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Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. A problem with supervised learning is intra- and interrater agreement which in some instances are far from perfect. This can affect the training and make evaluation more difficult. This thesis includes three papers where self-supervised deep neural networks were developed. In self-supervised learning, the input data to the networks themselves contain structures that are used as targets for the training and no labeling is necessary. In paper I, deep neural networks were trained to increase the number of-, or to recreate missing EEG-channels. The performance was at least on the same level as that of spherical interpolation, but unlike in the case of interpolation, missing data does not have to be identified manually first. Papers II and III involved developing deep neural networks for clustering analysis. The networks produced two-dimensional representations of EEG data and the training strategy was based on the principle of t-distributed stochastic neighbor embedding (t-SNE). In paper II, comparisons were made to parametric t-SNE and EEG-features obtained from time-frequency methods. The deep neural networks produced more distinct clustering when tested on data annotated for epileptiform discharges, seizure activity, or sleep-wakefulness. In paper III, the newly developed method was used to compare annotations of epileptiform discharges. Two experts performed independent annotations and classifiers were trained on these, using supervised learning, which in turn produced new annotations. The agreement when comparing two sets of annotations was not larger between the two experts than between an expert and a classifier. The analysis showed that differences in the annotations by the experts infl, Funding: The work was funded by Saad Nagi (grants RÖ-974228, RÖ-962769, and RÖ-941377), Magnus Thordstein (grant RÖ-986017), and Håkan Olaus-son (grants LIO-936017 and RÖ-941359).

Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. A problem with supervised learning is intra- and interrater agreement which in some instances are far from perfect. This can affect the training and make evaluation more difficult. This thesis includes three papers where self-supervised deep neural networks were developed. In self-supervised learning, the input data to the networks themselves contain structures that are used as targets for the training and no labeling is necessary. In paper I, deep neural networks were trained to increase the number of-, or to recreate missing EEG-channels. The performance was at least on the same level as that of spherical interpolation, but unlike in the case of interpolation, missing data does not have to be identified manually first. Papers II and III involved developing deep neural networks for clustering analysis. The networks produced two-dimensional representations of EEG data and the training strategy was based on the principle of t-distributed stochastic neighbor embedding (t-SNE). In paper II, comparisons were made to parametric t-SNE and EEG-features obtained from time-frequency methods. The deep neural networks produced more distinct clustering when tested on data annotated for epileptiform discharges, seizure activity, or sleep-wakefulness. In paper III, the newly developed method was used to compare annotations of epileptiform discharges. Two experts performed independent annotations and classifiers were trained on these, using supervised learning, which in turn produced new annotations. The agreement when comparing two sets of annotations was not larger between the two experts than between an expert and a classifier. The analysis showed that differences in the annotations by the experts infl, Funding: The work was funded by Saad Nagi (grants RÖ-974228, RÖ-962769, and RÖ-941377), Magnus Thordstein (grant RÖ-986017), and Håkan Olaus-son (grants LIO-936017 and RÖ-941359).

Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. A problem with supervised learning is intra- and interrater agreement which in some instances are far from perfect. This can affect the training and make evaluation more difficult. This thesis includes three papers where self-supervised deep neural networks were developed. In self-supervised learning, the input data to the networks themselves contain structures that are used as targets for the training and no labeling is necessary. In paper I, deep neural networks were trained to increase the number of-, or to recreate missing EEG-channels. The performance was at least on the same level as that of spherical interpolation, but unlike in the case of interpolation, missing data does not have to be identified manually first. Papers II and III involved developing deep neural networks for clustering analysis. The networks produced two-dimensional representations of EEG data and the training strategy was based on the principle of t-distributed stochastic neighbor embedding (t-SNE). In paper II, comparisons were made to parametric t-SNE and EEG-features obtained from time-frequency methods. The deep neural networks produced more distinct clustering when tested on data annotated for epileptiform discharges, seizure activity, or sleep-wakefulness. In paper III, the newly developed method was used to compare annotations of epileptiform discharges. Two experts performed independent annotations and classifiers were trained on these, using supervised learning, which in turn produced new annotations. The agreement when comparing two sets of annotations was not larger between the two experts than between an expert and a classifier. The analysis showed that differences in the annotations by the experts infl, Funding: The work was funded by Saad Nagi (grants RÖ-974228, RÖ-962769, and RÖ-941377), Magnus Thordstein (grant RÖ-986017), and Håkan Olaus-son (grants LIO-936017 and RÖ-941359).

Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. A problem with supervised learning is intra- and interrater agreement which in some instances are far from perfect. This can affect the training and make evaluation more difficult. This thesis includes three papers where self-supervised deep neural networks were developed. In self-supervised learning, the input data to the networks themselves contain structures that are used as targets for the training and no labeling is necessary. In paper I, deep neural networks were trained to increase the number of-, or to recreate missing EEG-channels. The performance was at least on the same level as that of spherical interpolation, but unlike in the case of interpolation, missing data does not have to be identified manually first. Papers II and III involved developing deep neural networks for clustering analysis. The networks produced two-dimensional representations of EEG data and the training strategy was based on the principle of t-distributed stochastic neighbor embedding (t-SNE). In paper II, comparisons were made to parametric t-SNE and EEG-features obtained from time-frequency methods. The deep neural networks produced more distinct clustering when tested on data annotated for epileptiform discharges, seizure activity, or sleep-wakefulness. In paper III, the newly developed method was used to compare annotations of epileptiform discharges. Two experts performed independent annotations and classifiers were trained on these, using supervised learning, which in turn produced new annotations. The agreement when comparing two sets of annotations was not larger between the two experts than between an expert and a classifier. The analysis showed that differences in the annotations by the experts infl, Funding: The work was funded by Saad Nagi (grants RÖ-974228, RÖ-962769, and RÖ-941377), Magnus Thordstein (grant RÖ-986017), and Håkan Olaus-son (grants LIO-936017 and RÖ-941359).

t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation, and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data are usually first transformed into a set of features, but it is not known which features are optimal. The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: (1) wakefulness and sleep; (2) interictal epileptiform discharges; and (3) seizure activity. The CNN encoders produced low-dimensional representations of the datasets with a structure that conformed well to the EEG characters and generalized to new data. Compared to parametric t-SNE for either a short-time Fourier transform or wavelet representation of the datasets, the developed CNN encoders performed equally well in separating categories, as assessed by support vector machines. The CNN encoders generally produced a higher degree of clustering, both visually and in the number of clusters detected by k-means clustering. The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data., Funding: Linkoping University; University Hospital of Linkoeping; ALF of Region OEstergoetland [LIO-936176, ROE-941359]; ITEA3/VINNOVA

t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation, and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data are usually first transformed into a set of features, but it is not known which features are optimal. The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: (1) wakefulness and sleep; (2) interictal epileptiform discharges; and (3) seizure activity. The CNN encoders produced low-dimensional representations of the datasets with a structure that conformed well to the EEG characters and generalized to new data. Compared to parametric t-SNE for either a short-time Fourier transform or wavelet representation of the datasets, the developed CNN encoders performed equally well in separating categories, as assessed by support vector machines. The CNN encoders generally produced a higher degree of clustering, both visually and in the number of clusters detected by k-means clustering. The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data., Funding: Linkoping University; University Hospital of Linkoeping; ALF of Region OEstergoetland [LIO-936176, ROE-941359]; ITEA3/VINNOVA

t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation, and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data are usually first transformed into a set of features, but it is not known which features are optimal. The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: (1) wakefulness and sleep; (2) interictal epileptiform discharges; and (3) seizure activity. The CNN encoders produced low-dimensional representations of the datasets with a structure that conformed well to the EEG characters and generalized to new data. Compared to parametric t-SNE for either a short-time Fourier transform or wavelet representation of the datasets, the developed CNN encoders performed equally well in separating categories, as assessed by support vector machines. The CNN encoders generally produced a higher degree of clustering, both visually and in the number of clusters detected by k-means clustering. The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data., Funding: Linkoping University; University Hospital of Linkoeping; ALF of Region OEstergoetland [LIO-936176, ROE-941359]; ITEA3/VINNOVA

