EEG Correlates of Central Origin of Cancer-Related Fatigue

The neurophysiological mechanism of cancer-related fatigue (CRF) remains poorly understood. EEG was examined during a sustained submaximal contraction (SC) task to further understand our prior research findings of greater central contribution to early fatigue during SC in CRF. Advanced cancer patients and matched healthy controls performed an elbow flexor SC until task failure while undergoing neuromuscular testing and EEG recording. EEG power changes over left and right sensorimotor cortices were analyzed and correlated with brief fatigue inventory (BFI) score and evoked muscle force, a measure of central fatigue. Brain electrical activity changes during the SC differed in CRF from healthy subjects mainly in the theta (4-8 Hz) and beta (12-30 Hz) bands in the contralateral (to the fatigued limb) hemisphere; changes were correlated with the evoked force. Also, the gamma band (30-50 Hz) power decrease during the SC did not return to baseline after 2 min of rest in CRF, an effect correlated with BFI score. In conclusion, altered brain electrical activity during a fatigue task in patients is associated with central fatigue during SC or fatigue symptoms, suggesting its potential contribution to CRF during motor performance. This information should guide the development and use of rehabilitative interventions that target the central nervous system to maximize function recovery.

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Acupressure Therapy as Alternative Treatment on Cancer-Related Fatigue in Cancer Patients

STRADA JURNAL ILMIAH KESEHATAN ◽

10.30994/sjik.v8i1.184 ◽

2019 ◽

Vol 8 (1) ◽

pp. 21-27

Author(s):

Yesiana Dwi Wahyu Werdani

Keyword(s):

Cancer Patients ◽

Rank Test ◽

Inclusion Criteria ◽

Wilcoxon Sign Rank Test ◽

Cancer Related Fatigue ◽

Brief Fatigue Inventory ◽

Post Test ◽

Wilcoxon Sign Rank ◽

The Central Nervous System ◽

Sign Rank Test

Cancer affects the neurophysiological changes in skeletal muscle progressively and leading to cancer-related fatigue (CRF). Acupressure therapy can stimulate local microcirculation improvement and increasing physical performance. The purpose was determine the influence of acupressure therapy on CRF in cancer patients. The design used pre-experiment one group pre-test post-test design. Samples were 30 cancer patients living at the Indonesian Cancer Foundation East Java Branch taken by purposive technique based on inclusion criteria. The instrument was the Brief Fatigue Inventory. Acupressure therapy is given to acupoint C6, GB20, P6, Li4, Li11, Sp 6, St 36, 2 times per week for 4 weeks. Statistics was verified with Wilcoxon Sign Rank Test p < 0.05. Result showed the effect of acupressure therapy on CRF p = 0.000. Acupressure therapy can activate acupoint on the meridians, stimulating myelin nerves in the muscles and impulses into the central nervous system and can facilitate the reduction fatigue.

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Fit Vs Faint: Assessing Work Causation in “Idiopathic Fall” Cases

Guides Newsletter ◽

10.1001/amaguidesnewsletters.2014.sepoct01 ◽

2014 ◽

Vol 19 (5) ◽

pp. 3-12

Author(s):

Lorne Direnfeld ◽

David B. Torrey ◽

Jim Black ◽

LuAnn Haley ◽

Christopher R. Brigham

Keyword(s):

Blood Flow ◽

Electrical Activity ◽

Loss Of Consciousness ◽

Brain Electrical Activity ◽

Medical Terminology ◽

Scientific Analysis ◽

Medical Causation ◽

The Individual ◽

The Brain ◽

Current Science

Abstract When an individual falls due to a nonwork-related episode of dizziness, hits their head and sustains injury, do workers’ compensation laws consider such injuries to be compensable? Bearing in mind that each state makes its own laws, the answer depends on what caused the loss of consciousness, and the second asks specifically what happened in the fall that caused the injury? The first question speaks to medical causation, which applies scientific analysis to determine the cause of the problem. The second question addresses legal causation: Under what factual circumstances are injuries of this type potentially covered under the law? Much nuance attends this analysis. The authors discuss idiopathic falls, which in this context means “unique to the individual” as opposed to “of unknown cause,” which is the familiar medical terminology. The article presents three detailed case studies that describe falls that had their genesis in episodes of loss of consciousness, followed by analyses by lawyer or judge authors who address the issue of compensability, including three scenarios from Arizona, California, and Pennsylvania. A medical (scientific) analysis must be thorough and must determine the facts regarding the fall and what occurred: Was the fall due to a fit (eg, a seizure with loss of consciousness attributable to anormal brain electrical activity) or a faint (eg, loss of consciousness attributable to a decrease in blood flow to the brain? The evaluator should be able to fully explain the basis for the conclusions, including references to current science.

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Identification of Brain Electrical Activity Related to Head Yaw Rotations

Sensors ◽

10.3390/s21103345 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3345

Author(s):

Enrico Zero ◽

Chiara Bersani ◽

Roberto Sacile

Keyword(s):

Electrical Activity ◽

Electrical Stimulus ◽

Head Movements ◽

Brain Electrical Activity ◽

Output Function ◽

Input Output ◽

Eeg Signals ◽

Head Turning ◽

Identification Technique ◽

The Brain

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

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