2018 |
Al-Hiyali, M I; Ishak, A J; Harun, H; Ahmad, S A; Sulaiman, Wan W A A review in modification food-intake behavior by brain stimulation: Excess weight cases Journal Article NeuroQuantology, 16 (12), pp. 86-97, 2018, ISSN: 13035150, (cited By 2). Abstract | Links | BibTeX | Tags: Amygdala, Anoxia, Article, Autism, Binge Eating Disorder, Body Mass, Body Weight, Brain Depth Stimulation, Depolarization, Dietary Intake, Drug Craving, Eating Disorder, Electric Current, Electroencephalogram, Electroencephalography, Energy Consumption, Energy Expenditure, Feeding Behavior, Food Intake, Functional Magnetic Resonance Imaging, Gender, Health Status, Homeostasis, Human, Hunger, Lifestyle, Nerve Cell Membrane Steady Potential, Nerve Excitability, Neurofeedback, Neuromodulation, Nutritional Assessment, Outcome Assessment, Questionnaires, Repetitive Transcranial Magnetic Stimulation, Signal Processing, Training, Transcranial Direct Current Stimulation, Transcranial Magnetic Stimulation, Underweight @article{Al-Hiyali201886, title = {A review in modification food-intake behavior by brain stimulation: Excess weight cases}, author = {M I Al-Hiyali and A J Ishak and H Harun and S A Ahmad and W A Wan Sulaiman}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062843670&doi=10.14704%2fnq.2018.16.12.1894&partnerID=40&md5=235f66cef05a144be23472641f70bd1d}, doi = {10.14704/nq.2018.16.12.1894}, issn = {13035150}, year = {2018}, date = {2018-01-01}, journal = {NeuroQuantology}, volume = {16}, number = {12}, pages = {86-97}, publisher = {Anka Publishers}, abstract = {Obesity and overweight are frequently prescribed for dysfunction in food-intake behavior. Due to the widely prevalence of obesity in last year’s, there is demand for more studies which are aimed to modify the food-intake behavior. For the past decades many researches has applied in modify food-intake by brain training or stimulation. This review for neuroscience studies in modifying food-intake behavior, it’s involved three sections; The first section explained the role of brain activity in food-intake regulation, general ideas about biomedical devices in food-intake behavior are discussed in second section and third section focused on brain-stimulation systems. Finally, this paper concluded with main points that need to be taken into account when designing experimental study for modification food-intake behavior by brain stimulation according to previous studies recommendation and challenges. © 2018, Anka Publishers. All Rights Reserved.}, note = {cited By 2}, keywords = {Amygdala, Anoxia, Article, Autism, Binge Eating Disorder, Body Mass, Body Weight, Brain Depth Stimulation, Depolarization, Dietary Intake, Drug Craving, Eating Disorder, Electric Current, Electroencephalogram, Electroencephalography, Energy Consumption, Energy Expenditure, Feeding Behavior, Food Intake, Functional Magnetic Resonance Imaging, Gender, Health Status, Homeostasis, Human, Hunger, Lifestyle, Nerve Cell Membrane Steady Potential, Nerve Excitability, Neurofeedback, Neuromodulation, Nutritional Assessment, Outcome Assessment, Questionnaires, Repetitive Transcranial Magnetic Stimulation, Signal Processing, Training, Transcranial Direct Current Stimulation, Transcranial Magnetic Stimulation, Underweight}, pubstate = {published}, tppubtype = {article} } Obesity and overweight are frequently prescribed for dysfunction in food-intake behavior. Due to the widely prevalence of obesity in last year’s, there is demand for more studies which are aimed to modify the food-intake behavior. For the past decades many researches has applied in modify food-intake by brain training or stimulation. This review for neuroscience studies in modifying food-intake behavior, it’s involved three sections; The first section explained the role of brain activity in food-intake regulation, general ideas about biomedical devices in food-intake behavior are discussed in second section and third section focused on brain-stimulation systems. Finally, this paper concluded with main points that need to be taken into account when designing experimental study for modification food-intake behavior by brain stimulation according to previous studies recommendation and challenges. © 2018, Anka Publishers. All Rights Reserved. |
Tsuchida, N; Hamada, K; Shiina, M; Kato, M; Kobayashi, Y; Tohyama, J; Kimura, K; Hoshino, K; Ganesan, V; Teik, K W; Nakashima, M; Mitsuhashi, S; Mizuguchi, T; Takata, A; Miyake, N; Saitsu, H; Ogata, K; Miyatake, S; Matsumoto, N GRIN2D variants in three cases of developmental and epileptic encephalopathy Journal Article Clinical Genetics, 94 (6), pp. 538-547, 2018, ISSN: 00099163, (cited By 4). Abstract | Links | BibTeX | Tags: Adolescent, Allele, Amino Acid Sequence, Amino Acid Substitution, Amino Terminal Sequence, Anemia, Antibiotic Agent, Antibiotic Therapy, Article, Atonic Seizure, Attention Deficit Disorder, Autism, Binding Affinity, Brain, Brain Atrophy, Carbamazepine, Case Report, Channel Gating, Chemistry, Children, Clinical Article, Clinical Feature, Clobazam, Clonazepam, Conformational Transition, Continuous Infusion, Contracture, Crystal Structure, Cysteine Ethyl Ester Tc 99m, Developmental Delay, Developmental Disorders, Electroencephalogram, Electroencephalography, Epilepsy, Epileptic Discharge, Ethosuximide, Eye Tracking, Febrile Convulsion, Female, Frontal Lobe Epilepsy, Gene, Gene Frequency, Genetic Variation, Genetics, Genotype, GRIN2D Protein, Heterozygosity, Home Oxygen Therapy, Human, Human Cell, Hydrogen Bond, Intellectual Impairment, Intelligence Quotient, Intractable Epilepsy, Ketamine, Lacosamide, Lamotrigine, Lennox Gastaut Syndrome, Levetiracetam, Magnetoencephalography, Male, Maternal Hypertension, Melatonin, Migraine, Missense Mutation, Molecular Dynamics, Molecular Dynamics Simulation, Mutation, Myoclonus Seizure, N Methyl Dextro Aspartic Acid Receptor, N Methyl Dextro Aspartic Acid Receptor 2D, N-Methyl-D-Aspartate, Neonatal Pneumonia, Neonatal Respiratory Distress Syndrome, Neuroimaging, Nuclear Magnetic Resonance Imaging, Phenobarbital, Premature Labor, Preschool, Preschool Child, Priority Journal, Protein Conformation, Proximal Interphalangeal Joint, Pyridoxine, Receptors, Respiratory Arrest, Sanger Sequencing, School Child, Single Photon Emission Computed Tomography, Sleep Disordered Breathing, Static Electricity, Stridor, Structure-Activity Relationship, Subglottic Stenosis, Superior Temporal Gyrus, Supramarginal Gyrus, Thiopental, Tonic Seizure, Valproic Acid, Wakefulness, Wechsler Intelligence Scale for Children, Whole Exome Sequencing @article{Tsuchida2018538, title = {GRIN2D variants in three cases of developmental and epileptic encephalopathy}, author = {N Tsuchida and K Hamada and M Shiina and M Kato and Y Kobayashi and J Tohyama and K Kimura and K Hoshino and V Ganesan and K W Teik and M Nakashima and S Mitsuhashi and T Mizuguchi and A Takata and N Miyake and H Saitsu and K Ogata and S Miyatake and N Matsumoto}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056487337&doi=10.1111%2fcge.13454&partnerID=40&md5=f0d32670db57261820bc244943cffd62}, doi = {10.1111/cge.13454}, issn = {00099163}, year = {2018}, date = {2018-01-01}, journal = {Clinical Genetics}, volume = {94}, number = {6}, pages = {538-547}, publisher = {Blackwell Publishing Ltd}, abstract = {N-methyl-d-aspartate (NMDA) receptors are glutamate-activated ion channels that are widely distributed in the central nervous system and essential for brain development and function. Dysfunction of NMDA receptors has been associated with various neurodevelopmental disorders. Recently, a de novo recurrent GRIN2D missense variant was found in two unrelated patients with developmental and epileptic encephalopathy. In this study, we identified by whole exome sequencing novel heterozygous GRIN2D missense variants in three unrelated patients with severe developmental delay and intractable epilepsy. All altered residues were highly conserved across vertebrates and among the four GluN2 subunits. Structural consideration indicated that all three variants are probably to impair GluN2D function, either by affecting intersubunit interaction or altering channel gating activity. We assessed the clinical features of our three cases and compared them to those of the two previously reported GRIN2D variant cases, and found that they all show similar clinical features. This study provides further evidence of GRIN2D variants being causal for epilepsy. Genetic diagnosis for GluN2-related disorders may be clinically useful when considering drug therapy targeting NMDA receptors. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd}, note = {cited By 4}, keywords = {Adolescent, Allele, Amino Acid Sequence, Amino Acid Substitution, Amino Terminal Sequence, Anemia, Antibiotic Agent, Antibiotic Therapy, Article, Atonic Seizure, Attention Deficit Disorder, Autism, Binding Affinity, Brain, Brain Atrophy, Carbamazepine, Case Report, Channel Gating, Chemistry, Children, Clinical Article, Clinical Feature, Clobazam, Clonazepam, Conformational Transition, Continuous Infusion, Contracture, Crystal Structure, Cysteine Ethyl Ester Tc 99m, Developmental Delay, Developmental Disorders, Electroencephalogram, Electroencephalography, Epilepsy, Epileptic Discharge, Ethosuximide, Eye Tracking, Febrile Convulsion, Female, Frontal Lobe Epilepsy, Gene, Gene Frequency, Genetic Variation, Genetics, Genotype, GRIN2D Protein, Heterozygosity, Home Oxygen Therapy, Human, Human Cell, Hydrogen Bond, Intellectual Impairment, Intelligence Quotient, Intractable Epilepsy, Ketamine, Lacosamide, Lamotrigine, Lennox Gastaut Syndrome, Levetiracetam, Magnetoencephalography, Male, Maternal Hypertension, Melatonin, Migraine, Missense Mutation, Molecular Dynamics, Molecular Dynamics Simulation, Mutation, Myoclonus Seizure, N Methyl Dextro Aspartic Acid Receptor, N Methyl Dextro Aspartic Acid Receptor 2D, N-Methyl-D-Aspartate, Neonatal Pneumonia, Neonatal Respiratory Distress Syndrome, Neuroimaging, Nuclear Magnetic Resonance Imaging, Phenobarbital, Premature Labor, Preschool, Preschool Child, Priority Journal, Protein Conformation, Proximal Interphalangeal Joint, Pyridoxine, Receptors, Respiratory Arrest, Sanger Sequencing, School Child, Single Photon Emission Computed Tomography, Sleep Disordered Breathing, Static Electricity, Stridor, Structure-Activity Relationship, Subglottic Stenosis, Superior Temporal Gyrus, Supramarginal Gyrus, Thiopental, Tonic Seizure, Valproic Acid, Wakefulness, Wechsler Intelligence Scale for Children, Whole Exome Sequencing}, pubstate = {published}, tppubtype = {article} } N-methyl-d-aspartate (NMDA) receptors are glutamate-activated ion channels that are widely distributed in the central nervous system and essential for brain development and function. Dysfunction of NMDA receptors has been associated with various neurodevelopmental disorders. Recently, a de novo recurrent GRIN2D missense variant was found in two unrelated patients with developmental and epileptic encephalopathy. In this study, we identified by whole exome sequencing novel heterozygous GRIN2D missense variants in three unrelated patients with severe developmental delay and intractable epilepsy. All altered residues were highly conserved across vertebrates and among the four GluN2 subunits. Structural consideration indicated that all three variants are probably to impair GluN2D function, either by affecting intersubunit interaction or altering channel gating activity. We assessed the clinical features of our three cases and compared them to those of the two previously reported GRIN2D variant cases, and found that they all show similar clinical features. This study provides further evidence of GRIN2D variants being causal for epilepsy. Genetic diagnosis for GluN2-related disorders may be clinically useful when considering drug therapy targeting NMDA receptors. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd |
Razi, N I M; Rahman, A W A; Kamaruddin, N Neurophysiological analysis of porn addiction to learning disabilities Conference Institute of Electrical and Electronics Engineers Inc., 2018, ISBN: 9781538675250, (cited By 2). Abstract | Links | BibTeX | Tags: Attention Deficit Hyperactivity Disorder, Autism Spectrum Disorders, Diseases, Dyslexia, Electroencephalography, Learning Disorder, Neurophysiological, Neurophysiology, Porn Addiction @conference{Razi2018272, title = {Neurophysiological analysis of porn addiction to learning disabilities}, author = {N I M Razi and A W A Rahman and N Kamaruddin}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060456087&doi=10.1109%2fICT4M.2018.00057&partnerID=40&md5=40b96e377414d3bed38a2803752c165a}, doi = {10.1109/ICT4M.2018.00057}, isbn = {9781538675250}, year = {2018}, date = {2018-01-01}, journal = {Proceedings - International Conference on Information and Communication Technology for the Muslim World 2018, ICT4M 2018}, pages = {272-277}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Learning disability results from a reduced intellectual ability that can be observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Such condition may expose the children from the unfiltered porn contents freely available from the Internet as they are unaware or have minimal understanding of the negative effects of the pornographic contents. The exposure to pornographic contents that are unmonitored may result to porn addiction as it may trigger excitement and pleasure induced. Hence, this paper attempts to explore the empirical evidence of the correlation between learning disability and pornography addiction by using the electroencephalogram (EEG) of children from a private psychology clinic. The experimental results show that, there are weak correlation between learning disability based on the EEG frequency bands and porn addiction. It can be hoped that such approach is a stepping step in further exploring the relationship between porn addiction and learning disability. © 2018 IEEE.}, note = {cited By 2}, keywords = {Attention Deficit Hyperactivity Disorder, Autism Spectrum Disorders, Diseases, Dyslexia, Electroencephalography, Learning Disorder, Neurophysiological, Neurophysiology, Porn Addiction}, pubstate = {published}, tppubtype = {conference} } Learning disability results from a reduced intellectual ability that can be observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Such condition may expose the children from the unfiltered porn contents freely available from the Internet as they are unaware or have minimal understanding of the negative effects of the pornographic contents. The exposure to pornographic contents that are unmonitored may result to porn addiction as it may trigger excitement and pleasure induced. Hence, this paper attempts to explore the empirical evidence of the correlation between learning disability and pornography addiction by using the electroencephalogram (EEG) of children from a private psychology clinic. The experimental results show that, there are weak correlation between learning disability based on the EEG frequency bands and porn addiction. It can be hoped that such approach is a stepping step in further exploring the relationship between porn addiction and learning disability. © 2018 IEEE. |
