Biology subjects

Netoff, T. I.

Publications and source records attributed to Netoff, T. I..

7 recordsLinked to original sources

bioRxiv2025

Metric Validation for Detection of Delayed and Directed Coupling

BackgroundThe brain functions as a complex network of billions of interconnected neurons, coordinating processes from basic reflexes to high-level cognition. Dysfunction in these networks contribute to neurological and psychiatric disorders, including epilepsy, depression, and Parkinsons disease. Understanding these network alterations is essential for developing effective therapies. However, reconstructing network topology from human electrophysiology data is challenging due to sparse sampling, measurement noise, and variable time delays in interregional communication. Effective connectivity (EC) metrics have been developed to infer directed neural interactions, but their accuracy and robustness under real-world data constraints remain unclear. This study empirically evaluates common EC metrics to determine which most accurately reconstruct network topology in the presence of data limitations. MethodsWe generated Erd[o]s-Renyi networks and simulated time series using a time-delayed vector autoregressive (VAR) model. We systematically varied network size, data length, measurement noise, and network coverage. Variations of four commonly used EC metrics, cross-correlation, Granger causality, mutual information, and transfer entropy, were evaluated for reconstruction accuracy using cosine distance to compare estimated and true connectivity matrices. ResultsMultivariate transfer entropy demonstrated the highest accuracy across various conditions but required significantly longer computation times. For small networks (<30 nodes), mutual information and Granger causality rapidly and accurately reconstructed networks. For larger networks, partial cross-correlation performed well with good computational efficiency. Notably, zero-lag metrics perform no better than chance in nearly all conditions. ConclusionThe choice of an EC metric should consider specific data constraints.While multivariate transfer entropy is the most reliable across conditions, its long runtime limits its practical application. For large networks, partial cross-correlation offers a faster and reasonably accurate alternative. Granger causality and mutual information are effective for small networks. Critically, time-lagged metrics are essential for accurate network reconstructions, as failing to account for time delays leads to reconstructions no more accurate than random network models.

bioinformatics↗

bioRxiv2024

Sequential estimation of nonstationary oscillator states and stimulus responses

Neural oscillations have been linked to multiple behaviors and neuropsychiatric disorders. The individual contributions to behavior from both oscillations and non-oscillatory activity are still unclear, complicating efforts to link neurophysiology to cognition, hindering the discovery of novel biomarkers, and preventing the development of effective therapeutics. To overcome these hurdles, it will be critical to investigate the biological origins of neural oscillations by characterizing the dynamic properties of different brain regions. The dynamical regime for a population of neurons generating oscillations in neural recordings can be discovered by stimulating the population and recording its subsequent response to stimulation. There are different dynamical regimes that can produce population-level neural oscillations. For certain dynamical regimes, like that of a nonlinear oscillator, the phase response curve (PRC) can help differentiate the dynamic state of the population. The PRC can be measured by stimulating the population across different phases of its oscillatory state. However, neural dynamics are non-stationary, so neural oscillations will vary in frequency and amplitude across a recording and the PRC can change over time. This non-stationarity could bias a PRC estimated from an electrophysiological experiment, preventing accurate characterization of a neural populations dynamics. This necessitates tools that can operate online to trigger stimulation and update PRC estimates. To that end, we develop online methods for tracking non-stationary oscillations and recovering PRCs corrupted by estimation errors. We validate the performance of our non-stationary oscillation estimator compared to both a known ground truth model and an alternative phase estimation approach. We demonstrate that a PRC can be recovered online underdifferent random error conditions in silico and that a similar amplitude response curve (ARC) can be estimated from physiologic data using online methods compared to offline approaches.

neuroscience↗

bioRxiv2023

Portable Arbitrary Pulse Generator for Driving μCoils for Micromagnetic Neurostimulation

Micromagnetic stimulation (MS) is a promising branch of neurostimulation technologies. Microcoil (coil) based magnetic stimulation uses micrometer sized coils that generate a time-varying magnetic field which as per Faradays Laws of Electromagnetic Induction induces an electric field on a conductive surface. This method of stimulation has the advantage of not requiring electrical contact with tissue, however these coils are not easy to operate. Large currents are required to generate the required magnetic field. These currents are too large for standard test equipment to provide, and additional power amplifiers are needed. To aid in the development and application of micromagnetic stimulation devices, we have created a compact single unit test setup for driving these devices called the Coil Driver. This unit is designed to drive small inductive loads up to {+/-}8 V at 5 A and 10 kHz.

