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Objectives: We investigated the auditory cognitive and behavioral functions during 2nd and 3rd trimesters of pregnancy using event-related potentials (ERPs) and different neuropsychology tests, respectively. Methods: The ERPs were studied by using a 128-sensor net, and PAS/COWA, WCST, ZCT, RAVLTIM/RAVLTDR/RAVLTTS, and BDI were tested for neuropsychology assessment. Total 39 subjects were recruited for control group (G1, n=15, non-pregnant), 2nd trimester group (G2, n=12, 13-26 weeks gestation), and 3rd trimester group (G3, n=12, 26- 40 weeks gestation). Auditory oddball paradigm was used during ERP study. Subjects silently counted only the target stimuli with attention by ignoring standard stimuli. Value of the mean differences of the target and standard stimuli were measured across groups in 10-20 electrode systems. Results: The P50, N100, and P300 ERP components were analyzed. The G3 (at F7 and C3) and G2 (at T4) groups evoked the highest significant amplitudes in P50, and G3 (at Cz and Pz) and G1 (at F8 and T4) evoked the highest significant amplitudes of N100 with significantly prolonged latencies at Cz and O1 (in G1) and F8 (in G2). However, significantly higher amplitudes of P300 were found at Fz (in G3) and FP1 (G2) (G2>G3). On the other hand, neuropsychology tests revealed that G2 possessed the highest significant score in WCST and G1 in ZCT. The G3 performed poorly in all tests. Conclusion: We concluded that pregnant groups performed better in auditory (attention/memory) and executive function assessment. Moreover, the 2nd trimester group has better auditory cognitive function compared to the 3rd trimester group due to the effect of hormonal changes during pregnancy, which might be a positive influence during the pregnancy period. Bangladesh Journal of Medical Science Vol.20(3) 2021 p.608-617

Adapting to a new traditional cultural environment has noteworthy implications for emotional manifestations, especially among immigrants who migrated to a new place. This study aimed to examine the effect of living duration (one-year cut-off period) among immigrants in Malaysia on their emotional manifestation towards the traditional culture of the ethnic majority. This study also examined the element of cultural items (games, clothes, or food) most affected during their adaptation in the new traditional culture environment. About 119 international graduate students (male; n=72, 61%) from a public university in West Malaysia who met the inclusion criteria took part in this cross-sectional study. They were selected using convenient sampling method. Using visualisation approach, two groups of immigrants with different living duration (less than a year versus a year or more) were asked to respond to the pictures of Malay traditional items (traditional food, game and clothes) that were projected onto the screen. While viewing the pictures, participants rated their emotional state of valence from one (low valence/high pleasantness) to nine (high valence/high pleasantness) according to the non-verbal pictorial measure of Self-Assessment Manikin Scale. Socio-demographic and acculturation scores were also collected. The study found the one-year duration of living in the country as insufficient for the immigrants to adapt to the new cultural environment. Traditional food seems to be the most affected cultural item. The tendency of immigrants to practice the Malay tradition explained 4% of the variance of immigrants’ emotional manifestation towards Malay traditional food. To conclude, the duration of living in a country is crucial to adapt to a new traditional culture. Unsurprisingly perhaps, traditional food in the particular country brought more emotional property than other cultural items.

Background: While there are studies on visual lexical processing in other languages among dyslexics, no studies were done in the Malay language. The origin of visual lexical processing might be different in the Malay language. We aimed to detect the source localisation of visual mismatch negativity (vMMN) during Malay orthographic lexicon stimulations, employing an event-related potential (ERP) study. Methods: Twelve dyslexic and twelve non-dyslexic children participated in this study. They pushed button ‘1’ when they saw real (meaningful) Malay words and button ‘2’ for pseudowords (meaningless). The source localisation of vMMN was performed in the grand average waveform by applying the standardised low-resolution brain electromagnetic tomography (sLORETA) method using Net Station software. Results: Left occipital (BA17) and left temporal (BA37) lobes were activated during real words in the non-dyslexic and dyslexic children, respectively. During pseudowords, BA18 and BA17 areas of the left occipital lobe were activated in the non-dyslexic and dyslexic children, separately. vMMN sources were found at the left temporal (BA37) and right frontal (BA11) lobes in nondyslexic and dyslexic children, respectively. Conclusion: Right frontal lobe is the decision-making area where vMMN source was found in dyslexic children. We concluded that dyslexic children required the decision-making area to detect Malay real and pseudowords.

Objective: Neuroticism is a medical condition associated with negative affect and is considered to predispose one to mental disorders. This study examined the effectsof arousal-evoking stimuli of various strengths on the severity of neuroticism. Materials and Methods: In the Event Related Potential (ERP)/electroencephalograph (EEG) recording session that was held at the Clinical Neuroscience Laboratory at a local hospital, Electroencephalogram was recorded in 58 participants (N=29 for moderate neuroticism and 29 for mild neuroticism) after they were screened for the severity of the neurotic trait. Universal emotional pictures were chosen randomly from the International Affective Picture System (IAPS) and were used as visual stimuli in the experiment. Visual stimuli weredivided into three categories (high, moderate, low) based on the IAPS normative mean values of arousal. Results: The significant interaction effect of P300 latency between neuroticism and arousal strength was found in the mid-frontal region. Meanwhile, independent of neuroticism, the main effects of arousal strength of the P300 (amplitude and latency) and N200 (latency) were observed in the mid-central region. Conclusion: There is a significant interaction between the severity of neuroticism and the emotional arousal strength, thus, points to the implication of the emotion process in the brain rewards system especially among individuals with neuroticism. Bangladesh Journal of Medical Science Vol.19(2) 2020 p.262-267

