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Decoding of the relationship between human brain activity and walking paths

Kamal SM, Sim S, Tee R, Nathan V, Aghasian E, Namazi H

Technol Health Care, 2020;28(4):381-390.
PMID: 31796717 DOI: 10.3233/THC-191965

BACKGROUND: The human brain controls all actions of the body. Walking is one of the most important actions that deals with the movement of the body. In fact, the brain controls and regulates human walking based on different conditions. One of the conditions that affects human walking is the complexity of path of movement. Therefore, the brain activity should change when a person walks on a path with different complexities.
OBJECTIVE: In this research we benefit from fractal analysis to study the effect of complexity of path of movement on the complexity of human brain reaction.
METHODS: For this purpose we calculate the fractal dimension of the electroencephalography (EEG) signal when subjects walk on different paths with different fractal dimensions (complexity).
RESULTS: The results of the analysis show that the complexity of brain activity increases with the increment of complexity of path of movement.
CONCLUSION: The method of analysis employed in this research can also be employed to analyse the reaction of the human heart and respiration when subjects move on paths with different complexities.

Matched MeSH terms: Brain
Fulltext A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students' Interest in the Mathematics Classroom

Babiker A, Faye I

Comput Intell Neurosci, 2021;2021:6617462.
PMID: 33564299 DOI: 10.1155/2021/6617462

Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted k-nearest neighbours (wkNN). The high classification accuracy of 85.7% with the sensitivity of 81.8% and specificity of 90% revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing.

Matched MeSH terms: Brain
Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli

Soundirarajan M, Pakniyat N, Sim S, Nathan V, Namazi H

Technol Health Care, 2021;29(1):99-109.
PMID: 32568131 DOI: 10.3233/THC-192085

BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles.
OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.
METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).
RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.
CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

Matched MeSH terms: Brain
Fulltext Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network

Al-Hiyali MI, Yahya N, Faye I, Hussein AF

Sensors (Basel), 2021 Aug 04;21(16).
PMID: 34450699 DOI: 10.3390/s21165256

The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger's disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

Matched MeSH terms: Brain Mapping
Radiological investigation of neurological disorders

Wastie NL, Chawla JC

Med J Malaysia, 1973 Jun;27(4):271-4.
PMID: 4270784
Matched MeSH terms: Brain Diseases/radiography*; Brain Neoplasms/radiography*
Value of cerebral angiography

Soo YS, Ang AH

Med J Malaya, 1971 Mar;25(3):168-74.
PMID: 4253242
Matched MeSH terms: Brain Diseases/radiography*; Brain Neoplasms/radiography
Cerebral cryptococcosis in Malaysia

Richardson PM, Mohandas A, Arumugasamy N

J Neurol Neurosurg Psychiatry, 1976 Apr;39(4):330-7.
PMID: 932751

Cryptococcal infection of the brain as encountered in a tropical country is reviewed. The meningitic form is not uncommon and there has been, in the last decade, an apparent, if not real, rise in incidence in Malaysia as in Singapore. Only exceptionally was there overt evidence of immunological deficiency. Hydrocephalus was present in about three-quarters of the patients with meningitis and shunts were employed readily. The presence of multiple small intracerebral cysts could be suspected clinically but treatment for this complication was ineffective. The antifungal agent used most frequently was 5-fluorocytosine. Resistance to this drug developed in about one patient in four. There is a need for further epidemiological studies and for a continuing search for new antifungal agents.

Matched MeSH terms: Brain Diseases/diagnosis*; Brain Diseases/drug therapy; Brain Diseases/epidemiology
The association between the behavior rating inventory of executive functioning and cognitive testing in children diagnosed with a brain tumor

de Vries M, de Ruiter MA, Oostrom KJ, Schouten-Van Meeteren AYN, Maurice-Stam H, Oosterlaan J, et al.

