Selective Modification of Somatosensory Evoked Potential during Voluntary Finger Movement in Humans

1997 ◽  
Vol 85 (1) ◽  
pp. 259-266 ◽  
Author(s):  
Y. Nishihira ◽  
K. Funase ◽  
H. Araki ◽  
K. Imanaka
Keyword(s):  
Evoked Potentials ◽  
Somatosensory Evoked Potentials ◽  
Voluntary Movement ◽  
Evoked Potential ◽  
Somatosensory Evoked Potential ◽  
Finger Movement ◽  
The Third ◽  
Middle Latency ◽  
The Relationship

We examined changes in somatosensory evoked potentials (SEPs) during voluntary movement of fingers innervated by the stimulated nerve and those not innervated by the stimulated nerve and the relationship to the kind of movement modality. Analysis showed that the amplitude of most components at F3, C3', and P3, except for P45 at C3, N35 and P45 at P3, decreased during voluntary finger movement tasks. Further, we found that the components of P40 at F3, P45 at C3', and N35 at P3 were increased during the voluntary pulling movement of the second and the third digits compared to those during the voluntary pushing movement of the fourth and the fifth digits, whereas all other components were decreased at F3, C3', and P3. We also found that not all components of SEPs were decreased while some SEPs in middle latency were increased. In conclusion, we confirmed the selectivity in attenuation of the SEPs. Moreover, we noted an interesting finding that the selectivity of attenuation of the SEPs was most frequently observed in the N20, P30 (P25 at F3), N35 (N30 at F3), and P45 (P40 at F3) components at F3, C3', and P3.

Migraine classification using somatosensory evoked potentials

Cephalalgia ◽  
2019 ◽  
Vol 39 (9) ◽  
pp. 1143-1155 ◽  
Author(s):  
Bingzhao Zhu ◽  
Gianluca Coppola ◽  
Mahsa Shoaran
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Evoked Potentials ◽  
Healthy Volunteers ◽  
Somatosensory Evoked Potentials ◽  
Evoked Potential ◽  
Machine Learning Algorithms ◽  
Somatosensory Evoked Potential ◽  
Support Vector ◽  
Extreme Gradient Boosting

Objective The automatic detection of migraine states using electrophysiological recordings may play a key role in migraine diagnosis and early treatment. Migraineurs are characterized by a deficit of habituation in cortical information processing, causing abnormal changes of somatosensory evoked potentials. Here, we propose a machine learning approach to utilize somatosensory evoked potential-based biomarkers for migraine classification in a noninvasive setting. Methods Forty-two migraine patients, including 29 interictal and 13 ictal, were recruited and compared with 15 healthy volunteers of similar age and gender distribution. The right median nerve somatosensory evoked potentials were collected from all subjects. State-of-the-art machine learning algorithms including random forest, extreme gradient-boosting trees, support vector machines, K-nearest neighbors, multilayer perceptron, linear discriminant analysis, and logistic regression were used for classification and were built upon somatosensory evoked potential features in time and frequency domains. A feature selection method was employed to assess the contribution of features and compare it with previous clinical findings, and to build an optimal feature set by removing redundant features. Results Using a set of relevant features and different machine learning models, accuracies ranging from 51.2% to 72.4% were achieved for the healthy volunteers-ictal-interictal classification task. Following model and feature selection, we successfully separated the three groups of subjects with an accuracy of 89.7% for the healthy volunteers-ictal, 88.7% for healthy volunteers-interictal, 80.2% for ictal-interictal, and 73.3% for healthy volunteers-ictal-interictal classification tasks, respectively. Conclusion Our proposed model suggests the potential use of somatosensory evoked potentials as a prominent and reliable signal in migraine classification. This non-invasive somatosensory evoked potential-based classification system offers the potential to reliably separate migraine patients in ictal and interictal states from healthy controls.

scholarly journals The Somatosensory Evoked Potential

1982 ◽  
Vol 9 (2) ◽  
pp. 65-77 ◽  
Author(s):  
Andrew Eisen
Keyword(s):  
Evoked Potentials ◽  
Somatosensory Evoked Potentials ◽  
Evoked Potential ◽  
Body Height ◽  
Negative Polarity ◽  
Somatosensory Evoked Potential ◽  
Far Field ◽  
Normal Means ◽  
Progressive Myoclonic Epilepsy ◽  
Tall Individual

