Transcranial and Transcutaneous Stimulation for Pain: What Have We Learned From the COVID-19 Pandemic Shutdown?

BACKGROUND: Transcranial magnetic stimulation (TMS) and transcutaneous magnetic stimulation (tMS) offer a novel noninvasive treatment option for chronic pain. While the recent COVID-19 pandemic caused by the SARS-CoV-2 virus resulted in a temporary interruption of the treatments for patients, it provided an excellent opportunity to assess the long-term sustainability of the treatment, and the feasibility of resuming the treatments after a brief period of interruption as no such data are available in current literature. METHODS: First, a list of patients whose pain/headache conditions have been stably controlled with either treatment for at least 6 months prior to the 3-month pandemic-related shutdown was generated. Those who returned for treatments after the shutdown were identified and their underlying pain diagnoses, pre- and posttreatment Mechanical Visual Analog Scale (M-VAS) pain scores, 3-item Pain, Enjoyment, and General Activity (PEG-3), and Patient Health Questionnaire-9 scores were assessed in 3 phases: Phase I (P1) consisted of a 6-month pre-COVID-19 period in which pain conditions were stably managed with either treatment modality; Phase II (P2) consisted of the first treatment visit period immediately after COVID-19 shutdown; and Phase III (P3) consisted of a 3-4 month post-COVID-19 shutdown period patients received up to 3 sessions of either treatment modality after the P2 treatment. RESULTS: For pre- and posttreatment M-VAS pain scores, mixed-effect analyses for both treatment groups demonstrated significant (P < 0.01) time interactions across all phases. For pretreatment M-VAS pain scores, TMS (n = 27) between-phase analyses indicated a significant (F = 13.572, P = 0.002) increase from 37.7 ± 27.6 at P1 to 49.6 ± 25.9 at P2, which then decreased significantly (F = 12.752, P = 0.001) back to an average score of 37.1 ± 24.7 at P3. Similarly, tMS (n = 25) between-phase analyses indicated the mean pretreatment pain score (mean ± standard deviation [SD]) increased significantly (F = 13.383, P = 0.003) from 34.9 ± 25.1 at P1 to 56.3 ± 27.0 at P2, which then decreased significantly (F = 5.464, P = 0.027) back to an average score of 41.9 ± 26.4 at P3. For posttreatment pain scores, the TMS group between-phase analysis indicated the mean posttreatment pain score (mean ± SD) increased significantly (F = 14.206, P = 0.002) from 25.6 ± 22.9 at P1 to 36.2 ± 23.4 at P2, which then significantly decreased (F = 16.063, P < 0.001) back to an average score of 23.2 ± 21.3 at P3. The tMS group between-phase analysis indicates a significant (F = 8.324, P = 0.012) interaction between P1 and P2 only with the mean posttreatment pain score (mean ± SD) increased from 24.9 ± 25.7 at P1 to 36.9 ± 26.7 at P2. The combined PEG-3 score between-phase analyses demonstrated similar significant (P < 0.001) changes across the phases in both treatment groups. CONCLUSIONS: Both TMS and tMS treatment interruptions resulted in an increase of pain/headache severity and interference of quality of life and functions. However, the pain/headache symptoms, patients' quality of life, or function can quickly be improved once the maintenance treatments were restarted.

Modelling microprosodic effects can lead to an audible improvement in articulatory synthesis.

When pitch is explicitly modelled for parametric speech synthesis, microprosodic variations of the fundamental frequency f0 are usually disregarded by current intonation models. While there are numerous studies dealing with the nature and the origin of microprosody, little research has been done on its audibility and its effect on the naturalness of synthetic speech. In this work, the influence of obstruent-related microprosodic variations on the perceived naturalness of articulatory speech synthesis was studied. A small corpus of 20 German words and sentences was re-synthesized using the state-of-the-art articulatory synthesizer VocalTractLab. The pitch contours of the real utterances were extracted and fitted with the Target-Approximation-Model. After the real microprosodic variations were removed from the obtained pitch contours, synthetic variations were applied based on a microprosody model. Subsequently, multiple stimuli with different microprosody amplitudes were synthesized and evaluated in a listening experiment. The results indicate that microprosodic variations are barely audible, but can lead to a greater perceived naturalness of the synthesized speech in certain cases.