Isolating Neural Signatures of Conscious Speech Perception with a No-Report Sine-Wave Speech Paradigm.

Identifying neural correlates of conscious perception is a fundamental endeavor of cognitive neuroscience. Most studies so far have focused on visual awareness along with trial-by-trial reports of task-relevant stimuli, which can confound neural measures of perceptual awareness with postperceptual processing. Here, we used a three-phase sine-wave speech paradigm that dissociated between conscious speech perception and task relevance while recording EEG in humans of both sexes. Compared with tokens perceived as noise, physically identical sine-wave speech tokens that were perceived as speech elicited a left-lateralized, near-vertex negativity, which we interpret as a phonological version of a perceptual awareness negativity. This response appeared between 200 and 300 ms after token onset and was not present for frequency-flipped control tokens that were never perceived as speech. In contrast, the P3b elicited by task-irrelevant tokens did not significantly differ when the tokens were perceived as speech versus noise and was only enhanced for tokens that were both perceived as speech and relevant to the task. Our results extend the findings from previous studies on visual awareness and speech perception and suggest that correlates of conscious perception, across types of conscious content, are most likely to be found in midlatency negative-going brain responses in content-specific sensory areas.

Towards interpretable speech biomarkers: exploring MFCCs.

While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.

Identifying value in healthcare transformation initiatives: an evaluation of an approach to benefits realisation.

Benefits realisation management (BRM) aims to facilitate the process of identifying, measuring and tracking desired benefits derived from a project. Improvement methodology frameworks often describe BRM as integral to identifying and measuring value derived from transformation initiatives within the National Health Service and beyond. Despite this, reporting of benefits realisation plans and methodological approaches to identifying and measuring benefits remains surprisingly scarce.This project aimed to pilot and evaluate the application of a purpose-designed benefits mapping template with seven newly funded transformation projects across three hospitals in the UK. The scope of the template was to identify key project benefits and metrics associated with the project initiatives. Plan-do-study-act (PDSA) cycles were used to capture the approach and utilisation of the template by project teams. These methods also enabled critical review of the template as an enabler to identifying relevant benefits and project metrics.Stakeholder engagement with the templates was variable. This was attributed to clinical pressures induced by the second wave of COVID-19 in the UK. Despite this, teams were able to produce completed templates outlining a number of wide-ranging benefits. Themes of benefits drawn from the maps include patient experience, patient outcomes, staff experience, access to care and efficiency. Qualitative feedback from teams included the reported value of a structured template to help recognise all the potential benefits associated with each project initiative. The PDSA cycles highlighted the template as an early step in BRM. Further components to this process are recommended to include consensus of the key metrics to be measured, a tool that summarises the reporting details of those metrics, and an effective means to collate reported metrics overtime.

Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study.

BACKGROUND: Human voice has increasingly been recognized as an effective indicator for the detection of cognitive disorders. However, the association of acoustic features with specific cognitive functions and mild cognitive impairment (MCI) has yet to be evaluated in a large community-based population. OBJECTIVE: This study aimed to investigate the association between acoustic features and neuropsychological (NP) tests across multiple cognitive domains and evaluate the added predictive power of acoustic composite scores for the classification of MCI. METHODS: This study included participants without dementia from the Framingham Heart Study, a large community-based cohort with longitudinal surveillance for incident dementia. For each participant, 65 low-level acoustic descriptors were derived from voice recordings of NP test administration. The associations between individual acoustic descriptors and 18 NP tests were assessed with linear mixed-effect models adjusted for age, sex, and education. Acoustic composite scores were then built by combining acoustic features significantly associated with NP tests. The added prediction power of acoustic composite scores for prevalent and incident MCI was also evaluated. RESULTS: The study included 7874 voice recordings from 4950 participants (age: mean 62, SD 14 years; 4336/7874, 55.07% women), of whom 453 were diagnosed with MCI. In all, 8 NP tests were associated with more than 15 acoustic features after adjusting for multiple testing. Additionally, 4 of the acoustic composite scores were significantly associated with prevalent MCI and 7 were associated with incident MCI. The acoustic composite scores can increase the area under the curve of the baseline model for MCI prediction from 0.712 to 0.755. CONCLUSIONS: Multiple acoustic features are significantly associated with NP test performance and MCI, which can potentially be used as digital biomarkers for early cognitive impairment monitoring.

