The COVID-19 pandemic, psychologists' professional quality of life and mental health.

Background: Psychologists are at known risk of work-related stress, secondary trauma, and burnout. The COVID-19 pandemic increased stress and anxiety for communities worldwide and corresponded with an increased demand for mental health services. Our study investigated the impact of COVID-19 on psychologists' professional quality of life, psychological symptoms, and work-related stress in Aotearoa, New Zealand (NZ). Method: Ninety-nine registered psychologists were recruited via NZ professional psychology organizations, representing 3% of the total workforce. Survey data collected included symptoms of compassion fatigue and satisfaction, psychological symptoms, COVID-19-related stress and resilience, and professional and personal circumstances during the third year of the pandemic, 2022. Results: Seventy percent reported that their work stress had increased, and 60% reported that their caseload intensity had increased during the COVID-19 pandemic. Psychologists reported receiving little to no additional personal or professional support, while 55% reported increased personal responsibilities during the pandemic (for example, closed childcare and schools during lockdowns). High rates of compassion fatigue (burnout and secondary traumatic stress) and low resilience were reported. We observed that psychological distress was higher than the community averages before the pandemic and comparable with frontline healthcare professionals. Compassion fatigue was associated with COVID-related stress, psychological distress, years in practice, and more frequent supervision, but not with working with at-risk clients, levels of personal support, or having children at home. Despite these difficulties, high Compassion Satisfaction scores were also reported, with over 90% indicating they had no intention of leaving the profession in the foreseeable future. Conclusion: Psychologists' compassion fatigue appears to have worsened during the COVID-19 pandemic, as have their symptoms of psychological distress. Increased workplace and clinical demands, telehealth difficulties, stress relating to the pandemic, inadequate support, and increased personal responsibilities were reported by psychologists. Mental health workforces are not immune to the personal and professional impacts of crises and are at risk of burnout and secondary traumatic stress. We hope that increased awareness and understanding of psychologists' own difficulties during COVID-19 can be used to better tackle future crises and support mental health professionals.

Specialization of amygdala subregions in emotion processing.

The amygdala is important for human fear processing. However, recent research has failed to reveal specificity, with evidence that the amygdala also responds to other emotions. A more nuanced understanding of the amygdala's role in emotion processing, particularly relating to fear, is needed given the importance of effective emotional functioning for everyday function and mental health. We studied 86 healthy participants (44 females), aged 18-49 (mean 26.12 ± 6.6) years, who underwent multiband functional magnetic resonance imaging. We specifically examined the reactivity of four amygdala subregions (using regions of interest analysis) and related brain connectivity networks (using generalized psycho-physiological interaction) to fear, angry, and happy facial stimuli using an emotional face-matching task. All amygdala subregions responded to all stimuli (p-FDR < .05), with this reactivity strongly driven by the superficial and centromedial amygdala (p-FDR < .001). Yet amygdala subregions selectively showed strong functional connectivity with other occipitotemporal and inferior frontal brain regions with particular sensitivity to fear recognition and strongly driven by the basolateral amygdala (p-FDR < .05). These findings suggest that amygdala specialization to fear may not be reflected in its local activity but in its connectivity with other brain regions within a specific face-processing network.

Resting-state amygdala subregion and precuneus connectivity provide evidence for a dimensional approach to studying social anxiety disorder.

Social anxiety disorder (SAD) is a prevalent and disabling mental health condition, characterized by excessive fear and anxiety in social situations. Resting-state functional magnetic resonance imaging (fMRI) paradigms have been increasingly used to understand the neurobiological underpinnings of SAD in the absence of threat-related stimuli. Previous studies have primarily focused on the role of the amygdala in SAD. However, the amygdala consists of functionally and structurally distinct subregions, and recent studies have highlighted the importance of investigating the role of these subregions independently. Using multiband fMRI, we analyzed resting-state data from 135 participants (42 SAD, 93 healthy controls). By employing voxel-wise permutation testing, we examined group differences of fMRI connectivity and associations between fMRI connectivity and social anxiety symptoms to further investigate the classification of SAD as a categorical or dimensional construct. Seed-to-whole brain functional connectivity analysis using multiple 'seeds' including the amygdala and its subregions and the precuneus, revealed no statistically significant group differences. However, social anxiety severity was significantly negatively correlated with functional connectivity of the precuneus - perigenual anterior cingulate cortex and positively correlated with functional connectivity of the amygdala (specifically the superficial subregion) - parietal/cerebellar areas. Our findings demonstrate clear links between symptomatology and brain connectivity in the absence of diagnostic differences, with evidence of amygdala subregion-specific alterations. The observed brain-symptom associations did not include disturbances in the brain's fear circuitry (i.e., disturbances in connectivity between amygdala - prefrontal regions) likely due to the absence of threat-related stimuli.

