal regions and no exogenous inputs, we proposed 64 effective connectivity models that were determined depending on whether an extrinsic connection existed between each pair of temporal regions. Each model was inverted using spDCM to estimate model parameters and compute model evidence.

Intratemporal effective connectivity models were composed of the 3 regions corresponding to the Hipp, PHG, and Amyg in the MTL ipsilateral to the seizure focus (Figure 1 A). The boundary of each region was initially defined according to the parcellation provided in the automated anatomical labeling brain atlas, 26 and 100 voxels of the greatest temporal similarity were selected among all voxels within the boundary by computing intervoxel cross‐correlation of time series. 27 A representative time series of the region was extracted as the principal or first eigenvariate of the selected 100 voxels with their time series corrected for the confounds included in the GLM.

For time series of the preprocessed rsfMRI data, a general linear model (GLM) was constructed to adjust for the effect of head movement and nonneuronal fluctuations. The GLM contained nuisance regressors comprising 6 head movement parameters estimated during the preprocessing step, the mean cerebrospinal fluid signal, and the mean white matter signal, in addition to a discrete cosine transform basis set for high‐pass filtering at 1/128 Hz.

All analyses of rsfMRI data were performed in SPM12 ( http://www.fil.ion.ucl.ac.uk/spm/ ). Preprocessing consisted of spatial realignment to correct for head movement, normalization into the same coordinate frame as the template brain in Montreal Neurological Institute space, and spatial smoothing with a Gaussian kernel of 8 mm full width at half maximum. A nonlinear deformation field for spatial normalization was derived from the segmentation of the structural MRI volume coregistered to the mean of the realigned rsfMRI volumes.

MRI data were acquired using a 3‐T Achieva system (Philips, Best, The Netherlands). During the resting state, 150 volumes of rsfMRI data from each participant were collected using a T2*‐weighted gradient echo echo‐planar imaging sequence: repetition time = 2000 milliseconds, echo time = 32 milliseconds, voxel size = 1.67 mm × 1.67 mm × 5.00 mm, matrix size = 144 × 144, and slice number = 32. The participants were instructed to remain in a relaxed state with eyes closed, and to avoid movement and structural thinking. In addition, structural MRI data were obtained using a 3‐dimensional sensitivity encoding sequence: voxel size = 0.50 mm × 0.50 mm × 1.00 mm, matrix size = 448 × 448, and slice number = 160.

Verbal memory function was assessed using a Korean version of the California Verbal Learning Test (CVLT) 23 to measure immediate recall, delayed recall, and recognition of words. A total score over 5 learning trials was calculated as the sum of individual task scores and then adjusted for age and education level. Language function was assessed using a Korean version of the Boston Naming Test (BNT) 24 to measure retrieval of words. A total score for 60 items was adjusted for age and education level. Adjusted scores on each cognitive test were compared between the healthy control group and each TLE subgroup, using 2 sample t tests. Two‐way analysis of variance (ANOVA) was used to test for the interaction of the 2 seizure factors in their effect on the CVLT and BNT test scores in TLE patients. Statistical significance was identified primarily when a P ‐value was < .05 with false discovery rate (FDR) estimation 25 to correct for multiple comparisons and secondarily when a P ‐value was < .05 but failed to pass multiple comparison correction.

According to the Engel classification, patients were assigned to 1 of 4 classes based on their seizure control status or seizure frequency: class I, seizure‐free or auras only; class II, no more than twice per year; class III, no more than once per month; class IV, more than once per month. 22 The 33 patients in class I and class II were categorized as having good seizure control (GSC), and the other 15 patients in class III and class IV were categorized as having poor seizure control (PSC). The seizure frequency was estimated for 6 months before the time of rsfMRI based on the information from seizure diaries. The seizure lateralization of each patient was determined according to the combination of seizure semiology, epileptiform discharges on EEG, and MTL damage on MRI. Twenty‐six patients were categorized as left TLE (LTLE), and the other 22 patients were categorized as right TLE (RTLE). The patients were then divided into 4 subgroups based on seizure frequency and lateralization: GSC/LTLE, 17 patients; GSC/RTLE, 16 patients; PSC/LTLE, 9 patients; and PSC/RTLE, 6 patients. Detailed demographic and clinical variables including seizure characteristics and MRI findings are summarized for the 4 TLE subgroups in Table 1 .

