The pathway of the blink reflex to a transcutaneous electrical stimulus at the stylomastoid region

1997 ◽  
Vol 103 (1) ◽  
pp. 66
Author(s):  
K Nakamura
Keyword(s):  
Blink Reflex ◽  
Electrical Stimulus

scholarly journals The Effect of Trigeminal Nociceptive Stimulation on Blink Reflexes and Pain Evoked by Stimulation of the Supraorbital Nerve

Cephalalgia ◽  
2003 ◽  
Vol 23 (7) ◽  
pp. 534-540 ◽  
Author(s):  
PD Drummond
Keyword(s):  
High Intensity ◽  
Recovery Period ◽  
Spatial Summation ◽  
Blink Reflex ◽  
Electrical Stimulus ◽  
Inhibitory Influence ◽  
Conditioning Stimulation ◽  
Left Hand ◽  
Supraorbital Nerve ◽  
Stimulation Of

The aim of this study was to investigate the effect of painful conditioning stimuli on pain and blink reflexes to supraorbital nerve stimulation. Electromyograph activity was recorded bilaterally from the orbicularis oculi muscles in 13 normal participants in response to low (2.3 mA) and high-intensity (18.6 mA) electrical stimulation of the left supraorbital nerve before, during and after the application of ice to the left or right temple or immersion of the left hand in ice-water for 60 s. The pain evoked by the high-intensity electrical stimulus was greater during painful conditioning stimulation of the ipsilateral temple than during the recovery period afterwards, and was greater than during painful conditioning stimulation of the contralateral temple. These findings imply that spatial summation of nociceptive signals across different divisions of the trigeminal nerve can heighten pain. However, painful conditioning stimulation, particularly to the right temple, strongly suppressed the R2 component of the blink reflex to the low-intensity stimulus, and also suppressed R2 to the high-intensity stimulus. Thus, an inhibitory influence (e.g. diffuse noxious inhibitory controls) appeared to mask ipsilateral segmental facilitation of R2 during ice-induced headache. This finding contrasts with recent electrophysiological evidence of trigeminal sensitization in migraine.

The effect of age on blink reflex excitability recovery

2011 ◽  
Vol 42 (01) ◽  
Author(s):  
M. Kofler ◽  
H. Kumru ◽  
J. Schaller
Keyword(s):  
Blink Reflex ◽  
Reflex Excitability ◽  
Effect Of Age

Utilidad del blink reflex en un caso de neurobehçet de tronco

2001 ◽  
Vol 32 (01) ◽  
pp. 67
Author(s):  
Mónica Ortega Galán ◽  
A.G. García ◽  
Victor Martínez ◽  
Jesús Calleja Fernández
Keyword(s):  
Blink Reflex

scholarly journals Effects of Transcranial Ultrasound Stimulation on Trigeminal Blink Reflex Excitability

2021 ◽  
Vol 11 (5) ◽  
pp. 645
Author(s):  
Andrea Guerra ◽  
Edoardo Vicenzini ◽  
Ettore Cioffi ◽  
Donato Colella ◽  
Antonio Cannavacciuolo ◽  
...  
Keyword(s):  
Substantia Nigra ◽  
Superior Colliculus ◽  
Blink Reflex ◽  
Transcranial Ultrasound ◽  
Recovery Cycle ◽  
Brain Areas ◽  
Nucleus Raphe Magnus ◽  
Ultrasound Stimulation ◽  
Raphe Magnus ◽  
Interstimulus Intervals

Recent evidence indicates that transcranial ultrasound stimulation (TUS) modulates sensorimotor cortex excitability. However, no study has assessed possible TUS effects on the excitability of deeper brain areas, such as the brainstem. In this study, we investigated whether TUS delivered on the substantia nigra, superior colliculus, and nucleus raphe magnus modulates the excitability of trigeminal blink reflex, a reliable neurophysiological technique to assess brainstem functions in humans. The recovery cycle of the trigeminal blink reflex (interstimulus intervals of 250 and 500 ms) was tested before (T0), and 3 (T1) and 30 min (T2) after TUS. The effects of substantia nigra-TUS, superior colliculus-TUS, nucleus raphe magnus-TUS and sham-TUS were assessed in separate and randomized sessions. In the superior colliculus-TUS session, the conditioned R2 area increased at T1 compared with T0, while T2 and T0 values did not differ. Results were independent of the interstimulus intervals tested and were not related to trigeminal blink reflex baseline (T0) excitability. Conversely, the conditioned R2 area was comparable at T0, T1, and T2 in the nucleus raphe magnus-TUS and substantia nigra-TUS sessions. Our findings demonstrate that the excitability of brainstem circuits, as evaluated by testing the recovery cycle of the trigeminal blink reflex, can be increased by TUS. This result may reflect the modulation of inhibitory interneurons within the superior colliculus.

scholarly journals Identification of Brain Electrical Activity Related to Head Yaw Rotations

Sensors ◽  
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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