Brain functional connectivity in sleep-related hypermotor epilepsy

Emotion dysregulation, impulsivity and anger rumination in borderline personality disorder: the role of amygdala and insula

Borderline Personality Disorder (BPD) is a severe mental disorder, characterized by deficits in emotion regulation, interpersonal dysfunctions, dissociation and impulsivity. Brain abnormalities have been generally explored; however, the specific contribution of different limbic structures to BPD symptomatology is not described. The aim of this study is to cover this gap, exploring functional and structural alterations of amygdala and insula and to highlight their contribution to neuropsychiatric symptoms. Twenty-eight BPD patients (23.7 ± 3.42 years; 6 M/22F) and twenty-eight matched healthy controls underwent a brain MR protocol (1.5 T, including a 3D T1-weighted sequence and resting-state fMRI) and a complete neuropsychiatric assessment. Volumetry, cortical thickness and functional connectivity of amygdala and insula were evaluated, along with correlations with the neuropsychiatric scales. BPD patients showed a lower cortical thickness of the left insula (p = 0.027) that negatively correlated with the Anger Rumination Scale (p = 0.019; r = - 0.450). A focused analysis on female patients showed a significant reduction of right amygdala volumes in BPD (p = 0.037), that correlate with Difficulties in Emotion Regulation Scale (p = 0.031; r =  - 0.415), Beck Depression Inventory (p = 0.009; r =  - 0.50) and Ruminative Response Scale (p = 0.045; r =  - 0.389). Reduced functional connectivity was found in BPD between amygdala and frontal pole, precuneus and temporal pole. This functional connectivity alterations correlated with Anger Rumination Scale (p = .009; r = - 0.491) and Barratt Impulsiveness Scale (p = 0.020; r = - 0.447). Amygdala and insula are altered in BPD patients, and these two limbic structures are implicated in specific neuropsychiatric symptoms, such as difficulty in emotion regulation, depression, anger and depressive rumination.

A scoping review of the functional magnetic resonance imaging-based functional connectivity of focal cortical dysplasia-related epilepsy

Focal cortical dysplasia (FCD) is the most frequent etiology of operable pharmacoresistant epilepsy in children. There is burgeoning evidence that FCD-related epilepsy is a disorder that involves distributed brain networks. Functional magnetic resonance imaging (fMRI) is a tool that allows one to infer neuronal activity and to noninvasively map whole-brain functional networks. Despite its relatively widespread availability at most epilepsy centers, the clinical application of fMRI remains mostly task-based in epilepsy. Another approach is to map and characterize cortical functional networks of individuals using resting state fMRI (rsfMRI). The focus of this scoping review is to summarize the evidence to date of investigations of the network basis of FCD-related epilepsy, and to highlight numerous potential future applications of rsfMRI in the exploration of diagnostic and therapeutic strategies for FCD-related epilepsy. There are numerous studies demonstrating a global disruption of cortical functional networks in FCD-related epilepsy. The underlying pathological subtypes of FCD influence overall functional network patterns. There is evidence that cortical functional network mapping may help to predict postsurgical seizure outcomes, highlighting the translational potential of these findings. Additionally, several studies emphasize the important effect of FCD interaction with cortical networks and the expression of epilepsy and its comorbidities.

Cognitive and functional connectivity impairment in post-COVID-19 olfactory dysfunction

OBJECTIVES

To explore the neuropsychological profile and the integrity of the olfactory network in patients with COVID-19-related persistent olfactory dysfunction (OD).

METHODS

Patients with persistent COVID-19-related OD underwent olfactory assessment with Sniffin' Sticks and neuropsychological evaluation. Additionally, both patients and a control group underwent brain MRI, including T1-weighted and resting-state functional MRI (rs-fMRI) sequences on a 3 T scanner. Morphometrical properties were evaluated in olfaction-associated regions; the rs-fMRI data were analysed using graph theory at the whole-brain level and within a standard parcellation of the olfactory functional network. All the MR-derived quantities were compared between the two groups and their correlation with clinical scores in patients were explored.

RESULTS

We included 23 patients (mean age 37 ± 14 years, 12 females) with persistent (mean duration 11 ± 5 months, range 2-19 months) COVID-19-related OD (mean score 23.63 ± 5.32/48, hyposmia cut-off: 30.75) and 26 sex- and age-matched healthy controls. Applying population-derived cut-off values, the two cognitive domains mainly impaired were visuospatial memory and executive functions (17 % and 13 % of patients). Brain MRI did not show gross morphological abnormalities. The lateral orbital cortex, hippocampus, and amygdala volumes exhibited a reduction trend in patients, not significant after the correction for multiple comparisons. The olfactory bulb volumes did not differ between patients and controls. Graph analysis of the functional olfactory network showed altered global and local properties in the patients' group (n = 19, 4 excluded due to artifacts) compared to controls. Specifically, we detected a reduction in the global modularity coefficient, positively correlated with hyposmia severity, and an increase of the degree and strength of the right thalamus functional connections, negatively correlated with short-term verbal memory scores.

