Service members are often exposed to harmful acoustic environments. Understanding how different acoustic exposures affect auditory processing is critical. We investigated peripheral and subcortical auditory deficits following continuous noise exposure or a single blast exposure to induce mild-to-moderate traumatic brain injury (TBI). Chinchillas were exposed to noise simulating aircraft-carrier for 1, 2, or 4 consecutive weeks (40 h/week at 87.5 dBA) or one single (1) blast at 150 kPa with bilateral hearing protection (earplugs). We employed a multi-metric auditory framework including tympanometry, wide-band middle-ear muscle reflex (WB-MEMR), otoacoustic emissions (DPOAE, SFOAE, and TEOAE), auditory brainstem response (ABR), and envelope following response (EFR) to amplitude-modulated stimuli. Noise: Reduced DPOAEs by 10–15 dB across all frequencies, SFOAEs decreased by 5–20 dB at mid-to-high frequencies, and WB-MEMR thresholds increased as early as 1 week. ABRs revealed mild-to-moderate threshold shifts across all frequencies (10–30 dB) while EFRs showed degraded temporal encoding. Blast: Subtle effects were observed for DPOAEs, SFOAEs, and WB-MEMR. Although hearing thresholds were mostly unaffected, temporal encoding was degraded as early as 3 days post-exposure followed by hypersensitivity. Both exposures showed mixed TEOAE effects across frequencies. Our study highlights the importance of implementing multi-metric auditory diagnostics.

Abstract Sensorineural hearing loss can result from different pathologies, but the primary diagnostic method is a threshold-based audiogram, which is insensitive to some forms of cochlear dysfunction. Individuals may experience difficulty understanding speech in noise despite normal audiometric thresholds. Because most cochlear insults damage both inner (IHCs) and outer hair cells (OHCs), the contribution of IHC dysfunction to auditory-nerve coding has been difficult to isolate. We used the IHC-selective ototoxicity of carboplatin in chinchillas to examine how IHC dysfunction, with preserved OHC function, affects temporal-envelope coding in auditory-nerve fibers (ANFs). Carboplatin produced 10–20% IHC loss with stereocilia damage in surviving IHCs, while OHC-dependent measures such as DPOAEs and ANF thresholds were unchanged. Suprathreshold ABR wave 1 was reduced, whereas wave 5 was preserved, suggesting central compensation. Both spontaneous and driven firing rates decreased following exposure. Mean vector strength to amplitude-modulated tones was unchanged, but response variability increased. Neurometric analysis (d′) and mutual information showed degraded AM detection in carboplatin-exposed fibers, an effect accounted for by reduced driven rate (i.e., normalizing spike counts across groups removed the group difference). Background noise degraded AM coding similarly in both groups. Pooled-neurometric modeling showed that population redundancy compensated for impaired fibers in quiet, but not in noise, where carboplatin-exposed pools remained worse. These findings indicate that IHC dysfunction degrades envelope coding by reducing neural output rather than by altering temporal synchrony. This study suggests IHC dysfunction is a phenotype consistent with “hidden hearing loss” (but distinct from cochlear synaptopathy), and motivates suprathreshold clinical assays.

Amplitude modulation (AM) conveys critical temporal cues for speech perception; prior work shows a close link between modulation detection and speech intelligibility. Slow modulation rates (e.g., as in speech envelopes) are critical but are difficult to measure because longer stimulus durations are needed to include sufficient modulation periods. Thus, current neurometric approaches that estimate modulation detection by repeating measurements across several modulation depths are very time intensive. This challenge is particularly pertinent for understanding neural mechanisms underlying modulation coding in hearing-loss conditions, which are directly investigated through single auditory-nerve (AN) fiber recordings in preclinical animal models of sensorineural hearing loss. The limited time available to record from single units is constraining. Similar challenges occur in electrophysiological measures such as envelope following responses (EFRs), where low signal-to-noise ratios require more repetitions and longer acquisition times. To address this challenge, we developed a swept-modulation-depth stimulus, in which modulation depth changes continuously over time. Using AN spike-train data and EFRs in the chinchilla model, we evaluated accuracy and efficiency of this method by comparing thresholds from swept and discrete paradigms. Preliminary results demonstrate accurate and efficient estimation of AM detection thresholds using swept stimuli, improving the practicality of physiological studies of modulation coding.