Child Neuropsychol, 2018 08;24(6):844-858.
PMID: 28693404 DOI: 10.1080/09297049.2017.1350262

Pediatric brain tumor survivors (PBTS) suffer from cognitive late effects, such as deteriorating executive functioning (EF). We explored the suitability of the Behavior Rating Inventory of Executive Function (BRIEF) to screen for these late effects. We assessed the relationship between the BRIEF and EF tasks, and between the BRIEF-Parent and BRIEF-Teacher, and we explored the clinical utility. Eighty-two PBTS (8-18 years) were assessed with EF tasks measuring attention, cognitive flexibility, inhibition, visual-, and working memory (WM), and with the BRIEF-Parent and BRIEF-Teacher. Pearson's correlations between the BRIEF and EF tasks, and between the BRIEF-Parent and BRIEF-Teacher were calculated. The BRIEF-Parent related poorly to EF tasks (rs < .26, ps > .01), but of the BRIEF-Teacher the WM-scale, Monitor-scale, Behavioral-Regulation-Index, and Meta-cognition-Index, and Total-score (rs > .31, ps < .01) related significantly to some EF tasks. When controlling for age, only the WM scale and Total score related significantly to the attention task (ps < .01). The inhibit scales of the BRIEF-Parent and BRIEF-Teacher correlated significantly (r = .33, p < .01). Children with clinically elevated scores on BRIEF scales that correlated with EF tasks performed worse on all EF tasks (ds 0.56-1.23, ps < .05). The BRIEF-Teacher Total and Index scores might better screen general EF in PBTS than the BRIEF-Parent. However, the BRIEF-Teacher is also not specific enough to capture separate EFs. Solely relying on the BRIEF as a screening measure of EFs in BPTS is insufficient. Questionnaires and tasks give distinctive, valuable information.

Matched MeSH terms: Brain Neoplasms/diagnosis*; Brain Neoplasms/mortality; Brain Neoplasms/pathology
Fulltext Central Diabetes Insipidus in neonatal brain abscess

Asmaa, H.A., Rohani, A.J., Farah Inaz, S.A., Rosli, F.J.

Malaysian Journal of Paediatrics and Child Health, 2016;22(101):0-0.
MyJurnal

Neonatal Central Diabetes Insipidus (CDI) is extremely rare and its causes include infection, trauma, hemorrhage or tumor. A high index of suspicion is necessary as early treatment is required to prevent further complications. We report a case of Neonatal CDI as a complication of a Serratia brain abscess.(Copied from article)

Matched MeSH terms: Brain Abscess
A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA

Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, et al.

Australas Phys Eng Sci Med, 2018 Sep;41(3):633-645.
PMID: 29948968 DOI: 10.1007/s13246-018-0656-5

Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.

Matched MeSH terms: Brain/physiology*; Brain Mapping*
Fulltext A Distributed Testbed for 5G Scenarios: An Experimental Study

Chamran MK, Yau KA, Noor RMD, Wong R

Sensors (Basel), 2019 Dec 19;20(1).
PMID: 31861500 DOI: 10.3390/s20010018

This paper demonstrates the use of Universal Software Radio Peripheral (USRP), together with Raspberry Pi3 B+ (RP3) as the brain (or the decision making engine), to develop a distributed wireless network in which nodes can communicate with other nodes independently and make decision autonomously. In other words, each USRP node (i.e., sensor) is embedded with separate processing units (i.e., RP3), which has not been investigated in the literature, so that each node can make independent decisions in a distributed manner. The proposed testbed in this paper is compared with the traditional distributed testbed, which has been widely used in the literature. In the traditional distributed testbed, there is a single processing unit (i.e., a personal computer) that makes decisions in a centralized manner, and each node (i.e., USRP) is connected to the processing unit via a switch. The single processing unit exchanges control messages with nodes via the switch, while the nodes exchange data packets among themselves using a wireless medium in a distributed manner. The main disadvantage of the traditional testbed is that, despite the network being distributed in nature, decisions are made in a centralized manner. Hence, the response delay of the control message exchange is always neglected. The use of such testbed is mainly due to the limited hardware and monetary cost to acquire a separate processing unit for each node. The experiment in our testbed has shown the increase of end-to-end delay and decrease of packet delivery ratio due to software and hardware delays. The observed multihop transmission is performed using device-to-device (D2D) communication, which has been enabled in 5G. Therefore, nodes can either communicate with other nodes via: (a) a direct communication with the base station at the macrocell, which helps to improve network performance; or (b) D2D that improve spectrum efficiency, whereby traffic is offloaded from macrocell to small cells. Our testbed is the first of its kind in this scale, and it uses RP3 as the distributed decision-making engine incorporated into the USRP/GNU radio platform. This work provides an insight to the development of a 5G network.

