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Fulltext Auditory Brainstem Response (ABR) Findings With Click and CE-Chirp Stimulations in Noise-Exposed Participants

Zakaria MN, Abdul Wahab NA, Awang MA

Noise Health, 2017 12 2;19(87):112-113.
PMID: 29192621 DOI: 10.4103/nah.NAH_2_17
Matched MeSH terms: Evoked Potentials, Auditory, Brain Stem
Test-retest reliability of speech-evoked auditory brainstem response in healthy children at a low sensation level

Zakaria MN, Jalaei B

Int J Pediatr Otorhinolaryngol, 2017 Nov;102:28-31.
PMID: 29106871 DOI: 10.1016/j.ijporl.2017.08.033

OBJECTIVE: Auditory brainstem responses evoked by complex stimuli such as speech syllables have been studied in normal subjects and subjects with compromised auditory functions. The stability of speech-evoked auditory brainstem response (speech-ABR) when tested over time has been reported but the literature is limited. The present study was carried out to determine the test-retest reliability of speech-ABR in healthy children at a low sensation level.
METHODS: Seventeen healthy children (6 boys, 11 girls) aged from 5 to 9 years (mean = 6.8 ± 3.3 years) were tested in two sessions separated by a 3-month period. The stimulus used was a 40-ms syllable /da/ presented at 30 dB sensation level.
RESULTS: As revealed by pair t-test and intra-class correlation (ICC) analyses, peak latencies, peak amplitudes and composite onset measures of speech-ABR were found to be highly replicable. Compared to other parameters, higher ICC values were noted for peak latencies of speech-ABR.
CONCLUSION: The present study was the first to report the test-retest reliability of speech-ABR recorded at low stimulation levels in healthy children. Due to its good stability, it can be used as an objective indicator for assessing the effectiveness of auditory rehabilitation in hearing-impaired children in future studies.

Matched MeSH terms: Evoked Potentials, Auditory, Brain Stem/physiology*
Statistical verification on the effect of hearing loss on speech-evoked auditory brainstem response

Zakaria MN

Int J Pediatr Otorhinolaryngol, 2017 10;101:264.
PMID: 28843698 DOI: 10.1016/j.ijporl.2017.08.016
Matched MeSH terms: Brain Stem; Evoked Potentials, Auditory, Brain Stem*
Fulltext ASPP2 deficiency causes features of 1q41q42 microdeletion syndrome

Zak J, Vives V, Szumska D, Vernet A, Schneider JE, Miller P, et al.

Cell Death Differ, 2016 Dec;23(12):1973-1984.
PMID: 27447114 DOI: 10.1038/cdd.2016.76

Chromosomal abnormalities are implicated in a substantial number of human developmental syndromes, but for many such disorders little is known about the causative genes. The recently described 1q41q42 microdeletion syndrome is characterized by characteristic dysmorphic features, intellectual disability and brain morphological abnormalities, but the precise genetic basis for these abnormalities remains unknown. Here, our detailed analysis of the genetic abnormalities of 1q41q42 microdeletion cases identified TP53BP2, which encodes apoptosis-stimulating protein of p53 2 (ASPP2), as a candidate gene for brain abnormalities. Consistent with this, Trp53bp2-deficient mice show dilation of lateral ventricles resembling the phenotype of 1q41q42 microdeletion patients. Trp53bp2 deficiency causes 100% neonatal lethality in the C57BL/6 background associated with a high incidence of neural tube defects and a range of developmental abnormalities such as congenital heart defects, coloboma, microphthalmia, urogenital and craniofacial abnormalities. Interestingly, abnormalities show a high degree of overlap with 1q41q42 microdeletion-associated abnormalities. These findings identify TP53BP2 as a strong candidate causative gene for central nervous system (CNS) defects in 1q41q42 microdeletion syndrome, and open new avenues for investigation of the mechanisms underlying CNS abnormalities.

Matched MeSH terms: Brain/abnormalities; Brain/pathology
Fulltext Reliable epileptic seizure detection using an improved wavelet neural network

Zainuddin Z, Huong LK, Pauline O

Australas Med J, 2013;6(5):308-14.
PMID: 23745153 DOI: 10.4066/AMJ.2013.1640

Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts.

