Displaying publications 21 - 40 of 65 in total

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  1. Complexity-based decoding of brain-skin relation in response to olfactory stimuli
    Omam S, Babini MH, Sim S, Tee R, Nathan V, Namazi H
    Comput Methods Programs Biomed, 2020 Feb;184:105293.
    PMID: 31887618 DOI: 10.1016/j.cmpb.2019.105293
    BACKGROUND AND OBJECTIVE: Human body is covered with skin in different parts. In fact, skin reacts to different changes around human. For instance, when the surrounding temperature changes, human skin will react differently. It is known that the activity of skin is regulated by human brain. In this research, for the first time we investigate the relation between the activities of human skin and brain by mathematical analysis of Galvanic Skin Response (GSR) and Electroencephalography (EEG) signals.

    METHOD: For this purpose, we employ fractal theory and analyze the variations of fractal dimension of GSR and EEG signals when subjects are exposed to different olfactory stimuli in the form of pleasant odors.

    RESULTS: Based on the obtained results, the complexity of GSR signal changes with the complexity of EEG signal in case of different stimuli, where by increasing the molecular complexity of olfactory stimuli, the complexity of EEG and GSR signals increases. The results of statistical analysis showed the significant effect of stimulation on variations of complexity of GSR signal. In addition, based on effect size analysis, fourth odor with greatest molecular complexity had the greatest effect on variations of complexity of EEG and GSR signals.

    CONCLUSION: Therefore, it can be said that human skin reaction changes with the variations in the activity of human brain. The result of analysis in this research can be further used to make a model between the activities of human skin and brain that will enable us to predict skin reaction to different stimuli.

    Matched MeSH terms: Brain/physiology*
  2. Functional Neuroproteomics: An Imperative Approach for Unravelling Protein Implicated Complexities of Brain
    Husain I, Ahmad W, Ali A, Anwar L, Nuruddin SM, Ashraf K, et al.
    CNS Neurol Disord Drug Targets, 2021;20(7):613-624.
    PMID: 33530918 DOI: 10.2174/1871527320666210202121624
    A proteome is defined as a comprehensive protein set either of an organ or an organism at a given time and under specific physiological conditions. Accordingly, the study of the nervous system's proteomes is called neuroproteomics. In the neuroproteomics process, various pieces of the nervous system are "fragmented" to understand the dynamics of each given sub-proteome in a much better way. Functional proteomics addresses the organisation of proteins into complexes and the formation of organelles from these multiprotein complexes that control various physiological processes. Current functional studies of neuroproteomics mainly talk about the synapse structure and its organisation, the major building site of the neuronal communication channel. The proteomes of synaptic vesicle, presynaptic terminal, and postsynaptic density, have been examined by various proteomics techniques. The objectives of functional neuroproteomics are: to solve the proteome of single neurons or astrocytes grown in cell cultures or from the primary brain cells isolated from tissues under various conditions, to identify the set of proteins that characterize specific pathogenesis, or to determine the group of proteins making up postsynaptic or presynaptic densities. It is usual to solve a particular sub-proteome like the heat-shock response proteome or the proteome responding to inflammation. Post-translational protein modifications alter their functions and interactions. The techniques to detect synapse phosphoproteome are available. However, techniques for the analysis of ubiquitination and sumoylation are under development.
    Matched MeSH terms: Brain/physiology*
  3. Fulltext Robust EEG/MEG Based Functional Connectivity with the Envelope of the Imaginary Coherence: Sensor Space Analysis
    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/physiology*
  4. Fulltext Modelling the differential effects of age on transcranial magnetic stimulation induced electric fields
    Alawi M, Lee PF, Deng ZD, Goh YK, Croarkin PE
    J Neural Eng, 2023 Mar 16;20(2).