Exploring individual fixel-based white matter abnormalities in epilepsy.

Diffusion MRI has provided insight into the widespread structural connectivity changes that characterize epilepsies. Although syndrome-specific white matter abnormalities have been demonstrated, studies to date have predominantly relied on statistical comparisons between patient and control groups. For diffusion MRI techniques to be of clinical value, they should be able to detect white matter microstructural changes in individual patients. In this study, we apply an individualized approach to a technique known as fixel-based analysis, to examine fibre-tract-specific abnormalities in individuals with epilepsy. We explore the potential clinical value of this individualized fixel-based approach in epilepsy patients with differing syndromic diagnoses. Diffusion MRI data from 90 neurologically healthy control participants and 10 patients with epilepsy (temporal lobe epilepsy, progressive myoclonus epilepsy, and Dravet Syndrome, malformations of cortical development) were included in this study. Measures of fibre density and cross-section were extracted for all participants across brain white matter fixels, and mean values were computed within select tracts-of-interest. Scanner harmonized and normalized data were then used to compute Z-scores for individual patients with epilepsy. White matter abnormalities were observed in distinct patterns in individual patients with epilepsy, both at the tract and fixel level. For patients with specific epilepsy syndromes, the detected white matter abnormalities were in line with expected syndrome-specific clinical phenotypes. In patients with lesional epilepsies (e.g. hippocampal sclerosis, periventricular nodular heterotopia, and bottom-of-sulcus dysplasia), white matter abnormalities were spatially concordant with lesion location. This proof-of-principle study demonstrates the clinical potential of translating advanced diffusion MRI methodology to individual-patient-level use in epilepsy. This technique could be useful both in aiding diagnosis of specific epilepsy syndromes, and in localizing structural abnormalities, and is readily amenable to other neurological disorders. We have included code and data for this study so that individualized white matter changes can be explored robustly in larger cohorts in future work.

Altered structural connectome of children with auditory processing disorder: a diffusion MRI study.

Auditory processing disorder (APD) is a listening impairment that some school-aged children may experience despite having normal peripheral hearing. Recent resting-state functional magnetic resonance imaging (MRI) has revealed an alteration in regional functional brain topology in children with APD. However, little is known about the structural organization in APD. We used diffusion MRI data to investigate the structural connectome of 58 children from 8 to 14 years old diagnosed with APD (n = 29) and children without hearing complaints (healthy controls, HC; n = 29). We investigated the rich-club organization and structural connection differences between groups. The APD group showed similar rich-club organization and edge-wise connection compared with the HC group. However, at the regional level, we observed increased average path length (APL) and betweenness centrality in the right inferior parietal lobule and inferior precentral gyrus, respectively, in the APD group. Only HCs demonstrated a positive association between APL and the listening-in-spatialized-noise-sentences task in the left orbital gyrus. In line with previous findings, the current results provide evidence for altered structural networks at the regional level in the APD group, suggesting the involvement of multimodal deficits and a role for structure-function alteration in the listening difficulties of children with APD.

mHealth Technologies for Managing Problematic Pornography Use: Content Analysis.