t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation, and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data are usually first transformed into a set of features, but it is not known which features are optimal. The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: (1) wakefulness and sleep; (2) interictal epileptiform discharges; and (3) seizure activity. The CNN encoders produced low-dimensional representations of the datasets with a structure that conformed well to the EEG characters and generalized to new data. Compared to parametric t-SNE for either a short-time Fourier transform or wavelet representation of the datasets, the developed CNN encoders performed equally well in separating categories, as assessed by support vector machines. The CNN encoders generally produced a higher degree of clustering, both visually and in the number of clusters detected by k-means clustering. The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data., Funding: Linkoping University; University Hospital of Linkoeping; ALF of Region OEstergoetland [LIO-936176, ROE-941359]; ITEA3/VINNOVA

t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation, and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data are usually first transformed into a set of features, but it is not known which features are optimal. The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: (1) wakefulness and sleep; (2) interictal epileptiform discharges; and (3) seizure activity. The CNN encoders produced low-dimensional representations of the datasets with a structure that conformed well to the EEG characters and generalized to new data. Compared to parametric t-SNE for either a short-time Fourier transform or wavelet representation of the datasets, the developed CNN encoders performed equally well in separating categories, as assessed by support vector machines. The CNN encoders generally produced a higher degree of clustering, both visually and in the number of clusters detected by k-means clustering. The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data., Funding: Linkoping University; University Hospital of Linkoeping; ALF of Region OEstergoetland [LIO-936176, ROE-941359]; ITEA3/VINNOVA

Background In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results. New Method Generative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21-channel EEGs. 5,144 h of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks. Comparison with Existing Method The results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5–100 subjects. Conclusions Using neural networks to restore or upsample EEG signals is a viable alternative to spherical spline interpolation., Funding: Linkoping University; University Hospital of Linkoping; ALF of Region Ostergotland [LIO-936176, RO-941359]; ITEA3/VINNOVA

Background In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results. New Method Generative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21-channel EEGs. 5,144 h of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks. Comparison with Existing Method The results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5–100 subjects. Conclusions Using neural networks to restore or upsample EEG signals is a viable alternative to spherical spline interpolation., Funding: Linkoping University; University Hospital of Linkoping; ALF of Region Ostergotland [LIO-936176, RO-941359]; ITEA3/VINNOVA

Background In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results. New Method Generative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21-channel EEGs. 5,144 h of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks. Comparison with Existing Method The results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5–100 subjects. Conclusions Using neural networks to restore or upsample EEG signals is a viable alternative to spherical spline interpolation., Funding: Linkoping University; University Hospital of Linkoping; ALF of Region Ostergotland [LIO-936176, RO-941359]; ITEA3/VINNOVA

Background In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results. New Method Generative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21-channel EEGs. 5,144 h of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks. Comparison with Existing Method The results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5–100 subjects. Conclusions Using neural networks to restore or upsample EEG signals is a viable alternative to spherical spline interpolation., Funding: Linkoping University; University Hospital of Linkoping; ALF of Region Ostergotland [LIO-936176, RO-941359]; ITEA3/VINNOVA

Background In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results. New Method Generative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21-channel EEGs. 5,144 h of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks. Comparison with Existing Method The results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5–100 subjects. Conclusions Using neural networks to restore or upsample EEG signals is a viable alternative to spherical spline interpolation., Funding: Linkoping University; University Hospital of Linkoping; ALF of Region Ostergotland [LIO-936176, RO-941359]; ITEA3/VINNOVA