Three decades have elapsed since Dawson (1947) recorded the first somatosensory evoked potential (SEP). Simple superimposition of individual responses was possible because the patient had progressive myoclonic epilepsy. In this disease the SEP amplitude is much enhanced (Shibasaki et al, 1978; Kelly et al, 1981). Subsequently Dawson (1951, 1954) presented his averager to the Physiological Society, thereby initiating the present-day explosive growth of evoked potentials.SEPs are made up of components with varying latencies. The components are best identified by latency and polarity as recorded at the scalp (P = positive and N = negative). Nevertheless, the nomenclature of somatosensory evoked potentials can be extremely confusing, mainly because the same component can have a different polarity depending on the electrode montage used. Generally speaking (but this is not a firm rule), far-field (subcortical) potentials are positive in polarity when a non-cephalic reference is used, whereas these same components have a negative polarity when the reference is on the scalp. It is therefore useful to always indicate the recording montage being employed. In addition, use of absolute latencies in the terminology can cause confusion because they are dependent upon length and body height. For example, the brachial plexus component usually occurs at about 9 msec, but may extend to as long as 11 or more msec in a very tall individual. Subsequent components then become difficult to identify in relation to normal means.

scholarly journals Effect of high rate rTMS on somatosensory evoked potential in migraine

Cephalalgia ◽  
2016 ◽  
Vol 37 (13) ◽  
pp. 1222-1230 ◽  
Author(s):  
Jayantee Kalita ◽  
Sanjeev K Bhoi ◽  
Usha K Misra
Keyword(s):  
Cortical Neurons ◽  
Evoked Potential ◽  
Therapeutic Response ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Hot Spot ◽  
High Rate ◽  
Somatosensory Evoked Potential ◽  
Sham Stimulation ◽  
The Third ◽  
Abductor Digiti Minimi

Background Sensitization and impaired habituation of cortical neurons have been reported in migraineurs. Repetitive transcranial magnetic stimulation (rTMS) may change these phenomena and be the basis of therapeutic response. We report the effect of 10 Hz rTMS on sensitization and habituation of median somatosensory evoked potential (SEP) in migraineurs, and correlate these changes with clinical response. Methods Migraineurs having four or more episodes of headache per month were included and their clinical details were noted. Three sessions of 10 Hz rTMS, 600 pulses in 412.4 seconds were delivered on the left frontal cortex corresponding to the hot spot of right abductor digiti minimi, on alternate days. Median SEP was done before and 30 minutes after the third rTMS session. Sensitization (block I N20 amplitude) and impaired habituation (if N20 amplitude of block 2 or 3 were not suppressed compared to block I) were noted. The reduction in frequency and severity of headache in the next month were noted and correlated with SEP changes. Results Ninety-four migraineurs were included; 56 received true rTMS and 38 sham stimulation. Following stimulation, reduction in N20 amplitude of block 1 correlated with a reduction in frequency and severity of headache at one month. The impaired habituation significantly improved in the true rTMS group compared to sham stimulation, and correlated with a reduction in the severity of headache but not with frequency. Conclusion In migraineurs, 10 Hz rTMS improves habituation and may be the biological basis of headache relief.

Scalp topographic mapping of middle-latency somatosensory evoked potentials in normal aging and dementia

1996 ◽  
Vol 26 (5) ◽  
pp. 311-319 ◽  
Author(s):  
R Ferri ◽  
S Del Gracco ◽  
M Elia ◽  
S.A. Musumeci ◽  
R Spada ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Somatosensory Evoked Potentials ◽  
Normal Aging ◽  
Topographic Mapping ◽  
Middle Latency

Scalp topography of the short-latency components of the respiratory-related evoked potential in children

1996 ◽  
Vol 80 (5) ◽  
pp. 1785-1791 ◽  
Author(s):  
P. W. Davenport ◽  
I. M. Colrain ◽  
P. M. Hill
Keyword(s):  
Evoked Potentials ◽  
Somatosensory Evoked Potentials ◽  
Evoked Potential ◽  
Central Sulcus ◽  
Normal Children ◽  
Polarity Inversion ◽  
Negative Peak ◽  
Scalp Topography ◽  
Scalp Distribution ◽  
Latency Range

Respiratory-related evoked potentials (RREPs) have been elicited by inspiratory occlusion and recorded at electroencephalographic (EEG) sites overlying the somatosensory cortex in adults. The present study was the first to be conducted in normal children and was designed to identify the scalp distribution of the early RREP components. EEG responses to occlusion were recorded from CZ-C3, CZ-C4, and 17 sites referenced to the linked earlobes. The RREP was observed in all subjects in the CZ-C3 and CZ-C4 electrode pairs. The earlobe-referenced recordings revealed two RREP patterns. The P1 and N1 peaks were found in C3, C4, P3, P4, T3, and T4. The RREPs recorded from the F3, F4, F7, and F8 electrodes did not exhibit either the P1 or N1 peaks. A negative peak (NF) occurred approximately 13 ms after the P1 peak. The results show that the RREPs to inspiratory occlusions were present bilaterally but diminished greatly over midline sites. Furthermore, consistent with mechanically and electrically elicited somatosensory evoked potentials, the RREP displayed a polarity inversion over the central sulcus in the early component latency range.

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