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At least 19 recordsLinked to original sources

bioRxiv2023

A Comparison of fMRI Data-Derived and Physiological Data-Derived Methods for Physiological Noise Correction

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.

neuroscience↗

bioRxiv2024

Physiological Harmony or Discord? Unveiling the Correspondence Between Subjective Arousal, Valence and Physiological Responses

Affective experiences are inevitably accompanied by physiological changes, however it is still a matter of intense debate whether events evoking similar affective experiences produce comparable physiological responses (fingerprint hypothesis) or variation is the norm within individuals (population hypothesis). We reanalyzed data from two independent samples (N = 491; N = 64), using representational similarity analysis (RSA) to examine the trial-by-trial similarity patterns of subjective experience of valence and arousal and affect-related physiological measures (skin conductance [SCR] and startle blink responses). Across different affect-inducing (passive picture viewing, passive sound listening, imagery tasks) tasks and samples (Ns=491, 64), we observed strong-to-decisive evidence for a correspondence between SCR and startle responses and models of arousal and valence that assume variation, especially between trials generally evoking higher responses. Our results show that similar affective experiences are rather reflected by distinct physiological responses and emphasize the importance of considering intraindividual variability in future studies to better understand how physiological changes contribute to conscious affective experiences in humans.

neuroscience↗

bioRxiv2026

There is an 'I' in team: Intrapersonal physiological synchrony complements interpersonal physiological synchrony in team interaction

Interpersonal physiological synchrony (IPS) between interaction partners has been studied as a marker of social connectedness and coordination. However, little is known about how such interpersonal synchrony relates to the intrapersonal coordination of individuals' own autonomic systems during team interaction. The present study examined whether IPS and intrapersonal physiological synchrony (IAPS), defined as the within-person coordination of heart rate (HR), respiration, and electrodermal activity (EDA), function as competing or complementary regulatory processes. It also examined whether IAPS is state- or trait-like and whether teams high or low in both IPS and IAPS had better outcomes. Fifty five newly formed triads (N = 165; Mage = 23.87, 77% female) participated in a laboratory interaction including passive shared experiences and active collaborative tasks while continuous physiological activity was recorded. Synchrony within individuals and between team members was quantified using multidimensional recurrence quantification analysis. Across interaction segments, IAPS was positively associated with IPS in both HR and EDA, with stronger and more consistent associations during active teamwork versus during passive social contexts. These findings contradict a trade-off hypothesis and instead support a synergistic relationship between IAPS and IPS. IAPS exhibited state-like dynamics and sensitivity to features of the context, i.e., consistency within similar interaction contexts and higher in passive versus active social segments. Further, IAPS had limited associations with individual difference measures. Exploratory analyses indicated that teams characterized by both high IAPS and high IPS demonstrated more favorable outcomes, reflected in either higher objective task performance or greater perceived team efficacy depending on physiological modality. Together, these results suggest that effective team coordination is supported not only by IPS but also by the capacity to maintain coherent internal autonomic organization. They further highlight IAPS as a dynamic component of social functioning that merits exploration alongside interpersonal processes.

physiology↗

bioRxiv2021

Physiologically based modeling of the effect of physiological and anthropometric variability on indocyanine green based liver function tests

