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  1. 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.
  2. Mok SY, Lim YM, Goh SY
    J. Neurosci. Methods, 2009 May 15;179(2):284-91.
    PMID: 19428539 DOI: 10.1016/j.jneumeth.2009.02.009
    A device to facilitate high-density seeding of dissociated neural cells on planar multi-electrode arrays (MEAs) is presented in this paper. The device comprises a metal cover with two concentric cylinders-the outer cylinder fits tightly on to the external diameter of a MEA to hold it in place and an inner cylinder holds a central glass tube for introducing a cell suspension over the electrode area of the MEA. An O-ring is placed at the bottom of the inner cylinder and the glass tube to provide a fluid-tight seal between the glass tube and the MEA electrode surface. The volume of cell suspension in the glass tube is varied according to the desired plating density. After plating, the device can be lifted from the MEA without leaving any residue on the contact surface. The device has enabled us to increase and control the plating density of neural cell suspension with low viability, and to prepare successful primary cultures from cryopreserved neurons and glia. The cultures of cryopreserved dissociated cortical neurons that we have grown in this manner remained spontaneously active over months, exhibited stable development and similar network characteristics as reported by other researchers.
  3. Javed E, Faye I, Malik AS, Abdullah JM
    J. Neurosci. Methods, 2017 11 01;291:150-165.
    PMID: 28842191 DOI: 10.1016/j.jneumeth.2017.08.020
    BACKGROUND: Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.

    METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

    RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.

    COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.

    CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

  4. Patar A, Dockery P, Howard L, McMahon S
    J. Neurosci. Methods, 2019 01 01;311:418-425.
    PMID: 30267723 DOI: 10.1016/j.jneumeth.2018.09.027
    BACKGROUND: The use of animals to model spinal cord injury (SCI) requires extensive post-operative care and can be expensive, which makes an alternative model extremely attractive. The use ofex vivo slice cultures is an alternative way to study the pathophysiological changes that can mimic in vivo conditions and support the 3Rs (replacement, reduction and refinement) of animal use in SCI research models.

    NEW METHOD: In this study the presence of reactive astrocytes and NG2 proteoglycans was investigated in two ex vivo models of SCI; stab injury and transection injury. Stereological analysis to measure immunohistochemical staining was performed on the scar and injury zones to detect astrocytes and the chondroitin sulphate proteoglycan NG2.

    RESULTS: The volume fraction (Vv) of reactive astrocytes and NG2 proteoglycans increased significantly between day 3 and day 10 post injury in both ex vivo models. This data shows how ex vivo SCI models are a useful research tool allowing reduction of research cost and time involved in carrying out animal studies, as well as reducing the numbers of animals used.

    COMPARISON WITH EXISTING METHOD: This is the first evidence of an ex vivo stab injury model of SCI and also the first comparison of immunohistochemical staining for injury markers within stab injured and transection injured ex vivo slice cultures.

    CONCLUSIONS: The use of organotypic slice culture models provide a simple way to study the cellular consequences following SCI and they can also be used as a platform for potential therapeutics regimes for the treatment of SCI.

  5. Mousavi Z, Yousefi Rezaii T, Sheykhivand S, Farzamnia A, Razavi SN
    J. Neurosci. Methods, 2019 08 01;324:108312.
    PMID: 31201824 DOI: 10.1016/j.jneumeth.2019.108312
    Using a smart method for automatic diagnosis in medical applications, such as sleep stage classification is considered as one of the important challenges of the last few years which can replace the time-consuming process of visual inspection done by specialists. One of the problems regarding the automatic diagnosis of sleep patterns is extraction and selection of discriminative features generally demanding high computational burden. This paper provides a new single-channel approach to automatic classification of sleep stages from EEG signal. The main idea is to directly apply the raw EEG signal to deep convolutional neural network, without involving feature extraction/selection, which is a challenging process in the previous literature. The proposed network architecture includes 9 convolutional layers followed by 2 fully connected layers. In order to make the samples of different classes balanced, we used a preprocessing method called data augmentation. The simulation results of the proposed method for classification of 2 to 6 classes of sleep stages show the accuracy of 98.10%, 96.86%, 93.11%, 92.95%, 93.55% and Cohen's Kappa coefficient of 0.98%, 0.94%, 0.90%, 0.86% and 0.89%, respectively. Furthermore, comparing the obtained results with the state-of-the-art methods reveals the performance improvement of the proposed sleep stage classification in terms of accuracy and Cohen's Kappa coefficient.
  6. Huang SG, Samdin SB, Ting CM, Ombao H, Chung MK
    J. Neurosci. Methods, 2020 Feb 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.

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