Sudirman, R; Hussin, S S; Airij, A G; Hai, C Z Profile indicator for autistic children using EEG biosignal potential of sensory tasks Conference Institute of Electrical and Electronics Engineers Inc., 2018, ISBN: 9781538612774, (cited By 0). Abstract | Links | BibTeX | Tags: Autistic Children, Biomedical Signal Processing, Brain, Children with Autism, Electroencephalography, Electrophysiology, Entropy Approximations, Independent Component Analysis, MATLAB, Neural Networks, Neurological Problems, Sensory Analysis, Sensory Profiles, Sensory Stimulation, Wavelet Packet Transforms @conference{Sudirman2018136, title = {Profile indicator for autistic children using EEG biosignal potential of sensory tasks}, author = {R Sudirman and S S Hussin and A G Airij and C Z Hai}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058032461&doi=10.1109%2fICBAPS.2018.8527403&partnerID=40&md5=30dbb1596f4a0529332713c087bd788d}, doi = {10.1109/ICBAPS.2018.8527403}, isbn = {9781538612774}, year = {2018}, date = {2018-01-01}, journal = {2nd International Conference on BioSignal Analysis, Processing and Systems, ICBAPS 2018}, pages = {136-141}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Electroencephalography (EEG) is a measure of voltages caused due to neural activities within the brain. EEG is a recommended tool for diagnosing neurological problems because it is non-invasive and can be recorded over a longer time-period. The children with Autism Spectrum Disorder (ASD) have difficulty in expressing their emotions due to their inability of proper information processing in brain. Therefore, this research aims to build a sensory profile with the help of EEG biosignal potential to distinguish among different sensory responses. The EEG signals acquired in this research identify different emotional states such as positive-thinking or super-learning and light-relaxation and are within the frequency range of 8-12 Hertz. 64 children participated in this research among which 34 were children with ASD and 30 were normal children. The EEG data was recoded while all the children were provided with vestibular, visual, sound, taste and vestibular sensory stimulations. The raw EEG data was filtered with the help of independent component analysis (ICA) using wavelet transform and EEGLAB software. Later, for building the sensory profile, entropy approximation, means and standard deviations were extracted from the filtered EEG signals. Along with that, the filtered EEG data was also fed to a neural networks (NN) algorithm which was implemented in MATLAB. Results from the acquired EEG signals depicted that during the sensory stimulation phase, the responses of all autistic children were in an unstable state. These findings will equip and aid their learning strategy in the future. © 2018 IEEE.}, note = {cited By 0}, keywords = {Autistic Children, Biomedical Signal Processing, Brain, Children with Autism, Electroencephalography, Electrophysiology, Entropy Approximations, Independent Component Analysis, MATLAB, Neural Networks, Neurological Problems, Sensory Analysis, Sensory Profiles, Sensory Stimulation, Wavelet Packet Transforms}, pubstate = {published}, tppubtype = {conference} } Electroencephalography (EEG) is a measure of voltages caused due to neural activities within the brain. EEG is a recommended tool for diagnosing neurological problems because it is non-invasive and can be recorded over a longer time-period. The children with Autism Spectrum Disorder (ASD) have difficulty in expressing their emotions due to their inability of proper information processing in brain. Therefore, this research aims to build a sensory profile with the help of EEG biosignal potential to distinguish among different sensory responses. The EEG signals acquired in this research identify different emotional states such as positive-thinking or super-learning and light-relaxation and are within the frequency range of 8-12 Hertz. 64 children participated in this research among which 34 were children with ASD and 30 were normal children. The EEG data was recoded while all the children were provided with vestibular, visual, sound, taste and vestibular sensory stimulations. The raw EEG data was filtered with the help of independent component analysis (ICA) using wavelet transform and EEGLAB software. Later, for building the sensory profile, entropy approximation, means and standard deviations were extracted from the filtered EEG signals. Along with that, the filtered EEG data was also fed to a neural networks (NN) algorithm which was implemented in MATLAB. Results from the acquired EEG signals depicted that during the sensory stimulation phase, the responses of all autistic children were in an unstable state. These findings will equip and aid their learning strategy in the future. © 2018 IEEE. |
Paudel, Y N; Shaikh, M F; Shah, S; Kumari, Y; Othman, I Role of inflammation in epilepsy and neurobehavioral comorbidities: Implication for therapy Journal Article European Journal of Pharmacology, 837 , pp. 145-155, 2018, ISSN: 00142999, (cited By 14). Abstract | Links | BibTeX | Tags: 3 Dioxygenase, Acetylsalicylic Acid, Adalimumab, Anakinra, Animals, Anti-Inflammatory Agents, Anxiety, Autacoid, Autism, Autism Spectrum Disorders, Behaviour Disorder, Belnacasan, Celecoxib, Cognition, Comorbidity, Complication, Cyclooxygenase 2, Cyclooxygenase 2 Inhibitor, Cytokine, Cytokines, Depression, Dexmedetomidine, Disease Association, Dopaminergic Transmission, Electroencephalogram, Electroencephalography, Epilepsy, Epileptogenesis, Esculetin, High Mobility Group B1 Protein, Human, Ibuprofen, Icariin, IImmunoglobulin Enhancer Binding Protein, Immunology, Indoleamine 2, Inflammation, Inflammation Mediators, Infliximab, Interleukin 1beta, Interleukin 6, Minocycline, Nerve Cell Plasticity, Nervous System Development, Nervous System Inflammation, Neuroendocrine Regulation, Neurotransmitter Release, Nonhuman, Palmidrol, Paracetamol, Physiology, Priority Journal, Prostaglandin E2, Psychology, Review, SC 51089, Schizophrenia, Toll-Like Receptor 4, Transforming Growth Factor Beta, Tryptophan Hydroxylase, Tumor Necrosis Factor, Unclassified Drug @article{Paudel2018145, title = {Role of inflammation in epilepsy and neurobehavioral comorbidities: Implication for therapy}, author = {Y N Paudel and M F Shaikh and S Shah and Y Kumari and I Othman}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053082063&doi=10.1016%2fj.ejphar.2018.08.020&partnerID=40&md5=27ff0199bae72f156425637a7ad02228}, doi = {10.1016/j.ejphar.2018.08.020}, issn = {00142999}, year = {2018}, date = {2018-01-01}, journal = {European Journal of Pharmacology}, volume = {837}, pages = {145-155}, publisher = {Elsevier B.V.}, abstract = {Epilepsy is a devastating condition affecting around 70 million people worldwide. Moreover, the quality of life of people with epilepsy (PWE) is worsened by a series of comorbidities. The neurobehavioral comorbidities discussed herein share a reciprocal and complex relationship with epilepsy, which ultimately complicates the treatment process in PWE. Understanding the mechanistic pathway by which these comorbidities are associated with epilepsy might be instrumental in developing therapeutic interventions. Inflammatory cytokine signaling in the brain regulates important brain functions including neurotransmitter metabolism, neuroendocrine function, synaptic plasticity, dopaminergic transmission, the kynurenine pathway, and affects neurogenesis as well as the neural circuitry of moods. In this review, we hypothesize that the complex relationship between epilepsy and its related comorbidities (cognitive impairment, depression, anxiety, autism, and schizophrenia) can be unraveled through the inflammatory mechanism that plays a prominent role in all these individual conditions. An ample amount of evidence is available reporting the role of inflammation in epilepsy and all individual comorbid condition but their complex relationship with epilepsy has not yet been explored through the prospective of inflammatory pathway. Our review suggests that epilepsy and its neurobehavioral comorbidities are associated with elevated levels of several key inflammatory markers. This review also sheds light on the mechanistic association between epilepsy and its neurobehavioral comorbidities. Moreover, we analyzed several anti-inflammatory therapies available for epilepsy and its neurobehavioral comorbidities. We suggest, these anti-inflammatory therapies might be a possible intervention and could be a promising strategy for preventing epileptogenesis and its related neurobehavioral comorbidities. © 2018 Elsevier B.V.