Biology subjects

Fransson, P.

Publications and source records attributed to Fransson, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Pathogen Host Shifts from a Niche Evolution Perspective

Emerging infectious diseases constitute a major health threat and are often associated with zoonotic spillover processes. Pathogen host shifts may involve evolutionary adaptation to new host species, which is of central concern in relation to future pandemics. Evolutionary host shifts require certain conditions to be met, including physiological and ecological factors. A pathogen strain infecting a new host species to which it is not well adapted will generally experience reduced success, which depends on the effective similarity between the new host species and the original reservoir host species from the pathogen's perspective. The adaptation process involves intermediate strains, or phenotypes, that exist at the cost of reduced transmission and replication rates, and the evolutionary outcome depends on factors associated with host species physiology and population interactions. The interplay between host similarity and interaction, and pathogen adaptation trade-offs, is therefore important in steering pathogen evolution and possible evolutionary host-shift outcomes. Here we apply niche-evolution theory to host-pathogen systems to investigate the combined effect of host species similarity and intra- and interspecific interactions on evolutionary host shifts. We identify the conditions under which evolutionary host shifts occur and show, for a two-host-species system, that evolutionary outcomes fall into four distinct categories.

evolutionary biology↗

bioRxiv2026

Time-resolved brain network community detection based on instantaneous phase of fMRI data

In this paper, we propose a novel method that estimates time-resolved communities, or networks, from parcellated fMRI data based on instantaneous phase of the parcel timeseries. Importantly, each community label reference a limited phase range across time and subjects. It thus avoids the relabelling problem that is common for community detection algorithms. Our aim was to enhance the temporal resolution of brain network analysis in a whole-brain context. The method provides insights into how brain regions synchronize both within and across subjects and reorganize during task performance. It offers a complementary perspective on network integration and segregation as concurrent processes, quantified through differences in instantaneous phase. We employed HCP motor task fMRI data to exemplify its practical application. Prior to calculation of instantaneous phase, signal timeseries were decomposed into two signal modes with minimally overlapping frequency ranges. We show that task specific motor movements (hand, feet, tongue) can be separated from block-design related activation (visual and attention networks) where the former was found in the slower mode and the latter in the higher frequency mode.

neuroscience↗

bioRxiv2026

Machine learning-based framework for predicting human infection potential of coronavirus associated with tri-amino acid motifs, KIQ and LEP in spike protein

Assessing the human infection potential of emerging coronaviruses remains a critical challenge for global health preparedness. In this study, we developed a machine learning-based framework to predict the human infection potential of coronaviruses and to identify associated sequence motifs using spike (S) protein sequences. A total of 3,904 complete S protein sequences were collected, annotated as human or non-human infection and encoded using trimer-based k-mer features. Model benchmarking was conducted across 27 machine learning algorithms, followed by hyperparameter optimization of the selected model. Robustness and generalizability were evaluated using k-fold cross-validation and independent external validation. Feature interpretability was further assessed using SHAP analysis to identify sequence determinants associated with infection potential. The Random Forest classifier achieved the best performance, with accuracy, sensitivity, and specificity of 97.8%, 99%, and 97.4%, respectively, and demonstrated stable predictive performance across validation datasets. Notably, the KIQ and LEP motifs were strongly associated with human infection coronaviruses and mapped to the HR1 and N-terminal domain regions of the S protein. Overall, this framework provides a practical approach for risk assessment and surveillance of emerging coronaviruses. Author summaryEmerging coronaviruses continue to threaten global public health, but rapidly identifying viruses with the potential to infect humans remains challenging. Traditional experimental approaches are time-consuming and resource-intensive, limiting their use for large-scale surveillance. In this study, we developed a machine learning based workflow to assess the human infection potential of coronaviruses using spike protein sequences. By analyzing sequence patterns across a diverse set of coronaviruses, our framework enables rapid screening of coronaviruses from multiple host species. Unlike previous studies focused on limited coronavirus genera, our approach integrates all four genera and systematically evaluates multiple learning strategies. Importantly, our analysis identifies conserved sequence motifs linked to human infection potential, bridging predictive performance with biological interpretability. Our findings demonstrate computational approaches support early warning systems for identifying high risk coronaviruses, contributing to prioritize viruses for experimental validation, guide surveillance efforts, and strengthen global pandemic preparedness under a One Health perspective.