DISCUSSION

Patients with persistent COVID-19-related OD showed an altered olfactory network connectivity correlated with hyposmia severity and neuropsychological performance. No significant morphological alterations were found in patients compared with controls.

Abnormal brain functional network dynamics in sleep-related hypermotor epilepsy

AIMS

This study aimed to use resting-state functional magnetic resonance imaging (rs-fMRI) to determine the temporal features of functional connectivity states and changes in connectivity strength in sleep-related hypermotor epilepsy (SHE).

METHODS

High-resolution T1 and rs-fMRI scanning were performed on all the subjects. We used a sliding-window approach to construct a dynamic functional connectivity (dFC) network. The k-means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network-based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed.

RESULTS

After k-means clustering, the SHE patients mainly exhibited two dFC states. The frequency of state 1 was higher, which was characterized by stronger connections within the networks; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks. SHE patients had greater fractional time and a mean dwell time in state 2 and had a larger number of state transitions. The NBS results showed that SHE patients had increased connectivity strength between networks. None of the properties was correlated with illness duration among patients with SHE.

CONCLUSION

The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures.

Diurnal variations of resting-state fMRI data: A graph-based analysis

Circadian rhythms (lasting approximately 24 h) control and entrain various physiological processes, ranging from neural activity and hormone secretion to sleep cycles and eating habits. Several studies have shown that time of day (TOD) is associated with human cognition and brain functions. In this study, utilizing a chronotype-based paradigm, we applied a graph theory approach on resting-state functional MRI (rs-fMRI) data to compare whole-brain functional network topology between morning and evening sessions and between morning-type (MT) and evening-type (ET) participants. Sixty-two individuals (31 MT and 31 ET) underwent two fMRI sessions, approximately 1 hour (morning) and 10 h (evening) after their wake-up time, according to their declared habitual sleep-wake pattern on a regular working day. In the global analysis, the findings revealed the effect of TOD on functional connectivity (FC) patterns, including increased small-worldness, assortativity, and synchronization across the day. However, we identified no significant differences based on chronotype categories. The study of the modular structure of the brain at mesoscale showed that functional networks tended to be more integrated with one another in the evening session than in the morning session. Local/regional changes were affected by both factors (i.e., TOD and chronotype), mostly in areas associated with somatomotor, attention, frontoparietal, and default networks. Furthermore, connectivity and hub analyses revealed that the somatomotor, ventral attention, and visual networks covered the most highly connected areas in the morning and evening sessions: the latter two were more active in the morning sessions, and the first was identified as being more active in the evening. Finally, we performed a correlation analysis to determine whether global and nodal measures were associated with subjective assessments across participants. Collectively, these findings contribute to an increased understanding of diurnal fluctuations in resting brain activity and highlight the role of TOD in future studies on brain function and the design of fMRI experiments.

Transcriptional substrates underlying functional connectivity profiles of subregions within the human sensorimotor cortex

The human sensorimotor cortex has multiple subregions showing functional commonalities and differences, likely attributable to their connectivity profiles. However, the molecular substrates underlying such connectivity profiles are unclear. Here, transcriptome-neuroimaging spatial correlation analyses were performed between transcriptomic data from the Allen human brain atlas and resting-state functional connectivity (rsFC) of 24 fine-grained sensorimotor subregions from 793 healthy subjects. Results showed that rsFC of six sensorimotor subregions were associated with expression measures of six gene sets that were specifically expressed in brain tissue. These sensorimotor subregions could be classified into the polygenic- and oligogenic-modulated subregions, whose rsFC were related to gene sets diverging on their numbers (hundreds vs. dozens) and functional characteristics. First, the former were specifically expressed in multiple types of neurons and immune cells, yet the latter were not specifically expressed in any cortical cell types. Second, the former were preferentially expressed during the middle and late stages of cortical development, while the latter showed no preferential expression during any stages. Third, the former were prone to be enriched for general biological functions and pathways, but the latter for specialized biological functions and pathways. Fourth, the former were enriched for neuropsychiatric disorders, whereas this enrichment was absent for the latter. Finally, although the identified genes were commonly associated with sensorimotor behavioral processes, the polygenic-modulated subregions associated genes were additionally related to vision and dementia. These findings may advance our understanding of the functional homogeneity and heterogeneity of the human sensorimotor cortex from the perspective of underlying genetic architecture.

Path Analysis: A Method to Estimate Altered Pathways in Time-varying Graphs of Neuroimaging Data

Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.

Disorders of arousal and sleep-related hypermotor epilepsy - overview and challenges night is a battlefield of sleep and arousal promoting forces

Arousability and reactivity to sensory stimuli are essential features of sleep, discriminating it from coma and keeping the sleeper in contact with the environment. Arousals and oscillations during sleep serve the reversibility of sleep and carry an alarm function awakening the sleeper in danger. In this review, we will explore mechanisms and circuits involved in arousal intrusions within the sleep texture, focusing on the significance of these phenomena in two sleep-related conditions: NREM sleep parasomnias and sleep-related hypermotor epilepsy. Knowledges and gaps in the field are discussed.

