Mechanistic bridges from receptors to whole-brain dynamics: mean-field reductions, validity domains, and computational trade-offs

Transcriptome-Inspired Spiking Simulations Uncover Human-Specific Prefrontal Dynamics and Provide a Mechanistic Platform for Species-Appropriate Disease Modeling

Whole-brain transcriptomic atlases are now widely available, yet computational neural models are almost exclusively parameterized from rodent data and used to infer human brain function, an extrapolation whose cost remains unquantified. To address this, we constructed a biophysically detailed, conductance-based Hodgkin–Huxley spiking microcircuit of a five-population prefrontal network, where every ion-channel, receptor, and gap-junction conductance was scaled by cell-type-specific gene expression. We parameterized the identical circuit using single-nucleus RNA-seq from mouse mPFC and human DLPFC, alongside a literature-derived baseline, and compared their high-frequency-oscillation (HFO) outputs across seven physiological and pathological states. While population firing rates differed only modestly between the two refinements (∼20% for pyramidal and PV cells), the oscillatory dynamics diverged dramatically. The human-refined circuit generated strongly synchronized PV activity and robust ripple- and fast-ripple-band power (e.g., healthy-wake ripple power, in arbitrary units: 322 vs. 24 and 22), whereas the mouse-refined and literature arms remained asynchronous (interneuron synchrony: 0.21 vs. 0.02). This human ≫mouse ≈ original hierarchy was statistically consistent across all seven states (significant arm differences in 75/77 comparisons). Mechanistically, the human transcriptome drove markedly stronger PV–PV electrical coupling (gap-junction scale: 1.78 vs. 0.96) paired with stronger recurrent pyramidal excitation, which collectively synchronized the fast-spiking PV population into a coherent rhythm that perisomatic inhibition then imposed on the local field potential. Critically, these results are model-dependent; the gene-to-conductance mapping is phenomenological, and mRNA expression does not linearly translate to functional conductance. Nonetheless, under this mapping the divergence localizes PV-mediated coupling and excitation–inhibition balance as the parameters most in need of human-specific recalibration. More broadly, this work establishes transcriptome-informed spiking simulation as a powerful strategy for uncovering species-specific computational principles and for building mechanistically grounded, human-relevant models of prefrontal circuit dysfunction, an approach that moves beyond generic rodent defaults to enable targeted, species-appropriate modeling of neurological and psychiatric disorders.

BRIDGE: A Computational Workflow from Single Neurons to Network of Mean-Field Models

BRIDGE provides a reproducible foundation for developing biologically informed mean-field models suitable for large-scale and whole-brain simulations, supporting the transition from generic homogeneous population models toward region-specific ones.

Can we trust subthalamic local field potentials? Geometric and dynamical constraints on the interpretation of extracellular recordings

The results show that the interpretability of extracellular signals depends critically on neuronal morphology and synchronization state, and provide a mechanistic framework for the use of STN LFPs as biomarkers in adaptive deep brain stimulation for Parkinson’s disease.

Whole-brain modeling of dynamic causal circuits in human cognition using amortized variational inference

State-dependent structured sparsification of cortical signal-transmission networks enhances visual coding

The brain can rapidly adjust sensory processing according to behavioral context from moment to moment without altering its underlying anatomical wiring. Such flexibility is thought to arise from dynamic reconfiguration of the effective network through which signals propagate, yet the principles governing such reconfiguration and its consequences for coding remain unclear. Here, we exploited the distinct stationary and locomotion states within the same recording sessions and addressed this question by inferring directed, millisecond-scale signal-transmission networks at single-neuron resolution in behaving mice. Locomotion was accompanied by a counterintuitive structured sparsification of the inferred network: interactions became fewer but more temporally precise, while the remaining interactions were more local, modular, feature-specific, and feedforward. We then used theoretical analysis and controlled perturbations of multi-area rate models to systematically determine how each empirically observed component of this reorganization affects population coding. We found that sparsification reduced shared variability; local organization reduced signal–noise alignment; feature-specific interactions sharpened selectivity; and a more feedforward architecture accelerated decoding. These results provide an experimentally grounded mechanism by which behavioral state reorganizes neuronal interactions given the same sensory inputs, and suggest structured sparsification as an underlying principle of network reconfiguration that supports accurate and faster sensory coding.

The Cerebellar Connectome

The cerebellum, long known for its role in motor control, has increasingly been implicated in cognitive and affective functions. Despite this broadened perspective, its connectivity remains undercharacterized relative to the cerebral cortex. Here, we present the first comprehensive cerebellar connectome analysis derived from a large-scale, meta-analytic database of over 7,800 high-resolution tract-tracing studies in the rat brain. Leveraging the neuroVIISAS framework, we constructed a directionally weighted, hierarchically organized cerebellar subnetwork integrating both intrinsic and extrinsic connections, including lateralization and interhemispheric projections. Our methodological pipeline involved region expansion, graph-theoretical filtering, and systematic edge weighting by anatomical significance. The resulting network, encompassing 862 regions and over 21,000 edges, was analyzed across multiple topological scales. Mesoscale analysis revealed hallmark properties of small-world and scale-free networks, while motif and modularity analyses identified non-random, functionally coherent microcircuits and subsystems. Local connectome metrics uncovered key integrative hubs–especially within brainstem-cerebellar loops–and exposed gradients of modularity, controllability, and vulnerability. A novel vulnerability analysis showed that the removal of high-significance edges leads to rapid and irregular degradation of clustering in the empirical network, in contrast to the robustness of rewired surrogate models. This indicates the presence of structurally privileged bottlenecks essential for cerebellar integration. Our results collectively highlight the cerebellum’s dual design: functionally specialized yet structurally efficient, with both local modularity and long-range integration. This study establishes a robust foundation for future multimodal, dynamic, and cross-species connectomic research. Integrating empirical data on neuronal dynamics, synaptic plasticity, and gene expression will be essential to fully realize the translational potential of cerebellar network models in both health and disease.