Biology subjects

Pille, M.

Publications and source records attributed to Pille, M..

7 recordsLinked to original sources

bioRxiv2026

The Virtual Child Brain: Modeling Neuromaturational Trajectories

IntroductionUnderstanding the mechanisms of human neuromaturation constitutes one of the fundamental questions of neuroscience. While it is well described that large-scale brain maturation is initiated within sensorimotor brain regions and progresses to associative cortex, the underlying developmental neurobiology remains to be fully characterized. Animal models have indicated that cortical inhibitory upregulation might be a driver of neurodevelopment. To investigate the hypothesis that cortical inhibitory upregulation plays a similar role in human neuromaturation, we developed a The Virtual Brain (TVB) based computational model (TVB-Child) to explore potential mechanisms of human neurodevelopment. Material and methodWe created neurodevelopmental dynamic brain network models capturing neurobiological maturation by using the large-scale brain simulator TVB and fitting brain network models to developmental functional MRI (fMRI) from the Human Connectome Project-Development (HCP-D) data set with 640 subjects with an age range of 6-21 years. Age-dependent trajectories in the fMRI data set were first analyzed by combined group-ICA/Dual Regression extracting subject-specific resting-state networks (RSN). Maturational topographical and topological redistribution of these networks were analyzed by linear and non-linear regression of RSN size and degree and strength centrality. Brain network models were fitted to the fMRI functional connectivity obtained from the HCP-D data set. Hypothesizing that cortical inhibition is a driver of neuromaturation, we analyzed spatiotemporal inhibition parameter gradients in the dynamic brain network model for the hypothesized significant correlations with fMRI RSN maturational trajectories. ResultsWhile during development frontoparietal (FP) and default mode network (DMN) grew and exhibited an increase in both degree and strength centrality, becoming dominant network hubs, the attention network underwent network pruning with a decrease in size and node degree. The primary sensory network changed little. For the fitted brain network models, we obtained a high degree of reproduction with correlation coefficients between empirical and simulated functional connectivities ranging between 0.80 and 0.95. Values of the feed forward inhibition model parameter [Formula] representing the strength of regional feedforward inhibitory input exhibited the most significant increase with age within the FP and DMN networks. A less pronounced, but significant, age-dependent increase of the inhibitory parameter values were seen in attention networks and no change within primary sensory networks. ConclusionOur study shows that high order (FP, DMN), attention and primary sensory networks exhibit distinct topographical and topological maturation trajectories. Moreover, brain network modeling revealed RSN-specific age-dependent inhibition trajectories, indicating that the model is able to reproduce and thus support candidate mechanisms of neurodevelopment. Graphical abstractOverview of the study combining empirical and modeling analyses of neuromaturation in 640 study participants from the HCP-D data set, age range 6-21 years old. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/737052v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1764432org.highwire.dtl.DTLVardef@17740ceorg.highwire.dtl.DTLVardef@3fa9a6org.highwire.dtl.DTLVardef@19afc80_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

An Interactive Brain Atlas of Knowledge

Biomedical knowledge about the brain increases every day, with a rapidly growing number of scientific publications, datasets, and software tools. While this informational plethora is not merely comprehensible by human beings, recent developments in information science and computational linguistics aim to make this knowledge programmatically accessible by literature mining. However, integrating these semantic methods into neuroimaging standards remains insufficient, hindering researchers from unraveling their full potential. Therefore, we developed the semantic meta-analysis platform The Virtual Brain adapter of semantics (TVBase) that projects biomedical knowledge preserved in over 36 million scientific articles onto a 3D standardized brain. The literature-mining platform SCAIView was used to extract ontologically defined biomedical entities and their associations with brain anatomy from the PubMed database. By querying a specific concept, the association strength with each anatomical term was calculated using entropy. To project the data onto a standardized brain, we created a unique transformation matrix that links over 800 anatomical terms to voxel coordinates of a parcellated standard brain. This novel method of knowledge projection extracts region-specific information about biomedical concepts from the literature to support translational multi-scale approaches to computational neuroscience. The multi-purpose software framework TVBase is openly available as a Python library. It aims for hypothesis-free neuroimaging pattern interpretation, hypothesis generation, and applications in personalized medicine.

neuroscience↗

bioRxiv2025

The Virtual Brain Ontology: A Digital Knowledge Framework for Reproducible Brain Network Modeling

Computational models of brain network dynamics offer mechanistic insights into brain function and disease, and are utilized for hypothesis generation, data interpretation, and the creation of personalized digital brain twins. However, results remain difficult to reproduce and compare because equations, parameters, networks, and numerical settings are reported inconsistently across the literature, and shared code is often not fully documented, standardized, or executable. We introduce The Virtual Brain Ontology (TVB-O), a semantic knowledge base, minimal metadata standard, and Python toolbox that simplifies the description, execution, and sharing of network simulations. TVB-O offers 1) a common vocabulary and ontology for core concepts and axioms representing current domain knowledge for simulating brain network dynamics, 2) a minimal, human- and machine-readable metadata specification for the information needed to reproduce an experiment, 3) a curated database of published models, brain networks, and study configurations, and 4) software that generates executable code for various simulation platforms and programming languages, including The Virtual Brain, Jax, or Julia. FAIR metadata and provenance-aware reports can be exported from TVB-Os model specification. It hereby enables a flexible framework for adopting new models and enhances reproducibility, comparability, and portability across simulators, while making assumptions explicit and linking models to biomedical knowledge and observation pathways. By reducing technical barriers and standardizing workflows, TVB-O broadens access to computational neuroscience and establishes a foundation for transparent, shareable "digital brain twins" that integrate with clinical pipelines and large-scale data resources.

