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Increasing efforts toward the prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. Wearable devices have emerged as a possible solution to aid in this process, but their use in real-life stress detection has not been systematically investigated. We aimed to determine the utility of ecological momentary assessments (EMA) and physiological arousal measured through wearable devices in detecting ecologically relevant stress states. Using EMA combined with wearable biosensors for ecological physiological assessments (EPA), we investigated the impact of an ecological stressor (ie, a high-stakes examination week) on physiological arousal and affect compared to a control week without examinations in first-year medical and biomedical science students (51/83, 61.4% female). We first used generalized linear mixed-effects models with maximal fitting approaches to investigate the impact of examination periods on subjective stress exposure, mood, and physiological arousal. We then used machine learning models to investigate whether we could use EMA, wearable biosensors, or the combination of both to classify momentary data (ie, beeps) as belonging to examination or control weeks. We tested both individualized models using a leave-one-beep-out approach and group-based models using a leave-one-subject-out approach. During stressful high-stakes examination (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal decreased on average during the examination week. Time-resolved analyses revealed peaks in physiological arousal associated with both momentary self-reported stress exposure and self-reported positive affect. Mediation models revealed that the decreased physiological arousal in the examination week was mediated by lower positive affect during the same period. We then used machine learning to show that while individualized EMA outperformed EPA in its ability to classify beeps as originating from examinations or from control weeks (1603/4793, 33.45% and 1648/4565, 36.11% error rates, respectively), a combination of EMA and EPA yields optimal classification (1363/4565, 29.87% error rate). Finally, when comparing individualized models to group-based models, we found that the individualized models significantly outperformed the group-based models across all 3 inputs (EMA, EPA, and the combination). This study underscores the potential of wearable biosensors for stress-related mental health monitoring. However, it emphasizes the necessity of psychological context in interpreting physiological arousal captured by these devices, as arousal can be related to both positive and negative contexts. Moreover, our findings support a personalized approach in which momentary stress is optimally detected when referenced against an individual’s own data.

Depression is characterized by a loss of positive and pronounced negative memory bias, which persists after remission. While theoretical accounts of depressive realism, emotional inertia, and mood-congruency substantiate the compelling evidence of weak positive memory in depression, they cannot fully explain negative memory bias in depression. We used Ecologically Momentary Assessments (EMA) of memory bias to provide insight into the accuracy and depression status-dependency of recall of previous positive and negative mood states. Currently- ( n  = 46), remitted- ( n  = 90), and never-depressed individuals ( n  = 55) provided positive mood and negative mood ratings (7x/day for six days), while also recalling their recent (i.e., previous prompt; 3x/day) or distal (i.e., one day lag; 1x/day) mood states. Currently depressed individuals displayed most accuracy and hence least bias in recall of both positive and negative mood; with accuracy in currently and remitted depressed individuals being independent of their current mood state. Conversely, mood at the time of recall significantly related to memory accuracy among never-depressed individuals with more negative mood, resulting in a depressotypic memory bias. Results are consistent with depressive realism and mood-congruency accounts, as well as with evidence for loss of positive memory bias (but not for negative memory bias) in depression.

Early detection of depression is crucial, yet current assessment methods depend on self-report questionnaires and clinical interviews, which are resource-intensive. Wearable devices provide a scalable way to assess physiological and behavioral features, but their predictive value across clinical and non-clinical populations remains insufficiently established. Wearable-derived features were collected from a student sample ( n  = 187) and an outpatient sample ( n  = 95). Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), and participants were categorized as screen-positive for depressed (≥ 10) or non-depressed (< 10). An elastic net regularized logistic regression model was used for classification, with performance evaluated in held-out test data. Sensitivity analyses controlled for age and bedtime, tested alternative PHQ-9 cutoffs, and comparisons to baseline models with and without wearable features. Across the combined sample ( n  = 282), the model achieved good discriminative performance (area under the curve = 0.82; accuracy = 79%). Sensitivity analyses revealed that sample was a strong predictor, but wearable-derived features still added incremental value. Minimum awake heart rate, variability in sleep duration, and maximum step count emerged as the strongest predictors. Wearable-derived features show promise for detecting depressive symptoms across clinical and non-clinical populations. Sample-specific factors should be considered in future research.

