Alterations of brain network hubs in reflex syncope: Evidence from a graph theoretical analysis based on DTI

Structural connectivity as a predictive factor for perampanel response in patients with epilepsy

OBJECTIVES

This study aimed to identify clinical characteristics that could predict the response to perampanel (PER) and determine whether structural connectivity is a predictive factor.

METHODS

We enrolled patients with epilepsy who received PER and were followed-up for a minimum of 12 months. Good PER responders, who were seizure-free or presented with more than 50 % seizure reduction, were classified separately from poor PER responders who had seizure reduction of less than 50 % or non-responders. A graph theoretical analysis was conducted based on diffusion tensor imaging to calculate network measures of structural connectivity among patients with epilepsy.

RESULTS

106 patients with epilepsy were enrolled, including 26 good PER responders and 80 poor PER responders. Good PER responders used fewer anti-seizure medications before PER administration compared to those by poor PER responders (3 vs. 4; p = 0.042). Early PER treatment was more common in good PER responders than poor PER responders (46.2 vs. 21.3 %, p = 0.014). Regarding cortical structural connectivity, the global efficiency was higher and characteristic path length was lower in good PER responders than in poor PER responders (0.647 vs. 0.635, p = 0.006; 1.726 vs. 1,759, p = 0.008, respectively). For subcortical structural connectivity, the mean clustering coefficient and small-worldness index were higher in good PER responders than in poor PER responders (0.821 vs. 0.791, p = 0.009; 0.597 vs. 0.560, p = 0.009, respectively).

CONCLUSION

This study demonstrated that early PER administration can predict a good PER response in patients with epilepsy, and structural connectivity could potentially offer clinical utility in predicting PER response.

Brain networks in migraine with and without aura: An exploratory arterial spin labeling MRI study

OBJECTIVES

The aim of this exploratory study was to investigate the underlying pathomechanisms of migraine with aura (MA) and migraine without aura (MO) in the interictal phase using a connectivity analysis.

METHODS

We prospectively enrolled patients who were newly diagnosed with migraine. All patients underwent brain MRI, including diffusion tensor imaging and arterial spin labeling perfusion MRI. We analyzed the differences between patients with MA and those with MO in structural connectivity based on diffusion tensor imaging and functional connectivity based on arterial spin labeling perfusion MRI using a graph theoretical analysis.

RESULTS

We enrolled 58 patients with migraine (11 patients with MA and 47 patients with MO). There were no differences between patients with MA and those with MO in the network measures of global structural connectivity. However, differences in global functional connectivity were found between the two groups. The assortative coefficient was lower in patients with MA than in those with MO (-0.050 vs. -0.012, p = .017). There were no differences in local structural and functional connectivity between patients with MA and those with MO.

CONCLUSION

We found differences in global functional connectivity between patients with MO and those with MA. The study of MA and MO using a connectivity analysis may shed light on migraine pathophysiology. We suggest it is worthwhile to investigate if changes in functional connectivity may serve as novel biomarkers in MA. In this regard, ASL MRI appears to be valuable in the context of network analysis, but further studies are needed to confirm our findings.

Network differences based on arterial spin labeling related to anti-seizure medication response in focal epilepsy

PURPOSE

The aim of this study was to determine whether anti-seizure medication (ASM) response is associated with structural connectivity in diffusion tensor imaging (DTI) or functional co-variance network in arterial spin labeling (ASL) magnetic resonance imaging (MRI) in patients with focal epilepsy.

METHODS

In this retrospective study conducted at a tertiary hospital, we enrolled 105 patients with focal epilepsy, of which 64 patients were good ASM responders, and 41 patients were poor ASM responders. All patients showed normal MRI findings on visual inspection and underwent DTI and ASL MRI from August 2018 to July 2020, with regular follow-up for at least 12 months after epilepsy diagnosis while taking ASMs. We calculated the structural connectivity based on DTI and functional co-variance network based on ASL MRI by using graph theory and analyzed their differences in relation to the ASM response.

RESULTS

No differences were observed in structural connectivity between the good and poor ASM responders. However, significant differences were observed in functional co-variance network between the good and poor ASM responders. In comparison with good ASM responders, poor ASM responders showed a significantly greater characteristic path length (2.557 vs. 1.753, p = 0.034) and a lower local efficiency (2.311 vs. 3.927, p = 0.048).

CONCLUSION

Significant differences were observed in functional co-variance network based on ASL MRI between the good and poor ASM responders. These findings suggest that functional co-variance network could serve as a new biomarker of ASM response in focal epilepsy.

