Biology subjects

Milosevic, L.

Publications and source records attributed to Milosevic, L..

15 recordsLinked to original sources

bioRxiv2026

Inhibitory Evoked Potentials as a Spatially Dependent Intraoperative Marker of Clinical Tremor Reduction

Deep brain stimulation (DBS) of the ventral intermediate nucleus (Vim) of the thalamus may be used to treat medication refractory essential tremor. Using recordings from in vivo human Vim neurons, our previous work has suggested that evoked potentials (that we termed quasi-evoked inhibition) ~2 ms following high frequency microstimulation pulses may be related to inhibitory synapses onto the Vim. Here, we investigate whether (i) quasi-evoked inhibition is related to clinical tremor reduction, and (ii) if quasi-evoked inhibition is dependent on the stimulation location within the Vim. By developing an objective determination of the presence or absence of quasi-evoked inhibition and utilizing accelerometer recordings, we showed that recordings with quasi-evoked inhibition at 100 Hz microstimulation exhibit greater tremor reduction than those without (P < 0.05, BF > 30). The number of stimulation pulses with quasi-evoked inhibition is also correlated with tremor reduction (rho = 0.18, P < 0.05) at all stimulation frequencies >=100 Hz. Furthermore, by analyzing microelectrode trajectories reconstructed from structural MRIs, we found that proximity to the ventral caudal border (P < 0.005) and to a previously established sweet spot (P < 0.05) are anti-correlated with the number of stimulation pulses with quasi-evoked inhibition. Our findings suggest that quasi-evoked inhibition is a potential biomarker of tremor reduction by means of network inhibition, and the more posterior regions of the Vim may allow for better recruitment of inhibition. This may be useful for closed-loop stimulation design.

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bioRxiv2026

A Unified Computational Framework for Deep Brain Stimulation at the Cellular and Network Levels

Deep brain stimulation (DBS) has been demonstrated to be a successful therapeutic intervention for neurological disorders, yet the mechanisms underlying its effects on neuronal circuits remain incompletely understood. In this study, we propose a comprehensive phenomenological computational model that accounts for the impact of electrical stimulation parameters on neuronal circuits while incorporating experimentally-validated synaptic and cellular constraints. We investigate how DBS pulses modulate spiking activity in populations of homogeneous neurons representing stimulated nuclei, systematically examining the influence of circuitry architecture, including synaptic connectivity strength (weak vs. strong) and organization (sparse vs. rich). To characterize how DBS-modulated neuronal activity propagates through downstream networks, we develop a simple encoder that reveals distinct encoding patterns arising from different architectural configurations of stimulated nuclei. Furthermore, by connecting stimulated nuclei to recurrently connected neuronal populations, we examine the propagation of DBS-modulated neuronal synchrony across various circuit motifs. Our results demonstrate that three critical factors shape DBS-modulated neuronal activity: (a) the intrinsic synaptic and cellular properties of stimulated nuclei, (b) the architectural organization of stimulated nuclei in terms of synaptic strength and connectivity density, and (c) the circuit motifs formed by postsynaptic targets of stimulated nuclei. This unified model provides a mechanistic framework for understanding DBS representation and propagation in neuronal networks, offering insights that may inform optimization of stimulation parameters for clinical applications. Author summaryComputational models of deep brain stimulation have proven to be supremely useful in disentangling the clinical benefits and adverse effects observed in the treatment of a variety of conditions. Despite this, the capacity for many of the existent computational models to account for micro/meso-circuit activation remains limited, as the major techniques rely on detailed characterization of tracts surrounding the DBS electrode, or depend on an non-physiologically constrained injected current intending to mimic the influence of electrical stimulation. The tract based methods only work for tracts that we have detailed characterization of, which are missing for many of the target structures, such as the basal ganglia. Given the restrictions of current methods we set out to define a phenomenological method that is applicable to as many simulation methods as possible, including those with missing details on tractography, while being able to readily integrate results of detailed simulations when available. Our approach is extensible and has examples implemented in some of the most popular computational neuroscience toolkits allowing for ready integration into existing network simulations. Further we demonstrate how this methodology supports interrogation of networks both for physiological responses but also computational dynamics, such as information multiplexing and delayed local evoked potentials.

