The mean percentage of correct answers for the semantic decision was 81±12.0 (range 46 – 96), 70±11.7 (range 50 – 96) and 71 ± 11.2 (range 52–96) for healthy, LHE and RHE subjects, respectively. There were no significant effects of age on task performance.
shows the results of a random effects GLM analysis of each group performing the STDT task. For healthy controls (), activation was detected in the bilateral inferior frontal gyrus (IFG), left middle frontal gyrus, medial frontal gyrus and superior frontal gyrus, bilateral parahippocampal and angular gyri, left superior temporal gyrus, lingual gyrus, posterior cingulate and precuneus consistent with our prior report [this is similar to in (26
) and in (58
)]. represent the GLM maps of LHE and RHE patients. On visual inspection, these activation patterns do not appear to be substantially different from the healthy controls suggesting that the standard GLM approach to detecting group differences may be suboptimal (healthy controls have higher intensity of the BOLD signal response likely related to a larger number of subjects enrolled in this group). Further, a previous study utilizing the same dataset tested for the differences in language lateralization indices between patients with LHE and RHE and found a significant effect (26
). Again, while this effect is present here, it does not appear to be reflected in the GLM activation patterns depicted in .
Figure 1 Results of a random effects GLM analysis of (a) 49 healthy controls (b) 30 LHE patients (c) 28 RHE patients where semantic decision was contrasted with tone decision (corrected p < 0.001). Slice range is between Z = −20 and Z = +55 mm (more ...)
shows the ICA maps (networks) sub-serving the semantic decision task, selected based on a post hoc analysis of timecourses of all enrolled subjects; the corresponding average IC timecourses are displayed in . All IC maps were detected at least in 20 (out of 25) runs of the ICA decomposition assuring a high degree of reliability. We assume that these IC maps represent the underlying brain networks that subserve the SDTD task in both, controls and patients with epilepsy as discussed in detail elsewhere (1
). The brain areas encompassed in IC maps () relevant to semantic decision aspects of the SDTD task are tabulated in . The inter-subject variability was calculated using previously established methods and is presented in (column 4) (51
). In brief, this method captures the variability of each IC time course when fitted to an optimal time course representing group effects (e.g., underlying pathology or performance effects). Accordingly, for the semantic decision, the components shown in show considerable variability in the epilepsy group compared to the healthy-control subjects. Based on a previously proposed model for STDT task (1
), the network shown in pertains to semantic decision making while the networks shown in pertain to speech production and general attentive processes subserving the STDT task.
Independent component maps of the semantic decision task common to all included subjects (controls, LHE and RHE). Slice range is between Z = −29 and Z = +31 mm (Talairach coordinates). All images are in radiologic orientation.
Independent component time courses (average) for maps shown in .
Table 2 Column three shows the correlation coefficients of the average time courses presented in and (for components presented in and , respectively) of each of the components listed in with the (more ...)
The laterality index (LI) was calculated for all subjects using the previously described formula LI = ((L−R)/(L+R)) where L is the number of voxels in the left hemisphere and R is the number voxels in the right hemisphere with all IC maps scaled to unity variance (1
). As previously (46
), we chose hemispheric ROIs to minimize the contributions (influence) of epilepsy on LI calculations. Nevertheless, the overall left laterality associated with semantic decision is readily observed in this population even when the atypical contributions of epilepsy patients are taken into account.
Average IC timecourses of the semantic decision were correlated with the on–off task reference function resulting in correlation coefficients of |r| ≥ 0.22 for healthy controls. The inherent drawback of this method (even in the case of average IC timecourses) is the assumption of a fixed hemodynamic response function (HRF) for all three groups. To circumvent this problem, we implemented a more flexible Bayesian approach in which the HRF was allowed to vary (46
). The resulting optimal timecourse capturing maximum amount of group differences (or performance/age effects) in the BOLD signal was then tested for the task-relatedness with the on-off reference function. A significant correlation provides confidence that the observed effects are, in fact, task-related and can justify the use of a simple on-off task reference function in further characterization of IC timecourses (or brain networks). This approach can be identified with typical optimization algorithms in machine learning applications (59
). Based on this more flexible method, for the healthy-controls, only IC maps shown in resulted in a significant correlation coefficient (p < 0.05). Similarly, language networks in patients with LHE shown in and language networks in patients with RHE shown in were significantly correlated (p < 0.05) with the task reference function.
We also investigated how the performance in semantic decision is influencing the task-related behavior of networks shown in . The more flexible optimal time course approach capable of representing the maximum amount of performance-effects on BOLD signal revealed that the IC maps shown in had significant influence on the task-related behavior (p < 0.05). Previously, we used a similar approach to investigate developmental trajectories associated with a covert verb generation (46
). Interestingly, the IC map shown in (highly left lateralized network) did not show significant performance-related BOLD signal changes reemphasizing the fact that this network subserving semantic decision is the dominant one as discussed elsewhere (1
). The alternative approach (first method) in which one investigates how the task performance is related to the task-relatedness (correlation coefficient) of each IC time course did not yield any significant results.
