Biology subjects

Shirinpour, S.

Publications and source records attributed to Shirinpour, S..

13 recordsLinked to original sources

bioRxiv2024

Layer-specific dynamics of local field potentials in monkey V1 during electrical stimulation

The mammalian neocortex, organized into six cellular layers or laminae, forms a cortical network within layers. Layer specific computations are crucial for sensory processing of visual stimuli within primary visual cortex. Laminar recordings of local field potentials (LFPs) are a powerful tool to study neural activity within cortical layers. Electric brain stimulation is widely used in basic neuroscience and in a large range of clinical applications. However, the layer-specific effects of electric stimulation on LFPs remain unclear. To address this gap, we conducted laminar LFP recordings of the primary visual cortex in monkeys while presenting a flash visual stimulus. Simultaneously, we applied a low frequency sinusoidal current to the occipital lobe with offset frequency to the flash stimulus repetition rate. We analyzed the modulation of visual-evoked potentials with respect to the applied phase of the electric stimulation. Our results reveal that only the deeper layers, but not the superficial layers, show phase-dependent changes in LFP components with respect to the applied current. Employing a cortical column model, we demonstrate that these in vivo observations can be explained by phase-dependent changes in the driving force within neurons of deeper layers. Our findings offer crucial insight into the selective modulation of cortical layers through electric stimulation, thus advancing approaches for more targeted neuromodulation.

biophysics↗

bioRxiv2024

Abnormal mu rhythm state-related cortical and corticospinal responses in chronic stroke

The motor cortexs activity is state-dependent. Specifically, the sensorimotor mu rhythm phase relates to fluctuating levels of primary motor cortex (M1) excitability, previously demonstrated in young and healthy volunteers. However, it is unknown whether this observation is generalizable to individuals with brain lesions after a stroke. We investigated the phase relationship between the mu rhythm and cortical excitability by combining real-time processing of electroencephalography (EEG) signals and transcranial magnetic stimulation (TMS) of M1. In 11 chronic subcortical stroke survivors and 12 similar-aged healthy volunteers, we applied TMS to M1 at the peak, falling, trough, and rising phase of the sensorimotor mu oscillation. As outcome measures, we investigated the M1-to-muscle excitability by measuring motor-evoked potentials (MEPs) and local cortical activation by measuring TMS-evoked potentials (TEPs). We found that M1-to-muscle excitability in stroke survivors and older volunteers shows a phase-dependency similar to that in young healthy adults. That is, MEPs were increased and decreased at the trough and peak of the mu rhythm, respectively. However, individuals with stronger stroke-related motor symptoms showed a decreased phase preference. Further, phase-dependency was abolished in the local cortical activity, as measured with EEG, in the stroke-affected hemisphere, in contrast to the non-affected hemisphere as well as either hemisphere in healthy volunteers. Altogether, these results shed light on the state-dependency of motor cortex excitability after stroke. Our results indicate that the strength of phase preference of TMS motor responses could indicate the severity of motor impairment. These results could enable the development of improved TMS paradigms for recovery of motor impairment after stroke.

neuroscience↗

bioRxiv2024

State dependent motor cortex stimulation reveals distinct mechanisms for corticospinal excitability and cortical responses

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method which can modulate brain activity by inducing electric fields in the brain. It is a popular tool to study causal brain-behavior relationships. However, brain states vary over time and affect the response of TMS. Neural oscillations can track the current brain state and are a promising marker to guide stimulation timing. Real-time, state-dependent brain stimulation has shown that neural oscillation phase modulates corticospinal excitability reflecting the connection from the primary motor cortex to a target muscle. However, such motor-evoked potentials (MEPs) only indirectly reflect motor cortex activation and are unavailable at other brain regions of interest. The direct and secondary cortical effects of phase-dependent brain stimulation remain an open question. In this study, we recorded the cortical responses during single-pulse transcranial magnetic stimulation (TMS) using electroencephalography (EEG) concurrently with the MEP measurements. TMS was delivered at peak, rising, trough, and falling phases of mu (8-13 Hz) and beta (14-30 Hz) oscillations in the motor cortex. The cortical responses were quantified through TMS-evoked potential components N15, P50, and N100 as peak-to-peak amplitudes (P50-N15 and P50-N100). We further analyzed whether the pre-stimulus frequency band power was predictive of the motor cortical responses. We found a significant main effect of neural oscillation phase on early evoked component (P50-N15). Furthermore, we found an interaction effect of oscillation phase and frequency on both early and late (P50-N100) components. Next, we compared the direct EEG response to the corticospinal excitability reflected by MEP amplitude. Interestingly, the preferred phase of the mu rhythm showed a 900 phase shift between the early TEP components and MEPs. The late component showed the same phase preference between EEG and MEPs. However, such a well-defined relationship did not exist for either of the components during beta phase specific stimulation. In addition, pre-TMS mu oscillatory power and phase significantly predicted both early and late cortical EEG responses when mu rhythm was targeted, indicating the independent causal effects of phase and power. However, only pre-TMS beta power significantly predicted the early and late TEP components when beta rhythm was targeted. Further analysis indicated that both pre-TMS mu and beta power jointly affect early cortical responses. In contrast, the late cortical responses were only influenced by pre-TMS mu power. These findings provide insight to mechanistic understanding of neural oscillation states in cortical and corticospinal activation in humans.

