Biology subjects

Frank, R.

Publications and source records attributed to Frank, R..

13 recordsLinked to original sources

bioRxiv2026

Social attention and coordinated movement produce dissociable spectral and network changes during dyadic improvisational dance

Dance is increasingly delivered as an intervention for social and affective functioning, but a live dyadic encounter confounds several distinct ingredients - social attention, verbal engagement, coordinated movement, and in-the-moment generation - so its neural effects cannot be assigned to any one of them. We recorded dual-brain mobile EEG from adult human participants of both sexes (16 female, 2 male) and a dance instructor across six five-minute conditions that vary these ingredients independently: solitary rest, direct eye gaze, conversation, teacher-led and participant-led mirroring, and free improvisation, with concurrent head-mounted accelerometry. Referencing each condition to eye gaze - socially engaging but nearly motionless - rather than to solitary rest partitioned the response. The components proved dissociable. Nonverbal social attention alone reorganized the power spectrum, flattening the aperiodic exponent (g = -0.97) and desynchronizing alpha-band oscillations (g = -0.90), while leaving inter-regional connectivity unchanged (amplitude g = +0.15; phase g = +0.11). Verbal engagement added high-frequency amplitude coupling (g = +2.61) without further spectral change. Coordinated movement added a decrease in theta/alpha coupling involving the frontoparietal-control network (g = -0.59), the signature of an externally directed state, alongside a further spectral shift (g = -0.61). Movement-vigor covariates, corticokinematic coherence, and aperiodic-adjusted power indicated that the spectral effects were cortical rather than myogenic. Four weeks of improvisational dance training increased empathic responsiveness to others' distress relative to a movie control (g = 0.82). Coordinated social movement thus acts on the brain componentially, with mutual attention rather than motor load establishing the engaged state.

neuroscience↗

bioRxiv2026

Amplitude without phase: interpersonal movement coordination grades inter-brain coupling

Interpersonal movement coordination is a foundational feature of human sociality, scaffolding communication, affiliation, and joint action from infancy onward. Whether this behavioral coupling is accompanied by genuine coupling between brains, rather than one manufactured by shared sensory input or motion artifact, has proven difficult to establish. We recorded dual-brain EEG with synchronized head-mounted accelerometry from participant-partner dyads (16 dyads, 155 dyad-condition recordings) across six conditions grading interpersonal coordination from stillness to fully reciprocal joint movement, using improvisational dance to elicit sustained, naturalistic, gradable coordination. Windowed cross-correlation confirmed graded, partner-specific movement coordination, including a lead-lag signature that reversed with who led. Against a pseudo-dyad surrogate, inter-brain synchrony took one specific form: amplitude-envelope coupling in the high-beta and gamma bands, robust to a leakage-corrected measure, with no phase-based or slow-band coupling. Within dyads, movement synchrony predicted the strength of this amplitude coupling (standardized beta = 0.54 in gamma), an association that strengthened monotonically with frequency, held under a dyad fixed-effects estimator, and was undiminished when the amount of movement was entered into the model. Movement synchrony and inter-brain coupling also co-fluctuated within interactions, above surrogate. Two control analyses excluded the leading alternatives: the coupling was not sensorimotor rhythm-tracking, since corticokinematic coherence was present but non-localized and not movement-selective, and not a shared movement-frequency rhythm linking the brains. These results identify interpersonal movement coordination as an organizing variable for inter-brain coupling and specify that coupling as fast-band amplitude co-modulation, not phase synchrony. Brains couple when bodies coordinate, not when they merely move together.

neuroscience↗

bioRxiv2026

The Choreography of Coupled Brains: Coordinated Movement and Multi-Scale Neural Synchrony in Musical Theater for Individuals with Developmental Disabilities

