Статті в журналах: "MEG data"

1

Lukka, Tuomas, Bernd Schoner, and Alec Marantz. "Phoneme discrimination from MEG data." Neurocomputing 31, no. 1-4 (March 2000): 153–65. http://dx.doi.org/10.1016/s0925-2312(99)00178-2.

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2

Chen, Zhihau, Alex Tretyakov, Hideki Takayasu, and Nobukazu Nakasato. "Spectral Analysis of Multichannel Meg Data." Fractals 06, no. 04 (December 1998): 395–400. http://dx.doi.org/10.1142/s0218348x98000432.

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We use the Discrete Wavelet Transform in order to study the power spectrum of data obtained in magnetoencephalographical measurements. α-wave phenomenon is found to occur independently 1/fβ noise, which is present over almost all channels. the β value is close to 1.

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3

Cheung, Michael J., Natasa Kovačević, Zainab Fatima, Bratislav Mišić, and Anthony R. McIntosh. "[MEG]PLS: A pipeline for MEG data analysis and partial least squares statistics." NeuroImage 124 (January 2016): 181–93. http://dx.doi.org/10.1016/j.neuroimage.2015.08.045.

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4

Ikeda, S., and K. Toyama. "Independent component analysis for noisy data — MEG data analysis." Neural Networks 13, no. 10 (December 2000): 1063–74. http://dx.doi.org/10.1016/s0893-6080(00)00071-x.

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5

Wu, Huanqi, Ruonan Wang, Yuyu Ma, Xiaoyu Liang, Changzeng Liu, Dexin Yu, Nan An, and Xiaolin Ning. "Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data." Bioengineering 11, no. 6 (June 13, 2024): 609. http://dx.doi.org/10.3390/bioengineering11060609.

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Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience.

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6

Askari, Pegah, Natascha Cardoso da Fonseca, Tyrell Pruitt, Joseph A. Maldjian, Sasha Alick-Lindstrom, and Elizabeth M. Davenport. "Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices." Brain Sciences 14, no. 2 (February 9, 2024): 173. http://dx.doi.org/10.3390/brainsci14020173.

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Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance.

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7

Litvak, Vladimir, Jérémie Mattout, Stefan Kiebel, Christophe Phillips, Richard Henson, James Kilner, Gareth Barnes, et al. "EEG and MEG Data Analysis in SPM8." Computational Intelligence and Neuroscience 2011 (2011): 1–32. http://dx.doi.org/10.1155/2011/852961.

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SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

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8

Gross, J., and A. A. Ioannides. "Linear transformations of data space in MEG." Physics in Medicine and Biology 44, no. 8 (July 22, 1999): 2081–97. http://dx.doi.org/10.1088/0031-9155/44/8/317.

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9

Luckhoo, Henry T., Matthew J. Brookes, and Mark W. Woolrich. "Multi-session statistics on beamformed MEG data." NeuroImage 95 (July 2014): 330–35. http://dx.doi.org/10.1016/j.neuroimage.2013.12.026.

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10

Ventrucci, Massimo, Claire Miller (née Ferguson), Joachim Gross, Jan-Mathijs Schoffelen, and Adrian W. Bowman. "Spatiotemporal smoothing of single trial MEG data." Journal of Neuroscience Methods 200, no. 2 (September 2011): 219–28. http://dx.doi.org/10.1016/j.jneumeth.2011.06.004.

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11

Belardinelli, P., L. Ciancetta, V. Pizzella, C. Del Gratta, and G. L. Romani. "Localizing complex neural circuits with MEG data." Cognitive Processing 7, no. 1 (January 21, 2006): 53–59. http://dx.doi.org/10.1007/s10339-005-0024-8.

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12

Lee, Haeji, Chun Kee Chung, and Jaehee Kim. "Statistical network analysis for epilepsy MEG data." Communications for Statistical Applications and Methods 30, no. 6 (November 30, 2023): 561–75. http://dx.doi.org/10.29220/csam.2023.30.6.561.

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13

YANG, JUHONG, YUKI SAITO, QIWEI SHI, JIANTING CAO, TOSHIHISA TANAKA, and TSUNEHIRO TAKEDA. "EMPIRICAL MODE DECOMPOSITION METHOD FOR MEG PHANTOM DATA ANALYSIS." Journal of Circuits, Systems and Computers 18, no. 08 (December 2009): 1467–80. http://dx.doi.org/10.1142/s0218126609005794.

