Resting-state functional MRI of the default mode network in epilepsy

Why did my seizures start now? Influences of lesion connectivity and genetic etiology on age at seizure onset in focal epilepsy

OBJECTIVE

Patients with focal, lesional epilepsy present with seizures at variable ages. Larger lesion size and overlap with sensorimotor or default mode network (DMN) have been associated with younger age at seizure onset in cohorts with mixed types of focal cortical dysplasia (FCD). Here, we studied determinants of age at seizure onset in patients with bottom-of-sulcus dysplasia (BOSD), a discrete type of FCD with highly localized epileptogenicity.

METHODS

Eighty-four patients (77% operated) with BOSD were studied. Demographic, histopathologic, and genetic findings were recorded. BOSD volume and anatomical, primary versus association, rostral versus caudal, and functional network locations were determined. Normative functional connectivity analyses were performed using each BOSD as a region of interest in resting-state functional magnetic resonance imaging data of healthy children. Variables were correlated with age at seizure onset.

RESULTS

Median age at seizure onset was 5.4 (interquartile range = 2-7.9) years. Of 50 tested patients, 22 had somatic and nine had germline pathogenic mammalian target of rapamycin (mTOR) pathway variants. Younger age at seizure onset was associated with greater BOSD volume (p = .002), presence of a germline pathogenic variant (p = .04), DMN overlap (p = .04), and increased functional connectivity with the DMN (p < .05, false discovery rate corrected). Location within sensorimotor cortex and networks was not associated with younger age at seizure onset in our relatively small but homogenous cohort.

SIGNIFICANCE

Greater lesion size, pathogenic mTOR pathway germline variants, and DMN connectivity are associated with younger age at seizure onset in small FCD. Our findings strengthen the suggested role of DMN connectivity in the onset of FCD-related focal epilepsy and reveal novel contributions of genetic etiology.

Commonality and variance of resting-state networks in common marmoset brains

Animal models of brain function are critical for the study of human diseases and development of effective interventions. Resting-state network (RSN) analysis is a powerful tool for evaluating brain function and performing comparisons across animal species. Several studies have reported RSNs in the common marmoset (Callithrix jacchus; marmoset), a non-human primate. However, it is necessary to identify RSNs and evaluate commonality and inter-individual variance through analyses using a larger amount of data. In this study, we present marmoset RSNs detected using > 100,000 time-course image volumes of resting-state functional magnetic resonance imaging data with careful preprocessing. In addition, we extracted brain regions involved in the composition of these RSNs to understand the differences between humans and marmosets. We detected 16 RSNs in major marmosets, three of which were novel networks that have not been previously reported in marmosets. Since these RSNs possess the potential for use in the functional evaluation of neurodegenerative diseases, the data in this study will significantly contribute to the understanding of the functional effects of neurodegenerative diseases.

Functional brain connectivity in children with focal epilepsy: A systematic review of functional MRI studies

Epilepsy is increasingly recognised as a brain network disorder and many studies have investigated functional connectivity (FC) in children with epilepsy using functional MRI (fMRI). This systematic review of fMRI studies, published up to November 2023, investigated profiles of FC changes and their clinical relevance in children with focal epilepsy compared to healthy controls. A literature search in PubMed and Web of Science yielded 62 articles. We categorised the results into three groups: 1) differences in correlation-based FC between patients and controls; 2) differences in other FC measures between patients and controls; and 3) associations between FC and disease variables (for example, age of onset), cognitive and seizure outcomes. Studies revealed either increased or decreased FC across multiple brain regions in children with focal epilepsy. However, findings lacked consistency: conflicting FC alterations (decreased and increased FC) co-existed within or between brain regions across all focal epilepsy groups. The studies demonstrated overall that 1) interhemispheric connections often displayed abnormal connectivity and 2) connectivity within and between canonical functional networks was decreased, particularly for the default mode network. Focal epilepsy disrupted FC in children both locally (e.g., seizure-onset zones, or within-brain subnetworks) and globally (e.g., whole-brain network architecture). The wide variety of FC study methodologies limits clinical application of the results. Future research should employ longitudinal designs to understand the evolution of brain networks during the disease course and explore the potential of FC biomarkers for predicting cognitive and postsurgical seizure outcomes.

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.