Of patients who had visited Ewha Womans University Mogdong Hospital for long‐term video‐encephalographic (EEG) monitoring as well as structural MRI and rsfMRI between 2011 and 2016, we enrolled 48 patients with unilateral TLE (age = 41.38 ± 12.56 years, 31 females). The clinical diagnosis of TLE was made according to the criteria of the International League Against Epilepsy. 20 , 21 All patients had unilateral anterior temporal interictal spikes or unilateral temporal lobe seizure onset on EEG and MRI evidence of hippocampal sclerosis or atrophy. Patients who had contralateral or extratemporal epileptiform discharges on EEG, previous brain surgery, chronic medical illness with central nervous system involvement other than epilepsy, contraindication for MRI, or history of drug abuse or psychiatric illness other than axis I depressive disorders were excluded from the study. For the control group, 45 age‐ and gender‐matched healthy subjects (age = 41.18 ± 13.48 years, 23 females) were recruited. The healthy controls had no history of neurological disorders and no MRI abnormalities. Written informed consent was obtained from all participants, and the study protocol was approved by the local ethical committee at Ewha Medical Center.

For the Amyg to PHG and Hipp to Amyg connections, connection strengths were significantly correlated with BNT scores in the GSC group, but their association was different; lower BNT scores were associated with larger reduction in connection strength for the Amyg to PHG connection (ρ = .3756, uncorrected P = .0341), but larger increase in connection strength for the Hipp to Amyg connection (ρ = −.4698, uncorrected P = .0067). When we tested whether the association was similar in the GSC/LTLE and GSC/RTLE subgroups, lower BNT scores correlated with larger reduction in connection strength for the Amyg to PHG connection in the GSC/RTLE subgroup (ρ = .8018, FDR‐corrected P = .0003), but not in the GSC/LTLE subgroup. By contrast, lower BNT scores were associated with stronger connection strength for the Hipp to Amyg connection in the GSC/LTLE subgroup (ρ = −.5604, FDR‐corrected P = .0193), but not in the GSC/RTLE subgroup.

For the Hipp to PHG connection, connection strengths positively correlated with BNT scores (ρ = .5916, uncorrected P = .0202) as well as with CVLT scores (ρ = .5152, uncorrected P = .0494) in the PSC group (Figure 5 ). However, the association was not maintained in either the PSC/LTLE or PSC/RTLE subgroups for scores on any of the cognitive tests.

For the 4 TLE subgroups, compared to the healthy controls, the strength of the Hipp to PHG connection was higher in PSC/LTLE ( t 52 = −2.0480, uncorrected P = .0456) and PSC/RTLE ( t 49 = −2.1667, uncorrected P = .0351) patients, whereas the strength of the Amyg to PHG connection was higher in GSC/LTLE patients ( t 60 = −2.2499, uncorrected P = .0281; Figure 4 ). In the 2‐way ANOVA, there was an effect of seizure frequency on the strength of the Hipp to PHG connection ( F 1,44 = 8.4458, P = .0057), but no interaction between the 2 seizure factors was observed.

Compared to the healthy controls, connection strengths were not altered in either LTLE or RTLE patients for all intrinsic and extrinsic connections. But the strength of the Hipp to PHG connection was higher in the PSC group ( t 58 = −2.8374, FDR‐corrected P = .0063), whereas the strength of the Amyg to PHG connection was higher in the GSC group ( t 76 = −2.2380, uncorrected P = .0282; Figure 3 ). Moreover, the strength of the Hipp to PHG ( t 46 = −2.9365, FDR‐corrected P = .0052) and Amyg to PHG ( t 46 = 2.1424, uncorrected P = .0375) connections differed between the GSC and PSC groups.

Among the 64 effective connectivity models considered, the fully connected model, that is, the network having all possible extrinsic connections between the 3 temporal regions (Figure 1 B), was selected to be the best in both the healthy controls (exceedance probability = 0.9244) and TLE patients (exceedance probability = 0.8104). When the healthy controls and TLE patients were considered together, the best model was still the fully connected model, with an exceedance probability of 0.9794.

