Prefrontal Cortex Acetylcholine Release, EEG Slow Waves, and Spindles Are Modulated by M2 Autoreceptors in C57BL/6J Mouse

2002 ◽  
Vol 87 (6) ◽  
pp. 2817-2822 ◽  
Author(s):  
Christopher L. Douglas ◽  
Helen A. Baghdoyan ◽  
Ralph Lydic
Keyword(s):  
Prefrontal Cortex ◽  
Slow Wave ◽  
Cortical Activation ◽  
Mouse Strains ◽  
Potential Contribution ◽  
Ach Release ◽  
Power Spectral ◽  
Eeg Data ◽  
Muscarinic Cholinergic ◽  
Eeg Power

Recent evidence suggests that muscarinic cholinergic receptors of the M2 subtype serve as autoreceptors modulating acetylcholine (ACh) release in prefrontal cortex. The potential contribution of M2 autoreceptors to excitability control of prefrontal cortex has not been investigated. The present study tested the hypothesis that M2 autoreceptors contribute to activation of the cortical electroencephalogram (EEG) in C57BL/6J (B6) mouse. This hypothesis was evaluated using microdialysis delivery of the muscarinic antagonist AF-DX116 (3 nM) while simultaneously quantifying ACh release in prefrontal cortex, number of 7- to 14-Hz EEG spindles, and EEG power spectral density. Mean ACh release in prefrontal cortex was significantly increased ( P < 0.0002) by AF-DX116. The number of 7- to 14-Hz EEG spindles caused by halothane anesthesia was significantly decreased ( P < 0.0001) by dialysis delivery of AF-DX116 to prefrontal cortex. The cholinergically induced cortical activation was characterized by a significant ( P < 0.05) decrease in slow-wave EEG power. Together, these neurochemical and EEG data support the conclusion that M2 autoreceptor enhancement of ACh release in prefrontal cortex activates EEG in contralateral prefrontal cortex of B6 mouse. EEG slow-wave activity varies across mouse strains, and the results encourage comparative phenotyping of cortical ACh release and EEG in additional mouse models.

scholarly journals Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment

2020 ◽  
Vol 20 (S12) ◽  
Author(s):  
Juan C. Mier ◽  
Yejin Kim ◽  
Xiaoqian Jiang ◽  
Guo-Qiang Zhang ◽  
Samden Lhatoo
Keyword(s):  
Sensitivity Analysis ◽  
Feature Extraction ◽  
Random Forest ◽  
Window Size ◽  
Extraction Process ◽  
Unexpected Death ◽  
Power Spectral ◽  
Eeg Data ◽  
Boosted Decision Trees ◽  
Future Work

Abstract Background Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021–1031, 2008; Nashef in Epilepsia 38:6–8, 1997). Methods This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687–5265, 2011). Results The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity. Conclusion The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient’s SUDEP risk.

Electrically coupled pacemaker neurons respond differently to same physiological inputs and neurotransmitters

1984 ◽  
Vol 51 (6) ◽  
pp. 1362-1374 ◽  
Author(s):  
E. Marder ◽  
J. S. Eisen
Keyword(s):  
Motor Neurons ◽  
Slow Wave ◽  
Lucifer Yellow ◽  
Electrical Coupling ◽  
Excitatory Postsynaptic Potential ◽  
Panulirus Interruptus ◽  
Bursting Neuron ◽  
Postsynaptic Potential ◽  
Pyloric Dilator ◽  
Muscarinic Cholinergic

The two pyloric dilator (PD) motor neurons and the single anterior burster (AB) interneuron are electrically coupled and together comprise the pacemaker for the pyloric central pattern generator of the stomatogastric ganglion of the lobster, Panulirus interruptus. Previous work (31) has shown that the AB neuron is an endogenously bursting neuron, while the PD neuron is a conditional burster. In this paper the effects of physiological inputs and neurotransmitters on isolated PD neurons and AB neurons were studied using the lucifer yellow photoinactivation technique (33). Stimulation of the inferior ventricular nerve (IVN) fibers at high frequencies elicits a triphasic response in AB and PD neurons: a rapid excitatory postsynaptic potential (EPSP) followed by a slow inhibitory postsynaptic potential (IPSP), followed by an enhancement of the pacemaker slow-wave depolarizations. Photoinactivation experiments indicate that the enhancement of the slow wave is due primarily to actions of the IVN fibers on the PD neurons but not on the AB neuron. Bath-applied dopamine dramatically alters the motor output of the pyloric system. Photoinactivation experiments show that 10(-4) M dopamine increases the amplitude and frequency of the slow-wave depolarizations recorded in the AB neurons but hyperpolarizes and inhibits the PD neurons. Bath-applied serotonin increases the frequency and amplitude of the slow-wave depolarizations in the AB neuron but has no effect on PD neurons. Pilocarpine, a muscarinic cholinergic agonist, stimulates slow-wave depolarization production in both PD neurons and the AB neuron, but the waveform and frequency of the slow waves elicited are quite different. These results show that although the electrically coupled PD and AB neurons always depolarize synchronously and act together as the pacemaker for the pyloric system, they respond differently to a neuronal input and to several putative neuromodulators. Thus, despite electrical coupling sufficient to ensure synchronous activity, the PD and AB neurons can be modulated independently.

scholarly journals Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain: a combined fMRI–EEG study

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130473 ◽  
Author(s):  
Tobias Larsen ◽  
John P. O'Doherty
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Temporal Dynamics ◽  
Brain Regions ◽  
Choice Task ◽  
Binary Choice ◽  
Intraparietal Sulcus ◽  
Trial Onset ◽  
Eeg Data ◽  
Spatio Temporal