Objectives: To evaluate auditory cognitive function in mild brain injury (MBI) patients, which is important to determine for rehabilitation and improve quality of their life. Methods: Participants (n=19/group) were divided into group 1 (G1-control), group 2 (G2/1st test-MBI/within 7 days of road traffic accident-RTA) and group 3 (G3/2nd test-MBI/2-6 months after RTA). Event related potentials (ERPs) were conducted using a 128-sensor net; participants counted silently rare target tone stimuli and ignored standard tones. Several neuropsychology tests like Verbal fluency test (PAS), Wisconsin Card Sorting Test (WCST), Rey Auditory Verbal and Learning Test (RAVLTIM, RAVLTDR and RAVLTTS) and Beck Depression Inventory (BDI) were subsequently administered. Results: Sensory (P50, N100) and cognitive (P300) ERP components were analysed from ERP waveforms. There were no significant group differences in amplitudes or latencies for all components across sites except P300 component amplitudes at T6 location. P50, N100 and P300 ERP components exhibited non-significantly increased amplitudes in G2 and G3 compared with G1 at all sites; non-significantly shorter latencies were identified at various sites. At several locations, G3 evoked non-significantly increased amplitudes and longer latencies with shorter latencies to other sites compared with G2 in all components. The MBI (G3) group exhibited significantly increased WCST, RAVLTIM and RAVLTDR scores compared with G1. Conclusion: These findings indicate MBI patients may have mild auditory, cognitive and executive dysfunctions with good auditory memory. MBI was associated with mild depression. Bangladesh Journal of Medical Science Vol.18(3) 2019 p.557-566

Background Research on audiovisual post-attentive integration has been carried out using a variety of experimental paradigms and experimental groups but not yet studied in dyslexia. We investigated post-attentive integration and topographic voltage distribution in children with dyslexia by analysing the P300 event-related potential (ERP) component. Methods We used a 128-child ERP net for the ERP experiment. Two types of stimuli were presented as either congruent or incongruent stimuli. Congruent stimuli included a matching auditory sound with an animal image, whereas incongruent stimuli included unmatched animal sounds. A total of 24 age-matched children were recruited in the control (n = 12) and dyslexia (n = 12) groups. Children pressed button '1' or '2' when presented with congruent or incongruent stimuli, respectively. The P300 amplitudes and latencies with topographic voltage distribution were analysed for both groups. Results The dyslexia group evoked significantly higher P300 amplitudes at the T4 area than the control group. No significant differences were found in cases of P300 latency. Moreover, the dyslexia group demonstrated a higher intensity of P300 voltage distribution in the right parietal and left occipital areas than the control group. Conclusion Post-attentive integration for children with dyslexia is higher and that this integration process implicated the parietal and occipital areas.

Background : We assessed cognitive function by using different colours. Colour has been used in different neuropsychology tests for diagnostic and therapeutic purposes. Purposes: As male and female hormones are different, it is important to investigate the effect of different colours on the male and female groups for planning their therapeutic strategy in different diseases. Methods: This prospective study was done between 2012 and 2014. We used the 128-sensor net for an event related potential (ERP) study in male and female groups (n= 22 in each group). Different colours were used as stimuli. Subjects pressed ‘button 1’ when they liked the colour and ‘button 2’ when they disliked it. Reaction time (RT) and differences in like and dislike stimuli were analysed. The values of the mean differences of like and dislike stimuli were calculated using a 10-20 electrode system of 19 electrodes. The amplitudes and latencies of the N200 and P300 ERP components were analysed. Results: No significant differences were found in the mean differences of the amplitudes and latencies of the N200 and P300 ERP components between the male and female groups across 19 electrode sites. RTs were non-significantly longer in the male group. However, colour reflected on the frontal-right occipital area in the female group and the frontal-left occipital area in the male group. Conclusion: There might be a possibility of delayed decision-making due to difficulty assessing emotion in the male group compared with the female group. Bangladesh Journal of Medical Science Vol.17(4) 2018 p.612-618

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.

The role of language in communication plays a crucial role in human development and function. In patients who have a surgical lesion at the functional language areas, surgery should be intricately planned to avoid incurring further morbidity. This normally requires extensive functional and anatomical mappings of the brain to identify regions that are involved in language processing and production. In our case report, regions of the brain that are important for language functions were studied before surgery by employing (a) extraoperative methods such as functional magnetic resonance imaging, transmagnetic stimulation, and magnetoencephalography; (b) during the surgery by utilizing intraoperative awake surgical methods such as an intraoperative electrical stimulation; and (c) a two-stage surgery, in which electrical stimulation and first mapping are made thoroughly in the ward before second remapping during surgery. The extraoperative methods before surgery can guide the neurosurgeon to localize the functional language regions and tracts preoperatively. This will be confirmed using single-stage intraoperative electrical brain stimulation during surgery or a two-stage electrical brain stimulation before and during surgery. Here, we describe two cases in whom one has a superficial lesion and another a deep-seated lesion at language-related regions, in which language mapping was done to preserve its function. Additional review on the neuroanatomy of language regions, language network, and its impairment was also described.