Sleep in Neurologic Disorders

Sleep is a complex brain state with fundamental relevance for cognitive functions, synaptic plasticity, brain resilience, and autonomic balance. Sleep pathologies may interfere with cerebral circuit organization, leading to negative consequences and favoring the development of neurologic disorders. Conversely, the latter can interfere with sleep functions. Accordingly, assessment of sleep quality is always recommended in the diagnosis of patients with neurologic disorders and during neurorehabilitation programs. This review investigates the complex interplay between sleep and brain pathologies, focusing on diseases in which the association with sleep disturbances is commonly overlooked and whereby major benefits may derive from their proper management.

Mechanisms of Drug Resistance in the Pathogenesis of Epilepsy: Role of Neuroinflammation. A Literature Review

Epilepsy is a chronic neurological disorder characterized by recurring spontaneous seizures. Drug resistance appears in 30% of patients and it can lead to premature death, brain damage or a reduced quality of life. The purpose of the study was to analyze the drug resistance mechanisms, especially neuroinflammation, in the epileptogenesis. The information bases of biomedical literature Scopus, PubMed, Google Scholar and SciVerse were used. To obtain full-text documents, electronic resources of PubMed Central and Research Gate were used. The article examines the recent research of the mechanisms of drug resistance in epilepsy and discusses the hypotheses of drug resistance development (genetic, epigenetic, target hypothesis, etc.). Drug-resistant epilepsy is associated with neuroinflammatory, autoimmune and neurodegenerative processes. Neuroinflammation causes immune, pathophysiological, biochemical and psychological consequences. Focal or systemic unregulated inflammatory processes lead to the formation of aberrant neural connections and hyperexcitable neural networks. Inflammatory mediators affect the endothelium of cerebral vessels, destroy contacts between endothelial cells and induce abnormal angiogenesis (the formation of “leaky” vessels), thereby affecting the blood–brain barrier permeability. Thus, the analysis of pro-inflammatory and other components of epileptogenesis can contribute to the further development of the therapeutic treatment of drug-resistant epilepsy.

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.

Altered intrinsic brain activity associated with outcome in frontal lobe epilepsy

Frontal lobe epilepsy (FLE) is the second most common type of the focal epilepsies. Our understanding of this disease has been revolutionized over the past decade, but variable treatment outcomes persist and the underlying functional mechanisms responsible for this have yet to be deciphered. This study was designed to determine how intrinsic brain connectivity related to treatment response in patients with FLE. 50 patients with FLE and 28 healthy controls were enrolled in this study and underwent functional MRI at baseline. At the end of 12-month follow up period, all patients with FLE were classified, based on their responses to AEDs treatment, into drug-responsive and drug-refractory groups. The amplitude of low-frequency fluctuation (ALFF) was calculated amongst the three groups in order to detect regional neural function integration. The responsive group showed decreased ALFF only in the left ventromedial prefrontal cortex (vmPFC), while the refractory group showed decreased ALFF in the left vmPFC, right superior frontal gyrus (SFG), and supramarginal gyrus (SMG) relative to healthy controls. In addition, both the responsive and refractory groups showed increased ALFF in the precuneus and postcentral gyrus when compared to the healthy controls. Furthermore, the refractory group exhibited significantly decreased ALFF in the left vmPFC, right SFG and SMG, relative to the responsive group. Focal spontaneous activity, as assessed by ALFF, was associated with response to antiepileptic treatment in patients with FLE. Patients with refractory frontal lobe epilepsy exhibited decreased intrinsic brain activity. Our findings provide novel neuroimaging evidence into the mechanisms of medically-intractable FLE at the brain level.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Resting-state connectivity alterations during transient global amnesia

While the pathophysiology of transient global amnesia (TGA) is not understood, due to the specific nature of the clinical deficits, transient dysfunction in the medial temporal lobe, especially in the hippocampus, is assumed; however, concomitant disturbances in other brain regions and in executive function have been postulated. In this study, a cohort of 16 patients was prospectively recruited from the emergency department for resting-state functional MRI (fMRI) during the acute stage of TGA, as confirmed by a standardized neuropsychological assessment. Twenty age- and sex-matched controls, as well as twenty patients with a history of TGA, were recruited for comparison. Functional data were processed using independent component analysis (ICA), allowing the complete automatic (data-driven) identification of spontaneous network dynamics. We documented a severe disturbance in anterograde episodic long-term memory in all patients. Group-based ICA of resting-state data in acute TGA patients versus that of controls and patients with a past TGA episode demonstrated reduced FC mainly of structures belonging to the executive network (EN), but also the hippocampus, confirming its pathophysiological involvement in the disorder, as well as areas belonging to the salience network and other subcortical regions. No significant differences were found when comparing connectivity in patients with a history of TGA and controls. Our findings strengthen previous empirical and theoretical accounts of hippocampal and executive dysfunction in TGA. The disruption of frontal, parietal and insular control regions, together with disruption in the hippocampus, provides a new interpretation for the pathophysiology and neuropsychological profile of this neurological disorder on a large-scale network level

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During TGA connectivity is reduced in areas within and outside the executive network, including the hippocampus.

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Relevant hubs within the salience network and subcortical regions are also involved.

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The acute stage of TGA is interpreted on a large-scale network level.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.