Matched MeSH terms: Brain Waves
Acute Intermittent Porphyria: A rare cause of hyponatraemia

Zainuddin NM, Sthaneshwar P, Vethakkan SRDB

Malays J Pathol, 2019 Dec;41(3):369-372.
PMID: 31901925

INTRODUCTION: Hyponatraemia is one of the most frequent laboratory findings in hospitalised patients. We present an unusual case of hyponatraemia in a 23-year-old female secondary to acute intermittent porphyria (AIP), a rare inborn error of metabolism.
CASE REPORT: The patient presented with upper respiratory tract infection, fever, seizures and abdominal pain. An initial diagnosis of encephalitis was made. In view of the unexplained abdominal pain with other clinical findings such as posterior reversible encephalopathy syndrome by CT brain, temporary blindness as well as hyponatraemia, acute intermittent porphyria was suspected. Urine delta aminolaevulinic acid (δ-ALA) and porphobilinogen were elevated confirming the diagnosis of AIP. Genetic studies were done for this patient. The patient had a complete resolution of her symptoms with carbohydrate loading and high caloric diet.
CONCLUSION: Although rare, AIP should be considered as a cause of hyponatraemia in a patient who presents with signs and/or symptoms that are characteristic of this disease.

Matched MeSH terms: Brain/pathology
Fulltext Presence of allelic loss and PTEN mutations in malignant gliomas from Malay patients

Zainuddin N, Jaafar H, Isa MN, Abdullah JM

Med J Malaysia, 2004 Oct;59(4):468-79.
PMID: 15779579

Loss of heterozygosity (LOH) on several loci and mutations on PTEN tumor suppressor gene (10q23.3) occur frequently in sporadic gliomas. We have performed polymerase chain reaction (PCR)-LOH analysis using microsatellite markers and single-stranded conformational polymorphism (SSCP) analysis to determine the incidence of allelic losses on chromosome 10q, 9p, 17p and 13q and mutations of exons 5, 6 and 8 of the PTEN gene in malignant gliomas. Twelve of 23 (52.2%) malignant glioma cases showed allelic losses whereas 7 of 23, (30.4%) samples showed aberrant band patterns and mutations of the PTEN gene. Four of these cases showed LOH on 10q23 and mutations of the PTEN gene. The data on LOH indicated the involvement of different genes in gliomagenesis whereas mutations of the PTEN gene indicated the role of PTEN tumor suppressor gene in the progression of glioma in Malay population.

Matched MeSH terms: Brain Neoplasms/genetics*
Loss of heterozygosity on chromosomes 10q, 9p, 17p and 13q in malays with malignant glioma

Zainuddin N, Jaafart H, Isa MN, Abdullah JM

Neurol Res, 2004 Jan;26(1):88-92.
PMID: 14977064

Recent advances in neuro-oncology have revealed different pathways of molecular oncogenesis in malignant gliomas including loss of heterozygosity on chromosomal regions harboring tumor suppressor genes. In the present study, we performed polymerase chain reaction-loss of heterozygosity (PCR-LOH) analysis using microsatellite markers to identify loss of heterozygosity on chromosomes 10q, 9p, 17p and 13q in the Malays with malignant gliomas. Of 12 cases with allelic losses, seven (58.3%) cases showed LOH on chromosome 10q, three (25.0%) cases showed LOH on chromosome 9p, four (33.3%) cases showed LOH on chromosome 17p and two (16.7%) cases showed LOH on chromosome 13q. The cases include five (41.7%) cases of glioblastoma multiforme, three (25.0%) cases of anaplastic astrocytoma, three (25.0%) cases of anaplastic oligodendroglioma and one (8.3%) case of anaplastic ependymoma. Four cases showed loss of heterozygosity on more than one locus. Our findings showed that loss of heterozygosity on specific chromosomal regions contributes to the molecular pathway of glioma progression in Malay population. In addition, these data provide useful evidence of molecular genetic alterations of malignant glioma in South East Asian patients, particularly in the East Coast of Malaysia.

