Displaying publications 1 - 20 of 44 in total

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  1. Khan DM, Kamel N, Muzaimi M, Hill T
    Brain Connect, 2021 02;11(1):12-29.
    PMID: 32842756 DOI: 10.1089/brain.2019.0721
    Introduction: With the recent technical advances in brain imaging modalities such as magnetic resonance imaging, positron emission tomography, and functional magnetic resonance imaging (fMRI), researchers' interests have inclined over the years to study brain functions through the analysis of the variations in the statistical dependence among various brain regions. Through its wide use in studying brain connectivity, the low temporal resolution of the fMRI represented by the limited number of samples per second, in addition to its dependence on brain slow hemodynamic changes, makes it of limited capability in studying the fast underlying neural processes during information exchange between brain regions. Materials and Methods: In this article, the high temporal resolution of the electroencephalography (EEG) is utilized to estimate the effective connectivity within the default mode network (DMN). The EEG data are collected from 20 subjects with alcoholism and 25 healthy subjects (controls), and used to obtain the effective connectivity diagram of the DMN using the Partial Directed Coherence algorithm. Results: The resulting effective connectivity diagram within the DMN shows the unidirectional causal effect of each region on the other. The variations in the causal effects within the DMN between controls and alcoholics show clear correlation with the symptoms that are usually associated with alcoholism, such as cognitive and memory impairments, executive control, and attention deficiency. The correlation between the exchanged causal effects within the DMN and symptoms related to alcoholism is discussed and properly analyzed. Conclusion: The establishment of the causal differences between control and alcoholic subjects within the DMN regions provides valuable insight into the mechanism by which alcohol modulates our cognitive and executive functions and creates better possibility for effective treatment of alcohol use disorder.
    Matched MeSH terms: Brain Mapping
  2. Al-Hiyali MI, Yahya N, Faye I, Hussein AF
    Sensors (Basel), 2021 Aug 04;21(16).
    PMID: 34450699 DOI: 10.3390/s21165256
    The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger's disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
    Matched MeSH terms: Brain Mapping
  3. Manan HA, Franz EA, Yahya N
    Eur J Cancer Care (Engl), 2021 Jul;30(4):e13428.
    PMID: 33592671 DOI: 10.1111/ecc.13428
    PURPOSE: Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is suggested to be a viable option for pre-operative mapping for patients with brain tumours. However, it remains an open issue whether the tool is useful in the clinical setting compared to task-based fMRI (T-fMRI) and intraoperative mapping. Thus, a systematic review was conducted to investigate the usefulness of this technique.

    METHODS: A systematic literature search of rs-fMRI methods applied as a pre-operative mapping tool was conducted using the PubMed/MEDLINE and Cochrane Library electronic databases following PRISMA guidelines.

    RESULTS: Results demonstrated that 50% (six out of twelve) of the studies comparing rs-fMRI and T-fMRI showed good concordance for both language and sensorimotor networks. In comparison to intraoperative mapping, 86% (six out of seven) studies found a good agreement to rs-fMRI. Finally, 87% (twenty out of twenty-three) studies agreed that rs-fMRI is a suitable and useful pre-operative mapping tool.

    CONCLUSIONS: rs-fMRI is a promising technique for pre-operative mapping in assessing the functional brain areas. However, the agreement between rs-fMRI with other techniques, including T-fMRI and intraoperative maps, is not yet optimal. Studies to ascertain and improve the sophistication in pre-processing of rs-fMRI imaging data are needed.