neuroscience↗

bioRxiv2023

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

Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity, and thereby cause changes in local neural oscillatory power. Despite an increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Here, we develop a computational neuronal network model of two-compartment pyramidal neurons and inhibitory interneurons which mimic the local cortical circuits. We model tACS with electric field strengths that are achievable in human applications. We then simulate intrinsic network activity and measure neural entrainment to investigate how tACS modulates ongoing endogenous oscillations. First, we show that intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV/mm), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV/mm), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that entrainment of ongoing cortical oscillations also depends on frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS induced entrainment via excitation-inhibition balance. Our model shows that pyramidal neurons are directly entrained by the exogenous electric field and drive the inhibitory neurons. Our findings can thus provide a mechanistic framework for understanding the intensity- and frequency-specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameters selection for tACS in cognitive studies and clinical applications.

neuroscience↗

bioRxiv2023

Experimental validation of computational models for the prediction of phase distribution during multi-channel transcranial alternating current stimulation

Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. Neural oscillations exhibit phase-dependent associations with cognitive functions, and tools to manipulate local oscillatory phases can affect communication across remote brain regions. A recent study demonstrated that multi-channel tACS can generate electric fields with a phase gradient or traveling waves in the brain. Computational simulations using phasor algebra can predict the phase distribution inside the brain and aid in informing parameters in tACS experiments. However, experimental validation of computational models for multi-phase tACS is still lacking. Here, we develop such a framework for phasor simulation and evaluate its accuracy using in vivo recordings in nonhuman primates. We extract the phase and amplitude of electric fields from intracranial recordings in two monkeys during multi-channel tACS and compare them to those calculated by phasor analysis using finite element models. Our findings demonstrate that simulated phases correspond well to measured phases (r = 0.9). Further, we systematically evaluated the impact of accurate electrode placement on modeling and data agreement. Finally, our framework can predict the amplitude distribution in measurements given calibrated tissues conductivity. Our validated general framework for simulating multi-phase, multi-electrode tACS provides a streamlined tool for principled planning of multi-channel tACS experiments.

bioengineering↗

bioRxiv2023

Induced neural phase precession through exogeneous electric fields

The gradual shifting of preferred neural spiking relative to local field potentials (LFPs), known as phase precession, plays a prominent role in neural coding. Correlations between the phase precession and behavior have been observed throughout various brain regions. As such, phase precession is suggested to be a global neural mechanism that promotes local neuroplasticity. However, causal evidence and neuroplastic mechanisms of phase precession are lacking so far. Here we show a causal link between LFP dynamics and phase precession. In three experiments, we modulated LFPs in humans, a non-human primate, and computational models using alternating current stimulation. We show that continuous stimulation of motor cortex oscillations in humans lead to a gradual phase shift of maximal corticospinal excitability by [~]90{degrees}. Further, exogenous alternating current stimulation induced phase precession in a subset of entrained neurons ([~]30%) in the non-human primate. Multiscale modeling of realistic neural circuits suggests that alternating current stimulation-induced phase precession is driven by NMDA-mediated synaptic plasticity. Altogether, the three experiments provide mechanistic and causal evidence for phase precession as a global neocortical process. Alternating current-induced phase precession and consequently synaptic plasticity is crucial for the development of novel therapeutic neuromodulation methods.

