Developmental Regulation of Matrix Metalloproteinases in Response to Multifactorial, Severe Traumatic Brain Injuries during Immaturity.

INTRODUCTION: A striking pattern in young children after severe TBI is when the entire cortical ribbon displays tissue damage: hemispheric hypodensity (HH). HH is often a result of abusive head trauma (AHT). We previously reported a model of HH in a gyrencephalic species where a combination of injuries consisting of (1) cortical impact, (2) midline shift, (3) subdural hematoma/subarachnoid hemorrhage, (4) traumatic seizures, and (5) brief apnea and hypoventilation resulted in extensive, hypoxic-ischemic-type injury. Importantly, this mechanism closely resembles that seen in children, with relative sparing of the contralateral cortex, thus ruling out a pure asphyxia mechanism. In this model, piglets of similar developmental stage to human toddlers (postnatal day 30, PND30) have extensive hypoxic-ischemic damage to the cortical ribbon with sparing of the contralateral hemisphere and deep gray matter areas. However, piglets of similar developmental stage to human infants (postnatal day 7, PND7) have less hypoxic-ischemic damage that is notably bilateral and patchy. We therefore sought to discover whether the extensive tissue damage observed in PND30 was due to a greater upregulation of matrix metalloproteinases (MMPs). MATERIALS AND METHODS: In PND7 or PND30 piglets receiving AHT injuries (cortical impact, midline shift, subdural hematoma/subarachnoid hemorrhage, traumatic seizures, and brief apnea and hypoventilation) or a sham injury, the pattern of albumin extravasation and MMP-9 upregulation throughout the brain was determined via immunohistochemistry, brain tissue adjacent to the cortical impact where the tissue damage spreads was collected for Western blots, and the gelatinase activity was determined over time in peripheral plasma. EEG was recorded, and piglets survived up to 24 h after injury administration. RESULTS: The pattern of albumin extravasation, indicating vasogenic edema, as well as increase in MMP-9, were both present at the same areas of hypoxic-ischemic tissue damage. Evidence from immunohistochemistry, Western blot, and zymogens demonstrate that MMP-2, -3, or -9 are constitutively expressed during immaturity and are not different between developmental stages; however, active forms are upregulated in PND30 but not PND7 after in response to AHT model injuries. Furthermore, peripheral active MMP-9 was downregulated after model injuries in PND7. CONCLUSIONS: This differential response to AHT model injuries might confer protection to the PND7 brain. Additionally, we find that immature gyrencephalic species have a greater baseline and array of MMPs than previously demonstrated in rodent species. Treatment with an oral or intravenous broad-spectrum matrix metalloproteinase inhibitor might reduce the extensive spread of injury in PND30, but the exposure to metalloproteinase inhibitors must be acute as to not interfere with the homeostatic role of matrix metalloproteinases in normal postnatal brain development and plasticity as well as post-injury synaptogenesis and tissue repair.

Evaluating the Impact of Mask Mandates and Political Party Affiliation on Mental Health Internet Search Behavior in the United States During the COVID-19 Pandemic: Generalized Additive Mixed Model Framework.

BACKGROUND: The impacts of the COVID-19 pandemic on mental health worldwide and in the United States have been well documented. However, there is limited research examining the long-term effects of the pandemic on mental health, particularly in relation to pervasive policies such as statewide mask mandates and political party affiliation. OBJECTIVE: The goal of this study was to examine whether statewide mask mandates and political party affiliations yielded differential changes in mental health symptoms across the United States by leveraging state-specific internet search query data. METHODS: This study leveraged Google search queries from March 24, 2020, to March 29, 2021, in each of the 50 states in the United States. Of the 50 states, 39 implemented statewide mask mandates-with 16 of these states being Republican-to combat the spread of COVID-19. This study investigated whether mask mandates were associated differentially with mental health in states with and without mandates by exploring variations in mental health search queries across the United States. In addition, political party affiliation was examined as a potential covariate to determine whether mask mandates had differential associations with mental health in Republican and Democratic states. Generalized additive mixed models were implemented to model associations among mask mandates, political party affiliation, and mental health search volume for up to 7 months following the implementation of a mask mandate. RESULTS: The results of generalized additive mixed models revealed that search volume for "restless" significantly increased following a mask mandate across all states, whereas the search volume for "irritable" and "anxiety" increased and decreased, respectively, following a mandate for Republican states in comparison with Democratic states. Most mental health search terms did not exhibit significant changes in search volume in relation to mask mandate implementation. CONCLUSIONS: These findings suggest that mask mandates were associated nonlinearly with significant changes in mental health search behavior, with the most notable associations occurring in anxiety-related search terms. Therefore, policy makers should consider monitoring and providing additional support for these mental health symptoms following the implementation of public health-related mandates such as mask mandates. Nevertheless, these results do not provide evidence for an overwhelming impact of mask mandates on population-level mental health in the United States.

