Biology subjects

Hermans, E.

Publications and source records attributed to Hermans, E..

7 recordsLinked to original sources

bioRxiv2026

Detection of Stress in Naturalistic Settings Through Passive Mobile Sensing

Unobtrusive stress detection using wearable sensors could enable scalable, continuous mental-health monitoring. However, stress is an inherently subjective state that can only be inferred indirectly from physiological signals, making generalizable detection in naturalistic settings challenging. Although prior work has focused on improving model performance, it remains unclear whether wearable physiology supports a shared cross-individual mapping to subjective stress or whether this relationship is fundamentally person-specific. We evaluated feature-based and deep-learning models across multiple physiological modalities using ecological momentary assessment (EMA) as the reference standard, comparing within- and between-individual modeling approaches. Within-individual models achieved modest but consistent improvements in stress detection, whereas between-individual models consistently failed to generalize, yielding negative R2 values despite multimodal fusion and high-capacity architectures. Error analyses revealed regression to the mean, reduced sensitivity to high-stress states, and residual associations with general physiological activation, highlighting the limited stress specificity of wearable physiology. These findings suggest that wearable stress detection is fundamentally a personalized inference problem and that future systems should prioritize individual adaptation and contextual modeling over universal stress predictors.

neuroscience↗

bioRxiv2024

Neural Mechanisms of Feedback Processing and Behavioral Adaptation during Neurofeedback Training

The acquisition of new skills is facilitated by providing individuals with feedback that reflects their performance. This process creates a closed loop that involves feedback processing and regulation recalibration to promote effective training. Functional magnetic resonance imaging (fMRI)-based neurofeedback is unique in applying this principle by delivering direct feedback on the self-regulation of brain activity. Understanding how feedback-driven learning occurs requires examining how feedback is evaluated and how regulation adjusts in response to feedback signals. In this pre-registered mega-analysis, we re-analyzed data from eight intermittent fMRI neurofeedback studies (N = 153 individuals) to investigate brain regions where activity and connectivity are linked to feedback processing and regulation recalibration (i.e., regulation after feedback) during training. We harmonized feedback scores presented during training in these studies and computed their linear associations with brain activity and connectivity using parametric general linear model analyses. We observed that, during feedback processing, feedback scores were positively associated with (1) activity in the reward system, dorsal attention network, default mode network, and cerebellum; and with (2) reward system-related connectivity within the salience network. During regulation recalibration, no significant associations were observed between feedback scores and either activity or associative learning-related connectivity. Our results suggest that neurofeedback is processed in the reward system, supporting the theory that reinforcement learning shapes this form of brain training. In addition, the involvement of large-scale networks in feedback processing, continuously transitioning between evaluating external feedback and internally assessing the adopted cognitive state, suggests that higher-level processing is integral to this type of learning. Our findings highlight the pivotal role of performance-related feedback as a driving force in learning, potentially extending beyond neurofeedback training to other feedback-based processes. Key PointsWe conducted a pre-registered mega-analysis integrating data from eight fMRI neurofeedback studies to examine feedback processing and regulation recalibration during neurofeedback training. During feedback processing, feedback was associated with activity in the reward system, dorsal attention network, default mode network, and cerebellum; as well as with reward system-related connectivity within the salience network. We found no positive results during regulation blocks; however, additional analyses suggest that recalibration may have already occurred during feedback presentation.

neuroscience↗

bioRxiv2024

Unravelling the neurocognitive mechanisms underlying counterconditioning in humans

Counterconditioning (CC) aims to enhance extinction of threat memories by establishing new associations of opposite valence. While its underlying neurocognitive mechanisms remain largely unexplored, previous studies suggest qualitatively different mechanisms from regular extinction. In this functional MRI study, participants underwent categorical threat conditioning (CS+/CS-: images of animals/tools), followed by either CC (CS+ images reinforced with monetary rewards, n=24) or regular extinction (n=24). The following day, we assessed spontaneous recovery of threat responses and episodic memory for CS+ and CS-category exemplars. While the ventromedial prefrontal cortex (vmPFC) was activated during regular extinction, participants undergoing CC showed persistent CS+-specific deactivation of the vmPFC and hippocampus, and CS+-specific activation of the nucleus accumbens (NAcc). The following day, physiological threat responses returned in the regular extinction group, but not in the CC group. Counterconditioning furthermore strengthened episodic memory for CS+ exemplars presented during CC, and retroactively also for CS+ exemplars presented during the threat conditioning phase. Our findings confirm that CC leads to more persistent extinction of threat memories, as well as altered consolidation of the threat conditioning episode. Crucially, we show a qualitatively different activation pattern during CC versus regular extinction, with a shift away from the vmPFC and towards the NAcc.

