Facilitation of Early and Middle Latency SEP after tDCS of M1: No Evidence of Primary Somatosensory Homeostatic Plasticity.

Homeostatic plasticity is a mechanism that stabilizes cortical excitability within a physiological range. Most homeostatic plasticity protocols have primed and tested the homeostatic response of the primary motor cortex (M1). This study investigated if a homeostatic response could be recorded from the primary sensory cortex (S1) after inducing homeostatic plasticity in M1. In 31 healthy participants, homeostatic plasticity was induced over M1 with a priming and testing block of transcranial direct current stimulation (tDCS) in two different sessions (anodal and cathodal). S1 excitability was assessed by early (N20, P25) and middle-latency (N33-P45) somatosensory evoked potentials (SEP) extracted from 4 electrodes (CP5, CP3, P5, P3). Baseline and post-measures (post-priming, 0-min, 10-min, and 20-min after homeostatic induction) were taken. Anodal M1 homeostatic plasticity induction significantly facilitated the N20-P25, P45 peak, and N33-P45 early SEP components up to 20-min post-induction, without any indication of a homeostatic response (i.e., reduced SEP). Cathodal homeostatic induction did not induce any significant effect on early or middle latency SEPs. M1 homeostatic plasticity induction by anodal stimulation protocol to the primary motor cortex did not induce a homeostatic response in SEPs.

Effects of pain on cortical homeostatic plasticity in humans: a systematic review.

Homeostatic plasticity (HP) is a negative feedback mechanism that prevents excessive facilitation or depression of cortical excitability (CE). Cortical HP responses in humans have been investigated by using 2 blocks of noninvasive brain stimulation with a no-stimulation block in between. A healthy HP response is characterized by reduced CE after 2 excitatory stimulation blocks and increased CE when using inhibitory stimulation. Conversely, impaired HP responses have been demonstrated in experimental and chronic pain conditions. Therefore, this systematic review aimed to provide an overview of the effect of pain on cortical HP in humans. Scopus, Embase, and PubMed were searched from inception until November 20, 2023. The included studies (1) compared experimental or clinical pain conditions with healthy controls, (2) induced HP using 2 blocks of stimulation with a no-stimulation interval, and (3) evaluated CE measures such as motor-evoked potentials. Four studies were included, consisting of 5 experiments and 146 participants, of whom 63 were patients with chronic pain and 48 were subjected to an experimental pain model. This systematic review found support for an HP impairment in pain compared with that in pain-free states, reflected by a lack of CE reduction after excitatory-excitatory HP induction over the primary motor cortex. Inhibitory-inhibitory HP induction did not produce a consistent HP response across studies, independent of pain or pain-free states. Standardization of HP induction protocols and outcome calculations is needed to ensure reproducibility and study comparison. Future HP studies may consider investigating sensory domains including nociception, which would further our understanding of abnormal HP regulation in pain conditions.

Comparative analysis of acoustic therapies for tinnitus treatment based on auditory event-related potentials.

Introduction: So far, Auditory Event-Related Potential (AERP) features have been used to characterize neural activity of patients with tinnitus. However, these EEG patterns could be used to evaluate tinnitus evolution as well. The aim of the present study is to propose a methodology based on AERPs to evaluate the effectiveness of four acoustic therapies for tinnitus treatment. Methods: The acoustic therapies were: (1) Tinnitus Retraining Therapy (TRT), (2) Auditory Discrimination Therapy (ADT), (3) Therapy for Enriched Acoustic Environment (TEAE), and (4) Binaural Beats Therapy (BBT). In addition, relaxing music was included as a placebo for both: tinnitus sufferers and healthy individuals. To meet this aim, 103 participants were recruited, 53% were females and 47% were males. All the participants were treated for 8 weeks with one of these five sounds, which were moreover tuned in accordance with the acoustic features of their tinnitus (if applied) and hearing loss. They were electroencephalographically monitored before and after their acoustic therapy, and wherefrom AERPs were estimated. The sound effect of acoustic therapies was evaluated by examining the area under the curve of those AERPs. Two parameters were obtained: (1) amplitude and (2) topographical distribution. Results: The findings of the investigation showed that after an 8-week treatment, TRT and ADT, respectively achieved significant neurophysiological changes over somatosensory and occipital regions. On one hand, TRT increased the tinnitus perception. On the other hand, ADT redirected the tinnitus attention, what in turn diminished the tinnitus perception. Tinnitus handicapped inventory outcomes verified these neurophysiological findings, revealing that 31% of patients in each group reported that TRT increased tinnitus perception, but ADT diminished it. Discussion: Tinnitus has been identified as a multifactorial condition highly associated with hearing loss, age, sex, marital status, education, and even, employment. However, no conclusive evidence has been found yet. In this study, a significant (but low) correlation was found between tinnitus intensity and right ear hearing loss, left ear hearing loss, heart rate, area under the curve of AERPs, and acoustic therapy. This study raises the possibility to assign acoustic therapies by neurophysiological response of patient.

