ArticleACS chemical neuroscience2017
Computational Modeling of Neurotransmitter Release Evoked by Electrical Stimulation: Nonlinear Approaches to Predicting Stimulation-Evoked Dopamine Release.
Article in ACS chemical neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Acupuncture for tumor-related depression: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Machine Learning for Neurotransmitter Monitoring by Fast Voltammetry: Current and Future Prospects.ACS chemical neuroscience · 2026Review
- Redefining Roles: A Paradigm Shift in Tryptophan-Kynurenine Metabolism for Innovative Clinical Applications.International journal of molecular sciences · 2024Review
- Toward Precise Modeling of Dopamine Release Kinetics: Comparison and Validation of Kinetic Models Using Voltammetry.ACS omega · 2024Article
- Defining a Path Toward the Use of Fast-Scan Cyclic Voltammetry in Human Studies.Frontiers in neuroscience · 2021Review
- Evaluation of electrochemical methods for tonic dopamine detectionTrends in analytical chemistry : TRAC · 2020Article
- Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review.Brain sciences · 2020Review
- Vesicular Antipsychotic Drug Release Evokes an Extra Phase of Dopamine Transmission.Schizophrenia bulletin · 2020Article
- Regional Variation in Striatal Dopamine Spillover and Release Plasticity.ACS chemical neuroscience · 2020Article
- Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring.Frontiers in neuroscience · 2019Review
- Design Choices for Next-Generation Neurotechnology Can Impact Motion Artifact in Electrophysiological and Fast-Scan Cyclic Voltammetry Measurements.Micromachines · 2018Article
- A Materials Roadmap to Functional Neural Interface Design.Advanced functional materials · 2018Article
- An improved genetic algorithm for designing optimal temporal patterns of neural stimulation.Journal of neural engineering · 2017Article
- Stochastic Simulation of Dopamine Neuromodulation for Implementation of Fluorescent Neurochemical Probes in the Striatal Extracellular Space.ACS chemical neuroscience · 2017Article
- WINCS Harmoni: Closed-loop dynamic neurochemical control of therapeutic interventions.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Neurochemical changes evoked by electrical stimulation of the nervous system have been linked to both therapeutic and undesired effects of neuromodulation therapies used to treat obsessive-compulsive disorder, depression, epilepsy, Parkinson's disease, stroke, hypertension, tinnitus, and many other indications. In fact, interest in better understanding the role of neurochemical signaling in neuromodulation therapies has been a focus of recent government- and industry-sponsored programs whose ultimate goal is to usher in an era of personalized medicine by creating neuromodulation therapies that respond to real-time changes in patient status. A key element to achieving these precision therapeutic interventions is the development of mathematical modeling approaches capable of describing the nonlinear transfer function between neuromodulation parameters and evoked neurochemical changes. Here, we propose two computational modeling frameworks, based on artificial neural networks (ANNs) and Volterra kernels, that can characterize the input/output transfer functions of stimulation-evoked neurochemical release. We evaluate the ability of these modeling frameworks to characterize subject-specific neurochemical kinetics by accurately describing stimulation-evoked dopamine release across rodent (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.