Compared to the healthy controls, BNT scores were lower in both GSC ( t 58 = 5.0400, FDR‐corrected P < .0001) and PSC ( t 41 = 6.4268, FDR‐corrected P < .0001) TLE patients, and in both LTLE ( t 52 = 7.5728, FDR‐corrected P < .0001) and RTLE ( t 47 = 3.5812, FDR‐corrected P = .0008) patients. In addition, BNT scores were lower in every TLE subgroup (Figure 2 ): GSC/LTLE ( t 43 = 6.3516, FDR‐corrected P < .0001), PSC/LTLE ( t 35 = 7.8600, FDR‐corrected P < .0001), GSC/RTLE ( t 41 = 3.1029, FDR‐corrected P = .0035), and PSC/RTLE ( t 32 = 3.2989, FDR‐corrected P = .0024). In the 2‐way ANOVA, an effect of seizure lateralization on BNT scores was observed ( F 1,43 = 6.5064, P = .0144), but neither an effect of seizure frequency nor interaction between the 2 seizure factors was seen (Table 1 ).

Comparison of scores on the California Verbal Learning Test (CVLT) and Boston Naming Test (BNT) between healthy controls and patients with temporal lobe epilepsy grouped by both seizure frequency and lateralization. Bars and error bars represent the mean and standard deviation, respectively, and asterisks indicate statistical significance. GSC, good seizure control; LTLE, left temporal lobe epilepsy; PSC, poor seizure control; RTLE, right temporal lobe epilepsy

Compared to the healthy controls, CVLT scores were lower in both GSC ( t 61 = 3.3240, FDR‐corrected P = .0015) and PSC ( t 43 = 3.3866, FDR‐corrected P = .0015) TLE patients, and in LTLE ( t 54 = 4.3928, FDR‐corrected P = .0001) but not in RTLE patients. For the 4 TLE subgroups, CVLT scores were lower in GSC/LTLE ( t 45 = 3.4304, FDR‐corrected P = .0013) and PSC/LTLE ( t 37 = 4.3062, FDR‐corrected P = .0001), but not in GSC/RTLE nor PSC/RTLE (Figure 2 ). In the 2‐way ANOVA, there was an effect of seizure lateralization on CVLT scores ( F 1,44 = 4.2527, P = .0451), but there was not an effect of seizure frequency, nor interaction between the 2 seizure factors (Table 1 ).

In TLE patients, age at epilepsy onset was 24.56 ± 12.98 years (range = 11‐57 years) and the epilepsy duration was 17.00 ± 12.74 years (range = 2‐57 years). All patients were on antiepileptic drug (AED) treatment, and the number of AEDs was related to seizure frequency ( P < .0001) because the TLE patients categorized as PSC were drug‐resistant but not related to seizure lateralization. Various AEDs were prescribed, including oxcarbazepine (600‐1800 mg/d), carbamazepine (600‐1600 mg/d), levetiracetam (1000‐3000 mg/d), lamotrigine (200‐400 mg/d), valproic acid (600‐2400 mg/d), topiramate (200‐400 mg/d), phenytoin (200‐300 mg/d), lacosamide (100‐200 mg/d), zonisamide (200‐300 mg/d), perampanel (6‐12 mg/d), and clonazepam (0.5‐1.0 mg/d). Detailed information about AED medications for the 4 TLE subgroups is provided in Table 1 .

4 DISCUSSION

Multiple seizure‐related clinical factors are believed to affect neuroimaging features and cognitive impairment. Here, we investigated possible interactive effects of 2 seizure factors, seizure frequency and lateralization, in TLE patients. Whereas impairment of verbal memory and language function was heavily influenced by seizure lateralization, alterations in effective connectivity between MTL regions was mainly affected by seizure frequency. Furthermore, the association between changes in connection strengths, specifically of the Amyg to PHG and Hipp to Amyg connections, and cognitive dysfunction, especially language impairment, was influenced by both seizure factors.

Clinically, multiple seizure factors need to be considered together in determining the severity of epilepsy29 with respect to interactions between the seizure factors. Most studies evaluate seizure frequency and lateralization as being important in TLE patients. Seizure frequency, despite its fluctuations in individual patients over even short time intervals,30 is often related to cognitive loss or outcome in TLE patients.31 Seizure lateralization has an influence on cognitive impairment, specifically material‐specific lateralization in TLE patients.7, 8 In this study, seizure frequency and lateralization revealed their interactive effect on alterations in neuroimaging features as well as in cognitive function.

