Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort.

INTRODUCTION: The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. METHODS: Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. RESULTS: Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. CONCLUSION: Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.

A growth chart of brain function from infancy to adolescence based on EEG.

BACKGROUND: In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. METHODS: We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states). FINDINGS: The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028). INTERPRETATION: A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts. FUNDING: This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.

Association between patent foramen ovale and migraine: evidence from a resting-state fMRI study.

A relationship between migraine without aura (MO) and patent foramen ovale (PFO) has been observed, but the neural basis underlying this relationship remains elusive. Utilizing independent component analysis via functional magnetic resonance imaging, we examined functional connectivity (FC) within and across networks in 146 patients with MO (75 patients with and 71 patients without PFO) and 70 healthy controls (35 patients each with and without PFO) to elucidate the individual effects of MO and PFO, as well as their interaction, on brain functional networks. The main effect of PFO manifested exclusively in the FC among the visual, auditory, default mode, dorsal attention and salience networks. Furthermore, the interaction effect between MO and PFO was discerned in brain clusters of the left frontoparietal network and lingual gyrus network, as well as the internetwork FC between the left frontoparietal network and the default mode network (DMN), the occipital pole and medial visual networks, and the dorsal attention and salience networks. Our findings suggest that the presence of a PFO shunt in patients with MO is accompanied by various FC changes within and across networks. These changes elucidate the intricate mechanisms linked to PFO-associated migraines and provide a basis for identifying novel noninvasive biomarkers.

EEG Source Imaging-Clinical Considerations for EEG Acquisition and Signal Processing for Improved Temporo-Spatial Resolution.

SUMMARY: EEG source imaging (ESI) has gained traction in recent years as a useful clinical tool for the noninvasive surgical work-up of patients with drug-resistant focal epilepsy. Despite its proven benefits for the temporo-spatial modeling of spike and seizure sources, ESI remains widely underused in clinical practice. This partly relates to a lack of clarity around an optimal approach to the acquisition and processing of scalp EEG data for the purpose of ESI. Here, we describe some of the practical considerations for the clinical application of ESI. We focus on patient preparation, the impact of electrode number and distribution across the scalp, the benefit of averaging raw data for signal analysis, and the relevance of modeling different phases of the interictal discharge as it evolves from take-off to peak. We emphasize the importance of recording high signal-to-noise ratio data for reliable source analysis. We argue that the accuracy of modeling cortical sources can be improved using higher electrode counts that include an inferior temporal array, by averaging interictal waveforms rather than limiting ESI to single spike analysis, and by careful interrogation of earlier phase components of these waveforms. No amount of postacquisition signal processing or source modeling sophistication, however, can make up for suboptimally recorded scalp EEG data in a poorly prepared patient.

Neuropsychological function in psychosis of epilepsy.

OBJECTIVE: The neuropsychological profile of patients with psychosis of epilepsy (POE) has received limited research attention. Recent neuroimaging work in POE has identified structural network pathology in the default mode network and the cognitive control network. This study examined the neuropsychological profile of POE focusing on cognitive domains subserved by these networks. METHODS: Twelve consecutive patients with a diagnosis of POE were prospectively recruited from the Comprehensive Epilepsy Programmes at The Royal Melbourne, Austin and St Vincent's Hospitals, Melbourne, Australia between January 2015 and February 2017. They were compared to 12 matched patients with epilepsy but no psychosis and 42 healthy controls on standardised neuropsychological tests of memory and executive functioning in a case-control design. RESULTS: Mean scores across all cognitive tasks showed a graded pattern of impairment, with the POE group showing the poorest performance, followed by the epilepsy without psychosis and the healthy control groups. This was associated with significant group-level differences on measures of working memory (p = < 0.01); immediate (p = < 0.01) and delayed verbal recall (p = < 0.01); visual memory (p < 0.001); and verbal fluency (p = 0.02). In particular, patients with POE performed significantly worse than the healthy control group on measures of both cognitive control (p = .005) and memory (p < .001), whereas the epilepsy without psychosis group showed only memory difficulties (delayed verbal recall) compared to healthy controls (p = .001). CONCLUSION: People with POE show reduced performance in neuropsychological functions supported by the default mode and cognitive control networks, when compared to both healthy participants and people with epilepsy without psychosis.

Validating ASHS-T1 automated entorhinal and transentorhinal cortical segmentation in Alzheimer's disease.