    Matched MeSH terms: Brain Mapping
  4. Ahmad, N. Z., Aini Ismafairus, A. H., Khairiah, A. H., Wan Ahmad Kamil, W. A., Mazlyfarina, M., Hanani, A. M.
    MyJurnal
    Introduction: This multiple-subject fMRI study continue to further investigate brain activation within and effective connectivity between the significantly (p
    Matched MeSH terms: Brain Mapping
  5. Dewey RS, Hall DA, Plack CJ, Francis ST
    Magn Reson Med, 2021 11;86(5):2577-2588.
    PMID: 34196020 DOI: 10.1002/mrm.28902
    PURPOSE: Detecting sound-related activity using functional MRI requires the auditory stimulus to be more salient than the intense background scanner acoustic noise. Various strategies can reduce the impact of scanner acoustic noise, including "sparse" temporal sampling with single/clustered acquisitions providing intervals without any background scanner acoustic noise, or active noise cancelation (ANC) during "continuous" temporal sampling, which generates an acoustic signal that adds destructively to the scanner acoustic noise, substantially reducing the acoustic energy at the participant's eardrum. Furthermore, multiband functional MRI allows multiple slices to be collected simultaneously, thereby reducing scanner acoustic noise in a given sampling period.

    METHODS: Isotropic multiband functional MRI (1.5 mm) with sparse sampling (effective TR = 9000 ms, acquisition duration = 1962 ms) and continuous sampling (TR = 2000 ms) with ANC were compared in 15 normally hearing participants. A sustained broadband noise stimulus was presented to drive activation of both sustained and transient auditory responses within subcortical and cortical auditory regions.

    RESULTS: Robust broadband noise-related activity was detected throughout the auditory pathways. Continuous sampling with ANC was found to give a statistically significant advantage over sparse sampling for the detection of the transient (onset) stimulus responses, particularly in the auditory cortex (P < .001) and inferior colliculus (P < .001), whereas gains provided by sparse over continuous ANC for detecting offset and sustained responses were marginal (p ~ 0.05 in superior olivary complex, inferior colliculus, medial geniculate body, and auditory cortex).

    CONCLUSIONS: Sparse and continuous ANC multiband functional MRI protocols provide differing advantages for observing the transient (onset and offset) and sustained stimulus responses.

    Matched MeSH terms: Brain Mapping
  6. Balafar MA, Ramli AR, Mashohor S
    Neurosciences (Riyadh), 2011 Jul;16(3):242-7.
    PMID: 21677615
    To improve the quality of expectation maximizing (EM) for brain image segmentation, and to evaluate the accuracy of segmentation results.
    Matched MeSH terms: Brain Mapping*
  7. Poznanski RR
    J Integr Neurosci, 2009 Sep;8(3):345-69.
    PMID: 19938210
    The continuity of the mind is suggested to mean the continuous spatiotemporal dynamics arising from the electrochemical signature of the neocortex: (i) globally through volume transmission in the gray matter as fields of neural activity, and (ii) locally through extrasynaptic signaling between fine distal dendrites of cortical neurons. If the continuity of dynamical systems across spatiotemporal scales defines a stream of consciousness then intentional metarepresentations as templates of dynamic continuity allow qualia to be semantically mapped during neuroimaging of specific cognitive tasks. When interfaced with a computer, such model-based neuroimaging requiring new mathematics of the brain will begin to decipher higher cognitive operations not possible with existing brain-machine interfaces.
    Matched MeSH terms: Brain Mapping/methods*
  8. 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 brain activity (P brain activity between hemispheres (P brain activity and behavioral performance.

    RESULTS: Performance was significantly enhanced during the 50 and 55 dB sound pressure level (SPL) conditions via the stochastic resonance mechanism [F(1,195) = 49.17, P 

    Matched MeSH terms: Brain Mapping*
  9. Manan HA, Franz EA, Yahya N
    Neuroradiology, 2020 Mar;62(3):353-367.
    PMID: 31802156 DOI: 10.1007/s00234-019-02322-w
    PURPOSE: Functional MRI (fMRI) can be employed to non-invasively localize brain regions involved in functional areas of language in patients with brain tumour, for applications including pre-operative mapping. The present systematic review was conducted to explore prevalence of different language paradigms utilised in conjunction with fMRI approaches for pre-operative mapping, with the aim of assessing their effectiveness and suitability.