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bioRxiv2026

Differentiating filter-induced oscillations from physiological stimulation-evoked potentials in intracranial recordings

IntroductionStimulation-evoked potentials (SEPs), recorded both during and after deep brain stimulation (DBS) surgery, have shown promise for guiding DBS targeting and programming. However, filtering protocols applied to stimulation trains produce an artifact we call a filter-induced oscillation (FIO) which closely mimics physiological SEPs. Hence, we outline the mechanistic origins of this distortion and describe a means of differentiating it from valid SEP activity. MethodsWe recorded in 18 patients undergoing DBS surgery targeting the subthalamic nucleus or globus pallidus internus. We stimulated target nuclei with cathode-first (CF) and anode-first (AF) pulses to record native SEPs, and in white matter tracts (null condition). Recordings were subsequently filtered to illustrate FIO. Next, we filtered harmonic frequencies of an artificial stimulation train to demonstrate FIO origins. Finally, FIO was deliberately generated in white matter recordings with a notch filter, and its behaviour contrasted with SEPs during AF and CF stimulation. ResultsFiltering stimulation trains produced FIOs that depended on filter order and corner frequency. We also showed that FIO emerges from filter-induced attenuations of harmonic frequencies which compose stimulation trains, producing oscillations of like frequency around pulses. Finally, FIOs reverse in polarity depending on AF or CF stimulation, whereas SEPs do not. ConclusionsGiven the potential for widespread adoption of SEPs in DBS targeting and programming, safe analytical protocols are imperative to avoid the induction of processing-related artifacts which can be misinterpreted as biological signals. Here we establish the necessary theory for identifying FIOs and tuning analytical pipelines to avoid their generation.

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bioRxiv2025

Frequency-dependent Inhibition during Deep Brain Stimulation of Thalamic Ventral Intermediate Nuclei

Deep brain stimulation (DBS) of the thalamic ventral intermediate nucleus (Vim) has been a standard therapy for essential tremor. It has been shown that high frequency ([≥]100Hz) DBS suppresses Vim neuronal firing and tremor activity, however, the underlying mechanisms are not fully understood. Here, we use in vivo recordings (single-unit) of Vim neurons (n=19, people with essential tremor) during different DBS frequencies to investigate whether neuronal suppression during high-frequency DBS occurs at synaptic/cellular levels (e.g., cell inhibition due to synaptic depression/fatigue during high-frequency DBS) or is influenced by network-level effects (e.g., recurrent inhibition). We propose a theoretical framework that explains DBS effects at both cellular and network levels, i.e., (continuous) high-frequency DBS not only depresses synapses projecting to Vim but also enables the recruitment of inhibitory neurons. A transient burst in the spiking activity of Vim during high-frequency DBS, prior to neuronal suppression, is likely providing sufficient network engagement to recruit inhibitory neurons that are silent during low-frequency DBS. Further, we detected a positive-going evoked-field potential effect, hereafter referred to as quasi-evoked inhibition, during high-frequency (100 Hz and 200 Hz) Vim-DBS in four out of 19 recording sites. Interestingly, it was observed that (i) neuronal suppression is stronger in these four neurons (P < 0.05), implying that inhibitory engagement during high-frequency DBS can further suppress neuronal firing, and (ii) quasi-evoked inhibition emerges after the transient burst (P < 1.00 x 10-7), i.e., the latter may give rise to the former. By removing DBS artifacts with a novel algorithm and characterizing the dynamics of quasi-evoked inhibitory activity, we showed that the likelihood of occurrence of this inhibitory activity negatively correlated with the instantaneous firing rate (P < 1.00 x 10-5). These results suggest that an excitatory-inhibitory balance is likely regulating Vim activities during high-frequency DBS. Our findings shed light on potential network mechanisms underlying Vim-DBS, which can provide insight for optimizing DBS by designing new stimulation patterns.

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bioRxiv2024

FiNNpy 2.0: Fast MEG source reconstruction

Herein, we present the 2.0 update of FiNNpy, which expands the toolkits initial scope from the analysis of multi-site electrophysiological activity to track information propagation to include source reconstruction capability for MEG signals. Following the toolkits de- sign guidelines, the new functionality has been optimized towards minimal resource consumption, making source reconstruction much faster, especially when executed in a parallelized fashion. Furthermore, several quality-of-life aspects are introduced to support source reconstruction, such as grouping source-space activity into cortical areas, a cohesive documentation & in-code extensive literature references, and wrapping of previously terminal exclusive functions of FreeSurfer.