Restoration of audibility through frequency-specific amplification is central to the clinical management of sensorineural hearing loss (SNHL). Yet, patients often struggle to understand audible speech, especially in noisy environments. These suprathreshold deficits are conventionally attributed to reduced frequency selectivity and to non-peripheral factors. However, our cross-species studies show that damage to cochlear hair cells not only broadens auditory filter “tips” as previously recognized, but can also distort the fundamental tonotopy of the cochlea such that the temporal responses of the base are commandeered by “off-frequency” (i.e., low-frequency) sound fluctuations through overzealous filter “tails.” This effect is especially pronounced with naturalistic stimuli and background noise possessing pink-like spectra, which contain intense low-frequency components alongside softer but informative higher-frequency content. In a chinchilla model of SNHL with noise-induced permanent threshold shifts, single-unit auditory-nerve measurements revealed that hypersensitive tuning-curve tails were the dominant driver of degraded speech envelope coding, even with sound amplifier gains akin to modern hearing aids. In parallel human studies, individuals with mild or moderate SNHL showed hypersensitive tuning-curve tails linked to impaired speech envelope tracking as measured through electroencephalography. Crucially, individual differences in the estimated degree of distorted tonotopy predicted aided speech-in-noise outcomes.

Despite major strides in conceptualizing and modeling the multifaceted nature of suicidal thought and behavior (STB) over the past few decades, the overall predictability of STB has not improved. This may be partly due to the dynamic nature of suicidal ideation (SI), which often fluctuates over hours, yet is largely overlooked in studies. Bolstered by the application and promise of natural language processing (NLP) across the mental health field, efforts toward richer operationalization of acute SI may include analyses on written data that occur alongside changes in SI, thus offering a better understanding of STB as it unfolds. Ecological momentary assessment (EMA) data from 268 participants with major depressive disorder (MDD) were utilized to investigate acute changes in SI. Data consisted of thrice-daily SI severity scores measured through self-report responses to item 9 of the Patient Health Questionnaire mobile version (MPHQ-9) as well as free-form diary text. Using difference scores and probability of acute change thresholds, eleven acute SI phase trajectory types were defined to label change in SI over three consecutive EMAs. In total, 5,938 acute SI trajectories were paired with the temporally centered diary entries. The Sentiment Analysis and Cognition Engine (SEANCE) tool was applied to quantify the written content of each diary entry across eight established lexica. Entry results were grouped based on phase trajectory type, and the Kruskal-Wallis test was employed with post-hoc multiple hypothesis correction to statistically compare SEANCE features between all group pairs. There were 131 statistically significant (adjusted p-value < 0.05) pairwise differences between acute SI phase trajectory groups, implicating 31 NLP features. Consistent with the literature, results highlighted qualities of writing that are generally associated with heightened SI, including personal pronoun usage, passivity, and negative valence. Patterns of significance also uncovered novel contextual nuance in terms of how characteristics such as verbosity, hostility, anger, and pleasantness present in relation to SI over short change trajectories. This work provides an accessible exploratory framework that capitalizes on the benefits of dense EMA sampling and NLP to profile and quantify acute SI trajectories. The use of the MPHQ’s item 9 to quantify SI is an important limitation as it is designed to also capture precursory SI, passive SI, and SI-adjacent behaviors, potentially overestimating the SI expressed by participants. Nonetheless, future research should continue to focus on short timeframes as there are likely important signals and interpretative nuances to SI expression that have yet to be fully detailed.

