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. Accordingly, this work investigated the association of screen completion with mental health-related search behaviors. Three-year internet search histories from N=7,572 Microsoft Bing users were paired with their respective depression, anxiety, bipolar disorder, or psychosis online screen completion and sociodemographic data available through Mental Health America. Data was transformed into network representations to model queries as discrete steps with probabilities and times-to-transition from one search type to another. Search data subsequent to screen completion was also modeled using Markov chains to simulate likelihood trajectories of different search types through time. Differences in querying dynamics relative to screen completion were observed, with searches involving treatment, diagnosis, suicidal ideation, and suicidal intent commonly emerging as the highest probability behavioral information seeking endpoints. Moreover, results pointed to the association of low risk states of psychopathology with transitions to extreme clinical outcomes (i.e., active suicidal intent). Future research is required to draw definitive conclusions regarding causal relationships between screens and search behavior.

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., source segregation). In normal-hearing listeners, temporal coherence of sound fluctuations across frequency channels supports this process by promoting grouping of elements belonging to a single acoustic source. We hypothesized that reduced spectral resolution-a hallmark of both electric/CI (from current spread) and acoustic (from broadened tuning) hearing with sensorineural hearing loss-degrades segregation based on temporal coherence. This is because reduced frequency resolution decreases the likelihood that a single sound source dominates the activity driving any specific channel; concomitantly, it increases the correlation in activity across channels. Consistent with our hypothesis, predictions from a physiologically plausible model of temporal-coherence-based segregation suggest that CI current spread reduces comodulation masking release (CMR; a correlate of temporal-coherence processing) and speech intelligibility in noise. These predictions are consistent with our behavioral data with simulated CI listening. Our model also predicts smaller CMR with increasing levels of outer-hair-cell damage. These results suggest that reduced spectral resolution relative to normal hearing impairs temporal-coherence-based segregation and speech-in-noise outcomes.

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). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.

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. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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. METHODS: To improve the estimation of cochlear frequency selectivity based on the CAP, we introduce a convolution model to fit forward-masked CAP waveforms. The model generates masking patterns that, when convolved with a unitary response, can predict the masking of the CAP waveform induced by Gaussian noise maskers. Model parameters, including those characterizing frequency selectivity, are fine-tuned by minimizing waveform prediction errors across numerous masking conditions, yielding robust estimates. RESULTS: The method was applied to click-evoked CAPs at the round window of anesthetized chinchillas using notched-noise maskers with various notch widths and attenuations. The estimated quality factor Q10 as a function of center frequency is shown to closely match the average quality factor obtained from AN fiber tuning curves, without the need for an empirical correction factor. CONCLUSION: This study establishes a moderately invasive method for estimating cochlear frequency selectivity with potential applicability to other animal species or humans. Beyond the estimation of frequency selectivity, the proposed model proved to be remarkably accurate in fitting forward-masked CAP responses and could be extended to study more complex aspects of cochlear signal processing (e.g., compressive nonlinearities).

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. We posit that understanding the relationship between objective data streams and individual depression symptoms is important for understanding the considerable heterogeneity in MDD. Thus, we conduct a comprehensive comparative study to explore the ability of machine learning to predict nine self-reported depressive symptoms with call and text logs. We created time series from the logs of over 300 participants by aggregating communication attributes- average length, count, or contacts- every 4, 6, 12, or 24 hours. We were most successful predicting movement irregularities with a balanced accuracy of 0.70. Further, we predicted suicidal ideation with a balanced accuracy of 0.67. Outgoing texts proved to be the most useful log type. This study provides valuable insights for future mobile health research aimed at personalizing assessment and intervention for MDD.