The current study aimed to validate entorhinal and transentorhinal cortical volumes measured by the automated segmentation tool Automatic Segmentation of Hippocampal Subfields (ASHS-T1). The study sample comprised 34 healthy controls (HCs), 37 individuals with amnestic mild cognitive impairment (aMCI), and 29 individuals with Alzheimer's disease (AD) dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Entorhinal and transentorhinal cortical volumes were assessed using ASHS-T1, manual segmentation, as well as a widely used automated segmentation tool, FreeSurfer v6.0.1. Mean differences, intraclass correlation coefficients, and Bland-Altman plots were computed. ASHS-T1 tended to underestimate entorhinal and transentorhinal cortical volumes relative to manual segmentation and FreeSurfer. There was variable consistency and low agreement between ASHS-T1 and manual segmentation volumes. There was low-to-moderate consistency and low agreement between ASHS-T1 and FreeSurfer volumes. There was a trend toward higher consistency and agreement for the entorhinal cortex in the aMCI and AD groups compared to the HC group. Despite the differences in volume measurements, ASHS-T1 was sensitive to entorhinal and transentorhinal cortical atrophy in both early and late disease stages. Based on the current study, ASHS-T1 appears to be a promising tool for automated entorhinal and transentorhinal cortical volume measurement in individuals with likely underlying AD.

An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography.

Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.

Increased cortical thickness in nodes of the cognitive control and default mode networks in psychosis of epilepsy.

OBJECTIVE: To explore the cortical morphological associations of the psychoses of epilepsy. METHODS: Psychosis of epilepsy (POE) has two main subtypes - postictal psychosis and interictal psychosis. We used automated surface-based analysis of magnetic resonance images to compare cortical thickness, area, and volume across the whole brain between: (i) all patients with POE (n = 23) relative to epilepsy-without psychosis controls (EC; n = 23), (ii) patients with interictal psychosis (n = 10) or postictal psychosis (n = 13) relative to EC, and (iii) patients with postictal psychosis (n = 13) relative to patients with interictal psychosis (n = 10). RESULTS: POE is characterised by cortical thickening relative to EC, occurring primarily in nodes of the cognitive control network; (rostral anterior cingulate, caudal anterior cingulate, middle frontal gyrus), and the default mode network (posterior cingulate, medial paracentral gyrus, and precuneus). Patients with interictal psychosis displayed cortical thickening in the left hemisphere in occipital and temporal regions relative to EC (lateral occipital cortex, lingual, fusiform, and inferior temporal gyri), which was evident to a lesser extent in postictal psychosis patients. There were no significant differences in cortical thickness, area, or volume between the postictal psychosis and EC groups, or between the postictal psychosis and interictal psychosis groups. However, prior to correction for multiple comparisons, both the interictal psychosis and postictal psychosis groups displayed cortical thickening relative to EC in highly similar regions to those identified in the POE group overall. SIGNIFICANCE: The results show cortical thickening in POE overall, primarily in nodes of the cognitive control and default mode networks, compared to patients with epilepsy without psychosis. Additional thickening in temporal and occipital neocortex implicated in the dorsal and ventral visual pathways may differentiate interictal psychosis from postictal psychosis. A novel mechanism for cortical thickening in POE is proposed whereby normal synaptic pruning processes are interrupted by seizure onset.

Dynamical Network Models From EEG and MEG for Epilepsy Surgery-A Quantitative Approach.

There is an urgent need for more informative quantitative techniques that non-invasively and objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) before surgical resection. However, there are major limitations of iEEG, such as the limited spatial sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG data. Recent advances in network analysis and dynamical network modeling provide a novel aspect toward a more objective assessment of the EZ. The advantage of such approaches is that they are data-driven and require less or no human input. Multiple studies have demonstrated success using these approaches when applied to iEEG data in characterizing the EZ and predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these studies-limited spatial sampling and the implicit assumption that iEEG electrodes, whether strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, it is of interest to clinicians and scientists to see whether the same analysis and modeling techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing the limitation of spatial sampling, while safely and objectively characterizing the EZ. This review aims to summarize current state of the art non-invasive methods that inform epilepsy surgery using network analysis and dynamical network models. We also present perspectives on future directions and clinical applications of these promising approaches.

Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery.

Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose virtual intracranial electroencephalography (ViEEG), which combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. We show the non-invasive ViEEG approach may have some advantage over intracranial electroencephalography (iEEG). Future work may be designed to test the potential of the virtual iEEG approach for use in surgical management of epilepsy.

Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis.

BACKGROUND: Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD). PURPOSE: To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD. STUDY TYPE: Systematic review and meta-analysis. DATA SOURCES: MEDLINE, Embase, and PsycINFO were searched through October 2021. FIELD STRENGTH: 1.0 T, 1.5 T, or 3.0 T. ASSESSMENT: Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). STATISTICAL TESTS: Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results. RESULTS: Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value. DATA CONCLUSION: While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Long-term mood, quality of life, and seizure freedom in intracranial EEG epilepsy surgery.

OBJECTIVES: To determine the long-term outcomes in patients undergoing intracranial EEG (iEEG) evaluation for epilepsy surgery in terms of seizure freedom, mood, and quality of life at St. Vincent's Hospital, Melbourne. METHODS: Patients who underwent iEEG between 1999 and 2016 were identified. Patients were retrospectively assessed between 2014 and 2017 by specialist clinic record review and telephone survey with standardized validated questionnaires for: 1) seizure freedom using the Engel classification; 2) Mood using the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E); 3) Quality-of-life outcomes using the QOLIE-10 questionnaire. Summary statistics and univariate analysis were performed to investigate variables for significance. RESULTS: Seventy one patients underwent iEEG surgery: 49 Subdural, 14 Depths, 8 Combination with 62/68 (91.9%) of those still alive, available at last follow-up by telephone survey or medical record review (median of 8.2 years). The estimated epileptogenic zone was 62% temporal and 38% extra-temporal. At last follow-up, 69.4% (43/62) were Engel Class I and 30.6% (19/62) were Engel Class II-IV. Further, a depressive episode (NDDI-E > 15)was observed in 34% (16/47), while a 'better quality of life' (QOLIE-10 score < 25) was noted in 74% (31/42). Quality of life (p < 0.001) but not mood (p = 0.24) was associated with seizure freedom. SIGNIFICANCE: Long-term seizure freedom can be observed in patients undergoing complex epilepsy surgery with iEEG evaluation and is associated with good quality of life.

Interictal spike localization for epilepsy surgery using magnetoencephalography beamforming.

OBJECTIVE: Magnetoencephalography (MEG) kurtosis beamforming is an automated localization method for focal epilepsy. Visual examination of virtual sensors, which are source activities reconstructed by beamforming, can improve performance but can be time-consuming for neurophysiologists. We propose a framework to automate the method and evaluate its effectiveness against surgical resections and outcomes. METHODS: We retrospectively analyzed MEG recordings of 13 epilepsy surgery patients who had one-year minimum post-operative follow-up. Kurtosis beamforming was applied and manual inspection was confined to morphological clusters. The region with the Maximum Interictal Spike Frequency (MISF) was validated against prospectively modelled sLORETA solutions and surgical resections linked to outcome. RESULTS: Our approach localized spikes in 12 out of 13 patients. In eight patients with Engel I surgical outcomes, beamforming MISF regions were concordant with surgical resection at overlap level for five patients and at lobar level for three patients. The MISF regions localized to spike onset and propagation modelled by sLORETA in two and six patients, respectively. CONCLUSIONS: Automated beamforming using MEG can predict postoperative seizure freedom at the lobar level but tends to localize propagated MEG spikes. SIGNIFICANCE: MEG beamforming may contribute to non-invasive procedures to predict surgical outcome for patients with drug-refractory focal epilepsy.

Enlarged hippocampal fissure in psychosis of epilepsy.

Psychosis of epilepsy (POE) can be a devastating condition, and its neurobiological basis remains unclear. In a previous study, we identified reduced posterior hippocampal volumes in patients with POE. The hippocampus can be further subdivided into anatomically and functionally distinct subfields that, along with the hippocampal fissure, have been shown to be selectively affected in other psychotic disorders and are not captured by gross measures of hippocampal volume. Therefore, in this study, we compared the volume of selected hippocampal subfields and the hippocampal fissure in 31 patients with POE with 31 patients with epilepsy without psychosis. Cortical reconstruction, volumetric segmentation, and calculation of hippocampal subfields and the hippocampal fissure were performed using FreeSurfer. The group with POE had larger hippocampal fissures bilaterally compared with controls with epilepsy, which was significant on the right. There were no significant differences in the volumes of the hippocampal subfields between the two groups. Our findings suggest abnormal development of the hippocampus in POE. They support and expand the neurodevelopmental model of psychosis, which holds that early life stressors lead to abnormal neurodevelopmental processes, which underpin the onset of psychosis in later life. In line with this model, the findings of the present study suggest that enlarged hippocampal fissures may be a biomarker of abnormal neurodevelopment and risk for psychosis in patients with epilepsy.