Spectrotemporal-modulation (STM) sensitivity is a strong predictor of speech recognition in noise, yet the neural mechanisms underlying this predictive power remain unclear. We investigate how the peripheral auditory system encodes STM stimuli by analyzing single-unit responses from chinchilla auditory-nerve (AN) fibers and simulating responses using a computational AN model. A central question is which cues listeners rely on when listening to this stimulus and how these cues are preserved or degraded in the auditory periphery following various forms of sensorineural hearing loss (SNHL). We analyze spike-train data to quantify envelope coding, TFS coding, and short-term place coding. To overcome the limitations of CF sampling in physiological recordings, we apply the Spectro-Temporal Manipulation Procedure (STMP), which simulates a population response while recording from a single unit by varying the stimulus sampling rate. Species-specific stimulus design is also explored, as chinchillas have broader cochlear tuning than humans. The computational model supports experimental design and parameter selection, allowing for a broader exploration of STM parameters than is practical experimentally. Preliminary modeling and physiology guide future efforts to understand how various SNHL subtypes affect STM coding and how those effects may explain the predictive power of STM sensitivity for speech-in-noise perception in individual listeners.

Even with prescriptive amplification, individuals with sensorineural hearing loss (SNHL) exhibit large variations in their ability to understand speech in noisy settings. Psychophysical assessments of sensitivity to spectro-temporal modulations (STM) have emerged as language-independent measures of suprathreshold auditory fidelity capable of predicting aided speech-in-noise (SPIN) performance. However, the physiological mechanisms underpinning this predictive power remain unclear. Emerging cross-species evidence suggests that SNHL not only broadens auditory filter “tips,” as traditionally recognized, but also distorts the fundamental tonotopicity of the cochlea. Specifically, the temporal response of any given cochlear section (especially in the basal half) can be commandeered by tonotopically “wrong” lower-frequency stimulus components. Furthermore, chinchilla models show that the degree of distorted tonotopy varies according to the specific cochlear pathology underlying the SNHL. Consistent with chinchilla data, human listeners with similar audiograms show substantial variability in suprathreshold tuning curve tip-to-tail ratios and corresponding electroencephalographic estimates of distorted tonotopy. The current study tests whether individual differences in markers of distorted tonotopy predict individual variability in STM sensitivity and SPIN performance. Preliminary data support the hypothesis that distorted tonotopy is a likely contributor to individual variations in STM sensitivity and SPIN outcomes.

In recent years, large language models (LLMs), including ChatGPT, have exponentially grown in application. Given existing barriers to mental health services, alongside the capability of LLMs to generate therapeutic responses, LLMs have potential to serve as accessible precursors, adjuncts, or alternatives to traditional therapy. However, little is known about the opinions of persons who have used LLMs for mental health-related problems. Thus, the current work investigated the positive and negative experiences of those who have interacted with ChatGPT for their mental health using posts from relevant Reddit threads (N = 1594). A 33-item coding scheme was applied to code the presence/absence of each item, and coded posts were modeled using an Ising network graph to explore pairwise and groupwise thematic associations of items. Results from the qualitative coding indicated that the codes representing positive sentiment, experiencing affect/emotion, and attaining personal benefit from using ChatGPT for therapy were among the most frequently coded. Moreover, the Ising network model revealed the most important associations were between items representing positive experiences with ChatGPT (e.g., it performs better than a human therapist in some way and is empathetic), negative experiences (e.g., restrictions worsened their mental health), and specific pathologies (e.g., used it for both anxiety and stress). In addition, the node representing ChatGPT as functioning well as a therapist was the most central node in the network Taken together, the current study indicates that, while users endorse several benefits from using ChatGPT for their mental health, they also report significant drawbacks, including restrictions by ChatGPT that can harm them or exacerbate their symptoms. Thus, ChatGPT may be a useful tool for persons not able to receive standard care, but it may not be as beneficial for persons with more severe pathologies or mental health concerns.

