arXiv subjects

Martin G. Frasch

Publications and source records attributed to Martin G. Frasch.

At least 19 recordsLinked to original sources

arXiv2026

Maternal Anxiety During Pregnancy and Predictive Processing Across Development: A Cross-Cohort Empirical Reappraisal

Prenatal maternal distress has been linked to differences in offspring brain and behavioural development, but is rarely framed computationally. Predictive-processing (PP) accounts hold that perception, cognition, and action arise from the interplay of incoming input and internally generated predictions, governed in part by precision. We reappraised eleven publications from two non-clinical prenatal cohorts for a coherent PP-compatible developmental pattern. Eight Leuven publications spanned adolescence to adulthood: externally cued response inhibition was largely preserved, whereas differences emerged when task goals, stimulus relevance, expected outcomes, and response policies had to be generated or maintained endogenously. At age 28, higher prenatal anxiety was associated with lower radial and mean diffusivity in the left superior posterior corona radiata, lower Vocabulary scores, weaker medial prefrontal to left inferior frontal connectivity, and greater unpleasantness of induced dyspnoea. Three Tilburg ERP publications covered infancy and early childhood: higher prenatal anxiety was associated with increased processing of repeated standard tones, of fearful over happy vocalizations, and of neutral over pleasant or unpleasant pictures. Maternal mindfulness, the only positive prenatal characteristic examined, showed the opposite association for the repeated standard, suggesting that positive and distress-related states may act differently on shared mechanisms. Across cohorts, findings were compatible with differences in how relevance and processing resources are allocated to predictable, threat-related, ambiguous, contextual, feedback-related, and bodily information. Because these publications did not formally estimate precision, prediction errors, or priors, we treat them as hypothesis-supporting rather than a formal test of the account.

q-bio.QM↗

arXiv2026

World-Time Compute with Verified Code World Models

LLMs generalize across a domain only after seeing many real, labeled examples, which most domains lack. We study a way to manufacture it cheaply. When a domain's dynamics can be written as code, one template instantiates into many world models: executable, verifiable programs over symbolic state, each an inexhaustible source of exactly-labeled trajectories. Fine-tuning an LLM on trajectories through many such worlds, which we call world-time compute, a training-time analogue of test-time compute, lifts generalization to held-out worlds it never trained on (synthesized world families). Gains are largest where capability is scarcest: +29 points at 0.5B; the largest model's lift is within noise, consistent with saturation. Labels can be trusted because the worlds are verified code: synthesized-then-checked dynamics are exact over 20-step rollouts and answer 10x out-of-distribution probes exactly (100%), whereas per-step LLM and MLP predictors compound error and collapse. Unlike domain randomization, each world is independently authored and verified; a corrupted-label control shows label exactness, not task variety, drives the gains. On real benchmarks (ARC-AGI grids, List Functions, CLRS) the same lever holds as per-world test-time training. On List Functions the harder cross-world form holds: one adapter trained on 128 disjoint worlds reaches 40% on held-out worlds versus 6% for a corrupted-label control (+34 points, CI [29, 39]). The gain is a saturating regularity, not a law: largest for few-step reasoning and small/weak models, fading for long chains, perception-induced tasks, and saturated tasks; cross-task transfer is weak without shared skill. Worlds are authored and served by OpenWorld, a zero-dependency framework (companion paper). Scope: symbolic state; pixel-native domains remain territory of learned models. All code, recipes, and this manuscript regenerate from one repository.

cs.LG↗

arXiv2026

Modularity Emerges from Action-Functional Constraints in Marine Metabolic Networks: A Biology-Scale Validation of the Network-Weighted Action Principle

