Effective Elicitation of Stuttering in Magnetic Resonance Imaging Data Collection Using a Suite of Connected Speech Tasks.

Purpose: Articulatory behaviors during moments of stuttering have been understudied, largely due to the technical difficulty of collecting such data. Tracking moving articulators during stuttering requires advanced instrumentation, and eliciting stuttering in a lab setting poses challenges for experimental design. To address these difficulties, we present a novel methodology that combines real-time vocal tract magnetic resonance imaging (MRI) with a suite of connected speech tasks to elicit stuttering.

Developing personalized algorithms for sensing mental health symptoms in daily life.

The integration of artificial intelligence (AI) and pervasive computing offers new opportunities to sense mental health symptoms and deliver just-in-time adaptive interventions via mobile devices. This pilot study tested personalized versus generalized machine learning models for detecting individual and family mental health symptoms as a foundational step toward JITAI development, using data collected through the Colliga app on smart devices. Over a 60-day period, data from 35 families resulted in approximately 14 million data points across 52 data streams.

Dampening versus amplification: Intrapersonal and interpersonal vocal affect dynamics during psychotherapy for depression.

Objective: Major depressive disorder (MDD) is associated with ineffective affect regulation. Vocal data can shed light on communication and expression during psychotherapy and provide high-resolution data for the study of affective arousal dynamics. Computerized vocal analyses were used to examine the extent to which intrapersonal and interpersonal vocal-arousal dynamics were linked to session outcomes and whether a session's dampening as compared to an amplification arousal trajectory would moderate this association.

Conversational Latency in Autistic Children With Heterogeneous Spoken Language Abilities.

Purpose: Conversational latency entails the temporal feature of turn-taking, which is understudied in autistic children. The current study investigated the influences of child-based and parental factors on conversational latency in autistic children with heterogeneous spoken language abilities.

Method: Participants were 46 autistic children aged 4-7 years.

The machine learning-based prediction of the sound pressure level from pathological and healthy speech signals.

Vocal intensity is quantified by sound pressure level (SPL). The SPL can be measured by either using a sound level meter or by comparing the energy of the recorded speech signal with the energy of the recorded calibration tone of a known SPL. Neither of these approaches can be used if speech is recorded in real-life conditions using a device that is not calibrated for SPL measurements.

The association of multilingualism with diverse language families and cognition among adults with and without education in India.

Objective: Early-life socioeconomic factors, such as education, closely associated with the opportunity to become multilingual (ML), are important determinants of late-life cognition. To study the cognitive advantage of multilingualism, it is critical to disentangle whether cognitive benefit is driven by multilingualism or education. With rich linguistic diversity across all socioeconomic gradients, India provides an excellent setting to examine the role of multilingualism on cognition among individuals with and without education.

Understanding Stress, Burnout, and Behavioral Patterns in Medical Residents Using Large-scale Longitudinal Wearable Recordings.

Medical residency training is often associated with physically intense and emotionally demanding tasks, requiring them to engage in extended working hours providing complex clinical care. Residents are hence susceptible to negative psychological effects, including stress and anxiety, that can lead to decreased well-being, affecting them achieving desired training outcomes. Understanding the daily behavioral patterns of residents can guide the researchers to identify the source of stress in residency training, offering unique opportunities to improve residency programs.

Knowledge-guided EEG Representation Learning.

Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labelled data in such scenarios. The ability to leverage large-scale unlabelled data to learn robust representations could help improve the performance of numerous inference tasks on biosignals.

Evaluating Atypical Gaze Patterns through Vision Models: The Case of Cortical Visual Impairment.

A wide range of neurological and cognitive disorders exhibit distinct behavioral markers aside from their clinical manifestations. Cortical Visual Impairment (CVI) is a prime example of such conditions, resulting from damage to visual pathways in the brain, and adversely impacting low- and high-level visual function. The characteristics impacted by CVI are primarily described qualitatively, challenging the establishment of an objective, evidence-based measure of CVI severity.

Association of machine-learning-rated supportive counseling skills with psychotherapy outcome.

Objective: This study applied a machine-learning-based skill assessment system to investigate the association between supportive counseling skills (empathy, open questions, and reflections) and treatment outcomes. We hypothesized that higher empathy and higher use of open questions and reflections would be associated with greater symptom reduction.

Method: We used a data set with 2,974 sessions, 610 clients, and 48 therapists collected from a university counseling center, which included 845,953 rated therapist statements.

Method for assessing visual saliency in children with cerebral/cortical visual impairment using generative artificial intelligence.

Cerebral/cortical visual impairment (CVI) is a leading cause of pediatric visual impairment in the United States and other developed countries, and is increasingly diagnosed in developing nations due to improved care and survival of children who are born premature or have other risk factors for CVI. Despite this, there is currently no objective, standardized method to quantify the diverse visual impairments seen in children with CVI who are young and developmentally delayed. We propose a method that combines eye tracking and an image-based generative artificial intelligence (AI) model (SegCLIP) to assess higher- and lower-level visual characteristics in children with CVI.

Direct articulatory observation reveals phoneme recognition performance characteristics of a self-supervised speech model.