Accurate evaluation of liver function is a central task in hepatology. Dynamic liver function tests (DLFT) based on the time-dependent elimination of a test substance provide an important tool for such a functional assessment. These tests are used in the diagnosis and monitoring of liver disease as well as in the planning of hepatobiliary surgery. A key challenge in the evaluation of liver function with DLFTs is the large inter-individual variability. Indocyanine green (ICG) is a widely applied test compound used for the evaluation of liver function. After an intravenous administration, pharmacokinetic (PK) parameters are calculated from the plasma disappearance curve of ICG which provide an estimate of liver function. The hepatic elimination of ICG is affected by physiological factors such as hepatic blood flow or binding of ICG to plasma proteins, anthropometric factors such as body weight, age, and sex, or the protein amount of the organic anion-transporting polypeptide 1B3 (OATP1B3) mediating the hepatic uptake of ICG. Being able to account for and better understand these various sources of inter-individual variability would allow to improve the power of ICG based DLFTs and move towards an individualized evaluation of liver function. Within this work we systematically analyzed the effect of various factors on ICG elimination by the means of computational modeling. For the analysis, a recently developed and validated physiologically based pharmacokinetics (PBPK) model of ICG distribution and hepatic elimination was utilized. Key results are (i) a systematic analysis of the variability in ICG elimination due to hepatic blood flow, cardiac output, OATP1B3 abundance, liver volume, body weight and plasma bilirubin level; (ii) the evaluation of the inter-individual variability in ICG elimination via a large in silico cohort of n=100000 subjects based on the NHANES cohort with special focus on stratification by age, sex, and body weight; (iii) the evaluation of the effect of various degrees of cirrhosis on variability in ICG elimination. The presented results are an important step towards individualizing liver function tests by elucidating the effects of confounding physiological and anthropometric parameters in the evaluation of liver function via ICG.

systems biology↗

bioRxiv2019

Modeling Dragons: Using linked mechanistic physiological and microclimate models to explore environmental, physiological, and morphological constraints on the early evolution of dinosaurs

We employed the widely-tested biophysiological modeling software, Niche Mapper™ to investigate the metabolic function of Late Triassic dinosaurs Plateosaurus and Coelophysis during global greenhouse conditions. We tested them under a variety of assumptions about resting metabolic rate, evaluated within six microclimate models that bound paleoenvironmental conditions at 12° N paleolatitude, as determined by sedimentological and isotopic proxies for climate within the Chinle Formation of the southwestern United States. Sensitivity testing of metabolic variables and simulated “metabolic chamber” analyses support elevated “ratite-like” metabolic rates and intermediate “monotreme-like” core temperature ranges in these species of early saurischian dinosaur. Our results suggest small theropods may have needed partial to full epidermal insulation in temperate environments, while fully grown prosauropods would have likely been heat stressed in open, hot environments and should have been restricted to cooler microclimates such as dense forests (under any vegitative cover) or those seen at higher latitudes and elevations. This is in agreement with the Late Triassic fossil record and may have contributed to the latitudinal gap in the Triassic prosauropod record.View Full Text

paleontology↗

bioRxiv2021

New insights on the correspondence between subjective affective experience and physiological responses from representational similarity analyses

Classical views suggest that experienced affect is related to a specific bodily response (Fingerprint hypothesis), whereas recent perspectives challenge this view postulating that similar affective experiences rather evoke different physiological responses. To further advance this debate in the field, we used representational similarity analysis (N= 64) to investigate the correspondence between subjective affect (arousal and valence ratings) and physiological reactions (skin conductance response [SCR], startle blink response, heart rate and corrugator activity) across various emotion induction contexts (picture viewing task, sound listening task and imagery task). Significant similarities were exclusively observed between SCR and arousal in the picture viewing task. However, none of the other physiological measures showed a significant relation with valence and arousal ratings in any of the tasks. These findings tend to support the populations hypothesis, suggesting that there is no clear match between the evoked physiological responses and the experienced subjective affect between individuals. Statement of relevanceThe subjective affective experience evoked by an event is accompanied by physiological responses. The correspondence between physiological response patterns and the experienced affect, however, is still under debate. Classical views (Fingerprint hypothesis) suggest that affect is related to a specific physiological response, whereas recent perspectives (Populations hypothesis) challenge this view, postulating rather different physiological responses. In the current study, we used representational similarity analysis (RSA) to examine the relation between affective experience, assessed using valence and arousal ratings, and the evoked physiological reactivity across three affect-inducing contexts. Results showed significant similarities exclusively between SCR and arousal in the passive picture viewing task. However, none of the other physiological measures showed a significant relation with valence and arousal ratings in any of the tasks, supporting the populations hypothesis. These findings invite to reframe the relation between physiology and affect from invariant and homogeneous to variant and context-dependent.