Biological systems operate under simultaneous energetic and informational constraints, yet direct evidence that such constraints shape real metabolic networks is limited. The Network-Weighted Action Principle predicts that networks under these constraints should organize toward high modularity. We tested this prediction in marine microbiome metabolic networks reconstructed from Tara Oceans metagenomes using two complementary approaches. Composite metrics of protein-deployment efficiency and functional-repertoire complexity (n=10) failed under causal-inference diagnostics, with apparent structure dominated by shared-component bias. In contrast, network modularity (n=7) was high (Q ~ 0.987), but this value was shown to arise from sparsity alone. The biologically meaningful signal is the excess over null models: modularity exceeded configuration-model, label-permutation, and bipartite-incidence nulls by Delta Q ~ 0.15-0.40 (p < 0.001), with the largest effect under the bipartite-incidence control. Fine-grained communities recovered by the network partition are not arbitrary: 25% recur across samples, and the most consistent modules map to known functional units, including enzyme subunits, biosynthetic sequences, and transporter complexes. Together, these results show that modularity excess - rather than absolute modularity - is the appropriate signature of biological organization, and that such excess is consistent with cost-minimization principles operating at the scale of natural metabolic networks.

q-bio.MN↗

arXiv2026

minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation

Modern machine learning optimizes for accuracy without explicit treatment of internal computational cost, even though physical and biological systems operate under intrinsic energy constraints. We evaluate energy-aware learning across 2,203 experiments spanning vision, text, neuromorphic, and physiological datasets with 10 seeds per configuration and factorial statistical analysis. Three findings emerge. First, architecture alone explains negligible variance in accuracy (partial eta^2 = 0.001), while the architecture x dataset interaction is large (partial eta^2 = 0.44, p < 0.001), demonstrating that optimal architecture depends critically on task modality and rejecting the assumption of a universal best architecture. Second, a controlled lambda-sweep across lambda in {0, 1e-5, 1e-4, 1e-3, 1e-2} validates a single-parameter energy-regularized objective L = L_CE + lambda * E(theta, x): across this range, internal activation energy decreases by approximately three orders of magnitude relative to the unregularized lambda=0 baseline, with negligible accuracy change (<0.5 percentage points) on both MNIST and Fashion-MNIST. Third, energy-first architectures inspired by an action-principle framework yield 5-33% within-modality training-efficiency gains over conventional baselines. These results emerge from a research program that interprets learning through a structural correspondence between the action functional in classical mechanics, free energy in statistical physics, and KL-regularized objectives in variational inference. We frame this correspondence as a design hypothesis, not a derivation.

cs.LG↗

arXiv2026

Minimum-Action Learning: Energy-Constrained Symbolic Model Selection for Physical Law Identification from Noisy Data

Identifying physical laws from noisy observational data is a central challenge in scientific machine learning. We present Minimum-Action Learning (MAL), a framework that selects symbolic force laws from a pre-specified basis library by minimizing a Triple-Action functional combining trajectory reconstruction, architectural sparsity, and energy-conservation enforcement. A wide-stencil acceleration-matching technique reduces noise variance by 10,000x, transforming an intractable problem (SNR ~0.02) into a learnable one (SNR ~1.6); this preprocessing is the critical enabler shared by all methods tested, including SINDy variants. On two benchmarks -- Kepler gravity and Hooke's law -- MAL recovers the correct force law with Kepler exponent p = 3.01 +/- 0.01 at ~0.07 kWh (40% reduction vs. prediction-error-only baselines). The raw correct-basis rate is 40% for Kepler and 90% for Hooke; an energy-conservation-based criterion discriminates the true force law in all cases, yielding 100% pipeline-level identification. Basis library sensitivity experiments show that near-confounders degrade selection (20% with added r^{-2.5} and r^{-1.5}), while distant additions are harmless, and the conservation diagnostic remains informative even when the correct basis is absent. Direct comparison with noise-robust SINDy variants, Hamiltonian Neural Networks, and Lagrangian Neural Networks confirms MAL's distinct niche: interpretable, energy-constrained model selection that combines symbolic basis identification with dynamical rollout validation.

cs.LG↗

arXiv2026

From Static Repositories to Agentic Knowledge Webs: ResearchTwin and the S-Index for Federated Human-AI Research Discovery