Verbal memory and language function, as assessed with the CVLT and BNT, were impaired in TLE patients. Seizure lateralization affected cognitive impairment in such a way that decline particularly in verbal memory was more profound in LTLE than in RTLE, consistent with a previous report.7

As neuroimaging features, causal interactions between the Hipp, PHG, and Amyg ipsilateral to the seizure focus were assessed by effective connectivity modeling. Extensive anatomical connections between the Amyg and PHG32 and between the Hipp and Amyg33 have been revealed in tracer studies of macaque monkeys. Furthermore, effective connectivity between the Hipp and PHG,34 between the Amyg and PHG,35 and between the Hipp and Amyg36 has been validated in human fMRI studies. In accordance with these findings, the fully connected model was the effective connectivity structure that best explained the observed complex cross spectral density as revealed by spDCM in the current study.

In the fully connected model, the strength of the Hipp to PHG and the Amyg to PHG connections was mainly affected by seizure frequency in TLE patients; connection strengths increased in the PSC group for the Hipp to PHG connection and in the GSC group for the Amyg to PHG connection. Previous task‐related fMRI studies demonstrated that causal interactions between the MTL regions could be modulated by external perturbation such as memory context in the retrieval of memory.34, 36 Our findings in rsfMRI of no experimental manipulations suggest that alterations in connection strengths may have been driven by TLE‐related pathology, which appears to be primarily affected by seizure frequency.

Although alterations in intratemporal effective connectivity were predominantly associated with seizure frequency, the relationship between effective connectivity changes and cognitive impairment appears to be affected by both seizure factors; the effect of seizure frequency on the association was dependent on the status of seizure lateralization, demonstrating the interactive effect of the 2 seizure factors. Specifically, the strength of the Amyg to PHG and Hipp to Amyg connections was related to the level of language impairment in the GSC group, but the association was preserved only in its LTLE subgroup.

In this study, we only considered changes in effective connectivity between intratemporal regions, but extratemporal regions as well as temporal regions are affected via anatomical and functional connections.37 In particular, temporal structures contralateral to the seizure focus could be involved in reconstitution mechanisms underlying the preservation of comorbid cognitive function.38 In future investigations, a broader picture that includes effective connectivity structures involving extratemporal and contratemporal regions could provide a more complete understanding of the reorganization of effective connectivity in TLE.

Despite its notable findings, this study has limitations. First, the small number of participants could affect the detection of statistically significant findings, especially when considering the need for multiple comparisons in univariate approaches. We were mainly interested in effects of the 2 seizure factors on individual variables and thus relied on univariate approaches. On the other hand, multivariate approaches such as multivariate analysis of variance (MANOVA) can provide alternate views. The outcome of MANOVA showed that effects of the 2 seizure factors were similar even when considering multivariate variables as a whole (see Section 1 of Appendix S1 for details). Second, the at least partly uncontrolled quality of rsfMRI data may have influenced our findings as well. Signal loss and distortion are especially severe in brain regions adjacent to air, such as the temporal lobe.39 When we assessed the signal‐to‐noise ratio, the PHG showed the lowest signal quality among the 3 temporal regions, possibly due to its location (see Section 2 of Appendix S1 for details). In addition, highly anisotropic spatial resolution of rsfMRI data might have led to distorted signals possibly through problematic interpolations during the preprocessing steps. Third, there has been debate about the suitability of DCM specifically regarding model selection. However, we think that such issues have been generally answered and clarified via continued developments of DCM. In particular, combinatorial explosion in an exhaustive search for finding the best model40 seems not to be a severe problem in spDCM for rsfMRI, because a computationally efficient scheme can be applied for a model space with relatively tractable size. In addition, in estimating effective connectivity, we used default values in spDCM as priors on the parameters of the neuronal model, because they have been shown to work well for a variety of simulation scenarios.14 However, as variations in neuronal states may be induced by epilepsy, thorough investigation of a better selection of the priors warrants further study.

In conclusion, we have shown that cognitive impairment is mainly affected by seizure lateralization, whereas intratemporal effective connectivity is primarily influenced by seizure frequency in patients with TLE. Furthermore, we revealed that the association between effective connectivity changes and cognitive impairment is interactively affected by both seizure frequency and lateralization. Our findings support the idea that seizure frequency and lateralization have an interactive effect on effective connectivity alterations in MTL and cognitive abnormalities, especially verbal memory and language function in patients with TLE.