Journal: Mediators of Inflammation

Article Title: Auricular Electroacupuncture Reduced Inflammation-Related Epilepsy Accompanied by Altered TRPA1, pPKC α , pPKC ε , and pERk1/2 Signaling Pathways in Kainic Acid-Treated Rats

doi: 10.1155/2014/493480

Figure Lengend Snippet: Establishment of epilepsy rats was monitored by electroencephalographic (EEG) signals. Baseline EEG activity in the sensorimotor cortex was characterized by 6–8 Hz activity in rats when awake (a). KA-induced temporal lobe seizures, including wet-dog shakes (WDS) with intermittent polyspike-like activity (b), facial myoclonia with continuous sharp waves (c), and paw tremor (PT) with continuous spike activity (d). All signals were counted and displayed as bar chart (e). Each type of seizure had its own characteristic EEG activity. Lt Cx = EEG recording of the left sensorimotor cortex; Rt Cx = EEG recording of the right sensorimotor cortex; EMG = EMG recording of the neck muscle.

Article Snippet: Electrodes were connected to an EEG- and EMG-monitoring machine (MPlOOWSW, BIOPAC Systems, Inc., CA, USA).

Techniques: Activity Assay

Journal: Sleep

Article Title: Activity of a subset of vesicular GABA-transporter neurons in the ventral zona incerta anticipates sleep onset

doi: 10.1093/sleep/zsaa268

Figure Lengend Snippet: Classification of transition points between wake–sleep states. The wake–sleep states were partitioned based on EEG, EMG, EEG power spectra and video recording of the animal behavior, criteria that were same as in our previous study [19]. The transition point is indicated by the gray dashed vertical line.

Article Snippet: Data analysis of calcium traces during sleep–wake states The EEG and EMG signals that were recorded via OmniPlex® (Plexon.com) were imported into a data visualization and physiological analysis software program (Neuroexplorer.com).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: A summary of select literature examples using CNN models with EEG or EMG signals.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: The protocol followed to process the EEG/EMG signals, generate the spectrogram and signal images, and train the CNN models using different EEG–EMG fusion methods. The top path (purple) shows the steps used to develop the CNN models based on spectrogram image inputs, while the bottom path (green) shows the steps used to develop the CNN models based on signal image inputs. For all EEG–EMG-fusion-based CNN model types (represented by the final step of all paths), an EEG and EMG only version was also trained, to provide a baseline comparison for evaluating EEG–EMG Fusion.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques: Comparison

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: A sample normalized spectrogram image to demonstrate the three EEG–EMG fusion methods used, where (A,B) show single-channel spectrograms and (C) visualizes a multi-channel spectrogram. (A) Shows the grouped method, where signal channels of the same type are grouped together within the image. (B) Shows the mixed method, where EEG and EMG channels are alternated to mix signal types. (C) Provides a visualization of the stacked method, where a multi-channel spectrogram is generated by combining the different EEG/EMG spectrograms in depth-wise manner.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques: Generated

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the single-channel spectrogram-based CNN models. (A) Shows the matrix for the grouped fusion method while (B) shows the matrix for the mixed fusion method. (C,D) Show the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the 1D convolution signal-image-based CNN models. (A) Shows the matrix for the EEG–EMG fusion model, while (B,C) how the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the split convolution signal-image-based CNN models. (A) Shows the matrix for the EEG–EMG fusion model, while (B,C) how the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Nature Communications

Article Title: Astrocytic chloride is brain state dependent and modulates inhibitory neurotransmission in mice

doi: 10.1038/s41467-023-37433-9

Figure Lengend Snippet: a [Cl − ] i in cortical astrocytes was imaged using mClY and fibre photometry in combination with EEG/EMG recordings in awake, freely moving, or spontaneously sleeping mice. b Representative traces of astrocytic [Cl − ] i , EEG, and EMG; colour code indicates sleep and awake periods. c Changes in [Cl − ] i during transiting from sleep to awake or from awake to sleep in expanded time scale. d Distribution of astrocytic [Cl − ] i during sleep and wakefulness. N = 1 representative mouse. e Average [Cl − ] i traces during transition from sleep to awake or awake to sleep, shading indicates ±SEM (standard error of the mean). N = 6 mice. f Mean [Cl − ] i and standard deviation (SD) during sleep and wakefulness. N = 6 mice, paired two-tailed t -test * p = 0.0296, ** p = 0.002. g Distribution of [Cl − ] i in awake and sleep states recorded from freely moving and naturally sleeping mice. N = 6 mice, paired two-tailed t -test, **** p < 0.001. h Distribution of YFP recorded from freely moving and naturally sleeping mice. N = 3 mice. i [Cl − ] i in cortical astrocytes was imaged in awake and resting (immobile) or voluntary running (mobile, 10 s immobility followed by more than 1 s mobility) mice. j Cross correlation of [Cl − ] i versus SD of EMG. Data represent mean ± SEM. N = 6 mice, the average Pearson correlation coefficient: 0.258. k Representative traces of astrocytic [Cl − ] i , EEG, and EMG; colour code indicates mobile and immobile periods. l [Cl − ] i trace during transition from immobile to mobile state, shading indicates ±SD. N = 7 mice. m Mean [Cl − ] i ( N = 7 mice) and standard deviation ( N = 6 mice) during immobile and mobile periods. Paired two-tailed t -test, ** p = 0.0096, p = 0.7490. n Distribution of [Cl − ] i recorded from awake freely moving, mobile or immobile mice. N = 6 mice, one sample t -test, **** p < 0.001. o Relative changes of [Cl − ] i when transitioning between sleep and awake ( N = 6 mice) versus immobile and mobile ( N = 7 mice). Paired two-tailed t -test. [Cl − ] i = mClY − ΔF/F (%). Data represent mean ± SEM. Source data are provided as a Source Data file.