In Sweden, a large family with a point mutation in the nerve growth factor-beta gene has previously been identified. The carriers of this mutation have reduced small-fiber density and selective deficits in deep pain and temperature modalities. The clinical findings in this population are described as hereditary sensory and autonomic neuropathy type V. The purpose of the current study was to investigate the prevalence of carpal tunnel syndrome in hereditary sensory and autonomic neuropathy type V based on clinical examinations and electrophysiological measurements. Further, the cross-sectional area of the median nerve at the carpal tunnel inlet was measured with ultrasonography. Out of 52 known individuals heterozygous for the nerve growth factor-beta mutation in Sweden, 23 participated in the current study (12 males, 11 females; mean age, 55 years; range, 25 to 86 years). All participants answered a health questionnaire and underwent clinical examination followed by median nerve conduction study in a case-control design, and measurement of the nerve cross-sectional area with ultrasonography. The diagnosis of carpal tunnel syndrome was made based on consensus criteria using patient history and nerve conduction study. The prevalence of carpal tunnel syndrome in the hereditary sensory and autonomic neuropathy group was 35% (95% CI 19-55%) or 52% (95% CI 37-74%) depending on whether those individuals who had classic symptoms of carpal tunnel syndrome but negative nerve conduction studies were included or not. Those who had a high likelihood of carpal tunnel syndrome based on classic/probable patient history with positive nerve conduction study had a significantly larger median nerve cross-sectional area than those who had an unlikely patient history with negative nerve conduction study. The prevalence of carpal tunnel syndrome was 10 to 25 times higher in individuals heterozygous for the nerve growth factor-beta mutation than the general S, Funding agencies:This work was supported by Region Norrbotten (FOU), ALF Grants Region Ostergotland (LIO-900631), and Svenska Lakaresallskapet (The Swedish Society of Medicine, SLS-935894). The funding bodies had no role in the design or the execution of this study.

In Sweden, a large family with a point mutation in the nerve growth factor-beta gene has previously been identified. The carriers of this mutation have reduced small-fiber density and selective deficits in deep pain and temperature modalities. The clinical findings in this population are described as hereditary sensory and autonomic neuropathy type V. The purpose of the current study was to investigate the prevalence of carpal tunnel syndrome in hereditary sensory and autonomic neuropathy type V based on clinical examinations and electrophysiological measurements. Further, the cross-sectional area of the median nerve at the carpal tunnel inlet was measured with ultrasonography. Out of 52 known individuals heterozygous for the nerve growth factor-beta mutation in Sweden, 23 participated in the current study (12 males, 11 females; mean age, 55 years; range, 25 to 86 years). All participants answered a health questionnaire and underwent clinical examination followed by median nerve conduction study in a case-control design, and measurement of the nerve cross-sectional area with ultrasonography. The diagnosis of carpal tunnel syndrome was made based on consensus criteria using patient history and nerve conduction study. The prevalence of carpal tunnel syndrome in the hereditary sensory and autonomic neuropathy group was 35% (95% CI 19-55%) or 52% (95% CI 37-74%) depending on whether those individuals who had classic symptoms of carpal tunnel syndrome but negative nerve conduction studies were included or not. Those who had a high likelihood of carpal tunnel syndrome based on classic/probable patient history with positive nerve conduction study had a significantly larger median nerve cross-sectional area than those who had an unlikely patient history with negative nerve conduction study. The prevalence of carpal tunnel syndrome was 10 to 25 times higher in individuals heterozygous for the nerve growth factor-beta mutation than the general S, Funding agencies:This work was supported by Region Norrbotten (FOU), ALF Grants Region Ostergotland (LIO-900631), and Svenska Lakaresallskapet (The Swedish Society of Medicine, SLS-935894). The funding bodies had no role in the design or the execution of this study.

Anodal transspinal Direct Current Stimulation (tsDCS) has been suggested as a means to treat neuropathic pain by reducing pain signalling/processing and Laser Evoked Potentials (LEPs) likewise as a method to evaluate such reduction. However, results in previous studies are disagreeing. To evaluate these claims using rigorous methodology, LEPs were evoked from hands and feet in healthy volunteers. The N2 potential and three psychophysic parameters (general- and pinprick pain, warmth) were used to evaluate the signalling and appreciation of pain respectively. This was made at three time points; at baseline, directly- and 30 min after low thoracic tsDCS (20 min, 2.5 mA, cathode on shoulder). The study was randomized, cross over, double blinded and placebo controlled. At the group level low thoracic anodal tsDCS produced reduced perceptions of all three tested pain qualities from the foot (p < 0.05 - p < 0.001). These reductions began during stimulation and became more pronounced during the 30 min after its cessation (p < 0.05 - p < 0.01). The LEP parameter alteration mirroring these changes was latency prolongation (p < 0.05 - p < 0.001) whereas amplitude reductions were in par with placebo stimulation. Similar but less pronounced and only transient (during stimulation, p < 0.05 - p < 0.001) changes, were seen for hand stimulation. The interindividual variation was large. The findings indicate that anodal tsDCS may become a technique to treat neuropathic pain by reducing pain signalling/processing and LEPs likewise a method to evaluate such reduction. (C) 2020 Elsevier Ltd. All rights reserved., Funding Agencies|ALF Grants; Region Ostergotland; Swedish Society of Medicine; Medical Research Council of Southeast Sweden; Ostergotland County Council; Linkoping Society of Medicine