bioinformatics↗

bioRxiv2025

Offset analgesia and onset hyperalgesia: A comparison between women with non-suicidal self-injury and healthy women

IntroductionIndividuals who engage in non-suicidal self-injury (NSSI) exhibit reduced pain sensitivity compared to the general population. It has been argued that this hypoalgesic characteristic may be attributable to hyper-effective pain modulation. However, empirical support for this theory remains inconsistent. ObjectiveThe aim of the study was to use a combined offset analgesia (OA) and onset hyperalgesia (OH) protocol to investigate if women with NSSI have a propensity to inhibit nociceptive signals to a higher degree (stronger OA response) and facilitate nociceptive signals to a lesser degree (weaker OH response), compared to a control group. MethodsData was collected from 76 women, 18-35 age (37 with NSSI and 39 healthy controls). The OA and OH protocol was combined with functional magnetic resonance imaging (fMRI). ResultsThe NSSI group displayed a weaker OH response, compared to the control group. This suggests that women with NSSI do not facilitate nociceptive signals to the same extent as healthy women. However, there were no significant differences between the groups regarding OA. Across all participants, we observed stronger activation in the primary sensory cortex during the OH condition, compared to the control condition. ConclusionsThe results offer partial support for the general hypothesis that women with NSSI demonstrate enhanced pain modulation.

neuroscience↗

bioRxiv2025

Whole-brain entrainment and anticipation in fMRI motor task data.

The readiness potential (RP), also known as the Bereichtschaftspotential, is commonly observed in EEG recordings as a slow build-up of negative electrical potential prior to self-directed motor movements (Kornhuber et al. 1965). In this study, we analyzed motor task-based functional magnetic resonance (fMRI) data acquired from 262 human participants, focusing on the degree of signal phase synchronization across different frequency spans and brain regions. A new method that clusters brain regions based on instantaneous phase allowed for time resolved estimation of cross-subject phase synchronization. We show that during rest periods that precede movement, an fMRI equivalent of the RP is gradually established in a network that encompasses the bilateral supplementary motor area and parts of the cingulo-opercular network, recently described as the brains Action Mode network (Dosenbach et al. 2025). Importantly, an anticipatory negative shift in the fMRI signal was observed in both single subjects and single epochs. Tongue movement elicited strong synchronization between the orbitofrontal, ventromedial, and temporal pole cortices. Our findings suggest a link between fMRI and electrophysiological recordings of anticipatory motor events in the brain. The method presented here grades brain synchronization along an in-phase and anti-phase continuum and has applications in clinical settings, as well as for cognitive brain mapping that goes beyond anticipation. Significance statementThe readiness potential (RP) is observed in EEG recordings as a slow build-up of negative electrical potential in the supplementary motor area (SMA) starting up to two seconds before self-initiated motor movements. It is believed to play a causal role in motor preparation. Given that it is only detectable by averaging 30-40 epochs, its existence as a real signal has been challenged by new modeling approaches which suggests the early RP-component is merely a summation of stochastic noise. In this study, we demonstrate that the equivalent of the RP is present in functional magnetic resonance imaging (fMRI) data in a distributed network known as the "action mode network". The RP-equivalent is present in single subjects and single epochs.

neuroscience↗

bioRxiv2024

Disclosing temperature sensitivity of West Nile virus transmission: novel computational approaches to mosquito-pathogen trait responses

Temperature influences the transmission of mosquito-borne pathogens with significant implications for disease risk in the context of climate change. Mathematical models of mosquito-borne infections rely on functions that capture mosquito-pathogen interactions in response to temperature to better estimate transmission dynamics. To derive these functions, experimental studies provide valuable data on the temperature sensitivity of mosquito life-history traits and pathogen transmission. However, the scarcity of experimental data and inconsistencies in methodologies for analysing and comparing temperature responses across mosquito species and pathogens present challenges to accurately modelling mosquito-borne infections. In this study, we investigated the thermal biology of West Nile virus (WNV), a major mosquito-borne infection. We critically reviewed existing experimental studies, obtaining temperature responses for eight mosquito-pathogen traits across 15 mosquito species. Using this data, we employed Bayesian hierarchical models to estimate temperature response functions for each trait and estimate their variation between species and experiments. We incorporate the resulting functions into mathematical models to estimate the temperature sensitivity of WNV transmission, focusing on six competent mosquito species of the genus Culex. Our study finds similarities in the temperature response among Culex species, with a general optimal transmission temperature around 24{degrees}C. We demonstrate that differing mechanistic assumptions in published models can result in a temperature optimum variation exceeding 3{degrees}C, underscoring the need for model scrutiny. Additionally, we identify substantial variability between trait temperature responses across experiments on the same mosquito species, indicating significant intra-species variation in trait performance. We provide recommendations for future experimental studies, emphasizing the critical need for additional data on all mosquito-pathogen traits, except for mosquito larva-to-adult development rate and survival, which are relatively well-documented. Incorporating additional data into our multi-species approach enhances the accuracy of temperature trait response estimates. These improvements are critical for forecasting future shifts in mosquito-borne disease risk due to climate change.