    PMID: 36240726 DOI: 10.1088/1741-2552/ac9a76
    Objective. The therapeutic application of noninvasive brain stimulation modalities such as transcranial magnetic stimulation (TMS) has expanded in terms of indications and patient populations. Often neurodevelopmental and neurodegenerative changes are not considered in research studies and clinical applications. This study sought to examine TMS dosing across time points in the life cycle.Approach. TMS induced electric fields with a figure-of-eight coil was simulated at left dorsolateral prefrontal cortex regions and taken in vertex as a control region. Realistic magnetic resonance imaging-based head models (N= 48) were concurrently examined in a cross-sectional study of three different age groups (children, adults, and elderlies).Main results. Age had a negative correlation with electric field peaks in white matter, grey matter and cerebrospinal fluid (P< 0.001). Notably, the electric field map in children displayed the widest cortical surface spread of TMS induced electric fields.Significance. Age-related anatomical geometry beneath the coil stimulation site had a significant impact on the TMS induced electric fields for different age groups. Safety considerations for TMS applications and protocols in children are warranted based on the present electric field findings.
    Matched MeSH terms: Brain/physiology
  5. Fulltext Local context influences memory for emotional stimuli but not electrophysiological markers of emotion-dependent attention
    Barnacle GE, Tsivilis D, Schaefer A, Talmi D
    Psychophysiology, 2018 04;55(4).
    PMID: 29023754 DOI: 10.1111/psyp.13014
    Emotional enhancement of free recall can be context dependent. It is readily observed when emotional and neutral scenes are encoded and recalled together in a "mixed" list, but diminishes when these scenes are encoded separately in "pure" lists. We examined the hypothesis that this effect is due to differences in allocation of attention to neutral stimuli according to whether they are presented in mixed or pure lists, especially when encoding is intentional. Using picture stimuli that were controlled for semantic relatedness, our results contradicted this hypothesis. The amplitude of well-known electrophysiological markers of emotion-related attention-the early posterior negativity (EPN), the late positive potential (LPP), and the slow wave (SW)-was higher for emotional stimuli. Crucially, the emotional modulation of these ERPs was insensitive to list context, observed equally in pure and mixed lists. Although list context did not modulate neural markers of emotion-related attention, list context did modulate the effect of emotion on free recall. The apparent decoupling of the emotional effects on attention and memory, challenges existing hypotheses accounting for the emotional enhancement of memory. We close by discussing whether findings are more compatible with an alternative hypothesis, where the magnitude of emotional memory enhancement is, at least in part, a consequence of retrieval dynamics.
    Matched MeSH terms: Brain/physiology*
  6. A rapid event-related potential (ERP) method for point-of-care evaluation of brain function: development of the Halifax Consciousness Scanner
    Sculthorpe-Petley L, Liu C, Hajra SG, Parvar H, Satel J, Trappenberg TP, et al.
    J Neurosci Methods, 2015 Apr 30;245:64-72.
    PMID: 25701685 DOI: 10.1016/j.jneumeth.2015.02.008
    Event-related potentials (ERPs) may provide a non-invasive index of brain function for a range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges that prevent full integration into clinical settings.
    Matched MeSH terms: Brain/physiology*
  7. Genomic instantiation of consciousness in neurons through a biophoton field theory
    Cacha LA, Poznanski RR
    J Integr Neurosci, 2014 Jun;13(2):253-92.