BACKGROUND: Several mobile apps are currently available that purportedly help with managing pornography addiction. However, the utility of these apps is unclear, given the lack of literature on the effectiveness of mobile health solutions for problematic pornography use. Little is also known about the content, structure, and features of these apps. OBJECTIVE: This study aims to characterize the purpose, content, and popularity of mobile apps that claim to manage pornography addiction. METHODS: The phrase "pornography addiction" was entered as a search term in the app stores of the two major mobile phone platforms (Android and iOS). App features were categorized according to a coding scheme that contained 16 categories. Apps were included in the analysis if they were described as helpful for reducing pornography use, and data were extracted from the store descriptions of the apps. Metrics such as number of user ratings, mean rating score, and number of installations were analyzed on a per-feature basis. RESULTS: In total, 170 apps from both app stores met the inclusion criteria. The five most common and popular features, both in terms of number of apps with each feature and minimum possible number of installations, were the ability to track the time since last relapse (apps with feature=72/170, 42.4%; minimum possible number of installations=6,388,000), tutorials and coaching (apps with feature=63/170, 37.1%; minimum possible number of installations=9,286,505), access to accountability partners or communities (apps with feature=51/170, 30%; minimum possible number of installations=5,544,500), content blocking or content monitoring (apps with feature=46/170, 27.1%; minimum possible number of installations=17,883,000), and a reward system for progress (apps with feature=34/170, 20%; minimum possible number of installations=4,425,300). Of these features, content-blocking apps had the highest minimum possible number of installations. Content blocking was also the most detected feature combination in a combinatorial analysis (with 28 apps having only this feature), but it also had the lowest mean consumer satisfaction rating (4.04) and second-lowest median rating (4.00) out of 5 stars. None of the apps reviewed contained references to literature that provided direct evidence for the app's efficacy or safety. CONCLUSIONS: There are several apps with the potential to provide low- or zero-cost real-time interventions for people struggling to manage problematic pornography use. Popular app features include blockers of pornographic content, behavior monitoring, and tutorials that instruct users how to eliminate pornography use. However, there is currently no empirical evidence to support the effectiveness and safety of these apps. Further research is required to be able to provide recommendations about which apps (and app features) are safe for public consumption.

Altered brain network topology in children with auditory processing disorder: A resting-state multi-echo fMRI study.

Children with auditory processing disorder (APD) experience hearing difficulties, particularly in the presence of competing sounds, despite having normal audiograms. There is considerable debate on whether APD symptoms originate from bottom-up (e.g., auditory sensory processing) and/or top-down processing (e.g., cognitive, language, memory). A related issue is that little is known about whether functional brain network topology is altered in APD. Therefore, we used resting-state functional magnetic resonance imaging data to investigate the functional brain network organization of 57 children from 8 to 14 years old, diagnosed with APD (n = 28) and without hearing difficulties (healthy control, HC; n = 29). We applied complex network analysis using graph theory to assess the whole-brain integration and segregation of functional networks and brain hub architecture. Our results showed children with APD and HC have similar global network properties -i.e., an average of all brain regions- and modular organization. Still, the APD group showed different hub architecture in default mode-ventral attention, somatomotor and frontoparietal-dorsal attention modules. At the nodal level -i.e., single-brain regions-, we observed decreased participation coefficient (PC - a measure quantifying the diversity of between-network connectivity) in auditory cortical regions in APD, including bilateral superior temporal gyrus and left middle temporal gyrus. Beyond auditory regions, PC was also decreased in APD in bilateral posterior temporo-occipital cortices, left intraparietal sulcus, and right posterior insular cortex. Correlation analysis suggested a positive association between PC in the left parahippocampal gyrus and the listening-in-spatialized-noise -sentences task where APD children were engaged in auditory perception. In conclusion, our findings provide evidence of altered brain network organization in children with APD, specific to auditory networks, and shed new light on the neural systems underlying children's listening difficulties.

White matter abnormalities characterize the acute stage of sports-related mild traumatic brain injury.

Sports-related concussion, a form of mild traumatic brain injury, is characterized by transient disturbances of brain function. There is increasing evidence that functional brain changes may be driven by subtle abnormalities in white matter microstructure, and diffusion MRI has been instrumental in demonstrating these white matter abnormalities in vivo. However, the reported location and direction of the observed white matter changes in mild traumatic brain injury are variable, likely attributable to the inherent limitations of the white matter models used. This cross-sectional study applies an advanced and robust technique known as fixel-based analysis to investigate fibre tract-specific abnormalities in professional Australian Football League players with a recent mild traumatic brain injury. We used the fixel-based analysis framework to identify common abnormalities found in specific fibre tracts in participants with an acute injury (≤12 days after injury; n = 14). We then assessed whether similar changes exist in subacute injury (>12 days and <3 months after injury; n = 15). The control group was 29 neurologically healthy control participants. We assessed microstructural differences in fibre density and fibre bundle morphology and performed whole-brain fixel-based analysis to compare groups. Subsequent tract-of-interest analyses were performed within five selected white matter tracts to investigate the relationship between the observed tract-specific abnormalities and days since injury and the relationship between these tract-specific changes with cognitive abnormalities. Our whole-brain analyses revealed significant increases in fibre density and bundle cross-section in the acute mild traumatic brain injury group when compared with controls. The acute mild traumatic brain injury group showed even more extensive differences when compared with the subacute injury group than with controls. The fibre structures affected in acute concussion included the corpus callosum, left prefrontal and left parahippocampal white matter. The fibre density and cross-sectional increases were independent of time since injury in the acute injury group, and were not associated with cognitive deficits. Overall, this study demonstrates that acute mild traumatic brain injury is characterized by specific white matter abnormalities, which are compatible with tract-specific cytotoxic oedema. These potential oedematous changes were absent in our subacute mild traumatic brain injury participants, suggesting that they may normalize within 12 days after injury, although subtle abnormalities may persist in the subacute stage. Future longitudinal studies are needed to elucidate individualized recovery after brain injury.