neuroscience↗

bioRxiv2023

Micromagnetic Stimulation (μMS) Controls Dopamine Release: An in vivo Study Using WINCS Harmoni

ObjectiveResearch into the role of neurotransmitters in regulating normal and pathologic brain functions has made significant progress. Yet, clinical trials that aim to improve therapeutic interventions do not take advantage of the in vivo changes in the neurochemistry that occur in real time during disease progression, drug interactions or response to pharmacological, cognitive, behavioral, and neuromodulation therapies. In this work, we used the WINCS Harmoni tool to study the real time in vivo changes in dopamine release in rodent brains for the micromagnetic neuromodulation therapy. ApproachAlthough still in its infancy, micromagnetic stimulation (MS) using micro-meter sized coils or microcoils (coils) has shown incredible promise in spatially selective, galvanic contact free and highly focal neuromodulation. These coils are powered by a time-varying current which generates a magnetic field. As per Faradays Laws of Electromagnetic Induction, this magnetic field induces an electric field in a conducting medium (here, the brain tissues). We used a solenoidal-shaped coil to stimulate the medial forebrain bundle (MFB) of the rodent brain in vivo. The evoked in vivo dopamine releases in the striatum were tracked in real time by carbon fiber microelectrodes (CFM) using fast scan cyclic voltammetry (FSCV). ResultsOur experiments report that coils can successfully activate the MFB in rodent brains, triggering dopamine release in vivo. We further show that the successful release of dopamine upon micromagnetic stimulation is dependent on the orientation of the coil. Furthermore, varied intensities of MS can control the concentration of dopamine releases in the striatum. SignificanceThis work helps us better understand the brain and its conditions arising from a new therapeutic intervention, like MS, at the level of neurotransmitter release. Despite its early stage, this study potentially paves the path for MS to enter the clinical world as a precisely controlled and optimized neuromodulation therapy.

neuroscience↗

bioRxiv2022

Micromagnetic Stimulation (μMS) Dose-Response of the Rat Sciatic Nerve

ObjectiveThe objective of this study was to investigate the effects of micromagnetic stimuli strength and frequency from the Magnetic Pen (MagPen) on the rat right sciatic nerve. The nerves response would be measured by recording muscle activity and movement of the right hind limb. ApproachThe MagPen was custom-built such that it can be held over the sciatic nerve in a stable manner. Rat leg muscle twitches were captured on video and movements were extracted using image processing algorithms. EMG recordings were also used to measure muscle activity. Main resultsThe MagPen prototype when driven by alternating current, generates time-varying magnetic field which as per Faradays Law of Electromagnetic Induction, induces an electric field for neuromodulation. The orientation dependent spatial contour maps for the induced electric field from the MagPen prototype has been numerically simulated. Furthermore, in this in vivo work on MS, a dose-response relationship has been reported by experimentally studying how the varying amplitude (Range: 25 mVp-p through 6 Vp-p) and frequency (Range: 100 Hz through 5 kHz) of the MagPen stimuli alters the hind limb movement. The primary highlight of this dose-response relationship is that at a higher frequency of the MS stimuli, significantly smaller amplitudes can trigger hind limb muscle twitch. This frequency-dependent activation can be justified following directly from the Faradays Law as the magnitude of the induced electric field is directly proportional to frequency. SignificanceThis work reports that MS can successfully activate the sciatic nerve in a dose-dependent manner. The MagPen probe, unlike electrodes, does not have a direct electrochemical interface with tissues rendering it much safer than an electrode. Magnetic fields create more precise activation than electrodes because they induce smaller volumes of activation. Finally, unique features of MS such as orientation dependence, directionality and spatial selectivity have been demonstrated.

bioengineering↗

bioRxiv2021

Strength-frequency curve for micromagnetic neurostimulation through EPSPs on rat hippocampal neurons and numerical modeling of magnetic microcoil (μcoil)