Performing together, which demands moving, singing, and coordinating in real time, is among the most social things people do, yet whether such coordination synchronizes not only behavior but brain activity, within and between performers, has rarely been examined during genuine performance. In this pilot study, we used mobile electroencephalography, including dual-brain hyperscanning of co-performing partners, to resolve neural change over a 16-week musical-theater program in adolescents and adults with developmental disabilities, the majority autistic. We examined four levels: behavior, spectral power, within-brain (source-space) connectivity, and between-brain synchrony, framing all estimates as effect sizes. Anxiety decreased from pre- to post-intervention, consistently across participant and parent reports. During performance, high-beta and gamma power and within-brain amplitude connectivity rose broadly across the cortex, with maximal effects seen in parieto-occipital regions. Between-brain coupling was similarly carried by amplitude co-fluctuation rather than phase; however, it was graded by the degree of interpersonal movement coordination (strongest when partners mirrored one another) and scaled with movement vigor. Local and within-brain changes were thus broad and state-like, whereas coupling between brains was specific to interpersonal coordination. Coordinated movement may organize neural synchrony across scales, positioning musical theater as a tractable model for studying the social brain in motion.

neuroscience↗

bioRxiv2026

Direct anti-inflammatory actions of N,N-dimethyltryptamine on microglia are revealed by proteomic profiling and receptor pharmacology

N,N-dimethyltryptamine (DMT) is an endogenous psychedelic tryptamine that has recently emerged as a promising therapeutic candidate for acute ischemic stroke. Although DMT consistently reduces infarct size, attenuates neuroinflammation, and improves functional outcome in experimental stroke, the cellular and receptor mechanisms underlying these effects remain poorly understood. Primary rat microglial cultures were used to examine the direct anti-inflammatory effects of DMT following lipopolysaccharide (LPS)-induced activation. Microglial morphology, phagocytosis, and proteomic alterations were analyzed. Radioligand binding assays determined the affinity of DMT for microglial sigma-1 receptors (Sig-1Rs). Pharmacological inhibition of Sig-1Rs and serotonin (5-HT) receptors was performed to define receptor-specific mechanisms. Translational relevance was evaluated in acute mouse brain slices subjected to mild oxygen-glucose deprivation (mOGD) and anoxic episodes, where microglial activation, spreading depolarizations (SDs), and neuronal injury were assessed. DMT directly suppressed LPS-induced microglial activation, promoted a homeostatic morphology, and reduced phagocytic activity. Proteomic profiling demonstrated that DMT selectively reprogrammed inflammatory pathways by suppressing proteins involved in cytokine and chemokine signaling and oxidative stress while largely preserving arachidonic acid-prostaglandin synthesis. DMT bound microglial Sig-1Rs with micromolar affinity comparable to that reported in whole-brain preparations. Pharmacological inhibition revealed that DMT-induced morphological reprogramming required both Sig-1R and serotonergic signaling, whereas suppression of phagocytosis was largely independent of either receptor pathway. In acute brain slices, DMT attenuated microglial activation, reduced SD propagation and ischemic neuronal injury, and tissue-level neuroprotection depended on serotonergic signaling. DMT directly targets microglia and selectively remodels inflammatory states rather than broadly suppressing microglial activation. The receptor mechanisms underlying its actions are context dependent, with Sig-1R and serotonergic signaling contributing differentially according to the cellular response and experimental model. These findings provide mechanistic insight into the neuroprotective actions of DMT and support its ongoing clinical translation as a potential therapy for ischemic stroke.