neuroscience↗

bioRxiv2022

Mesoscale neural effects of transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that is rapidly growing in popularity for studying causal brain-behavior relationships. However, its dose-dependent direct neural mechanisms, i.e., due to electric field or connectivity, and peripheral sensory co-stimulation effects remain debated. Understanding how TMS stimulation parameters affect brain responses is vital for the rational design of TMS protocols. Studying these mechanisms in humans is challenging due to the limited spatiotemporal resolution of available non-invasive neuroimaging methods. Here, we leverage invasive recordings of local field potentials in non-human primates to study TMS mesoscale responses. We demonstrate that early TMS-evoked potentials show a sigmoidal dose-response with stimulation intensity. We further show that stimulation responses are spatially specific. We employ several control conditions to dissociate direct neural responses from auditory and somatosensory co-activation. These results provide crucial evidence regarding TMS neural effects at the brain circuit level. Our findings are highly relevant for interpreting human TMS studies and biomarker developments for TMS target engagement in clinical applications.

neuroscience↗

bioRxiv2022

Systematic evaluation of anatomical details on transcranial electric stimulation and transcranial magnetic stimulation induced electric fields in a non-human primate model

Non-human primates (NHPs) have become key for translational research in noninvasive brain stimulation (NIBS). However, in order to create comparable stimulation conditions to humans it is vital to evaluate and match electric fields across species. Numerical models to simulate electric fields are an important tool for experimental planning in NHPs and translation to human studies. It is thus essential to evaluate how anatomical details in NHP models affect NIBS electric fields. Here, we create a highly accurate head model of a non-human primate (NHP) from 10.5 T MR data. We systematically evaluate how muscle tissue and head size (due to different MRI fields of view) affect simulation results in transcranial electric and magnetic stimulation (TES and TMS). Our findings indicate that the inclusion of muscle can affect TES electric field strength up to 29.5% while TMS is largely unaffected. Additionally, comparing a full head model to a cropped head model illustrates the impact of head size on electric fields for both TES and TMS. We find opposing effects between TES and TMS with an increase up to 13.5% for TES and a decrease up to 21.5% for TMS for the cropped head model compared to the full head model. Our results provide important insights into the level of anatomical detail needed for NHP head models and can inform future translational efforts for NIBS studies. HighlightsO_LIWe created a high resolution non-human primate FEM head model from 10.5 T MR data. C_LIO_LIWe ran transcranial electric and magnetic stimulation (TES and TMS) simulations to evaluate the effects of muscle and head size on the induced electric field in the brain. C_LIO_LIWe simulated both isotropic and anisotropic muscle conductivities. C_LIO_LIMuscle tissue can greatly affect simulation results for TES (increase electric field strength by up to 29.5%), but not for TMS. C_LIO_LIThere are opposing, but large effects of using a full head model versus a cropped head model for both TES (increase up to 13.5%) and TMS (decrease of up to 21.5%) C_LI

neuroscience↗

bioRxiv2022

The phase of sensorimotor mu and beta oscillations has the opposite effect on corticospinal excitability

Neural oscillations in the primary motor cortex (M1) shape corticospinal excitability. Power and phase of ongoing mu (8-13 Hz) and beta (14-30 Hz) activity may mediate motor cortical output. However, the functional dynamics of both mu and beta phase and power relationships and their interaction, are largely unknown. Here, we employ recently developed real-time targeting of the mu and beta rhythm, to apply phase-specific brain stimulation and probe motor corticospinal excitability non-invasively. For this, we used instantaneous read-out and analysis of ongoing oscillations, targeting four different phases (0{degrees}, 90{degrees}, 180{degrees}, and 270{degrees}) of mu and beta rhythms with suprathreshold single-pulse transcranial magnetic stimulation (TMS) to M1. Ensuing motor evoked potentials (MEPs) in the right first dorsal interossei muscle were recorded. Twenty healthy adults took part in this double-blind randomized crossover study. Mixed model regression analyses showed significant phase-dependent modulation of corticospinal output by both mu and beta rhythm. Strikingly, these modulations exhibit a double dissociation. MEPs are larger at the mu trough and rising phase and smaller at the peak and falling phase. For the beta rhythm we found the opposite behavior. Also, mu power, but not beta power, was positively correlated with corticospinal output. Power and phase effects did not interact for either rhythm, suggesting independence between these aspects of oscillations. Our results provide insights into real-time motor cortical oscillation dynamics, which offers the opportunity to improve the effectiveness of TMS by specifically targeting different frequency bands.

neuroscience↗

bioRxiv2021

Effects of transcranial alternating current stimulation on spiking activity in computational models of single neocortical neurons