}, note = {cited By 14}, keywords = {3 Dioxygenase, Acetylsalicylic Acid, Adalimumab, Anakinra, Animals, Anti-Inflammatory Agents, Anxiety, Autacoid, Autism, Autism Spectrum Disorders, Behaviour Disorder, Belnacasan, Celecoxib, Cognition, Comorbidity, Complication, Cyclooxygenase 2, Cyclooxygenase 2 Inhibitor, Cytokine, Cytokines, Depression, Dexmedetomidine, Disease Association, Dopaminergic Transmission, Electroencephalogram, Electroencephalography, Epilepsy, Epileptogenesis, Esculetin, High Mobility Group B1 Protein, Human, Ibuprofen, Icariin, IImmunoglobulin Enhancer Binding Protein, Immunology, Indoleamine 2, Inflammation, Inflammation Mediators, Infliximab, Interleukin 1beta, Interleukin 6, Minocycline, Nerve Cell Plasticity, Nervous System Development, Nervous System Inflammation, Neuroendocrine Regulation, Neurotransmitter Release, Nonhuman, Palmidrol, Paracetamol, Physiology, Priority Journal, Prostaglandin E2, Psychology, Review, SC 51089, Schizophrenia, Toll-Like Receptor 4, Transforming Growth Factor Beta, Tryptophan Hydroxylase, Tumor Necrosis Factor, Unclassified Drug}, pubstate = {published}, tppubtype = {article} } Epilepsy is a devastating condition affecting around 70 million people worldwide. Moreover, the quality of life of people with epilepsy (PWE) is worsened by a series of comorbidities. The neurobehavioral comorbidities discussed herein share a reciprocal and complex relationship with epilepsy, which ultimately complicates the treatment process in PWE. Understanding the mechanistic pathway by which these comorbidities are associated with epilepsy might be instrumental in developing therapeutic interventions. Inflammatory cytokine signaling in the brain regulates important brain functions including neurotransmitter metabolism, neuroendocrine function, synaptic plasticity, dopaminergic transmission, the kynurenine pathway, and affects neurogenesis as well as the neural circuitry of moods. In this review, we hypothesize that the complex relationship between epilepsy and its related comorbidities (cognitive impairment, depression, anxiety, autism, and schizophrenia) can be unraveled through the inflammatory mechanism that plays a prominent role in all these individual conditions. An ample amount of evidence is available reporting the role of inflammation in epilepsy and all individual comorbid condition but their complex relationship with epilepsy has not yet been explored through the prospective of inflammatory pathway. Our review suggests that epilepsy and its neurobehavioral comorbidities are associated with elevated levels of several key inflammatory markers. This review also sheds light on the mechanistic association between epilepsy and its neurobehavioral comorbidities. Moreover, we analyzed several anti-inflammatory therapies available for epilepsy and its neurobehavioral comorbidities. We suggest, these anti-inflammatory therapies might be a possible intervention and could be a promising strategy for preventing epileptogenesis and its related neurobehavioral comorbidities. © 2018 Elsevier B.V. |
2015 |
Khosrowabadi, R; Quek, C; Ang, K K; Wahab, A; Chen, Annabel S -H Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions Journal Article Applied Soft Computing Journal, 32 , pp. 335-346, 2015, ISSN: 15684946, (cited By 6). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Biomedical Signal Processing, Brain, Connectivity Feature, Connectivity Pattern, Diseases, Electroencephalography, Face Perceptions, Feature Extraction, Functional Connectivity, Pattern Recognition, Pattern Recognition Techniques @article{Khosrowabadi2015335, title = {Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions}, author = {R Khosrowabadi and C Quek and K K Ang and A Wahab and S -H Annabel Chen}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927922520&doi=10.1016%2fj.asoc.2015.03.030&partnerID=40&md5=5973f80db5649e5c61e344907819a18b}, doi = {10.1016/j.asoc.2015.03.030}, issn = {15684946}, year = {2015}, date = {2015-01-01}, journal = {Applied Soft Computing Journal}, volume = {32}, pages = {335-346}, publisher = {Elsevier Ltd}, abstract = {In this study, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Therefore, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (EEG) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 autistic children (age matched in 7-10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods. © 2015 Published by Elsevier B.V.}, note = {cited By 6}, keywords = {Autism Spectrum Disorders, Biomedical Signal Processing, Brain, Connectivity Feature, Connectivity Pattern, Diseases, Electroencephalography, Face Perceptions, Feature Extraction, Functional Connectivity, Pattern Recognition, Pattern Recognition Techniques}, pubstate = {published}, tppubtype = {article} } In this study, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Therefore, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (EEG) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 autistic children (age matched in 7-10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods. © 2015 Published by Elsevier B.V. |
2014 |
Bhat, S; Acharya, U R; Adeli, H; Bairy, G M; Adeli, A Automated diagnosis of autism: In search of a mathematical marker Journal Article Reviews in the Neurosciences, 25 (6), pp. 851-861, 2014, ISSN: 03341763, (cited By 34). Abstract | Links | BibTeX | Tags: Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis @article{Bhat2014851, title = {Automated diagnosis of autism: In search of a mathematical marker}, author = {S Bhat and U R Acharya and H Adeli and G M Bairy and A Adeli}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925286949&doi=10.1515%2frevneuro-2014-0036&partnerID=40&md5=04858a5c9860e9027e3113835ca2e11f}, doi = {10.1515/revneuro-2014-0036}, issn = {03341763}, year = {2014}, date = {2014-01-01}, journal = {Reviews in the Neurosciences}, volume = {25}, number = {6}, pages = {851-861}, publisher = {Walter de Gruyter GmbH}, abstract = {Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH.}, note = {cited By 34}, keywords = {Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis}, pubstate = {published}, tppubtype = {article} } Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH. |
Sudirman, R; Hussin, S S Sensory responses of autism via electroencephalography for Sensory Profile Conference Institute of Electrical and Electronics Engineers Inc., 2014, ISBN: 9781479956869, (cited By 3). Abstract | Links | BibTeX | Tags: Autism, Discrete Wavelet Transforms, Diseases, Electroencephalography, Electrophysiology, Independent Component Analysis, International System, Learning, Sensory Analysis, Sensory Profiles, Sensory Profiling, Sensory Stimulation, Signal Processing, Standard Deviation, Wavelet Packet Transforms @conference{Sudirman2014626, title = {Sensory responses of autism via electroencephalography for Sensory Profile}, author = {R Sudirman and S S Hussin}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946435600&doi=10.1109%2fICCSCE.2014.7072794&partnerID=40&md5=3e6f1cfe19eae4fad359d2493aebd7e0}, doi = {10.1109/ICCSCE.2014.7072794}, isbn = {9781479956869}, year = {2014}, date = {2014-01-01}, journal = {Proceedings - 4th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2014}, pages = {626-631}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {The aim of this study is to investigate the brain signals of autism children through electroencephalography (EEG) associated to physical tasks. The physical task was meant to stimulate the sensitivity correlation of sensory response of a child. A group of autism children was chosen for this study and were given by five sensory stimulations which are audio, taste, touch, visual and vestibular. The acquisition of brain signals was acquainted using EEG Neurofax 9200 and the electrode positions were using 10-20 International System placements. The preprocessing signals were analyzed using independent component analysis (ICA) using EEGLAB Software and Discrete Wavelet Transform (DWT). The alpha wave was selected by level 6 decomposition and the extracted features represents the characteristic of the sensory task. The means, standard deviations and approximation entropy were extracted on the clean signals and forms into Sensory Profile (Sensory Profiling). From the overall results, the behavior of each autism children has been observed unstable emotion while running the sensory stimulation. The observation also helps to improve their learning strategy for the future work in assessment. © 2014 IEEE.}, note = {cited By 3}, keywords = {Autism, Discrete Wavelet Transforms, Diseases, Electroencephalography, Electrophysiology, Independent Component Analysis, International System, Learning, Sensory Analysis, Sensory Profiles, Sensory Profiling, Sensory Stimulation, Signal Processing, Standard Deviation, Wavelet Packet Transforms}, pubstate = {published}, tppubtype = {conference} } The aim of this study is to investigate the brain signals of autism children through electroencephalography (EEG) associated to physical tasks. The physical task was meant to stimulate the sensitivity correlation of sensory response of a child. A group of autism children was chosen for this study and were given by five sensory stimulations which are audio, taste, touch, visual and vestibular. The acquisition of brain signals was acquainted using EEG Neurofax 9200 and the electrode positions were using 10-20 International System placements. The preprocessing signals were analyzed using independent component analysis (ICA) using EEGLAB Software and Discrete Wavelet Transform (DWT). The alpha wave was selected by level 6 decomposition and the extracted features represents the characteristic of the sensory task. The means, standard deviations and approximation entropy were extracted on the clean signals and forms into Sensory Profile (Sensory Profiling). From the overall results, the behavior of each autism children has been observed unstable emotion while running the sensory stimulation. The observation also helps to improve their learning strategy for the future work in assessment. © 2014 IEEE. |