While there is a growing body of functional magnetic resonance imaging (fMRI) evidence implicating a corpus of brain regions in value-based decision-making in humans, the limited temporal resolution of fMRI cannot address the relative temporal precedence of different brain regions in decision-making. To address this question, we adopted a computational model-based approach to electroencephalography (EEG) data acquired during a simple binary choice task. fMRI data were also acquired from the same participants for source localization. Post-decision value signals emerged 200 ms post-stimulus in a predominantly posterior source in the vicinity of the intraparietal sulcus and posterior temporal lobe cortex, alongside a weaker anterior locus. The signal then shifted to a predominantly anterior locus 850 ms following the trial onset, localized to the ventromedial prefrontal cortex and lateral prefrontal cortex. Comparison signals between unchosen and chosen options emerged late in the trial at 1050 ms in dorsomedial prefrontal cortex, suggesting that such comparison signals may not be directly associated with the decision itself but rather may play a role in post-decision action selection. Taken together, these results provide us new insights into the temporal dynamics of decision-making in the brain, suggesting that for a simple binary choice task, decisions may be encoded predominantly in posterior areas such as intraparietal sulcus, before shifting anteriorly.

scholarly journals CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

2020 ◽  
Author(s):  
Diego Fabian Collazos Huertas ◽  
Andres Marino Alvarez Meza ◽  
German Castellanos Dominguez
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Machine Learning Techniques ◽  
Continuous Wavelet ◽  
Brain Response ◽  
Power Spectral ◽  
Learning Techniques ◽  
Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

scholarly journals Modifications in Prefrontal Cortex Oxygenation in Linear and Curvilinear Dual Task Walking: A Combined fNIRS and IMUs Study

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6159
Author(s):  
Valeria Belluscio ◽  
Gabriele Casti ◽  
Marco Ferrari ◽  
Valentina Quaresima ◽  
Maria Sofia Sappia ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Dual Task ◽  
Near Infrared ◽  
Cognitive Task ◽  
Real Life ◽  
Motor Task ◽  
Hemoglobin Concentration ◽  
Cortical Activation ◽  
Functional Near Infrared Spectroscopy ◽  
Gait Quality

Increased oxygenated hemoglobin concentration of the prefrontal cortex (PFC) has been observed during linear walking, particularly when there is a high attention demand on the task, like in dual-task (DT) paradigms. Despite the knowledge that cognitive and motor demands depend on the complexity of the motor task, most studies have only focused on usual walking, while little is known for more challenging tasks, such as curved paths. To explore the relationship between cortical activation and gait biomechanics, 20 healthy young adults were asked to perform linear and curvilinear walking trajectories in single-task and DT conditions. PFC activation was assessed using functional near-infrared spectroscopy, while gait quality with four inertial measurement units. The Figure-of-8-Walk-Test was adopted as the curvilinear trajectory, with the “Serial 7s” test as concurrent cognitive task. Results show that walking along curvilinear trajectories in DT led to increased PFC activation and decreased motor performance. Under DT walking, the neural correlates of executive function and gait control tend to be modified in response to the cognitive resources imposed by the motor task. Being more representative of real-life situations, this approach to curved walking has the potential to reveal crucial information and to improve people’ s balance, safety, and life’s quality.

Assessment visual fatigue of watching 3DTV using EEG power spectral parameters

Displays ◽  
2014 ◽  
Vol 35 (5) ◽  
pp. 266-272 ◽  
Author(s):  
Chunxiao Chen ◽  
Jing Wang ◽  
Kun Li ◽  
Qiuyi Wu ◽  
Haowen Wang ◽  
...  
Keyword(s):  
Visual Fatigue ◽  
Spectral Parameters ◽  
Power Spectral ◽  
Eeg Power

EEG power spectral density under Propofol and its association with burst suppression, a marker of cerebral fragility

2019 ◽  
Vol 130 (8) ◽  
pp. 1311-1319 ◽  
Author(s):  
Cyril Touchard ◽  
Jérôme Cartailler ◽  
Charlotte Levé ◽  
Pierre Parutto ◽  
Cédric Buxin ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Burst Suppression ◽  
Power Spectral ◽  
Eeg Power

The Effects of Exposure to Red and Blue Light on Physiological Indices and Time Estimation

Perception ◽  
1985 ◽  
Vol 14 (1) ◽  
pp. 19-29 ◽  
Author(s):  
John A Caldwell ◽  
Gary E Jones
Keyword(s):  
Repeated Measures ◽  
Time Estimation ◽  
Peak Frequency ◽  
Dependent Measure ◽  
Physiological Indices ◽  
Power Spectral ◽  
Eeg Data ◽  
Verbal Estimation ◽  
Discriminant Analyses ◽  
Two Phases

The effects of exposure to red, white, and blue lights on time estimation and physiological indices were examined. Sixty subjects were exposed to a total of four presentation series of red, white, and blue lights. There were two phases of the experiment: a verbal estimation phase in which subjects were required to count out loud the length of each color while measures of eyeblinks, skin conductance, pulse volume, heart rate, and EEG activity were obtained; and a production phase in which subjects were required to produce several intervals while measures of EEG were obtained. The data on each dependent measure were subjected to three-way repeated-measures ANOVAS. EEG data were digitized and analyzed with power spectral, peak frequency, percentage of alpha activity, and discriminant analyses. Results indicate that color did not exert consistent significant effects on any of the dependent measures and raise serious questions about the assumption that ‘warm’ colors are more arousing than ‘cool’ colors.

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