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. Methods: Participants from the 2011-2014 National Health and Nutrition Examination Survey (N = 8367; N de- pressed = 762) with one-week of passively-collected, wrist worn actigraphy data and self-reported questionnaires assessing PA, sedentary, and sleep behaviors were analyzed. Results: Three negative binomial models investigated the effects of group, measurement type, and their interaction on PA, sedentary, and sleep behaviors. Individuals with depressive symptoms exhibited lower PA and sleep than individuals without depressive symptoms but did not differ in sedentary behaviors. Measurement type differed across all models: self-reported PA and sleep were lower, and self-reported sedentary behaviors were greater, than objective measurements. The interaction was significant only for PA; whereas objective PA was greater than subjective measurements for all individuals, the difference was far greater for individuals with depressive symptoms. Limitations: The absence of a clinically depressed sample and current manner of assessing subjective and objective measures may limit our generalizability and conclusions. Conclusion: Our study highlights discrepancies in objective and subjective reports across domains and emphasizes the importance of incorporating objective measurements to improve psychopathology assessment.

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. Individuals with MDD (N = 258) completed ecological momentary assessments three times a day (including the Patient Health Questionnaire-9 [PHQ-9] and a free-text diary entry) and weekly ecological momentary assessments (including a free-text response to a life events prompt) over a 90-day study period. Using natural language processing techniques, we generated 20 model features to detect and predict averages of and changes in weekly depression from diary entries. Four regression models detected and predicted total PHQ-9 and changes in PHQ-9, and two classification models detected and predicted moderate to severe depression. The models classified current (area under the receiver operating curve [AUC] = 0.68) and future depression (AUC = 0.63), and suggest that lower valence increased usage of "I"/"me"/"my," and lower valence of passages with "I"/"me" as the subject, influenced model predictions toward more severe depression, supporting prior research. These findings highlight that depressed individuals use less positive and more negative words when referring to themselves. Treatments targeting positive affect and digital interventions with written components may be beneficial for targeting MDD. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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. Moreover, understanding the detrimental or protective nature of positive emotion regulation on anhedonia can aid with future anhedonia-focused treatments. Therefore, the current study examined the temporal association between anhedonia dynamics and two different emotion rumination strategies in response to positive emotions: dampening and positive rumination. Depressed persons (N = 137) completed baseline measures of positive emotion regulation, difficulties regulating negative emotions, and anxiety, and completed ecological momentary assessments three times per day for 90 days regarding their depressive symptoms, including anhedonia. We assessed baseline dampening and amplifying scores to predict anhedonia dynamics through four linear models with interactions. Providing partial support for our hypotheses, results indicate that amplifying positivity is positively associated with fluctuations, instability, and acute changes in anhedonia over the course of 90 days; however, neither dampening, difficulties regulating negative emotions, nor anxiety were related to anhedonia dynamics. The current findings suggest that amplifying positivity may be able to predict changes in anhedonia over time and should further be examined as a potential protective factor of anhedonia.

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 current study used data collected from ecological momentary assessment (EMA) in a sample with MDD (N=55) and employed mixed effects models to detect and predict weekly and daily CU from anhedonia and depressed mood over 90 days. Results indicated that anhedonia and depressed mood were significantly associated with CU, yet varied at daily and weekly scales. Moreover, these associations varied in both strength and directionality. In weekly models, less anhedonia and greater depressed mood were associated with greater CU, and directionality of associations were reversed in the models looking at any CU (compared to none). Findings provide evidence that anhedonia and depressed mood demonstrate complex associations with CU and emphasize leveraging EMA-based studies to understand these associations with more fine-grained detail.

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.