The exponential growth of scientific literature, datasets, and code repositories has created a discovery bottleneck that impedes knowledge synthesis and reproducibility. Traditional dissemination formats -- static PDFs, siloed code hosting, and fragmented data repositories -- fail to represent the interconnected narrative of modern research, while conventional metrics such as the H-index neglect contributions from reusable code and shared datasets. We present ResearchTwin, an open-source federated platform that transforms a researcher's scholarly output into a conversational digital twin, with a preliminary evaluation of its deployed prototype. The system uses a Bimodal Glial-Neural Optimization (BGNO) architecture comprising a Multi-Modal Connector Layer, a Glial Layer for caching and rate management, and a Neural Layer implementing Retrieval-Augmented Generation with a provider-agnostic LLM backend. We formalize the S-index, building on our earlier QIC framework, into a composite metric that extends FAIR principles -- via a binary accessibility/licensing gate, field-normalized impact scoring, and geometric collaboration scaling -- to quantify multimodal research impact. A case study comparing two researchers with similar H-indexes but substantially different S-indexes demonstrates that the metric captures dimensions of impact -- particularly dataset and code contributions -- invisible to citation-based measures alone. ResearchTwin exposes an inter-agentic discovery API using Schema.org typed responses and HATEOAS navigation, enabling AI agents to discover cross-lab synergies. A three-tier federated architecture preserves data sovereignty while enabling global discoverability.

cs.DL↗

arXiv2026

Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation

Prenatal psychological stress affects 15-25% of pregnancies and increases risks of preterm birth, low birth weight, and adverse neurodevelopmental outcomes. Current screening relies on subjective questionnaires (PSS-10), limiting continuous monitoring. We developed deep learning models for stress detection from electrocardiography (ECG) using the FELICITy 1 cohort (151 pregnant women, 32-38 weeks gestation). A ResNet-34 encoder was pretrained via SimCLR contrastive learning on 40,692 ECG segments per subject. Multi-layer feature extraction enabled binary classification and continuous PSS prediction across maternal (mECG), fetal (fECG), and abdominal ECG (aECG). External validation used the FELICITy 2 RCT (28 subjects, different ECG device, yoga intervention vs. control). On FELICITy 1 (5-fold CV): mECG 98.6% accuracy (R2=0.88, MAE=1.90), fECG 99.8% (R2=0.95, MAE=1.19), aECG 95.5% (R2=0.75, MAE=2.80). External validation on FELICITy 2: mECG 77.3% accuracy (R2=0.62, MAE=3.54, AUC=0.826), aECG 63.6% (R2=0.29, AUC=0.705). Signal quality-based channel selection outperformed all-channel averaging (+12% R2 improvement). Mixed-effects models detected a significant intervention response (p=0.041). Self-supervised deep learning on pregnancy ECG enables accurate, objective stress assessment, with multi-layer feature extraction substantially outperforming single embedding approaches.

q-bio.QM↗

arXiv2025

Measuring the time-scale-dependent information flow between maternal and fetal heartbeats during the third trimester

Prenatal maternal stress alters maternal-fetal heart rate coupling, as demonstrated by the Fetal Stress Index derived from bivariate phase-rectified signal averaging. Here, we extend this framework using information-theoretical measures to elucidate underlying mechanisms. In 120 third-trimester pregnancies (58 stressed, 62 control), we computed transfer entropy (TE), entropy rate (ER), and sample entropy (SE) under multiple conditioning paradigms, employing mixed linear models for repeated measures. We identify dual coupling mechanisms at the short-term (0.5 - 2.5 s), but not long-term (2.5 - 5 s) time scales: (1) stress-invariant state-dependent synchronization, with maternal decelerations exerting approximately 60% coupling strength on fetal heart rate complexity - a fundamental coordination conserved across demographics; and (2) stress-sensitive temporal information transfer (TE), showing exploratory associations with maternal cortisol that require replication. A robust sex-by-stress interaction emerged in TE from mixed models, with exploratory female-specific coupling patterns absent in males. Universal acceleration predominance was observed in both maternal and fetal heart rates, stronger in fetuses and independent of sex or stress. We provide insight into the dependence of these findings on the sampling rate of the underlying data, identifying 4 Hz, commonly used for ultrasound-derived fetal heart rate recordings, as the necessary and sufficient sampling rate regime to capture the information flow. Information-theoretical analysis reveals that maternal-fetal coupling operates through complementary pathways with differential stress sensitivity, extending the Fetal Stress Index by elucidating causal foundations. Future studies should explore additional information-theoretical conditional approaches to resolve stress-specific and time-scale-specific differences in information flow.