Sidelobe Suppression Techniques for Near-Field Multistatic SAR.

Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery.

Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect.

BACKGROUND: The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. OBJECTIVE: Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. METHODS: Using COVID-19-related news headlines from a database of online news stories in conjunction with mental health-related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. RESULTS: Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA ß=-.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA ß=.221; P<.001) contributing generally to an increase in online mental health search term frequency. CONCLUSIONS: This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health-related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.

A perfect storm: The distribution of tissue damage depends on seizure duration, hemorrhage, and developmental stage in a gyrencephalic, multi-factorial, severe traumatic brain injury model.

The pathophysiology of extensive cortical tissue destruction observed in hemispheric hypodensity, a severe type of brain injury observed in young children, is unknown. Here, we utilize our unique, large animal model of hemispheric hypodensity with multifactorial injuries and insults to understand the pathophysiology of this severe type of traumatic brain injury, testing the effect of different stages of development. Piglets developmentally similar to human infants (1 week old, "infants") and toddlers (1 month old, "toddlers") underwent injuries and insults scaled to brain volume: cortical impact, creation of mass effect, placement of a subdural hematoma, seizure induction, apnea, and hypoventilation or a sham injury while anesthetized with a seizure-permissive regimen. Piglets receiving model injuries required overnight intensive care. Hemispheres were evaluated for damage via histopathology. The pattern of damage was related to seizure duration and hemorrhage pattern in "toddlers" resulting in a unilateral hemispheric pattern of damage ipsilateral to the injuries with sparing of the deep brain regions and the contralateral hemisphere. While "infants" had the equivalent duration of seizures as "toddlers", damage was less than "toddlers", not correlated to seizure duration, and was bilateral and patchy as is often observed in human infants. Subdural hemorrhagewas associate with adjacent focal subarachnoid hemorrhage. The percentage of the hemisphere covered with subarachnoid hemorrhage was positively correlated with damage in both developmental stages. In "infants", hemorrhage over the cortex was associated with damage to the cortex with sparing of the deep gray matter regions; without hemorrhage, damage was directed to the hippocampus and the cortex was spared. "Infants" had lower neurologic scores than "toddlers". This multifactorial model of severe brain injury caused unilateral, wide-spread destruction of the cortex in piglets developmentally similar to toddlers where both seizure duration and hemorrhage covering the brain were positively correlated to tissue destruction. Inherent developmental differences may affect how the brain responds to seizure, and thus, affects the extent and pattern of damage. Study into specifically how the "infant" brain is resistant to the effects of seizure is currently underway and may identify potential therapeutic targets that may reduce evolution of tissue damage after severe traumatic brain injury.

Associations between advertisement-supported media exposure and dietary quality among preschool-age children.

Foods of low nutritional quality are heavily marketed to children, and exposure to food ads shapes children's preferences and intake towards advertised foods. Whether food ad exposure independently relates to an overall lower diet quality among children remains unclear. We examined the association between ad-supported media use, a proxy for food ad exposure, and diet quality using the baseline data (2014-2015) from 535 3-5-year-olds in a community-based cohort study. Parents reported their child's dietary intake over 3 days via a diary, and diet quality was assessed with the Healthy Eating Index (HEI-2015) where higher scores reflect greater adherence to USDA dietary guidelines. Children's media exposure was measured through online parent surveys. Mean HEI score was 54.5 (SD = 9.4). In models adjusted for sociodemographic characteristics and metrics of parent diet quality, children's HEI scores were 0.5 points lower (adjusted beta = -0.5 [95% CI: 0.8, -0.1]; P < 0.01) for each 1-h increment in weekly viewing of ad-supported children's TV networks. Children's use of media that may have food ads (e.g., apps, online games) also related to a lower diet quality yet to a lesser extent (adjusted beta -0.2 [-0.2, -0.1]; P < 0.01). In contrast, children's ad-free media use was not associated with diet quality (P = 0.21). Findings support the premise that exposure to food advertisements via media may result in a lower quality diet among children independently of other risk factors.