ecology↗

bioRxiv2023

An eco-physiological model of forest photosynthesis and transpiration under combined nitrogen and water limitation

Although the separate effects of water and nitrogen (N) limitations on forest growth are well known, the question of how to predict their combined effects remains a challenge for modeling of climate change impacts on forests. Here, we address this challenge by developing a new eco-physiological model that accounts for plasticity in stomatal conductance and leaf N concentration. Based on an optimality principle, our model determines stomatal conductance and leaf N concentration by balancing carbon uptake maximization, hydraulic risk and cost of maintaining photosynthetic capacity. We demonstrate the accuracy of the model predictions by comparing them against gross primary production estimates from eddy covariance flux measurements and sap-flow scaled canopy transpiration in a long-term fertilized and an unfertilized Scots pine (Pinus sylvestris L.) forest in northern Sweden. The model also explains the response to N fertilization as a consequence of (i) reduced carbon cost of N uptake and (ii) increased leaf area per hydraulic conductance. The results suggest that leaves optimally coordinate N concentration and stomatal conductance both on short (weekly) time scales in response to weather conditions and on longer time scales in response to soil water and N availabilities.

ecology↗

bioRxiv2022

Optimal balancing of xylem efficiency and safety explains plant vulnerability to drought

O_LIIn vast areas of the world, the growth of forests and vegetation is water-limited and plant survival depends on the ability to avoid catastrophic hydraulic failure. Therefore, it is remarkable that plants take high hydraulic risks by operating at water potentials ({psi}) that induce partial failure of the water conduits (xylem). Here we present an eco-evolutionary optimality principle for xylem conduit design that explains this phenomenon. C_LIO_LIBased on the hypothesis that conductive efficiency and safety are optimally co-adapted to the environment, we derive a simple relationship between the intrinsic tolerance to negative water potential ({psi}50) and the environmentally dependent minimum xylem{psi} . C_LIO_LIThis relationship is constrained by a physiological tradeoff between xylem conductivity and safety, which is relatively strong at the level of individual conduits although it may be weak at the whole sapwood level. The model explains observed variation in{psi} 50 both across a large number of species, and along the xylem path in two species. The larger hydraulic safety margin in gymnosperms compared to angiosperms is explained as an adaptation to the gymnosperms lower capacity to recover from conductivity loss. C_LIO_LIThe constant xylem safety factor provides a powerful principle for simplifying and improving plant and vegetation models. C_LI

plant biology↗

bioRxiv2022

Brain network integration, flexibility and quasicyclicity during task and rest.

Previous studies have shown that a re-organization of the brains functional connectome expressed in terms of integration and segregation may play a pivotal role. However, it has been proven difficult to capture both processes within a single network-based framework. In this study we apply a hierarchical, spatiotemporally flexible network perspective onto fMRI data to track changes in integration and segregation in time. Our results show that network integration and segregation occur simultaneously in the brain. During task performance, global changes in synchronization between networks arise which are tied to the underlying temporal design of the experiment. We show that a hallmark property of the dynamics of the brains functional connectome is a presence of quasiperiodic patterns of network activation and deactivation, which during task performance becomes intertwined with the underlying temporal structure of the experimental paradigm. The proposed approach to study spatiotemporal changes in network reconfiguration during rest as well as task performance could be useful to identify aberrant network motifs in disease.

neuroscience↗

bioRxiv2021

Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain.

Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.

neuroscience↗

bioRxiv2021

Genetic influences on brain activation and large-scale functional connectivity during nociceptive processing: a twin study

Nociceptive processing in the human brain is a signal that enables harm avoidance, with large interindividual variance. The relative contributions of genes and environment to the neural structures that support nociception have not been studied in twins previously. Here, we employed a classic twin-design to determine brain structures influenced by additive genetics. We found genetic influences on nociceptive processing in the midcingulate cortex, bilateral posterior insulae and thalamus. In addition to brain activations, we found genetic contributions to large-scale functional connectivity during nociceptive processing. We conclude that additive genetics influence specific aspects of nociceptive processing, which improves our understanding of human nociceptive processing.

neuroscience↗

bioRxiv2020

Survival and growth of saprotrophic and mycorrhizal fungi in recalcitrant amine, amide and ammonium containing media

The elimination of hazardous compounds in chemical wastes can be a complex and technically demanding task. In the search for environmental-friendly technologies, fungal mediated remediation and removal procedures are of concern. In this study, we investigated whether there are fungal species that can survive and grow on solely amine-containing compounds. One compound containing a primary amine group; 2-diethylaminoethanol, one compound with a primary amide group; 2,6-dichlorobenzamide (BAM), and a third compound containing a quaternary ammonium group; N3-trimethyl(2-oxiranyl)methanaminium chloride, were selected. The choice of these compounds was motivated by their excessive use in large scale manufacturing of protein separation media (2-diethylaminoethanol and the quaternary amine). 2,6-dichlorobenzamide, the degradation product of the herbicide 2,6-dichlorobenzonitrile (dichlobenil), was chosen since it is an extremely recalcitrant compound. Utilising part of the large fungal diversity in Northern European forests, a screening study using 48 fungal isolates from 42 fungal species, including saprotrophic and mycorrhizal fungi, was performed to test for growth responses to the chosen compounds. The ericoid mycorrhizal fungus Rhizoscyphus ericae showed the best overall growth on 2-diethylaminoethanol and BAM in the 1-20 gL-1 concentration range. A 3500% and 450% increase in biomass, respectively, was observed. For N3-trimethyl(2-oxiranyl)methanaminium chloride, the peak growth occurred at 1 gL-1. In a second experiment, including three of the most promising species (Laccaria laccata, Hygrophorus camarophyllus and Rhizoscyphus ericae) from the screening experiment, a simulated process water containing 1.9% (w/v) 2-diethylaminoethanol and 0.8% (w/v) N3-trimethyl(2-oxiranyl)methanaminium chloride was used. Laccaria laccata showed the best biomass growth increase (380%) relative to a growth control, while the growth increase for Rhizoscyphus ericae and H. camarophyllus were 292% and 136% respectively, showing that also mycorrhizal fungal species can use amine- and amide-containing substrates as nutrients. These results show the potential of certain fungal species to be used in alternative green wastewater treatment procedures.

microbiology↗

bioRxiv2020

Normal Olfactory Functional Connectivity Despite Life-Long Absence of Olfactory Experiences

Congenital blindness is associated with atypical morphology, and functional connectivity within and from, visual cortical regions; changes that are hypothesized to originate from a life-long absence of visual input and could be regarded as a general (re)organization principle of sensory cortices. Challenging this is the fact that individuals with congenital anosmia (life-long olfactory sensory loss) display little to no morphological changes in primary olfactory cortex. To determine whether olfactory input from birth is essential to establish and maintain normal functional connectivity in olfactory processing regions, akin to the visual system, we assessed differences in functional connectivity within olfactory cortex between individuals with congenital anosmia (n=33) and matched controls (n=34). Specifically, we assessed differences in connectivity between core olfactory processing regions as well as differences in regional homogeneity and homotopic connectivity within primary olfactory cortex. In contrast to congenital blindness, none of the analyses indicated atypical connectivity in individuals with congenital anosmia. In fact, post-hoc Bayesian analysis provided support for an absence of group differences. These results suggest that a lifelong absence of olfactory experience has limited impact on the functional connectivity in olfactory cortex, a finding that indicates a clear difference between sensory modalities in how sensory cortical regions develop.

neuroscience↗

bioRxiv2020

Differentiating ADHD subtypes and simulation of in silico therapeutic neuronal excitation in a whole-brain nonlinear dynamic modelling frame-work

