Biology subjects

Lankarany, M.

Publications and source records attributed to Lankarany, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Computational simulation reveals the critical role of spike-timing-dependent plasticity in synchrony

Emerging research on desynchronization reveals strong relations to memory formation and recollection with much of the evidence originating from studies of the hippocampus, a brain region central to memory processing. However, growing evidence emphasizing the role of desynchronization is accompanied by challenges, most notably the synchronization/desynchronization conundrum. The conundrum arises from an apparent contradiction between learning and Shannon's information theory: spike-timing-dependent plasticity (STDP) acquires information via synchronized populational activity whereas information theory suggests that information channel is encoded through higher variability. Both frameworks are well-founded. Learning is widely accepted as the mathematical abstraction of STDP while Shannon's information theory showcases its power ubiquitously across fields from communication systems to neuroscience. Here, we propose that learning and Shannon's information theory are not necessarily opposing principles. Rather, learning plays a critical role in the process of synchronization and desynchronization. To justify our claim, we conduct computational simulations based on a Hodgkin-Huxley neuronal model coupled with NMDA, AMPA, and GABA synapses with inputs of biologically realistic spiking data. Our results show that enabling STDP significantly influences synchronization and desynchronization dynamics. Specifically, we find that STDP and desynchronization form a regulatory loop, in which STDP regulates the level of synchrony, and synchrony regulates subsequent learning strength. Further study reveals STDP is capable of switching neurons from synchronization to desynchronization. This framework mitigates the synchronization/desynchronization conundrum by clarifying the relationship between desynchronization and learning, and offers a new perspective on how synchrony contributes to memory formation and recollection.

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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

Network Dynamics and State-Dependent Effects of Electrical Stimulation in Recurrent Excitatory-Inhibitory Populations

Deep Brain Stimulation (DBS) is an established clinical treatment for a variety of neurological disorders, including Parkinsons Disease where it has been shown to reduce motor symptoms as well as disrupt pathological beta oscillations in the basal ganglia. The mechanisms of action of DBS on the collective activity of neuronal circuits is not fully understood. We use a recurrently-connected excitatory-inhbitory network based on the Brunel network architecture that can produce activity in a variety of states. Using a model of DBS that can reproduce observed effects such as antidromic activation, local somatic suppression, and axonal activation, we characterize the effect of stimulation across the entire parameter space of the network. We show that the effects of stimulation are dependent on the baseline state of the network, with the level of beta suppression dependent on the level of inhibition and the external drive. Specifically, networks with higher inhibition and lower drive show greater disruption of beta oscillations. We further show that networks in different states are preferentially sensitive to different frequencies of stimulation, suggesting that alternative protocols to the clinically standard high-frequency stimulation may have therapeutic efficacy.

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bioRxiv2026

Bifurcation Structure and Cross Nuclei Universality Govern Frequency-Selective Deep Brain Stimulation

High-frequency deep brain stimulation (DBS, >90 Hz) reliably suppresses Parkinsonian motor symptoms, whereas sub-therapeutic frequencies (<60 Hz) worsen them, yet the circuit mechanism underlying this frequency selectivity remains unresolved. We develop an analytically tractable excitatory-inhibitory continuous attractor neural network with threshold-linear transfer functions. The model predicts a boundary equilibrium bifurcation (BEB) at a critical DBS frequency fth that simultaneously accounts for two observations that prior models have treated separately: abrupt excitatory suppression and a concurrent linear rise in GABAergic output with stimulation rate. A separate, orthogonal bifurcation condition -- the Hopf boundary [Formula]-- governs endogenous beta oscillations independently of stimulation frequency; DBS suppresses these oscillations by destroying the active fixed point via the BEB, not by crossing the Hopf boundary. The predicted signature -- an oscillation frequency that remains at f0 up to the suppression threshold, then drops discontinuously -- is testable with existing intraoperative protocols. The sub-critical spectral sharpness index C= 2f0/{Delta}fFWHM diverges at the Hopf boundary and provides a parameter-free biomarker of pathological synchrony computable from any local field potential recording. Fitting the model to intraoperative VIM single-unit recordings across nine stimulation frequencies yields physiologically plausible parameters consistent with the observed transient and steady-state dynamics. Frequency-response data from four DBS nuclei (STN, SNr, VIM, RT) each conform to a rectified quadratic derived from the mean-field equations; nucleus pairs sharing circuit role collapse onto common master curves (R2 [≥] 0.988 per nucleus, collapsed R2 [≥] 0.988) after a two-parameter rescaling, with no further free parameters. SignificanceHigh-frequency deep brain stimulation (DBS) relieves Parkinsons disease motor symptoms, while sub-therapeutic frequencies worsen them; the circuit origin of this [~]90 Hz threshold has lacked explanation. We show that one bifurcation -- a boundary equilibrium bifurcation (BEB) at a critical frequency fth -- jointly explains the abrupt loss of excitatory firing and the concurrent rise in GABAergic output. The Hopf boundary governing endogenous beta oscillations is independent of stimulation frequency, so DBS suppresses beta by destroying the active state through the BEB, not by shifting the Hopf boundary: disease progression and therapy act on separate axes. Frequency-response curves from four DBS nuclei collapse onto two universality-class master curves after a two-parameter rescaling, yielding cross-nucleus predictions without further fitting.