A systematic review of the characteristics of adolescents with major depressive disorder in randomised controlled treatment trials

Background and Objectives. Concerns exist around the generalizability of randomised controlled trials (RCTs) for adolescents with major depressive disorder (MDD). This review assesses whether adolescents with MDD treated in RCTs are representative of clinical samples. Methods. A systematic narrative review of selection criteria used in RCTs for adolescent MDD (PROSPERO CRD42018096298). Included were studies assessing psychological, pharmacological or combination treatments. Results. 52 studies were included. Overall, the reporting of selection criteria (defined as both inclusion and exclusion criteria), in the 23 psychotherapy trials was low (52% did not report on comorbid emotional disorders and 48% did not report on suicidal ideation). In contrast, the majority of selection criteria were reported in the 22 medication trials and the 7 combination trials. Where selection criteria were reported, most adolescents with comorbidities were excluded from psychotherapy and medication trials. The 7 combination trials included more adolescents with comorbidities. Of note, only 10 of the 52 studies reported on self-harm as a selection criteria. Conclusion. Reporting of the characteristics of depressed adolescents was poor in psychotherapy trials. Both psychotherapy and medication trials excluded many adolescents with co-morbid conditions, however combination trials tended to be more inclusive. There is concern that many RCTs for adolescent MDD may not be generalizable to clinical populations, particularly with regards to comorbidity, self-harm and suicidal ideation. The findings suggest that clinicians need to view the evidence base and guidelines developed from RCTs with discernment. Pragmatic trials are needed with representative clinical populations and comprehensive reporting of the selection criteria.

Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study.

BACKGROUND: Self-monitoring food intake is a cornerstone of national recommendations for health, but existing apps for this purpose are burdensome for users and researchers, which limits use. OBJECTIVE: We developed and pilot tested a new app (COCO Nutritionist) that combines speech understanding technology with technologies for mapping foods to appropriate food composition codes in national databases, for lower-burden and automated nutritional analysis of self-reported dietary intake. METHODS: COCO was compared with the multiple-pass, interviewer-administered 24-hour recall method for assessment of energy intake. COCO was used for 5 consecutive days, and 24-hour dietary recalls were obtained for two of the days. Participants were 35 women and men with a mean age of 28 (range 20-58) years and mean BMI of 24 (range 17-48) kg/m2. RESULTS: There was no significant difference in energy intake between values obtained by COCO and 24-hour recall for days when both methods were used (mean 2092, SD 1044 kcal versus mean 2030, SD 687 kcal, P=.70). There were also no significant differences between the methods for percent of energy from protein, carbohydrate, and fat (P=.27-.89), and no trend in energy intake obtained with COCO over the entire 5-day study period (P=.19). CONCLUSIONS: This first demonstration of a dietary assessment method using natural spoken language to map reported foods to food composition codes demonstrates a promising new approach to automate assessments of dietary intake.

Identification of digital voice biomarkers for cognitive health.

AIM: Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia. METHODS: Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted with the OpenSMILE toolkit (v2.1). The association of each acoustic feature with incident dementia was assessed by Cox proportional hazards models. RESULTS: Our study included 6, 528 voice recordings from 4, 849 participants (mean age 63 ± 15 years old, 54.6% women). The majority of participants (71.2%) had one voice recording, 23.9% had two voice recordings, and the remaining participants (4.9%) had three or more voice recordings. Although all asymptomatic at the time of examination, participants who developed dementia tended to have shorter segments than those who were dementia free (P < 0.001). Additionally, 14 acoustic features were significantly associated with dementia after adjusting for multiple testing (P < 0.05/48 = 1 × 10-3). The most significant acoustic feature was jitterDDP_sma_de (P = 7.9 × 10-7), which represents the differential frame-to-frame Jitter. A voice based linear classifier was also built that was capable of predicting incident dementia with area under curve of 0.812. CONCLUSIONS: Multiple acoustic and linguistic features are identified that are associated with incident dementia among asymptomatic participants, which could be used to build better prediction models for passive cognitive health monitoring.

Classifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech.

Alzheimer's Disease (AD) is a form of dementia that affects the memory, cognition, and motor skills of patients. Extensive research has been done to develop accessible, cost-effective, and non-invasive techniques for the automatic detection of AD. Previous research has shown that speech can be used to distinguish between healthy patients and afflicted patients. In this paper, the ADReSS dataset, a dataset balanced by gender and age, was used to automatically classify AD from spontaneous speech. The performance of five classifiers, as well as a convolutional neural network and long short-term memory network, was compared when trained on audio features (i-vectors and x-vectors) and text features (word vectors, BERT embeddings, LIWC features, and CLAN features). The same audio and text features were used to train five regression models to predict the Mini-Mental State Examination score for each patient, a score that has a maximum value of 30. The top-performing classification models were the support vector machine and random forest classifiers trained on BERT embeddings, which both achieved an accuracy of 85.4% on the test set. The best-performing regression model was the gradient boosting regression model trained on BERT embeddings and CLAN features, which had a root mean squared error of 4.56 on the test set. The performance on both tasks illustrates the feasibility of using speech to classify AD and predict neuropsychological scores.