Wearable OPM-MEG: A changing landscape for epilepsy.

Magnetoencephalography with optically pumped magnometers (OPM-MEG) is an emerging and novel, cost-effective wearable system that can simultaneously record neuronal activity with high temporal resolution ("when" neuronal activity occurs) and spatial resolution ("where" neuronal activity occurs). This paper will first outline recent methodological advances in OPM-MEG compared to conventional superconducting quantum interference device (SQUID)-MEG before discussing how OPM-MEG can become a valuable and noninvasive clinical support tool in epilepsy surgery evaluation. Although OPM-MEG and SQUID-MEG share similar data features, OPM-MEG is a wearable design that fits children and adults, and it is also robust to head motion within a magnetically shielded room. This means that OPM-MEG can potentially extend the application of MEG into the neurobiology of severe childhood epilepsies with intellectual disabilities (e.g., epileptic encephalopathies) without sedation. It is worth noting that most OPM-MEG sensors are heated, which may become an issue with large OPM sensor arrays (OPM-MEG currently has fewer sensors than SQUID-MEG). Future implementation of triaxial sensors may alleviate the need for large OPM sensor arrays. OPM-MEG designs allowing both awake and sleep recording are essential for potential long-term epilepsy monitoring.

EEG signatures change during unilateral Yogi nasal breathing.

Airflow through the left-and-right nostrils is said to be entrained by an endogenous nasal cycle paced by both poles of the hypothalamus. Yogic practices suggest, and scientific evidence demonstrates, that right-nostril breathing is involved with relatively higher sympathetic activity (arousal states), while left-nostril breathing is associated with a relatively more parasympathetic activity (stress alleviating state). The objective of this study was to further explore this laterality by controlling nasal airflow and observing patterns of cortical activity through encephalographic (EEG) recordings. Thirty subjects participated in this crossover study. The experimental session consisted of a resting phase (baseline), then a period of unilateral nostril breathing (UNB) using the dominant nasal airway, followed by UNB using the non-dominant nasal airway. A 64-channel EEG was recorded throughout the whole session. The effects of nostril-dominance, and nostril-lateralization were assessed using the power spectral density of the neural activity. The differences in power-spectra and source localization were calculated between EEG recorded during UNB and baseline for delta, theta, alpha, beta and gamma bands. Cluster-based permutation tests showed that compared to baseline, EEG spectral power was significantly (1) decreased in all frequency bands for non-dominant nostril UNB, (2) decreased in alpha, beta and gamma bands for dominant nostril UNB, (3) decreased in all bands for left nostril UNB, and (4) decreased in all bands except delta for right nostril UNB. The beta band showed the most widely distributed changes across the scalp. our source localisation results show that breathing with the dominant nostril breathing increases EEG power in the left inferior frontal (alpha band) and left parietal lobule (beta band), whereas non-dominant nostril breathing is related to more diffuse and bilateral effects in posterior areas of the brain.These preliminary findings may stimulate further research in the area, with potential applications to tailored treatment of brain disorders associated with disruption of sympathetic and parasympathetic activity.

Resting-state neuroimaging in social anxiety disorder: a systematic review.