Investigation of altered spontaneous brain activity in patients with bronchial asthma using the percent amplitude of fluctuation method: a resting-state functional MRI study

Purpose

To explore the regions of aberrant spontaneous brain activity in asthma patients and their potential impacts using the Percent amplitude of fluctuation (PerAF) analysis method.

Patients and methods

In this study, a total of 31 bronchial asthma (BA) patients were ultimately included, comprising 17 males and 14 females. Subsequently, 31 healthy control subjects (HCS) were recruited, consisting of 17 males and 14 females, and they were matched with the BA group based on age, sex, and educational status. The PerAF analysis technique was employed to study the differences in spontaneous brain activity between the two groups. The SPM12 toolkit was used to carry out a two sample t-test on the collected fMRI data, in order to examine the differences in PerAF values between the asthma patients and the healthy controls. We employed the Montreal Cognitive Assessment (MoCA) scale and the Hamilton Depression Scale (HAMD) to evaluate the cognitive and emotional states of the two groups. Pearson correlation analysis was utilized to ascertain the relationship between changes in the PerAF values within specific brain regions and cognitive as well as emotional conditions.

Results

Compared with the healthy control group, areas of the brain with reduced PerAF in asthma patients included the inferior cerebellum, fusiform gyrus, right inferior orbital frontal gyrus, left middle orbital frontal gyrus, left/right middle frontal gyrus (MFG), dorsal lateral superior frontal gyrus (SFGdl), left superior temporal gyrus (STG), precuneus, right inferior parietal lobule (IPL), and left/right angular gyrus. BA patients exhibit mild cognitive impairments and a propensity for emotional disturbances. Furthermore, the perAF values of the SFGdl region are significantly positively correlated with the results of the MoCA cognitive assessment, while negatively correlated with the HAMD evaluation.

Conclusion

Through the application of PerAF analysis methods, we discovered that several brain regions in asthma patients that control the amplitude of respiration, vision, memory, language, attention, and emotional control display abnormal changes in intrinsic brain activity. This helps characterize the neural mechanisms behind cognitive, sensory, and motor function impairments in asthma patients, providing valuable insights for potential therapeutic targets and disease management strategies.

Functional brain connectivity in young adults with post-stroke epilepsy

Approximately 1 in 10 young stroke patients (18-50 years) will develop post-stroke epilepsy, which is associated with cognitive impairment. While previous studies have shown altered brain connectivity in patients with epilepsy, little is however known about the changes in functional brain connectivity in young stroke patients with post-stroke epilepsy and their relationship with cognitive impairment. Therefore, we aimed to investigate whether young ischaemic stroke patients have altered functional networks and whether this alteration is related to cognitive impairment. We included 164 participants with a first-ever cerebral infarction at young age (18-50 years), along with 77 age- and sex-matched controls, from the Follow-Up of Transient Ischemic Attack and Stroke patients and Unelucidated Risk Factor Evaluation study. All participants underwent neuropsychological testing and resting-state functional MRI to generate functional connectivity networks. At follow-up (10.5 years after the index event), 23 participants developed post-stroke epilepsy. Graph theoretical analysis revealed functional network reorganization in participants with post-stroke epilepsy, in whom a weaker (i.e. network strength), less-integrated (i.e. global efficiency) and less-segregated (i.e. clustering coefficient and local efficiency) functional network was observed compared with the participants without post-stroke epilepsy group and the controls (P < 0.05). Regional analysis showed a trend towards decreased clustering coefficient, local efficiency and nodal efficiency in contralesional brain regions, including the caudal anterior cingulate cortex, posterior cingulate cortex, precuneus, superior frontal gyrus and insula in participants with post-stroke epilepsy compared with those without post-stroke epilepsy. Furthermore, participants with post-stroke epilepsy more often had impairment in the processing speed domain than the group without post-stroke epilepsy, in whom the network properties of the precuneus were positively associated with processing speed performance. Our findings suggest that post-stroke epilepsy is associated with functional reorganization of the brain network after stroke that is characterized by a weaker, less-integrated and less-segregated brain network in young ischaemic stroke patients compared with patients without post-stroke epilepsy. The contralesional brain regions, which are mostly considered as hub regions, might be particularly involved in the altered functional network and may contribute to cognitive impairment in post-stroke epilepsy patients. Overall, our findings provide additional evidence for a potential role of disrupted functional network as underlying pathophysiological mechanism for cognitive impairment in patients with post-stroke epilepsy.