neuroscience↗

bioRxiv2021

Using wearable biosensors and ecological momentary assessments for the detection of prolonged stress in real life

BackgroundIncreasing efforts toward 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. MethodsUsing ecological momentary assessments (EMA) combined with wearable biosensors for ecological physiological assessments (EPA), we investigated the impact of an ecological stressor (i.e., an exam week) on physiological arousal and affect. With this paradigm we investigated whether we could use wearable devices to detect stress states using machine learning models. ResultsDuring stressful high-stake exam (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal was decreased on average during the exam week. Time-resolved analyses revealed peaks in physiological arousal associated with both self-reported stress and self-reported positive affect, while the overall decrease in physiological arousal was mediated by lower positive affect during the stress period. We then used machine learning to show that a combination of EMA and physiology yields optimal identification of stress states. ConclusionsOur findings highlight the potential of wearable biosensors in stress-related mental-health monitoring, but critically show that psychological context is essential for interpreting physiological arousal detected using these devices.

physiology↗

bioRxiv2021

Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience. HighlightsO_LIAcute stress affects the allocation of neural resources between large-scale brain networks C_LIO_LIWe provide a first successful demonstration of neurofeedback training based on stress-related large-scale brain networks C_LIO_LINovel approach has the potential to train control over central response to stressors in real-life C_LIO_LICould build foundation for future clinical interventions to increase resilience C_LI

neuroscience↗

bioRxiv2020

The tumor suppressor protein PTEN undergoes amyloid-like aggregation in tumor cells.

Protein aggregation is an underappreciated mechanism that may contribute to the loss- and oncogenic-gain-of-function of mutant tumor suppressors such as p53 and axin. In the present study, we describe amyloid-like aggregation behaviour of the second most frequently mutated tumor suppressor in human cancer, PTEN. In silico analysis revealed a particularly high aggregation vulnerability for this protein, which was corroborated by in vitro aggregation assays. In cultured tumor cells, we found that under stress conditions, PTEN readily undergoes amyloid-like aggregation as a result of mutation. However, we also show that severe dysregulation of protein homeostasis may lead to aggregation of wild-type PTEN. These observations were supported by a small survey of patient-derived uterine tumor tissues, which found that more than 25% of tumors analyzed displayed wild-type PTEN aggregation. Finally, in an exploratory clinical study we found that PTEN aggregation status was correlated with a decline in clinical outcome. Our findings establish that the tumor suppressor PTEN is highly aggregation-prone and our work suggests that protein aggregation might be an underestimated but prevalent component of cancer cell biology.

cancer biology↗

bioRxiv2020

Dynamic analysis of sugar metabolism reveals the mechanisms of action of synthetic sugar analogs

Synthetic sugar analogs are widely applied in metabolic oligosaccharide engineering (MOE) and as novel drugs to interfere with glycoconjugate biosynthesis. However, mechanistic insights on their exact metabolism in the cell and over time are mostly lacking. We developed sensitive ion-pair UHPLC-QqQ mass spectrometry methodology for analysis of sugar metabolites in organisms and in model cells and identified novel low abundant nucleotide sugars in human cells, such as ADP-glucose and UDP-arabinose, and CMP-sialic acid (CMP-NeuNAc) in Drosophila. Dynamic tracing of propargyloxycarbonyl (Poc) labeled analogs, commonly used for MOE, revealed that ManNPoc is metabolized to both CMP-NeuNPoc and UDP-GlcNPoc. Finally, combined treatment of B16-F10 melanoma cells with antitumor compound 3Fax-NeuNAc and 13C-labeled GlcNAc revealed that endogenous CMP-NeuNAc levels started to decrease before a subsequent decrease of ManNAc 6-phosphate was observed. This implicates 3Fax-NeuNAc first acts as a substrate for cytosolic CMP-sialic acid synthetase and subsequently its product CMP-3Fax-NeuNAc functions as a feed-back inhibitor for UDP-GlcNAc 2-epimerase/N-acetylmannosamine kinase. Thus, dynamic analysis of sugar metabolites provides key insights into the time-dependent metabolism of synthetic sugars, which is important for the rational design of analogs with optimized effects.

biochemistry↗