Ecological momentary assessment (EMA) is increasingly recognized as a vital tool for tracking the fluctuating nature of mental states and symptoms in psychiatric research. However, determining the optimal sampling rate - that is, deciding how often participants should be queried to report their symptoms - remains a significant challenge. To address this issue, our study utilizes the Nyquist-Shannon theorem from signal processing, which establishes that any sampling rate more than twice the highest frequency component of a signal is adequate. We applied the Nyquist-Shannon theorem to analyze two EMA datasets on depressive symptoms, encompassing a combined total of 35,452 data points collected over periods ranging from 30 to 90 days per individual. Our analysis of both datasets suggests that the most effective sampling strategy involves measurements at least every other week. We find that measurements at higher frequencies provide valuable and consistent information across both datasets, with significant peaks at weekly and daily intervals. Ideal frequency for measurements remains largely consistent, regardless of the specific symptoms used to estimate depression severity. For conditions in which abrupt or transient symptom dynamics are expected, such as during treatment, more frequent data collection is recommended. However, for regular monitoring, weekly assessments of depressive symptoms may be sufficient. We discuss the implications of our findings for EMA study optimization, address our study's limitations, and outline directions for future research.

The anatomical structure of the thalamus renders its segmentation on 3DT1 images harder due to its low tissue contrast, and not well-defined boundaries. We aimed to investigate the differences in the precision of publicly available segmentation techniques on 3DT1 images acquired at 1.5 T and 3 T machines compared to the thalamic manual segmentation in a pediatric population. Sixty-eight subjects were recruited between the ages of one and 18 years. Manual segmentation of the thalamus was done by three junior raters, and then corrected by an experienced rater. Automated segmentation was then performed with FSL Anat, FIRST, FreeSurfer, MRICloud, and volBrain. A mask of the intersections between the manual and automated segmentation was created for each algorithm to measure the degree of similitude (DICE) with the manual segmentation. The DICE score was shown to be highest using volBrain in all subjects (0.873 ± 0.036), as well as in the 1.5 T (0.871 ± 0.037), and the 3 T (0.875 ± 0.036) groups. FSL-Anat and FIRST came in second and third. MRICloud was shown to have the lowest DICE values. When comparing 1.5 T to 3 T groups, no significant differences were observed in all segmentation methods, except for FIRST (p = 0.038). Age was not a significant predictor of DICE in any of the measurements. When using automated segmentation, the best option in both field strengths would be the use of volBrain. This will achieve results closest to the manual segmentation while reducing the amount of time and computing power needed by researchers.

The endeavor to understand the human brain has seen more progress in the last few decades than in the previous two millennia. Still, our understanding of how the human brain relates to behavior in the real world and how this link is modulated by biological, social, and environmental factors is limited. To address this, we designed the Healthy Brain Study (HBS), an interdisciplinary, longitudinal, cohort study based on multidimensional, dynamic assessments in both the laboratory and the real world. Here, we describe the rationale and design of the currently ongoing HBS. The HBS is examining a population-based sample of 1,000 healthy participants (age 30–39) who are thoroughly studied across an entire year. Data are collected through cognitive, affective, behavioral, and physiological testing, neuroimaging, bio-sampling, questionnaires, ecological momentary assessment, and real-world assessments using wearable devices. These data will become an accessible resource for the scientific community enabling the next step in understanding the human brain and how it dynamically and individually operates in its bio-social context. An access procedure to the collected data and bio-samples is in place and published on https://www.healthybrainstudy.nl/en/data-and-methods/access . Trail registration: https://www.trialregister.nl/trial/7955 .

[This corrects the article DOI: 10.1371/journal.pone.0260952.].