neuroscience↗

bioRxiv2018

Physiological predictors of reproductive performance in the European Starling (Sturnus vulgaris): I. Univariate analysis

Costs of reproduction are assumed to be widespread, and to have a physiological basis, but the mechanisms driving decreased survival and future fecundity remain elusive. Our overall goals were to assess whether physiological variables are related to workload ability or immediate fitness consequences and if they mediate future survival or reproductive success. We investigated individual variation in physiological state at two breeding stages (incubation, chick-rearing) in female European starlings (Sturnus vulgaris), for first-and second-broods over two years. Specifically, we measured a suite of 13 physiological traits related to aerobic/metabolic capacity, oxidative stress and muscle damage, intermediary metabolism and energy supply, and immune function. We tested for relationships to traits for workload (e.g. nest visit rate) and fitness (current reproduction, survival and future reproduction).\n\nThere was little co-variation among the 13 physiological traits, either in incubating or chick-rearing birds. There were some systematic differences in incubation versus chick-rearing physiology. Chick-rearing birds had lower hematocrit and plasma creatine kinase but higher hemoglobin, triglyceride and uric acid levels. Only plasma corticosterone was repeatable between incubation and chick-rearing. We assessed relationships in a univariate manner, and found very few significant relationships between incubation or chick-rearing physiology and measures of workload, current productivity, future fecundity or survival. Variability in ecological context may complicate the relationship between physiology and behavior. Additionally, individuals may regulate different aspects of their physiology independently, making detection of physiological mediators of cost of reproduction difficult. In a companion paper (Cohen et al. submitted) we explore the utility of multivariate analysis (Principal Components Analysis, Mahalanobis distance) of the same data to account for potentially complex physiological integration.

ecology↗

bioRxiv2022

Unsupervised physiological noise correction of fMRI data using phase and magnitude information (PREPAIR)

Of the sources of noise which affect BOLD fMRI, respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultra-high field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method which uses the information carried in both the phase and the magnitude of EPI data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals which are sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations which are not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR not only agree well with those from external devices, but also retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most commonly used approach based on external recordings, RETROICOR. In comparison with widely used physiological noise correction tools which do not use external signals, PESTICA and FIX, PREPAIR removed more respiratory and cardiac noise and achieved a larger increase in tSNR at both 3 T and 7 T.

neuroscience↗

bioRxiv2026

An Expert-Informed Synthetic Animal Data Generator: A Physiology-Consistent Generative Framework for High-Fidelity Animal Digital Twins

Digital twins are increasingly recognized as a transformative technology for precision livestock farming; however, a major bottleneck in their development remains the scarcity of high-quality, high-granularity physiological data. This study introduces the expert-informed conditional diffusion (EICD) framework, a novel approach to synthesizing high-fidelity metabolic time-series trajectories by embedding mechanistic biological principles directly into the generative process. While traditional generative models often prioritize statistical pattern-matching over biological reality, frequently resulting in physiological hallucinations, the EICD framework utilizes a physiology loss function (PhLF) to act as a form of mechanistic regularization. This guardrail penalizes samples that contradict expert-defined constraints, such as the laws of porcine bioenergetics, effectively steering the model toward a realistic physiological manifold. The framework was validated using an empirical dataset of growing pigs under varying thermal conditions. Quantitative results demonstrate near-perfect statistical distributional fidelity, with the model achieving an average Jensen-Shannon divergence (JSD) of 0.062 and a Kullback-Leibler divergence (KLD) of 0.19. The full EICD model produced a mean energy expenditure (EE) of 284.94 {+/-} 38.70 kJ/kg/day, mirroring the empirical average of 281.33 {+/-} 41.58 kJ/kg/day. In contrast, the standard generative diffusion model (i.e., with no physiology guardrail) exhibited significant distributional drift, yielding a mean EE of 334.41 kJ/kg/day. The biological integrity of the model was further assessed using the biological violation rate (BVR), a novel metric defined as the percentage of generated samples that fall outside the physically possible metabolic boundaries established by species-specific laws. While the standard diffusion model produced frequent biological artifacts, the EICD framework successfully suppressed these hallucinations, ensuring that synthetic trajectories remain strictly grounded in mechanistic laws. Despite these advancements, limitations remain at physiological extremes where individual stochasticity is high. By providing a reliable method for generating physiology-consistent synthetic data, this framework provides a robust foundation for the next generation of animal digital twins. HighlightsO_LIA novel expert-informed conditional diffusion (EICD) framework is proposed for physiology-consistent synthetic data generation in precision livestock farming. C_LIO_LIA physiology loss function (PhLF) embeds species-specific bioenergetic laws directly into the generative process as a mechanistic guardrail. C_LIO_LIThe framework achieves near-perfect distributional fidelity (JSD = 0.062) while suppressing physiological hallucinations (BVR = 0.93%). C_LIO_LIAn ablation study confirms that biological consistency is not an emergent property of standard diffusion models but requires explicit mechanistic constraints. C_LIO_LIThe framework provides a scalable solution for synthetic data augmentation in precision livestock farming, supporting the 3Rs and enabling high-throughput in silico experimentation. C_LI