2013 |
Modugumudi, Y R; Santhosh, J; Anand, S Efficacy of collaborative virtual environment intervention programs in emotion expression of children with autism Journal Article Journal of Medical Imaging and Health Informatics, 3 (2), pp. 321-325, 2013, ISSN: 21567018, (cited By 4). Abstract | Links | BibTeX | Tags: Adolescent, Adult, Article, Autism, Children, Clinical Article, Collaborative Virtual Environment, Controlled Study, DSM-IV, Electroencephalogram, Electroencephalography, Electrooculogram, Emotion, Environment, Event Related Potential, Facial Expression, Female, Human, Latent Period, Male, Recognition, School Child @article{Modugumudi2013321, title = {Efficacy of collaborative virtual environment intervention programs in emotion expression of children with autism}, author = {Y R Modugumudi and J Santhosh and S Anand}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881262807&doi=10.1166%2fjmihi.2013.1167&partnerID=40&md5=c8e767c8eba2bbbec5ff36a43eb59af6}, doi = {10.1166/jmihi.2013.1167}, issn = {21567018}, year = {2013}, date = {2013-01-01}, journal = {Journal of Medical Imaging and Health Informatics}, volume = {3}, number = {2}, pages = {321-325}, abstract = {Exploratory empirical studies on Collaborative Virtual Environments (CVEs) were conducted to determine if children with autism could make basic emotional recognition effectively, with the use of CVEs as assistive technology. In this paper we report the results of electro-physiological study of two groups of autistic children after an intervention program with and without using Collaborative Virtual Environment. The group trained with CVE showed better results compared to the group trained without Collaborative virtual Environment. There is an emphasized early emotion expression positivity component at around 120 ms latency for CVE trained group which clearly distinguishes the CVE untrained group. Also there are differences observed in Event Related Potential component at about 170 ms latency after the stimulus. Results indicate that the Collaborative Virtual Environments are effective in training Autistic children. © 2013 American Scientific Publishers.}, note = {cited By 4}, keywords = {Adolescent, Adult, Article, Autism, Children, Clinical Article, Collaborative Virtual Environment, Controlled Study, DSM-IV, Electroencephalogram, Electroencephalography, Electrooculogram, Emotion, Environment, Event Related Potential, Facial Expression, Female, Human, Latent Period, Male, Recognition, School Child}, pubstate = {published}, tppubtype = {article} } Exploratory empirical studies on Collaborative Virtual Environments (CVEs) were conducted to determine if children with autism could make basic emotional recognition effectively, with the use of CVEs as assistive technology. In this paper we report the results of electro-physiological study of two groups of autistic children after an intervention program with and without using Collaborative Virtual Environment. The group trained with CVE showed better results compared to the group trained without Collaborative virtual Environment. There is an emphasized early emotion expression positivity component at around 120 ms latency for CVE trained group which clearly distinguishes the CVE untrained group. Also there are differences observed in Event Related Potential component at about 170 ms latency after the stimulus. Results indicate that the Collaborative Virtual Environments are effective in training Autistic children. © 2013 American Scientific Publishers. |
Shams, W K; Wahab, A Source-temporal-features for detection EEG behavior of autism spectrum disorder Conference 2013, ISBN: 9781479901340, (cited By 1). Abstract | Links | BibTeX | Tags: ASD, Autism Spectrum Disorders, Brain Activity, Children with Autism, Classification (of information), Communication, Diseases, Electroencephalography, Electronic Document, Information Technology, Multi-Layer Perception, Temporal Features, Time Difference of Arrival @conference{Shams2013, title = {Source-temporal-features for detection EEG behavior of autism spectrum disorder}, author = {W K Shams and A Wahab}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879037124&doi=10.1109%2fICT4M.2013.6518913&partnerID=40&md5=db31715811e1e8fdf62c9d61daf8e6f6}, doi = {10.1109/ICT4M.2013.6518913}, isbn = {9781479901340}, year = {2013}, date = {2013-01-01}, journal = {2013 5th International Conference on Information and Communication Technology for the Muslim World, ICT4M 2013}, abstract = {This study introduces a new model to capture the abnormal brain activity of children with Autism Spectrum Disorder (ASD) during eyes open and eyes closed resting conditions. EEG data was collected from normal subjects' ages (4 to 9) years and ASD subjects match group. Time Difference of Arrival (TDOA) approach was applied with EEG data raw for feature extracted at time domain. The neural network, Multilayer Perception (MLP) was used to distinguish between the two groups during the two tasks. Results show significant accuracy around 98% for both tasks and clearly discriminate for the features in z-dimension his electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. © 2013 IEEE.}, note = {cited By 1}, keywords = {ASD, Autism Spectrum Disorders, Brain Activity, Children with Autism, Classification (of information), Communication, Diseases, Electroencephalography, Electronic Document, Information Technology, Multi-Layer Perception, Temporal Features, Time Difference of Arrival}, pubstate = {published}, tppubtype = {conference} } This study introduces a new model to capture the abnormal brain activity of children with Autism Spectrum Disorder (ASD) during eyes open and eyes closed resting conditions. EEG data was collected from normal subjects' ages (4 to 9) years and ASD subjects match group. Time Difference of Arrival (TDOA) approach was applied with EEG data raw for feature extracted at time domain. The neural network, Multilayer Perception (MLP) was used to distinguish between the two groups during the two tasks. Results show significant accuracy around 98% for both tasks and clearly discriminate for the features in z-dimension his electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. © 2013 IEEE. |
2012 |