There has been a growing interest in resting-state brain alterations in people with social anxiety disorder. However, the evidence has been mixed and contested and further understanding of the neurobiology of this disorder may aid in informing methods to increase diagnostic accuracy and treatment targets. With this systematic review, we aimed to synthesize the findings of the neuroimaging literature on resting-state functional activity and connectivity in social anxiety disorder, and to summarize associations between brain and social anxiety symptoms to further characterize the neurobiology of the disorder. We systematically searched seven databases for empirical research studies. Thirty-five studies met the inclusion criteria, with a total of 1611 participants (795 people with social anxiety disorder and 816 controls). Studies involving resting-state seed-based functional connectivity analyses were the most common. Individuals with social anxiety disorder (vs. controls) displayed both higher and lower connectivity between frontal-amygdala and frontal-parietal regions. Frontal regions were the most consistently implicated across other analysis methods, and most associated with social anxiety symptoms. Small sample sizes and variation in the types of analyses used across studies may have contributed to the inconsistencies in the findings of this review. This review provides novel insights into established neurobiological models of social anxiety disorder and provides an update on what is known about the neurobiology of this disorder in the absence of any overt tasks (i.e., resting state). The knowledge gained from this body of research enabled us to also provide recommendations for a more standardized imaging pre-processing approach to examine resting-state brain activity and connectivity that could help advance knowledge in this field. We believe this is warranted to take the next step toward clinical translation in social anxiety disorder that may lead to better treatment outcomes by informing the identification of neurobiological targets for treatment.

Automatic detection of generalized paroxysmal fast activity in interictal EEG using time-frequency analysis.

OBJECTIVE: Markup of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG markup is a time-consuming, subjective, and the specialized task where the human reviewer needs to visually inspect a large amount of data to facilitate accurate clinical decisions. In this study, we aimed to develop a framework for automated detection of generalized paroxysmal fast activity (GPFA), a generalized IED seen in scalp EEG recordings of patients with the severe epilepsy of Lennox-Gastaut syndrome (LGS). METHODS: We studied 13 children with LGS who had GPFA events in their interictal EEG recordings. Time-frequency information derived from manually marked IEDs across multiple EEG channels was used to automatically detect similar events in each patient's interictal EEG. We validated true positives and false positives of the proposed spike detection approach using both standalone scalp EEG and simultaneous EEG-functional MRI (EEG-fMRI) recordings. RESULTS: GPFA events displayed a consistent low-high frequency arrangement in the time-frequency domain. This 'bimodal' spectral feature was most prominent over frontal EEG channels. Our automatic detection approach using this feature identified EEG events with similar time-frequency properties to the manually marked GPFAs. Brain maps of EEG-fMRI signal change during these automatically detected IEDs were comparable to the EEG-fMRI brain maps derived from manual IED markup. CONCLUSION: GPFA events have a characteristic bimodal time-frequency feature that can be automatically detected from scalp EEG recordings in patients with LGS. The validity of this time-frequency feature is demonstrated by EEG-fMRI analysis of automatically detected events, which recapitulates the brain maps we have previously shown to underlie generalized IEDs in LGS. SIGNIFICANCE: This study provides a novel methodology that enables a fast, automated, and objective inspection of generalized IEDs in LGS. The proposed framework may be extendable to a wider range of epilepsy syndromes in which monitoring the burden of epileptic activity can aid clinical decision-making and faster assessment of treatment response and estimation of future seizure risk.

Intracranial brain stimulation modulates fMRI-based network switching.

The extent to which functional MRI (fMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous electrical stimulation (es-fMRI) and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced after intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is likely increased in epilepsy, we hypothesised that intracranial stimulation would reduce the brain's switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved es-fMRI connectivity. Network switching and synchrony was decreased after the first brain stimulation, followed by a more consistent pattern of network switching over time. This change was commonly observed in cortical networks and adjacent to the electrode targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in epilepsy.

Temporal complexity of fMRI is reproducible and correlates with higher order cognition.

It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.

Artificial intelligence for clinical decision support in neurology.

Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-level clinical decision tools across healthcare settings. Despite the clinical promise of artificial intelligence, machine and deep-learning algorithms are not a one-size-fits-all solution for all types of clinical data and questions. In this article, we provide an overview of the core concepts of artificial intelligence, particularly contemporary deep-learning methods, to give clinician and neuroscience researchers an appreciation of how artificial intelligence can be harnessed to support clinical decisions. We clarify and emphasize the data quality and the human expertise needed to build robust clinical artificial intelligence models in neurology. As artificial intelligence is a rapidly evolving field, we take the opportunity to iterate important ethical principles to guide the field of medicine is it moves into an artificial intelligence enhanced future.

Reducing the influence of intramodular connectivity in participation coefficient.

Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coefficient (PC) is typically used to measure this attribute, although its value also depends on the size and connectedness of the module it belongs to and may lead to nonintuitive identification of highly connected nodes. Here, we develop a normalized PC that reduces the influence of intramodular connectivity compared with the conventional PC. Using brain, C. elegans, airport, and simulated networks, we show that our measure of participation is not influenced by the size or connectedness of modules, while preserving conceptual and mathematical properties, of the classic formulation of PC. Unlike the conventional PC, we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules.