EEG frequency band analysis in chronic neuropathic pain: A linear and nonlinear approach to classify pain severity.

BACKGROUND AND OBJECTIVE: Chronic neuropathic pain (NP) is a chronic pain condition that severely impacts a patient's life. Pain management has proved to be inefficient due to a lack of a simple clinical tool that may identify and monitor NP. A low-cost, noninvasive tool that provides relevant information on NP is the electroencephalogram (EEG). However, the commonly used linear EEG features have proved to be limited in characterizing NP pathophysiology. This study sought to determine whether nonlinear EEG features such as approximate entropy (ApEn) would better differentiate pain severity than absolute band power. METHODS: A non-parametric statistical approach based on the Brief Pain Inventory (BPI), along with linear and nonlinear EEG features, is proposed in this study. For this purpose, thirty-six chronic NP patients were recruited, and 22 channels were registered. Additionally, a control database of 13 participants with no NP was used as a reference, where 19 channels were registered. For both groups, EEG was recorded for 10 min in a resting state: 5 min with eyes open (EO) and 5 min with eyes closed (EC). Absolute band power and ApEn EEG features in the five clinical frequency bands (delta, theta, alpha, beta, and gamma) were estimated for all channels in both groups. As a result, 220-dimensional and 190-dimensional feature vectors were obtained for experimental and control classes respectively. For the experimental class, NP patients were grouped according to their BPI evaluation in three groups: low, moderate, and high pain. Finally, feature vectors were compared between groups using Kruskal Wallis and post-hoc Dunn's tests. RESULTS: ApEn revealed significant statistical difference (p <=0.0001) in most frequency bands and conditions among the groups. In contrast, power had less significant differences between groups, particularly with EO. Furthermore, NP groups were notably clustered using only ApEn in theta, alpha, and beta bands. CONCLUSIONS: The results indicate that ApEn effectively characterizes the different severities of chronic NP rather than the commonly used linear features. ApEn and other nonlinear techniques (e.g., spectral entropy, Shannon entropy) might be a more suitable methodology to monitor chronic NP experience.

Chronic neuropathic pain is more than a perception: Systems and methods for an integral characterization.

The management of chronic neuropathic pain remains a challenge, because pain is subjective, and measuring it objectively is usually out of question. However, neuropathic pain is also a signal provided by maladaptive neuronal activity. Thus, the integral management of chronic neuropathic pain should not only rely on the subjective perception of the patient, but also on objective data that measures the evolution of neuronal activity. We will discuss different objective and subjective methods for the characterization of neuropathic pain. Additionally, the gaps and proposals for an integral management of chronic neuropathic pain will also be discussed. The current management that relies mostly on subjective measures has not been sufficient, therefore, this has hindered advances in pain management and clinical trials. If an integral characterization is achieved, clinical management and stratification for clinical trials could be based on both questionnaires and neuronal activity. Appropriate characterization may lead to an increased effectiveness for new therapies, and a better quality of life for neuropathic pain sufferers.

Identification of Neuropathic Pain Severity based on Linear and Non-Linear EEG Features.

The lack of an integral characterization of chronic neuropathic pain (NP) has led to pharmacotherapy mismanagement and has hindered advances in clinical trials. In this study, we attempted to identify chronic NP by fusing psychometric (based on the Brief Inventory of Pain - BIP), and both linear and non-linear electroencephalographic (EEG) features. For this purpose, 35 chronic NP patients were recruited voluntarily. All the volunteers answered the BIP; and additionally, 22 EEG channels positioned in accordance with the 10/20 international system were registered for 10 minutes at resting state: 5 minutes with eyes open and 5 minutes with eyes closed. EEG Signals were sampled at 250 Hz within a bandwidth between 0.1 and 100 Hz. As linear features, absolute band power was obtained per clinical frequency band: delta (0.1~4 Hz), theta (4~8 Hz), alpha (8~12 Hz), beta (12~30 Hz) and gamma (30~100 Hz); considering five regions: prefrontal, frontal, central, parietal and occipital. As non-linear features, approximate entropy was calculated per channel and per clinical frequency band with addition of the broadband (0.1~100 Hz). Resulting feature vectors were grouped in line with the BIP outcome. Three groups were considered: low, moderate, and high pain. Finally, BIP-EEG patterns were classified in those three classes, achieving 96% accuracy. This result improves a previous work of a SVM classifier that used exclusively linear EEG features and showed an accuracy between 87% and 90% per class to predict central NP after spinal cord injury.