Altered intrinsic brain activity associated with outcome in frontal lobe epilepsy

Static and dynamic changes in amplitude of Low-Frequency fluctuations in patients with Self-Limited epilepsy with centrotemporal Spikes (SeLECTS): A Resting-State fMRI study

OBJECTIVE

This study aims to explore differences in the static and dynamic amplitude of low-frequency fluctuations (sALFF and dALFF) in resting-state functional MRI (rs-fMRI) data between patients with Benign childhood epilepsy with centrotemporal spikes (SeLECTS) and healthy controls (HCs).

MATERIALS AND METHODS

We recruited 45 patient with SeLECTS and 55 HCs, employing rs-fMRI to assess brain activity. The analysis utilized a two-sample t-test for primary comparisons, supplemented by stratification and matching based on clinical and demographic characteristics to ensure comparability between groups. Post hoc analyses assessed the relationships between sALFF/dALFF alterations and clinical demographics, incorporating statistical adjustments for potential confounders and performing sensitivity analysis to test the robustness of our findings.

RESULTS

Our analysis identified significant differences in sALFF and dALFF between patient with SeLECTS and HCs. Notably, increases in sALFF and dALFF were observed in the right middle temporal gyrus and left superior temporal gyrus among patient with SeLECTS, while a decrease in dALFF was seen in the right cerebellum crus 1. Additionally, a positive correlation was found between abnormal dALFF variability in specific brain regions and various clinical and demographic factors of patient with SeLECTS, with age being one such influential factor.

CONCLUSION

This investigation provides insights into the assessment of local brain activity in SeLECTS through both static and dynamic approaches. It highlights the significance of non-invasive neuroimaging techniques in understanding the complexities of epilepsy syndromes like SeLECTS and emphasizes the need to consider a range of clinical and demographic factors in neuroimaging studies of neurological disorders.

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.

Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study

BACKGROUND

Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy.

METHODS

Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN.

RESULTS

Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables.

CONCLUSIONS

Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.

Cognition of Time and Thinking Beyond

A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

Topological abnormality of structural covariance network in MRI-negative frontal lobe epilepsy

Background

Frontal lobe epilepsy (FLE) is the second most common type of focal epilepsy, however, imaging studies of FLE have been far less than Temporal lobe epilepsy (TLE) and the structural findings were not consistent in previous literature.

Object

Investigate the changes in cortical thickness in patients with FLE and the alteration of the structural covariance networks (SCNs) of cortical thickness with graph-theory.

Method

Thirty patients with FLE (18 males/12 females; 28.33 ± 11.81 years) and 27 demographically matched controls (15 males/12 females; 29.22 ± 9.73 years) were included in this study with high-resolution structural brain MRI scans. The cortical thickness was calculated, and structural covariance network (SCN) of cortical thickness were reconstructed using 68 × 68 matrix and analyzed with graph-theory approach.

Result

Cortical thickness was not significantly different between two groups, but path length and node betweenness were significantly increased in patients with FLE, and the regional network alterations were significantly changed in right precentral gyrus and right temporal pole (FDR corrected, p < 0.05). Comparing to HC group, network hubs were decreased and shifted away from frontal lobe.

Conclusion

The topological properties of cortical thickness covariance network were significantly altered in patients with FLE, even without obvious surface-based morphological damage. Graph-theory based SCN analysis may provide sensitive neuroanatomical biomarkers for FLE.

Brain function and network features in patients with chronic epilepsy before and after antiseizure medication withdrawal

OBJECTIVES

A considerable proportion of epilepsy patients who achieved long-term seizure freedom with standardized treatment of antiseizure medication will attempt to withdraw medications. Epilepsy is currently considered as a network disease, however, the characteristics of brain function and neural network before and after medication withdrawal remain to be discovered.

METHODS

Resting-state functional magnetic resonance imaging was obtained for 32 healthy controls, 32 seizure-free patients initiating medication tapering (PG1 group), and 16 seizure-free patients that had completely discontinued medications (PG2 group). Amplitude of low-frequency fluctuation and regional homogeneity were calculated to measure local functional activity. Global and nodal metrics of small-world network were calculated based on Graph theory. One-way analysis of variance was applied to analyze intergroup difference, withpost hoc analysis being conducted for each pair of groups.

RESULTS

Sex, age at scanning and other clinical variables showed no significant difference between groups. As compared to control, the amplitude of low-frequency fluctuation, regional homogeneity or nodal metrics of neural network in some brain areas were abnormal in the PG1 or PG2 group; when compared between patient groups, significant between-group differences were also found in the amplitude of low-frequency fluctuation, regional homogeneity or nodal metrics. But, the global metrics of neural network showed no differences among groups.

CONCLUSIONS

The global metrics of patients with long-term seizure freedom were normal either before or after antiseizure medication withdrawal, while the local functional activity and nodal metrics in some brain areas were abnormal and differed between before and after antiseizure medication withdrawal.

Distinct changes of brain cortical thickness relate to post-treatment outcomes in children with epilepsy

PURPOSE

In the current study, we examined the potential of neuroanatomic measures to cluster patients into different subgroups and established their clinical relevance to post-treatment outcomes.