Brain networks and epilepsy development in patients with Alzheimer disease

INTRODUCTION

This study aimed to investigate the association between brain networks and epilepsy development in patients with Alzheimer disease (AD).

METHODS

We enrolled patients newly diagnosed with AD at our hospital who underwent three-dimensional T1-weighted magnetic resonance imaging at the time of AD diagnosis and included healthy controls. We obtained the cortical, subcortical, and thalamic nuclei structural volumes using FreeSurfer and applied graph theory to obtain the global brain network and intrinsic thalamic network based on the structural volumes using BRAPH.

RESULTS

We enrolled 25 and 56 patients with AD with and without epilepsy development, respectively. We also included 45 healthy controls. The global brain network differed between the patients with AD and healthy controls. The local efficiency (2.026 vs. 3.185, p = .048) and mean clustering coefficient (0.449 vs. 1.321, p = .024) were lower, whereas the characteristic path length (0.449 vs. 1.321, p = .048) was higher in patients with AD than in healthy controls. Both global and intrinsic thalamic networks were significantly different between AD patients with and without epilepsy development. In the global brain network, local efficiency (1.340 vs. 2.401, p = .045), mean clustering coefficient (0.314 vs. 0.491, p = .045), average degree (27.442 vs. 41.173, p = .045), and assortative coefficient (-0.041 vs. -0.011, p = .045) were lower, whereas the characteristic path length (2.930 vs. 2.118, p = .045) was higher in patients with AD with epilepsy development than in those without. In the intrinsic thalamic network, the mean clustering coefficient (0.646 vs. 0.460, p = .048) was higher, whereas the characteristic path length (1.645 vs. 2.232, p = .048) was lower in patients with AD with epilepsy development than in those without.

CONCLUSION

We found that the global brain network differs between patients with AD and healthy controls. In addition, we demonstrated significant associations between brain networks (both global brain and intrinsic thalamic networks) and epilepsy development in patients with AD.

Functional alteration due to structural damage is network dependent: insight from multiple sclerosis

Little is known about how the brain's functional organization changes over time with respect to structural damage. Using multiple sclerosis as a model of structural damage, we assessed how much functional connectivity (FC) changed within and between preselected resting-state networks (RSNs) in 122 subjects (72 with multiple sclerosis and 50 healthy controls). We acquired the structural, diffusion, and functional MRI to compute functional connectomes and structural disconnectivity profiles. Change in FC was calculated by comparing each multiple sclerosis participant's pairwise FC to controls, while structural disruption (SD) was computed from abnormalities in diffusion MRI via the Network Modification tool. We used an ordinary least squares regression to predict the change in FC from SD for 9 common RSNs. We found clear differences in how RSNs functionally respond to structural damage, namely that higher-order networks were more likely to experience changes in FC in response to structural damage (default mode R2 = 0.160-0.207, P < 0.001) than lower-order sensory networks (visual network 1 R2 = 0.001-0.007, P = 0.157-0.387). Our findings suggest that functional adaptability to structural damage depends on how involved the affected network is in higher-order processing.

Altered topological organization of resting-state functional networks in children with infantile spasms

Covering neuroimaging evidence has demonstrated that epileptic symptoms are associated with the disrupted topological architecture of the brain network. Infantile spasms (IS) as an age-specific epileptic encephalopathy also showed abnormal structural or functional connectivity in specific brain regions or specific networks. However, little is known about the topological alterations of whole-brain functional networks in patients with IS. To fill this gap, we used the graph theoretical analysis to investigate the topological properties (whole-brain small-world property and modular interaction) in 17 patients with IS and 34 age- and gender-matched healthy controls. The functional networks in both groups showed efficient small-world architecture over the sparsity range from 0.05 to 0.4. While patients with IS showed abnormal global properties characterized by significantly decreased normalized clustering coefficient, normalized path length, small-worldness, local efficiency, and significantly increased global efficiency, implying a shift toward a randomized network. Modular analysis revealed decreased intra-modular connectivity within the default mode network (DMN) and fronto-parietal network but increased inter-modular connectivity between the cingulo-opercular network and occipital network. Moreover, the decreased intra-modular connectivity in DMN was significantly negatively correlated with seizure frequency. The inter-modular connectivity between the cingulo-opercular and occipital network also showed a significant correlation with epilepsy frequency. Together, the current study revealed the disrupted topological organization of the whole-brain functional network, which greatly advances our understanding of neuronal architecture in IS and may contribute to predict the prognosis of IS as disease biomarkers.