    PMID: 25012712 DOI: 10.1142/S0219635214400081
    A theoretical framework is developed based on the premise that brains evolved into sufficiently complex adaptive systems capable of instantiating genomic consciousness through self-awareness and complex interactions that recognize qualitatively the controlling factors of biological processes. Furthermore, our hypothesis assumes that the collective interactions in neurons yield macroergic effects, which can produce sufficiently strong electric energy fields for electronic excitations to take place on the surface of endogenous structures via alpha-helical integral proteins as electro-solitons. Specifically the process of radiative relaxation of the electro-solitons allows for the transfer of energy via interactions with deoxyribonucleic acid (DNA) molecules to induce conformational changes in DNA molecules producing an ultra weak non-thermal spontaneous emission of coherent biophotons through a quantum effect. The instantiation of coherent biophotons confined in spaces of DNA molecules guides the biophoton field to be instantaneously conducted along the axonal and neuronal arbors and in-between neurons and throughout the cerebral cortex (cortico-thalamic system) and subcortical areas (e.g., midbrain and hindbrain). Thus providing an informational character of the electric coherence of the brain - referred to as quantum coherence. The biophoton field is realized as a conscious field upon the re-absorption of biophotons by exciplex states of DNA molecules. Such quantum phenomenon brings about self-awareness and enables objectivity to have access to subjectivity in the unconscious. As such, subjective experiences can be recalled to consciousness as subjective conscious experiences or qualia through co-operative interactions between exciplex states of DNA molecules and biophotons leading to metabolic activity and energy transfer across proteins as a result of protein-ligand binding during protein-protein communication. The biophoton field as a conscious field is attributable to the resultant effect of specifying qualia from the metabolic energy field that is transported in macromolecular proteins throughout specific networks of neurons that are constantly transforming into more stable associable representations as molecular solitons. The metastability of subjective experiences based on resonant dynamics occurs when bottom-up patterns of neocortical excitatory activity are matched with top-down expectations as adaptive dynamic pressures. These dynamics of on-going activity patterns influenced by the environment and selected as the preferred subjective experience in terms of a functional field through functional interactions and biological laws are realized as subjectivity and actualized through functional integration as qualia. It is concluded that interactionism and not information processing is the key in understanding how consciousness bridges the explanatory gap between subjective experiences and their neural correlates in the transcendental brain.
    Matched MeSH terms: Brain/physiology*
  8. A comparison of brain phantom relative permittivity with CST simulation library and existing research
    Chew KM, Seman N, Sudirman R, Yong CY
    Biomed Mater Eng, 2014;24(6):2161-7.
    PMID: 25226914 DOI: 10.3233/BME-141027
    The development of human-like brain phantom is important for data acquisition in microwave imaging. The characteristics of the phantom should be based on the real human body dielectric properties such as relative permittivity. The development of phantom includes the greymatter and whitematter regions, each with a relative permittivity of 38 and 28 respectively at 10 GHz frequency. Results were compared with the value obtained from the standard library of Computer Simulation Technology (CST) simulation application and the existing research by Fernandez and Gabriel. Our experimental results show a positive outcome, in which the proposed mixture was adequate to represent real human brain for data acquisition.
    Matched MeSH terms: Brain/physiology*
  9. The effects of aging on the brain activation pattern during a speech perception task: an fMRI study
    Manan HA, Franz EA, Yusoff AN, Mukari SZ
    Aging Clin Exp Res, 2015 Feb;27(1):27-36.
    PMID: 24906677 DOI: 10.1007/s40520-014-0240-0
    In the present study, brain activation associated with speech perception processing was examined across four groups of adult participants with age ranges between 20 and 65 years, using functional MRI (fMRI). Cognitive performance demonstrates that performance accuracy declines with age. fMRI results reveal that all four groups of participants activated the same brain areas. The same brain activation pattern was found in all activated areas (except for the right superior temporal gyrus and right middle temporal gyrus); brain activity was increased from group 1 (20-29 years) to group 2 (30-39 years). However, it decreased in group 3 (40-49 years) with further decreases in group 4 participants (50-65 years). Result also reveals that three brain areas (superior temporal gyrus, Heschl's gyrus and cerebellum) showed changes in brain laterality in the older participants, akin to a shift from left-lateralized to right-lateralized activity. The onset of this change was different across brain areas. Based on these findings we suggest that, whereas all four groups of participants used the same areas in processing, the engagement and recruitment of those areas differ with age as the brain grows older. Findings are discussed in the context of corroborating evidence of neural changes with age.
    Matched MeSH terms: Brain/physiology*
  10. Asynchronous brain machine interface-based control of a wheelchair
    Hema CR, Paulraj MP, Yaacob S, Adom AH, Nagarajan R
    Adv Exp Med Biol, 2011;696:565-72.
    PMID: 21431597 DOI: 10.1007/978-1-4419-7046-6_57
    A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.
