Advancing Computational Neurobiology

One powerful application of multi-scale modeling is the ability to identify mechanistic changes at the cellular scale that give rise to emergent network, circuit, and behavioral effects at the clinical scale. To facilitate the development and testing of these complex models, we developed Neuroblox, a neuroCAD platform designed to accelerate biotech innovation and precision therapeutics for neuropsychiatric disorders and cognitive performance.

Neuroblox permits users to load prebuilt (experimentally validated) neural circuits that can be modified. Likewise, users can upload their own experimental data to model neural circuits from modular components. In both cases, Neuroblox allows users to simulate treatment effects across spatio-temporal scales and predict outcomes: from molecular targets to biomarkers to psychiatric/cognitive symptoms. Neuroblox uses libraries of modular neurobiological building blocks ("blox") that snap together like components in a circuit diagram.

Circuits include the corticostriatal, corticothalamic, and prefrontal-limbic. Modules are plug-ins for those circuits optimized for specific applications: e.g., Pharma, Metabolic, Deep Brain Stimulation.

Demo

Documentation

Code

For Neuroblox biotech partnerships, please contact our commercial arm: www.neuroblox.ai

One significant area of focus by our team has been neurometabolism and its predictive and preventative impact on the clinical trajectory of brain aging. In particular, we have worked at all scales to systematically develop a mechanistic understanding of how metabolic interventions can restore age-related disruptions in brain network function.

Our research demonstrates that ketosis—achieved through ketone administration—strengthens brain-wide signaling by modulating key neurobiological processes: regulating K+ ion channels that are disrupted by aging, optimizing glutamate/GABA signaling, and elevating antioxidants alongside energy metabolism markers.

Our team has pioneered methods to identify and quantify control circuit regulation of intact feedback loops from human neuroimaging data. This matters because distinct types of neurotransmitter-based dysregulation for key circuits (prefrontal-limbic for threat, corticostriatal for learning and reward, ventral stream and thalamocortical loop for sensory integration) are thought to underlie most disorders of cognition, mood, and behavior.

However, “dysregulation” cannot be characterized by standard neuroimaging analyses focused solely on activation maps and/or connectivity. Our contributions include adapting techniques from complex systems analysis and control systems engineering to neuroimaging time series, and quantifying circuit-based regulatory parameters such as feedback strength, sensitivity to perturbation, and control error.

A complementary body of work establishes critical methodological foundations for translating neuroimaging research into reliable clinical neurodiagnostic tools. This work addresses challenges in extracting robust individual-specific parameters from noisy neural data.

Our research provides solutions to measurement noise contamination through data-driven correction methods for scanner-induced distortions in fMRI time series and principled parameter estimation techniques for stochastic neural processes. A key contribution is the development of computationally efficient modeling frameworks that significantly increase the speed and flexibility of dynamic causal modeling—essential for clinical applications requiring real-time parameter estimation.

The integration of novel behavioral paradigms with advanced computational methods enables functional dissection of neural circuit activity at the individual level, moving beyond group-averaged approaches toward personalized neurodiagnostics. This work directly addresses the gap between research-grade neuroimaging and clinical implementation.

The first article introduces a dynamic phantom developed by our team, BrainDancer™ (Patent 11,061,093), which amplifies signal-to-noise ratio to enable single-subject-level fMRI.