Impaired cardiac energy metabolism in embryos lacking adrenergic stimulation.

As development proceeds from the embryonic to fetal stages, cardiac energy demands increase substantially, and oxidative phosphorylation of ADP to ATP in mitochondria becomes vital. Relatively little, however, is known about the signaling mechanisms regulating the transition from anaerobic to aerobic metabolism that occurs during the embryonic period. The main objective of this study was to test the hypothesis that adrenergic hormones provide critical stimulation of energy metabolism during embryonic/fetal development. We examined ATP and ADP concentrations in mouse embryos lacking adrenergic hormones due to targeted disruption of the essential dopamine ß-hydroxylase (Dbh) gene. Embryonic ATP concentrations decreased dramatically, whereas ADP concentrations rose such that the ATP/ADP ratio in the adrenergic-deficient group was nearly 50-fold less than that found in littermate controls by embryonic day 11.5. We also found that cardiac extracellular acidification and oxygen consumption rates were significantly decreased, and mitochondria were significantly larger and more branched in adrenergic-deficient hearts. Notably, however, the mitochondria were intact with well-formed cristae, and there was no significant difference observed in mitochondrial membrane potential. Maternal administration of the adrenergic receptor agonists isoproterenol or l-phenylephrine significantly ameliorated the decreases in ATP observed in Dbh-/- embryos, suggesting that α- and ß-adrenergic receptors were effective modulators of ATP concentrations in mouse embryos in vivo. These data demonstrate that adrenergic hormones stimulate cardiac energy metabolism during a critical period of embryonic development.

Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach.

Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is likely to benefit from web-based PPIs, in order to triage persons who may not benefit from other interventions. In the current study, we used machine learning to predict individual response to a web-based PPI, in order to investigate baseline prognostic indicators of likelihood of response (N = 120). Our models demonstrated moderate correlations (happiness: r Test = 0.30 ± 0.09; depressive symptoms: r Test = 0.39 ± 0.06), indicating that baseline features can predict changes in happiness and depressive symptoms at a 6-month follow-up. Thus, machine learning can be used to predict outcome changes from a web-based PPI and has important clinical implications for matching individuals to PPIs based on their individual characteristics.

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.

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.e., moderate depressive symptoms or greater) and (b) general population. Using data from the National Health and Nutrition Examination Survey, a large nationally representative sample (N = 8,378), we employed a convolutional neural network to determine which depressive symptoms in each population could be detected by wrist-worn, minute-level actigraphy data. Findings indicated a small-moderate correspondence between the predictions and observed outcomes for mood, psychomotor, and suicide items (area under the receiver operating characteristic curve [AUCs] = 0.58-0.61); a moderate-large correspondence for anhedonia (AUC = 0.64); and a large correspondence for fatigue (AUC = 0.74) in the clinical subpopulation (n = 766); and a small-moderate correspondence for sleep, appetite, psychomotor, and suicide items (AUCs = 0.56-0.60) in the general population (n = 8,378). Thus, individual depressive symptoms can be detected in individuals who likely meet the criteria for MDD, suggesting that wrist-worn actigraphy may be suitable for passively assessing these symptoms, providing important clinical implications for the diagnosis and treatment of MDD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Plasma per- and polyfluoroalkyl substance mixtures during pregnancy and duration of breastfeeding in the New Hampshire birth cohort study.

BACKGROUND: Prior studies suggest that prenatal per- and polyfluoroalkyl substances (PFAS) exposures are associated with shorter breastfeeding duration. Studies assessing PFAS mixtures and populations in North America are sparse. METHODS: We quantified PFAS concentrations in maternal plasma collected during pregnancy in the New Hampshire Birth Cohort Study (2010-2017). Participants completed standardized breastfeeding surveys at regular intervals until weaning (n = 813). We estimated associations between mixtures of 5 PFAS and risk of stopping exclusive breastfeeding before 6 months or any breastfeeding before 12 months using probit Bayesian kernel machine regression. For individual PFAS, we calculated the relative risk and hazard ratio (HR) of stopping breastfeeding using modified Poisson regression and accelerated failure time models respectively. RESULTS: PFAS mixtures were associated with stopping exclusive breastfeeding before 6 months, primarily driven by perfluorooctanoate (PFOA). We observed statistically significant trends in the association of perfluorohexane sulfonate (PFHxS), PFOA, and perfluorononanoate (PFNA) (p-trends≤0.02) with stopping exclusive breastfeeding. Participants in the highest PFOA quartile had a 28% higher risk of stopping exclusive breastfeeding before 6 months compared to those in the lowest quartile (95% Confidence Interval: 1.04, 1.56). Similar trends were observed for PFHxS and PFNA with exclusive breastfeeding (p-trends≤0.05). PFAS were not associated with stopping any breastfeeding before 12 months. CONCLUSIONS: In this cohort, we observed that participants with greater overall plasma PFAS concentrations had greater risk of stopping exclusive breastfeeding before 6 months and associations were driven largely by PFOA. These findings further support the growing literature indicating that PFAS may be associated with shorter duration of breastfeeding.

