Biology subjects

Bikson, M.

Publications and source records attributed to Bikson, M..

33 records · Page 2Linked to original sources

bioRxiv2023

A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning

Closed-loop neuromodulation measures dynamic neural or physiological activity to optimize interventions for clinical and nonclinical behavioral, cognitive, wellness, attentional, or general task performance enhancement. Conventional closed-loop stimulation approaches can contain biased biomarker detection (decoders and error-based triggering) and stimulation-type application. We present and verify a novel deep learning framework for designing and deploying flexible, data-driven, automated closed-loop neuromodulation that is scalable using diverse datasets, agnostic to stimulation technology (supporting multi-modal stimulation: tACS, tDCS, tFUS, TMS), and without the need for personalized ground-truth performance data. Our approach is based on identified periods of responsiveness - detected states that result in a change in performance when stimulation is applied compared to no stimulation. To demonstrate our framework, we acquire, analyze, and apply a data-driven approach to our open sourced GX dataset, which includes concurrent physiological (ECG, EOG) and neuronal (EEG) measures, paired with continuous vigilance/attention-fatigue tracking, and High-Definition transcranial electrical stimulation (HD-tES). Our frameworks decision process for intervention application identified 88.26% of trials as correct applications, showed potential improvement with varying stimulation types, or missed opportunities to stimulate, whereas 11.25% of trials were predicted to stimulate at inopportune times. With emerging datasets and stimulation technologies, our unifying and integrative framework; leveraging deep learning (Convolutional Neural Networks - CNNs); demonstrates the adaptability and feasibility of automated multimodal neuromodulation for both clinical and nonclinical applications.

neuroscience↗

bioRxiv2022

Computational modeling of posteroanterior lumbar traction by an automated massage bed: predicting intervertebral disc stresses and deformation

Spinal traction is a physical intervention that provides constant or intermittent stretching axial force to the lumbar vertebrae to gradually distract spinal tissues into better alignment, reduce intervertebral disc (IVD) pressure, and manage lower back pain (LBP). However, such axial traction may change the normal lordotic curvature, and result in unwanted side effects and/or inefficient reduction of the IVD pressure. An alternative to axial traction has been recently tested, consisting of posteroanterior (PA) traction in supine posture, which was recently shown effective to increase the intervertebral space and lordotic angle using MRI. PA traction aims to maintain the lumbar lordosis curvature throughout the spinal traction therapy while reducing the intradiscal pressure. In this study, we developed finite element simulations of mechanical therapy produced by a commercial thermo-mechanical massage bed capable of spinal PA traction. The stress relief produced on the lumbar discs by the posteroanterior traction system was investigated on human subject models with different BMI (normal, overweight, moderate obese and extreme obese BMI cases). We predict typical traction levels lead to significant distraction stresses in the lumbar discs, thus producing a stress relief by reducing the compression stresses normally experienced by these tissues. Also, the stress relief experienced by the lumbar discs was effective in all BMI models, and it was found maximal in the normal BMI model. These results are consistent with prior observations of therapeutic benefits derived from spinal AP traction.

bioengineering↗

bioRxiv2022

Computational modeling of deep tissue heating by an automatic thermal massage bed: predicting the effects on circulation

Automatic thermal and mechanical massage beds support selfmanaged treatment, including reduction of pain and stress, enhanced circulation, and improved mobility. As the devices become more sophisticated (increasing the degrees of freedom), it is essential to identify the settings that best target the desired tissue. To that end, we developed an MRI-derived model of the lower back and simulated the physiological effects of a commercial thermal-mechanical massage bed. Here we specifically estimated the tissue temperature and increased circulation under steady-state conditions for typical thermal actuator settings (i.e., 45-65{degrees}C). Energy transfer across nine tissues was simulated with finite element modeling (FEM) and the resulting heating was coupled to blood flow with an empirically-guided model of temperature-dependent circulation. Our findings indicate that thermal massage increases tissue temperature by 3-8{degrees}C and 1-3{degrees}C at depths of 2 and 3 cm, respectively. Importantly, due to the rapid (non-linear) increase of circulation with local temperature, this is expected to increase blood flow four-fold (4x) at depths occupied by deep tissue and muscle. These predictions are consistent with prior clinical observations of therapeutic benefits derived from spinal thermal massage.

bioengineering↗

bioRxiv2020

Adaptive current-flow models of ECT: Explaining individual static impedance, dynamic impedance, and brain current delivery

