Biology subjects

Friedrich, P.

Publications and source records attributed to Friedrich, P..

12 recordsLinked to original sources

bioRxiv2026

MedMCP: An Open Agentic Ecosystem for Medical Imaging Workflows

Deep learning has driven rapid progress in medical image analysis, with methods for tasks such as organ segmentation, anomaly detection, and radiology report generation now matching or surpassing expert performance on several benchmarks. While many methods are released as open source, their routine adoption by clinicians and medical researchers remains limited. The bottleneck is not a lack of capable tools, but the absence of infrastructure to make them accessible to non-technical users. To break this barrier, we present MedMCP, an agentic framework that solves this last-mile problem along two axes: (1) a local agentic workspace that lets users apply state-of-the-art image analysis methods through natural language, and (2) a community-driven tool ecosystem that lets developers deploy and distribute capabilities as validated, independently testable servers. MedMCP enforces a strict orchestration-execution boundary in which a local language model translates user intent into workflow plans, while all image analysis is delegated to domain-specific tools. The framework runs entirely on-premise, every side-effecting operation passes through a human-in-the-loop permission gate, and a full provenance trail links each output to the tools, parameters, and inputs that produced it. An inspectable workspace lets users verify intermediate results rather than blindly trusting the agent, and completed sessions distill into deterministic, shareable workflows that re-execute without the agent, turning one-off interactions into reproducible analyses. This paper introduces the project's general idea and describes the design principles that guide its development. Beyond a single toolkit, we envision MedMCP as a community-driven platform where each contributed tool becomes easily accessible, compounding individual efforts into shared capability.

bioinformatics↗

bioRxiv2024

Self organisation of invasive breast cancer driven by the interplay of active and passive nematic dynamics

In invasive breast cancer, cell clusters of varying sizes and shapes are embedded in the fibrous extracellular matrix (ECM). Although the prevailing view attributes this structure to increasing disorder resulting from loss of function and dedifferentiation, our findings reveal that it arises through a process of active self-organization driven by cancer cell motility. Simulations and histological analyses of tumours from over 2,000 breast cancer patients reveal that motile, aligned cancer cells within clusters move as active nematic aggregates through the surrounding highly aligned ECM fibres, which form a confining, passive nematic phase. Cellular motion leads to cluster splitting and coalescence. The degree of cluster activity, combined with heterogeneity in cell motility, is reflected in specific scaling behaviours for cluster shape, size distribution, and the distance between cluster boundaries and nematic defects in ECM alignment. Increased activity estimates correlate with tumour progression and are associated with a poorer prognosis for patients.

biophysics↗

bioRxiv2024

Test Retest Reliability of Meta Analytic Networks During Naturalistic Viewing

Functional connectivity analyses have given considerable insights into human brain function and organization. As research moves towards clinical application, test-retest reliability has become a main focus of the field. So far, the majority of studies have relied on resting-state paradigms to examine brain connectivity, based on its low demand and ease of implementation. However, the reliability of resting-state measures is mostly moderate, potentially due to its unconstrained nature. Recently, naturalistic viewing paradigms have gained popularity because they probe the human brain under more ecologically valid conditions, thereby possibly increasing reliability. Therefore, we here compared the reliability of graph metrics extracted from resting-state and naturalistic viewing in functional networks, across two sessions. We show that naturalistic viewing can increase reliability over resting-state, but that its effect varies between stimuli and networks. Furthermore, we demonstrate that the effect of naturalistic viewing differs between two cohorts with Asian and European cultural backgrounds. Taken together, our study encourages the use of naturalistic viewing to increase reliability, but emphasizes the need to carefully select the appropriate stimulus and network for the respective research question.

neuroscience↗

bioRxiv2024

Inter- and intra-subject similarity in network functional connectivity across a full narrative movie

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging (fMRI), are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. However, in how far NV elicits similarity within and between subjects on a network level, particularly depending on emotions portrayed in movies, is currently unknown. We used the studyforrest dataset to investigate the inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into 8 consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results showed that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. Lowest similarity both within and between subjects was seen in the emotional regulation network and networks associated with long-term memory processing, which might be explained by specific features and content of the movie. We conclude that detailed characterization of movie features is crucial for NV research.

neuroscience↗

bioRxiv2023

Sex classification from functional brain connectivity: Generalization to multiple datasets

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.

neuroscience↗

bioRxiv2022

To start or to discontinue the pill - changes in progestogens reflected by resting-state connectivity and positive mood

Oral contraceptive (OC) intake has been associated with alterations in functional brain architecture and socio-affective processes. However, most previous studies have been limited by cross-sectional designs and/or did not account for synthetic sex hormone concentrations. The aim of this longitudinal study was to determine the effects of starting vs discontinuing OCs on socio-affective functions such as mood and emotion cognition, and to identify their possible neuroendocrinological substrates. To this end, 88 young healthy women performed the behavioral and fMRI measures twice, three to eight months apart: 26 natural cycling women twice during menstruation, 26 OC users twice during OC intake, 25 OC discontinuers and 11 OC starters before and after discontinuation or start, respectively. In addition to mean-based analyses, we used intersubject representational similarity analyses to determine relationships between interindividual variability in within-subject changes of hormone profiles, including concentrations of endogenous and synthetic hormones, region-specific resting state functional connectivity (parcelwise RSFC) and socio-affective measures. Across the whole sample, interindividual patterns of changes in RSFC of fronto-parietal regions, parts of the left hippocampus and the right cerebellum reflected change patterns of progestogen levels. For the right superior orbitofrontal gyrus (OFG), a trinity of idiosyncratic patterns was found in changes of progestogens, RSFC and positive mood. Active OC intake was associated with higher self-reported depressive symptoms in OC discontinuers (and starters). Emotion recognition performance was not associated with changes in hormone profiles or RSFC. Overall, progestogens rather than estrogens appear to be associated with functional brain architecture of the frontal and subcortical/cerebellar regions and positive mood. The right superior OFG represents a possible neural substrate for progestogen-induced changes in positive mood. This study indicates the importance of a multidimensional, longitudinal approach when being interested in effects of hormonal contraception on womens brain and behavior.

