An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth

Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95% confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies

The importance of discriminative power rather than significance when evaluating potential clinical biomarkers in epilepsy research

OBJECTIVE: The quest for epilepsy biomarkers is on the rise. Variables with statistically significant group-level differences are often misinterpreted as biomarkers with sufficient discriminative power. This study aimed to demonstrate the relationship between significant group-level differences and a variable's power to discriminate between individuals. METHODS: We simulated normal-distributed datasets from hypothetical populations with varying sample sizes (25-800), effect sizes (Cohen's d: .25-2.50), and variability (standard deviation: 10-35) to assess the impact of these parameters on significance and discriminative power. The simulation data were illustrated by assessing the discriminative power of a potential real-case biomarker-the EEG beta band power-to diagnose generalized epilepsy, using data from 66 children with generalized epilepsy and 385 controls. Additionally, we evaluated recently reported epilepsy biomarkers by comparing their effect sizes to our simulation-derived effect size criterion. RESULTS: Group size affects significance but not discriminative power. Discriminative power is much more related to variability and effect size. Our real data example supported these simulation results by demonstrating that group-level significance does not translate, one to one, into discriminative power. Although we found a significant difference in the beta band power between children with and without epilepsy, the discriminative power was poor due to a small effect size. A Cohen's d of at least 1.25 is required to reach good discriminative power in univariable prediction modeling. Slightly over 60% of the biomarkers in our literature search met this criterion. SIGNIFICANCE: Rather than statistical significance of group-level differences, effect size should be used as an indicator of a variable's biomarker potential. The minimal required effects size for individual biomarkers-a Cohen's d of 1.25-is large. This calls for multivariable approaches, in which combining multiple variables with smaller effect sizes could increase the overall effect size and discriminative power

Epidemiology of Epilepsy in Nigeria: A Community-Based Study From 3 Sites

BACKGROUND: We determined the prevalence, incidence, and risk factors for epilepsy in Nigeria. METHODS: We conducted a door-to-door survey to identify cases of epilepsy in 3 regions. We estimated age-standardized prevalence adjusted for nonresponse and sensitivity and the 1-year retrospective incidence for active epilepsy. To assess potential risk factors, we conducted a case-control study by collecting sociodemographic and risk factor data. We estimated odds ratios using logistic regression analysis and corresponding population attributable fractions (PAFs). RESULTS: We screened 42,427 persons (age ≥6 years), of whom 254 had confirmed active epilepsy. The pooled prevalence of active epilepsy per 1,000 was 9.8 (95% confidence interval [CI] 8.6-11.1), 17.7 (14.2-20.6) in Gwandu, 4.8 (3.4-6.6) in Afikpo, and 3.3 (2.0-5.1) in Ijebu-Jesa. The pooled incidence per 100,000 was 101.3 (95% CI 57.9-167.6), 201.2 (105.0-358.9) in Gwandu, 27.6 (3.3-128.0) in Afikpo, and 23.9 (3.2-157.0) in Ijebu-Jesa. Children's significant risk factors included febrile seizures, meningitis, poor perinatal care, open defecation, measles, and family history in first-degree relatives. In adults, head injury, poor perinatal care, febrile seizures, family history in second-degree relatives, and consanguinity were significant. Gwandu had more significant risk factors. The PAF for the important factors in children was 74.0% (71.0%-76.0%) and in adults was 79.0% (75.0%-81.0%). CONCLUSION: This work suggests varied epidemiologic numbers, which may be explained by differences in risk factors and population structure in the different regions. These variations should differentially determine and drive prevention and health care responses

Low-level laser therapy/photobiomodulation in the management of side effects of chemoradiation therapy in head and neck cancer: part 2: proposed applications and treatment protocols