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bioRxiv2023

Uncovering network mechanism underlying thalamic Deep Brain Stimulation using a novel firing rate model

Thalamic ventral intermediate nucleus (Vim) is the primary surgical target of deep brain stimulation (DBS) for reducing symptoms of essential tremor. High-frequency Vim-DBS ([≥]100Hz) has been clinically effective, generating two experimentally-observed features in Vim spiking activity: 1) a large transient excitatory response (lasting <1s), followed by 2) a suppressed steady-state consisting of oscillations. Yet, mechanisms underlying these observations have not been fully understood by previous studies. In this work, we developed a network rate model and a novel parameter optimization method that accurately fit in-vivo single-unit recordings of Vim in human patients with essential tremor receiving a wide range of DBS frequencies (5[~]200Hz). Our model incorporates both the DBS-induced synaptic plasticity of Vim neurons, and the recurrent connections among excitatory and inhibitory neurons in Vim-network. We hypothesized that besides inducing synaptic depression, the therapeutic mechanism of high-frequency Vim-DBS could be to engage more inhibitory neurons in stabilizing the underlying circuits. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/570924v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1e78cbcorg.highwire.dtl.DTLVardef@c8bde1org.highwire.dtl.DTLVardef@12a3bb3org.highwire.dtl.DTLVardef@1b5a0dd_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Interrogating basal ganglia circuit function in Parkinson's disease and dystonia

BackgroundThe dichotomy between the hypo-versus hyperkinetic nature of Parkinsons disease (PD) and dystonia, respectively, is thought to be reflected in the underlying basal ganglia pathophysiology. ObjectiveInvestigate differences in globus pallidus internus (GPi) neuronal activity, and short- and long-term plasticity of direct pathway projections. ResultsGPi neurons were slower, burstier, and less regular in dystonia. In PD, symptom severity positively correlated with the power of low-beta frequency spiketrain oscillations. In dystonia, symptom severity negatively correlated with firing rate, and positively correlated with neuronal variability and the power of theta frequency spiketrain oscillations. Dystonia was moreover associated with less long-term plasticity and slower synaptic depression. ConclusionWe substantiated claims of hyper-versus hypofunctional GPi output in PD versus dystonia, and provided cellular-level validation of the pathological nature of theta and low-beta oscillations in respective disorders. Such circuit changes may be underlain by disease-related differences in plasticity of striato-pallidal synapses.

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bioRxiv2022

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

ObjectiveDeep Brain Stimulation (DBS) is an effective treatment for movement disorders, including Parkinsons disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei--including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr) and ventral intermediate nucleus (Vim)--across different DBS frequencies. Materials and MethodsBoth synthetic and experimental data were utilized in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work 1, and the experimental data were provided using single-unit microelectrode recordings (MER) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr and Vim--across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies. ResultsOur model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies. ConclusionThe result of our model fitting were in agreement with experimental single-unit microelectrode recording data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS, and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

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bioRxiv2022

Single-neuron bursts encode pathological oscillations in Parkinson's disease and essential tremor

Deep brain stimulation procedures offer an invaluable opportunity to study disease through intracranial recordings from awake patients. Herein, we address the relationship between singleneuron and aggregate-level (local field potential; LFP) activities in the subthalamic nucleus (STN) and thalamic ventral intermediate nucleus (Vim) of patients with Parkinsons disease (n=19) and essential tremor (n=16), respectively. Both disorders have been characterized by pathologically elevated LFP oscillations, as well as an increased tendency for neuronal bursting. Our findings suggest that periodic single-neuron bursts encode both pathophysiological beta (13-33Hz; STN) and tremor (4-10Hz; Vim) LFP oscillations, evidenced by strong time-frequency and phase-coupling relationships between the bursting and LFP signals. Spiking activity occurring outside of bursts had no relationship to the LFP. In STN, bursting activity most commonly preceded the LFP oscillation, suggesting that neuronal bursting generated within STN may give rise to an aggregate-level LFP oscillation. In Vim, LFP oscillations most commonly preceded bursting activity, suggesting that neuronal firing may be entrained by periodic afferent inputs. In both STN and Vim, the phasecoupling relationship between LFP and high-frequency oscillation (HFO) signals closely resembled the relationships between the LFP and single-neuron bursting. This suggests that periodic singleneuron bursting is likely representative of a higher spatial and temporal resolution readout of periodic increases in the amplitude of HFOs, which themselves may be a higher resolution readout of aggregate-level LFP oscillations. Overall, our results may reconcile "rate" and "oscillation" models of Parkinsons disease and shed light onto the single-neuron basis and origin of pathophysiological oscillations in movement disorders. SignificanceIn surgical patients with Parkinsons disease and essential tremor, we leverage intracranial recordings to establish a link between pathophysiological phenomena across various scales of observation (spatio-temporal resolutions). We provide insights and reconcile theories about aberrant neurocircuit phenomena which underly theses debilitating, medically refractory movement disorders. Furthermore, our connectivity analyses between single-neuron and local field potential activities may shed light on the origin of the deleterious neural oscillations underlying these disorders. Ultimately, our findings may aid in the development or investigation of targeted therapies to address or correct underlying neurocircuit dysfunction, which can include neuropharmaceuticals, but also novel neuromodulatory strategies like closed-loop deep brain stimulation targeting pathophysiological oscillations and phase-dependent stimulation methods seeking to stimulate "at the right time/phase."