Matched MeSH terms: Brain Neoplasms/genetics*
Fulltext Psychosis induced by cefuroxime and metronidazole

Zahiruddin O, Shanooha M, Mohd Azhar MY

Med J Malaysia, 2014 Feb;69(1):33-4.
PMID: 24814627 MyJurnal

We report a case 35-year-old lady who developed acute psychosis following administration of cefuroxime and metronidazole. Earliest mood changes occurred on the second day of antibiotics therapy. She developed hallucinations, delusions and bizarre behavior 1 day after the completion of the antibiotic therapy. All the relevant investigations including CT brain were normal. The psychosis resolved completely within 5 days of antipsychotic treatment.

Matched MeSH terms: Brain
Inherited metabolic disorders presenting as hypoxic ischaemic encephalopathy: A case series of patients presenting at a tertiary care hospital in Pakistan

Zahid M, Khan AH, Yunus ZM, Chen BC, Steinmann B, Johannes H, et al.

J Pak Med Assoc, 2019 Mar;69(3):432-436.
PMID: 30890842

In spite of the efforts and interventions by the Government of Pakistan and The World Health Organization, the neonatal mortality in Pakistan has declined by only 0.9% as compared to the global average decline of 2.1% between 2000 and 2010. This has resulted in failure to achieve the global Millennium Development Goal 4. Hypoxic-ischaemic encephalopathy, still birth, sepsis, pneumonia, diarrhoea and birth defects are commonly attributed as leading causes of neonatal mortality in Pakistan. Inherited metabolic disorders often present at the time of birth or the first few days of life. The clinical presentation of the inherited metabolic disorders including hypotonia, seizure and lactic acidosis overlap with clinical features of hypoxic-ischaemic encephalopathy and sepsis. Thus, these disorders are often either missed or wrongly diagnosed as hypoxicischaemic encephalopathy or sepsis unless the physicians actively investigate for the underlying inherited metabolic disorders. We present 4 neonates who had received the diagnosis of hypoxic-ischaemic encephalopathy and eventually were diagnosed to have various inherited metabolic disorders. Neonates with sepsis and hypoxic-ischaemic encephalopathy-like clinical presentation should be evaluated for inherited metabolic disorders.

Matched MeSH terms: Hypoxia-Ischemia, Brain/diagnosis*
Decoding of visual information from human brain activity: A review of fMRI and EEG studies

Zafar R, Malik AS, Kamel N, Dass SC, Abdullah JM, Reza F, et al.

J Integr Neurosci, 2015 Jun;14(2):155-68.
PMID: 25939499 DOI: 10.1142/S0219635215500089

Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding. This paper concludes that it's not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.

Matched MeSH terms: Brain/blood supply*; Brain/physiology*
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*
Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

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

J Integr Neurosci, 2017;16(3):275-289.
PMID: 28891512 DOI: 10.3233/JIN-170016

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Matched MeSH terms: Brain
The relation between upper limb muscle and brain activity in two precision levels of repetitive light tasks

Zadry HR, Dawal SZ, Taha Z

Int J Occup Saf Ergon, 2011;17(4):373-84.
PMID: 22152503

A study was conducted to investigate the effects of repetitive light tasks of low and high precision on upper limb muscles and brain activities. Surface electromyography (EMG) and electroencephalography (EEG) were used to measure the muscle and brain activity of 10 subjects. The results show that the root-mean-square (RMS) and mean power frquency (MPF) of the muscle activity and the mean power of the EEG alpha bands were higher on the high-precision task than on the low-precision one. There was also a high and significant correlation between upper limb muscle and brain activity during the tasks. The longer the time and the more precise the task, the more the subjects become fatigued both physically and mentally. Thus, these results could be potentially useful in managing fatigue, especially fatique related to muscle and mental workload.