Tan, E H; Razak, S A; Abdullah, J M; Yusoff, Mohamed A A De-novo mutations and genetic variation in the SCN1A gene in Malaysian patients with generalized epilepsy with febrile seizures plus (GEFS+) Journal Article Epilepsy Research, 102 (3), pp. 210-215, 2012, ISSN: 09201211, (cited By 2). Abstract | Links | BibTeX | Tags: Alanine, Amino Acid Substitution, Arginine, Article, Asparagine, Aspartic Acid, Children, Clinical Article, Clinical Feature, Controlled Study, Disease Association, DNA Mutational Analysis, DNA Sequence, Electroencephalography, Epilepsy, Febrile, Febrile Convulsion, Female, Gene, Gene Frequency, Gene Identification, Generalized, Generalized Epilepsy, Genetic Association, Genetic Predisposition, Genetic Screening, Genetic Variability, Glycine, Histidine, Human, Infant, Malaysia, Male, Missense Mutation, Molecular Pathology, Mutation, Mutational Analysis, Mutator Gene, Nav1.1 Voltage-Gated Sodium Channel, Onset Age, Patient Assessment, Polymorphism, Preschool Child, Priority Journal, Promoter Region, School Child, Seizure, Sequence Analysis, Single Nucleotide, Single Nucleotide Polymorphism, Sodium Channel Nav1.1, Voltage Gated Sodium Channel Alpha1 Subunit Gene @article{Tan2012210, title = {De-novo mutations and genetic variation in the SCN1A gene in Malaysian patients with generalized epilepsy with febrile seizures plus (GEFS+)}, author = {E H Tan and S A Razak and J M Abdullah and A A Mohamed Yusoff}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870296042&doi=10.1016%2fj.eplepsyres.2012.08.004&partnerID=40&md5=25cc4eeb07db2492a7c04c6b3b3b2167}, doi = {10.1016/j.eplepsyres.2012.08.004}, issn = {09201211}, year = {2012}, date = {2012-01-01}, journal = {Epilepsy Research}, volume = {102}, number = {3}, pages = {210-215}, abstract = {Generalized epilepsy with febrile seizures plus (GEFS+) comprises a group of clinically and genetically heterogeneous epilepsy syndrome. Here, we provide the first report of clinical presentation and mutational analysis of SCN1A gene in 36 Malaysian GEFS+ patients. Mutational analysis of SCN1A gene revealed twenty seven sequence variants (missense mutation and silent polymorphism also intronic polymorphism), as well as 2 novel de-novo mutations were found in our patients at coding regions, c.5197A>G (N1733D) and c.4748A>G (H1583R). Our findings provide potential genetic insights into the pathogenesis of GEFS+ in Malaysian populations concerning the SCN1A gene mutations. © 2012 Elsevier B.V.}, note = {cited By 2}, keywords = {Alanine, Amino Acid Substitution, Arginine, Article, Asparagine, Aspartic Acid, Children, Clinical Article, Clinical Feature, Controlled Study, Disease Association, DNA Mutational Analysis, DNA Sequence, Electroencephalography, Epilepsy, Febrile, Febrile Convulsion, Female, Gene, Gene Frequency, Gene Identification, Generalized, Generalized Epilepsy, Genetic Association, Genetic Predisposition, Genetic Screening, Genetic Variability, Glycine, Histidine, Human, Infant, Malaysia, Male, Missense Mutation, Molecular Pathology, Mutation, Mutational Analysis, Mutator Gene, Nav1.1 Voltage-Gated Sodium Channel, Onset Age, Patient Assessment, Polymorphism, Preschool Child, Priority Journal, Promoter Region, School Child, Seizure, Sequence Analysis, Single Nucleotide, Single Nucleotide Polymorphism, Sodium Channel Nav1.1, Voltage Gated Sodium Channel Alpha1 Subunit Gene}, pubstate = {published}, tppubtype = {article} } Generalized epilepsy with febrile seizures plus (GEFS+) comprises a group of clinically and genetically heterogeneous epilepsy syndrome. Here, we provide the first report of clinical presentation and mutational analysis of SCN1A gene in 36 Malaysian GEFS+ patients. Mutational analysis of SCN1A gene revealed twenty seven sequence variants (missense mutation and silent polymorphism also intronic polymorphism), as well as 2 novel de-novo mutations were found in our patients at coding regions, c.5197A>G (N1733D) and c.4748A>G (H1583R). Our findings provide potential genetic insights into the pathogenesis of GEFS+ in Malaysian populations concerning the SCN1A gene mutations. © 2012 Elsevier B.V. |
Tan, E H; Yusoff, A A M; Abdullah, J M; Razak, S A Generalized epilepsy with febrile seizure plus (GEFS+) spectrum: Novel de novo mutation of SCN1A detected in a Malaysian patient Journal Article Journal of Pediatric Neurosciences, 7 (2), pp. 123-125, 2012, ISSN: 18171745, (cited By 3). Abstract | Links | BibTeX | Tags: Adolescent, Anxiety Disorder, Article, Autism, Carbamazepine, Case Report, Computer Assisted Tomography, Electroencephalogram, Electroencephalography, Febrile Convulsion, Gene, Generalized Epilepsy, Generalized Epilepsy with Febrile Seizure Plus, Human, Karyotype, Malaysia, Male, Medical History, Mental Deficiency, Missense Mutation, Nuclear Magnetic Resonance Imaging, Phenotype, SCN1A Gene, Tonic Clonic Seizure, Topiramate, Valproic Acid @article{Tan2012123, title = {Generalized epilepsy with febrile seizure plus (GEFS+) spectrum: Novel de novo mutation of SCN1A detected in a Malaysian patient}, author = {E H Tan and A A M Yusoff and J M Abdullah and S A Razak}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870194979&doi=10.4103%2f1817-1745.102575&partnerID=40&md5=b73f0bdb583e84404e0fff232faf30cb}, doi = {10.4103/1817-1745.102575}, issn = {18171745}, year = {2012}, date = {2012-01-01}, journal = {Journal of Pediatric Neurosciences}, volume = {7}, number = {2}, pages = {123-125}, abstract = {In this report, we describe a 15-year-old Malaysian male patient with a de novo SCN1A mutation who experienced prolonged febrile seizures after his first seizure at 6 months of age. This boy had generalized tonic clonic seizure (GTCS) which occurred with and without fever. Sequencing analysis of voltage-gated sodium channel a1-subunit gene, SCN1A, confirmed a homozygous A to G change at nucleotide 5197 (c.5197A > G) in exon 26 resulting in amino acid substitution of asparagines to aspartate at codon 1733 of sodium channel. The mutation identified in this patient is located in the pore-forming loop of SCN1A and this case report suggests missense mutation in pore-forming loop causes generalized epilepsy with febrile seizure plus (GEFS+) with clinically more severe neurologic phenotype including intellectual disabilities (mental retardation and autism features) and neuropsychiatric disease (anxiety disorder).}, note = {cited By 3}, keywords = {Adolescent, Anxiety Disorder, Article, Autism, Carbamazepine, Case Report, Computer Assisted Tomography, Electroencephalogram, Electroencephalography, Febrile Convulsion, Gene, Generalized Epilepsy, Generalized Epilepsy with Febrile Seizure Plus, Human, Karyotype, Malaysia, Male, Medical History, Mental Deficiency, Missense Mutation, Nuclear Magnetic Resonance Imaging, Phenotype, SCN1A Gene, Tonic Clonic Seizure, Topiramate, Valproic Acid}, pubstate = {published}, tppubtype = {article} } In this report, we describe a 15-year-old Malaysian male patient with a de novo SCN1A mutation who experienced prolonged febrile seizures after his first seizure at 6 months of age. This boy had generalized tonic clonic seizure (GTCS) which occurred with and without fever. Sequencing analysis of voltage-gated sodium channel a1-subunit gene, SCN1A, confirmed a homozygous A to G change at nucleotide 5197 (c.5197A > G) in exon 26 resulting in amino acid substitution of asparagines to aspartate at codon 1733 of sodium channel. The mutation identified in this patient is located in the pore-forming loop of SCN1A and this case report suggests missense mutation in pore-forming loop causes generalized epilepsy with febrile seizure plus (GEFS+) with clinically more severe neurologic phenotype including intellectual disabilities (mental retardation and autism features) and neuropsychiatric disease (anxiety disorder). |
2011 |