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).

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: "I have felt down, depressed, or hopeless". Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.

The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults.

BACKGROUND: Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective states in MDD and BPD, with BPD characterized by instability, we expected heightened instability of MDD symptoms among depressed adults with BPD traits. The current study examined whether BPD symptoms predicted instability in depression symptoms, measured by ecological momentary assessments (EMAs). METHODS: The sample included 207 adults with MDD (76 % White, 82 % women) recruited from across the United States. At the start of the study, participants completed a battery of mental health screens including BPD severity and neuroticism. Participants completed EMAs tracking their depression symptoms three times a day over a 90-day period. RESULTS: Using self-report scores assessing borderline personality disorder (BPD) traits along with neuroticism scores and sociodemographic data, Bayesian and frequentist linear regression models consistently indicated that BPD severity was not associated with depression symptom change through time. LIMITATIONS: Diagnostic sensitivity and specificity may be restricted by use of a self-report screening tool for capturing BPD severity. Additionally, this clinical sample of depressed adults lacks a comparison group to determine whether subclinical depressive symptoms present differently among individuals with BPD only. CONCLUSIONS: The unexpected findings shed light on the interplay between these disorders, emphasizing the need for further research to understand their association.

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 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.

Depression deconstructed: Wearables and passive digital phenotyping for analyzing individual symptoms.

Wearable technology enables unobtrusive collection of longitudinally dense data, allowing for continuous monitoring of physiology and behavior. These digital phenotypes, or device-based indicators, are frequently leveraged to study depression. However, they are usually considered alongside questionnaire sum-scores which collapse the symptomatic gamut into a general representation of severity. To explore the contributions of passive sensing streams more precisely, associations of nine passive sensing-derived features with self-report responses to Center for Epidemiologic Studies Depression (CES-D) items were modeled. Using data from the NetHealth study on N=469 college students, this work generated mixed ordinal logistic regression models to summarize contributions of pulse, movement, and sleep data to depression symptom detection. Emphasizing the importance of the college context, wearable features displayed unique and complementary properties in their heterogeneously significant associations with CES-D items. This work provides conceptual and exploratory blueprints for a reductionist approach to modeling depression within passive sensing research.

Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention.

OBJECTIVE: Generalized anxiety disorder (GAD) is a prevalent mental health disorder that often goes untreated. A core aspect of GAD is worry, which is associated with negative health outcomes, accentuating a need for simple treatments for worry. The present study leveraged pretreatment individual differences to predict personalized treatment response to a digital intervention. METHODS: Linear mixed-effect models were used to model changes in daytime and nighttime worry duration and frequency for 163 participants who completed a six-day worry postponement intervention. Ensemble-based machine learning regression and classification models were implemented to predict changes in worry across the intervention. Model feature importance was derived using SHapley Additive exPlanation (SHAP). RESULTS: Moderate predictive performance was obtained for predicting changes in daytime worry duration (test r2 = 0.221, AUC = 0.77) and nighttime worry frequency (test r2 = 0.164, AUC = 0.72), while poor predictive performance was obtained for nighttime worry duration and daytime worry frequency. Baseline levels of worry and subjective health complaints were most important in driving model predictions. LIMITATIONS: A complete-case analysis was leveraged to analyze the present data, which was collected from participants that were Dutch and majority female. CONCLUSIONS: This study suggests that treatment response to a digital intervention for GAD can be accurately predicted using baseline characteristics. Particularly, this worry postponement intervention may be most beneficial for individuals with high baseline worry but fewer subjective health complaints. The present findings highlight the complexities of and need for further research into daily worry dynamics and the personalizable utility of digital interventions.

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.

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.