Matched MeSH terms: Brain
Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity

Jatoi MA, Kamel N, Musavi SHA, López JD

Curr Med Imaging Rev, 2019;15(2):184-193.
PMID: 31975664 DOI: 10.2174/1573405613666170629112918

BACKGROUND: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms.
METHODS: Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared.
RESULTS: The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB.
CONCLUSION: In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

Matched MeSH terms: Brain/physiology*; Brain Mapping/methods
Comparison of continuous sampling with active noise cancelation and sparse sampling for cortical and subcortical auditory functional MRI

Dewey RS, Hall DA, Plack CJ, Francis ST

Magn Reson Med, 2021 11;86(5):2577-2588.
PMID: 34196020 DOI: 10.1002/mrm.28902

PURPOSE: Detecting sound-related activity using functional MRI requires the auditory stimulus to be more salient than the intense background scanner acoustic noise. Various strategies can reduce the impact of scanner acoustic noise, including "sparse" temporal sampling with single/clustered acquisitions providing intervals without any background scanner acoustic noise, or active noise cancelation (ANC) during "continuous" temporal sampling, which generates an acoustic signal that adds destructively to the scanner acoustic noise, substantially reducing the acoustic energy at the participant's eardrum. Furthermore, multiband functional MRI allows multiple slices to be collected simultaneously, thereby reducing scanner acoustic noise in a given sampling period.
METHODS: Isotropic multiband functional MRI (1.5 mm) with sparse sampling (effective TR = 9000 ms, acquisition duration = 1962 ms) and continuous sampling (TR = 2000 ms) with ANC were compared in 15 normally hearing participants. A sustained broadband noise stimulus was presented to drive activation of both sustained and transient auditory responses within subcortical and cortical auditory regions.
RESULTS: Robust broadband noise-related activity was detected throughout the auditory pathways. Continuous sampling with ANC was found to give a statistically significant advantage over sparse sampling for the detection of the transient (onset) stimulus responses, particularly in the auditory cortex (P < .001) and inferior colliculus (P < .001), whereas gains provided by sparse over continuous ANC for detecting offset and sustained responses were marginal (p ~ 0.05 in superior olivary complex, inferior colliculus, medial geniculate body, and auditory cortex).
CONCLUSIONS: Sparse and continuous ANC multiband functional MRI protocols provide differing advantages for observing the transient (onset and offset) and sustained stimulus responses.

Matched MeSH terms: Brain Mapping
Fish as a model in social neuroscience: conservation and diversity in the social brain network

Ogawa S, Pfaff DW, Parhar IS

Biol Rev Camb Philos Soc, 2021 06;96(3):999-1020.
PMID: 33559323 DOI: 10.1111/brv.12689

Mechanisms for fish social behaviours involve a social brain network (SBN) which is evolutionarily conserved among vertebrates. However, considerable diversity is observed in the actual behaviour patterns amongst nearly 30000 fish species. The huge variation found in socio-sexual behaviours and strategies is likely generated by a morphologically and genetically well-conserved small forebrain system. Hence, teleost fish provide a useful model to study the fundamental mechanisms underlying social brain functions. Herein we review the foundations underlying fish social behaviours including sensory, hormonal, molecular and neuroanatomical features. Gonadotropin-releasing hormone neurons clearly play important roles, but the participation of vasotocin and isotocin is also highlighted. Genetic investigations of developing fish brain have revealed the molecular complexity of neural development of the SBN. In addition to straightforward social behaviours such as sex and aggression, new experiments have revealed higher order and unique phenomena such as social eavesdropping and social buffering in fish. Finally, observations interpreted as 'collective cognition' in fish can likely be explained by careful observation of sensory determinants and analyses using the dynamics of quantitative scaling. Understanding of the functions of the SBN in fish provide clues for understanding the origin and evolution of higher social functions in vertebrates.