Razali, N; Wahab, A 2D Affective Space Model (ASM) for detecting autistic children Conference 2011, ISBN: 9781612848433, (cited By 8). Abstract | Links | BibTeX | Tags: Autistic Children, Brain Disorders, Brain Imaging, Brain Imaging Techniques, Brain Signals, Children with Autism, Consumer Electronics, Data Collection, Diseases, Electroencephalogram, Electroencephalography, Feature Extraction, Frequency Domains, Functional Magnetic Resonance Imaging, Gaussian Mixture Model, Magnetic Resonance Imaging, Multi Layer Perceptron, Multilayer Perceptron, Multilayers, Positron Emission Tomography, Resonance, Space Models, Verification Results @conference{Razali2011536, title = {2D Affective Space Model (ASM) for detecting autistic children}, author = {N Razali and A Wahab}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052392399&doi=10.1109%2fISCE.2011.5973888&partnerID=40&md5=f6ea401148e6558b861e4df6407e527e}, doi = {10.1109/ISCE.2011.5973888}, isbn = {9781612848433}, year = {2011}, date = {2011-01-01}, journal = {Proceedings of the International Symposium on Consumer Electronics, ISCE}, pages = {536-541}, abstract = {There are many research works have been done on autism cases using brain imaging techniques. In this paper, the Electroencephalogram (EEG) was used to understand and analyze the functionality of the brain to identify or detect brain disorder for autism in term of motor imitation. Thus, the portability and affordability of the EEG equipment makes it a better choice in comparison with other brain imaging device such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and megnetoencephalography (MEG). Data collection consists of both autistic and normal children with the total of 6 children for each group. All subjects were asked to clinch their hand by following video stimuli which presented in 1 minute time. Gaussian mixture model was used as a method of feature extraction for analyzing the brain signals in frequency domain. Then, the extraction data were classified using multilayer perceptron (MLP). According to the verification result, the percentage of discriminating between both groups is up to 85% in average by using k-fold validation. © 2011 IEEE.}, note = {cited By 8}, keywords = {Autistic Children, Brain Disorders, Brain Imaging, Brain Imaging Techniques, Brain Signals, Children with Autism, Consumer Electronics, Data Collection, Diseases, Electroencephalogram, Electroencephalography, Feature Extraction, Frequency Domains, Functional Magnetic Resonance Imaging, Gaussian Mixture Model, Magnetic Resonance Imaging, Multi Layer Perceptron, Multilayer Perceptron, Multilayers, Positron Emission Tomography, Resonance, Space Models, Verification Results}, pubstate = {published}, tppubtype = {conference} } There are many research works have been done on autism cases using brain imaging techniques. In this paper, the Electroencephalogram (EEG) was used to understand and analyze the functionality of the brain to identify or detect brain disorder for autism in term of motor imitation. Thus, the portability and affordability of the EEG equipment makes it a better choice in comparison with other brain imaging device such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and megnetoencephalography (MEG). Data collection consists of both autistic and normal children with the total of 6 children for each group. All subjects were asked to clinch their hand by following video stimuli which presented in 1 minute time. Gaussian mixture model was used as a method of feature extraction for analyzing the brain signals in frequency domain. Then, the extraction data were classified using multilayer perceptron (MLP). According to the verification result, the percentage of discriminating between both groups is up to 85% in average by using k-fold validation. © 2011 IEEE. |
Shams, Khazaal W; Rahman, Abdul A W Characterizing autistic disorder based on principle component analysis Conference 2011, ISBN: 9781457714184, (cited By 6). Abstract | Links | BibTeX | Tags: Autism, Brain Function, Brain Signals, Classification Process, Data Dimensions, Diseases, Electroencephalogram Signals, Electroencephalography, Frequency Domain Analysis, Industrial Electronics, Motor Movements, Motor Tasks, PCA, Principal Component Analysis, Signal Detection, Time Frequency Domain @conference{KhazaalShams2011653, title = {Characterizing autistic disorder based on principle component analysis}, author = {W Khazaal Shams and A W Abdul Rahman}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855644760&doi=10.1109%2fISIEA.2011.6108797&partnerID=40&md5=c486566e2d7ff404d830704c0b404067}, doi = {10.1109/ISIEA.2011.6108797}, isbn = {9781457714184}, year = {2011}, date = {2011-01-01}, journal = {2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011}, pages = {653-657}, abstract = {Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. However, Electroencephalogram (EEG) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open-eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task. © 2011 IEEE.}, note = {cited By 6}, keywords = {Autism, Brain Function, Brain Signals, Classification Process, Data Dimensions, Diseases, Electroencephalogram Signals, Electroencephalography, Frequency Domain Analysis, Industrial Electronics, Motor Movements, Motor Tasks, PCA, Principal Component Analysis, Signal Detection, Time Frequency Domain}, pubstate = {published}, tppubtype = {conference} } Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. However, Electroencephalogram (EEG) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open-eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task. © 2011 IEEE. |
2010 |
Othman, M; Wahab, A Affective face processing analysis in autism using electroencephalogram Conference 2010, ISBN: 9789791948913, (cited By 7). Abstract | Links | BibTeX | Tags: Affective Face Processing, Analysis Results, Autism Spectrum Disorders, Brain Wave, Diseases, Electroencephalogram, Electroencephalography, Emotion, Emotion Models, Eye Contact, Facial Expression, Human Emotion, Information Technology @conference{Othman2010, title = {Affective face processing analysis in autism using electroencephalogram}, author = {M Othman and A Wahab}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052372671&doi=10.1109%2fICT4M.2010.5971907&partnerID=40&md5=4d5f8a317d6a9c93e1ab7186a9b99b52}, doi = {10.1109/ICT4M.2010.5971907}, isbn = {9789791948913}, year = {2010}, date = {2010-01-01}, journal = {Proceeding of the 3rd International Conference on Information and Communication Technology for the Moslem World: ICT Connecting Cultures, ICT4M 2010}, pages = {E23-E27}, abstract = {Past research in the area of psychology has indicated the inability of Autism Spectrum Disorder (ASD) patients for interpreting other people's emotion. This impairment is due to their lack of social motivation and eye contact during communication, causing insufficient information to the brain for interpreting emotional faces. This paper investigates human brainwaves for understanding affective face processing of ASD children. Pattern classification results are explained based on the 2-dimensional emotion model. The 2-dimensional model explains human emotion in terms of the pleasant/ unpleasantness (or valence) and intensity (or arousal). Analysis results revealed that emotion of the non-autistic group is altered towards matching the affective faces currently displayed on the computer monitor. Emotion dynamics of ASD children, however, indicated the trend for reversed valence while watching emotionally related facial expressions. © 2010 IEEE.}, note = {cited By 7}, keywords = {Affective Face Processing, Analysis Results, Autism Spectrum Disorders, Brain Wave, Diseases, Electroencephalogram, Electroencephalography, Emotion, Emotion Models, Eye Contact, Facial Expression, Human Emotion, Information Technology}, pubstate = {published}, tppubtype = {conference} } Past research in the area of psychology has indicated the inability of Autism Spectrum Disorder (ASD) patients for interpreting other people's emotion. This impairment is due to their lack of social motivation and eye contact during communication, causing insufficient information to the brain for interpreting emotional faces. This paper investigates human brainwaves for understanding affective face processing of ASD children. Pattern classification results are explained based on the 2-dimensional emotion model. The 2-dimensional model explains human emotion in terms of the pleasant/ unpleasantness (or valence) and intensity (or arousal). Analysis results revealed that emotion of the non-autistic group is altered towards matching the affective faces currently displayed on the computer monitor. Emotion dynamics of ASD children, however, indicated the trend for reversed valence while watching emotionally related facial expressions. © 2010 IEEE. |