Article Snippet: On the day of recording, mice were connected to the fibre optic implants and recordings were performed for 2–4 h. EEG and EMG signals were amplified (National Instruments Inc.) and filtered (EEG signal: high-pass at 1 Hz and low-pass at 100 Hz; EMG signal: high-pass at 10 Hz and low-pass at 100 Hz), and a notch filter of 50 Hz was used to reduce power line noise.

Techniques: Standard Deviation, Two Tailed Test

Journal: The Journal of Neuroscience

Article Title: Dynamic Network Activation of Hypothalamic MCH Neurons in REM Sleep and Exploratory Behavior

doi: 10.1523/JNEUROSCI.0305-19.2019

Figure Lengend Snippet: Deep-brain imaging of MCH neurons. A, Schematic of transfection of MCH neurons in MCH-Cre mice with AAV-DIO-GCaMP6 followed by placement of the GRIN lens in region transfected with GCaMP6 (slow or medium). The miniscope is attached to the GRIN lens via a baseplate on the skull. B, Photomicrograph depicts the location of the GRIN lens (outlined in dashed lines) atop the body of GCaMP6s containing neurons in the hypothalamus in a representative MCH-Cre mouse. The brain region containing the GRIN lens was sectioned along the coronal axis of the brain, and tissue containing the GCaMP6s neurons were identified. f, Fornix. Scale bar, 300 μm. C, Immunohistochemistry revealed that GCaMP6s-infected neurons (green) were also immunopositive for MCH. The coronal sections were incubated with the MCH antibody and visualized using a Leica confocal microscope. Scale bar, 80 μm. D, The field of view of the GRIN lens with fluorescence (ΔF/F0) in somata and processes during REM sleep in neurons extracted automatically by PCA-ICA analysis. We have labeled the three neurons (labeled 1, 2, and 3) whose Ca2+ fluorescence is plotted in E. E, GCaMP6s fluorescence (ΔF/F0) in MCH neurons is associated with REM sleep. Ca2+ imaging was performed simultaneously with recording of cortical EEG and EMG activity in the nuchal muscles. Behavioral video recordings were obtained and examined to identify behaviors such as walking, eating, grooming, or eating. Activity in the EEG (depicted as power spectra, 0.3–15 Hz) and the EMG is used to identify wake, NREM, and REM sleep states (labeled as hypnogram). The traces depict the change in fluorescence (ΔF/F) during wake–sleep bouts of the three neurons identified in D. In each neuron, the ΔF/F0 (expressed as a z-score) varies with the wake–sleep state of the animal, with peak fluorescence associated with REM sleep. The hypnogram categorizes the sleep–wake states in the following colors: purple, active wake; blue, quiet wake; green, NREM; yellow, pre-REM sleep; red, REM sleep. F, The same field of view as in D, but this image shows the PCA-ICA extracted neurons (ΔF/F0) while the mouse was engaged in exploring novel objects placed in its home cage. This image shows that some neurons that were evident in REM sleep (D) were also activated during exploratory behavior. However, some neurons in D were not evident during exploratory behavior, indicating selective activation of these neurons during REM sleep (D). Thirty percent of the neurons were activated during REM sleep but not during exploratory behavior, indicating that a subset of MCH neurons is selectively active in REM sleep. G, GCaMP6s fluorescence in MCH neurons while exploring novel objects. The traces are from the same neurons represented in REM sleep (E). Note that the GCaMP6s has a rapid response and a slow rate of decay, which makes it difficult to infer whether the imaged neuron fired as single spikes or in clusters.

Article Snippet: The sleep–wake states were identified based on EEG, EMG and video data (Neuroexplorer; Plexon).

Techniques: Imaging, Transfection, Immunohistochemistry, Infection, Incubation, Microscopy, Fluorescence, Labeling, Activity Assay, Muscles, Activation Assay