Matched MeSH terms: Brain
TRPM4 inhibition improves spatial memory impairment and hippocampal long-term potentiation deficit in chronic cerebral hypoperfused rats

Hazalin NAMN, Liao P, Hassan Z

Behav Brain Res, 2020 09 01;393:112781.
PMID: 32619565 DOI: 10.1016/j.bbr.2020.112781

Chronic cerebral hypoperfusion (CCH) been well characterized as a common pathological status contributing to neurodegenerative diseases such as Alzheimer's disease and vascular dementia. CCH is an important factor that leads to cognitive impairment, but the underlying neurobiological mechanism is poorly understood and no effective treatment is available. Recently, transient receptor potential melastatin 4 (TRPM4) cation channel has been identified as an important molecular element in focal cerebral ischemia. Over activation of the channel is a major molecular mechanism of oncotic cell death. However, the role of TRPM4 in CCH that propagates global brain hypoxia have not been explored. Therefore, the present study is designed to investigate the effect of TRPM4 inhibition on the cognitive functions of the rats following CCH via permanent bilateral occlusion of common carotid arteries (PBOCCA) model. In this model, treatment with siRNA suppressed TRPM4 expression at both the mRNA and protein levels and improved cognitive deficits of the CCH rats without affecting their motor function. Furthermore, treatment with siRNA rescued the LTP impairment in CCH-induced rats. Consistent with the restored of LTP, western blot analysis revealed that siRNA treatment prevented the reduction of synaptic proteins, including calcium/calmodulin-dependent kinase II alpha (CaMKIIα) and brain-derived neurotrophic factor (BDNF) in brain regions of CCH rats. The present findings provide a novel role of TRPM4 in restricting cognitive functions in CCH and suggest inhibiting TRPM4 may represent a promising therapeutic strategy in targeting ion channels to prevent the progression of cognitive deficits induced by ischemia.

Matched MeSH terms: Brain/physiopathology; Brain Ischemia/physiopathology*
Information-based decoding of the coupling among human brain activity and movement paths

Kamal SM, Dawi NM, Namazi H

Technol Health Care, 2021;29(6):1109-1118.
PMID: 33749623 DOI: 10.3233/THC-202744

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements.
OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view.
METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents.
RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

Matched MeSH terms: Brain
Structured Imaging Approach for Viral Encephalitis

Ramli NM, Bae YJ

Neuroimaging Clin N Am, 2023 Feb;33(1):43-56.
PMID: 36404046 DOI: 10.1016/j.nic.2022.07.002

MR imaging is essential in diagnosing viral encephalitis. Clinical features, cerebrospinal fluid analysis and pathogen confirmation by polymerase chain reaction can be supported by assessing imaging features. MR imaging patterns with typical locations can identify pathogens such as temporal lobe for herpes simplex virus type 1; bilateral thalami for Japanese encephalitis and influenza virus ; and brainstem for enterovirus and rabies. In this article, we have reviewed representative viral encephalitis and its MR imaging patterns. In addition, we also presented acute viral encephalitis without typical MR imaging patterns, such as dengue and varicella-zoster virus encephalitis.

Matched MeSH terms: Brain Stem
Fulltext Bayesian brain in tinnitus: Computational modeling of three perceptual phenomena using a modified Hierarchical Gaussian Filter

Hu S, Hall DA, Zubler F, Sznitman R, Anschuetz L, Caversaccio M, et al.