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Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from real-world measured data and represent them corresponding to the human brain functions. This usually depends on how to reduce a high level noise from the measurement. In this paper, a novel multistage MEG data analysis method based on the empirical mode decomposition (EMD) and independent component analysis (ICA) approaches is proposed for the feature extraction. Moreover, EMD and ICA algorithms are investigated for analyzing the MEG single-trial data which is recorded from the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in high level noise reduction by EMD associated with ICA approach and source localization by equivalent current dipole fitting method.

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14

Marhl, Urban, Anna Jodko-Władzińska, Rüdiger Brühl, Tilmann Sander, and Vojko Jazbinšek. "Transforming and comparing data between standard SQUID and OPM-MEG systems." PLOS ONE 17, no. 1 (January 19, 2022): e0262669. http://dx.doi.org/10.1371/journal.pone.0262669.

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Optically pumped magnetometers (OPMs) have recently become so sensitive that they are suitable for use in magnetoencephalography (MEG). These sensors solve operational problems of the current standard MEG, where superconducting quantum interference device (SQUID) gradiometers and magnetometers are being used. The main advantage of OPMs is that they do not require cryogenics for cooling. Therefore, they can be placed closer to the scalp and are much easier to use. Here, we measured auditory evoked fields (AEFs) with both SQUID- and OPM-based MEG systems for a group of subjects to better understand the usage of a limited sensor count OPM-MEG. We present a theoretical framework that transforms the within subject data and equivalent simulation data from one MEG system to the other. This approach works on the principle of solving the inverse problem with one system, and then using the forward model to calculate the magnetic fields expected for the other system. For the source reconstruction, we used a minimum norm estimate (MNE) of the current distribution. Two different volume conductor models were compared: the homogeneous conducting sphere and the three-shell model of the head. The transformation results are characterized by a relative error and cross-correlation between the measured and the estimated magnetic field maps of the AEFs. The results for both models are encouraging. Since some commercial OPMs measure multiple components of the magnetic field simultaneously, we additionally analyzed the effect of tangential field components. Overall, our dual-axis OPM-MEG with 15 sensors yields similar information to a 62-channel SQUID-MEG with its field of view restricted to the right hemisphere.

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15

Duman, Ali Nabi, and Ahmet E. Tatar. "Topological data analysis for revealing dynamic brain reconfiguration in MEG data." PeerJ 11 (July 20, 2023): e15721. http://dx.doi.org/10.7717/peerj.15721.

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In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.

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16

Gjini, Klevest, Susan M. Bowyer, Frank Wang, and Nash N. Boutros. "Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data." Clinical EEG and Neuroscience 51, no. 1 (August 4, 2019): 34–44. http://dx.doi.org/10.1177/1550059419867561.

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This study investigated the magneto- and electroencephalography (MEG and EEG, respectively) resting state to identify the deviations closely associated with the deficit syndrome (DS) in schizophrenia patients. Ten subjects in each group (control, DS, and nondeficit schizophrenia [NDS]) were included. Subjects underwent MEG-EEG recordings during a resting state condition. MEG coherence source imaging (CSI) in source space and spectral analysis in sensor space were performed. Significant differences were found between the 2 patient groups: (1) MEG and EEG spectral analysis showed significantly higher power at low frequencies (delta band) at sensor space in DS compared with NDS patients; (2) source analysis revealed larger power in the DS compared with NDS group at low frequencies in the frontal region; (3) NDS patients showed significantly higher MEG signal relative power in beta bands in sensor space compared with DS patients; (4) both DS and NDS patients showed higher EEG absolute power at higher beta band compared to controls; and (5) patients with DS were found to have a significantly higher MEG CSI than controls in the beta frequency band. These data support the observation of increased power in the low-frequency EEG/MEG rhythms associated with the DS. Increased power in the beta rhythms was more associated with the NDS.

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17

Goel, Anshika, Saurav Roy, Khushboo Punjabi, Ritwick Mishra, Manjari Tripathi, Deepika Shukla, and Pravat K. Mandal. "PRATEEK: Integration of Multimodal Neuroimaging Data to Facilitate Advanced Brain Research." Journal of Alzheimer's Disease 83, no. 1 (August 31, 2021): 305–17. http://dx.doi.org/10.3233/jad-210440.