Matched MeSH terms: Brain Waves/physiology*
Development of statistical models for predicting muscle and mental activities during repetitive precision tasks

Zadry HR, Dawal SZ, Taha Z

Int J Occup Saf Ergon, 2016 Sep;22(3):374-83.
PMID: 27053140 DOI: 10.1080/10803548.2016.1150094

This study was conducted to develop muscle and mental activities on repetitive precision tasks. A laboratory experiment was used to address the objectives. Surface electromyography was used to measure muscle activities from eight upper limb muscles, while electroencephalography recorded mental activities from six channels. Fourteen university students participated in the study. The results show that muscle and mental activities increase for all tasks, indicating the occurrence of muscle and mental fatigue. A linear relationship between muscle activity, mental activity and time was found while subjects were performing the task. Thus, models were developed using those variables. The models were found valid after validation using other students' and workers' data. Findings from this study can contribute as a reference for future studies investigating muscle and mental activity and can be applied in industry as guidelines to manage muscle and mental fatigue, especially to manage job schedules and rotation.

Matched MeSH terms: Brain Waves/physiology*
Inter-hemispheric EEG coherence analysis in Parkinson's disease: assessing brain activity during emotion processing

Yuvaraj R, Murugappan M, Ibrahim NM, Sundaraj K, Omar MI, Mohamad K, et al.

J Neural Transm (Vienna), 2015 Feb;122(2):237-52.
PMID: 24894699 DOI: 10.1007/s00702-014-1249-4

Parkinson's disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3-AF4, F7-F8, F3-F4, FC5-FC6, T7-T8, P7-P8, and O1-O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities.

Matched MeSH terms: Brain/physiopathology*; Brain Waves/physiology*
Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study

Yuvaraj R, Murugappan M, Ibrahim NM, Omar MI, Sundaraj K, Mohamad K, et al.

J Integr Neurosci, 2014 Mar;13(1):89-120.
PMID: 24738541 DOI: 10.1142/S021963521450006X

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

Matched MeSH terms: Brain Mapping
Fulltext On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Yuvaraj R, Murugappan M, Mohamed Ibrahim N, Iqbal M, Sundaraj K, Mohamad K, et al.

Behav Brain Funct, 2014;10:12.
PMID: 24716619 DOI: 10.1186/1744-9081-10-12

While Parkinson's disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing.

Matched MeSH terms: Brain/physiopathology*
Emotion processing in Parkinson's disease: an EEG spectral power study

Yuvaraj R, Murugappan M, Omar MI, Ibrahim NM, Sundaraj K, Mohamad K, et al.

Int J Neurosci, 2014 Jul;124(7):491-502.
PMID: 24168328 DOI: 10.3109/00207454.2013.860527

Although an emotional deficit is a common finding in Parkinson's disease (PD), its neurobiological mechanism on emotion recognition is still unknown. This study examined the emotion processing deficits in PD patients using electroencephalogram (EEG) signals in response to multimodal stimuli.

Matched MeSH terms: Brain/physiopathology*
Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia

Yuvaraj R, Murugappan M, Acharya UR, Adeli H, Ibrahim NM, Mesquita E

Behav Brain Res, 2016 Feb 1;298(Pt B):248-60.
PMID: 26515932 DOI: 10.1016/j.bbr.2015.10.036

Successful emotional communication is crucial for social interactions and social relationships. Parkinson's Disease (PD) patients have shown deficits in emotional recognition abilities although the research findings are inconclusive. This paper presents an investigation of six emotions (happiness, sadness, fear, anger, surprise, and disgust) of twenty non-demented (Mini-Mental State Examination score >24) PD patients and twenty Healthy Controls (HCs) using Electroencephalogram (EEG)-based Brain Functional Connectivity (BFC) patterns. The functional connectivity index feature in EEG signals is computed using three different methods: Correlation (COR), Coherence (COH), and Phase Synchronization Index (PSI). Further, a new functional connectivity index feature is proposed using bispectral analysis. The experimental results indicate that the BFC change is significantly different among emotional states of PD patients compared with HC. Also, the emotional connectivity pattern classified using Support Vector Machine (SVM) classifier yielded the highest accuracy for the new bispectral functional connectivity index. The PD patients showed emotional impairments as demonstrated by a poor classification performance. This finding suggests that decrease in the functional connectivity indices during emotional stimulation in PD, indicating functional disconnections between cortical areas.

Matched MeSH terms: Brain

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