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. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting 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. Potential cases of PANS and PANDAS were identified by including children age 3-12 years at the time they received one of five new diagnoses: avoidant/restrictive food intake disorder, other specified eating disorder, separation anxiety disorder of childhood, obsessive-compulsive disorder, or other specified disorders involving an immune mechanism, not elsewhere classified. Tic disorders was not used as a diagnostic code to identify cases. Data were abstracted; cases were classified as PANDAS or PANS if standard definitions were met. Results: The combined study population consisted of 95,498 individuals. The majority were non-Hispanic Caucasian (85%), 48% were female and the mean age was 7.1 (SD 3.1) years. Of 357 potential cases, there were 13 actual cases [mean age was 6.0 (SD 1.8) years, 46% female and 100% non-Hispanic Caucasian]. The estimated annual incidence of PANDAS/PANS was 1/11,765 for children between 3 and 12 years with some variation between different geographic areas. Conclusion: Our results indicate that PANDAS/PANS is a rare disorder with substantial heterogeneity across geography and time. A prospective investigation of the same question is warranted.

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., as an objective measure for universal newborn hearing screening. In research, the ABR is widely used for estimating hearing thresholds and cochlear synaptopathy in animal models of hearing loss. The ABR contains multiple waves representing neural activity across different peripheral auditory pathway stages, which arise within the first 10 ms after stimulus onset. Multi-channel (e.g., 32 or higher) caps provide robust measures for a wide variety of EEG applications for the study of human hearing. However, translational studies using preclinical animal models typically rely on only a few subdermal electrodes. NEW METHOD: We evaluated the feasibility of a 32-channel rodent EEG mini-cap for improving the reliability of ABR measures in chinchillas, a common model of human hearing. RESULTS: After confirming initial feasibility, a systematic experimental design tested five potential sources of variability inherent to the mini-cap methodology. We found each source of variance minimally affected mini-cap ABR waveform morphology, thresholds, and wave-1 amplitudes. COMPARISON WITH EXISTING METHOD: The mini-cap methodology was statistically more robust and less variable than the conventional subdermal-needle methodology, most notably when analyzing ABR thresholds. Additionally, fewer repetitions were required to produce a robust ABR response when using the mini-cap. CONCLUSIONS: These results suggest the EEG mini-cap can improve translational studies of peripheral auditory evoked responses. Future work will evaluate the potential of the mini-cap to improve the reliability of more centrally evoked (e.g., cortical) EEG responses.

Coupling Titanium and Chromium Catalysis in a Reaction Network for the Reprogramming of [BH4 ]- as Electron Transfer and Hydrogen Atom Transfer Reagent for Radical Chemistry.

We describe a unique catalytic system with an efficient coupling of Ti- and Cr-catalysis in a reaction network that allows the use of [BH4 ]- as stoichiometric hydrogen atom and electron donor in catalytic radical chemistry. The key feature is a relay hydrogen atom transfer from [BH4 ]- to Cr generating the active catalysts under mild conditions. This enables epoxide reductions, regiodivergent epoxide opening and radical cyclizations that are not possible with cooperative catalysis with radicals or by epoxide reductions via Meinwald rearrangement and ensuing carbonyl reduction. No typical SN 2-type reactivity of [BH4 ]- salts is observed.

Induced alpha and beta electroencephalographic rhythms covary with single-trial speech intelligibility in competition.

Neurophysiological studies suggest that intrinsic brain oscillations influence sensory processing, especially of rhythmic stimuli like speech. Prior work suggests that brain rhythms may mediate perceptual grouping and selective attention to speech amidst competing sound, as well as more linguistic aspects of speech processing like predictive coding. However, we know of no prior studies that have directly tested, at the single-trial level, whether brain oscillations relate to speech-in-noise outcomes. Here, we combined electroencephalography while simultaneously measuring intelligibility of spoken sentences amidst two different interfering sounds: multi-talker babble or speech-shaped noise. We find that induced parieto-occipital alpha (7-15 Hz; thought to modulate attentional focus) and frontal beta (13-30 Hz; associated with maintenance of the current sensorimotor state and predictive coding) oscillations covary with trial-wise percent-correct scores; importantly, alpha and beta power provide significant independent contributions to predicting single-trial behavioral outcomes. These results can inform models of speech processing and guide noninvasive measures to index different neural processes that together support complex listening.