The group differences in task-relatedness were investigated between the three groups using similar methods as described above. In the first approach, group differences were investigated in the task-relatedness based on correlation coefficients between IC timecourses and the on-off task reference function. When LHE patients were compared with healthy controls, significant differences were detected in both left lateralized networks as shown in ; the former was more active (task-related) in the LHE while the latter was more active in the healthy-controls. Similarly, in the RHE group, networks shown in were significantly less active when compared to the healthy controls. However, this method did not detect any statistically significant differences between the left and right epilepsy groups. This was a surprise as we anticipated significant group differences between the LHE and RHE groups due to the chronic nature of the epilepsy and the highly left lateralized nature of the SDTD task. A possible explanation for this finding may be our assumption of an invariant HRF for all three groups irrespective of the underlying pathology. While the alternative Bayesian approach detected significant task-related differences in several networks () between LHE and RHE groups, this method did not detect significant task-related differences between the epilepsy groups in the network shown in which is the main network subserving semantic decision (1
). The detected differences in networks shown in pertain to reasoning, semantic memory retrieval and general attentive control according to the hypothesized cognitive model for semantic decision discussed elsewhere (1
). The other differences are in the verbal encoding and mental imagery module () which are related to stored mental image activation as described in detail elsewhere (1
). Using the Bayesian approach we also examined task-related differences between healthy controls and LHE patients. Significant differences were detected in networks shown in . More significantly, we detected highly significant group differences in the task-relatedness of the main network () subserving semantic decision. As mentioned earlier, the anticipated group differences (in the temporal dynamics) between the healthy controls and LHE patients were detected in the highly left lateralized network of the SDTD task when analyzed using the Bayesian approach. The assumption of a variable HRF between the two groups effectively provided the necessary detection power to decipher the effects of chronic LHE on the main network () subserving semantic decision. In a similar way, this data-driven approach also detected significant differences between healthy-controls and RHE patients in networks shown in . The main findings in this comparison are the detected differences in the highly left lateralized networks as shown in . Thus, irrespective of the epilepsy focus, (in this case the right hemisphere) the chronic nature (or the underlying pathology) of this disease affects the main left lateralized networks subserving semantic decision as seen in healthy controls.
Additional IC maps and corresponding timecourses detected in patients with epilepsy are shown in and . These differences were detected based on the ICA timecourse analysis described in the methods section with few additional steps. In addition to the standard analyses, a separate ICA analysis of the LHE group detected the IC map shown in while the maps shown in were detected in the RHE group. This additional analysis provided further evidence to support our findings of different effects of LHE and RHE on language circuitry. Briefly, this complementary analysis investigated how the phase of the average Fourier component of each IC map (at the frequency) compared to the phase of the on–off task reference function (shifted by 3 seconds relative to the task itself to account for the hemodynamic delay) (60
). A paired t test revealed that the IC maps shown in are not significantly different between LHE (4a) and RHE (4b and c combined) patients when compared to healthy controls. This suggests that the nodes shown in correspond to the nodes shown in , the main module subserving semantic memory encoding and retrieval in healthy controls as described previously (1
). An additional analysis was performed to investigate any group differences in task-relatedness of these networks using the first method (i.e., correlating with the on-off task reference function). The task-relatedness of all three networks in the epilepsy group (both, left and right hemispheric epilepsy) did not differ when compared to healthy controls. Finally, the task-relatedness of these three nodes () did not differ significantly between the two epilepsy groups even after taking into account the 3 seconds shift as described above.
Figure 4 Independent component maps of semantic decision task specific to epilepsy patients; a – components specific to the LHE patients; b and c – components specific to the RHE patients. Slice range is between Z = −29 and Z = +31 mm (Talairach (more ...)
The corresponding average independent component time courses for maps shown in .
The Bayesian method detected significant differences in the task-relatedness between the LHE and the healthy-controls (). In evaluating the maximum likelihood reference time course, the null distribution for the slope found via the Monte Carlo simulation (repeatedly performing the algorithm on Gaussian noise) yielded a result of 2.77842 ± 0.183569 for the null distribution. As indicated above, this significant difference in the slope from the null distribution indicates a significant group difference effect in the task-relatedness of the network shown in between the LHE and the healthy-controls. In addition, the temporal differences were also task-related in the network shown in when calculated by correlating the optimal group difference reference function with the on–off task reference function (p < 0.05). Likewise, the RHE group showed significant task-related differences in all networks () when compared to the healthy-controls; the null distribution yielded a result of 2.87054±0.200004 for this contrast. Finally, when LHE () and RHE groups were compared (, combined), all three networks showed temporal differences [when compared with the null distribution (3.19832±0.208003)], while two networks shown in showed additional task-related behavior.