Mental health disorders—including depression, anxiety, trauma-related, and psychotic conditions—are pervasive and impairing, representing considerable challenges for both individual well-being and public health. Often the first challenges to treatment include financial, geographic, and stigmatic barriers, which limit the accessibility of traditional assessment measures. Further, compounded by frequent misdiagnosis or delayed detection, there is a need for effective, accessible, and scalable approaches to identification and management. Considering advances in computing and the ubiquitous nature of personal mobile and wearable technology, this narrative review examines the utilization of passive sensor data as a screening and diagnostic tool for mental disorders. As an alternative to traditional screening measures, passive sensing offers a tool to overcome barriers that prevent many from seeking services. We critically assess the literature up to September 2023, exploring the use of passive data—such as heart rate variability, movement patterns, and geolocation—to predict mental health outcomes across a spectrum of disorders. From a translational perspective, our review explores the state of passive sensing science, with special emphasis on the capacity for the science to be implemented in real world clinical and general populations, a novelty specific to this review to the best of our knowledge. Toward this aim, we consider multiple study factors, including participant demographics, data collection methods, sensor modalities, outcome measures, and analytic modeling approaches. We find that passive sensing features, such as GPS, heart rate, and actigraphy offer promise for enhancing early detection and improving the diagnostic process for mental disorders. Despite this promise, however, our findings highlight important limitations in passive sensing research including (1) a trend toward smaller, specialized samples, (2) a predominance of data collection apps built on the Android operating system, and (3) a reliance on self-reported measures as proxies for important clinical outcomes. These limitations ultimately stymie efforts to implement and scale important research findings in larger and more heterogeneous populations. With future translational research in mind, we emphasize the importance of validating passive sensing findings with larger, more diverse samples and ensuring assessment tools can be deployed across multiple device types and operating systems. Further, where possible, we emphasize the need for robust, objectively validated outcome measures, such as by clinician assessment. We conclude that careful consideration of translational factors in the design of future research will aid in enhancing the impact of future passive sensing studies, ultimately enhancing mental health outcomes on a broad scale.

The medial olivocochlear (MOC) efferent system is less explored than the ascending auditory pathway but likely contributes in important ways to neural coding and perception. These effects are thought to vary across stimulus configurations, anesthetic states, and subtypes of sensorineural hearing loss (SNHL). To explore effective assays of MOC effects on neural coding, we have recorded Interleaved otoacoustic emissions (OAEs) and envelope following responses (EFRs) from several pre-clinical SNHL chinchilla models. Our preliminary observations include increases in OAEs with inner-hair-cell loss and anesthesia, which may be due to reduced efferent strength. Additionally, we observed enhanced EFRs with an added noise masker, which also could be related to efferent effects. We are using sedated and awake OAE comparisons to develop a standard efferent assay for use in neural-coding studies. Interleaved recording of OAEs and EFRs track cochlear-gain and neural-coding changes during acoustic stimuli. A recently developed modeling framework (Farhadi et al., 2023 JASA) that includes different MOC projection pathways, including midbrain modulation-sensitive inputs, is used to guide most-effective stimulus selection. Ultimately, this model-guided experimental framework will provide unique guidance for testing MOC hypotheses related to neural coding.

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. Methods The study sample included ∼508 thousand Bing users with searches for one of six SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline) from April-December 2022, comprising 510 million queries. Cox proportional hazard models were employed to examine 30 topics (e.g., shopping, tourism, health) and 195 health symptoms (e.g., anxiety, weight gain, impotence), using each SSRI as a reference. We assessed the relative hazard ratios between drugs and, where feasible, ranked the SSRIs based on their observed effects. We used Cox proportional hazard models in order to account for both the likelihood of users searching for a particular topic or symptom and the associated time to that search. The temporal aspect aided in distinguishing between potential symptoms of the disorder, short-term medication side effects, and later appearing side effects. Results Differences were found in search behaviors associated with each SSRI. E.g., fluvoxamine was associated with a significantly higher likelihood of searching weight gain compared to all other SSRIs (HRs 1.85-2.93). Searches following citalopram were associated with significantly higher rates of later impotence queries compared to all other SSRIs (HRs 5.11-7.76), except fluvoxamine. Fluvoxamine was associated with a significantly higher rate of health related searches than all other SSRIs (HRs 2.11-2.36). Conclusions Our study reveals new insights into the varying SSRI impacts, suggesting distinct symptom profiles. This novel use of large-scale, naturalistic search data contributes to pharmacovigilance efforts, enhancing our understanding of intra-class variation among SSRIs, potentially uncovering previously unidentified drug effects.

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.

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).