Testing domain knowledge and risk of bias of a large-scale general artificial intelligence model in mental health.

Background: With a rapidly expanding gap between the need for and availability of mental health care, artificial intelligence (AI) presents a promising, scalable solution to mental health assessment and treatment. Given the novelty and inscrutable nature of such systems, exploratory measures aimed at understanding domain knowledge and potential biases of such systems are necessary for ongoing translational development and future deployment in high-stakes healthcare settings. Methods: We investigated the domain knowledge and demographic bias of a generative, AI model using contrived clinical vignettes with systematically varied demographic features. We used balanced accuracy (BAC) to quantify the model's performance. We used generalized linear mixed-effects models to quantify the relationship between demographic factors and model interpretation. Findings: We found variable model performance across diagnoses; attention deficit hyperactivity disorder, posttraumatic stress disorder, alcohol use disorder, narcissistic personality disorder, binge eating disorder, and generalized anxiety disorder showed high BAC (0.70 ≤ BAC ≤ 0.82); bipolar disorder, bulimia nervosa, barbiturate use disorder, conduct disorder, somatic symptom disorder, benzodiazepine use disorder, LSD use disorder, histrionic personality disorder, and functional neurological symptom disorder showed low BAC (BAC ≤ 0.59). Interpretation: Our findings demonstrate initial promise in the domain knowledge of a large AI model, with performance variability perhaps due to the more salient hallmark symptoms, narrower differential diagnosis, and higher prevalence of some disorders. We found limited evidence of model demographic bias, although we do observe some gender and racial differences in model outcomes mirroring real-world differential prevalence estimates.

Leveraging deep learning models to understand the daily experience of anxiety in teenagers over the course of a year.

INTRODUCTION: Anxiety disorders are a prevalent and severe problem that are often developed early in life and can disrupt the daily lives of affected individuals for many years into adulthood. Given the persistent negative aspects of anxiety, accurate and early assessment is critical for long term outcomes. Currently, the most common method for anxiety assessment is through point-in-time measures like the GAD-7. Unfortunately, this survey and others like it can be subject to recall bias and do not fully capture the variability in an individual's day-to-day symptom experience. The current work aims to evaluate how point-in-time assessments like the GAD-7 relate to daily measurements of anxiety in a teenage population. METHODS: To evaluate this relationship, we leveraged data collected at four separate three week intervals from 30 teenagers (age 15-17) over the course of a year. The specific items of interest were a single item anxiety severity measure collected three times per day and end-of-month GAD-7 assessments. Within this sample, 40 % of individuals reported clinical levels of generalized anxiety disorder symptoms at some point during the study. The first component of analysis was a visual inspection assessing how daily anxiety severity fluctuated around end-of-month reporting via the GAD-7. The second component was a between-subjects comparison assessing whether individuals with similar GAD-7 scores experienced similar symptom dynamics across the month as represented by latent features derived from a deep learning model. With this approach, similarity was operationalized by hierarchical clustering of the latent features. RESULTS: The aim clearly indicated that an individual's daily experience of anxiety varied widely around what was captured by the GAD-7. Additionally, when hierarchical clustering was applied to the three latent features derived from the (LSTM) encoder (r = 0.624 for feature reconstruction), it was clear that individuals with similar GAD-7 outcomes were experiencing different symptom dynamics. Upon further inspection of the latent features, the LSTM model appeared to rely as much on anxiety variability over the course of the month as it did on anxiety severity (p < 0.05 for both mean and RMSSD) to represent an individual's experience. DISCUSSION: This work serves as further evidence for the heterogeneity within the experience of anxiety and that more than just point-in-time assessments are necessary to fully capture an individual's experience.

Induced Alpha And Beta Electroencephalographic Rhythms Covary With Single-Trial Speech Intelligibility In Competition.