bioengineering↗

bioRxiv2022

Octopamine and tyramine signaling in Aedes aegypti: characterization, distribution and potential role in the Dengue vector development and physiology

In insects, the biogenic amines octopamine (OA) and tyramine (TA) are involved in controlling several physiological and behavioural processes. OA and TA act as neurotransmitters, neuromodulators or neurohormones, performing their functions by binding to specific receptors belonging to the G protein-coupled receptor (GPCR) superfamily. OA and TA along with their receptors are involved in reproduction, smell perception, metabolism, and homeostasis. Moreover, OA and TA receptors are targets for insecticides and antiparasitic agents, such as the formamidine Amitraz. In the dengue and yellow fever vector, Aedes aegypti, limited research has been previously reported on their OA or TA receptors. Here, we identify and characterize the OA and TA receptors in A. aegypti. Bioinformatic tools have been used to identify four OA and three TA receptors in the genome of A. aegypti. The seven receptors are expressed in all developmental stages of A. aegypti; however, their highest transcript abundance is observed in the adult compared to the larval stages. Among several adult A. aegypti tissues examined, including the central nervous system, antennae and rostrum, midgut, Malpighian tubules, ovaries, and testes, the type 2 TA receptor (TAR2) transcript is most abundant in the ovaries and the type 3 TA receptor (TAR3) is enriched in the Malpighian tubules, leading us to hypothesize putative roles for these receptors in reproduction and diuresis, respectively. Furthermore, a blood meal influenced OA and TA receptor transcript expression patterns in adult female tissues at several time points post blood meal, suggesting these receptors may play key physiological roles associated with feeding. To better understand OA and TA signaling in A. aegypti, the transcript expression profiles of key enzymes in their biosynthetic pathway, namely tyrosine decarboxylase (Tdc) and tyramine {beta}-hydroxylase (T{beta}h), were examined in developmental stages, adult tissues, and brains from blood-fed females. These findings provide information for better understanding the physiological roles of OA, TA, and their receptors in A. aegypti, and additionally, may help in the development of novel strategies for the control of these human disease vectors. Author summaryAedes aegypti is the primary vector for dengue, chikungunya, and yellow fever - debilitating diseases that together are responsible for hundreds of millions of infections and thousands of deaths annually worldwide. Understanding the A. aegypti physiology may be critical for the development of new control strategies. In insects, the biogenic amines dopamine, serotonin, tyramine and octopamine play important roles in controlling various physiological processes. In A. aegypti, both serotonin and dopamine are implicated in blood feeding behavior and development. Conversely, the role of octopamine (OA) and tyramine (TA) in A. aegypti physiology is still poorly characterized. Both OA and TA exert their physiological actions by interacting with and activating different receptors, the tyramine (TAR) and the octopamine (OAR) receptors. Here, we show the characterization of the OA and TA receptors in A. aegypti. In the A. aegypti genome we identify a total of four OA receptors and three TA receptors, suggesting for each receptor a particular role in the development and physiology of this insect. This work contributes to better understanding the roles of OA, TA, and their receptors, in A. aegypti development and physiology. Furthermore, it may be crucial in identifying novel strategies for the mosquitoes control.