Background Sex is a psychobiological factor that is important in the process of emotion. This study determines the effect of sex on the electropsychological process of various intensities of emotional arousal. Methods In the Event-related Potential (ERP) session, electroencephalographic (EEG) data was recorded for 90 participants, 60% of whom were females. The participants responded to 30 universal emotional pictures, randomly chosen from the International Affective Picture System (IAPS), which were classified as invoking high, moderate, and low intensity of emotional arousal. Results From the analysis of variance of two-way mixed design, the interaction between sex and emotional intensity was observed in the occipital regions (O2), indexed by the amplitude of P300 and N200 components. Males exhibited higher amplitude of P300 and N200 components (in the occipital region) as responded to high and low emotional arousal stimuli than females. Conclusion Sex is a fundamental factor that modulates psychological states in reaction to emotional stimuli.

Background Previous studies from animal models have shown that pre-synaptic NMDA receptors (preNMDARs) are present in the cortex, but the role of inhibition mediated by preNMDARs during epileptogenesis remains unclear. In this study, we wanted to observe the changes in GABAergic inhibition through preNMDARs in sensory-motor and visual cortical pyramidal neurons after pilocarpine-induced status epilepticus. Methods Using a pilocarpine-induced epileptic mouse model, sensory-motor and visual cortical slices were prepared, and the whole-cell patch clamp technique was used to record spontaneous inhibitory post-synaptic currents (sIPSCs). Results The primary finding was that the mean amplitude of sIPSC from the sensory-motor cortex increased significantly in epileptic mice when the recording pipette contained MK-801 compared to control mice, whereas the mean sIPSC frequency was not significantly different, indicating that post-synaptic mechanisms are involved. However, there was no significant pre-synaptic inhibition through preNMDARs in the acute brain slices from pilocarpine-induced epileptic mice. Conclusion In the acute case of epilepsy, a compensatory mechanism of post-synaptic inhibition, possibly from ambient GABA, was observed through changes in the amplitude without significant changes in the frequency of sIPSC compared to control mice. The role of preNMDAR-mediated inhibition in epileptogenesis during the chronic condition or in the juvenile stage warrants further investigation.

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%).

Neuroplasticity has been subjected to a great deal of research in the last century. Recently, significant emphasis has been placed on the global effect of localized plastic changes throughout the central nervous system, and on how these changes integrate in a pathological context. The present study aimed to demonstrate the functional cortical reorganization before and after surgery using magnetoencephalography (MEG) in a participant with brain tumor. Results of Visual Evoked Magnetic Field (VEF) based on functional MEG study revealed significantly different of MEG N100 waveforms before and after surgery. Larger and additional new locations for visual activation areas after the surgery were found suggesting neuroplasticity. The present study highlight a physiological plasticity in a teenage brain and the alterations regarding neural plasticity and network remodeling described in pathological contexts in higher-order visual association areas.

The present study discussed functional reorganization and alteration in respond to the slow-growing tumour, hemangiopericytoma in the occipital cortex. Visual evoked field (VEF) and auditory evoked field (AEF) using magnetoencephalography (MEG) was used to evaluate the source localization and brain activity. Results of VEF source localization show a typical brain waves. Brain activity of the occipital lobe demonstrate low activation in the ipsilateral to the tumour. However, result shows the activation on the contralateral hemisphere was high and bigger in activation volume. AEF result shows an identical source localization and both side of the temporal lobe are activated. This result suggests that there is a positive plasticity in auditory cortex and slow-growing tumour can induce functional reorganization and alteration to the brain. DOI : http://dx.doi.org./10.17576/JSKM-2018-1602-11

This article examines how hormonal changes may affect the neuronal networking and mechanisms of cognitive function. Hormones are the chemical regulators of the human body and function critically to maintain various processes, such as growth, emotions and even cognition. Numerous studies have examined the relationship between hormonal effects and cognitive function; these studies have investigated different factors, such as aging, pregnancy, post-natal states, emotions and stress. Different types of hormones produce different outcomes for the human body and mind. Hormones may also contribute to both positive and negative outcomes, depending on whether the hormone levels are too low or too high. To investigate the hormonal effects on cognitive function, the sources of localisation must be localised, so that the neuronal network can be realised. Furthermore, cognitive function does not rely on a specific brain region but is determined by the neuronal network interactions. Thus, it is worthwhile to know the neural mechanisms behind cognitive functions that are affected by hormones.

not available Bangladesh Journal of Medical Science Vol.18(3) 2019 p.665-667

Visual function is mainly located within the bilateral hemisphere of the occipital lobes of the brain. However, our functional magnetoencephalography (MEG) result has demonstrated the reorganization of brain activity in the occipital area in patients with left-sided brain tumour. The results showed that brain laterality changes from bilateral to unilateral activation of the occipital area. Right occipital area (contralateral areas to the tumour), shows increase intensity of activation. Diffusion tensor imaging (DTI) with fibre tracking was performed to further investigate this brain laterality modification and the findings confirmed there is an alteration in the left hemisphere fibre optic tracts. This functional modification and changes of the brain laterality and optic tracts in the brain is suspected to be the result of tumour growth induced changes. The present observation will be discussed in term of the mechanism of tumour induced reorganization and changes with the corroborating evidence from MEG, DTI and neuropsychological assessment.