Neurophysiological studies suggest that intrinsic brain oscillations influence sensory processing, especially of rhythmic stimuli like speech. Prior work suggests that brain rhythms may mediate perceptual grouping and selective attention to speech amidst competing sound, as well as more linguistic aspects of speech processing like predictive coding. However, we know of no prior studies that have directly tested, at the single-trial level, whether brain oscillations relate to speech-in-noise outcomes. Here, we combined electroencephalography while simultaneously measuring intelligibility of spoken sentences amidst two different interfering sounds: multi-talker babble or speech-shaped noise. We find that induced parieto-occipital alpha (7-15 Hz; thought to modulate attentional focus) and frontal beta (13-30 Hz; associated with maintenance of the current sensorimotor state and predictive coding) oscillations covary with trial-wise percent-correct scores; importantly, alpha and beta power provide significant independent contributions to predicting single-trial behavioral outcomes. These results can inform models of speech processing and guide noninvasive measures to index different neural processes that together support complex listening.

Digitally filling the access gap in mental health care: An investigation of the association between rurality and online engagement with validated self-report screens across the United States.

Mental health disorders are highly prevalent, yet few persons receive access to treatment; this is compounded in rural areas where mental health services are limited. The proliferation of online mental health screening tools are considered a key strategy to increase identification, diagnosis, and treatment of mental illness. However, research on real-world effectiveness, especially in hard to reach rural communities, is limited. Accordingly, the current work seeks to test the hypothesis that online screening use is greater in rural communities with limited mental health resources. The study utilized a national, online, population-based cohort consisting of Microsoft Bing search engine users across 18 months in the United States (representing approximately one-third of all internet searches), in conjunction with user-matched data of completed online mental health screens for anxiety, bipolar, depression, and psychosis (N = 4354) through Mental Health America, a leading non-profit mental health organization in the United States. Rank regression modeling was leveraged to characterize U.S. county-level screen completion rates as a function of rurality, health-care availability, and sociodemographic variables. County-level rurality and mental health care availability alone explained 42% of the variance in MHA screen completion rate (R2 = 0.42, p < 5.0 × 10-6). The results suggested that online screening was more prominent in underserved rural communities, therefore presenting as important tools with which to bridge mental health-care gaps in rural, resource-deficient areas.

A theory of mechanical stress-induced H2O2 signaling waveforms in Planta.

Recent progress in nanotechnology-enabled sensors that can be placed inside of living plants has shown that it is possible to relay and record real-time chemical signaling stimulated by various abiotic and biotic stresses. The mathematical form of the resulting local reactive oxygen species (ROS) wave released upon mechanical perturbation of plant leaves appears to be conserved across a large number of species, and produces a distinct waveform from other stresses including light, heat and pathogen-associated molecular pattern (PAMP)-induced stresses. Herein, we develop a quantitative theory of the local ROS signaling waveform resulting from mechanical stress in planta. We show that nonlinear, autocatalytic production and Fickian diffusion of H2O2 followed by first order decay well describes the spatial and temporal properties of the waveform. The reaction-diffusion system is analyzed in terms of a new approximate solution that we introduce for such problems based on a single term logistic function ansatz. The theory is able to describe experimental ROS waveforms and degradation dynamics such that species-dependent dimensionless wave velocities are revealed, corresponding to subtle changes in higher moments of the waveform through an apparently conserved signaling mechanism overall. This theory has utility in potentially decoding other stress signaling waveforms for light, heat and PAMP-induced stresses that are similarly under investigation. The approximate solution may also find use in applied agricultural sensing, facilitating the connection between measured waveform and plant physiology.

Predicting symptom response and engagement in a digital intervention among individuals with schizophrenia and related psychoses.