    METHODS: A systematic literature search of brain tumours in the context of fMRI methods applied to pre-operative mapping for language functional areas was conducted using PubMed/MEDLINE and Scopus electronic database following PRISMA guidelines. The article search was conducted between the earliest record and March 1, 2019. References and citations were checked in Google Scholar database.

    RESULTS: Twenty-nine independent studies were identified, comprising 1031 adult participants with 976 patients characterised with different types and sizes of brain tumours, and the remaining 55 being healthy controls. These studies evaluated functional language areas in patients with brain tumours prior to surgical interventions using language-based fMRI. Results demonstrated that 86% of the studies used a Word Generation Task (WGT) to evoke functional language areas during pre-operative mapping. Fifty-seven percent of the studies that used language-based paradigms in conjunction with fMRI as a pre-operative mapping tool were in agreement with intra-operative results of language localization.

    CONCLUSIONS: WGT was most commonly utilised and is proposed as a suitable and useful technique for a language-based paradigm fMRI for pre-operative mapping. However, based on available evidence, WGT alone is not sufficient. We propose a combination and convergence paradigms for a more sensitive and specific map of language function for pre-operative mapping. A standard guideline for clinical applications should be established.

    Matched MeSH terms: Brain Mapping/methods*
  10. Sanchez Bornot JM, Wong-Lin K, Ahmad AL, Prasad G
    Brain Topogr, 2018 11;31(6):895-916.
    PMID: 29546509 DOI: 10.1007/s10548-018-0640-0
    The brain's functional connectivity (FC) estimated at sensor level from electromagnetic (EEG/MEG) signals can provide quick and useful information towards understanding cognition and brain disorders. Volume conduction (VC) is a fundamental issue in FC analysis due to the effects of instantaneous correlations. FC methods based on the imaginary part of the coherence (iCOH) of any two signals are readily robust to VC effects, but neglecting the real part of the coherence leads to negligible FC when the processes are truly connected but with zero or π-phase (modulus 2π) interaction. We ameliorate this issue by proposing a novel method that implements an envelope of the imaginary coherence (EIC) to approximate the coherence estimate of supposedly active underlying sources. We compare EIC with state-of-the-art FC measures that included lagged coherence, iCOH, phase lag index (PLI) and weighted PLI (wPLI), using bivariate autoregressive and stochastic neural mass models. Additionally, we create realistic simulations where three and five regions were mapped on a template cortical surface and synthetic MEG signals were obtained after computing the electromagnetic leadfield. With this simulation and comparison study, we also demonstrate the feasibility of sensor FC analysis using receiver operating curve analysis whilst varying the signal's noise level. However, these results should be interpreted with caution given the known limitations of the sensor-based FC approach. Overall, we found that EIC and iCOH demonstrate superior results with most accurate FC maps. As they complement each other in different scenarios, that will be important to study normal and diseased brain activity.
    Matched MeSH terms: Brain Mapping/methods
  11. Sabel BA, Hamid AIA, Borrmann C, Speck O, Antal A
    Int J Psychophysiol, 2020 08;154:80-92.
    PMID: 30978369 DOI: 10.1016/j.ijpsycho.2019.04.002
    BACKGROUND: Modifying brain activity using non-invasive, low intensity transcranial electrical brain stimulation (TES) has rapidly increased during the past 20 years. Alternating current stimulation (ACS), for example, has been shown to alter brain rhythm activities and modify neuronal functioning in the visual system. Daily application of transorbital ACS to patients with optic nerve damage induces functional connectivity reorganization, and partially restores vision. While ACS is thought to mainly modify neuronal mechanisms, e.g. changes in brain oscillations that can be detected by EEG, it is still an open question, whether and how it may alter BOLD activity.

    OBJECTIVE: We evaluated whether transorbital ACS modulates BOLD activity in early visual cortex using high-resolution 7 Tesla functional magnetic resonance imaging (fMRI).