ObjectiveThe objective of this study was to measure the effect of micromagnetic stimulation (MS) on hippocampal neurons, by using single microcoil (coil) prototype, Magnetic Pen (MagPen). MagPen will be used to stimulate the CA3 region magnetically and excitatory post synaptic potential (EPSP) response measurements will be made from the CA1 region. The threshold for micromagnetic neurostimulation as a function of stimulation frequency of the current driving the coil will be demonstrated. Finally, the optimal stimulation frequency of the current driving the coil to minimize power will be estimated. ApproachA biocompatible, watertight, non-corrosive prototype, MagPen was built, and customized such that it is easy to adjust the orientation of the coil and its distance over the hippocampal tissue in an in vitro recording setting. Finite element modeling (FEM) of the coil design was performed to estimate the spatial profiles of the magnetic flux density (in T) and the induced electric fields (in V/m). The induced electric field profiles generated at different values of current applied to the coil can elicit a neuron response, which was validated by numerical modeling. The modeling settings for the coil were replicated in experiments on rat hippocampal neurons. Main resultsThe preferred orientation of MagPen over the Schaffer Collateral fibers was demonstrated such that they elicit a neuron response. The recorded EPSPs from CA1 region due to MS at CA3 region were validated by applying tetrodotoxin (TTX). Application of TTX to the hippocampal slice blocked the EPSPs from MS while after prolonged TTX washout, a partial recovery of the EPSP from MS was observed. Finally, it was interpreted through numerical analysis that increasing frequency of the current driving the coil, led to a decrease in the current amplitude threshold for micromagnetic neurostimulation. SignificanceThis work reports that micromagnetic neurostimulation can be used to evoke population EPSP responses in the CA1 region of the hippocampus. It demonstrates the strengthfrequency curve for MS and its unique features related to orientation dependence of the coils, spatial selectivity and stimulation threshold related to distance dependence. Finally, the challenges related to MS experiments were studied including ways to overcome them.

bioengineering↗

bioRxiv2021

Realistic alpha oscillations and transient responses in a cortical microcircuit model

Neural-mass modeling of neural population data (EEG, ECoG, or LFPs) has shown promise both in elucidating the neural processes underlying cortical rhythms and changes in brain state, as well as offering a framework for testing the interplay between these rhythms and information processing. Models of cortical alpha rhythms (8 - 12 Hz) and their impact in visual sensory processing have been at the forefront of this effort, with the Jansen-Rit being one of the more popular models in this domain. The Jansen-Rit model, however, fails in reproducing key physiological observations including the level of inputs that cortical neurons receive and their responses to visual transients. To address these issues we generated a neural mass model that complies better with synaptic mediated dynamics, intrinsic alpha behavior, and produces realistic responses. The model is robust to many changes in parameter values but critically depends on the ratio of excitation to inhibition, producing response transients whose features are dependent on this ratio and alpha phase and power. The model is sufficiently flexible so as to be able to easily replicate the range of low frequency oscillations observed in different studies. Consistent with experimental observations, we find phase-dependent response dynamics to both visual and electrical stimulation using this model. The model suggests that stimulation facilitates alpha at particular phases and suppresses it in others due to a phase dependent lag in inhibitory responses. Hence, the model generates insight into the physiological parameters responsible for intrinsic oscillations and testable hypotheses regarding the interactions between visual and electrical stimulation on those oscillations. Author summaryWhile there is increasing evidence of the fundamental role brain states play in shaping information processing in the cerebral cortex, a mechanistic understanding of how those brain states are manifested and alter the signals underlying sensory processing and decision making has proved challenging. To address this issue we have modeled a well established signature of inattention in visual cortex: synchronized low frequency (8 - 12 Hz) oscillations. To allow for inferences regarding the local generation of these rhythms within a cortical microcircuit we used a neural mass model approach that incorporates physiologically realistic interactions between 3 populations of neurons. Our model is able to explain a variety of experimental observations that previous neural mass models have not, including spontaneous rhythms in the absence of input, the faithful transmission of strong input transients, a range of oscillation frequencies, and phase dependent visual responses. The model is robust to a range of parameters, but critically depends on the balance between local excitation and inhibition.

neuroscience↗