neuroscience↗

bioRxiv2026

Distinct mechanistic pathways of early tauopathy revealed by MAPT mutations

Tau pathology underlies a broad spectrum of neurodegenerative disorders, collectively termed tauopathies, yet these diseases exhibit striking heterogeneity in their biological mechanisms and clinical outcomes. The basis for this heterogeneity remains poorly understood. Here, we address this question using knock-in mouse models expressing two distinct frontotemporal dementia-associated tau mutations to define how different tau variants drive divergent pathogenic programs in vivo. We find that the two mutations give rise to fundamentally different trajectories of tau pathogenesis. One trajectory is marked by progressive tau hyperphosphorylation and cytoskeletal destabilization occurring in the absence of detectable tau seed formation. In contrast, an alternative trajectory is characterized by tau hypophosphorylation, early seed formation, and alterations in nucleotide metabolism and chromatin organization, without overt cytoskeletal disruption. With aging, tau in this latter pathway transitions to a hyperphosphorylated state and forms mature fibrillar aggregates. Genetic enhancement of {beta}-amyloid selectively accelerates fibril formation, particularly in the model exhibiting early seeding. Together, these findings demonstrate that distinct tau mutations can engage separable pathogenic mechanisms, providing a biological framework for the heterogeneity observed across tauopathies, and highlighting the need for mechanism-informed therapeutic strategies and patient stratification. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/705716v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@11eb652org.highwire.dtl.DTLVardef@153cc53org.highwire.dtl.DTLVardef@8b670eorg.highwire.dtl.DTLVardef@1afaad_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical abstractSchematic representation comparing two trajectories of tau pathology. The S305N mutation promotes a 4R isoform shift, cytoskeletal damage and synapse loss, and accumulation of soluble hyperphosphorylated tau. Tau remains soluble even at old ages. In contrast, the P301S mutation generates hypophosphorylated, seed-competent tau that forms fibrils. The effect of amyloid is slow in the S305N, but results in accelerated acceleration of pathology in the P301S. Figure made with Biorender.com.

neuroscience↗

bioRxiv2025

Active learning for improving out-of-distribution lab-in-the-loop experimental design

The accurate prediction of antibody-antigen binding is crucial for developing antibody-based therapeutics and advancing immunological research. Library-on-library approaches, where many antigens are probed against many antibodies, can identify specific interacting pairs. Machine learning models can predict target binding by analyzing many-to-many relationships between antibodies and antigens. However, these models face challenges when predicting interactions when test antibodies and antigens are not represented in the training data, a scenario known as out-of-distribution prediction. Generating experimental binding data is costly, limiting the availability of comprehensive datasets. Active learning can reduce costs by starting with a small labeled subset of data and iteratively expanding the labeled dataset. Few active learning approaches are available to handle data with many-to-many relationships as, for example, obtained from library-on-library screening approaches. In this study, we developed and evaluated fourteen novel active learning strategies for antibody-antigen binding prediction in a library-on-library setting and evaluated their out-of-distribution performance using the Absolut! simulation framework. We found that three of the fourteen algorithms tested significantly outperformed the baseline where random data are iteratively labeled. The best algorithm reduced the number of required antigen mutant variants by up to 35%, and sped up the learning process by 28 steps compared to the random baseline. These findings demonstrate that active learning can improve experimental efficiency in a library-on-library setting and advance antibody-antigen binding prediction.

bioinformatics↗

bioRxiv2024

Nimodipine reduces microglial activation in vitro as evidenced by morphological phenotype, phagocytic activity and next generation RNA sequencing

