Clinical use of Electroencephalography in the Assessment of Acute Thermal Pain: A Narrative Review Based on Articles From 2009 to 2019

2021 ◽  
pp. 155005942110262
Author(s):  
Chloé Savignac ◽  
Don Daniel Ocay ◽  
Yacine Mahdid ◽  
Stefanie Blain-Moraes ◽  
Catherine E. Ferland
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Experimental Pain ◽  
Alpha Power ◽  
Contact Heat ◽  
Noxious Heat ◽  
Thermal Pain ◽  
Contact Heat Evoked Potentials ◽  
Central Regions ◽  
Acute Thermal Pain

Nowadays, no practical system has successfully been able to decode and predict pain in clinical settings. The inability of some patients to verbally express their pain creates the need for a tool that could objectively assess pain in these individuals. Neuroimaging techniques combined with machine learning are seen as possible candidates for the identification of pain biomarkers. This review aimed to address the potential use of electroencephalographic features as predictors of acute experimental pain. Twenty-six studies using only thermal stimulations were identified using a PubMed and Scopus search. Combinations of the following terms were used: “EEG,” “Electroencephalography,” “Acute,” “Pain,” “Tonic,” “Noxious,” “Thermal,” “Stimulation,” “Brain,” “Activity,” “Cold,” “Subjective,” and “Perception.” Results revealed that contact-heat-evoked potentials have been widely recorded over central areas during noxious heat stimulations. Furthermore, a decrease in alpha power over central regions was revealed, as well as increased theta and gamma powers over frontal areas. Gamma and theta rhythms were associated with connectivity between sensory and affective regions involved in pain processing. A machine learning analysis revealed that the gamma band is a predominant predictor of acute thermal pain. This review also addressed the need of supplementing current spectral features with techniques that allow the investigation of network dynamics.

The effect of nerve compression and capsaicin on contact heat evoked potentials (CHEPs) related to Aδ and C fibers

2012 ◽  
Vol 3 (3) ◽  
pp. 187-187
Author(s):  
C.S. Madsen ◽  
B. Johnsen ◽  
A. Fuglsang-Frederiksen ◽  
T.S. Jensen ◽  
N.B. Finnerup
Keyword(s):  
Evoked Potentials ◽  
Pain Intensity ◽  
Radial Nerve ◽  
Nerve Compression ◽  
Contact Heat ◽  
Noxious Heat ◽  
Superficial Radial Nerve ◽  
C Fibers ◽  
Contact Heat Evoked Potentials ◽  
C Fiber

Abstract Background/aims Brief noxious heat stimuli activate Aδ and C fibers, and contact heat evoked potentials (CHEPs) can be recorded from the scalp. Under standard conditions, late responses related to AS fibers can be recorded. This study examines C-fiber responses to contact heat stimuli. Methods A preferential A-fiber blockade by compression to the superficial radial nerve was applied in 22 healthy subjects. Quality and intensity of heat evoked pain (NRS, 0–10), and CHEPs were examined at baseline, during nerve compression, and during further nerve compression with topical capsaicin (5%). Results During the A-fiber blockade, 3 subjects had CHEPs with latencies below 400 ms, 8 subjects within 400–800 ms and 6 subjects later than 800 ms. Pain intensity to contact heat stimuli was reduced and fewer subjects reported the heat stimuli as stinging. Following acute capsaicin application, ultralate CHEPs with latencies >800 ms could be recorded in 13 subjects, pain intensity to the contact heat stimuli was increased (p <0.01) and more subjects reported the heat stimuli as being more warm/hot-burning. Conclusion The results indicate that following a compression to the superficial radial nerve, CHEPs compatible within complete A fibers or C fibers were recorded. Following sensitization with capsaicin, C-fiber responses were recorded in 62% of subjects.

