Biology subjects

Popovic, M. R.

Publications and source records attributed to Popovic, M. R..

7 recordsLinked to original sources

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↗

bioRxiv2022

Inferring Cognitive State Underlying Conflict Choices in Verbal Stroop Task Using Heterogeneous Input Discriminative-Generative Decoder Model

The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g., the response time), remains to be identified. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals response time) recorded in 10 Parkinsons disease patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy population. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural- and behavioral data simultaneously and estimate cognitive states underlying conflict and nonconflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems. HighlightsO_LIResearch highlight 1 C_LIO_LIResearch highlight 2 C_LI

neuroscience↗

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.

neuroscience↗

bioRxiv2021

A generic sequential stimulation adapter for reducing muscle fatigue during functional electrical stimulation

BackgroundClinical applications of conventional functional electrical stimulation (FES) administered via a single electrode is limited by rapid onset neuromuscular fatigue. "Sequential" (SEQ) stimulation, involving rotation of pulses between multiple active electrodes, has been shown to reduce fatigue compared to conventional FES. However, there has been limited adoption of SEQ in research and clinical settings. MethodsThe SEQ adapter is a small, battery-powered device that transforms the output of any commercially available electrical stimulator into SEQ stimulation. We examined the output of the adaptor across a range of clinically relevant stimulation pulse parameters to verify the signal integrity preservation ability of the SEQ adapter. Pulse frequency, amplitude, and duration were varied across discrete states between 4-200 Hz, 10-100 mA, and 50-2000 s, respectively. ResultsA total of 420 trials were conducted, with 80 stimulation pulses per trial. The SEQ adapter demonstrated excellent preservation of signal integrity, matching the pulse characteristics of the originating stimulator within 1% error. The SEQ adapter operates as expected at pulse frequencies up to 160 Hz, with a noted failure mode at 200 Hz. ConclusionThe SEQ adapter represents an effective and low-cost solution to increase the utilization of SEQ in existing rehabilitation paradigms.

bioengineering↗

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.

neuroscience↗

bioRxiv2020

Volitional Control of Individual Neurons in the Human Brain

Can the human brain, a complex interconnected structure of over 80 billion neurons learn to control itself at the most elemental scale - a single neuron. We directly linked the firing rate of a single (direct) neuron to the position of a box on a screen, which participants tried to control. Remarkably, all subjects upregulated the firing rate of the direct neuron in memory structures of their brain. Learning was accompanied by improved performance over trials, simultaneous decorrelation of the direct neuron to local neurons, and direct neuron to beta frequency oscillation phase-locking. Such previously unexplored neuroprosthetic skill learning within memory related brain structures, and associated beta frequency phase-locking implicates the ventral striatum. Our demonstration that humans can volitionally control neuronal activity in mnemonic structures, may provide new ways of probing the function and plasticity of human memory without exogenous stimulation.

neuroscience↗

bioRxiv2018

Cluster Tendency Assessment in Neuronal Spike Data

Sorting spikes from extracellular recording into clusters associated with distinct single units (putative neurons) is a fundamental step in analyzing neuronal populations. Such spike sorting is intrinsically unsupervised, as the number of neurons are not known a priori. Therefor, any spike sorting is an unsupervised learning problem that requires either of the two approaches: specification of a fixed value c for the number of clusters to seek, or generation of candidate partitions for several possible values of c, followed by selection of a best candidate based on various post-clustering validation criteria. In this paper, we investigate the first approach and evaluate the utility of several methods for providing lower dimensional visualization of the cluster structure and on subsequent spike clustering. We also introduce a visualization technique called improved visual assessment of cluster tendency (iVAT) to estimate possible cluster structures in data without the need for dimensionality reduction. Experimental results are conducted on two datasets with ground truth labels. In data with a relatively small number of clusters, iVAT is beneficial in estimating the number of clusters to inform the initialization of clustering algorithms. With larger numbers of clusters, iVAT gives a useful estimate of the coarse cluster structure but sometimes fails to indicate the presumptive number of clusters. We show that noise associated with recording extracellular neuronal potentials can disrupt computational clustering schemes, highlighting the benefit of probabilistic clustering models. Our results show that t-Distributed Stochastic Neighbor Embedding (t-SNE) provides representations of the data that yield more accurate visualization of potential cluster structure to inform the clustering stage. Moreover, The clusters obtained using t-SNE features were more reliable than the clusters obtained using the other methods, which indicates that t-SNE can potentially be used for both visualization and to extract features to be used by any clustering algorithm.

neuroscience↗