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      Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study

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          Abstract

          Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data‐driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision‐making for patients with intractable epilepsy.

          Abstract

          In this study, we use hidden Markov modeling (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric epilepsy patients. This data‐driven model produces an output unique to each patient, and is used to localize the epileptogenic area(s). In two patients, where more than one focus is identified, the HMM provides additional information about the relationship between the epileptiform activity arising in those areas.

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          Most cited references31

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          Global, regional, and national burden of epilepsy, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

          Summary Background Seizures and their consequences contribute to the burden of epilepsy because they can cause health loss (premature mortality and residual disability). Data on the burden of epilepsy are needed for health-care planning and resource allocation. The aim of this study was to quantify health loss due to epilepsy by age, sex, year, and location using data from the Global Burden of Diseases, Injuries, and Risk Factors Study. Methods We assessed the burden of epilepsy in 195 countries and territories from 1990 to 2016. Burden was measured as deaths, prevalence, and disability-adjusted life-years (DALYs; a summary measure of health loss defined by the sum of years of life lost [YLLs] for premature mortality and years lived with disability), by age, sex, year, location, and Socio-demographic Index (SDI; a compound measure of income per capita, education, and fertility). Vital registrations and verbal autopsies provided information about deaths, and data on the prevalence and severity of epilepsy largely came from population representative surveys. All estimates were calculated with 95% uncertainty intervals (UIs). Findings In 2016, there were 45·9 million (95% UI 39·9–54·6) patients with all-active epilepsy (both idiopathic and secondary epilepsy globally; age-standardised prevalence 621·5 per 100 000 population; 540·1–737·0). Of these patients, 24·0 million (20·4–27·7) had active idiopathic epilepsy (prevalence 326·7 per 100 000 population; 278·4–378·1). Prevalence of active epilepsy increased with age, with peaks at 5–9 years (374·8 [280·1–490·0]) and at older than 80 years of age (545·1 [444·2–652·0]). Age-standardised prevalence of active idiopathic epilepsy was 329·3 per 100 000 population (280·3–381·2) in men and 318·9 per 100 000 population (271·1–369·4) in women, and was similar among SDI quintiles. Global age-standardised mortality rates of idiopathic epilepsy were 1·74 per 100 000 population (1·64–1·87; 1·40 per 100 000 population [1·23–1·54] for women and 2·09 per 100 000 population [1·96–2·25] for men). Age-standardised DALYs were 182·6 per 100 000 population (149·0–223·5; 163·6 per 100 000 population [130·6–204·3] for women and 201·2 per 100 000 population [166·9–241·4] for men). The higher DALY rates in men were due to higher YLL rates compared with women. Between 1990 and 2016, there was a non-significant 6·0% (−4·0 to 16·7) change in the age-standardised prevalence of idiopathic epilepsy, but a significant decrease in age-standardised mortality rates (24·5% [10·8 to 31·8]) and age-standardised DALY rates (19·4% [9·0 to 27·6]). A third of the difference in age-standardised DALY rates between low and high SDI quintile countries was due to the greater severity of epilepsy in low-income settings, and two-thirds were due to a higher YLL rate in low SDI countries. Interpretation Despite the decrease in the disease burden from 1990 to 2016, epilepsy is still an important cause of disability and mortality. Standardised collection of data on epilepsy in population representative surveys will strengthen the estimates, particularly in countries for which we currently have no or sparse data and if additional data is collected on severity, causes, and treatment. Sizeable gains in reducing the burden of epilepsy might be expected from improved access to existing treatments in low-income countries and from the development of new effective drugs worldwide. Funding Bill & Melinda Gates Foundation.
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            Moving magnetoencephalography towards real-world applications with a wearable system

            Summary Imaging human brain function with techniques such as magnetoencephalography1 (MEG) typically requires a subject to perform tasks whilst their head remains still within a restrictive scanner. This artificial environment makes the technique inaccessible to many people, and limits the experimental questions that can be addressed. For example, it has been difficult to apply neuroimaging to investigation of the neural substrates of cognitive development in babies and children, or in adult studies that require unconstrained head movement (e.g. spatial navigation). Here, we develop a new type of MEG system that can be worn like a helmet, allowing free and natural movement during scanning. This is possible due to the integration of new quantum sensors2,3 that do not rely on superconducting technology, with a novel system for nulling background magnetic fields. We demonstrate human electrophysiological measurement at millisecond resolution whilst subjects make natural movements, including head nodding, stretching, drinking and playing a ball game. Results compare well to the current state-of-the-art, even when subjects make large head movements. The system opens up new possibilities for scanning any subject or patient group, with myriad applications such as characterisation of the neurodevelopmental connectome, imaging subjects moving naturally in a virtual environment, and understanding the pathophysiology of movement disorders.
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              Fast transient networks in spontaneous human brain activity

              To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states. DOI: http://dx.doi.org/10.7554/eLife.01867.001
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                Author and article information

                Contributors
                zelekha.seedat@nottingham.ac.uk
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                19 October 2022
                January 2023
                : 44
                : 1 ( doiID: 10.1002/hbm.v44.1 )
                : 66-81
                Affiliations
                [ 1 ] Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy University of Nottingham Nottingham UK
                [ 2 ] Young Epilepsy St Pier's Lane Lingfield RH7 6PW UK
                [ 3 ] Oxford Centre for Human Brain Activity University Department of Psychiatry, Warneford Hospital Oxford UK
                [ 4 ] Department of Radiology Children's Hospital of Philadelphia Philadelphia Pennsylvania USA
                [ 5 ] Aston Brain Centre Aston University Birmingham UK
                [ 6 ] Pediatric Epilepsy Program, Division of Child Neurology CHOP Philadelphia Pennsylvania USA
                [ 7 ] Centre for Human Brain Health, School of Psychology University of Birmingham Birmingham UK
                [ 8 ] Departments of Neurology and Paediatrics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USA
                Author notes
                [*] [* ] Correspondence

                Zelekha A. Seedat, Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK.

                Email: zelekha.seedat@ 123456nottingham.ac.uk

                Author information
                https://orcid.org/0000-0001-5453-2289
                https://orcid.org/0000-0002-8164-0274
                https://orcid.org/0000-0002-8687-8185
                Article
                HBM26118
                10.1002/hbm.26118
                9783449
                36259549
                ec9910d3-d225-418b-b816-ec47efaeca7f
                © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 September 2022
                : 11 October 2021
                : 26 September 2022
                Page count
                Figures: 9, Tables: 2, Pages: 16, Words: 11021
                Funding
                Funded by: Engineering and Physical Sciences Research Council , doi 10.13039/501100000266;
                Award ID: EP/L016052/1
                Funded by: euSNN
                Award ID: MSCA‐ITN H2020‐860563
                Funded by: Medical Research Council , doi 10.13039/501100007155;
                Award ID: RG89702
                Award ID: RG94383
                Funded by: Wellcome Trust , doi 10.13039/100010269;
                Award ID: 106183/Z/14/Z
                Award ID: 203139/Z/16/Z
                Award ID: 215573/Z/19/Z
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                January 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.3 mode:remove_FC converted:23.12.2022

                Neurology
                epilepsy,hidden markov model,interictal activity,magnetoencephalography
                Neurology
                epilepsy, hidden markov model, interictal activity, magnetoencephalography

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