    METHODS: In this feasibility study transorbital ACS in the alpha range and sham ACS was applied in a random block design in five healthy subjects for 20 min at 1 mA. Brain activation in the visual areas V1, V2 and V3 were measured using 7 Tesla fMRI-based retinotopic mapping at the time points before (baseline) and after stimulation. In addition, we collected data from one hemianopic stroke patient with visual cortex damage after ten daily sessions with 25-50 min stimulation duration.

    RESULTS: In healthy subjects transorbital ACS increased the activated cortical surface area, decreased the fMRI response amplitude and increased coherence in the visual cortex, which was most prominent in the full field task. In the patient, stimulation improved contrast sensitivity in the central visual field. BOLD amplitudes and coherence values were increased in most early visual areas in both hemispheres, with the most pronounced activation detected during eccentricity testing in retinotopic mapping.

    CONCLUSIONS: This feasibility study showed that transorbital ACS modifies BOLD activity to visual stimulation, which outlasts the duration of the AC stimulation. This is in line with earlier neurophysiological findings of increased power in EEG recordings and functional connectivity reorganization in patients with impaired vision. Accordingly, the larger BOLD response area after stimulation can be explained by more coherent activation and lower variability in the activation. Alternatively, increased neuronal activity can also be taken into account. Controlled trials are needed to systematically evaluate the potential of repetitive transorbital ACS to improve visual function after visual pathway stroke and to determine the cause-effect relationship between neural and BOLD activity changes.

    Matched MeSH terms: Brain Mapping
  12. Nisar H, Malik AS, Ullah R, Shim SO, Bawakid A, Khan MB, et al.
    Adv Exp Med Biol, 2015;823:159-74.
    PMID: 25381107 DOI: 10.1007/978-3-319-10984-8_9
    The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.
    Matched MeSH terms: Brain Mapping
  13. Parida S, Dehuri S, Cho SB, Cacha LA, Poznanski RR
    J Integr Neurosci, 2015 Sep;14(3):355-68.
    PMID: 26455882 DOI: 10.1142/S0219635215500223
    Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.
    Matched MeSH terms: Brain Mapping
  14. Huang SG, Samdin SB, Ting CM, Ombao H, Chung MK
    J Neurosci Methods, 2020 02 01;331:108480.
    PMID: 31760059 DOI: 10.1016/j.jneumeth.2019.108480
    BACKGROUND: Recent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain resting-state fMRI is to compute the correlation between anatomical regions via sliding time windows. However, the direct use of the sample correlation matrices is not reliable due to the image acquisition and processing noises in resting-sate fMRI.

    NEW METHOD: To overcome these limitations, we propose a new statistical model that smooths out the noise by exploiting the geometric structure of correlation matrices. The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm.

    RESULTS: The proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between males and females is identified.

    COMPARISON WITH EXISTING METHODS: We demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states.

    CONCLUSIONS: We propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity.