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Background: In vivo neuroimaging modalities such as magnetic resonance imaging (MRI), functional MRI (fMRI), magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), and quantitative susceptibility mapping (QSM) are useful techniques to understand brain anatomical structure, functional activity, source localization, neurochemical profiles, and tissue susceptibility respectively. Integrating unique and distinct information from these neuroimaging modalities will further help to enhance the understanding of complex neurological diseases. Objective: To develop a processing scheme for multimodal data integration in a seamless manner on healthy young population, thus establishing a generalized framework for various clinical conditions (e.g., Alzheimer’s disease). Methods: A multimodal data integration scheme has been developed to integrate the outcomes from multiple neuroimaging data (fMRI, MEG, MRS, and QSM) spatially. Furthermore, the entire scheme has been incorporated into a user-friendly toolbox- “PRATEEK”. Results: The proposed methodology and toolbox has been tested for viability among fourteen healthy young participants. The data-integration scheme was tested for bilateral occipital cortices as the regions of interest and can also be extended to other anatomical regions. Overlap percentage from each combination of two modalities (fMRI-MRS, MEG-MRS, fMRI-QSM, and fMRI-MEG) has been computed and also been qualitatively assessed for combinations of the three (MEG-MRS-QSM) and four (fMRI-MEG-MRS-QSM) modalities. Conclusion: This user-friendly toolbox minimizes the need of an expertise in handling different neuroimaging tools for processing and analyzing multimodal data. The proposed scheme will be beneficial for clinical studies where geometric information plays a crucial role for advance brain research.

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18

Gramfort, Alexandre, Martin Luessi, Eric Larson, Denis A. Engemann, Daniel Strohmeier, Christian Brodbeck, Lauri Parkkonen, and Matti S. Hämäläinen. "MNE software for processing MEG and EEG data." NeuroImage 86 (February 2014): 446–60. http://dx.doi.org/10.1016/j.neuroimage.2013.10.027.

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19

Lapalme, Ervig, Jean-Marc Lina, and Jérémie Mattout. "Data-driven parceling and entropic inference in MEG." NeuroImage 30, no. 1 (March 2006): 160–71. http://dx.doi.org/10.1016/j.neuroimage.2005.08.067.

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20

Brookes, Matthew J., Johanna M. Zumer, Claire M. Stevenson, Joanne R. Hale, Gareth R. Barnes, Jiri Vrba, and Peter G. Morris. "Investigating spatial specificity and data averaging in MEG." NeuroImage 49, no. 1 (January 2010): 525–38. http://dx.doi.org/10.1016/j.neuroimage.2009.07.043.

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21

Hong, J., and S. C. Jun. "Single-trial Analysis for MEG/EEG spatiotemporal data." NeuroImage 47 (July 2009): S145. http://dx.doi.org/10.1016/s1053-8119(09)71470-3.

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22

Maris, Eric, and Robert Oostenveld. "Nonparametric statistical testing of EEG- and MEG-data." Journal of Neuroscience Methods 164, no. 1 (August 2007): 177–90. http://dx.doi.org/10.1016/j.jneumeth.2007.03.024.

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23

Sequeira, H., L. De Zorzi, F. D'Hondt, F. Lepore, and J. Honoré. "Emotional vision and anxiety: Behavioral and meg data." International Journal of Psychophysiology 131 (October 2018): S30. http://dx.doi.org/10.1016/j.ijpsycho.2018.07.093.

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24

Moran, J. E., S. Bowyer, and N. Tepley. "Multi-Resolution FOCUSS source imaging of MEG Data." Biomedizinische Technik/Biomedical Engineering 46, s2 (2001): 112–14. http://dx.doi.org/10.1515/bmte.2001.46.s2.112.

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25

Zhang, Jian, and Li Su. "Temporal Autocorrelation-Based Beamforming With MEG Neuroimaging Data." Journal of the American Statistical Association 110, no. 512 (October 2, 2015): 1375–88. http://dx.doi.org/10.1080/01621459.2015.1054488.

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26

Wilson, H., A. Moiseev, S. Podin, and M. Quraan. "Continuous head localization and data correction in MEG." International Congress Series 1300 (June 2007): 623–26. http://dx.doi.org/10.1016/j.ics.2007.02.051.

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27

Hasasneh, Ahmad, Nikolas Kampel, Praveen Sripad, N. Jon Shah, and Jürgen Dammers. "Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data." Journal of Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1350692.

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We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.

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28

Aine, C. J., L. Sanfratello, D. Ranken, E. Best, J. A. MacArthur, T. Wallace, K. Gilliam, et al. "MEG-SIM: A Web Portal for Testing MEG Analysis Methods using Realistic Simulated and Empirical Data." Neuroinformatics 10, no. 2 (November 10, 2011): 141–58. http://dx.doi.org/10.1007/s12021-011-9132-z.