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bioRxiv2022

FiNN: A toolbox for neurophysiological network analysis

Recently, neuroscience has seen a shift from localist approaches to network-wide investigations of brain function. Neurophysiological signals across different spatial and temporal scales provide insight into neural communication. However, additional methodological considerations arise when investigating network-wide brain dynamics rather than local effects. Specifically, larger amounts of data, investigated across a higher dimensional space, are necessary. Here, we present FiNN (Find Neurophysiological Networks), a novel toolbox for the analysis of neurophysiological data with a focus on functional and effective connectivity. FiNN provides a wide range of data processing methods, and statistical and visualization tools to facilitate inspection of connectivity estimates and the resulting metrics of brain dynamics. The Python toolbox (https://github.com/neurophysiological-analysis/FiNN) and its documentation (https://neurophysiological-analysis.github.io/FiNN/) are freely available. We evaluated FiNN against a number of established frameworks on both a conceptual and an implementation level. We found FiNN to require much less processing time and memory than other toolboxes. In addition, FiNN adheres to a design philosophy of easy access and modifiability, while providing efficient data processing implementations. Since the investigation of network-level neural dynamics is experiencing increasing interest, we place FiNN at the disposal of the neuroscientific community as open-source software.

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bioRxiv2022

Directional Absolute Coherence: a phase-based measure of effective connectivity for neurophysiology data

Communication between neural structures is a topic of much clinical and scientific interest and has been linked to a variety of behavioural, cognitive, and psychiatric measures. Here, we introduce a novel effective connectivity measure, termed the directional absolute coherence (DAC). Combining aspects of magnitude squared coherence, imaginary coherence, and phase slope index, DAC provides an estimate of connectivity that is resistant to volume conduction, encapsulates the directionality of neural communication, and is bound to the interval of -1 and 1. To highlight the properties of this newly proposed method, we compare DAC to a number of established connectivity methods using data recorded from the subthalamic nucleus of patients with Parkinsons disease with deep brain stimulation electrodes. By applying a combination of real and simulated data, we demonstrate that DAC provides a reliable estimate of the magnitude and direction of connectivity, independent of the phase difference between brain signals. As such, DAC facilitates a reliable investigation of inter-regional neural communication, rendering it a valuable tool for gaining a deeper understanding of the functional architecture of the brain and its relationship to behaviour and cognition. A Python implementation of DAC is freely available at https://github.com/neurophysiological-analysis/FiNN. Highlights- Volume conduction and limited interpretability affect many connectivity methods. - DAC is a combined approach to overcome these pitfalls. - DAC augments information content and interpretability in comparison to other methods. - DAC allows for reliable estimation of effective connectivity.

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bioRxiv2022

Direct Modulation Index: a measure of phase amplitude coupling for neurophysiology data

Neural communication across different spatial and temporal scales is a topic of great interest in clinical and basic science. Phase-amplitude coupling (PAC) has attracted particular interest due to its functional role in a wide range of cognitive and motor functions. Here, we introduce a novel measure termed the direct modulation index (dMI). Based on the classical modulation index, dMI provides an estimate of PAC that is bound to an absolute interval between 0 and +1, resistant against noise, and reliable even for small amounts of data. To highlight the properties of this newly-proposed measure, we evaluated dMI by comparing it to the classical modulation index, mean vector length, and phase-locking value using simulated data. We ascertained that dMI provides a more accurate estimate of PAC than the existing methods and that is resilient to varying noise levels and signal lengths. As such, dMI permits a reliable investigation of PAC, which may reveal insights crucial to our understanding of functional brain architecture in key contexts such as behaviour and cognition. A Python toolbox that implements dMI and other measures of PAC is freely available at https://github.com/neurophysiological-analysis/FiNN. Highlights- Neural coupling measures are sensitive to higher harmonics of the target oscillation. - dMI achieves frequency-specificity by sine-fitting the phase-amplitude histogram. - Increased robustness to noise and signal duration in comparison to other measures. - dMI allows for reliable estimation of phase-amplitude coupling.

