Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder.

Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing techniques have been used to predict depression, with pronoun and emotion usage being identified as important features. However, it is unclear how depressed individuals use positive and negative words when writing about themselves.

Whole-Genome Sequencing Reveals Individual and Cohort Level Insights into Chromosome 9p Syndromes.

Previous genomic efforts on chromosome 9p deletion and duplication syndromes have utilized low resolution strategies (i.e., karyotypes, chromosome microarrays).

A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.

Background: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid use (NPOU) and treatment discontinuation among persons receiving medications for OUD (MOUD) can provide a proactive approach to these challenges. Our study uses ecological momentary assessment (EMA) and deep learning to predict momentary NPOU, medication nonadherence, and treatment retention in MOUD patients.

Acute Onset Neuropsychiatric Conditions in Children and Adolescents following SARS-CoV-2 Infection: A Case Series.

Introduction: Neuropsychiatric symptoms following SARS-CoV-2 infection have been described in a substantial proportion of patients, acute, subacute, and chronic. Understanding of the neurological and neuropsychiatric sequelae of this virus is an emerging field of study with rapidly evolving descriptions of its impact on the central and peripheral nervous system.

Case Presentation: Here, we report a series of 8 pediatric patients presenting with acute onset neuropsychiatric symptoms following SARS-CoV-2 infection who received comprehensive medical and psychiatric evaluation and treatment in our research-based Neuroimmune Psychiatric Disorders Program.

A comparison of objective and subjective measures of physical activity, sedentary and sleep behaviors between persons with and without depressive symptoms.

Background: Major Depressive Disorder (MDD) is characterized by negative recall biases, which may impact how individuals with depressive symptoms report physical activity (PA), sedentary, and sleep behaviors. Additionally, there are discrepancies between subjective and objective behaviors in MDD. Thus, the current study investigated whether individuals with depressive symptoms differ in their subjective and objective PA, sedentary, and sleep behaviors, and whether the magnitude of these discrepancies differ from those in individuals without depressive symptoms.

MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI.

Anhedonia in flux: Understanding the associations of emotion regulation and anxiety with anhedonia dynamics in a sample with major depressive disorder.

Negative rumination and emotion regulation difficulties have been consistently linked with depression. Despite anhedonia-the lack of interest in pleasurable experiences-being a cardinal symptom of depression, emotion regulation of positive emotions, including dampening, are considered far less in the literature. Given that anhedonia may manifest through blunted responses to previously positive or enjoyable experiences, it is vital to understand how different positive emotion regulation strategies impact anhedonia symptom severity and how it can vary or change over time.

Use of passively collected actigraphy data to detect individual depressive symptoms in a clinical subpopulation and a general population.

The presentation of major depressive disorder (MDD) can vary widely due to its heterogeneity, including inter- and intraindividual symptom variability, making MDD difficult to diagnose with standard measures in clinical settings. Prior work has demonstrated that passively collected actigraphy can be used to detect MDD at a disorder level; however, given the heterogeneous nature of MDD, comprising multiple distinct symptoms, it is important to measure the degree to which various MDD symptoms may be captured by such passive data. The current study investigated whether individual depressive symptoms could be detected from passively collected actigraphy data in a (a) clinical subpopulation (i.

Evaluating a mobile app's effects on depression and anxiety in medication-treated opioid use disorder.

Depression and anxiety frequently co-occur with opioid use disorder (OUD) yet are often overlooked in standard OUD treatments. This study evaluated the feasibility, acceptability, and preliminary effectiveness of a mobile application designed to address these symptoms in individuals receiving medications for OUD (MOUD). A randomized controlled trial recruited N = 63 adults with OUD who received MOUD and screened positive for moderate depression or generalized anxiety.

A large-scale observational comparison of antidepressants and their effects.

Background: Selective Serotonin Reuptake Inhibitors (SSRIs) represent a diverse class of medications widely prescribed for depression and anxiety. Despite their common use, there is an absence of large-scale, real-world evidence capturing the heterogeneity in their effects on individuals. This study addresses this gap by utilizing naturalistic search data to explore the varied impact of six different SSRIs on user behavior.

MoodCapture: Depression Detection Using In-the-Wild Smartphone Images.

MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: .

Mental Health and Mobile Communication Profiles of Crowdsourced Participants.

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are highly prevalent and burdensome. To increase mental health screening rates, the digital health research community has been exploring the ability to augment self reporting instruments with digital logs. Crowdsourced workers are being increasingly recruited for behavioral health research studies as demographically representative samples are desired for later translational applications.

From mood to use: Using ecological momentary assessments to examine how anhedonia and depressed mood impact cannabis use in a depressed sample.

Anhedonia and depressed mood are two cardinal symptoms of major depressive disorder (MDD). Prior work has demonstrated that cannabis consumers often endorse anhedonia and depressed mood, which may contribute to greater cannabis use (CU) over time. However, it is unclear (1) how the unique influence of anhedonia and depressed mood affect CU and (2) how these symptoms predict CU over more proximal periods of time, including the next day or week (rather than proceeding weeks or months).

The Trajectories of Online Mental Health Information Seeking: Modeling Search Behavior Before and After Completion of Self-report Screens.

There is an appreciable mental health treatment gap in the United States. Efforts to bridge this gap and improve resource accessibility have led to the provision of online, clinically-validated tools for mental health self-assessment. In theory, these screens serve as an invaluable component of information-seeking, representing the preparative and action-oriented stages of this process while altering or reinforcing the search content and language of individuals as they engage with information online.

Impact of Reduced Spectral Resolution on Temporal-Coherence-Based Source Segregation.

Hearing-impaired listeners struggle to understand speech in noise, even when using cochlear implants (CIs) or hearing aids. Successful listening in noisy environments depends on the brain's ability to organize a mixture of sound sources into distinct perceptual streams (i.e.

Estimation of Cochlear Frequency Selectivity Using a Convolution Model of Forward-Masked Compound Action Potentials.

Purpose: Frequency selectivity is a fundamental property of the peripheral auditory system; however, the invasiveness of auditory nerve (AN) experiments limits its study in the human ear. Compound action potentials (CAPs) associated with forward masking have been suggested as an alternative to assess cochlear frequency selectivity. Previous methods relied on an empirical comparison of AN and CAP tuning curves in animal models, arguably not taking full advantage of the information contained in forward-masked CAP waveforms.

Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form.

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample.

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9).

Mobile Communication Log Time Series to Detect Depressive Symptoms.

Major Depressive Disorder (MDD) is highly prevalent and characterized by often debilitating behavioral and cognitive symptoms. MDD is poorly understood, likely due to considerable heterogeneity and self-report-driven symptomatology. While researchers have been exploring the ability of machine learning to screen for MDD, much less attention has been paid to individual symptoms.

Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.

Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability.

Estimate of the incidence of PANDAS and PANS in 3 primary care populations.

Objective: Pediatric Autoimmune Neuropsychiatric Disorder Associated with Streptococcal infection (PANDAS) and Pediatric Acute-Onset Neuropsychiatric syndrome (PANS) are presumed autoimmune complications of infection or other instigating events. To determine the incidence of these disorders, we performed a retrospective review for the years 2017-2019 at three academic medical centers.

Methods: We identified the population of children receiving well-child care at each institution.

A multi-channel EEG mini-cap can improve reliability for recording auditory brainstem responses in chinchillas.

Background: Disabling hearing loss affects nearly 466 million people worldwide (World Health Organization). The auditory brainstem response (ABR) is the most common non-invasive clinical measure of evoked potentials, e.g.