zoology↗

bioRxiv2018

Physiological predictors of reproductive performance in the European Starling (Sturnus vulgaris): II. Multivariate Analysis

Physiological variation is generally thought or supposed to underlie variation in fitness related traits in wild animals, including reproductive effort, reproductive success, and survival. However, physiological markers of individual quality have proven elusive. In this paper and its companion, we use data on 14 physiological parameters measured in 152 observations of 93 individual European starlings (Sturnus vulgaris) over 2 years in an attempt to understand how physiological variation relates to variation in current breeding productivity, future fecundity, and survival. The companion paper, focusing on univariate analysis, showed that individual physiological parameters have little relationship with these performance measures. Here, we used more sophisticated statistical approaches in an attempt to extract a multivariate signal from the biomarkers - physiological dysregulation as calculated via statistical distance, and a number of principal components analysis approaches. Broadly speaking, there was a surprising lack of association between physiology and performance: while some physiological summary measures were associated with some performance measures, the associations were not particularly strong or robust given the large number of statistical tests conducted. This implies either that there are relatively few links between physiology and performance, or, more likely, that the complexity of these relationships exceeds our ability to measure and model it, even using state-of-the-art statistical approaches. This is likely particularly true because our population was quite heterogeneous; we nonetheless urge caution regarding the over-interpretation of isolated significant findings in the literature.

ecology↗

bioRxiv2025

Functional MRI Signatures of Autonomic Physiology in Aging

While traditionally regarded as "noise", blood-oxygenation-level-dependent (BOLD) fMRI fluctuations coupled to systemic physiology--such as heart rate and respiratory changes--also hold valuable information about brain vascular properties and autonomic function. In this study, we leverage these physiological BOLD signals to characterize age-related changes in brain physiology. Using a large dataset from the Lifespan Human Connectome Project Aging study, we investigated how the spatiotemporal BOLD-fMRI signatures of autonomic physiology, specifically heart rate and respiratory variation, change with advancing age. Our findings reveal that aging is associated with globally slower respiratory fMRI responses, alongside faster cardiac fMRI responses and enhanced brain-cardiac signal coupling. Moreover, we show that the impact of age on physiological fMRI signals exhibits a notable turning point after age 60, suggesting a critical role of declining vascular health and autonomic function in aging. The potential impact of age-related changes in brain structure, tissue perfusion, and in-scan arousal states on the identified physiological fMRI patterns is also tested and discussed. Altogether, our results underscore significant age effects in the fMRI signatures of systemic physiology, emphasizing the pivotal role of altered vascular properties and autonomic function in aging. Methodologically, this study also demonstrates the utility of resting-state fMRI for extracting multi-parametric information about brain physiology, offering new biomarker opportunities that complement established functional connectivity metrics.

physiology↗

bioRxiv2024

The impact of tropodithietic acid on microbial physiology under varying culture complexities