Human GABRG2 generalized epilepsy: Increased somatosensory and striatothalamic connectivity.

OBJECTIVE: To map functional MRI (fMRI) connectivity within and between the somatosensory cortex, putamen, and ventral thalamus in individuals from a family with a GABAergic deficit segregating with febrile seizures and genetic generalized epilepsy. METHODS: We studied 5 adults from a family with early-onset absence epilepsy and/or febrile seizures and a GABAA receptor subunit gamma2 pathogenic variant (GABRG2[R43Q]) vs 5 age-matched controls. We infer differences between participants with the GABRG2 pathogenic variant and controls in resting-state fMRI connectivity within and between the somatosensory cortex, putamen, and ventral thalamus. RESULTS: We observed increased fMRI connectivity within the somatosensory cortex and between the putamen and ventral thalamus in all individuals with the GABRG2 pathogenic variant compared with controls. Post hoc analysis showed less pronounced changes in fMRI connectivity within and between the primary visual cortex and precuneus. CONCLUSIONS: Although our sample size was small, this preliminary study suggests that individuals with a GABRG2 pathogenic variant, raising risk of febrile seizures and generalized epilepsy, display underlying increased functional connectivity both within the somatosensory cortex and in striatothalamic networks. This human network model aligns with rodent research and should be further validated in larger cohorts, including other individuals with generalized epilepsy with and without known GABA pathogenic variants.

Genetic absence epilepsy: Effective connectivity from piriform cortex to mediodorsal thalamus.

OBJECTIVE: The objective of the study was to quantify effective connectivity from the piriform cortex to mediodorsal thalamus, in Genetic Absence Epilepsy Rats from Strasbourg (GAERS). METHODS: Local field potentials (LFPs) were recorded using microelectrode arrays implanted in the mediodorsal thalamus and piriform cortex, in three urethane anesthetized GAERS and three control rats. Screw electrodes were placed in the primary motor cortex to identify epileptiform discharges. We used transfer entropy to measure effective connectivity from piriform cortex to mediodorsal thalamus prior to and during generalized epileptiform discharges. RESULTS: We observed increased theta band effective connectivity from piriform cortex to mediodorsal thalamus, prior to and during epileptiform discharges in GAERS compared with controls. Increased effective connectivity was also observed in beta and gamma bands from the piriform cortex to mediodorsal thalamus, but only during epileptiform discharges. CONCLUSIONS: This preliminary study suggests that increased effective theta connectivity from the piriform cortex to the mediodorsal thalamus may be a feature of the 'epileptic network' associated with genetic absence epilepsy. Our findings indicate an underlying predisposition of this direct pathway to propagate epileptiform discharges in genetic absence epilepsy.

Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection.

OBJECTIVE: The process of manually marking up epileptic spikes for simultaneous electroencephalogram (EEG) and resting state functional MRI (rsfMRI) analysis in epilepsy studies is a tedious and subjective task for a human expert. The aim of this study was to evaluate whether automatic EEG spike detection can facilitate EEG-rsfMRI analysis, and to assess its potential as a clinical tool in epilepsy. METHODS: We implemented a fast algorithm for detection of uniform interictal epileptiform discharges (IEDs) in one-hour scalp EEG recordings of 19 refractory focal epilepsy datasets (from 16 patients) who underwent a simultaneous EEG-rsfMRI recording. Our method was based on matched filtering of an IED template (derived from human markup) used to automatically detect other 'similar' EEG events. We compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only. RESULTS: In contrast to human markup, automatic IED detection takes a much shorter time to detect IEDs and export an output text file containing spike timings. In 13/19 focal epilepsy datasets, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases automatic spike detection revealed additional brain regions not seen with human EEG markup. Additional events detected by our automated method independently revealed similar patterns of activation to a human markup. Overall, automatic IED detection provides greater statistical power in EEG-rsfMRI analysis compared to human markup in a short timeframe. CONCLUSIONS: Automatic spike detection is a simple and fast method that can reproduce comparable and, in some cases, even superior results compared to the common practice of manual EEG markup in EEG-rsfMRI analysis of epilepsy. SIGNIFICANCE: Our study shows that IED detection algorithms can be effectively used in epilepsy clinical settings. This work further helps in translating EEG-rsfMRI research into a fast, reliable and easy-to-use clinical tool for epileptologists.