METHODS

We included seventy-two children with epilepsy (aged 14-195 months) who were treated with anti-seizure medication alone and 39 healthy participants (aged 36-60 months). High-resolution T1-weighted imaging was performed for all participants, and brain cortical thickness measurements were obtained for 68 cortical regions for each of them. Amongst the patients, data-driven hierarchical cluster analysis was performed using the selected cortical thickness measures as features. The average thickness measures in each of the 68 brain regions were then compared between patient subgroups and healthy controls.

RESULTS

Two distinct patient subgroups were identified but were not related to the clinical types. Patients within subgroup 1 (n = 56) had a significantly higher rate of recurrent seizure than those in subgroup 2 (n = 16) (41.1% vs. 14.3%, p<0.05), while the follow-up time or medication did not differ between them. This finding was further confirmed by a recent follow-up through phone calls. The demographic variables, rate of electroencephalogram abnormalities, or sleep problems did not significantly differ between patient subgroups. Compared with healthy controls, patients in subgroup 1 showed significantly increased cortical thickness in the neocortex, whereas patients in subgroup 2 only showed regional cortical thinning in the right superior temporal gyrus.

CONCLUSION

These findings suggest the potential existence of distinct subgroups of children with epilepsy that were especially relevant to the differential patterns of post-treatment outcomes, with regional cortical thinning in the temporal regions relative to controls predicting lower risk of recurrent seizure.

Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study

Background:

Because about 30% of epileptic patients suffer from refractory epilepsy, an efficient automatic seizure prediction tool is in great demand to improve their life quality.

Methods:

In this work, time-domain discriminating preictal and interictal features were efficiently extracted from the intracranial electroencephalogram of twelve patients, i.e., six with temporal and six with frontal lobe epilepsy. The performance of three types of feature selection methods was compared using Matthews’s correlation coefficient (MCC).

Results:

Kruskal Wallis, a non-parametric approach, was found to perform better than the other approaches due to a simple and less resource consuming strategy as well as maintaining the highest MCC score. The impact of dividing the electroencephalogram signals into various sub-bands was investigated as well. The highest performance of Kruskal Wallis may suggest considering the importance of univariate features like complexity and interquartile ratio (IQR), along with autoregressive (AR) model parameters and the maximum (MAX) cross-correlation to efficiently predict epileptic seizures.

Conclusion:

The proposed approach has the potential to be implemented on a low power device by considering a few simple time domain characteristics for a specific sub-band. It should be noted that, as there is not a great deal of literature on frontal lobe epilepsy, the results of this work can be considered promising.

Abnormal blood oxygen level-dependent fluctuations and remote connectivity in sleep-related hypermotor epilepsy

OBJECTIVE

Sleep-related hypermotor epilepsy (SHE) is a form of the epileptic syndrome that involves stereotyped hypermotor seizures and presents as asymmetric tonic or dystonic posturing events. We aimed to investigate the brain activities of SHE patients using structural and functional magnetic resonance imaging (fMRI).

METHODS

A total of 41 patients with SHE and 41 age- and sex-matched healthy controls (HCs) were prospectively enrolled and assessed using fMRI. The two groups were compared in amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), and potential correlations between these measures and clinical features were also examined. The involvement of functional network integration was explored by analyzing seed-based functional connectivity.

RESULTS

In SHE patients, ALFF in the right precentral gyrus was significantly higher than in HCs, and ReHo in the left postcentral and right precentral gyrus was higher. None of the brain regions had lower ALFF or ReHo compared to HCs. ReHo in the left postcentral gyrus and ALFF in the right precentral gyrus were both negatively correlated with epilepsy duration. Patients with SHE had higher functional connectivity mainly in the precuneus, postcentral gyrus, and supplementary motor area. However, none of the brain regions in SHE group presented lower functional connectivity than in HCs. SHE is associated with disrupted regional and interregional functional activities.

CONCLUSIONS

The patients showed abnormalities within the sensorimotor gyrus and supplementary motor area, suggesting spontaneous fluctuations correlated with remote functional brain network. These results at the whole-brain level argue for further investigation into connectivity disturbance in SHE.

Intrinsic hippocampal and thalamic networks in temporal lobe epilepsy with hippocampal sclerosis according to drug response

PURPOSE

The aim of this study was to investigate whether intrinsic hippocampal or thalamic networks in patients with temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS) were different according to antiepileptic drug (AED) response.

METHODS

We enrolled 80 patients with TLE with HS and 40 healthy controls. Of the patients with TLE with HS, 43 were classified as a drug-resistant epilepsy (DRE) group, whereas 37 patients were enrolled as a drug-controlled epilepsy (DCE) group. We investigated the structural connectivity of the global brain, intrinsic hippocampal, and intrinsic thalamic networks based on structural volumes in the patients with DRE and DCE, and analyzed the differences between them.

RESULTS

There were significant alterations of the intrinsic hippocampal network compared with healthy controls. The average degree and the global efficiency were decreased, whereas the characteristic path length was increased in the patients with DRE compared with those in healthy controls. In the patients with DCE, only the small-worldness index was decreased compared with healthy controls. Compared to the patients with DCE, the mean clustering coefficient was increased in the patients with DRE.

CONCLUSION

We found that the intrinsic hippocampal network in patients with TLE with HS was different according to AED response. The patients with DRE had more severe disruptions of the intrinsic hippocampal network than those with DCE compared with healthy controls. These findings suggested that the hippocampal network might be related to AED response and could be a new biomarker of medical outcome in patients with TLE with HS.