Anodal transspinal Direct Current Stimulation (tsDCS) has been suggested as a means to treat neuropathic pain by reducing pain signalling/processing and Laser Evoked Potentials (LEPs) likewise as a method to evaluate such reduction. However, results in previous studies are disagreeing. To evaluate these claims using rigorous methodology, LEPs were evoked from hands and feet in healthy volunteers. The N2 potential and three psychophysic parameters (general- and pinprick pain, warmth) were used to evaluate the signalling and appreciation of pain respectively. This was made at three time points; at baseline, directly- and 30 min after low thoracic tsDCS (20 min, 2.5 mA, cathode on shoulder). The study was randomized, cross over, double blinded and placebo controlled. At the group level low thoracic anodal tsDCS produced reduced perceptions of all three tested pain qualities from the foot (p < 0.05 - p < 0.001). These reductions began during stimulation and became more pronounced during the 30 min after its cessation (p < 0.05 - p < 0.01). The LEP parameter alteration mirroring these changes was latency prolongation (p < 0.05 - p < 0.001) whereas amplitude reductions were in par with placebo stimulation. Similar but less pronounced and only transient (during stimulation, p < 0.05 - p < 0.001) changes, were seen for hand stimulation. The interindividual variation was large. The findings indicate that anodal tsDCS may become a technique to treat neuropathic pain by reducing pain signalling/processing and LEPs likewise a method to evaluate such reduction. (C) 2020 Elsevier Ltd. All rights reserved., Funding Agencies|ALF Grants; Region Ostergotland; Swedish Society of Medicine; Medical Research Council of Southeast Sweden; Ostergotland County Council; Linkoping Society of Medicine

Anodal transspinal Direct Current Stimulation (tsDCS) has been suggested as a means to treat neuropathic pain by reducing pain signalling/processing and Laser Evoked Potentials (LEPs) likewise as a method to evaluate such reduction. However, results in previous studies are disagreeing. To evaluate these claims using rigorous methodology, LEPs were evoked from hands and feet in healthy volunteers. The N2 potential and three psychophysic parameters (general- and pinprick pain, warmth) were used to evaluate the signalling and appreciation of pain respectively. This was made at three time points; at baseline, directly- and 30 min after low thoracic tsDCS (20 min, 2.5 mA, cathode on shoulder). The study was randomized, cross over, double blinded and placebo controlled. At the group level low thoracic anodal tsDCS produced reduced perceptions of all three tested pain qualities from the foot (p < 0.05 - p < 0.001). These reductions began during stimulation and became more pronounced during the 30 min after its cessation (p < 0.05 - p < 0.01). The LEP parameter alteration mirroring these changes was latency prolongation (p < 0.05 - p < 0.001) whereas amplitude reductions were in par with placebo stimulation. Similar but less pronounced and only transient (during stimulation, p < 0.05 - p < 0.001) changes, were seen for hand stimulation. The interindividual variation was large. The findings indicate that anodal tsDCS may become a technique to treat neuropathic pain by reducing pain signalling/processing and LEPs likewise a method to evaluate such reduction. (C) 2020 Elsevier Ltd. All rights reserved., Funding Agencies|ALF Grants; Region Ostergotland; Swedish Society of Medicine; Medical Research Council of Southeast Sweden; Ostergotland County Council; Linkoping Society of Medicine