Application of machine learning analysis based on diffusion tensor imaging to identify REM sleep behavior disorder

PURPOSE

We evaluated the feasibility of machine learning analysis using diffusion tensor imaging (DTI) parameters to identify patients with idiopathic rapid eye movement (REM) sleep behavior disorder (RBD). We hypothesized that patients with idiopathic RBD could be identified via machine learning analysis based on DTI.

METHODS

We enrolled 20 patients with newly diagnosed idiopathic RBD at a tertiary hospital. We also included 20 healthy subjects as a control group. All of the subjects underwent DTI. We obtained the conventional DTI parameters and structural connectomic profiles from the DTI. We investigated the differences in conventional DTI measures and structural connectomic profiles between patients with idiopathic RBD and healthy controls. We then used machine learning analysis using a support vector machine (SVM) algorithm to identify patients with idiopathic RBD using conventional DTI and structural connectomic profiles.

RESULTS

Several regions showed significant differences in conventional DTI measures and structural connectomic profiles between patients with idiopathic RBD and healthy controls. The SVM classifier based on conventional DTI measures revealed an accuracy of 87.5% and an area under the curve of 0.900 to identify patients with idiopathic RBD. Another SVM classifier based on structural connectomic profiles yielded an accuracy of 75.0% and an area under the curve of 0.833.

CONCLUSION

Our findings demonstrate the feasibility of machine learning analysis based on DTI to identify patients with idiopathic RBD. The conventional DTI parameters might be more important than the structural connectomic profiles in identifying patients with idiopathic RBD.

Temporal lobe epilepsy with or without hippocampal sclerosis: Structural and functional connectivity using advanced MRI techniques

BACKGROUND AND PURPOSE

The aim of this study was to investigate the differences in structural connectivity based on diffusion tensor imaging (DTI) and functional connectivity based on arterial spin labeling (ASL) MRI between temporal lobe epilepsy (TLE) patients with and without hippocampal sclerosis (HS).

METHODS

We enrolled 50 patients with TLE, including 25 patients with HS and 25 patients without HS, who underwent brain MRI, including DTI and ASL. We calculated the network parameters of structural connectivity based on DTI and functional connectivity based on ASL using a graph theoretical analysis. The parameters included global network measures (radius, diameter, characteristic path length, global efficiency, local efficiency, mean clustering coefficient, transitivity, assortative coefficient, and small-worldness index) and a local network measure (betweenness centrality).

RESULTS

The global and local network measures of structural connectivity were not different between TLE patients with and without HS. However, significant differences in functional connectivity existed between the two groups. The radius and diameter of the global network measures in the TLE patients with HS were significantly increased compared with those without HS (4.140 vs. 3.140, p = 0.045; 6.812 vs. 5.132, p = 0.049; respectively). No differences were detected between other global network measures of functional connectivity and local network measure.

CONCLUSIONS

Significant differences in global network measures of functional connectivity based on ASL existed between TLE patients with and without HS. These findings suggest that TLE patients with HS exhibit a more disconnected functional brain network than those without HS.

Alterations of brain network hubs in reflex syncope: Evidence from a graph theoretical analysis based on DTI

OBJECTIVE

We evaluated global topology and organization of regional hubs in the brain networks and microstructural abnormalities in the white matter of patients with reflex syncope.

METHODS

Twenty patients with reflex syncope and thirty healthy subjects were recruited, and they underwent diffusion tensor imaging (DTI) scans. Graph theory was applied to obtain network measures based on extracted DTI data, using DSI Studio. We then investigated differences in the network measures between the patients with reflex syncope and the healthy subjects. We also analyzed microstructural abnormalities of white matter using tract-based spatial statistics analysis (TBSS).

RESULTS

Measures of global topology were not different between patients with reflex syncope and healthy subjects. However, in reflex syncope patients, the strength measures of the right angular, left inferior frontal, left middle orbitofrontal, left superior medial frontal, and left middle temporal gyrus were lower than in healthy subjects. The betweenness centrality measures of the left middle orbitofrontal, left fusiform, and left lingual gyrus in patients were lower than those in healthy subjects. The PageRank centrality measures of the right angular, left middle orbitofrontal, and left superior medial frontal gyrus in patients were lower than those in healthy subjects. Regarding the analysis of the white matter microstructure, there were no differences in the fractional anisotropy and mean diffusivity values between the two groups.

CONCLUSIONS

We have identified a reorganization of network hubs in the brain network of patients with reflex syncope. These alterations in brain network may play a role in the pathophysiologic mechanism underlying reflex syncope.