Physiological noise substantially affects fMRI data quality, particularly in areas near fluid-filled cavities and arteries such as the brainstem. Peripheral physiological signals can be recorded alongside fMRI and incorporated into general linear models to remove variance associated with cardiac and respiratory cycles using methods such as Retrospective Image Correction (RETROICOR). In contrast, data-driven noise reduction methods such as anatomical component correction (aCompCor) and independent component analysis based automatic removal of motion artifacts (ICA-AROMA) do not rely on additional peripheral physiological recordings. These methods have shown efficacy in correcting for motion, scanner as well as physiological artifacts. This raises the question of whether logistically demanding peripheral recordings offer added value. We therefore used nuisance regression methods based on peripherally recorded physiology (RETROICOR, heart rate, respiratory volume) as well as fMRI data itself (ICA-AROMA, aCompCor) to account for noise in a resting-state data set. Subsequently, we compared variance explained by the respective methods and improvements in temporal signal-to-noise ratio throughout different regions of interest in the brain. Consistent with prior research, RETROICOR, heart rate and respiratory volume explain significant variance throughout the brain with peaks around areas of strong cardiac pulsations. ICA-AROMA and aCompCor account for a substantial proportion of the variance explained by the methods using peripheral physiology. Nonetheless, the methods based on peripheral physiological recordings retain unique explanatory power. Analysis revealed a pattern of unreliability of ICA-AROMA to consistently identify and remove physiological noise across recordings in each participant, which is compensated by RETROICOR, heart rate and respiratory volume. This unreliability partially results from misclassifications in the noise selection models of ICA-AROMA. Our results suggest that it is advisable to additionally apply cleaning based on peripheral physiological recordings, especially when assessing inter-individual differences and areas with regionally high levels of physiological noise.

Controlled laboratory stress induction procedures are very effective in inducing physiological and subjective stress. However, whether such stress responses are representative for stress reactivity in real life is not clear. Using a combined within-subject functional MRI laboratory stress and ecological momentary assessment stress paradigm, we investigated dynamic shifts in large-scale neural network configurations under stress and how these relate to affective reactivity to stress in real life. Laboratory stress induction resulted in significantly increased cortisol levels, and shifts in task-driven neural activity including increased salience network (SN) activation in an oddball task and decreased default mode network activity in a memory retrieval task. Crucially, individuals showing increased SN reactivity specifically in the early phase of the acute stress response also expressed increased affective reactivity in real life. Our findings provide (correlational) evidence that real-life affective stress reactivity is driven primarily by vigilant attentional reorienting mechanisms associated with SN.

Physiological noise has been shown to have a large impact on the quality of functional MRI data, especially in areas close to fluid-filled cavities and arteries, such as the brainstem. Commonly, physiological recordings during scanning are transformed with methods such as RETROICOR and used as nuisance regressors in general linear models to remove variance associated with cardiac and respiratory cycles from the data. In contrast, modern pre-processing pipelines such as fMRIPrep, have created easy access to streamlined data-driven noise reduction methods such as aCompCor and ICA-AROMA. In combination, these methods have shown efficacy in correcting for motion, scanner as well as physiological artifacts. Given the ease of usability, it has to be questioned, whether there is any added benefit to applying logistically demanding methods such as RETROICOR. To answer this question, we applied RETROICOR, ICA-AROMA and aCompCor to a resting-state data set and compared variance explained by the respective methods and improvements in temporal signal-to-noise ratio throughout different regions of interest in the brain. In line with previous literature, RETROICOR significantly explains variance throughout the brain with peaks around areas of strong cardiac pulsations. ICA-AROMA and aCompCor largely account for the same variance. Nonetheless, RETROICOR retains unique explanatory power in individual participants. Further analysis points towards a pattern of unreliability of ICA-AROMA and aCompCor to consistently remove physiological noise across recordings, which is compensated by RETROICOR. While some of this inconsistency could be attributed to misclassifications in the noise selection models of ICA-AROMA, most is likely the consequence of secondary factors such as fMRI sequence parameters (e.g. long TR) limiting the efficiency of aCompCor and ICA-AROMA. Thus, it is advisable to additionally apply RETROICOR, especially when assuming regionally high levels of physiological noise.Competing Interest StatementThe authors have declared no competing interest.