q-bio.QM↗

arXiv2025

The QIC-Index: A Novel, Data-Centric Metric for Quantifying the Impact of Research Data Sharing

We introduce the QIC-Index, a novel metric to address the failure of publication-centric metrics to value research data sharing. The QIC-Index quantifies the impact of individual data objects by calculating a score based on their Quality (Q), Impact (I), and Collaboration (C). By rewarding the sharing of high-quality, impactful, and collaborative data, our framework aligns individual incentives with the goals of open science and aims to foster a more transparent and efficient research culture.

cs.DL↗

arXiv2025

Cross-Domain Transfer of Depression Voice Biomarkers Depends on the Outcome Instrument: Leakage-Controlled Cross-Sectional Evaluation Study

Whether voice biomarkers of depression generalize across clinical settings is largely untested. Generalization is usually framed as a question about populations. It is also a question about the outcome instrument a model is scored against -- a dimension largely unexamined. In a US-nationwide online study, 446 sessions from 390 pregnant participants at 22 weeks' gestation (analytical N=316) gave four voice recordings each, the PHQ-8 and a modified 9-item EPDS. Discrimination was assessed under leakage-controlled cross-validation with inside-fold selection, participant-grouped folds, a negative-control harness and multiplicity adjustment across a pre-specified 4 task x 4 outcome grid. The prenatal-trained model was applied to two general-psychiatric corpora (DAIC-WOZ, N=189; E-DAIC, N=219) under pure held-out inference; an open-source model trained on ~35,000 individuals was applied without refitting to all three. The pre-registered within-cohort outcome performed at chance (AUC 0.494, 95% CI 0.431-0.560), and no grid cell survived multiplicity adjustment under either of two modeling paradigms. The prenatal-trained model did not transfer (AUC 0.505, 0.478). Transfer in the reverse direction depended on the instrument, not the population: the general-population model reached 0.706-0.708 on general-psychiatric speech, 0.510 against the PHQ-8 in pregnant women, and 0.645 against the modified EPDS in the same women. The mechanism is visible at item level: the model tracks affective distress and is flat on somatic items, while the PHQ-8 in pregnancy is dominated by somatic content that gestation elevates. Validation should specify population, elicitation task and outcome instrument; a tool validated against one instrument should not be assumed to work against another. This version supersedes v1: a label scoring defect found in peer review is corrected and all analyses redone.

q-bio.QM↗

arXiv2023

Brain development dictates energy constraints on neural architecture search: cross-disciplinary insights on optimization strategies

Present day artificial neural architecture search (NAS) strategies are essentially prediction-error-optimized. That holds true for AI functions in general. From the developmental neuroscience perspective, I present evidence for the central role of metabolically, rather than prediction-error-optimized neural architecture search (NAS). Supporting evidence is drawn from the latest insights into the glial-neural organization of the human brain and the dynamic coordination theory which provides a mathematical foundation for the functional expression of this optimization strategy. This is relevant to devising novel NAS strategies in AI, especially in AGI. Additional implications arise for causal reasoning from deep neural nets. Together, the insights from developmental neuroscience offer a new perspective on NAS and the foundational assumptions in AI modeling.

q-bio.NC↗

arXiv2022

Vagus nerve manipulation and microglial plasticity in the prenatal brain

The efferent and afferent effects of the vagus nerve on the developing brain have remained enigmatic. Here we review the evidence of such effects on microglial plasticity in the sheep model of human fetal development, one of the most recognized and deployed models of human fetal physiology. We show that vagotomy alters microglial phenotype and that this effect is hormetic under conditions of mild systemic inflammation, as may occur antepartum with chorioamnionitis. We present the methodology to assess not only biomarker-based microglial activation but also the morphometric features of the microglia. Together, these assessments provide a more comprehensive toolbox of glial phenotypical characterizations, especially in the context of investigating the locoregional vagal control of glial function. The presented findings support the earlier discoveries in preclinical and clinical models of adult physiology whereby vagotomy appeared neuroprotective for Parkinson disease, explained, at least in part, by the effects on microglia. In addition, we present the approach to measure and the findings on regional cerebral blood flow changes in relation to vagus nerve manipulation. In summary, the body of evidence underscores the importance of both the efferent and the afferent vagal pathways, via the vagus nerve, in the programming of microglial phenotype in the developing brain. The significance of these relationships for developing and treating early susceptibility to neuroinflammatory and neurodegenerative disorders in later life requires further studies.