Altered dynamic functional network connectivity within default mode network of epileptic children with generalized tonic-clonic seizures

OBJECTIVE

Generalized tonic-clonic seizures (GTCS) is a group of epileptic disorders characterized by widespread generalized spike-and-waves discharges along with unresponsiveness and convulsions. Abnormal connectivity in the DMN is the common findings in children with generalized epilepsy. However, the neural mechanisms underlying the altered brain connectivity of DMN in children with GTCS remain unclear. The aim of the current study was to explore the temporal properties of functional connectivity states by dynamic functional connectivity (dFC) within the DMN of GTCS children.

METHODS

We collected resting-state functional MRI data from 22 GTCS children and 29 age-matched healthy controls. Sliding window approach and k-mean clustering analysis were applied to analyze the dFC and identify transient states of the DMN. Furthermore, the relationship between the dynamic properties and clinical features was assessed.

RESULTS

The dFC analyses identified two reoccurring states: a more frequent and weak connected state (State 1) and a less frequent and strong connected state (State 2). Relative to the normal control, GTCS children spent more time in State 1 showing weak connections and spent less time in State 2 showing strong connections. Dynamic functional network connectivity strength within the DMN showed both increase and decrease in patient group. In addition, the changes of dynamic metric were found to be correlated with epilepsy duration.

SIGNIFICANT

Our findings imply abnormal interactions and the state dynamics in DMN of the children with GTCS. These disruptions of temporal dynamic in DMN may provide significance for understanding the neural mechanism underlying the GTCS in children and suggest that dFC method can be considered as a valuable tool in children with epilepsy.

Deep learning resting state fMRI lateralization of temporal lobe epilepsy

OBJECTIVE

Localization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for non-invasive techniques to localize seizures for surgical decision-making. We investigate the use of deep learning using resting state functional MRI (RS-fMRI) to identify the hemisphere of seizure onset in temporal lobe epilepsy (TLE) patients.

METHODS

2132 healthy controls and 32 pre-operative TLE patients were studied. All participants underwent structural MRI and RS-fMRI. Healthy control data was used to generate training samples for a 3D convolutional neural network (3DCNN). RS-fMRI was synthetically altered in randomly lateralized regions in the healthy control participants. The model was then trained to classify the hemisphere containing synthetic noise. Finally, the model was tested on TLE patients to assess its performance for detecting biological seizure-onset zones, and gradient-weighted class activation mapping (Grad-CAM) identified the strongest predictive regions.

RESULTS

The 3DCNN classified healthy control hemispheres known to contain synthetic noise with 96% accuracy, and TLE hemispheres clinically identified to be seizure onset zones with 90.6% accuracy. Grad-CAM identified a range of temporal, frontal, parietal, and subcortical regions that were strong anatomical predictors of the seizure onset zone, while the resting state networks which colocalized with Grad-CAM results included default mode, medial temporal, and dorsal attention networks. Lastly, in an analysis of a subset of patients with post-surgical outcomes, the 3DCNN leveraged a more focal set of regions to achieve classification in patients with Engel class > 1 compared to Engel class 1.

SIGNIFICANCE

Non-invasive techniques capable of localizing the seizure-onset zone could improve pre-surgical planning in patients with intractable epilepsy. We have demonstrated the ability of deep learning to identify the correct hemisphere of the seizure onset zone in TLE patients using RS-fMRI with high accuracy. This approach represents a novel technique of seizure lateralization that could improve preoperative surgical planning.

Correlation Between the Functional Connectivity of Basal Forebrain Subregions and Vigilance Dysfunction in Temporal Lobe Epilepsy With and Without Focal to Bilateral Tonic-Clonic Seizure

PURPOSE

Previous research has shown that subcortical brain regions are related to vigilance in temporal lobe epilepsy (TLE). However, it is unknown whether alterations in the function and structure of basal forebrain (BF) subregions are associated with vigilance impairment in distinct kinds of TLE. We aimed to investigate changes in the structure and function BF subregions in TLE patients with and without focal to bilateral tonic-clonic seizures (FBTCS) and associated clinical features.

METHODS

A total of 50 TLE patients (25 without and 25 with FBTCS) and 25 healthy controls (HCs) were enrolled in this study. The structural and functional alterations of BF subregions in TLE were investigated using voxel-based morphometry (VBM) and resting-state functional connectivity (rsFC) analysis. Correlation analyses were utilized to investigate correlations between substantially altered imaging characteristics and clinical data from patients.