    Matched MeSH terms: Brain/physiology
  11. EEG spectral analysis on Muslim prayers
    Doufesh H, Faisal T, Lim KS, Ibrahim F
    Appl Psychophysiol Biofeedback, 2012 Mar;37(1):11-8.
    PMID: 21965118 DOI: 10.1007/s10484-011-9170-1
    This study investigated the proposition of relaxation offered by performing the Muslim prayers by measuring the alpha brain activity in the frontal (F3-F4), central (C3-C4), parietal (P3-P4), and occipital (O1-O2) electrode placements using the International 10-20 System. Nine Muslim subjects were asked to perform the four required cycles of movements of Dhuha prayer, and the EEG were subsequently recorded with open eyes under three conditions, namely, resting, performing four cycles of prayer while reciting the specific verses and supplications, and performing four cycles of acted salat condition (prayer movements without any recitations). Analysis of variance (ANOVA) tests revealed that there were no significant difference in the mean alpha relative power (RP(α)) between the alpha amplitude in the Dhuha prayer and the acted conditions in all eight electrode positions. However, the mean RP(α) showed higher alpha amplitude during the prostration position of the Dhuha prayer and acted condition at the parietal and occipital regions in comparison to the resting condition. Findings were similar to other studies documenting increased alpha amplitude in parietal and occipital regions during meditation and mental concentration. The incidence of increased alpha amplitude suggested parasympathetic activation, thus indicating a state of relaxation. Subsequent studies are needed to delineate the role of mental concentration, and eye focus, on alpha wave amplitude while performing worshipping acts.
    Matched MeSH terms: Brain/physiology
  12. A study on discrete wavelet-based noise removal from EEG signals
    Asaduzzaman K, Reaz MB, Mohd-Yasin F, Sim KS, Hussain MS
    Adv Exp Med Biol, 2010;680:593-9.
    PMID: 20865544 DOI: 10.1007/978-1-4419-5913-3_65
    Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
    Matched MeSH terms: Brain/physiology
  13. Enhanced mu rhythm extraction using blind source separation and wavelet transform
    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/physiology*
  14. Assessment of computerized analysis algorithms for one dimension physiologic data with multiple databases
    Lee YK, Bister M, Salleh YM, Blanchfield P
    Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:5004-7.
    PMID: 19163841 DOI: 10.1109/IEMBS.2008.4650338
    Software technology enables computerized analysis to offer second opinion in various screening and diagnostic tasks to assist the clinicians. Yet, the performance of these computerized methods for medical images is questioned by experts in CAD research, owing to the use of different databases and criteria for evaluating the computer results for comparison. This paper intends to substantiate this statement by illustrating the effects of such issues with the use of 1D physiologic data and multiple databases. For this purpose, the detection of desaturation events in Sp02 and spike events in EEG are used. This is the first time that comparison between different algorithms on a common basis is carried out on an individual effort. The appraisal for all the algorithms is made on the same databases and criteria. It is surprising to find that issues for 2/3D images concur with those found in 1D data here. In evaluating the accuracy of a new algorithm, a single independent database gives results fast. This paper reveals weaknesses of such an approach. It is hoped that the supportive evidence shown here is enough for researchers to innovate a better platform for credibility in reporting performance comparison of computerized analysis algorithms.
    Matched MeSH terms: Brain/physiology*
  15. Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications
    Kasabov N, Scott NM, Tu E, Marks S, Sengupta N, Capecci E, et al.
    Neural Netw, 2016 Jun;78:1-14.
    PMID: 26576468 DOI: 10.1016/j.neunet.2015.09.011
    The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.
    Matched MeSH terms: Brain/physiology
  16. Fulltext P300 correlates with learning & memory abilities and fluid intelligence
    Amin HU, Malik AS, Kamel N, Chooi WT, Hussain M
    J Neuroeng Rehabil, 2015;12:87.