Research has increasingly focused on understanding marine bacterial physiology under environmentally relevant conditions. Biotic interactions have a key influence on microbial physiology and are often modeled in the lab by manipulating the complexity of microbial cultures. Notably, findings from low-complexity cultures that have been re-evaluated in more complex systems, occasionally lead to different outcomes. Here, we assess how the genomic capability of bacteria to produce secondary metabolites, specifically the antibiotic tropodithietic acid (TDA), influences microbial physiology and interactions, under varying microbial complexity. We investigate the impact of TDA production on microbial physiology across systems with increasing complexity: from bacterial mono-cultures, to bacterial co-cultures of Phaeobacter inhibens with Dinoroseobacter shibae, and a more complex tri-culture that includes both bacteria and their algal host Emiliania huxleyi. In these systems, we examine both wild-type (WT) P. inhibens bacteria and mutant bacteria with a tdaB deletion ({Delta}tdaB) that are not capable of producing TDA. This systematic approach allowed us to explore the relationship between the tdaB gene, microbial physiology, and system complexity. Our findings show that deleting the tdaB gene affected bacteria-bacteria interactions in co-cultures but not in tri-cultures with the algal host. Additionally, our data revealed that algal death was delayed in cultures containing P. inhibens {Delta}tdaB mutants compared to those with WT bacteria. Results of our study highlight the importance of microbial complexity in the study of bacterial physiology and point to the understudied role of TDA in algal-bacterial interactions. ImportanceThis study advances our understanding of marine bacterial physiology under varying levels of microbial complexity. We uncover how the production of secondary metabolites, specifically the antibiotic tropodithietic acid (TDA), influences microbial interactions. Our systematic approach, which includes bacterial mono-cultures, co-cultures, and tri-cultures involving algal hosts, allows us to evaluate the impact of the tdaB gene on microbial interactions. Notably, our findings reveal that the deletion of this gene affects bacteria-bacteria interactions in co-culture but does not have the same effect in more complex systems that include algae. Additionally, the observation that algal death is delayed in cultures with TDA-deficient bacteria underscores the significance of TDA in these interactions. Overall, our research highlights the influence of the microbial culture complexity on bacterial physiology and emphasizes the overlooked role of TDA in algal-bacterial dynamics.

microbiology↗

bioRxiv2025

Athlytics: A Computational Framework for Longitudinal Analysis of Exercise Physiology Metrics from Wearable Sensor Data

The proliferation of wearable sensors provides large-scale longitudinal physiological data collected in real-world settings, offering unprecedented opportunities to investigate dynamic human responses to exercise interventions. However, systematically quantifying key physiological indicators related to adaptation and fatigue from these dense time-series data, particularly from popular platform APIs like Strava, and performing standardized, reproducible integrative analyses currently lacks established open-source workflows in R, posing significant practical and computational barriers. Researchers often expend considerable effort on custom programming, limiting analytical scale and efficiency. To overcome this critical bottleneck and empower broader research applications, we developed and introduce Athlytics, a computational framework specifically designed for processing Strava API data directly within R for longitudinal exercise physiology analysis. This framework provides a dedicated means to seamlessly integrate data acquisition with the calculation of key physiological indicators (such as ACWR reflecting training stress balance, EF assessing aerobic efficiency, and physiological decoupling indicating cardiovascular stability), based on activity summary data while carefully addressing necessary approximations for composite load metrics like TSS/HRSS, and multidimensional time-series visualization. By providing standardized function interfaces, Athlytics significantly lowers the technical barrier for conducting complex longitudinal analyses, enabling researchers to efficiently test hypotheses regarding the dynamic interplay between training stimuli, physiological efficiency, and stress responses. This work provides an open-source computational tool that fills a critical gap, contributing by substantially enhancing the feasibility, efficiency, and reproducibility of quantitative exercise physiology research utilizing widely available physiological sensor data through standardization and automation. The package (https://github.com/HzaCode/Athlytics) provides an important foundation for standardizing and applying computational methods in this field.

bioinformatics↗

bioRxiv2025

TRACC-PHYSIO: Time-domain Resolution-Aligned Cross-Correlation to estimate PHYSIOlogical coupling and time delays in dynamic MRI