BackgroundImprovements in electroconvulsive therapy (ECT) outcomes have followed refinement in device electrical output and electrode montage. The physical properties of the ECT stimulus, together with those of the patients head, determine the impedances measured by the device and govern current delivery to the brain and ECT outcomes. ObjectiveHowever, the precise relations among physical properties of the stimulus, patient head anatomy, and patient-specific impedance to the passage of current are long-standing questions in ECT research and practice. MethodsWe developed anatomical MRI-derived models of transcranial electrical stimulation (tES) that included changes in tissue conductivity due to local electrical current flow. These "adaptive" models simulate ECT both during therapeutic stimulation using high (~1 A) current and when dynamic impedance is measured, as well as prior to stimulation when low (~1 mA) current is used to measure static impedance. We modeled two scalp layers: a superficial scalp layer with adaptive conductivity that increases with electric field up to a subject specific maximum [Formula], and a deep scalp layer with a subject-specific fixed conductivity ({sigma}DS). ResultsWe demonstrate that variation in these scalp parameters explain clinical data on subject-specific static impedance and dynamic impedance, their imperfect correlation across subjects, their relationships to seizure threshold, and the role of head anatomy. Adaptive tES models demonstrate that current flow changes local tissue conductivity which in turn shapes current delivery to the brain in a manner not accounted for in fixed tissue conductivity models. ConclusionsOur predictions that variation in individual skin properties, rather than other aspects of anatomy, largely govern the relationship between static impedance, dynamic impedance, and current delivery to the brain, are themselves subject to assumptions about tissue properties. Broadly, our novel pipeline for tES models is important in ongoing efforts to optimize devices, personalize interventions, and explain clinical findings.

bioengineering↗

bioRxiv2020

Neurovascular-modulation

Neurovascular-modulation is based on two principles that derive directly from brain vascular ultra-structure, namely an exceptionally dense capillary bed (BBB length density: 972 mm/mm3) and a blood-brain-barrier (BBB) resistivity ({rho} ~ 1x105 {Omega}.m) much higher than brain parenchyma/interstitial space ({rho} ~ 4 {Omega}.m) or blood ({rho} ~ 1 {Omega}.m). Principle 1: Electrical current crosses between the brain parenchyma (interstitial space) and vasculature, producing BBB electric fields (EBBB) that are > 400x of the average parenchyma electric field ([E]BRAIN), which in turn modulates transport across the BBB. Specifically, for a BBB space constant ({lambda}BBB) and wall thickness (dth-BBB): analytical solution for maximum BBB electric field (EABBB) is given as: ([E]BRAIN x {lambda}BBB) / dth-BBB. Direct vascular stimulation suggests novel therapeutic strategies such as boosting metabolic capacity or interstitial fluid clearance. Boosting metabolic capacity impacts all forms of neuromodulation, including those applying intensive stimulation or driving neuroplasticity. Boosting interstitial fluid clearance has broad implications as a treatment for neurodegenerative disease including Alzheimers disease. Principle 2: Electrical current in the brain parenchyma is distorted around brain vasculature, amplifying neuronal polarization. Specifically, vascular ultra-structure produces ~50% modulation of the average parenchyma electric field ([E]BRAIN) over the ~40 m inter-capillary distance. The divergence of EBRAIN (activating function) is thus ~100 kV/m2 per unit average parenchyma electric field ([E]BRAIN). This impacts all forms of neuromodulation, including Deep Brain Stimulation (DBS), Spinal Cord Stimulation (SCS), Transcranial Magnetic Stimulation (TMS), Electroconvulsive Therapy (ECT), and transcranial electrical stimulation (tES) techniques such a transcranial Direct Current Stimulation (tDCS). Specifically, whereas spatial profile of EBRAIN along neurons is traditionally assumed to depend on macroscopic anatomy, it instead depends on local vascular ultra-structure.

neuroscience↗

bioRxiv2020

Group and Individual Level Variations between Symmetric and Asymmetric DLPFC Montages for tDCS over Large Scale Brain Network Nodes

Two challenges to optimizing transcranial direct current stimulation (tDCS) are selecting between, often similar, electrode montages and accounting for inter-individual differences in response. These two factors are related by how tDCS montage determines current flow through the brain considered across or within individuals. MRI-based computational head models (CHMs) predict how brain anatomy determine electric field (EF) patterns for a given tDCS montage. Because conventional tDCS produces diffuse brain current flow, stimulation outcomes may be understood as modulation of global networks. Therefore, we developed network-led, rather than region-led, approach. We specifically considered two common frontal tDCS montages that nominally target the dorsolateral prefrontal cortex; asymmetric unilateral (anode/cathode: F4/Fp1) and symmetric bilateral (F4/F3) electrode montages. CHMs of 66 participants were constructed. We showed that cathode location significantly affects EFs in the limbic network. Furthermore, using a finer parcellation of large-scale networks, we found significant differences in some of main nodes within a network, even if there is no difference at the network level. This study generally demonstrates a methodology for considering the components of large-scale networks in CHMs instead of targeting a single region and specifically provides insight into how symmetric vs asymmetric frontal tDCS may differentially modulate networks across a population.