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bioRxiv2021

Inferring Deep Brain Stimulation Induced Short-term Synaptic Plasticity Using Novel Dual Optimization Algorithm

Experimental evidence in both human and animal studies demonstrated that deep brain stimulation (DBS) can induce short-term synaptic plasticity (STP) in the stimulated nucleus. Given that DBS-induced STP may be connected to the therapeutic effects of DBS, we sought to develop an appropriate computational predictive model that infers the dynamics of STP in response to DBS at different frequencies. Existing methods for estimating STP - either model-based or model-free approaches - require access to pre-synaptic spiking activity. However, in the context of DBS, extracellular stimulation (e.g. DBS) can be used to elicit presynaptic activations directly. We present a model-based approach that integrates multiple individual frequencies of DBS-like electrical stimulation as pre-synaptic spikes and infers parameters of the Tsodyks-Markram (TM) model from post-synaptic currents of the stimulated nucleus. By distinguishing between the steady-state and transient responses of the TM model, we develop a novel dual optimization algorithm that infers the model parameters in two steps. First, the TM model parameters are calculated by integrating multiple frequencies of stimulation to estimate the steady state response of post-synaptic current through a closed-form analytical solution. The results of this step are utilized as the initial values for the second step in which a non-derivative optimization algorithm is used to track the transient response of the post-synaptic potential across different individual frequencies of stimulation. Moreover, we apply our algorithm to empirical data recorded from acute rodent brain slices of the subthalamic nucleus (STN) during DBS-like stimulation to infer dynamics of STP for inhibitory synaptic inputs.

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bioRxiv2021

Persistent synaptic inhibition of the subthalamic nucleus by high frequency stimulation

BackgroundDeep brain stimulation (DBS) provides symptomatic relief in a growing number of neurological indications, but local synaptic dynamics in response to electrical stimulation that may relate to its mechanism of action have not been fully characterized. ObjectiveThe objectives of this study were to (1) study local synaptic dynamics during high frequency extracellular stimulation of the subthalamic nucleus (STN), and (2) compare STN synaptic dynamics with those of the neighboring substantia nigra pars reticulata (SNr). MethodsTwo microelectrodes were advanced into the STN and SNr of patients undergoing DBS surgery for PD. Neuronal firing and evoked field potentials (fEPs) were recorded with one microelectrode during stimulation from an adjacent microelectrode. ResultsExcitatory and inhibitory fEPs could be discerned within the STN and their amplitudes predicted bidirectional effects on neuronal firing (p = .007). There were no differences between STN and SNr inhibitory fEP dynamics at low stimulation frequencies (p > .999). However, inhibitory neuronal responses were sustained over time in STN during high frequency stimulation, but not SNr (p < .001) where depression of inhibitory input was coupled with a return of neuronal firing (p = .003). InterpretationPersistent inhibitory input to the STN suggests a local synaptic mechanism for the suppression of subthalamic firing during high frequency stimulation. Moreover, differences in the resiliency versus vulnerability of inhibitory inputs to the STN and SNr suggest a projection source- and frequency-specificity for this mechanism. The feasibility of targeting electrophysiologically-identified neural structures may provide insight into how DBS achieves frequency-specific modulation of neuronal projections.

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bioRxiv2020

A theoretical framework for the site-specific and frequency-dependent neuronal effects of deep brain stimulation

With the growing interest in the expansion of deep brain stimulation indications, we aimed to provide experimental and computational insights into the brain-region-specific and frequency-dependent effects of extracellular stimulation on human neuronal activity. Experimentally, we demonstrated microstimulation-evoked excitatory neuronal responses in the ventral intermediate nucleus and reticular thalamus, and inhibitory responses in the subthalamic nucleus and substantia nigra pars reticulata; hypothesized to be the result of simultaneous activations of convergent afferent inputs. Higher stimulation frequencies led to a loss of site-specificity and convergence towards neuronal suppression; hypothesized to be mediated by synaptic depression. These experimental findings were reproduced by a computational framework in which relative distributions of convergent excitatory/inhibitory afferents were embedded within a model of short-term synaptic plasticity for the prediction of site-specific and frequency-dependent responses to extracellular stimulation. This theoretical framework may aid in the design of physiologically-informed stimulation paradigms in existing or prospective deep brain stimulation indications.

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