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

scholarly journals High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Xudong Lin ◽  
Xin Duan ◽  
Claire Jacobs ◽  
Jeremy Ullmann ◽  
Chung-Yuen Chan ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Brain Activity

Frequency-Specific Changes of Resting Brain Activity in Parkinson’s Disease: A Machine Learning Approach

Neuroscience ◽  
2020 ◽  
Vol 436 ◽  
pp. 170-183 ◽  
Author(s):  
Zhi-yao Tian ◽  
Long Qian ◽  
Lei Fang ◽  
Xue-hua Peng ◽  
Xiao-hu Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Brain Activity ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Caffeine boosts preparatory attention for reward-related stimulus information

2019 ◽  
Author(s):  
Berry van den Berg ◽  
Marlon de Jong ◽  
Marty G. Woldorff ◽  
Monicque M. Lorist
Keyword(s):  
Brain Activity ◽  
Cognitive Task ◽  
Relevant Information ◽  
Caffeine Intake ◽  
Alpha Power ◽  
Behavioral Measures ◽  
Placebo Condition ◽  
Electrical Brain Activity ◽  
Preparatory Attention ◽  
Stroop Stimulus

AbstractBoth the intake of caffeine-containing substances and the prospect of reward for performing a cognitive task have been associated with improved behavioral performance. To investigate the possible common and interactive influences of caffeine and reward-prospect on preparatory attention, we tested 24 participants during a 2-session experiment in which they performed a cued-reward color-word Stroop task. On each trial, participants were presented with a cue to inform them whether they had to prepare for presentation of a Stroop stimulus and whether they could receive a reward if they performed well on that trial. Prior to each session, participants received either coffee with caffeine (3 mg/kg bodyweight) or with placebo (3 mg/kg bodyweight lactose). In addition to behavioral measures, electroencephalography (EEG) measures of electrical brain activity were recorded. Results showed that both the intake of caffeine and the prospect of reward improved speed and accuracy, with the effects of caffeine and reward-prospect being additive on performance. Neurally, reward-prospect resulted in an enlarged contingent negative variation (CNV) and reduced posterior alpha power (indicating increased cortical activity), both hallmark neural markers for preparatory attention. Moreover, the CNV enhancement for reward-prospect trials was considerably more pronounced in the caffeine condition as compared to the placebo condition. These results thus suggest that caffeine intake boosts preparatory attention for task-relevant information, especially when performance on that task can lead to reward.

scholarly journals Replay in minds and machines

2021 ◽  
Author(s):  
Lennart Wittkuhn ◽  
Samson Chien ◽  
Sam Hall-McMaster ◽  
Nicolas W. Schuck
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Activity Patterns ◽  
Representation Learning ◽  
Parallel Machine ◽  
Artificial Agents ◽  
Active Behavior ◽  
Performance Improvements ◽  
Experience Replay ◽  
Data Efficiency

Experience-related brain activity patterns have been found to reactivate during sleep, wakeful rest, and brief pauses from active behavior. In parallel, machine learning research has found that experience replay can lead to substantial performance improvements in artificial agents. Together, these lines of research have significantly expanded our understanding of the potential computational benefits replay may provide to biological and artificial agents alike. We provide an overview of findings in replay research from neuroscience and machine learning and summarize the computational benefits an agent can gain from replay that cannot be achieved through direct interactions with the world itself. These benefits include faster learning and data efficiency, less forgetting, prioritizing important experiences, as well as improved planning and generalization. In addition to the benefits of replay for improving an agent's decision-making policy, we highlight the less-well studied aspect of replay in representation learning, wherein replay could provide a mechanism to learn the structure and relevant aspects of the environment. Thus, replay might help the agent to build task-appropriate state representations.

scholarly journals Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Laura Cornelissen ◽  
Seong-Eun Kim ◽  
Patrick L Purdon ◽  
Emery N Brown ◽  
Charles B Berde
Keyword(s):  
General Anesthesia ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Body Movement ◽  
Video Recording ◽  
Brain State ◽  
Alpha Power ◽  
State Monitoring ◽  
Spatio Temporal ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) approaches may provide important information about developmental changes in brain-state dynamics during general anesthesia. We used multi-electrode EEG, analyzed with multitaper spectral methods and video recording of body movement to characterize the spatio-temporal dynamics of brain activity in 36 infants 0–6 months old when awake, and during maintenance of and emergence from sevoflurane general anesthesia. During maintenance: (1) slow-delta oscillations were present in all ages; (2) theta and alpha oscillations emerged around 4 months; (3) unlike adults, all infants lacked frontal alpha predominance and coherence. Alpha power was greatest during maintenance, compared to awake and emergence in infants at 4–6 months. During emergence, theta and alpha power decreased with decreasing sevoflurane concentration in infants at 4–6 months. These EEG dynamic differences are likely due to developmental factors including regional differences in synaptogenesis, glucose metabolism, and myelination across the cortex. We demonstrate the need to apply age-adjusted analytic approaches to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.

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