Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.

Brain-computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials.gov, NCT03567213) exploring different strategies for BCI communication. The 3-stage approach reported here relies on recurrent neural networks to identify, decode and synthesize speech from electrocorticographic (ECoG) signals acquired across motor, premotor and somatosensory cortices. We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the participant from a vocabulary of 6 keywords previously used for decoding commands to control a communication board. Evaluation of the intelligibility of the synthesized speech indicates that 80% of the words can be correctly recognized by human listeners. Our results show that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words while preserving the participant's voice profile, and provide further evidence for the stability of ECoG for speech-based BCIs.

The Hidden Battle Within: Shedding Light on the Co-existence of Sarcopenia and Sarcopenic Obesity among Participants with Type 2 Diabetes in a Tertiary Care Hospital, Gujarat.

Introduction: Type 2 diabetes (T2DM) is characterised by chronic hyperglycaemia due to abnormal insulin secretion and/or utilisation. Currently, sarcopenia has emerged as a new complication of T2DM, which increases the risk of physical disability, and even death. The study aims to estimate the prevalence of sarcopenia and sarcopenic obesity (SO) as well as their association with various other factors related to T2DM. Methods: The study was an observational hospital-based cross-sectional study conducted among diabetic patients who came to the non-communicable diseases (NCD) clinic of a tertiary care hospital in Gujarat, India, from April 2023 to June 2023. Adult patients with T2DM attending follow-ups were included, with a diagnosis of T2DM for at least 1 year from the date of their electronic medical records, regardless of their mode of therapeutic treatment. They were on regular medical reviews with two or more visits to the study site in the past 1 year. Then a self-structured standard questionnaire was used to collect the data, containing socio-demographic characteristics, clinical profiles, anthropometric assessment (comprising weight, height and body mass index [BMI]), bio-impedance indices like body fat%, skeletal muscle% and handgrip by hand dynamometer. Results: In the study, a total of 404 participants participated. Their mean age was 55 ± 13.5 years and their mean body fat (BF) % was 30 ± 7.4%. BF%-defined obesity was found in 260 (64.4%) participants. A total of 362 (89.6%) had possible sarcopenia, 183 (45.3%) had sarcopenia and 124 (30.7%) had SO. Age (OR: 2.6, CI: 1.7-3.9), duration of diabetes for more than 7 years (OR: 7.5, CI: 3.65-15.4) and BF%-defined obesity (OR: 2.2, CI: 3.6-15) were statistically associated with Sarcopenia, in similar pattern age (OR: 2.4, CI: 1.5-3.7), and duration of diabetes more than 7 years (OR: 18.9, CI: 5.7-62) were associated with SO (P < 0.05). Conclusion: Older age, longer diabetes duration and BF%-defined obesity are associated with an increased likelihood of developing sarcopenia and sarcopenic obesity. Healthcare providers should prioritise regular screening for sarcopenia and SO in elderly individuals with diabetes to facilitate early detection and intervention.

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling.

Background: Brain-computer interfaces (BCIs) can restore communication in movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command "click" decoders provide a basic yet highly functional capability. Methods: We sought to test the performance and long-term stability of click-decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis (ALS). We trained the participant's click decoder using a small amount of training data (< 44 minutes across four days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. Results: Using this click decoder to navigate a switch-scanning spelling interface, the study participant was able to maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation interrupted testing with this fixed model, a new click decoder achieved comparable performance despite being trained with even less data (< 15 min, within one day). Conclusion: These results demonstrate that a click decoder can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.

Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months.

Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.

Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.

Recent studies have shown that speech can be reconstructed and synthesized using only brain activity recorded with intracranial electrodes, but until now this has only been done using retrospective analyses of recordings from able-bodied patients temporarily implanted with electrodes for epilepsy surgery. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a clinical trial participant (ClinicalTrials.gov, NCT03567213) with dysarthria due to amyotrophic lateral sclerosis (ALS). We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the user from a vocabulary of 6 keywords originally designed to allow intuitive selection of items on a communication board. Our results show for the first time that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words that are intelligible to human listeners while preserving the participants voice profile.

Geospatial Mapping of Indoor Air Quality and Respiratory Illnesses in an Urban Slum.

Introduction Air pollution is a well-recognized determinant of health. The general perception has focused primarily on outdoor pollution, and indoor pollution which may be due to smoking, biomass use, an extension of outdoor pollution, etc. has been neglected. It is therefore imperative to understand the levels of indoor pollution and find out if these are associated with high rates of illnesses, particularly, respiratory diseases. Material and methods This was a cross-sectional study involving 300 houses and 727 participants in an urban slum, selected through simple random sampling. Indoor air quality was assessed using the Prana C -Air Plus air quality monitor (Prana Air, New Delhi, India). The instrument detected formaldehyde, air quality index (AQI), temperature, humidity, PM2.5, PM10 particles, and total volatile organic (TVO) compounds. Socio-demographic details were noted, and geospatial mapping was done using Q-GIS software (www.qgis.org). A questionnaire was used to survey the residents of those houses. Ethical committee clearance was obtained before starting the project. Results The mean distribution of pollution parameters over the entire study area was AQI - 67.4±65.48, PM 2.5 - 37.6±35.82 µg/m3, formaldehyde - 0.09±0.37 mg/m3, PM 10 - 43.9±38.59 µg/m3, TVO compounds - 0.43±2.13 mg/m3, CO2 - 1128.9±323.86 ppm, temperature - 23.7±21.2 degree Celsius, and PM 1 - 24.3±20.5 µg/m3; 2.6% of the participants had respiratory diseases, and a significant association was found between the AQI, TVO compounds and ventilation, and respiratory diseases (p<0.05). Conclusion Indoor air pollution not unlike outdoor pollution can have dramatic health effects and needs to be addressed to lower the overall respiratory disease burden. The AQI, TVOC, and poor ventilation/cross-ventilation are associated with respiratory illnesses. Geospatial mapping shows a concentration of cases in areas of high pollution.

Seizure triggers identified postictally using a smart watch reporting system.

Persons with epilepsy (PWE) often report that seizure triggers can influence the occurrence and timing of seizures. Some previous studies of seizure triggers have relied on retrospective daily seizure diaries or surveys pertaining to all past seizures, recent and/or remote, in respondents. To assess the characteristics of seizure triggers at the granularity of individual seizures, we used a seizure-tracking app, called EpiWatch, on a smart watch system (Apple Watch and iPhone) in a national study of PWE. Participants tracked seizures during a 16-month study period using the EpiWatch app. Seizure tracking was initiated during a pre-ictal state or as the seizure was occurring and included collection of biosensor data, responsiveness testing, and completion of an immediate post-seizure survey. The survey evaluated seizure types, auras or warning symptoms, loss of awareness, use of rescue medication, and seizure triggers for each tracked seizure. Two hundred and thirty four participants tracked 2493 seizures. Ninety six participants reported triggers in 650 seizures: stress (65.8%), lack of sleep (30.5%), menstrual cycle (19.7%), and overexertion (18%) were the most common. Participants often reported having multiple combined triggers, frequent stress with lack of sleep, overexertion, or menses. Participants who reported triggers were more likely to be taking 3 or more anti-seizure medications compared to participants who did not report triggers. Participants were able to interact with the app and use mobile technology in this national study to record seizures and report common seizure triggers. These findings demonstrate the promise of longitudinal, self-reported data to improve our understanding of epilepsy and its related comorbidities.