The threshold length for fiber-induced acute pleural inflammation: shedding light on the early events in asbestos-induced mesothelioma.

Suspicion has been raised that high aspect ratio nanoparticles or nanofibers might possess asbestos-like pathogenicity. The pleural space is a specific target for disease in individuals exposed to asbestos and by implication of nanofibers. Pleural effects of fibers depends on fiber length, but the key threshold length beyond which adverse effects occur has never been identified till now because all asbestos and vitreous fiber samples are heterogeneously distributed in their length. Nanotechnology advantageously allows for highly defined length distribution of synthetically engineered fibers that enable for in-depth investigation of this threshold length. We utilized the ability to prepare silver nanofibers of five defined length classes to demonstrate a threshold fiber length for acute pleural inflammation. Nickel nanofibers and carbon nanotubes were then used to strengthen the relationship between fiber length and pleural inflammation. A method of intrapleural injection of nanofibers in female C57Bl/6 strain mice was used to deliver the fiber dose, and we then assessed the acute pleural inflammatory response. Chest wall sections were examined by light and scanning electron microscopy to identify areas of lesion; furthermore, cell-nanowires interaction on the mesothelial surface of the parietal pleura in vivo was investigated. Our results showed a clear threshold effect, demonstrating that fibers beyond 4 µm in length are pathogenic to the pleura. The identification of the threshold length for nanofiber-induced pathogenicity in the pleura has important implications for understanding the structure-toxicity relationship for asbestos-induced mesothelioma and consequent risk assessment with the aim to contribute to the engineering of synthetic nanofibers by the adoption of a benign-by-design approach.

Multistream articulatory feature-based models for visual speech recognition.

We study the problem of automatic visual speech recognition (VSR) using dynamic Bayesian network (DBN)-based models consisting of multiple sequences of hidden states, each corresponding to an articulatory feature (AF) such as lip opening (LO) or lip rounding (LR). A bank of discriminative articulatory feature classifiers provides input to the DBN, in the form of either virtual evidence (VE) (scaled likelihoods) or raw classifier margin outputs. We present experiments on two tasks, a medium-vocabulary word-ranking task and a small-vocabulary phrase recognition task. We show that articulatory feature-based models outperform baseline models, and we study several aspects of the models, such as the effects of allowing articulatory asynchrony, of using dictionary-based versus whole-word models, and of incorporating classifier outputs via virtual evidence versus alternative observation models.

Enzyme-mediated individual nanoparticle release assay.

Numerous methods have been developed to measure the presence of macromolecular species in a sample; however, the number of methods that detect functional activity or modulators of that activity is more limited. To address this limitation, an approach was developed that uses the optical detection of nanoparticles as a measure of enzyme activity. Nanoparticles are increasingly being used as biological labels in static binding assays; here, we describe their use in a release assay format, where the enzyme-mediated liberation of individual nanoparticles from a surface is measured. A double-stranded fragment of DNA is used as the initial tether to bind the nanoparticles to a solid surface. The nanoparticle spatial distribution and number are determined using dark-field optical microscopy and digital image capture. Site-specific cleavage of the DNA tether results in nanoparticle release. The methodology and validation of this approach for measuring enzyme-mediated, individual DNA cleavage events, rapidly, with high specificity, and in real-time are described. This approach was used to detect and discriminate between nonmethylated and methylated DNA, and demonstrates a novel platform for high-throughput screening of modulators of enzyme activity.

Building a cardiology practice.

This study was designed to quantify the clinical and marketing effectiveness of the Pocket EKG Clinical Based Marketing Program by measuring its impact on new patient visits, patient satisfaction, payor negotiations, and patient management at Pikes Peak Cardiology (PPC), Colorado Springs, Colorado. New patient visits were found to increase by 22% for 6.5 consecutive years. Ninety-two percent of patients surveyed found that the Pocket EKG Card promoted loyalty to the cardiology practice. The Pocket EKG Patient Satisfaction Survey was proven to satisfy Health Plan Employer Data and Information Set (HEDIS) guidelines as required in payor contracting. Finally, access to a baseline electrocardiogram contributed to a 54% reduction in unnecessary hospitalization. The Pocket EKG Clinical Based Marketing Program proved to successfully market PPC to its three customers: patients, payors, and primary care physicians.