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Purpose: This study aimed to evaluate whether long-term averaged spectrum (LTAS) descriptors for reading and monologue are suitable to detect worsening of dysarthria in patients with Parkinson's disease (PD) treated with subthalamic nucleus deep brain stimulation (STN-DBS) with potential effect of ON and OFF stimulation conditions and types of connected speech. Method: Four spectral moments based on LTAS were computed for monologue and reading passage collected from 23 individuals with PD treated with bilateral STN-DBS and 23 age- and gender-matched healthy controls. Speech performance of patients with PD was compared in ON and OFF STN-DBS conditions. Results: All LTAS spectral moments including mean, standard deviation, skewness, and kurtosis across both monologue and reading passage were able to significantly distinguish between patients with PD in both stimulation conditions and control speakers. The spectral mean was the only LTAS measure sensitive to capture better speech performance in STN-DBS ON, as compared to the STN-DBS OFF stimulation condition (p < 0.05). Standardized reading passage was more sensitive compared to monologue in detecting dysarthria severity via LTAS descriptors with an area under the curve of up to 0.92 obtained between PD and control groups. Conclusions: Our findings confirmed that LTAS is a suitable approach to objectively describe changes in speech impairment severity due to STN-DBS therapy in patients with PD. We envisage these results as an important step toward a continuum development of technological solutions for the automated assessment of stimulation-induced dysarthria.

Purpose: This study aimed to evaluate the reliability of different approaches for estimating the articulation rates in connected speech of Parkinsonian patients with different stages of neurodegeneration compared to healthy controls. Method: Monologues and reading passages were obtained from 25 patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), 25 de novo patients with Parkinson's disease (PD), 20 patients with multiple system atrophy (MSA), and 20 healthy controls. The recordings were subsequently evaluated using eight syllable localization algorithms, and their performances were compared to a manual transcript used as a reference. Results: The Google & Pyphen method, based on automatic speech recognition followed by hyphenation, outperformed the other approaches (automated vs. hand transcription: r > 0.87 for monologues and r > 0.91 for reading passages, p < 0.001) in precise feature estimates and resilience to dysarthric speech. The Praat script algorithm achieved sufficient robustness (automated vs. hand transcription: r > 0.65 for monologues and r > 0.78 for reading passages, p < 0.001). Compared to the control group, we detected a slow rate in patients with MSA and a tendency toward a slower rate in patients with iRBD, whereas the articulation rate was unchanged in patients with early untreated PD. Conclusions: The state-of-the-art speech recognition tool provided the most precise articulation rate estimates. If speech recognizer is not accessible, the freely available Praat script based on simple intensity thresholding might still provide robust properties even in severe dysarthria. Automated articulation rate assessment may serve as a natural, inexpensive biomarker for monitoring disease severity and a differential diagnosis of Parkinsonism.

Background: Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone. Objective: The aim was to develop a fully automated and noise‐resistant smartphone‐based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real‐world scenario. Methods: This cross‐sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application. The participants also performed an active, regular reading of a short passage on their smartphone. Smartphone data were continuously collected for up to 3 months after the standard in‐person assessments at the clinic. Results: A total of 3525 calls that led to 5990 minutes of preprocessed speech were extracted from 72 participants, comprising 21 iRBD patients, 26 PD patients, and 25 controls. With a high area under the curve of 0.85 between iRBD patients and controls, the combination of passive and active smartphone data provided a comparable or even more sensitive evaluation than laboratory examination using a high‐quality microphone. The most sensitive features to induce prodromal neurodegeneration in iRBD included imprecise vowel articulation during phone calls (P = 0.03) and monopitch in reading (P = 0.05). Eighteen minutes of speech corresponding to approximately nine calls was sufficient to obtain the best sensitivity for the screening. Conclusion: We consider the developed tool widely applicable to deep longitudinal digital phenotyping data with future applications in neuroprotective trials, deep brain stimulation optimization, neuropsychiatry, speech therapy, population screening, and beyond. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research note is to provide a performance comparison of available algorithms for the automated evaluation of oral diadochokinesis using speech samples from patients with amyotrophic lateral sclerosis (ALS). Method: Four different algorithms based on a wide range of signal processing approaches were tested on a sequential motion rate /pa/-/ta/-/ka/ syllable repetition paradigm collected from 18 patients with ALS and 18 age- and gender-matched healthy controls (HCs). Results: The best temporal detection of syllable position for a 10-ms tolerance value was achieved for ALS patients using a traditional signal processing approach based on a combination of filtering in the spectrogram, Bayesian detection, and polynomial thresholding with an accuracy rate of 74.4%, and for HCs using a deep learning approach with an accuracy rate of 87.6%. Compared to HCs, a slow diadochokinetic rate (p < 0.001) and diadochokinetic irregularity (p < 0.01) were detected in ALS patients. Conclusions: The approaches using deep learning or multiple-step combinations of advanced signal processing methods provided a more robust solution to the estimation of oral DDK variables than did simpler approaches based on the rough segmentation of the signal envelope. The automated acoustic assessment of oral diadochokinesis shows excellent potential for monitoring bulbar disease progression in individuals with ALS.