Combined Isoflurane-Remifentanil Anaesthesia Permits Resting-State fMRI in Children with Severe Epilepsy and Intellectual Disability.

Head motion is a significant barrier to functional MRI (fMRI) in patients who are unable to tolerate awake scanning, including young children or those with cognitive and behavioural impairments. General anaesthesia minimises motion and ensures patient comfort, however the optimal anaesthesia regimen for fMRI in the paediatric setting is unknown. In this study, we tested the feasibility of anaesthetised fMRI in 11 patients (mean age = 9.8 years) with Lennox-Gastaut syndrome, a severe form of childhood-onset epilepsy associated with intellectual disability. fMRI was acquired during clinically-indicated MRI sessions using a synergistic anaesthesia regimen we typically administer for epilepsy neurosurgery: combined low-dose isoflurane (≤ 0.8% end-tidal concentration) with remifentanil (≤ 0.1 mcg/kg/min). Using group-level independent component analysis, we assessed the presence of resting-state networks by spatially comparing results in the anaesthetised patients to resting-state network templates from the 'Generation R' study of 536 similarly-aged non-anaesthetised healthy children (Muetzel et al. in Hum Brain Mapp 37(12):4286-4300, 2016). Numerous resting-state networks commonly studied in non-anaesthetised healthy children were readily identifiable in the anaesthetised patients, including the default-mode, sensorimotor, and frontoparietal networks. Independent component time-courses associated with these networks showed spectral characteristics suggestive of a neuronal origin of fMRI signal fluctuations, including high dynamic range and temporal frequency power predominantly below 0.1 Hz. These results demonstrate the technical feasibility of anaesthetised fMRI in children, suggesting that combined isoflurane-remifentanil anaesthesia may be an effective strategy to extend the emerging clinical applications of resting-state fMRI (for example, neurosurgical planning) to the variety of patient groups who may otherwise be impractical to scan.

Performance gains with Compute Unified Device Architecture-enabled eddy current correction for diffusion MRI.

Correcting for eddy currents, movement-induced distortion and gradient inhomogeneities is imperative when processing diffusion MRI (dMRI) data, but is highly computing resource-intensive. Recently, Compute Unified Device Architecture (CUDA) was implemented for the widely-used eddy-correction software, 'eddy', which reduces processing time and allows more comprehensive correction. We investigated processing speed, performance and compatibility of CUDA-enabled eddy-current correction processing compared to commonly-used non-CUDA implementations. Four representative dMRI datasets from the Human Connectome Project, Alzheimer's Disease Neuroimaging Initiative and Chronic Diseases Connectome Project were processed on high-specification and regular workstations through three different configurations of 'eddy'. Processing times and graphics processing unit (GPU) resources used were monitored and compared. Using CUDA reduced the 'eddy' processing time by a factor of up to five. The CUDA slice-to-volume correction method was also faster than non-CUDA eddy except when datasets were large. We make a series of recommendations for eddy configuration and hardware. We suggest that users of eddy-correction software for dMRI processing utilise CUDA and take advantage of the slice-to-volume correction option. We recommend that users run eddy on computers with at least 32GB motherboard random access memory (RAM), and a graphics card with at least 4.5GB RAM and 3750 cores to optimise processing time.

Focal cortical hypermetabolism in atypical benign rolandic epilepsy.