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29

Henderson, Paul, Richard K. Russell, and David C. Wilson. "The validity of hospital discharge data." European Journal of Gastroenterology & Hepatology 22, no. 7 (July 2010): 899. http://dx.doi.org/10.1097/meg.0b013e32833424fa.

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30

Feng, Yulong, Wei Xiao, Teng Wu, Jianwei Zhang, Jing Xiang, and Hong Guo. "An Automatic Identification Method for the Blink Artifacts in the Magnetoencephalography with Machine Learning." Applied Sciences 11, no. 5 (March 9, 2021): 2415. http://dx.doi.org/10.3390/app11052415.

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Magnetoencephalography (MEG) detects very weak magnetic fields originating from the neurons so as to study human brain functions. The original detected MEG data always include interference generated by blinks, which can be called blink artifacts. Blink artifacts could cover the MEG signal we are interested in, and therefore need to be removed. Commonly used artifact cleaning algorithms are signal space projection (SSP) and independent component analysis (ICA). These algorithms need to locate the blink artifacts, which is typically done with the identification of the blink signals in the electrooculogram (EOG). The EOG needs to be measured by electrodes placed near the eye. In this work, a new algorithm is proposed for automatic and on-the-fly identification of the blink artifacts from the original detected MEG data based on machine learning; specifically, the artificial neural network (ANN). Seven hundred and one blink artifacts contained in eight MEG signal data sets are harnessed to verify the effect of the proposed blink artifacts identification algorithm. The results show that the method can recognize the blink artifacts from the original detected MEG data, providing a feasible MEG data-processing approach that can potentially be implemented automatically and simultaneously with MEG data measurement.

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31

Oliveira, Túlio Henrique Versiani de, Keila Karine Duarte Campos, Nícia Pedreira Soares, Karina Braga Pena, Wanderson Geraldo Lima, and Frank Silva Bezerra. "Influence of Sexual Dimorphism on Pulmonary Inflammatory Response in Adult Mice Exposed to Chloroform." International Journal of Toxicology 34, no. 3 (April 13, 2015): 250–57. http://dx.doi.org/10.1177/1091581815580172.

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Chloroform is an organic solvent used as an intermediate in the synthesis of various fluorocarbons. Despite its widespread use in industry and agriculture, exposure to chloroform can cause illnesses such as cancer, especially in the liver and kidneys. The aim of the study was to analyze the effects of chloroform on redox imbalance and pulmonary inflammatory response in adult C57BL/6 mice. Forty animals were divided into 4 groups (N = 10): female (FCG) and male (MCG) controls, and females (FEG) and males (MEG) exposed to chloroform (7.0 ppm) 3 times/d for 20 minutes for 5 days. Total and differential cell counts, oxidative damage analysis, and protein carbonyl and antioxidant enzyme catalase (CAT) activity measurements were performed. Morphometric analyses included alveolar area (Aa) and volume density of alveolar septa (Vv) measurements. Compared to FCG and MCG, inflammatory cell influx, oxidative damage to lipids and proteins, and CAT activity were higher in FEG and MEG, respectively. Oxidative damage and enzyme CAT activity were higher in FEG than in FCG. The Aa was higher in FEG and MEG than in FCG and MCG, respectively. The Vv was lower in FEG and MEG than in FCG and MCG, respectively. This study highlights the risks of occupational chloroform exposure at low concentrations and the intensity of oxidative damage related to gender. The results validate a model of acute exposure that provides cellular and biochemical data through short-term exposure to chloroform.

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32

Ahlfors, Seppo P., and Maria Mody. "Overview of MEG." Organizational Research Methods 22, no. 1 (November 9, 2016): 95–115. http://dx.doi.org/10.1177/1094428116676344.

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Magnetoencephalography (MEG) is a method to study electrical activity in the human brain by recording the neuromagnetic field outside the head. MEG, like electroencephalography (EEG), provides an excellent, millisecond-scale time resolution, and allows the estimation of the spatial distribution of the underlying activity, in favorable cases with a localization accuracy of a few millimeters. To detect the weak neuromagnetic signals, superconducting sensors, magnetically shielded rooms, and advanced signal processing techniques are used. The analysis and interpretation of MEG data typically involves comparisons between subject groups and experimental conditions using various spatial, temporal, and spectral measures of cortical activity and connectivity. The application of MEG to cognitive neuroscience studies is illustrated with studies of spoken language processing in subjects with normal and impaired reading ability. The mapping of spatiotemporal patterns of activity within networks of cortical areas can provide useful information about the functional architecture of the brain related to sensory and cognitive processing, including language, memory, attention, and perception.