bioengineering↗

bioRxiv2019

Temporal interference stimulation targets deep brain regions by modulating neural oscillations

Temporal interference (TI) stimulation of the brain generates amplitude-modulated electric fields oscillating in the kHz range. A validated current-flow model of the human head estimates that amplitude-modulated electric fields are stronger in deep brain regions, while unmodulated electric fields are maximal at the cortical regions. The electric field threshold to modulate carbachol-induced gamma oscillations in rat hippocampal slices was determined for unmodulated 0.05-2 kHz sine waveforms, and 5 Hz amplitude-modulated waveforms with 0.1-2 kHz carrier frequencies. The neuronal effects are replicated with a computational network model to explore the underlying mechanisms. Experiment and model confirm the hypothesis that spatial selectivity of temporal interference stimulation depends on the phasic modulation of neural oscillations only in deep brain regions. This selectivity is governed by network adaption (e.g. GABAb) that is faster than the amplitude-modulation frequency. The applied current required depends on the neuronal membrane time-constant (e.g. axons) approaching the kHz carrier frequency of temporal interference stimulation.

neuroscience↗

bioRxiv2019

Realistic Anatomically Detailed Open-Source Spinal Cord Stimulation (RADO-SCS) Model

ObjectiveComputational current flow models of spinal cord stimulation (SCS) are widely used in device development, clinical trial design, and patient programming. Proprietary models of varied sophistication have been developed. An open-source model with state-of-the-art precision would serve as a standard for SCS simulation. ApproachWe developed a sophisticated SCS modeling platform, named Realistic Anatomically Detailed Open-Source Spinal Cord Stimulation (RADO-SCS) model. This platform consists of realistic and detailed spinal cord and ancillary tissues anatomy derived based on prior imaging and cadaveric studies. Represented tissues within the T9-T11 spine levels include vertebrae, intravertebral discs, epidural space, dura, CSF, white-matter, gray-matter, dorsal and ventral roots and rootlets, dorsal root ganglion, sympathetic chain, thoracic aorta, epidural space vasculature, white-matter vasculature, and thorax. As an exemplary, a bipolar SCS montage was simulated to illustrate the model workflow from the electric field calculated from a finite element model (FEM) to activation thresholds predicted for individual axons populating the spinal cord. Main ResultsCompared to prior models, RADO-SCS meets or exceeds detail for every tissue compartment. The resulting electric fields in white and gray-matter, and axon model activation thresholds are broadly consistent with prior stimulations. SignificanceThe RADO-SCS can be used to simulate any SCS approach with both unprecedented resolution (precision) and transparency (reproducibility). Freely available online, the RADO-SCS will be updated continuously with version control.

neuroscience↗

bioRxiv2019

Transcranial Electrical Stimulation Motor Threshold Combined with Reverse-Calculated Electric Field Modeling Can Determine Individualized tDCS Dosage

BackgroundUnique amongst brain stimulation tools, transcranial direct current stimulation (tDCS) currently lacks an easy method for individualizing dosage.\n\nObjectiveCan one individually dose tDCS? We developed a novel method of reverse-calculating electric-field (E-field) models based on Magnetic Resonance Imaging (MRI) scans that can determine individualized tDCS dose. We also sought to develop an MRI-free method of individualizing tDCS dose by measuring transcranial magnetic stimulation (TMS) motor threshold (MT) and single pulse, suprathreshold transcranial electrical stimulation (TES) MT and regressing it against E-field modeling.\n\nMethodsIn 29 healthy adults, we acquired TMS MT, TES MT, and structural MRI scans with a fiducial marking the motor hotspot. We then computed a \"reverse-calculated tDCS dose\" of tDCS applied at the scalp needed to cause a 1.00V/m E-field at the cortex. Finally, we examined whether the predicted E-field values correlated with each participants measured TMS MT or TES MT.\n\nResultsWe were able to determine a reverse-calculated tDCS dose for each participant. The Transcranial Electrical Stimulation MT, but not the Transcranial Magnetic Stimulation MT, significantly correlated with the calculated tDCS dose determined by E-field modeling (R2 = 0.509, p < 0.001).\n\nConclusionsReverse-calculation E-field modeling, alone or in combination with TES MT, shows promise as a method to individualize tDCS dose. The large range of the reverse-calculated tDCS doses between subjects underscores the likely need to individualize tDCS dose. If these results are confirmed in future studies, TES MT may evolve into an inexpensive and quick method to individualize tDCS dose.