Functional neuroimaging modalities play an important role in deciding the diagnosis and course of treatment of neuronal dysfunction and degeneration. This article presents an analytical tool with visualization by exploiting the strengths of the MEG (magnetoencephalographic) neuroimaging technique. The tool automates MEG data import (in tSSS format), channel information extraction, time/frequency decomposition, and circular graph visualization (connectogram) for simple result inspection. For advanced users, the tool also provides magnitude squared coherence (MSC) values allowing personalized threshold levels, and the computation of default model from MEG data of control population. Default model obtained from healthy population data serves as a useful benchmark to diagnose and monitor neuronal recovery during treatment. The proposed tool further provides optional labels with international 10-10 system nomenclature in order to facilitate comparison studies with EEG (electroencephalography) sensor space. Potential applications in epilepsy and traumatic brain injury studies are also discussed.

Human brain has a complex structure with the billions of neurons, so it is a difficult and challenging task to predict the behavior of human brain. Different methods and classifiers are used to measure and classify the brain activities with higher accuracy and reliability. In this paper, instead of using mostly used classifier (support vector machine), prediction of the brain activity is done by estimating the match score densities. This method is based on likelihood ratio test which helps in finding the optimal combination of match scores. The distributions of match scores are modeled for different classes based on density score fusion in which the densities of different classes are estimated from the training data set and match scores are found by fusing the estimated densities with the testing data. The fusion is done with the data extracted from distributed activation patterns using multivariate pattern analysis (MVPA) against a visual task. MVPA is an intense strategy which helps in better understanding of the human brain. The match score-based technique is used in different biometric systems but never been used for the prediction of brain activity. In order to test the performance of proposed method, prediction accuracy is compared with the support vector machine using two data sets of different modalities, one is electroencephalography (EEG) and the other is functional magnetic resonance imaging (fMRI). The results show that the proposed method predicts the novel data with improved accuracy of 66.1% and 69.3% compared with support vector machine which have 64.15% and 65.7% for fMRI and EEG data sets, respectively.

Memory plays an important role in human life. Memory can be divided into two categories, i.e., long term memory and short term memory (STM). STM or working memory (WM) stores information for a short span of time and it is used for information manipulations and fast response activities. WM is generally involved in the higher cognitive functions of the brain. Different studies have been carried out by researchers to understand the WM process. Most of these studies were based on neuroimaging modalities like fMRI, EEG, MEG etc., which use standalone processes. Each neuroimaging modality has some pros and cons. For example, EEG gives high temporal resolution but poor spatial resolution. On the other hand, the fMRI results have a high spatial resolution but poor temporal resolution. For a more in depth understanding and insight of what is happening inside the human brain during the WM process or during cognitive tasks, high spatial as well as high temporal resolution is desirable. Over the past decade, researchers have been working to combine different modalities to achieve a high spatial and temporal resolution at the same time. Developments of MRI compatible EEG equipment in recent times have enabled researchers to combine EEG-fMRI successfully. The research publications in simultaneous EEG-fMRI have been increasing tremendously. This review is focused on the WM research involving simultaneous EEG-fMRI data acquisition and analysis. We have covered the simultaneous EEG-fMRI application in WM and data processing. Also, it adds to potential fusion methods which can be used for simultaneous EEG-fMRI for WM and cognitive tasks.

The elderly population rising rapidly in Malaysia and contributes to the increasing number of cognitive problems including mild cognitive impairment (MCI). However, due to limited information regarding this problem which may progress towards severe neurologic degeneration, this problem rarely diagnosed and left untreated. Hence, the use of electroencephalography (EEG) biomarker is seen to be important with the spectral power, coherence and synchronization between the both halves of brain explain the pathophysiology underlined. This method is becoming popular for its capabilities in quantifying changes in brain electrical activity and provide early signs of brain impairment. This paper reviews the incidence of mild cognitive impairment in elderly as an early cognitive deterioration signal. It continues with the role of quantitative EEG analysis in providing the physiological meaning of their brain. This paper also provides the information about neurochemical changes which associated with MCI. In addition, this paper proposes an idea of study towards examining physiology, neurochemicals, diet and lifestyles to promote healthy lifestyle in elderly.International Journal of Human and Health Sciences Vol. 04 No. 04 October’20 Page : 267-270

Background: Although there is increasing research interest in auditory cognitive function among pregnant women, little is known about auditory cognitive function during the second (mid) and third trimesters of pregnancy. Therefore, we investigated auditory cognitive and behavioral functions during the mid- and third trimesters of pregnancy using event related potentials (ERPs) and neuropsychological tests, respectively. Methods: ERPs were examined using a 128-sensor net, and the PAS, WCST, ZCT, RAVLTIM, RAVLTDR, RAVLTTS were administered as neuropsychological assessment tools. Thirty-nine participants were recruited as a control group (G1, n=15, non-pregnant), mid trimester (G2, n=12, 13-26 weeks), and third trimester (G3, n=12, 26-40 weeks). The auditory oddball paradigm was used during the ERP examination. Subjects silently counted the number of occurrences of a target tone while ignoring the standard tone. Results: The value of mean differences of two stimuli were measured in case of amplitudes of P50, N100 and P300 ERP components. The highest (significantly) amplitudes were found in three, three and two sites in P50, N100 and P300 ERP components, respectively. Pregnant group (G2 and G3 both) evoked the highest (significantly) amplitudes in three, one and two sites in P50, N100 and P300 components, respectively, comparing with the control group (G1). Within pregnant group, G3 possesses the highest (significantly) amplitudes at 2 sites (out of 3) in P50, 1 site (out of 3) in N100 and 1 site (out of 2) in P300 components. The highest amplitude of P300 was observed in G2 comparing with the G3. G2 subjects achieved the highest (significantly/nearly significantly) scores on the WCST, RAVLTIM; where G1 subjects had the highest score in ZCT among groups. Conclusion: These findings indicate that pregnant women exhibit good auditory attention, memory and executive function. Pregnant women exhibited better auditory cognitive function in the second trimester compared with those in the third trimester of pregnancy.