q-bio.TO↗

arXiv2022

The vagus nerve regulates immunometabolic homeostasis in the ovine fetus near term: impact on terminal ileum

The contribution of the vagus nerve to inflammation and glucosensing in the fetus is not understood. We hypothesized that vagotomy (Vx) will trigger a rise in systemic glucose levels and this will be enhanced during systemic and organ-specific inflammation. Efferent vagus nerve stimulation (VNS) should reverse this phenotype. Near-term fetal sheep (n=57) were surgically prepared with vascular catheters and ECG electrodes as control and treatment groups (lipopolysaccharide (LPS), Vx+LPS, Vx+LPS+selective efferent VNS). Fetal arterial blood samples were drawn for 7 days to profile inflammation (IL-6), insulin, blood gas and metabolism (glucose). At 54 h, a necropsy was performed; terminal ileum macrophages; CD11c (M1 phenotype) immunofluorescence was quantified to detect inflammation. Across the treatment groups, blood gas and cardiovascular changes indicated mild septicemia. At 3 h, in the LPS group IL-6 peaked; that peak was decreased in Vx+LPS400 and doubled in Vx+LPS800 group; the efferent VNS sped up the reduction of the inflammatory response profile over 54 h. M1 macrophage activity was increased in the LPS and Vx+LPS800 groups only. Glucose and insulin levels in the Vx+LPS group were respectively 1.3-fold and 2.3-fold higher vs. control at 3 h, and the efferent VNS normalized glucose levels. Complete withdrawal of vagal innervation results in a 72h delayed onset of sustained increase in glucose levels for at least 54h and intermittent hyperinsulinemia. Under conditions of moderate fetal inflammation, this is related to higher levels of gut inflammation; the efferent VNS reduces the systemic inflammatory response as well as restores both the levels of glucose and terminal ileum inflammation, but not the insulin levels. Our findings reveal a novel regulatory, hormetic, role of the vagus nerve in the immunometabolic response to endotoxin in near-term fetuses.

q-bio.TO↗

arXiv2021

The role of the vagus nerve during fetal development and its relationship with the environment

The autonomic nervous system (ANS) regulatory capacity begins before birth as the sympathetic and parasympathetic activity contributes significantly to the fetus' development. Several studies have shown how vagus nerve is involved in many vital processes during fetal, perinatal and postnatal life: from the regulation of inflammation through the anti-inflammatory cholinergic pathway, which may affect the functioning of each organ, to the production of hormones involved in bioenergetic metabolism. In addition, the vagus nerve has been recognized as the primary afferent pathway capable of transmitting information to the brain from every organ of the body. Therefore, this hypothesis paper aims to review the development of ANS during fetal and perinatal life, focusing particularly on the vagus nerve, to identify possible "critical windows" that could impact its maturation. These "critical windows" could help clinicians know when to monitor fetuses to effectively assess the developmental status of both ANS and specifically the vagus nerve. In addition, this paper will focus on which factors (i.e. fetal characteristics and behaviors, maternal lifestyle and pathologies, placental health and dysfunction, labor, incubator conditions, and drug exposure) may have an impact on the development of the vagus during the above-mentioned "critical window" and how. This analysis could help clinicians and stakeholders define precise guidelines for improving the management of fetuses and newborns, particularly to reduce the potential adverse environmental impacts on ANS development that may lead to persistent long-term consequences. Since the development of ANS and the vagus influence have been shown to be reflected in cardiac variability, this paper will rely in particular on studies using fetal heart rate variability (fHRV) to monitor the continued growth and health of both animal and human fetuses.

q-bio.TO↗

arXiv2021

Detection of preventable fetal distress during labor from scanned cardiotocogram tracings using deep learning