BackgroundNimodipine, an L-type voltage-gated calcium channel blocker, achieves vasorelaxation by suppressing Ca2+-dependent activation of cerebrovascular smooth muscle cells and is used to prevent delayed ischemic deficit following subarachnoid hemorrhage. Our preclinical drug repurposing studies raised the possibility that nimodipine may attenuate the pro-inflammatory shift in microglial function in response to brain injury. We analyzed the effects of nimodipine on activated microglia at the level of morphological and functional phenotypes, as well as their transcriptomic profile. MethodsLive brain slice preparations from C57BL/6 mice and primary microglia cultures from the cortex of neonatal Sprague Dawley rats were used. Brain slices were subjected to ischemia, and microglial cultures were activated with lipopolysaccharide (LPS; 20 ng/ml). Both preparations were treated with nimodipine (5-10-20 M). The degree of arborization was evaluated in Iba1-stained microglia and expressed as a transformation index (TI). Phagocytic activity of cultured microglia was visualized using fluorescent microbeads. TNF levels in the cultures were measured with ELISA. Total RNA was isolated from microglia and processed for next generation RNA sequencing to determine differentially expressed genes. ResultsNimodipine suppressed the ameboid morphological transformation and increased phagocytosis triggered by ischemia in brain slices and LPS in microglia cultures. At the transcriptional level, LPS resulted in a pro-inflammatory microglial phenotype, affecting the expression of cytokines, the complement system and phagocytosis-related genes. Focusing on the role of calcium in microglial activation, LPS increased RNA transcription of ionotropic purinergic and some TRP channels but decreased the expression of voltage- and ligand-gated calcium channels. In the endoplasmic reticulum, LPS downregulated gene expression of Ryr and IP3 receptors and increased transcription of the SERCA calcium pump gene. Nimodipine co-administered with LPS altered the expression of 110 genes in the opposite direction to LPS activation, of which at least 20 were associated with microglial immune response, 7 with cell adhesion and 2 with autophagy regulation. ConclusionThe effect of nimodipine goes beyond cerebral vasorelaxation. Nimodipine attenuates microglial activation by modulating Ca2+-dependent gene expression involved in intracellular signaling cascades to drive microglial immune responses. Consideration should be given to expanding the medical field of indication of nimodipine.

neuroscience↗

bioRxiv2024

Brain and grammar: revealing electrophysiological basic structures with competing statistical models

Acoustic, lexical and syntactic information is simultaneously processed in the brain. Therefore, distinguishing the electrophysiological activity pertaining to these components requires complex and indirect strategies. Capitalizing on previous works which factor out acoustic information, we could concentrate on the lexical and syntactic contribution to language processing by testing competing statistical models. We exploited EEG recordings and compared different surprisal models selectively involving lexical information, part of speech or syntactic structures in various combinations. EEG responses were recorded in 32 participants during listening to affirmative active declarative sentences and compared the activation corresponding to basic syntactic structures, such as noun phrases vs verb phrases. Lexical and syntactic processing activates different frequency bands, different time windows and different networks. Moreover, surprisal models based on part of speech inventory only do not explain well the electrophysiological data, while those including syntactic information do. Finally, we confirm previous measures obtained with intracortical recordings independently supporting the original hypothesis addressed here in a robust way.

neuroscience↗

bioRxiv2022

Breadth of Fc-mediated effector function delineates grades of clinical immunity following human malaria challenge

Controlled human malaria challenge studies in semi-immune volunteers provide an unparalleled opportunity to robustly identify mechanistic correlates of protection. We leveraged this platform to undertake a head-to-head comparison of seven functional antibody assays that are relevant to immunity against the erythrocytic merozoite stage of Plasmodium falciparum. Fc-mediated effector functions were strongly associated with protection from clinical symptoms of malaria and exponential parasite multiplication while the gold standard growth inhibition assay was not. The breadth of Fc-mediated effector function clearly discriminated grades of clinical immunity following challenge. These findings present a paradigm shift in the understanding of the mechanisms that underpin immunity to malaria and have important implications for vaccine development.