    PMID: 26400233 DOI: 10.1186/s12984-015-0077-6
    Educational psychology research has linked fluid intelligence with learning and memory abilities and neuroimaging studies have specifically associated fluid intelligence with event related potentials (ERPs). The objective of this study is to find the relationship of ERPs with learning and memory recall and predict the memory recall score using P300 (P3) component.
    Matched MeSH terms: Brain/physiology
  17. Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review
    Hamedi M, Salleh ShH, Noor AM
    Neural Comput, 2016 06;28(6):999-1041.
    PMID: 27137671 DOI: 10.1162/NECO_a_00838
    Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.
    Matched MeSH terms: Brain/physiology*
  18. Variations in prestimulus neural activity predict the emotion-enhanced memory effect
    Yick YY, Buratto LG, Schaefer A
    Neuroreport, 2016 08 03;27(11):864-8.
    PMID: 27295027 DOI: 10.1097/WNR.0000000000000628
    Here, we report evidence that electrophysiological neural activity preceding the onset of emotional pictures can predict whether they will be remembered or forgotten 24 h later, whereas the same effect was not observed for neutral pictures. In contrast to previous research, we observed this effect using a paradigm in which participants could not predict the emotional or the neutral content of the pictures before their onset. These effects were obtained alongside significant behavioural effects of superior recognition memory for emotional compared with neutral items. These findings suggest that the preferential encoding of emotional events in memory is determined by fluctuations in the availability of processing resources just before event onset. This explanation argues in favour of mediational models of emotional memory, which contend that emotional information is preferentially encoded because it mobilizes a greater amount of processing resources than neutral information.
    Matched MeSH terms: Brain/physiology*
  19. Fulltext Fractional Diffusion Based Modelling and Prediction of Human Brain Response to External Stimuli
    Namazi H, Kulish VV
    Comput Math Methods Med, 2015;2015:148534.
    PMID: 26089955 DOI: 10.1155/2015/148534
    Human brain response is the result of the overall ability of the brain in analyzing different internal and external stimuli and thus making the proper decisions. During the last decades scientists have discovered more about this phenomenon and proposed some models based on computational, biological, or neuropsychological methods. Despite some advances in studies related to this area of the brain research, there were fewer efforts which have been done on the mathematical modeling of the human brain response to external stimuli. This research is devoted to the modeling and prediction of the human EEG signal, as an alert state of overall human brain activity monitoring, upon receiving external stimuli, based on fractional diffusion equations. The results of this modeling show very good agreement with the real human EEG signal and thus this model can be used for many types of applications such as prediction of seizure onset in patient with epilepsy.
    Matched MeSH terms: Brain/physiology*
  20. The effects of negative emotion on encoding-related neural activity predicting item and source recognition
    Yick YY, Buratto LG, Schaefer A
    Neuropsychologia, 2015 Jul;73:48-59.
    PMID: 25936685 DOI: 10.1016/j.neuropsychologia.2015.04.030
    We report here a study that obtained reliable effects of emotional modulation of a well-known index of memory encoding--the electrophysiological "Dm" effect--using a recognition memory paradigm followed by a source memory task. In this study, participants performed an old-new recognition test of emotionally negative and neutral pictures encoded 1 day before the test, and a source memory task involving the retrieval of the temporal context in which pictures had been encoded. Our results showed that Dm activity was enhanced for all emotional items on a late positivity starting at ~400 ms post-stimulus onset, although Dm activity for high arousal items was also enhanced at an earlier stage (200-400 ms). Our results also showed that emotion enhanced Dm activity for items that were both recognised with or without correct source information. Further, when only high arousal items were considered, larger Dm amplitudes were observed if source memory was accurate. Three main conclusions are drawn from these findings. First, negative emotion can enhance encoding processes predicting the subsequent recognition of central item information. Second, if emotion reaches high levels of arousal, the encoding of contextual details can also be enhanced over and above the effects of emotion on central item encoding. Third, the morphology of our ERPs is consistent with a hybrid model of the role of attention in emotion-enhanced memory (Pottage and Schaefer, 2012).
    Matched MeSH terms: Brain/physiology*
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