Sudirman, ; Saidin, S; Safri, Mat N Study of electroencephalography signal of autism and down syndrome children using FFT Conference 2010, ISBN: 9781424476473, (cited By 15). Abstract | Links | BibTeX | Tags: Alpha Value, Autism, Down Syndrome, EEG Signals, Electroencephalography, Electrophysiology, Fast Fourier Transforms, Industrial Electronics, Metadata, User Interfaces, Visual Evoked Potential, Visualization @conference{Sudirman2010401, title = {Study of electroencephalography signal of autism and down syndrome children using FFT}, author = {Sudirman and S Saidin and N Mat Safri}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251542066&doi=10.1109%2fISIEA.2010.5679434&partnerID=40&md5=17fce4f69b27a3cc644f36c118b6ec6e}, doi = {10.1109/ISIEA.2010.5679434}, isbn = {9781424476473}, year = {2010}, date = {2010-01-01}, journal = {ISIEA 2010 - 2010 IEEE Symposium on Industrial Electronics and Applications}, pages = {401-406}, abstract = {Electroencephalography (EEG) signal between normal and special children is slightly different. Different types of special children will generate different shape of EEG patterns depend on their neurological function. This paper demonstrates the classification of EEG signal for special children: to determine and to classify level and pattern of EEG signal for autism and Down syndrome children. EEG signal was recorded and captured from normal and special children based on their visual response using Visual Evoked Potential (VEP) method. The data is analyzed using Fast Fourier Transform (FFT), so that, normal and special children can be distinguished based on alpha (α) value. As a result, alpha value for normal children at 10 Hz is higher than autism and Down syndrome children. A friendly user interface was built for easy storage and visualization. ©2010 IEEE.}, note = {cited By 15}, keywords = {Alpha Value, Autism, Down Syndrome, EEG Signals, Electroencephalography, Electrophysiology, Fast Fourier Transforms, Industrial Electronics, Metadata, User Interfaces, Visual Evoked Potential, Visualization}, pubstate = {published}, tppubtype = {conference} } Electroencephalography (EEG) signal between normal and special children is slightly different. Different types of special children will generate different shape of EEG patterns depend on their neurological function. This paper demonstrates the classification of EEG signal for special children: to determine and to classify level and pattern of EEG signal for autism and Down syndrome children. EEG signal was recorded and captured from normal and special children based on their visual response using Visual Evoked Potential (VEP) method. The data is analyzed using Fast Fourier Transform (FFT), so that, normal and special children can be distinguished based on alpha (α) value. As a result, alpha value for normal children at 10 Hz is higher than autism and Down syndrome children. A friendly user interface was built for easy storage and visualization. ©2010 IEEE. |
Othman, M; Wahab, A Understanding autistic children perception through EEG Conference 2010, ISBN: 9781617820267, (cited By 0). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Autistic Children, Behavioral Research, Children with Autism, Computer Applications, Control Subject, Electroencephalography, Emotion, Emotional State, Empirical Studies, Facial Expression, Mel Frequency Cepstral Coefficients, Multilayer-Percheptron (MLP), Speech Recognition @conference{Othman2010315, title = {Understanding autistic children perception through EEG}, author = {M Othman and A Wahab}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883660524&partnerID=40&md5=df9dac75053fbfa693b4823d5a0a77ad}, isbn = {9781617820267}, year = {2010}, date = {2010-01-01}, journal = {23rd International Conference on Computer Applications in Industry and Engineering 2010, CAINE 2010 - Including SNA 2010 Workshop}, pages = {315-320}, abstract = {Autistic children are known as having difficulties understanding human's facial expressions, making them incapable of interpreting the emotional states of others. This paper seeks to understand autistic children perception by analyzing brain signals using MFCC and MLP. An empirical study was conducted on 6 autistic and 6 typically developing children. Subjects' brainwaves were monitored while watching calm, happy and sad faces. Experimental results show that it is possible to discriminate the emotions of autistic children against control subjects with the accuracy of 76.61%. Brainwaves of autistic children also showed the trend of reversed emotions compared to normal children while watching happy and sad faces.}, note = {cited By 0}, keywords = {Autism Spectrum Disorders, Autistic Children, Behavioral Research, Children with Autism, Computer Applications, Control Subject, Electroencephalography, Emotion, Emotional State, Empirical Studies, Facial Expression, Mel Frequency Cepstral Coefficients, Multilayer-Percheptron (MLP), Speech Recognition}, pubstate = {published}, tppubtype = {conference} } Autistic children are known as having difficulties understanding human's facial expressions, making them incapable of interpreting the emotional states of others. This paper seeks to understand autistic children perception by analyzing brain signals using MFCC and MLP. An empirical study was conducted on 6 autistic and 6 typically developing children. Subjects' brainwaves were monitored while watching calm, happy and sad faces. Experimental results show that it is possible to discriminate the emotions of autistic children against control subjects with the accuracy of 76.61%. Brainwaves of autistic children also showed the trend of reversed emotions compared to normal children while watching happy and sad faces. |
2018 |
A review in modification food-intake behavior by brain stimulation: Excess weight cases Journal Article NeuroQuantology, 16 (12), pp. 86-97, 2018, ISSN: 13035150, (cited By 2). |
GRIN2D variants in three cases of developmental and epileptic encephalopathy Journal Article Clinical Genetics, 94 (6), pp. 538-547, 2018, ISSN: 00099163, (cited By 4). |
Neurophysiological analysis of porn addiction to learning disabilities Conference Institute of Electrical and Electronics Engineers Inc., 2018, ISBN: 9781538675250, (cited By 2). |
Profile indicator for autistic children using EEG biosignal potential of sensory tasks Conference Institute of Electrical and Electronics Engineers Inc., 2018, ISBN: 9781538612774, (cited By 0). |
Role of inflammation in epilepsy and neurobehavioral comorbidities: Implication for therapy Journal Article European Journal of Pharmacology, 837 , pp. 145-155, 2018, ISSN: 00142999, (cited By 14). |
2015 |
Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions Journal Article Applied Soft Computing Journal, 32 , pp. 335-346, 2015, ISSN: 15684946, (cited By 6). |
2014 |
Automated diagnosis of autism: In search of a mathematical marker Journal Article Reviews in the Neurosciences, 25 (6), pp. 851-861, 2014, ISSN: 03341763, (cited By 34). |
Sensory responses of autism via electroencephalography for Sensory Profile Conference Institute of Electrical and Electronics Engineers Inc., 2014, ISBN: 9781479956869, (cited By 3). |
2013 |
Efficacy of collaborative virtual environment intervention programs in emotion expression of children with autism Journal Article Journal of Medical Imaging and Health Informatics, 3 (2), pp. 321-325, 2013, ISSN: 21567018, (cited By 4). |
Source-temporal-features for detection EEG behavior of autism spectrum disorder Conference 2013, ISBN: 9781479901340, (cited By 1). |
2012 |
De-novo mutations and genetic variation in the SCN1A gene in Malaysian patients with generalized epilepsy with febrile seizures plus (GEFS+) Journal Article Epilepsy Research, 102 (3), pp. 210-215, 2012, ISSN: 09201211, (cited By 2). |
Generalized epilepsy with febrile seizure plus (GEFS+) spectrum: Novel de novo mutation of SCN1A detected in a Malaysian patient Journal Article Journal of Pediatric Neurosciences, 7 (2), pp. 123-125, 2012, ISSN: 18171745, (cited By 3). |
2011 |
2D Affective Space Model (ASM) for detecting autistic children Conference 2011, ISBN: 9781612848433, (cited By 8). |
Characterizing autistic disorder based on principle component analysis Conference 2011, ISBN: 9781457714184, (cited By 6). |
2010 |
Affective face processing analysis in autism using electroencephalogram Conference 2010, ISBN: 9789791948913, (cited By 7). |
Study of electroencephalography signal of autism and down syndrome children using FFT Conference 2010, ISBN: 9781424476473, (cited By 15). |
Understanding autistic children perception through EEG Conference 2010, ISBN: 9781617820267, (cited By 0). |