Recent advances in non-linear computational and dynamical modelling have opened up the possibility to param etrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully understand disorders of the brain as it may provide a quantitative platform that is capable of binding multiple neurophysiological processes to phenotype profiles. In this study, we apply a newly developed adaptive frequency-based model of whole-brain oscillations to resting-state fMRI data acquired from healthy controls and a cohort of attention deficit hyperactivity disorder (ADHD) subjects. As expected, we found that healthy control subjects differed from ADHD in terms of attractor dynamics. However, we also found a marked dichotomy in neural dynamics within the ADHD cohort. Next, we classified the ADHD group according to the level of distance of each individuals empirical network from the two model-based simulated networks. Critically, the model was mirrored in the empirical behavior data with the two ADHD subgroups displaying distinct behavioral phenotypes related to emotional instability (i.e., depression and hypomanic personality traits). Finally, we investigated the applicability and feasibility of our whole-brain model in a therapeutic setting by conducting in silico excitatory stimulations to parsimoniously mimic clinical neuro-stimulation paradigms in ADHD. We tested the effect of stimulating any individual brain region on the key network measures derived from the simulated brain network and its contribution in rectifying the brain dynamics to that of the healthy brain, separately for each ADHD subgroup. This showed that this was indeed possible for both subgroups. However, the current effect sizes were small suggesting that the stimulation protocol needs to be tailored at the individual level. These findings demonstrate the potential of this new modelling framework to unveil hidden neurophysiological profiles and establish tailored clinical interventions.

neuroscience↗

bioRxiv2019

Dynamic synergetic configurations of resting-state networks in ADHD.

Attention deficit hyperactivity disorder (ADHD) is characterized by high distractibility and impaired executive functions. Notably, there is mounting evidence suggesting that ADHD could be regarded as a default mode network (DMN) disorder. In particular, failure in regulating the dynamics of activity and interactions of the DMN and cognitive control networks have been hypothesized as the main source of task interference causing attentional problems. On the other hand, previous studies indicated pronounced fluctuations in the strength of functional connections over time, particularly for the inter-network connections between the DMN and fronto-parietal control networks. Hence, characterization of connectivity disturbances in ADHD disorder requires a thorough assessment of time-varying FCs. In this study, we propose a dynamical systems perspective to assess how the DMN over time recruits different configurations of network segregation and integration. Specifically, we were interested in configurations for which both intra- and inter-network connections are retained, as opposed to commonly used methods which assess network segregation as a single measure. From resting-state fMRI data, we extracted three different stable configurations of FC patterns for the DMN, namely synergies. We provide evidence supporting our hypothesis that the ADHD brain differs in both recruitment rate and the topology of specific synergies between resting-state networks.

neuroscience↗

bioRxiv2018

Brain network segregation and integration during an epoch-related working memory fMRI experiment

The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0- and 2-back epochs. During the interspersed epochs of baseline, many subnetworks, including the default mode, visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fast-evolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

neuroscience↗

bioRxiv2017

A common framework for the problem of derivingestimates of dynamic functional brain connectivity.

The research field of dynamic functional connectivity explores the temporal properties of brain connectivity. To date, many methods have been proposed, which are based on quite different assumptions. In order to understand in which way the results from different techniques can be compared to each other, it is useful to be able to formulate them within a common theoretical framework. In this study, we describe such a framework that is suitable for many of the dynamic functional connectivity methods that have been proposed. Our overall intention was to derive a theoretical framework that was constructed such that a wide variety of dynamic functional connectivity techniques could be expressed and evaluated within the same framework. At the same time, care was given to the fact that key features of each technique could be easily illustrated within the framework and thus highlighting critical assumptions that are made. We aimed to create a common framework which should serve to assist comparisons between different analytical methods for dynamic functional brain connectivity and promote an understanding of their methodological advantages as well as potential drawbacks.\n\nHighlightsDifferent approaches to compute dynamic functional brain connectivity have been proposed, each with their own assumptions.\n\nWe present a theoretical framework that encompasses a large majority of proposed methods.\n\nOur common framework facilitates comparisons between different methods and illustrates their underlying assumptions.

neuroscience↗

bioRxiv2017

A simulation and comparison of dynamic functional connectivity methods

There is a current interest in quantifying brain dynamic functional connectivity (DFC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brains dynamics. However, given that the ground truth for DFC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exists many DFC methods it is difficult to assess differences in dynamic brain connectivity between studies. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating DFC (sliding window, tapered sliding window, temporal derivative, spatial distance and jackknife correlation). In particular, we were interested in each methods ability to track changes in covariance over time, which is a key property in DFC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future DFC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. In this paper, we present dfcbenchmarker, which is a Python package where researchers can easily submit and compare their own DFC methods to evaluate its performance.

neuroscience↗