RESULTS

FBTCS patients had a lower rsFC between Ch1-3 and the bilateral striatum as well as the left cerebellum posterior lobe than non-FBTCS patients. In comparison to non-FBTCS patients, the rsFC between Ch4 and the bilateral amygdala was also lower in FBTCS patients. Compared to HCs, the TLE patients had reduced rsFC between the BF subregions and the cerebellum, striatum, default mode network, frontal lobe, and occipital lobes. In the FBTCS group, the rsFC between the left Ch1-3 and striatum was positive correlated with the vigilance measures. In the non-FBTCS group, the rsFC between the left Ch4 and striatum was significantly negative correlated with the alertness measure.

CONCLUSION

These results extend current understanding of the pathophysiology of impaired vigilance in TLE and imply that the BF subregions may serve as critical nodes for developing and categorizing TLE biomarkers.

Increased resting state connectivity in the anterior default mode network of idiopathic epileptic dogs

Epilepsy is one of the most common chronic, neurological diseases in humans and dogs and considered to be a network disease. In human epilepsy altered functional connectivity in different large-scale networks have been identified with functional resting state magnetic resonance imaging. Since large-scale resting state networks have been consistently identified in anesthetised dogs’ application of this technique became promising in canine epilepsy research. The aim of the present study was to investigate differences in large-scale resting state networks in epileptic dogs compared to healthy controls. Our hypothesis was, that large-scale networks differ between epileptic dogs and healthy control dogs. A group of 17 dogs (Border Collies and Greater Swiss Mountain Dogs) with idiopathic epilepsy was compared to 20 healthy control dogs under a standardized sevoflurane anaesthesia protocol. Group level independent component analysis with dimensionality of 20 components, dual regression and two-sample t test were performed and revealed significantly increased functional connectivity in the anterior default mode network of idiopathic epileptic dogs compared to healthy control dogs (p = 0.00060). This group level differences between epileptic dogs and healthy control dogs identified using a rather simple data driven approach could serve as a starting point for more advanced resting state network analysis in epileptic dogs.

The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches

A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.

Spatiotemporal distribution and age of seizure onset in a pediatric epilepsy surgery cohort with cortical dysplasia

OBJECTIVE

Focal Cortical Dysplasias (CD) are a common etiology of refractory pediatric epilepsy and are amenable to epilepsy surgery. We investigated the association of lesion volume and location to age of seizure onset among children with CD who underwent epilepsy surgery.

METHODS

A retrospective study of epilepsy surgery patients with pathologically-confirmed CD. Regions of interest (ROI) determined preoperative lesion volumes on 1.5 T and 3 T T2 and SPGR MRIs, and location in 7 distributed neural networks. Descriptive and inferential statistics were used.

RESULTS

Fifty-five patients were identified: 35 girls (56.5 %). Median age of seizure onset: 19.0 months (range 0.02 months - 16.0 years). Median age of surgery: 7.8 years (range 2.89 months - 24.45 years). CD were frontal (n = 21, 38 %); temporal (n = 15, 27 %); parietal (n = 10, 18 %); occipital (n = 3, 5%); multilobar (n = 6, 11 %). Frontal FCD had seizure onset < 1-year-old (P = 0.10); temporal lobe CD seizure onset was more likely > 5-years-old (P= 0.06). Median lesion volume for CD was 23.23 cm³ (range: 1.87-591.73 cm³). Larger CD lesions were associated with earlier epilepsy (P = 0.01, r = -0.16). We did not find that lesions proximal to early maturing cortical regions were associated with earlier seizure onset. We found an association with CD location in the default mode network (DMN) and age onset < 5years old (P = 0.03). Age of seizure onset was negatively correlated with percent of CD overlapping motor cortex (P = 0.001, r =-0.794) but not with CD overlap of the visual cortex (P = 0.35). There was no effect of CD type on age of epilepsy onset.

SIGNIFICANCE

Larger CD lesions are associated with earlier onset epilepsy. CD most commonly occurs within the DMN and Limbic network, and DMN is associated with seizure onset before 5-years-old. Percent of CD overlapping motor cortex correlates with earlier seizure onset. These observations may reflect patterns of brain maturation or regional differences in clinical expression of seizures.