Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

npj Digital Medicine, Год журнала: 2019, Номер 2(1)

Опубликована: Ноя. 25, 2019

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- cost-efficient strategies to analyze interpret these advance human health. The recent rise of machine learning as powerful technique integrate multimodality, multifidelity data, reveal correlations between intertwined phenomena presents special opportunity in this regard. However, alone ignores fundamental laws physics can result ill-posed problems or non-physical solutions. Multiscale modeling successful strategy multiscale, multiphysics uncover mechanisms that explain emergence function. multiscale often fails efficiently combine large datasets from different sources levels resolution. Here we demonstrate naturally complement each other create robust predictive models underlying manage explore massive design spaces. We review current literature, highlight applications opportunities, address open questions, discuss potential challenges limitations four overarching topical areas: ordinary differential equations, partial data-driven approaches, theory-driven approaches. Towards goals, leverage expertise applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, medicine. Our multidisciplinary perspective suggests integrating provide new insights into disease mechanisms, help identify targets treatment strategies, inform decision making benefit

Язык: Английский

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Multiscale Modeling Meets Machine Learning: What Can We Learn?

Archives of Computational Methods in Engineering, Год журнала: 2020, Номер 28(3), С. 1017 - 1037

Опубликована: Фев. 17, 2020

Язык: Английский

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Self-Organized Criticality in the Brain

Frontiers in Physics, Год журнала: 2021, Номер 9

Опубликована: Июль 7, 2021

Self-organized criticality (SOC) refers to the ability of complex systems evolve toward a second-order phase transition at which interactions between system components lead scale-invariant events that are beneficial for performance. For last two decades, considerable experimental evidence has accumulated mammalian cortex with its diversity in cell types, interconnectivity, and plasticity might exhibit SOC. Here, we review findings isolated, layered preparations self-organize four dynamical motifs presently identified intact vivo : up-states, oscillations, neuronal avalanches, coherence potentials. During synchronization observed nested theta/gamma oscillations embeds can be by robust power law scaling avalanche sizes slope −3/2 critical branching parameter 1. This precise coordination, tracked negative transients local field potential (nLFP) spiking activity pyramidal neurons using two-photon imaging, emerges autonomously superficial layers organotypic cultures acute slices, is homeostatically regulated, exhibits separation time scales, reveals unique size vs. quiet dependencies. A subclass potentials, maintenance course propagated synchrony. Avalanches emerge under conditions strong external drive. The balance excitation inhibition (E/I), as well neuromodulators such dopamine, establishes powerful control parameters dynamics. rich repertoire not dissociated cultures, lack differentiation into cortical phenotype expected first-order transition. avalanches provide compelling SOC brain.

Язык: Английский

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The quest for multiscale brain modeling

Trends in Neurosciences, Год журнала: 2022, Номер 45(10), С. 777 - 790

Опубликована: Июль 27, 2022

Язык: Английский

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Intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics

Journal of Neural Engineering, Год журнала: 2024, Номер 21(2), С. 026024 - 026024

Опубликована: Март 26, 2024

Abstract Objective . Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity and thereby cause changes in local oscillatory power. Despite its increased use cognitive clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Approach We developed a computational neuronal network model two-compartment pyramidal neurons (PY) inhibitory interneurons, which mimic cortical circuits. modeled with electric field strengths that achievable human applications. then simulated intrinsic measured entrainment investigate how modulates ongoing endogenous oscillations. Main results The intensity-specific effects non-linear. At low intensities (<0.3 mV mm −1 ), desynchronizes firing relative higher (>0.3 entrained exogenous field. further explore parameter space find oscillations also depends on frequency by following an Arnold tongue. Moreover, networks amplify tACS-induced via synaptic coupling effects. Our shows PY directly drive neurons. Significance presented this study provide mechanistic framework for understanding intensity- frequency-specific oscillating fields networks. This is crucial rational selection studies

Язык: Английский

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Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data

eLife, Год журнала: 2020, Номер 9

Опубликована: Янв. 22, 2020

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy disease states. Relating these macroscopic signals to underlying cellular- circuit-level generators is a limitation that constrains using MEG/EEG reveal novel principles information processing or translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has graphical user interface designed help researchers clinicians interpret the neural origins MEG/EEG. HNN's core neocortical circuit model accounts biophysical electrical currents generating Data can be directly compared simulated parameters easily manipulated develop/test hypotheses on signal's origin. Tutorials teach users simulate commonly measured signals, including event related potentials rhythms. ability associate across scales makes it unique tool translational neuroscience research.

Язык: Английский

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Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

Neuron, Год журнала: 2019, Номер 103(3), С. 395 - 411.e5

Опубликована: Июнь 11, 2019

Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven neural circuits that span multiple scales increasingly being used to understand brain function in health and disease. But their adoption reuse has been limited by specialist knowledge required evaluate use them. To address this, we have developed Open Source Brain, a platform sharing, viewing, analyzing, simulating standardized from different regions species. Model structure parameters can be automatically visualized dynamical explored through browser-based simulations. Infrastructure collaborative interaction, development, testing also provided. We demonstrate how existing components reused constructing new inhibition-stabilized cortical networks match recent experimental results. These features Brain improve accessibility, transparency, reproducibility facilitate wider community.

Язык: Английский

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Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Frontiers in Neuroinformatics, Год журнала: 2022, Номер 16

Опубликована: Июнь 27, 2022

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as widely-used NEURON environment computational neuroscience. Developing and maintaining over several decades required attention competing needs backwards compatibility, evolving computer architectures, addition new scales physical processes, accessibility users, efficiency flexibility specialists. In order meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system release workflow, better documentation. With help a source-to-source compiler NMODL domain-specific language enhanced NEURON's ability run efficiently, via CoreNEURON engine, on variety hardware including GPUs. Through implementation optimized in-memory transfer mechanism this performance backend is made easily accessible training model-development paths from laptop workstation supercomputer cloud platform. Similarly, been able accelerate reaction-diffusion through use just-in-time compilation. We show that efforts growing developer base, simpler more robust software distribution, wider range supported integration with other scientific workflows, biophysical biochemical models.

Язык: Английский

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Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics

Cell Reports, Год журнала: 2023, Номер 42(11), С. 113378 - 113378

Опубликована: Ноя. 1, 2023

We developed a detailed model of macaque auditory thalamocortical circuits, including primary cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator NetPyNE tool. The A1 simulates cortical column with over 12,000 neurons 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, connectivity across six layers. It is reciprocally connected to MGB thalamus, which includes interneurons core matrix-layer-specific projections A1. multiscale measures, physiological firing rates, local field potentials (LFPs), current source densities (CSDs), electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity in response broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously frequency bands comparable those recorded vivo. elucidate population-specific contributions observed oscillation events relate them presynaptic input patterns. offers quantitative theoretical framework integrate interpret predict its underlying cellular circuit mechanisms.

Язык: Английский

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Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics

Cell Reports, Год журнала: 2023, Номер 42(6), С. 112574 - 112574

Опубликована: Июнь 1, 2023

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, dendritic synapse locations are constrained by experimental data. The includes long-range inputs from seven thalamic regions noradrenergic inputs. Connectivity depends on cell class depth at sublaminar resolution. accurately predicts in vivo layer- cell-type-specific responses (firing rates LFP) associated behavioral states (quiet wakefulness movement) manipulations (noradrenaline receptor blockade thalamus inactivation). generate mechanistic hypotheses underlying the observed activity analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate interpret M1 data sheds light multiscale dynamics several conditions behaviors.

Язык: Английский

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