Physiological brain pulsations, primarily driven by cardiac and respiratory activity, play a key role in driving neurofluid circulation and waste clearance. Capturing the temporal dynamics of cardiac- and respiratory-driven brain pulsations (0.2-1.5 Hz) requires fast imaging with TRs near 100 ms, which is often unachievable in functional MRI or dynamic diffusion MRI. As a result, valuable physiological information remains hidden in these datasets. Here, we introduce TRACC-PHYSIO, a time-domain analytical framework designed to quantify physiological coupling and pulse time delays in dynamic MRI without requiring a fast acquisition. TRACC-PHYSIO uses cross-correlation to detect co-fluctuations between slowly sampled dynamic MRI data and simultaneously recorded physiological waveforms. It measures two key metrics: the peak Coupling Coefficient (peak CorrCoeff), quantifying the strength of co-fluctuations, and the TimeDelay, reflecting the relative arrival time of the physiological impulse in the brain with millisecond-level temporal resolution. The primary aim of this study is to validate TRACC-PHYSIO through systematic simulations that model realistic dynamic MR signals with mixed physiological components. We comprehensively evaluate TRACC-PHYSIOs performance under a wide range of conditions, including varying cardiac-to-respiratory composition ratios, TRs, and acquisition times. Results demonstrate that TRACC-PHYSIO can robustly assess coupling strengths and time delays for both cardiac (TRACC-Cardiac) and respiratory (TRACC-Respiratory) components, even in datasets with long TRs up to 3 seconds. By enabling a reliable time-domain coupling analysis, TRACC-PHYSIO opens new avenues for revealing brain pulsation mechanisms and elucidating the physiological drivers of neurofluid dynamics in health and disease. This stimulation study provides a valuable reference for interpreting TRACC-PHYSIO results and understanding associated uncertainties in future applications. Highlights- TRACC-PHYSIO is a time-domain method developed to estimate cardiac and respiratory coupling and their pulsation time delays in dynamic MRI without requiring high temporal resolution. - TRACC-PHYSIO was validated through systematic simulations across varying physiological compositions and MR acquisition parameters. - Results demonstrated that TRACC-PHYSIO reliably quantifies cardiac and respiratory components in dynamic MR signals. - The stimulation study provides a useful reference for interpreting TRACC-PHYSIO results and understanding associated uncertainties in future applications.

bioengineering↗

bioRxiv2018

Genome-wide selection scans integrated with association mapping reveal mechanisms of physiological adaptation across a salinity gradient in killifish

Adaptive divergence between marine and freshwater environments is important in generating phyletic diversity within fishes, but the genetic basis of adaptation to freshwater habitats remains poorly understood. Available approaches to detect adaptive loci include genome scans for selection, but these can be difficult to interpret because of incomplete knowledge of the connection between genotype and phenotype. In contrast, genome wide association studies (GWAS) are powerful tools for linking genotype to phenotype, but offer limited insight into the evolutionary forces shaping variation. Here, we combine GWAS and selection scans to identify loci important in the adaptation of complex physiological traits to freshwater environments. We focused on freshwater (FW)-native and brackish water (BW)-native populations of the Atlantic killifish (Fundulus heteroclitus) as well as a population that is a natural admixture of these two populations. We measured phenotypes for multiple physiological traits that differ between populations and that may contribute to adaptation across osmotic niches (salinity tolerance, hypoxia tolerance, metabolic rate, and body shape) and used a reduced representation approach for genome-wide genotyping. Our results show patterns of population divergence in physiological capabilities that are consistent with local adaptation. Selection scans between BW-native and FW-native populations identified genomic regions that presumably aect fitness between BW and FW environments, while GWAS revealed loci that contribute to variation for each physiological trait. There was substantial overlap in the genomic regions putatively under selection and loci associated with the measured physiological traits, suggesting that these phenotypes are important for adaptive divergence between BW and FW environments. Our analysis also implicates candidate genes likely involved in physiological capabilities, some of which validate a priori hypotheses. Together, these data provide insight into the mechanisms that enable diversification of fishes across osmotic boundaries.\n\nAuthor SummaryIdentifying the genes that underlie adaptation is important for understanding the evolutionary process, but this is technically challenging. We bring multiple lines of evidence to bear for identifying genes that underlie adaptive divergence. Specifically, we integrate genotype-phenotype association mapping with genome-wide scans for signatures of natural selection to reveal genes that underlie phenotypic variation and that are adaptive in populations of killifish that are diverging between marine and freshwater environments. Because adaptation is likely manifest in multiple physiological traits, we focus on hypoxia tolerance, salinity tolerance, and metabolic rate; traits that are divergent between marine and freshwater populations. We show that each of these phenotypes is evolving by natural selection between environments; genetic variants that contribute to variation in these physiological traits tend to be evolving by natural selection between marine and freshwater populations. Furthermore, one of our top candidate genes provides a mechanistic explanation for previous hypotheses that suggest the adaptive importance of cellular tight junctions. Together, these data demonstrate a powerful approach to identify genes involved in adaptation and help to reveal the mechanisms enabling transitions of fishes across osmotic boundaries.