Despite broad application during labor and delivery, there remains considerable debate about the value of electronic fetal monitoring (EFM). EFM includes the surveillance of the fetal heart rate (FHR) patterns in conjunction with the maternal uterine contractions providing a wealth of data about fetal behavior and the threat of diminished oxygenation and perfusion. Adverse outcomes universally associate a fetal injury with the failure to timely respond to FHR pattern information. Historically, the EFM data, stored digitally, are available only as rasterized pdf images for contemporary or historical discussion and examination. In reality, however, they are rarely reviewed systematically. Using a unique archive of EFM collected over 50 years of practice in conjunction with adverse outcomes, we present a deep learning framework for training and detection of incipient or past fetal injury. We report 94% accuracy in identifying early, preventable fetal injury intrapartum. This framework is suited for automating an early warning and decision support system for maintaining fetal well-being during the stresses of labor. Ultimately, such a system could enable a physician to timely respond during labor and prevent adverse outcomes. When adverse outcomes cannot be avoided, they can provide guidance to the early neuroprotective treatment of the newborn.

q-bio.QM↗

arXiv2021

Prenatal stress perturbs fetal iron homeostasis in a sex-specific manner

What is the influence of chronic maternal prenatal stress (PS) on fetal iron homeostasis? In a prospective case-control study in 164 pregnant women, we show that cord blood transferrin saturation is lower in male stressed neonates. The total effect of PS exposure on fetal ferritin revealed a decrease of 15.4% compared with controls. Electrocardiogram-based Fetal Stress Index (FSI) identified affected fetuses non-invasively during the third trimester of gestation. FSI-based timely detection of fetuses affected by PS can support early individualized iron supplementation and neurodevelopmental follow-up to prevent long-term sequelae due to PS-exacerbated impairment of the iron homeostasis.

q-bio.QM↗

arXiv2021

Update on the multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs: auditory, sensory and high-frequency sensory evoked potentials

We expand from a spontaneous to an evoked potentials (EP) data set of brain electrical activities as electrocorticogram (ECoG) and electrothalamogram (EThG) in juvenile pig under various sedation, ischemia and recovery states. This EP data set includes three stimulation paradigms: auditory (AEP, 40 and 2000 Hz), sensory (SEP, left and right maxillary nerve) and high-frequency oscillations (HFO) SEP. This permits derivation of electroencephalogram (EEG) biomarkers of corticothalamic communication under these conditions. The data set is presented in full band sampled at 2000 Hz. We provide technical validation of the evoked responses for the states of sedation, ischemia and recovery. This extended data set now permits mutual inferences between spontaneous and evoked activities across the recorded modalities. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.

q-bio.NC↗

arXiv2021

Distance to healthy cardiovascular dynamics from fetal heart rate scale-dependent features in pregnant sheep model of human labor predicts cardiovascular decompensation

The overarching goal of the present work is to contribute to the understanding of the relations between fetal heart rate (FHR) temporal dynamics and the well-being of the fetus, notably in terms of predicting cardiovascular decompensation (CVD). It makes uses of an established animal model of human labor, where fourteen near-term ovine fetuses subjected to umbilical cord occlusions (UCO) were instrumented to permit regular intermittent measurements of metabolites, pH, and continuous recording of electrocardiogram (ECG) and systemic arterial blood pressure (to identify CVD) during UCO. ECG-derived FHR was digitized at the sampling rate of 1000 Hz and resampled to 4Hz, as used in clinical routine. We focused on four FHR variability features which are tunable to temporal scales of FHR dynamics, robustly computable from FHR sampled at $4$Hz and within short-time sliding windows, hence permitting a time-dependent, or local, analysis of FHR which helps dealing with signal noise. Results show the sensitivity of the proposed features for early detection of CVD, correlation to metabolites and pH, useful for early acidosis detection and the importance of coarse time scales (2.5 to 8 seconds) which are not disturbed by the low FHR sampling rate. Further, we introduce the performance of an individualized self-referencing metric of the distance to healthy state, based on a combination of the four features. We demonstrate that this novel metric, applied to clinically available FHR temporal dynamics alone, accurately predicts the time occurrence of CVD which heralds a clinically significant degradation of the fetal health reserve to tolerate the trial of labor.

physics.med-ph↗