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33

Zhang, Junpeng, Sarang S. Dalal, Srikantan S. Nagarajan, and Dezhong Yao. "COHERENT MEG/EEG SOURCE LOCALIZATION IN TRANSFORMED DATA SPACE." Biomedical Engineering: Applications, Basis and Communications 22, no. 05 (October 2010): 351–65. http://dx.doi.org/10.4015/s1016237210002110.

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In some cases, different brain regions give rise to strongly-coherent electrical neural activities. For example, pure tone evoked activations of the bilateral auditory cortices exhibit strong coherence. Conventional 2nd order statistics-based spatio-temporal algorithms, such as MUSIC (MUltiple SIgnal Classification) and beamforming encounter difficulties in localizing such activities. In this paper, we proposed a novel solution for this case. The key idea is to map the measurement data into a new data space through a transformation prior to the localization. The orthogonal complement of the lead field matrix for the region to be suppressed is generated as the transformation matrix. Using a priori knowledge or another independent imaging method, such as sLORETA (standard LOw REsolution brain electromagnetic TomogrAphy), the coherent source regions can be primarily identified. And then, in the transformed data space a conventional spatio-temporal method, such as MUSIC, can be used to accomplish the localization of the remaining coherent sources. Repeatedly applying the method will achieve localization of all the coherent sources. The algorithm was validated by simulation experiments as well as by the reconstructions of real bilateral auditory cortical coherent activities.

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34

Zhang, Jian. "Depth-invariant beamforming for functional connectivity with MEG data." Statistics and Its Interface 15, no. 3 (2022): 359–71. http://dx.doi.org/10.4310/21-sii700.

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35

Kuhl, Patricia K. "Ken Stevens, motor theory, and infant MEG brain data." Journal of the Acoustical Society of America 137, no. 4 (April 2015): 2328. http://dx.doi.org/10.1121/1.4920503.

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36

Jas, Mainak, Denis A. Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort. "Autoreject: Automated artifact rejection for MEG and EEG data." NeuroImage 159 (October 2017): 417–29. http://dx.doi.org/10.1016/j.neuroimage.2017.06.030.

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37

Trujillo-Barreto, N. J., E. Martínez-Montes, P. A. Valdés-Sosa, and L. Melie-García. "Bayesian model for EEG/MEG and fMRI data fusion." NeuroImage 13, no. 6 (June 2001): 270. http://dx.doi.org/10.1016/s1053-8119(01)91613-1.

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38

Witton, C., T. Patel, P. L. Furlong, G. B. Henning, S. F. Worthen, and J. B. Talcott. "Sensory thresholds obtained from MEG data: Cortical psychometric functions." NeuroImage 63, no. 3 (November 2012): 1249–56. http://dx.doi.org/10.1016/j.neuroimage.2012.08.013.

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39

Roś, Beata P., Fetsje Bijma, Mathisca C. M. de Gunst, and Jan C. de Munck. "A three domain covariance framework for EEG/MEG data." NeuroImage 119 (October 2015): 305–15. http://dx.doi.org/10.1016/j.neuroimage.2015.06.020.

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40

Jun, Sung C., John S. George, Juliana Paré-Blagoev, Sergey M. Plis, Doug M. Ranken, David M. Schmidt, and C. C. Wood. "Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data." NeuroImage 28, no. 1 (October 2005): 84–98. http://dx.doi.org/10.1016/j.neuroimage.2005.06.003.

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41

Kozinska, D., F. Carducci, and K. Nowinski. "Automatic alignment of EEG/MEG and MRI data sets." Clinical Neurophysiology 112, no. 8 (August 2001): 1553–61. http://dx.doi.org/10.1016/s1388-2457(01)00556-9.

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42

Nolte, G., F. Shahbazi Avarvand, and A. Ewald. "Localizing interacting brain activity from EEG and MEG data." International Journal of Psychophysiology 85, no. 3 (September 2012): 347. http://dx.doi.org/10.1016/j.ijpsycho.2012.06.152.

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43

Marzetti, Laura, Federico Chella, Vittorio Pizzella, and Guido Nolte. "Disentangling coupled brain systems from EEG and MEG data." International Journal of Psychophysiology 108 (October 2016): 8. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.024.

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44

Sequeira, Henrique, Fabien D'Hondt, and Jacques Honoré. "Emotional salience and cognitive processing: ERP and MEG data." International Journal of Psychophysiology 108 (October 2016): 38. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.127.