In Sweden, a large family with a point mutation in the nerve growth factor-beta gene has previously been identified. The carriers of this mutation have reduced small-fiber density and selective deficits in deep pain and temperature modalities. The clinical findings in this population are described as hereditary sensory and autonomic neuropathy type V. The purpose of the current study was to investigate the prevalence of carpal tunnel syndrome in hereditary sensory and autonomic neuropathy type V based on clinical examinations and electrophysiological measurements. Further, the cross-sectional area of the median nerve at the carpal tunnel inlet was measured with ultrasonography. Out of 52 known individuals heterozygous for the nerve growth factor-beta mutation in Sweden, 23 participated in the current study (12 males, 11 females; mean age, 55 years; range, 25 to 86 years). All participants answered a health questionnaire and underwent clinical examination followed by median nerve conduction study in a case-control design, and measurement of the nerve cross-sectional area with ultrasonography. The diagnosis of carpal tunnel syndrome was made based on consensus criteria using patient history and nerve conduction study. The prevalence of carpal tunnel syndrome in the hereditary sensory and autonomic neuropathy group was 35% (95% CI 19-55%) or 52% (95% CI 37-74%) depending on whether those individuals who had classic symptoms of carpal tunnel syndrome but negative nerve conduction studies were included or not. Those who had a high likelihood of carpal tunnel syndrome based on classic/probable patient history with positive nerve conduction study had a significantly larger median nerve cross-sectional area than those who had an unlikely patient history with negative nerve conduction study. The prevalence of carpal tunnel syndrome was 10 to 25 times higher in individuals heterozygous for the nerve growth factor-beta mutation than the general S, Funding agencies:This work was supported by Region Norrbotten (FOU), ALF Grants Region Ostergotland (LIO-900631), and Svenska Lakaresallskapet (The Swedish Society of Medicine, SLS-935894). The funding bodies had no role in the design or the execution of this study.

In Sweden, a large family with a point mutation in the nerve growth factor-beta gene has previously been identified. The carriers of this mutation have reduced small-fiber density and selective deficits in deep pain and temperature modalities. The clinical findings in this population are described as hereditary sensory and autonomic neuropathy type V. The purpose of the current study was to investigate the prevalence of carpal tunnel syndrome in hereditary sensory and autonomic neuropathy type V based on clinical examinations and electrophysiological measurements. Further, the cross-sectional area of the median nerve at the carpal tunnel inlet was measured with ultrasonography. Out of 52 known individuals heterozygous for the nerve growth factor-beta mutation in Sweden, 23 participated in the current study (12 males, 11 females; mean age, 55 years; range, 25 to 86 years). All participants answered a health questionnaire and underwent clinical examination followed by median nerve conduction study in a case-control design, and measurement of the nerve cross-sectional area with ultrasonography. The diagnosis of carpal tunnel syndrome was made based on consensus criteria using patient history and nerve conduction study. The prevalence of carpal tunnel syndrome in the hereditary sensory and autonomic neuropathy group was 35% (95% CI 19-55%) or 52% (95% CI 37-74%) depending on whether those individuals who had classic symptoms of carpal tunnel syndrome but negative nerve conduction studies were included or not. Those who had a high likelihood of carpal tunnel syndrome based on classic/probable patient history with positive nerve conduction study had a significantly larger median nerve cross-sectional area than those who had an unlikely patient history with negative nerve conduction study. The prevalence of carpal tunnel syndrome was 10 to 25 times higher in individuals heterozygous for the nerve growth factor-beta mutation than the general S, Funding agencies:This work was supported by Region Norrbotten (FOU), ALF Grants Region Ostergotland (LIO-900631), and Svenska Lakaresallskapet (The Swedish Society of Medicine, SLS-935894). The funding bodies had no role in the design or the execution of this study.

Anodal transspinal Direct Current Stimulation (tsDCS) has been suggested as a means to treat neuropathic pain by reducing pain signalling/processing and Laser Evoked Potentials (LEPs) likewise as a method to evaluate such reduction. However, results in previous studies are disagreeing. To evaluate these claims using rigorous methodology, LEPs were evoked from hands and feet in healthy volunteers. The N2 potential and three psychophysic parameters (general- and pinprick pain, warmth) were used to evaluate the signalling and appreciation of pain respectively. This was made at three time points; at baseline, directly- and 30 min after low thoracic tsDCS (20 min, 2.5 mA, cathode on shoulder). The study was randomized, cross over, double blinded and placebo controlled. At the group level low thoracic anodal tsDCS produced reduced perceptions of all three tested pain qualities from the foot (p < 0.05 - p < 0.001). These reductions began during stimulation and became more pronounced during the 30 min after its cessation (p < 0.05 - p < 0.01). The LEP parameter alteration mirroring these changes was latency prolongation (p < 0.05 - p < 0.001) whereas amplitude reductions were in par with placebo stimulation. Similar but less pronounced and only transient (during stimulation, p < 0.05 - p < 0.001) changes, were seen for hand stimulation. The interindividual variation was large. The findings indicate that anodal tsDCS may become a technique to treat neuropathic pain by reducing pain signalling/processing and LEPs likewise a method to evaluate such reduction. (C) 2020 Elsevier Ltd. All rights reserved., Funding Agencies|ALF Grants; Region Ostergotland; Swedish Society of Medicine; Medical Research Council of Southeast Sweden; Ostergotland County Council; Linkoping Society of Medicine