Introduction: Despite existing work examining the effectiveness of smartphone digital interventions for schizophrenia at the group level, response to digital treatments is highly variable and requires more research to determine which persons are most likely to benefit from a digital intervention. Materials and methods: The current work utilized data from an open trial of patients with psychosis (N = 38), primarily schizophrenia spectrum disorders, who were treated with a psychosocial intervention using a smartphone app over a one-month period. Using an ensemble of machine learning models, pre-intervention data, app use data, and semi-structured interview data were utilized to predict response to change in symptom scores, engagement patterns, and qualitative impressions of the app. Results: Machine learning models were capable of moderately (r = 0.32-0.39, R2 = 0.10-0.16, MAE norm = 0.13-0.29) predicting interaction and experience with the app, as well as changes in psychosis-related psychopathology. Conclusion: The results suggest that individual smartphone digital intervention engagement is heterogeneous, and symptom-specific baseline data may be predictive of increased engagement and positive qualitative impressions of digital intervention in patients with psychosis. Taken together, interrogating individual response to and engagement with digital-based intervention with machine learning provides increased insight to otherwise ignored nuances of treatment response.

Underlying neural mechanisms of degraded speech intelligibility following noise-induced hearing loss: The importance of distorted tonotopy.

Listeners with sensorineural hearing loss (SNHL) have substantial perceptual deficits, especially in noisy environments. Unfortunately, speech-intelligibility models have limited success in predicting the performance of listeners with hearing loss. A better understanding of the various suprathreshold factors that contribute to neural-coding degradations of speech in noisy conditions will facilitate better modeling and clinical outcomes. Here, we highlight the importance of one physiological factor that has received minimal attention to date, termed distorted tonotopy, which refers to a disruption in the mapping between acoustic frequency and cochlear place that is a hallmark of normal hearing. More so than commonly assumed factors (e.g., threshold elevation, reduced frequency selectivity, diminished temporal coding), distorted tonotopy severely degrades the neural representations of speech (particularly in noise) in single- and across-fiber responses in the auditory nerve following noise-induced hearing loss. Key results include: 1) effects of distorted tonotopy depend on stimulus spectral bandwidth and timbre, 2) distorted tonotopy increases across-fiber correlation and thus reduces information capacity to the brain, and 3) its effects vary across etiologies, which may contribute to individual differences. These results motivate the development and testing of noninvasive measures that can assess the severity of distorted tonotopy in human listeners. The development of such noninvasive measures of distorted tonotopy would advance precision-audiological approaches to improving diagnostics and rehabilitation for listeners with SNHL.

An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia.

INTRODUCTION: Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical categories, they are each heterogenous in their symptom profiles, with considerable transdiagnostic features. Although movement and sleep abnormalities exist in both disorders, little is known of the precise nature of these changes longitudinally. Passively-collected longitudinal data from wearable sensors is well suited to characterize naturalistic features which may cross traditional diagnostic categories (e.g., highlighting behavioral markers not captured by self-report information). METHODS: The present analyses utilized raw minute-level actigraphy data from three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respectively, to interrogate naturalistic behavioral differences between groups. Subjects' week-long actigraphy data was processed without diagnostic labels via unsupervised machine learning clustering methods, in order to investigate the natural bounds of psychopathology. Further, actigraphic data was analyzed across time to determine timepoints influential in model outcomes. RESULTS: We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we found statistically significant inter-group differences, with depressed persons showing the highest behavioral variability. LIMITATIONS: However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information. CONCLUSIONS: Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifically in discriminating between MDD and schizophrenia.

Cross-species experiments reveal widespread cochlear neural damage in normal hearing.

Animal models suggest that cochlear afferent nerve endings may be more vulnerable than sensory hair cells to damage from acoustic overexposure and aging. Because neural degeneration without hair-cell loss cannot be detected in standard clinical audiometry, whether such damage occurs in humans is hotly debated. Here, we address this debate through co-ordinated experiments in at-risk humans and a wild-type chinchilla model. Cochlear neuropathy leads to large and sustained reductions of the wideband middle-ear muscle reflex in chinchillas. Analogously, human wideband reflex measures revealed distinct damage patterns in middle age, and in young individuals with histories of high acoustic exposure. Analysis of an independent large public dataset and additional measurements using clinical equipment corroborated the patterns revealed by our targeted cross-species experiments. Taken together, our results suggest that cochlear neural damage is widespread even in populations with clinically normal hearing.