BACKGROUND Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. METHODS In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. RESULTS Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. CONCLUSIONS The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Epilepsy is a neurological disorder of the brain, characterized by recurrent seizures, i.e., unusual behaviors. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the detection and analysis of epileptic seizures. However, it is often difficult to identify critical changes in the EEG waveform by visual inspection, thus opening up a vast research challenges for developing automated methods for quantifying such subtle changes. Moreover, manual interpretation becomes complex due to nonlinear and non-stationary nature of the EEG signals. Hence, it is necessary to develop a quantitative method to automatically identify the normal and epileptic brain activities. In this study, we propose a supervised machine learning method to classify non-ictal (normal or preictal or interictal) and ictal (during seizure) brain activities from EEG signals. Several time-domain and time-frequency domain features are extracted to characterize EEG signals, which are then applied to linear discriminant analysis (LDA) or nonlinear support vector machine (SVM) classifier in order classify brain epilepsy. A preliminary experiment with two sets of EEG signals from ‘focal’ and ‘nonfocal’ channels (50 EEG signals per set, each of 10 second duration) of five subjects shows 79.20% accuracy of the epilepsy classification using a combination of features from the time frequency domain.

To localize sensorimotor cortical activation in 10 patients with frontoparietal tumors using quantitative magnetoencephalography (MEG) with noise-normalized approaches. Somatosensory evoked magnetic fields (SEFs) were elicited in 10 patients with somatosensory tumors and in 10 control participants using electrical stimulation of the median nerve via the right and left wrists. We localized the N20m component of the SEFs using dynamic statistical parametric mapping (dSPM) and standardized low-resolution brain electromagnetic tomography (sLORETA) combined with 3D magnetic resonance imaging (MRI). The obtained coordinates were compared between groups. Finally, we statistically evaluated the N20m parameters across hemispheres using non-parametric statistical tests. The N20m sources were accurately localized to Brodmann area 3b in all members of the control group and in seven of the patients; however, the sources were shifted in three patients relative to locations outside the primary somatosensory cortex (SI). Compared with the affected (tumor) hemispheres in the patient group, N20m amplitudes and the strengths of the current sources were significantly lower in the unaffected hemispheres and in both hemispheres of the control group. These results were consistent for both dSPM and sLORETA approaches. Tumors in the sensorimotor cortex lead to cortical functional reorganization and an increase in N20m amplitude and current-source strengths. Noise-normalized approaches for MEG analysis that are integrated with MRI show accurate and reliable localization of sensorimotor function.

Cognitive state classification is a challenging task. Many studies were reported using different neuroimaging modalities for classification of the cognitive states of the human brain e.g., EEG, fMRI, MEG etc. However, functional MRI seems to be appropriate for these papers as due to its good spatial resolution and localizing the brain activated regions. In this paper, our objective is to identify the different cognitive brain states. For example, classifying the patterns of high and low cognitive loads. We acquired the fMRI data on the healthy participants. First, data is preprocessed to remove the artifacts and motions corrections. Next, regions of interest were extracted from functional brain volumes of the two states. Data reduction is also performed and data were passed to machine learning classifier i.e., support vector machine. The results showed that high and low cognitive loads were successfully classified with good accuracy.

BACKGROUND Previous studies have revealed that self-related tasks (items) receive more attention than non-self-related, and that they elicit event-related potential (ERP) components with larger amplitudes. Since personality has been reported as one of the biological correlates influencing these components, as well as our behavioural differences, it is important to examine how it affects our self-consciousness in relation to tasks of varied relevance and the neurological basis. METHODS A total of 33 male and female undergraduate Malaysian medical students of Universiti Sains Malaysia (USM) participated in the study. The participants were divided into two groups, Ambivert (n = 18) and Extravert (n = 15) groups, using the USM personality inventory questionnaire. In the ERP experiment, squares containing standard stimuli of any word other than self and non-self-related nouns (e.g., Bola, Gigi, Anak, etc.; in English: Ball, Teeth, Kids, etc., respectively), those containing self-related pronouns (Saya, Kami or Kita; in English: I, Us or We, respectively), and non-self-related pronouns (Dia, Anda or Mereka; in English: He/She, You or They, respectively), were shown 58%, 21% and 21% of the time, respectively, in a three-stimulus visual oddball paradigm. All words were presented in Bahasa Melayu. The participants were instructed to press 1 for self and 2 for non-self, and ignore standard stimuli. RESULTS Comparison of both N200 and P300 amplitudes for self-related and non-self-related pronouns in the Extravert group revealed significant differences at seven electrode sites, with self-related having larger amplitude at anterior electrodes and less at posterior. This was not seen in the Ambivert group. CONCLUSION The present study suggests that self-relevant pronouns are psychologically more important to extraverts than to ambiverts; hence, they have more self-awareness. This may be due to large amount of dopamine in the brains of extraverts, which is more concentrated in the frontal lobe.