evolutionary biology↗

bioRxiv2019

A physiological approach to the ecology and evolution of flowers

For most angiosperms, flowers are critical to reproduction because they increase rates of outcrossing. Flowers are highly variable in numerous traits, including size, shape, and color. Most of this variation is thought to have arisen due to selection by pollinators. Yet, non-pollinator selection is increasingly being recognized as contributing to floral trait evolution. One such non-pollinator agent of selection that often opposes pollinator selection includes the physiological and resource costs of producing and maintaining flowers. Yet, our understanding of the physiological function of flowers remains limited. Here I argue for a comparative, physiological framework for studying flowers and highlight recent work elucidating some of the basic physiological structure-function relationships of flowers and apply an energy balance model to show how two pollination traits (flower size and flower color) can interact with physiological traits. This modeling shows that (1) physiologically meaningful estimates of flower size can be easily gleaned from available floras, (2) the range of flower size most common in the California flora is the range in which complex, non-linear dynamics in flower energy balance occur, and (3) that intraspecific variation in flower color can, by these conservative estimates, cause large (up to 8{degrees}C) changes in flower temperature. The strong interaction between pollination traits (flower size and color) and physiological traits (surface conductance to water vapor) suggests that a more unified framework for understanding the evolution of floral form and function would include physiological traits that represent the costs of flower production and maintenance.

ecology↗

bioRxiv2026

PAMG-AT: A Physiological Attention Multi-Graph Model with Adaptive Topology for Stress Detection using Wearable Devices

Stress detection with wearable physiological sensors is vital in digital health and affective computing. Conventional machine learning techniques usually examine physiological signals separately, missing the intricate inter-signal connections involved in the human stress response. While deep neural networks offer high accuracy, they function as black boxes, offering minimal understanding of the physiological processes behind stress detection. This study introduces a hierarchical graph neural network framework for WESAD stress detection, establishing a methodology for affective computing that emphasizes interpretability and extensibility while maintaining strong predictive performance. We proposed PAMG-AT (Physiological Attention Multi-Graph with Adaptive Topology) which is a hierarchical graph neural network architecture, for stress detection using multimodal physiological signals. In this framework, physiological features serve as nodes within a knowledge-driven graph, while edges represent established physiological relationships, including cardiac-electrodermal coupling and cardio-respiratory interaction. The architecture employs a three-level attention mechanism: spatial encoding via Graph Attention Networks (GAT) to assess feature importance, temporal modeling with a Transformer to capture dynamics across time windows, and global pooling for classification. The model is evaluated using three sensor configurations (chest-only, wrist-only, and hybrid) on the WESAD dataset, employing rigorous Leave-One-Subject-Out (LOSO) cross-validation. PAMG-AT achieves competitive performance, with 94.59% accuracy ({+/-}6.8%) for chest sensors, 91.76% ({+/-}9.2%) for wrist sensors, and 92.80% ({+/-}8.33%) for the hybrid configuration. The proposed method provides interpretability via attention weights, revealing that ECG-EDA relationships (cardiac-electrodermal coupling) are most predictive of stress. Three low-responder subjects (S2, S3, S9) with atypical physiological stress patterns demonstrate lower accuracy (81-87%), offering clinically valuable insights for personalized stress management. The effective wrist-only configuration, achieving 91.76% accuracy, supports practical deployment in consumer wearables.

bioinformatics↗