OBJECTIVE: Atypical benign rolandic epilepsy (BRE) is an underrecognized and poorly understood manifestation of a common epileptic syndrome. Most consider it a focal epileptic encephalopathy in which frequent, interictal, centrotemporal spikes lead to negative motor seizures and interfere with motor and sometimes speech and cognitive abilities. We observed focal cortical hypermetabolism on PET in three children with atypical BRE and investigated the spatial and temporal relationship with their centrotemporal spikes. METHODS: EEG, MRI and PET were performed clinically in three children with atypical BRE. The frequency and source localization of centrotemporal spikes was determined and compared with the location of maximal metabolic activity on PET. RESULTS: Cortical hypermetabolism on thresholded PET t-maps and current density reconstructions of centrotemporal spikes overlapped in each child, in the central sulcus region, the distances between the "centers of maxima" being 2 cm or less. Hypermetabolism was not due to recent seizures or frequent centrotemporal spikes at the time of FDG uptake. SIGNIFICANCE: The findings suggest that localized, increased cortical activity, in the region of the EEG focus, underlies the negative clinical manifestations of atypical BRE. Similar findings are reported in the broader group of epileptic encephalopathies associated with electrical status epilepticus in sleep.

Default mode network dysfunction in idiopathic generalised epilepsy.

In order for fMRI findings to be valid and replicable, they must first adhere to quality standardisation. Currently, fMRI literature investigating idiopathic generalised epilepsy is heterogeneous in terms of design, acquisition, processing, and analysis. The present study reported the quality, methods, and functional connectivity findings of fMRI research investigating idiopathic generalised epilepsies, targeting studies that best represent valid and replicable methodologies. Twenty-four studies were identified in the present systematic review. The default mode network showed more significantly altered connectivity than other resting state networks. These networks and associated regions of interest were frequently deactivated at rest amongst all idiopathic generalised epilepsy subtypes compared to controls. This review highlights the need for standardization in acquisition techniques and parameters, processing steps, and analysis techniques, if research in this field is to be replicable. There is also a need for further investigation into default mode network connectivity in the juvenile absence epilepsy population, as a common subtype of idiopathic generalized epilepsy.

Single-subject manual independent component analysis and resting state fMRI connectivity outcomes in patients with juvenile absence epilepsy.

The quality of fMRI data impacts functional connectivity measures and consequently, the decisions that clinicians and researchers make regarding functional connectivity interpretation. The present study used resting state fMRI to investigate resting state network connectivity in a sample of patients with Juvenile Absence Epilepsy. Single-subject manual independent component analysis was used in two levels, whereby all noise components were removed, and cerebrospinal fluid pulsation components only were isolated and removed. Improved temporal signal to noise ratios and functional connectivity metrics were observed in each of the cleaning levels for both epilepsy and control cohorts. Results showed full, single-subject manual independent component analysis reduced the number of functional connectivity correlations and increased the strength of these correlations. Similar effects were also observed for the cerebrospinal fluid pulsation only cleaned data relative to the uncleaned, and fully cleaned data. Single-subject manual independent component analysis coupled with short TR multiband acquisition can significantly improve the validity of findings derived from fMRI data sets.

The epileptic network of Lennox-Gastaut syndrome: Cortically driven and reproducible across age.

OBJECTIVE: To identify brain regions underlying interictal generalized paroxysmal fast activity (GPFA), and their causal interactions, in children and adults with Lennox-Gastaut syndrome (LGS). METHODS: Concurrent scalp EEG-fMRI was performed in 2 separately analyzed patient groups with LGS: 10 children (mean age 8.9 years) scanned under isoflurane-remifentanil anesthesia and 15 older patients (mean age 31.7 years) scanned without anesthesia. Whole-brain event-related analysis determined GPFA-related activation in each group. Results were used as priors in a dynamic causal modeling (DCM) analysis comparing evidence for different neuronal hypotheses describing initiation and propagation of GPFA between cortex, thalamus, and brainstem. RESULTS: A total of 1,045 GPFA events were analyzed (cumulative duration 1,433 seconds). In both pediatric and older groups, activation occurred in distributed association cortical areas, as well as the thalamus and brainstem (p < 0.05, corrected for family-wise error). Activation was similar across individual patients with structural, genetic, and unknown etiologies of epilepsy, particularly in frontoparietal cortex. In both groups, DCM revealed that GPFA was most likely driven by prefrontal cortex, with propagation occurring first to the brainstem and then from brainstem to thalamus. CONCLUSIONS: We show reproducible evidence of a cortically driven process within the epileptic network of LGS. This network is present early (in children) and late (in older patients) in the course of the syndrome and across diverse etiologies of epilepsy, suggesting that LGS reflects shared "secondary network" involvement. A cortical-to-subcortical hierarchy is postulated whereby GPFA rapidly propagates from prefrontal cortex to the brainstem via extrapyramidal corticoreticular pathways, whereas the thalamus is engaged secondarily.