neuroscience↗

bioRxiv2022

A topography-based predictive framework for naturalistic viewing fMRI

Recent work has shown great interest in understanding individual differences in complex brain function under naturalistic viewing (NV) conditions. However, methods specifically designed for achieving this goal remain limited. Here, we propose a novel approach, called TOpography-based Predictive Framework (TOPF), to investigate individual differences in evoked brain activity on NV fMRI data. Specifically, TOPF identifies individual-specific evoked activity topographies in a data- driven manner and examines their behavioural relevance using a machine learning predictive framework. Our results show that these topographies successfully predict individual phenotypes across cognition, emotion and personality on unseen subjects, and the identified predictive brain regions are neurobiologically interpretable. Further, the prediction accuracy exceeds that of the commonly-used functional connectivity-based features. Conceptually, we highlight the importance of examining multivariate evoked activity patterns for studying brain-behaviour relationships. In summary, we provide a powerful tool for understanding individual differences and brain-behaviour relationships on NV fMRI data.

neuroscience↗

bioRxiv2021

Population-level asymmetry of the cerebral cortex: reproducibility, lifespan changes, heritability, and individual differences

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the genetic and later influences that shape them, such as age. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in 7 datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry correlates phenotypically and genetically in specific regions, and is low-moderately heritable (max h2SNP [~]19%). In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises in early life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

neuroscience↗

bioRxiv2021

A multiscale approach for computing gated ligand binding from molecular dynamics and Brownian dynamics simulations

We develop an approach to characterise the effects of gating by a multi-conformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterise them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multi-state gating theory from which we derive a gating factor{gamma} that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open and wide-open) and two inaccessible (closed and a newly identified, parted) macrostate conformations. The parted conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because{gamma} =0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease - that have a wide range of experimental association constants (~104 - 1010 M-1s-1) - yields gated rate constants in the range ~0.5-5.7 x 108 M-1s-1. This indicates that, whereas the association rate of some inhibitors could be described by the model, for many inhibitors either subsequent conformational transitions or alternate binding mechanisms may be rate-limiting. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands. Graphical TOC Entry O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biophysics↗

bioRxiv2020

Fear learning sculpts functional brain connectivity at rest beyond the traditional fear network in humans

Neuroscientific research has identified specific brain networks involved in the acquisition of fear memories. Using fMRI to assess changes in resting-state functional connectivity (RSFC) induced by fear acquisition, single brain regions from these networks have also been linked to fear memory consolidation. However, previous studies only examined RSFC changes within restricted sets of brain regions or without a proper control group, leaving our knowledge about fear consolidation outside of traditional fear networks incomplete. Here, we tested a group of 84 healthy participants in a differential fear conditioning paradigm and quantified RSFC changes between 358 cortical and 16 subcortical brain areas. Subsequent to fear learning, 21 functional connections exhibited significant RSFC changes. Importantly, these connections were not restricted to the traditional fear networks but also comprised various frontal and visual areas. Our findings indicate that fear memory consolidation is a complex process that integrates relevant information across the entire brain.

neuroscience↗

bioRxiv2020

Anchoring the human olfactory system to a functional gradient

Margulies et al. (2016) demonstrated the existence of at least five independent functional connectivity gradients in the human brain. However, it is unclear how these functional gradients might link to anatomy. The dual origin theory proposes that differences in cortical cytoarchitecture originate from two trends of progressive differentiation between the different layers of the cortex, referred to as the hippocampocentric and olfactocentric systems. When conceptualising the functional connectivity gradients within the evolutionary framework of the Dual Origin theory, the first gradient likely represents the hippocampocentric system anatomically. Here we expand on this concept and demonstrate that the fifth gradient likely links to the olfactocentric system. We describe the anatomy of the latter as well as the evidence to support this hypothesis. Together, the first and fifth gradients might help to model the Dual Origin theory of the human brain and inform brain models and pathologies.

neuroscience↗

bioRxiv2020

Mapping the principal gradient onto the corpus callosum

Gradients capture some of the variance of the resting-state functional magnetic resonance imaging (rsfMRI) signal. Amongst these, the principal gradient depicts a functional processing hierarchy that spans from sensory-motor cortices to regions of the default-mode network. While the cortex has been well characterised in terms of gradients little is known about its underlying white matter. For instance, comprehensive mapping of the principal gradient on the largest white matter tract, the corpus callosum, is still missing. We hence mapped the principal gradient onto the callosal midsection using the 7T human connectome project dataset. We further explored how quantitative measures and variability in callosal midsection connectivity relate to the principal gradient values. In so doing, we demonstrated that the extreme values of the principal gradient are located within the callosal genu and the posterior body, have lower connectivity variability but a larger spatial extent along the midsection of the corpus callosum than mid-range values. Our results shed light on the relationship between the brains functional hierarchy and the corpus callosum. We further speculate about how these results may bridge the gap between functional hierarchy, brain asymmetries, and evolution.

neuroscience↗