Human brain is considered as complex system having different mental states e.g., rest, active or cognitive states. It is well understood fact that brain activity increases with the cognitive load. This paper describes the cognitive and resting state classification based on EEG features. Previously, most of the studies used linear features. EEG signals are non-stationary in nature and have complex dynamics which is not fully mapped by linear methods. Here, we used non-linear feature extraction methods to classify the cognitive and resting states of the human brain. Data acquisition were carried out on eight healthy participants during cognitive state i.e., IQ task and rest conditions i.e., eyes open. After preprocessing, EEG features were extracted using both linear as well as non-linear. Further, these features were passed to the classifier. Results showed that with support vector machine (SVM), we achieved 87.5% classification accuracy with linear and 92.1% classification accuracy with non-linear features.

Language plays an important part in human for expressing their thoughts and emotions, where the source of function originated from the brain. This study aims to estimate the early and long latency language-related magnetic fields (LRFs) in patients with brain tumor. The study hypothesized that the brain tumor would alter the patients LRFs as compared to healthy subjects. Three patients with brain tumor which invades the language region in the brain were selected as subjects while two normal healthy subjects were taken as a control group regardless of gender. All the subjects had to undergo a Magnetoencephalography (MEG) scanning while doing a task related to language function. The subjects were shown four characters Malay semantic word and had to do silent reading while asked to lessen their movement (such as blinking) during the whole scanning procedure. The waveform was taken between the latency fields of -150 to 850 ms, and then filters were applied before analyzing the signals. The areas of analysis were done at the Occipital, Temporal, and Frontal region of the brain while the components taken were N100, N200, N400 and N600 with respect to the location. The LRFs were taken from both left and right side of the brain. The results obtained show significant difference of LRFs between the patient and control groups in term of both latency and amplitude. The latency and amplitude readings were inconsistent for all patients while the consistency for the control group was constant. The dominance of brain activation was shown to be on the left side for both control and patient group. As conclusion, there is no difference between patient and control group LRFs due to limitation of study.

Electroencephalography is most common noninvasive neuroimaging modality and it is widely used for measuring brain electrical signals. Measurement of electrical signals from the scalp requires high density electrodes and low noise amplifier. It is well known fact that neural activity increased with increasing the mental work e.g., IQ task in our case. In this paper, non-linear features have been used to classify the active and resting states of the human brain. We have used EEG acquired from 08 healthy participants during IQ task and resting conditions. Nonlinear feature e.g., Approximate entropy, sample entropy and Composite permutation entropy index (CPEI) have been computed from recorded EEG data. These nonlinear features were fed to the classifier and we are able to classify the active and rest conditions. Also for classification, SVM produced better results with 89.1% and 92.5% accuracy for eyes open (EO) vs IQ and eyes open (EO) vs eyes close (EC) conditions respectively as compared to other classifiers. Also results compared with linear features extraction methods.

Multimodal neuroimaging techniques have shown better understanding of human brain functions and dysfunctions as compared to the standalone modality. EEG and fMRI are widely used in neuroscience community. EEG signals recorded at scalp have high temporal resolution and poor spatial resolution due to limited number of electrodes. Functional Magnetic Resonance Imaging (fMRI) has a very high spatial resolution and low temporal resolution due to slow scanning. The complementary nature of EEG and fMRI can be combined to get high temporal and spatial resolution at the same time. Simultaneous EEG-fMRI imaging can help researchers to develop better understanding of brain mechanism and functionality. Simultaneous EEG-fMRI can be used for assessment of person's cognitive abilities which include working memory, attention and learning process as well as in diseases like schizophrenia, epilepsy, etc. However, simultaneous EEG-fMRI brings many challenges in terms of data acquisition and data quality point of view. In this paper, we have addressed the main issues involved during simultaneous EEG-fMRI data recording. Further, pros and cons of combined EEG-fMRI are discussed.

Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activity pattern in term of images and electric potential respectively. To get higher spatiotemporal resolution of human brain from these two complementary neuroimaging modalities, simultaneous EEG-fMRI can be helpful. In this paper, we proposed a framework for classifying the brain activity patterns with simultaneous EEG-fMRI. We have acquired five human participants' data with simultaneous EEG-fMRI by showing different object categories. Further, combined analysis of EEG and fMRI data was carried out. Extracted information through combine analysis is passed to support vector machine (SVM) classifier for classification purpose. We have achieved better classification accuracy using simultaneous EEG-fMRI i.e., 81.8% as compared to fMRI data standalone. This shows that multimodal neuroimaging can improve the classification accuracy of brain activity patterns as compared to individual modalities reported in literature.

Electroencephalography (EEG) and functional magnetic resonance (fMRI) both are considered as non-invasive neuroimaging modalities. Both are used for understanding brain functionalities in cognitive neuroscience as well as in clinical applications. EEG gives high temporal resolution and it has poor spatial resolution. On the other hand, fMRI has very high spatial resolution and poor temporal resolution. For deep understanding of neural mechanisms inside human brain, it is desirable to get the higher spatiotemporal resolution of human brain at the same time. Concurrent EEG-fMRI data recording solve the problem of higher spatiotemporal resolution. It can be also helpful to understand the neural mechanism inside human brain effectively. The concurrent EEG-fMRI recording requires MRI compatible EEG equipment which can be placed inside the higher magnetic field of MRI scanner and also synchronization is required to make setup concurrent. To get higher signal to noise ratio (SNR), optimization of data acquisition parameters plays a significant role. In this paper, we discussed the some real issues during data acquisition and their optimization. We have developed the concurrent EEG-fMRI setup and also successfully recorded the EEG-fMRI data concurrently by optimizing the data acquisition parameters involved. Artifacts have been removed from the data and further, data fusion framework is proposed for combine analysis of EEG and fMRI data.

Background: Magnetoencephalography (MEG) is a method of functional neuroimaging. The concomitant use of MEG and electrocorticography has been found to be useful in elucidating neural oscillation and network, and to localize epileptogenic zone and functional cortex. We describe our early experience using MEG in neurosurgical patients, emphasizing on its impact on patient management as well as the enrichment of our knowledge in neurosciences. Materials and Methods: A total of 10 subjects were included; five patients had intraaxial tumors, one with an extraaxial tumor and brain compression, two with arteriovenous malformations, one with cerebral peduncle hemorrhage and one with sensorimotor cortical dysplasia. All patients underwent evoked and spontaneous MEG recordings. MEG data was processed at band-pass filtering frequency of between 0.1 and 300 Hz with a sampling rate of 1 kHz. MEG source localization was performed using either overdetermined equivalent current dipoles or underdetermined inversed solution. Neuromag collection of events software was used to study brain network and epileptogenic zone. The studied data were analyzed for neural oscillation in three patients; brain network and clinical manifestation in five patients; and for the location of epileptogenic zone and eloquent cortex in two patients. Results: We elucidated neural oscillation in three patients. One demonstrated oscillatory phenomenon on stimulation of the motor-cortex during awake surgery, and two had improvement in neural oscillatory parameters after surgery. Brain networks corresponding to clinico-anatomical relationships were depicted in five patients, and two networks were illustrated here. Finally, we demonstrated epilepsy cases in which MEG data was found to be useful in localizing the epileptogenic zones and functional cortices. Conclusion: The application of MEG while enhancing our knowledge in neurosciences also has a useful role in epilepsy and awake surgery.

EEG and fMRI are non-invasive imaging modalities widely used to understand the brain functions and dysfunction in cognitive neuroscience. EEG provides us the high temporal resolution while functional MRI gives very high spatial resolution and vice versa. To get the better spatiotemporal resolution of human brain neural activities, simultaneous EEG-fMRI data acquisition provides solution to the problem. The development requires MRI compatible EEG equipment which can be successfully integrated and synchronized to make the setup concurrent. In this paper, we discussed some real issues during data acquisition and how to optimize the setup for better SNR. We have developed the simultaneous EEG-fMRI setup at hospital Universiti Sains Malaysia (USM) and also successfully recorded the EEG-fMRI data simultaneously for event related design.

Transcranial magnetic stimulation (TMS) elicits short latency excitatory responses in the target muscles, termed motor evoked potential (MEP). When TMS is delivered during a voluntary contraction, the MEP is followed by a period of silence called silent period (SP). These MEP parameters are in general recordable by electromyography (EMG). Mechanomyography (MMG) on the other hand is the mechanical counterpart of EMG. Thus, this study has been conducted to observe whether the MEP parameters from MMG signals showed similar trait of EMG recordings. Five normal healthy male subjects were included in this study. The subjects were required to perform right biceps brachii muscles contraction at diverse graded of load level at 5, 10, 20, 30, 40, 60, and 100% maximum voluntary contraction (MVC). MEPs by single pulse TMS on left hemisphere were obtained from both EMG electrode and MMG accelerometer at rest and at different levels of predetermined load level. MEP amplitude and area obtained both from EMG and MMG record were increased with the increase of muscle contraction with a maximum of 60% MVC. With increasing the level of contraction there was a shortening of onset latency and decreasing in the length of silent period in both EMG and MMG signals. We also recorded the EMG- and MMG-MEP from the right extensor carpi radialis muscle during transcutaneous electric nerve stimulation in order to observe neural changes in sensory stimulation from both EMG and MMG responses. The EMG-MEP was not visible in electrical artifact whereas it was obvious in MMG responses. In accordance with other study, this study showed that the voluntary contraction of biceps brachii muscle influenced the MEP parameter which are moreover obtainable by MMG even in electrical noise may provide insight for future study.

Epilepsy is the brain disorder disease having more than 50 million people worldwide. The treatment for epilepsy is medication and surgery. Some patients are not cured with medicine and surgery. One third of the patients still remain with uncontrolled epilepsy. They need constant monitoring for epileptic seizures. Better treatment can be provided by the doctors or precautionary measures can be taken by the patients themselves if any abnormal brain activity or seizure is predicted before its occurrence. The pre-ictal period has some information about the occurrence of epileptic seizure in EEG signals. The brain behaves normal in inter-ictal and postictal periods. For epilepsy, long duration EEG recording are required from days to week. This keeps the patients to stay in the hospital for many days. Our proposed methodology is to predict the epileptic seizure and monitor the brain abnormality in real time. Still there is no epileptic seizure prediction algorithm using EEG available for clinical applications. Our aim is to study and develop a good epileptic seizure prediction algorithm/method with high value of sensitivity and specificity using scalp EEG i-e noninvasive approach. Also a comprehensive survey is done to find the limitations and research issues related to this. The proposed pattern recognition approach has great potential to be used in real time monitoring for epileptic patients and it can be helpful in improving the quality of life of the patients.

Fusion of EEG and fMRI data helps researchers to provide a more comprehensive understanding of neural basis for the functional behavior in human brain. EEG and fMRI Joint analysis for cognitive tasks indicates plausible results to obtain a better spatiotemporal resolution of event related responses in the brain. Joint-ICA as a multivariate data analysis method, assumes more than two features type (modalities) have common mixing data and it tries to maximizes independency among joint components. Here, we study the performance of five ICA algorithms when applied to joint analysis of EEG/fMRI data. We use the visualization and computational tools to quantitatively analyze the performance of different ICA algorithms for EEG/fMRI fusion and discuss the results for the simulation and real data.

Anatomical and physiological target localization of the Subthalamic nucleus (STN) has a paramount importance for the proper treatment of advanced Parkinson's disease (PD) patient using Deep Brain Stimulation microelectrode. Square root of the mean value of the square of the input values is the measure of the power in the signal and the general level of activity of the signal can be assessed by its value. In the future we would like to conduct a study in a large sample size to predict the neuronal activity of deep brain structures of the whole electrode trajectory. This might help us to determine the Deep Brain Stimulation intensity for the treatment of Parkinson's disease.

We retrospectively reviewed the MEG data of one patient with post traumatic epilepsy, in which we observed unifocal periodic lateralized epileptiform discharges (PLEDs) during offline analysis which was evoked by only auditory stimuli. The patient underwent evaluation of her epilepsy including medical history, long-term video-EEG monitoring and structural MRI at University Sains Malaysia Hospital (USMH), Kelantan, Malaysia. The ictal onset zone of this patient was determined clinically by considering the results of the evaluation prior to obtaining the MEG. In all routine EEGs, there had neither PLEDs like findings nor interictal spike. We concluded that unifocal PLEDs can localize the ictal onset zone in epilepsy patient which can be detected by MEG but not in EEG and careful observation is highly recommended during recording of MEG which might be helpful for the surgical treatment of epilepsy patient.

Several devices or physical agents have been used for the well being of patients in neurorehabilitation setup. Physical agents improve the quality of life. Miswak-tree twig which is Arabic term of Kayu Sugi in Malay used to cleaning the tooth which has historical and traditional value in different parts of the world. The objective of our present study was to examine the effect of Miswak induced short exercise on excitement level of brain by recording magnetic flux which is related to intracranial electrical current. Spontaneous Magnetoencephalography (MEG) recording was done before and after tooth brushing in four conditions (rest, left hand pinch grip, right hand pinch grip and both hand pinch grip) and neural signal was recorded from eight regions of brain (left and right frontal, parietal, temporal and occipital region). Spectral analysis from 20sec epoch data by Fast Fourier Transform (FFT) was done to obtain mean value of power for alpha (7 - 13 Hz) and beta (13 - 30Hz) frequency bands. The relative power of alpha band was also measured from eight regions of brain. We also recorded visual evoked field (VEF) after and before brushing and measured P100m latency and amplitude but no significant changes were found in latency and amplitude. From spontaneous MEG multivariate analysis of variances showed significant reduction of alpha power after Miswak induced brief exercise in left and right frontal region at both hand pinch condition. But no changes were found in the relative alpha power in regions across the conditions. These results suggest that brain excitement level increased after short duration tooth brushing exercise and the measurement of power is reliable rather than relative power in neurofeedback.

BACKGROUND: The major function of rehabilitation and physical medicine specialists is to provide the proper therapy that helps in improving the physical activities of impaired, disabled and handicapped persons through improvement in their muscle strength. In performing their function, the rehabilitation team should always take heed of the social and mental well-being of such patients. Having observed millions of Muslims perform the salat (prayer) regularly at specified times throughout the world, we postulated that salat, along with its various postures, can play a role in increasing psychological well-being including self-reliance and self-esteem, improving musculo-skeletal fitness, motor behavior and cerebral blood flow that may be beneficial in the rehabilitation of geriatric and disabled persons. SUBJECTS AND METHODS: The various postures of salat were studied and a range of joint motions were measured by goniometer, an instrument for measuring angles. Brain blood pressure was calculated from the effect of gravity on blood pressure at different positions. RESULTS: We found that during the offering of salat, most of the joints and muscles of the body were involved in physical activities with little effort, which probably play a vital role in cerebral blood flow and postural reflexes. CONCLUSION: The physical activities involved in the performance of salat helps in the rehabilitation process in disabled geriatric patients by improving blood flow and increasing musculoskeletal fitness. The salat prayer involves little effort (standing, bowing, prostration and sitting), has a short duration and is beneficial for mental and physical health. More studies are needed in future to determine the full beneficial effects of the salat prayer on the rehabilitative process of disabled persons.

Background Following brain injury, development of hippocampal sclerosis often led to the temporal lobe epilepsy which is sometimes resistant to common anti-epileptic drugs. Cellular and molecular changes underlying epileptogenesis in animal models were studied, however, the underlying mechanisms of kainic acid (KA) mediated neuronal damage in rat hippocampal neuron cell culture alone has not been elucidated yet. Methods Embryonic day 18 (E-18) rat hippocampus neurons were cultured with poly-L-lysine coated glass coverslips. Following optimisation, KA (0.5 μM), a chemoconvulsant agent, was administered at three different time-points (30, 60 and 90 min) to induce seizure in rat hippocampal neuronal cell culture. We examined cell viability, neurite outgrowth density and immunoreactivity of the hippocampus neuron culture by measuring brain derived neurotrophic factor (BDNF), γ-amino butyric acid A (GABAA) subunit α-1 (GABRA1), tyrosine receptor kinase B (TrkB), and inositol trisphosphate receptor (IP3R/IP3) levels. Results The results revealed significantly decreased and increased immunoreactivity changes in TrkB (a BDNF receptor) and IP3R, respectively, at 60 min time point. Conclusion The current findings suggest that TrkB and IP3 could have a neuroprotective role which could be a potential pharmacological target for anti-epilepsy drugs.