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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.
A device to facilitate preparation of high-density neural cell cultures in MEAs

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.
Deep convolutional neural network for classification of sleep stages from single-channel EEG signals

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.
Fulltext Statistical model for dynamically-changing correlation matrices with application to brain connectivity

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.
Classification of ERP signal from amnestic mild cognitive impairment with type 2 diabetes mellitus using single-scale multi-input convolution neural network

Wen D, Cheng Z, Li J, Zheng X, Yao W, Dong X, et al.

J Neurosci Methods, 2021 Nov 01;363:109353.
PMID: 34492241 DOI: 10.1016/j.jneumeth.2021.109353

BACKGROUND: The application of deep learning models to electroencephalogram (EEG) signal classification has recently become a popular research topic. Several deep learning models have been proposed to classify EEG signals in patients with various neurological diseases. However, no effective deep learning model for event-related potential (ERP) signal classification is yet available for amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM).
METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task.
RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression.
CONCLUSIONS: The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.
Analysis of reactive astrocytes and NG2 proteoglycan in ex vivo rat models of spinal cord injury

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.
Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA

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.
Enhancing therapeutic efficacy of fingolimod via intranasal delivery in an ethidium bromide-induced model of multiple sclerosis

Sharma A, Sharma N, Khanna K, Arora A, Mehan S, Sharma A

J Neurosci Methods, 2025 Mar 08;418:110415.
PMID: 40058465 DOI: 10.1016/j.jneumeth.2025.110415

BACKGROUND: Multiple sclerosis (MS) is a chronic inflammatory autoimmune neurological characterized by muscle weakness, numbness, tingling, vision problems, and difficulty in coordination and balance caused by the damage of myelin content around the nerve fibres. The recent literature is evident that along with their lymphocyte attack prevention mechanism, fingolimod (FNG) can serve as neuroprotective also by ensuring their improved brain availability. Therefore, in this project brain availability of FNG was enhanced by delivering the FNG in the form of nanoparticles.
NEW METHODS: Fingolimod-loaded solid lipid nanoparticles (FNG-SLNs) were prepared using the solvent evaporation method and formulation factors (lipid concentration; X1, speed; X2, surfactant concentration; X3) and response were established by factorial design. FNG-SLNs were characterized for particle size, entrapment efficiency and in-vitro drug release. Optimized formulations were characterized for in-vivo efficacy study in ethidium bromide-induced MS rat model.
RESULT: Obtained data revealed that the particle size and entrapment efficiency of FNG-SLNs optimized formulation was 125.4 nm and 79.86 % w/w respectively. In-vitro drug release study showed an initial burst release of the FNG up to 32.52 % in 30 min followed by sustained drug release up to 78.22 % in 24 h. Furthermore, in-vivo data of FNG-SLNs on ethidium bromide-induced MS rat model revealed better treatment response by showing several evidence such as signs of remyelination, restoration of neuron shape, and the recovered oligodendrocytes.
COMPARISON WITH EXISTING METHODS: To the best of our knowledge this article demonstrates improved efficacy of FNG using SLNs.
CONCLUSION: This study demonstrates the successful development of FNG-SLNs to enhance the therapeutic efficacy of FNG for the treatment of multiple sclerosis.