neuroscience↗

bioRxiv2019

Computational Finite Element Method (FEM) forward modeling workflow for transcranial Direct Current Stimulation (tDCS) current flow on MRI-derived head: Simpleware and COMSOL Multiphysics tutorial

Transcranial Direct Current Stimulation (tDCS) dose designs are often based on computational Finite Element Method (FEM) forward modeling studies. These FEM models educate researchers about the resulting current flow (intensity and pattern) and so the resulting neurophysiological and behavioral changes based on tDCS dose (mA), resistivity of head tissues (e.g. skin, skull, CSF, brain), and head anatomy. Moreover, model support optimization of montage to target specific brain regions. Computational models are thus an ancillary tool used to inform the design, set-up and programming of tDCS devices, and investigate the role of parameters such as electrode assembly, current directionality, and polarity of tDCS in optimizing therapeutic interventions. Computational FEM modeling pipeline of tDCS initiates with segmentation of an exemplary magnetic resonance imaging (MRI) scan of a template head into multiple tissue compartments to develop a higher resolution (< 1 mm) MRI derived FEM model using Simpleware ScanIP. Next, electrode assembly (anode and cathode of variant dimension) is positioned over the brain target and meshed at different mesh densities. Finally, a volumetric mesh of the head with electrodes is imported in COMSOL and a quasistatic approximation (stead-state solution method) is implemented with boundary conditions such as inward normal current density (anode), ground (cathode), and electrically insulating remaining boundaries. A successfully solved FEM model is used to visualize the model prediction via different plots (streamlines, volume plot, arrow plot).

neuroscience↗

bioRxiv2019

Direct current stimulation boosts associative Hebbian synaptic plasticity and maintains its pathway specificity

There is evidence that transcranial direct current stimulation can boost learning performance. Arguably, this boost is related to synaptic plasticity. However, the precise effects on synaptic plasticity and its underlying mechanisms are not known. We hypothesized that direct current stimulation modulates endogenous Hebbian plasticity mechanisms due to its ability to polarize cellular membrane. To test this we induced long term plasticity (LTP) using theta-burst stimulation (TBS) in rat hippocampus, and measured the effects of concurrent direct current stimulation (DCS). Soma-depolarizing DCS increased TBS-induced LTP. Oscillating current stimulation is equally effective provided the soma-depolarizing phase is time-aligned with the theta-bursts, suggesting that only instantaneous depolarization is relevant. Importantly, the effect is pathway-specific and associative. These findings are consistent with classic theory on the role of post-synaptic membrane potential in Hebbian plasticity. These data suggest that the effects of direct current stimulation are specific because they modulate endogenous Hebbian plasticity, thus inheriting its exquisite functional specificity.

neuroscience↗

bioRxiv2018

transcranial Direct Current Stimulation Studies Open Database (tDCS-OD)

Transcranial direct current stimulation (tDCS) is increasingly evaluated for a myriad of clinical indications and performance enhancement mainly related to cognitive and motor applications. As a result tDCS has been tested with diverse inclusion criteria and subject populations. tDCS is customized for applications by many factors, including stimulation dose (montage, current, time), associated training and subject inclusion/exclusion criteria. The rapid proliferation of applications, technological advancements and emerging scientific discoveries presents challenges to the organization and consolidation of tDCS data - which can lead to scientific, clinical and public confusions. In this paper, we develop a system to summarize and consolidate methodological aspects of tDCS, concentrating on study design and the physical parameters of the stimulation. We introduce a community-driven, open access database, where these parameters, including stimulation intensities, duration, electrode sizes, montages, and subject information, are noted for relevant tDCS publications. The transcranial Direct Current Stimulation Studies Open Database (tDCS-OD) (http://tdcsdatabase.com) will support constructive dialogue on research and clinical tDCS applications, critical evaluation of past work, and identification of promising protocols, while reducing ambiguity about stimulation methodology. The database design allows ongoing updates and editing, with transparent version control. At the moment, the database includes information about 56,613 tDCS sessions.