The cross-correlation between annual lightning frequency and solar activity and the heliospheric magnetic field (HMF) is examined on a global scale using corrected data from the World Wide Lightning Location Network (WWLLN) for the period 2009 to 2022. Relatively large regions with significant cross-correlation coefficients (p<0.05) between the yearly lightning rates and sunspot number (SSN) are found in eastern Africa, part of South America overlapping with the South Atlantic Anomaly, and the Indian Ocean and west coast of Australia. The main region that shows a significant correlation between lightning activity and the By component of the HMF and the magnetopause reconnection Kan–Lee electric field matches the South Atlantic Anomaly quite well. Also shown are areas that show a significant cross-correlation of lightning activity with the El Niño–Southern Oscillation index. Similar areas of significant cross-correlation are obtained if simulated thunder days are used instead of lightning counts. Possible mechanisms leading to the observed correlations and limitations of the current study are discussed. The findings of the present study do not support previous works indicating that cosmic ray intensity is in phase with the global occurrence of lightning, but they do not rule out the role of cosmic rays in lightning ignition in developed thunderclouds and the role of energetic particles precipitating from the magnetosphere in the significant correlation between lightning and the By component of the HMF (SSN) in the South Atlantic Anomaly. [ABSTRACT FROM AUTHOR]

Introduction: Dysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson's disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development. This study validates a digital measure for speech intelligibility, the ki: SB-M intelligibility score, across various motor speech disorders and languages following the Digital Medicine Society (DiMe) V3 framework. Methods: The study used four datasets: healthy controls (HCs) and patients with PD, HD, PSP, and ALS from Czech, Colombian, and German populations. Participants' speech intelligibility was assessed using the ki: SB-M intelligibility score, which is derived from automatic speech recognition (ASR) systems. Verification with inter-ASR reliability and temporal consistency, analytical validation with correlations to gold standard clinical dysarthria scores in each disease, and clinical validation with group comparisons between HCs and patients were performed. Results: Verification showed good to excellent inter-rater reliability between ASR systems and fair to good consistency. Analytical validation revealed significant correlations between the SB-M intelligibility score and established clinical measures for speech impairments across all patient groups and languages. Clinical validation demonstrated significant differences in intelligibility scores between pathological groups and healthy controls, indicating the measure's discriminative capability. Discussion: The ki: SB-M intelligibility score is a reliable, valid, and clinically relevant tool for assessing speech intelligibility in motor speech disorders. It holds promise for improving clinical trials through automated, objective, and scalable assessments. Future studies should explore its utility in monitoring disease progression and therapeutic efficacy as well as add data from further dysarthrias to the validation. [ABSTRACT FROM AUTHOR]

In this work, we investigate various types of ionospheric disturbances observed over Europe following the earthquake that occurred in Turkey on 6 February 2023. By combining observations from Doppler sounding systems, ionosondes, and GNSS receivers, we are able to discern different types of disturbances, propagating with different velocities and through different mechanisms. We can detect co‐seismic ionospheric disturbances close to the epicenter, as well as ionospheric signatures of acoustic waves propagating as a consequence of propagating seismic waves. Unlike the vast majority of past ionospheric co‐seismic disturbance studies that are primarily based on Total Electron Content variations, reflecting disturbances propagating around the F‐region peak, the focus of the present study is the manifestation of disturbances at different ionospheric altitudes by exploiting complementary ionospheric remote sensing techniques. This is particularly highlighted through ionospheric earthquake‐related signatures established as specific ionogram deformations known as multiple‐cusp signatures which appear as additional cusps at the base of the F‐region attributed to electron density irregularities generated by Rayleigh surface waves that generate acoustic waves propagating up to the ionosphere. Therefore this study underlines the advantage that multi‐instrument investigations offer in identifying the propagation of earthquake‐related ionospheric disturbances at different ionospheric altitudes and distances from the earthquake epicenter. Key Points: The 2023 Turkey earthquake induced a spectrum of ionospheric disturbances as shown by networks of GNSS receivers, ionosondes and HF DopplerDisturbances appeared as shock acoustic wave‐induced traveling ionospheric disturbances in the near field and Rayleigh wave‐induced ionogram multiple‐cusp signature at longer distancesMulti‐instrument remote sensing techniques can detect ionospheric disturbances at a range of altitudes‐distances from the epicenter [ABSTRACT FROM AUTHOR]

The cross-correlation between lightning occurrence and cosmic ray intensity, solar activity and solar wind is examined on a global scale using data from the World Wide Lightning Location Network (WWLLN) for the period 2009 to 2022. The cross-correlation coefficients vary depending on the position on the globe. Positive cross-correlation between lightning occurrence and Sun spot number is found in most of Africa, South and Central America, while in parts of Europe and Southeast Asia the cross-correlation is negative. Positive cross-correlation between lightning occurrence and By component of heliospheric magnetic field is found for Southern part of South America, part of Europe, and northwestern Asia. Possible mechanisms are discussed. Although local weather and climate play a dominant role in lightning occurrence, observations suggest that changes in solar UV radiation during the solar cycle, together with global circulation and atmospheric waves, may modulate lightning occurrence in tropical and subtropical regions, while the polarity of the heliospheric magnetic field, atmospheric circulation and waves affect lightning occurrence more in mid- and high-latitude regions. The observed changes in cosmic ray intensity play an insignificant role in the global occurrence of lightning. [ABSTRACT FROM AUTHOR]