Hear Res, 2021 10;410:108338.
PMID: 34469780 DOI: 10.1016/j.heares.2021.108338

Recently, Bayesian brain-based models emerged as a possible composite of existing theories, providing an universal explanation of tinnitus phenomena. Yet, the involvement of multiple synergistic mechanisms complicates the identification of behavioral and physiological evidence. To overcome this, an empirically tested computational model could support the evaluation of theoretical hypotheses by intrinsically encompassing different mechanisms. The aim of this work was to develop a generative computational tinnitus perception model based on the Bayesian brain concept. The behavioral responses of 46 tinnitus subjects who underwent ten consecutive residual inhibition assessments were used for model fitting. Our model was able to replicate the behavioral responses during residual inhibition in our cohort (median linear correlation coefficient of 0.79). Using the same model, we simulated two additional tinnitus phenomena: residual excitation and occurrence of tinnitus in non-tinnitus subjects after sensory deprivation. In the simulations, the trajectories of the model were consistent with previously obtained behavioral and physiological observations. Our work introduces generative computational modeling to the research field of tinnitus. It has the potential to quantitatively link experimental observations to theoretical hypotheses and to support the search for neural signatures of tinnitus by finding correlates between the latent variables of the model and measured physiological data.

Matched MeSH terms: Brain
Statistical models for brain signals with properties that evolve across trials

Ombao H, Fiecas M, Ting CM, Low YF

Neuroimage, 2018 Oct 15;180(Pt B):609-618.
PMID: 29223740 DOI: 10.1016/j.neuroimage.2017.11.061

Most neuroscience cognitive experiments involve repeated presentations of various stimuli across several minutes or a few hours. It has been observed that brain responses, even to the same stimulus, evolve over the course of the experiment. These changes in brain activation and connectivity are believed to be associated with learning and/or habituation. In this paper, we present two general approaches to modeling dynamic brain connectivity using electroencephalograms (EEGs) recorded across replicated trials in an experiment. The first approach is the Markovian regime-switching vector autoregressive model (MS-VAR) which treats EEGs as realizations of an underlying brain process that switches between different states both within a trial and across trials in the entire experiment. The second is the slowly evolutionary locally stationary process (SEv-LSP) which characterizes the observed EEGs as a mixture of oscillatory activities at various frequency bands. The SEv-LSP model captures the dynamic nature of the amplitudes of the band-oscillations and cross-correlations between them. The MS-VAR model is able to capture abrupt changes in the dynamics while the SEv-LSP directly gives interpretable results. Moreover, it is nonparametric and hence does not suffer from model misspecification. For both of these models, time-evolving connectivity metrics in the frequency domain are derived from the model parameters for both functional and effective connectivity. We illustrate these two models for estimating cross-trial connectivity in selective attention using EEG data from an oddball paradigm auditory experiment where the goal is to characterize the evolution of brain responses to target stimuli and to standard tones presented randomly throughout the entire experiment. The results suggest dynamic changes in connectivity patterns over trials with inter-subject variability.

Matched MeSH terms: Brain
Fulltext Treatment, Therapy and Management of Metabolic Epilepsy: A Systematic Review

Lin Lin Lee V, Kar Meng Choo B, Chung YS, P Kundap U, Kumari Y, Shaikh MF

Int J Mol Sci, 2018 Mar 15;19(3).
PMID: 29543761 DOI: 10.3390/ijms19030871

Metabolic epilepsy is a metabolic abnormality which is associated with an increased risk of epilepsy development in affected individuals. Commonly used antiepileptic drugs are typically ineffective against metabolic epilepsy as they do not address its root cause. Presently, there is no review available which summarizes all the treatment options for metabolic epilepsy. Thus, we systematically reviewed literature which reported on the treatment, therapy and management of metabolic epilepsy from four databases, namely PubMed, Springer, Scopus and ScienceDirect. After applying our inclusion and exclusion criteria as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we reviewed a total of 43 articles. Based on the reviewed articles, we summarized the methods used for the treatment, therapy and management of metabolic epilepsy. These methods were tailored to address the root causes of the metabolic disturbances rather than targeting the epilepsy phenotype alone. Diet modification and dietary supplementation, alone or in combination with antiepileptic drugs, are used in tackling the different types of metabolic epilepsy. Identification, treatment, therapy and management of the underlying metabolic derangements can improve behavior, cognitive function and reduce seizure frequency and/or severity in patients.

Matched MeSH terms: Brain Diseases, Metabolic/etiology; Brain Diseases, Metabolic/physiopathology; Brain Diseases, Metabolic/therapy*

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