neuroscience↗

bioRxiv2018

Synaptic transmission modulates while non-synaptic processes govern the transition from pre-ictal to seizure activity in vitro

It is well established that non-synaptic mechanisms can generate electrographic seizures after blockade of synaptic function. We investigated the interaction of intact synaptic activity with non-synaptic mechanisms in the isolated CA1 region of rat hippocampal slices using the \"elevated-K+\" model of epilepsy. Elevated K+ ictal bursts share waveform features with other models of electrographic seizures, including non-synaptic models where chemical synaptic transmission is suppressed, such as the low-Ca2+model. These features include a prolonged (several seconds) negative field shift associated with neuronal depolarization and superimposed population spikes. When population spikes are disrupted for up to several seconds, intracellular recording demonstrated that the prolonged suppression of population spikes during ictal activity was due to depolarization block of neurons. Elevated-K+ ictal bursts were often preceded by a build-up of \"pre-ictal\" epileptiform discharges that were characterized as either \"slow-transition\" (localized and with a gradual increase in population spike amplitude, reminiscent non-synaptic neuronal aggregate formation, presumed mediated by extracellular K+ concentrations ([K+])o accumulation), or \"fast-transition\" (with a sudden increase in population spike amplitude, presumed mediated by field effects). When ictal activity had a fast-transition it was preceded by fast-transition pre-ictal activity; otherwise population spikes developed gradually at ictal event onset. Addition of bicuculline, a GABAA receptor antagonist, suppressed population spike generation during ictal activity, reduced pre-ictal activity, and increased the frequency of ictal discharges. Nipecotic acid and NNC-711, both of which block GABA re-uptake, increased population spike amplitude during ictal bursts and promoted the generation of preictal activity. By contrast, addition of ionotropic glutamate-receptor antagonists (NBQX, D-APV) had no consistent effect on ictal burst waveform or frequency and did not fully suppress pre-ictal activity. Similarly, CGP 55848, a GABAB receptor antagonist, has no significant effect on pre-ictal activity or burst frequency (although it did increase burst duration slightly). Our results are consistent with the hypothesis that non-synaptic mechanisms underpin the generation of ictal bursts in CA1 and that GABAA synaptic mechanisms can shape event development by delaying event initiation and counteracting depolarization block.

neuroscience↗

bioRxiv2017

Realistic vOlumetric-Approach to Simulate Transcranial Electric Stimulation -- ROAST -- a fully automated open-source pipeline

Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on the magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subjects MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each step, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool - ROAST - is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement. The electric field estimated with the open-source tools used by ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS, a well-established open-source modeling pipeline. However, we do find large differences when volumetric segmentation are converted into surfaces that are used in SimNIBS to generate volumetric meshes. Evaluation on intracranial recordings from human subjects suggests that ROAST outperforms newer versions of SimNIBS in predicting field distribution and magnitudes, but that an older version of SimNIBS performs similarly. We hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We release ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling at https://www.parralab.org/roast/.

biophysics↗

bioRxiv2017

tDCS changes in motor excitability are specific to orientation of current flow

Measurements and models of current flow in the brain during transcranial Direct Current Stimulation (tDCS) indicate stimulation of regions in-between electrodes. Moreover, the cephalic cortex result in local fluctuations in current flow intensity and direction, and animal studies suggest current flow direction relative to cortical columns determines response to tDCS. Here we test this idea by measuring changes in cortico-spinal excitability by Transcranial Magnetic Stimulation Motor Evoked Potentials (TMS-MEP), following tDCS applied with electrodes aligned orthogonal (across) or parallel to M1 in the central sulcus. Current flow models predicted that the orthogonal electrode montage produces consistently oriented current across the hand region of M1 that flows along cortical columns, while the parallel electrode montage produces none-uniform current directions across the M1 cortical surface. We find that orthogonal, but not parallel, orientated tDCS modulates TMS-MEPs. We also show modulation is sensitive to the orientation of the TMS coil (PA or AP), which is through to select different afferent pathways to M1. Our results are consistent with tDCS producing directionally specific neuromodulation in brain regions in-between electrodes, but shows nuanced changes in excitability that are presumably current direction relative to column and axon pathway specific. We suggest that the direction of current flow through cortical target regions should be considered for targeting and dose-control of tDCS.\n\nHighlights O_LIDirection of current flow is important for tDCS after-effects.\nC_LIO_LItDCS modulates excitability between two electrodes.\nC_LIO_LItDCS differentially modulates PA and AP inputs into M1.\nC_LI\n\nAbbreviations\n\nFundingThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

neuroscience↗