The Brain Activity in Brodmann Area 17: A Potential Bio-Marker to Predict Patient Responses to Antiepileptic Drugs

Aberrant functional connectivity associated with drug response in patients with newly diagnosed epilepsy

OBJECTIVE

To analyze the local functional activity and connectivity features of the brain associated with drug response inpatients newly diagnosed with epilepsy (NDE) who are naïve to anti-seizure medication (ASM).

METHODS

Recruited patients, underwent functional magnetic resonance imaging at baseline, and were assigned to the well-controlled (WC, n = 28) or uncontrolled (UC, n = 11) groups based on their response to ASM. Healthy participants were included in the control group (HC, n = 29). The amplitudes of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were used to measure local functional activity, and voxel-wise degree centrality (DC) and seed-based functional connectivity (FC) were used to evaluate the connecting intensity of the brain areas.

RESULTS

Compared to the HC and WC groups, the UC group had higher ALFF values in the left posterior central gyrus (PoCG.L) and left inferior temporal gyrus (ITG.L) and higher DC in the bilateral PoCG (Gaussian random field correction, voxel-level P < 0.001, and cluster-level P < 0.05). Both PoCG and ITG.L in the UC group showed stronger FC with multiple brain regions, mainly located in the occipital and temporal lobes, compared to the HC or WC group, while the WC group showed decreased or similar FC compared to the HC group.

INTERPRETATION

Excessive enhancement of brain functional activity or connecting intensity in ASM-naïve patients with NDE may be associated with a higher risk of poor drug response.

Can we predict drug response by functional connectivity in patients with juvenile myoclonic epilepsy?

OBJECTIVES

We investigated functional connectivity based on EEG using graph theoretical analysis in patients with newly diagnosed juvenile myoclonic epilepsy (JME), and whether it could play a role as a biomarker predicting antiepileptic drug (AED) response.

METHODS

We consecutively enrolled 38 patients with JME and 40 normal controls. The initial EEG was undertaken at the time of diagnosis of JME in a drug-naïve state. The second EEG was done after at least 12 months from the time of the initial EEG. We classified the patients with JME into two groups according to AED response at the time of taking the second EEG. We investigated functional connectivity based on graph theoretical analysis using connectivity measures of the coherence and phase locking value.

RESULTS

In the analysis of functional connectivity using coherence as a connectivity measure, the global efficiency and local efficiency in the AED poor responders (N = 4) decreased, whereas the small-worldness index increased. In the analysis of functional connectivity using phase locking value as a connectivity measure, the global efficiency and local efficiency in the AED poor responders decreased. However, in the AED good responders (N = 34), none of the network measures were different from those in healthy controls.

CONCLUSIONS

We newly found that there were significant differences of functional connectivity based on initial EEG according to AED response in the patients with JME. This suggests that brain connectivity could play a role as a new biomarker predicting AED response in patients with JME.

Predicting the antiepileptic drug response by brain connectivity in newly diagnosed focal epilepsy

OBJECTIVE

Growing evidence has suggested that epilepsy is a disease with alterations in brain connectivity. The aim of this study was to investigate whether the changes in brain connectivity can predict the response to an antiepileptic drug (AED) in patients with a newly diagnosed focal epilepsy of unknown etiology.

METHODS

This observational study was independently performed at two tertiary hospitals (Group A and B). Thirty-eight patients with newly diagnosed focal epilepsy of unknown etiology were enrolled in Group A and 46 patients in Group B. We divided these patients into two groups according to their seizure control after AED treatment: AED good and poor responders. We defined the AED good responders as those in whom had seizure free for at least the last 6 months while AED poor responders who were not. All of the subjects underwent diffusion tensor imaging, and graph theoretical analysis was applied to reveal the brain connectivity. We investigated the difference in the clinical characteristics and network measurements between the two groups.

RESULTS

Of the network measures, the assortativity coefficient in the AED good responders was significantly higher than that in the AED poor responders in both Groups A and B (- 0.0239 vs. - 0.0473, p = 0.0110 in Group A; 0.0173 vs. - 0.0180, p = 0.0024 in Group B). The Kaplan-Meier survival analysis revealed that the time to failure to retain the first AED was significantly longer in the patients with assortative networks (assortativity coefficient > 0) than in those with disassortative networks (assortativity coefficient < 0) in Group B.

CONCLUSION

We demonstrated that the assortativity coefficient differed between patients with newly diagnosed focal epilepsy of unknown etiology according to their AED responses, which suggests that the changes in brain connectivity could be a biomarker for predicting the responses to AED.

Can we predict drug response by volumes of the corpus callosum in newly diagnosed focal epilepsy?

OBJECTIVE

The aim of this study was to investigate whether volumes of the corpus callosum could predict a response to antiepileptic drugs in patients with newly diagnosed focal epilepsy.

METHODS

Fifty-three patients with newly diagnosed focal epilepsy of unknown etiology and healthy subjects were enrolled in this study. First, we analyzed the differences in the volumes of the corpus callosum between patients with epilepsy and healthy subjects. Second, we divided patients with epilepsy into antiepileptic drug responders and drug nonresponders groups, according to their seizure controls, and evaluated the differences in the volumes of the corpus callosum between the groups. Third, we conducted correlation analyses between the volumes of the corpus callosum and mean diffusion measures in healthy subjects.

RESULTS

The volumes of the corpus callosum in patients with epilepsy were significantly lower than those in normal controls (p = .0001). Among epilepsy patients, the volumes of the corpus callosum were significantly lower in antiepileptic drug responders compared with nonresponders (p = .0481), which was the only independent variable for predicting antiepileptic drug response (OR = 10.07, p = .0434). In addition, we found that the volumes of the corpus callosum were significantly correlated with the mean diffusion measures (fractional anisotropy, r = .408, p = .0027; mean diffusivity, r = -0.403, p = .0028) in normal controls.

CONCLUSIONS

We demonstrated that the volumes of the corpus callosum were different according to antiepileptic drug responses in patients with newly diagnosed focal epilepsy, which might suggest that the volumes of the corpus callosum could be a new biomarker for predicting responses to antiepileptic drugs.