neuroscience↗

bioRxiv2025

Fast and Easy Whole-Brain Network Model Parameter Estimation with Automatic Differentiation

Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and extensible framework for gradient-based optimization of brain network models build on JAX. Leveraging automatic differentiation, we demonstrate direct optimization of thousands of parameters, from global coupling (N=2) to regional dynamics (N=168) to full structural connectivity matrices (N=14,028), across functional MRI and magnetoencephalography data. Forward simulations achieve 10x CPU speedup over reference implementations and scale efficiently to GPU and multi-device configurations. We establish best practices through documented workflows that combine coarse parameter space exploration with gradient refinement, often yielding superior solutions faster than gradient methods alone. By bridging mechanistic neuroscience with modern machine learning infrastructure, TVB-Optim enables large-scale personalized brain models, bringing computational neuroscience closer to clinical translation.

neuroscience↗

bioRxiv2024

The Virtual Brain links transcranial magnetic stimulation evoked potentials and inhibitory neurotransmitter changes in major depressive disorder

BackgroundTranscranial magnetic stimulation evoked potentials (TEPs) show promise as a biomarker in major depressive disorder (MDD), but the origin of the increased TEP amplitude in these patients remains unclear. Gamma aminobutyric acid (GABA) may be involved, as TEP peak amplitude is known to increase with GABAergic activity in healthy controls. We employed a computational modeling approach to investigate this phenomenon. MethodsWhole-brain simulations in The Virtual Brain (thevirtualbrain.org), employing the Jansen and Rit neural mass model, were optimized to simulate TEPs of healthy individuals (Nsubs=20, 14 females, 24.5{+/-}4.9 years). To mimic MDD-like impaired inhibition, a GABAergic deficit was introduced to the simulations by altering one of two selected inhibitory parameters, the inhibitory synaptic decay rate b or the number of inhibitory synapses C4. The TEP amplitude was quantified and compared for all simulations. ResultsThe inhibitory synaptic decay rate showed a quadratic correlation (r=0.99, p<0.001) and the number of inhibitory synapses a negative exponential correlation (r=0.99, p<0.001) with the TEP amplitude. Moreover, significant correlations between these simulation-derived values and all TEP peaks and troughs were detected (p<0.001). Thus, under local parameter changes, we were able to alter the TEP amplitude towards pathological levels, i.e. creating an MDD-like increase of the global mean field amplitude in line with empirical results. ConclusionsOur model suggests specific GABAergic deficits as the cause of increased TEP amplitude in MDD patients, which may serve as therapeutic targets. This work highlights the potential of whole-brain simulations in the investigation of neuropsychiatric diseases.

neuroscience↗

bioRxiv2023

Knocking out CD70 rescues CD70-specific nanoCAR T cells from antigen induced exhaustion

CD70 is an attractive target for chimeric antigen receptor (CAR) T cell therapy as treatment for both solid and liquid malignancies. However, functionality of CD70-specific CARs is only modest. Here, we optimized a CD70-specific VHH based CAR (nanoCAR). We evaluated the nanoCARs in clinically relevant models in vitro, using co-cultures of CD70-specific nanoCAR T cells with malignant rhabdoid tumor organoids, and in vivo by using a diffuse large B cell lymphoma (DLBCL) patient-derived xenograft (PDX) model. Whereas the nanoCAR T cells were highly efficient in organoid co-cultures, they showed only modest efficacy in the PDX model. Knocking out CD70 expression by the nanoCAR T cells resulted in dramatically enhanced functionality in the PDX model, suggesting that endogenous CD70 interaction with the nanoCAR induces exhaustion. Through single-cell transcriptomics, we obtained evidence that CD70KO CD70-specific nanoCAR T cells are protected from antigen induced exhaustion. Our data shows that CARs targeted to endogenous T cell antigens, negatively affect CAR T cell functionality by inducing an exhausted state which can be overcome by knocking out the specific target, in this case CD70.

immunology↗

bioRxiv2022

Single-cell profiling identifies a spectrum of human unconventional intraepithelial T lineage cells

In the human thymus, a CD10+ PD-1+ TCR{beta}+ differentiation pathway diverges from the conventional single positive T cell lineages at the early double positive stage. These cells are phenotypically and functionally similar to murine unconventional intraepithelial lymphocyte (uIEL) precursors. Here, the progeny of the human uIEL lineage was identified in antigen-inexperienced blood. The uIELs in thymus and peripheral blood share a transcriptomic profile, characterized by hallmark transcription factors (i.e. ZNF683 and IKZF2), and polyclonal TCR repertoire with autoreactive features, exhibiting a bias towards early TCR alpha chain rearrangements. Single-cell RNA sequencing confirmed a common developmental trajectory between the thymic and peripheral uIELs, and clearly delineated this unconventional lineage in peripheral blood. This population is phenotypically defined as CD3+ TCR{beta}+ CD4- CCR7- CD26-. It contains CD10+ recent thymic emigrants, Helios+ KIR+ CD8+ Tregs and CD8+ T cells. Thus, the uIEL lineage represents a well-defined but heterogeneous, unconventional TCR{beta}+ lineage mostly confined in human within the CD8 single positive T cells. SummaryBilliet et al. identify the postthymic progeny of the intraepithelial lymphocyte precursors in human based on shared characteristics of the T cell receptor repertoire and the transcriptome. This lineage represents a well-defined but heterogeneous, unconventional TCR{beta}+ lineage mostly confined within the CD8 single positive T cells.

immunology↗