In Sweden, a large family with a point mutation in the nerve growth factor-beta gene has previously been identified. The carriers of this mutation have reduced small-fiber density and selective deficits in deep pain and temperature modalities. The clinical findings in this population are described as hereditary sensory and autonomic neuropathy type V. The purpose of the current study was to investigate the prevalence of carpal tunnel syndrome in hereditary sensory and autonomic neuropathy type V based on clinical examinations and electrophysiological measurements. Further, the cross-sectional area of the median nerve at the carpal tunnel inlet was measured with ultrasonography. Out of 52 known individuals heterozygous for the nerve growth factor-beta mutation in Sweden, 23 participated in the current study (12 males, 11 females; mean age, 55 years; range, 25 to 86 years). All participants answered a health questionnaire and underwent clinical examination followed by median nerve conduction study in a case-control design, and measurement of the nerve cross-sectional area with ultrasonography. The diagnosis of carpal tunnel syndrome was made based on consensus criteria using patient history and nerve conduction study. The prevalence of carpal tunnel syndrome in the hereditary sensory and autonomic neuropathy group was 35% (95% CI 19-55%) or 52% (95% CI 37-74%) depending on whether those individuals who had classic symptoms of carpal tunnel syndrome but negative nerve conduction studies were included or not. Those who had a high likelihood of carpal tunnel syndrome based on classic/probable patient history with positive nerve conduction study had a significantly larger median nerve cross-sectional area than those who had an unlikely patient history with negative nerve conduction study. The prevalence of carpal tunnel syndrome was 10 to 25 times higher in individuals heterozygous for the nerve growth factor-beta mutation than the general S, Funding agencies:This work was supported by Region Norrbotten (FOU), ALF Grants Region Ostergotland (LIO-900631), and Svenska Lakaresallskapet (The Swedish Society of Medicine, SLS-935894). The funding bodies had no role in the design or the execution of this study.

Anodal transspinal Direct Current Stimulation (tsDCS) has been suggested as a means to treat neuropathic pain by reducing pain signalling/processing and Laser Evoked Potentials (LEPs) likewise as a method to evaluate such reduction. However, results in previous studies are disagreeing. To evaluate these claims using rigorous methodology, LEPs were evoked from hands and feet in healthy volunteers. The N2 potential and three psychophysic parameters (general- and pinprick pain, warmth) were used to evaluate the signalling and appreciation of pain respectively. This was made at three time points; at baseline, directly- and 30 min after low thoracic tsDCS (20 min, 2.5 mA, cathode on shoulder). The study was randomized, cross over, double blinded and placebo controlled. At the group level low thoracic anodal tsDCS produced reduced perceptions of all three tested pain qualities from the foot (p < 0.05 - p < 0.001). These reductions began during stimulation and became more pronounced during the 30 min after its cessation (p < 0.05 - p < 0.01). The LEP parameter alteration mirroring these changes was latency prolongation (p < 0.05 - p < 0.001) whereas amplitude reductions were in par with placebo stimulation. Similar but less pronounced and only transient (during stimulation, p < 0.05 - p < 0.001) changes, were seen for hand stimulation. The interindividual variation was large. The findings indicate that anodal tsDCS may become a technique to treat neuropathic pain by reducing pain signalling/processing and LEPs likewise a method to evaluate such reduction. (C) 2020 Elsevier Ltd. All rights reserved., Funding Agencies|ALF Grants; Region Ostergotland; Swedish Society of Medicine; Medical Research Council of Southeast Sweden; Ostergotland County Council; Linkoping Society of Medicine