Neural oscillations are a key mechanism for information transfer in brain circuits. Rhythmic fluctuations of local field potentials control spike timing through cyclic membrane de- and hyperpolarization. Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation method which can directly interact with brain oscillatory activity by imposing an oscillating electric field on neurons. Despite its increasing use, the basic mechanisms of tACS are still not fully understood. Here, we investigate in a computational study the effects of tACS on morphologically realistic neurons with ongoing spiking activity. We characterize the membrane polarization as a function of electric field strength and subsequent effects on spiking activity in a set of 25 neurons from different neocortical layers. We find that tACS does not affect the firing rate of investigated neurons for electric field strengths applicable to human studies. However, we find that the applied electric fields entrain the spiking activity of large pyramidal neurons at < 1mV/mm field strengths. Our model results are in line with recent experimental studies and can provide a mechanistic framework to understand the effects of oscillating electric fields on single neuron activity. They highlight the importance of neuron morphology in responsiveness to electrical stimulation and suggest that large pyramidal neurons are most likely the prime target for tACS.

neuroscience↗

bioRxiv2020

Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique widely used in research and clinical applications. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research and provide a framework between the physical input parameters and the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields in whole-brain volume conductor models. However, multi-scale computational modeling approaches to predict TMS cellular and subcellular responses, crucial to understanding TMS plasticity inducing protocols, are not available so far. Here, we develop a multi-scale Neuron Modeling for TMS toolbox (NeMo-TMS) that enables researchers to easily generate accurate neuron models from morphological reconstructions, couple them to the external electric fields induced by TMS, and to simulate the cellular and subcellular responses of the neurons. Both single-pulse and rTMS protocols can be simulated and results visualized in 3D. We openly share our toolbox and provide example scripts and datasets for the user to explore. NeMo-TMS toolbox (https://github.com/OpitzLab/NeMo-TMS) allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS.

neuroscience↗

bioRxiv2019

Experimental Evaluation of Methods for Real-Time EEG Phase-Specific Transcranial Magnetic Stimulation

Brain oscillations reflect system-level neural dynamics and capture the current brain state. These brain rhythms can be measured noninvasively in humans with electroencephalography (EEG). Up and down states of brain oscillations capture local changes in neuronal excitability. This makes them a promising target for non-invasive brain stimulation methods such as Transcranial Magnetic Stimulation (TMS). Real-time EEG-TMS systems record ongoing brain signals, process the data, and deliver TMS stimuli at a specific brain state. Despite their promise to increase the temporal specificity of stimulation, best practices and technical solutions are still under development. Here, we implement and compare state-of-the-art methods (Fourier based, Autoregressive Prediction) for real-time EEG-TMS and evaluate their performance both in silico and experimentally. We further propose a new robust algorithm for delivering real-time EEG phase-specific stimulation based on short prerecorded EEG training data (Educated Temporal Prediction). We found that Educated Temporal Prediction performs at the same level or better than Fourier-based or Autoregressive methods both in silico and in vivo, while being computationally more efficient. Further, we document a dependency of EEG signal-to-noise ratio (SNR) on algorithm accuracy across all algorithms. In conclusion, our results can give important insights for real-time TMS-EEG technical development as well as experimental design.

neuroscience↗

bioRxiv2018

Comparative Modeling of Transcranial Magnetic and Electric Stimulation in Mouse, Monkey, and Human

Transcranial magnetic stimulation (TMS) and transcranial electric stimulation (TES) are increasingly popular methods to noninvasively affect brain activity. However, their mechanism of action and dose-response characteristics remain under active investigation. Translational studies in animals play a pivotal role in these efforts due to a larger neuroscientific toolset enabled by invasive recordings. In order to translate knowledge gained in animal studies to humans, it is crucial to generate comparable stimulation conditions with respect to the induced electric field in the brain. Here, we conduct a finite element method (FEM) modeling study of TMS and TES electric fields in a mouse, capuchin monkey, and human model. We systematically evaluate the induced electric fields and analyze their relationship to head and brain anatomy. We find that with increasing head size, TMS-induced electric field strength first increases and then decreases according to a two-term exponential function. TES-induced electric field strength strongly decreases from smaller to larger specimen with up to 100x fold differences across species. Our results can serve as a basis to compare and match stimulation parameters across studies in animals and humans.\n\nHIGHLIGHTSO_LITranslational research in brain stimulation should account for large differences in induced electric fields in different organisms\nC_LIO_LIWe simulate TMS and TES electric fields using anatomically realistic finite element models in three species: mouse, monkey, and human\nC_LIO_LITMS with a 70 mm figure-8 coil creates an approximately 2-times weaker electric field in a mouse brain than in monkey and human brains, where electric field strength is comparable\nC_LIO_LITwo-electrode TES creates an approximately 100-times stronger electric field in a mouse brain and 3.5-times stronger electric field in a monkey brain than in a human brain\nC_LI

neuroscience↗