Variability in speech pronunciation is widely observed across different linguistic backgrounds, which impacts modern automatic speech recognition performance. Here, we evaluate the performance of a self-supervised speech model in phoneme recognition using direct articulatory evidence. Findings indicate significant differences in phoneme recognition, especially in front vowels, between American English and Indian English speakers.

Can a Machine Distinguish High and Low Amount of Social Creak in Speech?

Objectives: Increased prevalence of social creak particularly among female speakers has been reported in several studies. The study of social creak has been previously conducted by combining perceptual evaluation of speech with conventional acoustical parameters such as the harmonic-to-noise ratio and cepstral peak prominence. In the current study, machine learning (ML) was used to automatically distinguish speech of low amount of social creak from speech of high amount of social creak.

Vertical larynx actions and intergestural timing stability in Hausa ejectives and implosives.

The current project undertakes a kinematic examination of vertical larynx actions and intergestural timing stability within multi-gesture complex segments such as ejectives and implosives that may possess specific temporal goals critical to their articulatory realization. Using real-time MRI (rtMRI) speech production data from Hausa non-pulmonic and pulmonic consonants, this study illuminates speech timing between oral constriction and vertical larynx actions within segments and the role this intergestural timing plays in realizing phonological contrasts and processes in varying prosodic contexts. Results suggest that vertical larynx actions have greater magnitude in the production of ejectives compared to their pulmonic counterparts, but implosives and pulmonic consonants are differentiated not by vertical larynx magnitude but by the intergestural timing patterns between their oral and vertical larynx gestures.

People make mistakes: Obtaining accurate ground truth from continuous annotations of subjective constructs.

Accurately representing changes in mental states over time is crucial for understanding their complex dynamics. However, there is little methodological research on the validity and reliability of human-produced continuous-time annotation of these states. We present a psychometric perspective on valid and reliable construct assessment, examine the robustness of interval-scale (e.

Machine-Learning-Based Prediction of Client Distress From Session Recordings.

Natural language processing (NLP) is a subfield of machine learning that may facilitate the evaluation of therapist-client interactions and provide feedback to therapists on client outcomes on a large scale. However, there have been limited studies applying NLP models to client outcome prediction that have (a) used transcripts of therapist-client interactions as direct predictors of client symptom improvement, (b) accounted for contextual linguistic complexities, and (c) used best practices in classical training and test splits in model development. Using 2,630 session recordings from 795 clients and 56 therapists, we developed NLP models that directly predicted client symptoms of a given session based on session recordings of the previous session (Spearman's rho =0.

Multimodal analysis of temporal affective variability within treatment for depression.

Objective: Affective flexibility, the capacity to respond to life's varying environmental changes in a dynamic and adaptive manner, is considered a central aspect of psychological health in many psychotherapeutic approaches. The present study examined whether affective two-dimensional (i.e.

Scaling Representation Learning From Ubiquitous ECG With State-Space Models.

Ubiquitous sensing from wearable devices in the wild holds promise for enhancing human well-being, from diagnosing clinical conditions and measuring stress to building adaptive health promoting scaffolds. But the large volumes of data therein across heterogeneous contexts pose challenges for conventional supervised learning approaches. Representation Learning from biological signals is an emerging realm catalyzed by the recent advances in computational modeling and the abundance of publicly shared databases.

Unique affective profile of music-evoked nostalgia: An extension and conceptual replication of Barrett et al.'s (2010) study.

Nostalgia is a mixed emotion, often evoked by music. This study sought to conceptually replicate and extend Barrett et al.'s (see record 2010-09991-008) pioneering work exploring music-evoked nostalgia, where the authors identified person- and context-level predictors of the experience of nostalgia in music.

A machine learning approach to improve implementation monitoring of family-based preventive interventions in primary care.

Background: Evidence-based parenting programs effectively prevent the onset and escalation of child and adolescent behavioral health problems. When programs have been taken to scale, declines in the quality of implementation diminish intervention effects. Gold-standard methods of implementation monitoring are cost-prohibitive and impractical in resource-scarce delivery systems.

Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial.

We present data from the Heart Rate Variability and Emotion Regulation (HRV-ER) randomized clinical trial testing effects of HRV biofeedback. Younger (N = 121) and older (N = 72) participants completed baseline magnetic resonance imaging (MRI) including T-weighted, resting and emotion regulation task functional MRI (fMRI), pulsed continuous arterial spin labeling (PCASL), and proton magnetic resonance spectroscopy (H MRS). During fMRI scans, physiological measures (blood pressure, pulse, respiration, and end-tidal CO) were continuously acquired.

Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review.

Background: Smartphones and wearable biosensors can continuously and passively measure aspects of behavior and physiology while also collecting data that require user input. These devices can potentially be used to monitor symptom burden; estimate diagnosis and risk for relapse; predict treatment response; and deliver digital interventions in patients with obsessive-compulsive disorder (OCD), a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course and may uniquely benefit from these technologies.

Objective: Given the speed at which mobile and wearable technologies are being developed and implemented in clinical settings, a continual reappraisal of this field is needed.