    Matched MeSH terms: Brain Mapping
  15. Al-Ezzi A, Kamel N, Faye I, Gunaseli E
    Sensors (Basel), 2021 Jun 15;21(12).
    PMID: 34203578 DOI: 10.3390/s21124098
    Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1-3 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (13-21 Hz), and high beta (22-30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = -0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.
    Matched MeSH terms: Brain Mapping
  16. Abdullah JM
    Malays J Med Sci, 2021 Apr;28(2):1-14.
    PMID: 33958956 DOI: 10.21315/mjms2021.28.2.1
    Last year, there was an increase in the amount of manpower in Malaysia, especially in terms of the numbers of neurosurgeons, cognitive neuroscientists and clinical psychologists. One way to increase the number of cognitive neurotechnologists in the country in 2021 is to allow neuroscientists to register as neurotechnologists with the Malaysian Board of Technologists (MBOT). The Malaysian Brain Mapping project has risen from its humble beginnings as an initiative of the Universiti Sains Malaysia Brain Mapping Group in 2017. There is currently a proposal for its entry into the national arena via the Precision Medicine Initiative with the Academy Science Malaysia, the Ministry of Science, Technology and Innovation, Ministry of Higher Education and Ministry of Health. The current Malaysian Government's Science, Technology, Innovation and Economy (STIE) plan was launched in 2020, leading to the establishment of neurotechnology as one of 10 STIE drivers.
    Matched MeSH terms: Brain Mapping
  17. Begum T, Reza F, Ahmed I, Abdullah JM
    J Integr Neurosci, 2014 Mar;13(1):71-88.
    PMID: 24738540 DOI: 10.1142/S0219635214500058
    Simple geometric and organic shapes and their arrangement are being used in different neuropsychology tests for the assessment of cognitive function, special memory and also for the therapy purpose in different patient groups. Until now there is no electrophysiological evidence of cognitive function determination for simple geometric, organic shapes and their arrangement. Then the main objective of this study is to know the cortical processing and amplitude, latency of visual induced N170 and P300 event related potential components on different geometric, organic shapes and their arrangement and different educational influence on it, which is worthwhile to know for the early and better treatment for those patient groups. While education influenced on cognitive function by using auditory oddball task, little is known about the influence of education on cognitive function induced by visual attention task in case of the choice of geometric, organic shapes and their arrangements. Using a 128-electrode sensor net, we studied the responses of the choice of the different geometric and organic shapes randomly in experiment 1 and their arrangements in experiment 2 in the high, medium and low education groups. In both experiments, subjects push the button "1" or "2" if like or dislike, respectively. Total 45 healthy subjects (15 in each group) were recruited. ERPs were measured from 11 electrode sites and analyzed to see the evoked N170/N240 and P300 ERP components. There were no differences between like and dislike in amplitudes even in latencies in every stimulus in both experiments. We fixed geometric shapes and organic shapes stimuli only, not like and dislike. Upon the stimulus types, N170 ERP component was found instead of N240, in occipito-temporal (T5, T6, O1 and O2) locations where the amplitude is the highest at O2 location and P300 was distributed in the central (Cz and Pz) locations in both experiments in all groups. In experiment 1, significant low amplitude and non-significant larger latency of the N170 component are found out at O1 location for both stimuli in low education group comparing medium education groups, but in experiment 2, there is no significant difference between stimuli among groups in amplitude and latency. In both experiments, P300 component was found in Cz and Pz locations though the amplitudes are higher at Cz than Pz areas. In experiment 1, medium education group evoked significantly (geometric shape stimuli, P = 0.05; organic shape stimuli, P = 0.02) higher amplitude of P300 component comparing low education group at Cz location. Whereas, there is no significant difference of amplitudes among groups across stimuli in Cz and Pz locations in experiment 2. Latencies have no significant differences in both experiments among groups also, but longer latency are found in low education group at Cz location comparing medium education group, though not significant. We conclude that simple geometric shapes, organic shapes and their arrangements evoked visual N170 component at temporo-occipital areas with right lateralization and P300 ERP component at centro-parietal areas. Significant low amplitude of N170 and P300 ERP components and longer latencies during different shape stimuli in low education group prove that, low education significantly influence on visual cognitive functions in low education group.
    Matched MeSH terms: Brain Mapping*
  18. Ng SC, Raveendran P
    IEEE Trans Biomed Eng, 2009 Aug;56(8):2024-34.
    PMID: 19457744 DOI: 10.1109/TBME.2009.2021987
    The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.
    Matched MeSH terms: Brain Mapping/methods
  19. Yazdani S, Yusof R, Karimian A, Mitsukira Y, Hematian A
    PLoS One, 2016;11(4):e0151326.
    PMID: 27096925 DOI: 10.1371/journal.pone.0151326
    Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.
    Matched MeSH terms: Brain Mapping/methods*
  20. Ting CM, Seghouane AK, Khalid MU, Salleh ShH
    Neural Comput, 2015 Sep;27(9):1857-71.
    PMID: 26161816 DOI: 10.1162/NECO_a_00765
    We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.
    Matched MeSH terms: Brain Mapping*
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