Purpose: There is a large body of evidence supporting the efficacy of low-level laser therapy (LLLT), more recently termed photobiomodulation (PBM) for the management of oral mucositis (OM) in patients undergoing radiotherapy for head and neck cancer (HNC). Recent advances in PBM technology, together with a better understanding of mechanisms involved and dosimetric parameters may lead to the management of a broader range of complications associated with HNC treatment. This could enhance patient adherence to cancer therapy, and improve quality of life and treatment outcomes. The mechanisms of action, dosimetric, and safety considerations for PBM have been reviewed in part 1. Part 2 discusses the head and neck treatment side effects for which PBM may prove to be effective. In addition, PBM parameters for each of these complications are suggested and future research directions are discussed. Methods: Narrative review and presentation of PBM parameters are based on current evidence and expert opinion. Results: PBM may have potential applications in the management of a broad range of side effects of (chemo)radiation therapy (CRT) in patients being treated for HNC. For OM management, optimal PBM parameters identified were as follows: wavelength, typically between 633 and 685 nm or 780–830 nm; energy density, laser or light-emitting diode (LED) output between 10 and 150 mW; dose, 2–3 J (J/cm2), and no more than 6 J/cm2 on the tissue surface treated; treatment schedule, two to three times a week up to daily; emission type, pulsed (<100 Hz); and route of delivery, intraorally and/or transcutaneously. To facilitate further studies, we propose potentially effective PBM parameters for prophylactic and therapeutic use in supportive care for dermatitis, dysphagia, dry mouth, dysgeusia, trismus, necrosis, lymphedema, and voice/speech alterations. Conclusion: PBM may have a role in supportive care for a broad range of complications associated with the treatment of HNC with CRT. The suggested PBM irradiation and dosimetric parameters, which are potentially effective for these complications, are intended to provide guidance for well-designed future studies. It is imperative that such studies include elucidating the effects of PBM on oncology treatment outcomes.National Institutes of Health (U.S.) (NIH grant R01AI050875

Low level laser therapy/photobiomodulation in the management of side effects of chemoradiation therapy in head and neck cancer: part 1: mechanisms of action, dosimetric, and safety considerations

Purpose: There is a large body of evidence supporting the efficacy of low level laser therapy (LLLT), more recently termed photobiomodulation (PBM), for the management of oral mucositis (OM) in patients undergoing radiotherapy for head and neck cancer (HNC). Recent advances in PBM technology, together with a better understanding of mechanisms involved, may expand the applications for PBM in the management of other complications associated with HNC treatment. This article (part 1) describes PBM mechanisms of action, dosimetry, and safety aspects and, in doing so, provides a basis for a companion paper (part 2) which describes the potential breadth of potential applications of PBM in the management of side-effects of (chemo)radiation therapy in patients being treated for HNC and proposes PBM parameters. Methods: This study is a narrative non-systematic review. Results: We review PBM mechanisms of action and dosimetric considerations. Virtually, all conditions modulated by PBM (e.g., ulceration, inflammation, lymphedema, pain, fibrosis, neurological and muscular injury) are thought to be involved in the pathogenesis of (chemo)radiation therapy-induced complications in patients treated for HNC. The impact of PBM on tumor behavior and tumor response to treatment has been insufficiently studied. In vitro studies assessing the effect of PBM on tumor cells report conflicting results, perhaps attributable to inconsistencies of PBM power and dose. Nonetheless, the biological bases for the broad clinical activities ascribed to PBM have also been noted to be similar to those activities and pathways associated with negative tumor behaviors and impeded response to treatment. While there are no anecdotal descriptions of poor tumor outcomes in patients treated with PBM, confirming its neutrality with respect to cancer responsiveness is a critical priority. Conclusion: Based on its therapeutic effects, PBM may have utility in a broad range of oral, oropharyngeal, facial, and neck complications of HNC treatment. Although evidence suggests that PBM using LLLT is safe in HNC patients, more research is imperative and vigilance remains warranted to detect any potential adverse effects of PBM on cancer treatment outcomes and survival.National Institutes of Health (U.S.) (grant R01AI050875

Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing

Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform

What Electrophysiology Tells Us About Alzheimer’s Disease::A Window into the Synchronization and Connectivity of Brain Neurons

Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer’s disease (AD), despite a surge in recent validated evidence. This Position Paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity reflecting thalamocortical and cortico-cortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies

Accounting for the complex hierarchical topology of EEG phase-based functional connectivity in network binarisation

Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG phase-based functional connectivity, we test the hypothesis that such binarisation should take into account the complex hierarchical structure found in functional connectivity. We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold (CST), minimum spanning trees, efficiency-cost optimisation and union of shortest path graphs, with arbitrary proportional thresholds and weighted networks. We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences. We also test the robustness of these techniques to random and targeted topological attacks.We find that the CST performs consistenty well in state-of-the-art modelling of EEG network topology, robustness to topological network attacks, and in three real datasets, agreeing with our hypothesis of hierarchical complexity. This provides interesting new evidence into the relevance of considering a large number of edges in EEG functional connectivity research to provide informational density in the topology.Comment: Accepted for publication in PLOS One, 27th September 201

Seizure prediction : ready for a new era

Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin

Dynamics of large-scale electrophysiological networks: a technical review

For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography / electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity