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Fulltext Quantitative assessment of visual cortex function with fMRI at 7 Tesla-test-retest variability

Abd Hamid AI, Speck O, Hoffmann MB

Front Hum Neurosci, 2015;9:477.
PMID: 26388756 DOI: 10.3389/fnhum.2015.00477

fMRI-based retinotopic mapping was used to assess systematic variations in activated cortical surface area, amplitude, and coherence across sessions. Seven healthy subjects were scanned at 7 T in three separate sessions with intervals of 51.4 ± 5.4 days (Sessions 1 and 2) and 167.9 ± 24.4 days (Sessions 2 and 3). We found a reduction between Sessions 1 and 2 for activated cortical surface area, between Sessions 1 and 3 for amplitude, and between Sessions 1 and 2/3 for coherence. The results do not support head motion as a major cause of the observed effect seen in Session 1, suggesting that cognitive effects were the underlying cause of change. The phase correlations for both eccentricity and polar angle mapping were highly correlated between sessions, demonstrating the stability of the maps. Furthermore, the sensitivity in determining inter-session changes of cortical surface area, response amplitude, and coherence were, at a 5% significance level, estimated to be 1.5, 6, and 5%, respectively. Any future longitudinal fMRI study should carefully evaluate activation across sessions to determine the eligibility of inclusion of all time points. This experimental design provides guidance in methodological issues of clinical longitudinal fMRI-studies, specifically regarding effects of subject experience.
Fulltext Working Memory From the Psychological and Neurosciences Perspectives: A Review

Chai WJ, Abd Hamid AI, Abdullah JM

Front Psychol, 2018;9:401.
PMID: 29636715 DOI: 10.3389/fpsyg.2018.00401

Since the concept of working memory was introduced over 50 years ago, different schools of thought have offered different definitions for working memory based on the various cognitive domains that it encompasses. The general consensus regarding working memory supports the idea that working memory is extensively involved in goal-directed behaviors in which information must be retained and manipulated to ensure successful task execution. Before the emergence of other competing models, the concept of working memory was described by the multicomponent working memory model proposed by Baddeley and Hitch. In the present article, the authors provide an overview of several working memory-relevant studies in order to harmonize the findings of working memory from the neurosciences and psychological standpoints, especially after citing evidence from past studies of healthy, aging, diseased, and/or lesioned brains. In particular, the theoretical framework behind working memory, in which the related domains that are considered to play a part in different frameworks (such as memory's capacity limit and temporary storage) are presented and discussed. From the neuroscience perspective, it has been established that working memory activates the fronto-parietal brain regions, including the prefrontal, cingulate, and parietal cortices. Recent studies have subsequently implicated the roles of subcortical regions (such as the midbrain and cerebellum) in working memory. Aging also appears to have modulatory effects on working memory; age interactions with emotion, caffeine and hormones appear to affect working memory performances at the neurobiological level. Moreover, working memory deficits are apparent in older individuals, who are susceptible to cognitive deterioration. Another younger population with working memory impairment consists of those with mental, developmental, and/or neurological disorders such as major depressive disorder and others. A less coherent and organized neural pattern has been consistently reported in these disadvantaged groups. Working memory of patients with traumatic brain injury was similarly affected and shown to have unusual neural activity (hyper- or hypoactivation) as a general observation. Decoding the underlying neural mechanisms of working memory helps support the current theoretical understandings concerning working memory, and at the same time provides insights into rehabilitation programs that target working memory impairments from neurophysiological or psychological aspects.
Fulltext Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM

Lai CQ, Ibrahim H, Abd Hamid AI, Abdullah JM

Sensors (Basel), 2020 Sep 14;20(18).
PMID: 32937801 DOI: 10.3390/s20185234

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.
Fulltext Effects of alternating current stimulation on the healthy and diseased brain

Abd Hamid AI, Gall C, Speck O, Antal A, Sabel BA

Front Neurosci, 2015;9:391.
PMID: 26578858 DOI: 10.3389/fnins.2015.00391

Cognitive and neurological dysfunctions can severely impact a patient's daily activities. In addition to medical treatment, non-invasive transcranial alternating current stimulation (tACS) has been proposed as a therapeutic technique to improve the functional state of the brain. Although during the last years tACS was applied in numerous studies to improve motor, somatosensory, visual and higher order cognitive functions, our knowledge is still limited regarding the mechanisms as to which type of ACS can affect cortical functions and altered neuronal oscillations seem to be the key mechanism. Because alternating current send pulses to the brain at predetermined frequencies, the online- and after-effects of ACS strongly depend on the stimulation parameters so that "optimal" ACS paradigms could be achieved. This is of interest not only for neuroscience research but also for clinical practice. In this study, we summarize recent findings on ACS-effects under both normal conditions and in brain diseases.
Hemispheric Lateralization of Auditory Working Memory Regions During Stochastic Resonance: An fMRI Study

Othman EA, Yusoff AN, Mohamad M, Abdul Manan H, Abd Hamid AI, Giampietro V

J Magn Reson Imaging, 2020 06;51(6):1821-1828.
PMID: 31794119 DOI: 10.1002/jmri.27016

BACKGROUND: The auditory and prefrontal cortex supports auditory working memory processing. Many neuroimaging studies have shown hemispheric lateralization of auditory working memory brain regions in the presence of background noise, but few studies have focused on the lateralization of these regions during stochastic resonance.
PURPOSE: To investigate the effects of stochastic resonance on lateralization of auditory working memory regions, and also to examine the brain-behavior relationship during stochastic resonance.
STUDY TYPE: Cross-sectional.
POPULATION/SUBJECTS: Forty healthy young adults (18-24 years old).
FIELD STRENGTH/SEQUENCE: 3.0T, T1 , and T2 *-weighted imaging.
ASSESSMENT: The auditory working memory performance was assessed using a backward recall task. Functional magnetic resonance imaging (fMRI) was used to measure brain activity during task performance. Functional MRI data were analyzed using SPM12 and WFU PickAtlas.
STATISTICAL TESTS: One-way independent analyses of variance (ANOVA) were conducted on the behavioral and functional data to examine the main effect of noise level on performance (P
Effects of white noise on word recall performance and brain activity in healthy adolescents with normal and low auditory working memory

Othman E, Yusoff AN, Mohamad M, Abdul Manan H, Abd Hamid AI, Giampietro V

Exp Brain Res, 2020 Apr;238(4):945-956.
PMID: 32179941 DOI: 10.1007/s00221-020-05765-3

The present study examined the impact of white noise on word recall performance and brain activity in 40 healthy adolescents, split in two groups (normal and low) depending on their auditory working memory capacity (AWMC). Using functional magnetic resonance imaging, participants performed a backward recall task under four different signal-to-noise ratio (SNR) conditions: 15, 10, 5, and 0-dB SNR. Behaviorally, normal AWMC individuals scored significantly higher than low AWMC individuals across noise levels. Whole-brain analyses showed brain activation not to be statistically different between groups across noise levels. In the normal group, a significant positive relationship was found between performance and number of activated voxels in the right superior frontal gyrus. In the low group, significant positive correlations were found between performance and number of activated voxels in left superior frontal gyrus, left inferior frontal gyrus, and left anterior cingulate cortex. These findings suggest that the strategic structure involved in the enhancement of AWM performance may differ in normal and low AWMC individuals.
Fulltext Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Lai CQ, Ibrahim H, Abd Hamid AI, Abdullah MZ, Azman A, Abdullah JM

Comput Intell Neurosci, 2020;2020:8923906.
PMID: 32256555 DOI: 10.1155/2020/8923906

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.
Fulltext Brain Activation during Addition and Subtraction Tasks In-Noise and In-Quiet

Abd Hamid AI, Yusoff AN, Mukari SZ, Mohamad M

Malays J Med Sci, 2011 Apr;18(2):3-15.
PMID: 22135581 MyJurnal

In spite of extensive research conducted to study how human brain works, little is known about a special function of the brain that stores and manipulates information-the working memory-and how noise influences this special ability. In this study, Functional magnetic resonance imaging (fMRI) was used to investigate brain responses to arithmetic problems solved in noisy and quiet backgrounds.
Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector

Ooi AZH, Embong Z, Abd Hamid AI, Zainon R, Wang SL, Ng TF, et al.

Sensors (Basel), 2021 Sep 24;21(19).
PMID: 34640698 DOI: 10.3390/s21196380

Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.
Fulltext Neurodevelopmental effects of childhood malnutrition: A neuroimaging perspective

Galler JR, Bringas-Vega ML, Tang Q, Rabinowitz AG, Musa KI, Chai WJ, et al.

Neuroimage, 2021 05 01;231:117828.
PMID: 33549754 DOI: 10.1016/j.neuroimage.2021.117828

Approximately one in five children worldwide suffers from childhood malnutrition and its complications, including increased susceptibility to inflammation and infectious diseases. Due to improved early interventions, most of these children now survive early malnutrition, even in low-resource settings (LRS). However, many continue to exhibit neurodevelopmental deficits, including low IQ, poor school performance, and behavioral problems over their lifetimes. Most studies have relied on neuropsychological tests, school performance, and mental health and behavioral measures. Few studies, in contrast, have assessed brain structure and function, and to date, these have mainly relied on low-cost techniques, including electroencephalography (EEG) and evoked potentials (ERP). The use of more advanced methods of neuroimaging, including magnetic resonance imaging (MRI) and functional near-infrared spectroscopy (fNIRS), has been limited by cost factors and lack of availability of these technologies in developing countries, where malnutrition is nearly ubiquitous. This report summarizes the current state of knowledge and evidence gaps regarding childhood malnutrition and the study of its impact on neurodevelopment. It may help to inform the development of new strategies to improve the identification, classification, and treatment of neurodevelopmental disabilities in underserved populations at the highest risk for childhood malnutrition.
Fulltext Low intensity white noise improves performance in auditory working memory task: An fMRI study

Othman E, Yusoff AN, Mohamad M, Abdul Manan H, Giampietro V, Abd Hamid AI, et al.

Heliyon, 2019 Sep;5(9):e02444.
PMID: 31687551 DOI: 10.1016/j.heliyon.2019.e02444

Research suggests that white noise may facilitate auditory working memory performance via stochastic resonance. Stochastic resonance is quantified by plotting cognitive performance as a function of noise intensity. The plot would appear as an inverted U-curve, that is, a moderate noise is beneficial for performance whereas too low and too much noise attenuates performance. However, knowledge about the optimal signal-to-noise ratio (SNR) needed for stochastic resonance to occur in the brain, particularly in the neural network of auditory working memory, is limited and demand further investigation. In the present study, we extended previous works on the impact of white noise on auditory working memory performance by including multiple background noise levels to map out the inverted U-curve for the stochastic resonance. Using functional magnetic resonance imaging (fMRI), twenty healthy young adults performed a word-based backward recall span task under four signal-to-noise ratio conditions: 15, 10, 5, and 0-dB SNR. Group results show significant behavioral improvement and increased activation in frontal cortices, primary auditory cortices, and anterior cingulate cortex in all noise conditions, except at 0-dB SNR, which decreases activation and performance. When plotted as a function of signal-to-noise ratio, behavioral and fMRI data exhibited a noise-benefit inverted U-shaped curve. Additionally, a significant positive correlation was found between the activity of the right superior frontal gyrus (SFG) and performance in 5-dB SNR. The predicted phenomenon of SR on auditory working memory performance is confirmed. Findings from this study suggest that the optimal signal-to-noise ratio to enhance auditory working memory performance is within 10 to 5-dB SNR and that the right SFG may be a strategic structure involved in enhancement of auditory working memory performance.
Fulltext The Brain-Gut Clinic in Hospital Universiti Sains Malaysia: Pioneering New Service to Advance Neuro-Gastroenterology and Motility in Malaysia

Lee YY, Izham N, Mohd Zulkifly MF, Mohamed Mustafar MF, Ismail AK, Mohamed Shah NFFN, et al.

Malays J Med Sci, 2023 Jun;30(3):1-7.
PMID: 37425382 DOI: 10.21315/mjms2023.30.3.1

Neurogastroenterology and motility is a new but advanced subspecialty within gasteroenterology that cater to difficult, persistent and refractory gut-brain symptoms. Hospital USM has the country's first and new state-of-the art motility lab that was recently launched on the 25 May 2023, and is covered in nationwide media. Another first is the Brain-Gut Clinic, established on the 16 November 2022. The clinic is a new concept that builds on unique multiple disciplines in relation to the gut-brain axis. It is hoped that there will be more awareness on the existence of neurogastroenterology and motility among doctors and community, and that more research can be forthcoming to reduce the disease burden.

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