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45

Babajani-Feremi, Abbas, Hamid Soltanian-Zadeh, and John E. Moran. "Integrated MEG/fMRI Model Validated Using Real Auditory Data." Brain Topography 21, no. 1 (May 14, 2008): 61–74. http://dx.doi.org/10.1007/s10548-008-0056-3.

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46

Philip, Beril Susan, Girijesh Prasad, and D. Jude Hemanth. "Non-stationarity Removal Techniques in MEG Data: A Review." Procedia Computer Science 215 (2022): 824–33. http://dx.doi.org/10.1016/j.procs.2022.12.085.

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47

Han, Yujin, Junsik Kim, and Jaehee Kim. "Artificial neural network for classifying with epilepsy MEG data." Korean Journal of Applied Statistics 37, no. 2 (April 30, 2024): 139–55. http://dx.doi.org/10.5351/kjas.2024.37.2.139.

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48

Verkhlyutov, V. M., E. O. Burlakov, K. G. Gurtovoy, and V. L. Vvedensky. "RECOGNITION OF ORAL SPEECH ACCORDING TO MEG DATA BY COVARIANCE FILTERS." Журнал высшей нервной деятельности им. И.П. Павлова 73, no. 6 (November 1, 2023): 800–808. http://dx.doi.org/10.31857/s0044467723060126.

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Анотація:

Speech recognition based on EEG and MEG data is the first step in the development of BCI and AI systems for their further use in inner speech decoding. Great advances in this direction have been made using ECoG and stereo-EEG. At the same time, there are few works on this topic on the analysis of data obtained by non-invasive methods of recording brain activity. Our approach is based on the evaluation of connections in the space of sensors with the identification of a pattern of MEG connectivity specific for a given segment of speech. We tested our method on 7 subjects. In all cases, our processing pipeline was quite reliable and worked either without recognition errors or with a small number of errors. After “training”, the algorithm is able to recognise a fragment of oral speech with a single presentation. For recognition, we used segments of the MEG recording 50–1200 ms from the beginning of the sound of the word. For high-quality recognition, a segment of at least 600 ms was required. Intervals longer than 1200 ms worsened the recognition quality. Bandpass filtering of the MEG showed that the quality of recognition is equally effective in the entire frequency range. Some decrease in the level of recognition is observed only in the range of 9–14 Hz.

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49

Itälinna, Veera, Hanna Kaltiainen, Nina Forss, Mia Liljeström, and Lauri Parkkonen. "Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data." PLOS Computational Biology 19, no. 11 (November 9, 2023): e1011613. http://dx.doi.org/10.1371/journal.pcbi.1011613.

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New biomarkers are urgently needed for many brain disorders; for example, the diagnosis of mild traumatic brain injury (mTBI) is challenging as the clinical symptoms are diverse and nonspecific. EEG and MEG studies have demonstrated several population-level indicators of mTBI that could serve as objective markers of brain injury. However, deriving clinically useful biomarkers for mTBI and other brain disorders from EEG/MEG signals is hampered by the large inter-individual variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as deviations with respect to the normal variation. To this end, a normative dataset comprising 621 healthy participants was used to determine the variation in power spectra across the cortex. In addition, we constructed normative datasets based on age-matched subsets of the full normative data. To discriminate patients from healthy control subjects, we trained support-vector-machine classifiers on the quantitative deviation maps for 25 mTBI patients and 20 controls not included in the normative dataset. The best performing classifier made use of the full normative data across the entire age and frequency ranges. This classifier was able to distinguish patients from controls with an accuracy of 79%. Inspection of the trained model revealed that low-frequency activity in the theta frequency band (4–8 Hz) is a significant indicator of mTBI, consistent with earlier studies. The results demonstrate the feasibility of using normative modeling of MEG data combined with machine learning to advance diagnosis of mTBI and identify patients that would benefit from treatment and rehabilitation. The current approach could be applied to a wide range of brain disorders, thus providing a basis for deriving MEG/EEG-based biomarkers.

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Grova, Christophe, Maria Aiguabella, Rina Zelmann, Jean-Marc Lina, Jeffery A. Hall, and Eliane Kobayashi. "Intracranial EEG potentials estimated from MEG sources: A new approach to correlate MEG and iEEG data in epilepsy." Human Brain Mapping 37, no. 5 (March 2, 2016): 1661–83. http://dx.doi.org/10.1002/hbm.23127.

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