immunology↗

bioRxiv2021

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Generative machine learning (ML) has been postulated to be a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody binding parameters. The simulation framework enables both the computation of antibody-antigen 3D-structures as well as functions as an oracle for unrestricted prospective evaluation of the antigen specificity of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (1D) data can be used to design native-like conformational (3D) epitope-specific antibodies, matching or exceeding the training dataset in affinity and developability variety. Furthermore, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Finally, we validated that the antibody design insight gained from simulated antibody-antigen binding data is applicable to experimental real-world data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design. HighlightsO_LIA large-scale dataset of 70M [3 orders of magnitude larger than the current state of the art] synthetic antibody-antigen complexes, that reflect biological complexity, allows the prospective evaluation of antibody generative deep learning C_LIO_LICombination of generative learning, synthetic antibody-antigen binding data, and prospective evaluation shows that deep learning driven antibody design and discovery at an unconstrained level is feasible C_LIO_LITransfer learning (low-N learning) coupled to generative learning shows that antibody-binding rules may be transferred across unrelated antibody-antigen complexes C_LIO_LIExperimental validation of antibody-design conclusions drawn from deep learning on synthetic antibody-antigen binding data C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/451480v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1d947bcorg.highwire.dtl.DTLVardef@1f4c51corg.highwire.dtl.DTLVardef@b8df67org.highwire.dtl.DTLVardef@1530ea5_HPS_FORMAT_FIGEXP M_FIG We leverage large synthetic ground-truth data to demonstrate the (A,B) unconstrained deep generative learning-based generation of native-like antibody sequences, (C) the prospective evaluation of conformational (3D) affinity, paratope-epitope pairs, and developability. (D) Finally, we show increased generation quality of low-N-based machine learning models via transfer learning. C_FIG

immunology↗

bioRxiv2021

A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: The lack of a unified ML formalization of immunological antibody specificity prediction problems and the unavailability of large-scale synthetic benchmarking datasets of real-world relevance. Here, we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based 3D-antibody-antigen binding structures with ground-truth access to conformational paratope, epitope, and affinity. We formalized common immunological antibody specificity prediction problems as ML tasks and confirmed that for both sequence and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework thus enables real-world relevant development and benchmarking of ML strategies for biotherapeutics design. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/451258v3_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@4c3a6aorg.highwire.dtl.DTLVardef@8f1deborg.highwire.dtl.DTLVardef@1f55ec6org.highwire.dtl.DTLVardef@2135dd_HPS_FORMAT_FIGEXP M_FIG The software framework Absolut! enables (A,B) the generation of virtually arbitrarily large numbers of synthetic 3D-antibody-antigen structures, (C,D) the formalization of antibody specificity as machine learning (ML) tasks as well as the exploration of ML strategies for real-world antibody-antigen binding or paratope-epitope prediction. C_FIG HighlightsO_LISoftware framework Absolut! to generate an arbitrarily large number of synthetic 3D-antibody-antigen structures that contain biological layers of antibody-antigen binding complexity that render ML predictions challenging C_LIO_LIImmunological antibody specificity prediction problems formalized as machine learning tasks for which the in silico complexes are immediately usable as benchmark datasets C_LIO_LIExploration of machine learning prediction accuracy as a function of architecture, dataset size, choice of negatives, and sequence-structure encoding C_LIO_LIRelative ML performance learnt on Absolut! datasets transfers to experimental datasets C_LI

immunology↗

bioRxiv2021

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

bioinformatics↗

bioRxiv2020

Malignant astrocyte swelling and impaired glutamate clearance drive the expansion of injurious spreading depolarization foci

Spreading depolarizations (SD) indicate infarct maturation and predict worse clinical outcome in ischemic stroke. We demonstrate here in rodents that brain edema formation upon ischemic stroke impairs astroglial glutamate clearance and increases the tissue area invaded by SD. The cytotoxic glutamate accumulation predisposes an extensive bulk of tissue for a yet undescribed simultaneous depolarization (SiD). We confirm in rat brain slices under hypo-osmotic stress that SiD is the pathological expansion of prior SD foci, is associated with astrocyte swelling and triggers oncotic neuron death. The blockade of astrocytic aquaporin-4 channels and Na+/K+/Cl- co-transporters, or volume-regulated anion channels mitigated slice edema, glutamate accumulation and SiD occurrence. Reversal of slice edema by hyperosmotic treatment counteracted glutamate accumulation and prevented SiD. In contrast, paralysis of astrocyte metabolism or inhibition of astrocyte glutamate uptake reproduced the SiD phenotype. We discuss our results in the light of evidence for SiD in the human cortex. Our results emphasize the need of preventive osmotherapy in ischemic stroke.

neuroscience↗