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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

A computational framework with voltage-dependent synaptic function explains LTP-dominant plasticity during functional electrical stimulation therapy

Functional Electrical Stimulation (FES) therapy is a widely used neurorehabilitation technique that restores motor function by delivering electrical stimulation to target muscles during voluntary contraction. Despite its clinical effectiveness, the mechanisms by which FES therapy induces neuroplasticity remain poorly understood. Previous work has proposed that positive plasticity arises from Hebbian interactions at corticospinal-motoneuronal synapses when voluntary descending motor commands coincide with antidromic firing elicited by FES therapy. However, if spike-timing-dependent plasticity (STDP) is assumed to underlie this Hebbian mechanism, an unresolved question remains: why does FES therapy produce long-term potentiation (LTP) reliably, rather than the mixture of LTP and LTD predicted from classical STDP rules? Here, we test the hypothesis that interactions between voluntary descending spikes and stimulation-evoked antidromic spikes generate multi-spike patterns that bias plasticity toward potentiation. To investigate this mechanism, we developed a computational framework implementing a voltage-dependent plasticity rule that incorporates postsynaptic membrane dynamics and higher-order spike interactions. This framework enables simulation of synaptic plasticity during FES therapy while systematically varying stimulation frequency, input heterogeneity, and spike timing structure. Our simulations show that voltage-dependent dynamics strongly bias synaptic changes toward LTP during FES therapy-like conditions. In particular, physiological interspike interval variability promotes potentiation, whereas highly regular inputs bias synapses toward depression. These results indicate that postsynaptic voltage dynamics and spike-interaction structure, rather than pairwise spike timing alone, govern plasticity outcomes during FES therapy. Our findings provide a mechanistic explanation for why FES therapy reliably induces LTP-dominant plasticity and offer a computational framework for optimizing neuromodulation therapies.

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bioRxiv2025

DIME: Data-driven Importance MEtric Guides Localization of the Seizure Onset Zone from Intracranial EEG Data

Epilepsy affects over 50 million individuals, many of whom require surgical treatment that is dependent on accurate localization of the seizure onset zone (SOZ). Conventional SOZ biomarkers are based on strictly defined intracranial electroen-cephalography (iEEG) phenomena and cannot benefit from increased datasets. The scarcity of SOZ-labeled iEEG data impedes biomarker development. We introduce the Data-driven Importance MEtric (DIME) to guide SOZ localization in an interpretable pipeline that improves with ictal-labeled iEEG data. We apply DIME to an open-source dataset (n=21; 13 successful; 8 failed) for SOZ localization and surgical outcome prediction. The highest DIME-ranked electrode belonged to the clinically annotated SOZ for 69.2% of patients with successful surgery (p < 0.001). DIME scores were significantly higher in SOZ electrodes than nonSOZ electrodes in both successful and failed surgeries (p < 0.001), though the DIME distribution for successful cases differed from failed cases (p = 0.002). DIME predicted surgical outcome with 92.3% recall and 66.7% accuracy.

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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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bioRxiv2025

Semi-Automated Detection, Annotation, and Prognostic Assessment of Ictal Chirps in Intracranial EEG from Patients with Epilepsy

We analyzed the spectro-temporal characteristics of ictal "chirp" events in intracranial EEG (iEEG) recordings from 13 epilepsy patients, using a custom derivative dataset of 22,721 spectrograms that we generated from the Epilepsy-iEEG-Multicenter Dataset. Ictal chirps, transient frequency-modulated patterns, were semi-automatically annotated to assess their relationship with seizure onset zones (SOZs) and surgical outcomes. Preprocessing included notch filtering (60 Hz, 120 Hz) and bandpass filtering (1-60 Hz), followed by segmentation into 60-second windows. Spectrograms were generated via Short-Time Fourier Transform (STFT) with a Hann window (87.5% overlap) and converted to dB scale. Chirps were annotated by manually drawing bounding boxes, followed by automated ridge detection, model fitting, and feature extraction (start/end time-frequency, duration, direction, RMSE, R2). Spatial, Spectro-temporal, and clinical features were analyzed using heatmaps, hierarchical clustering, statistical tests (Mann-Whitney U), and outcome prediction models. Patient-channel mappings revealed clustering of chirp patterns among specific patient pairs, correlating with shared clinical profiles. Flow-based analysis demonstrated prognostic value: very high spectral durations in SOZ regions were associated with favorable surgical outcomes (80.43% success rate), whereas very high temporal durations in SOZ correlated with poorer outcomes (51.35% risk). Statistical comparisons showed significant differences between SOZ and non-SOZ chirps: SOZ chirps exhibited longer spectral durations (10.13 {+/-} 6.35 Hz vs. 8.51 {+/-} 5.66 Hz, *p* < 0.001), shorter temporal durations (6.76 {+/-} 5.83 s vs. 7.14 {+/-} 5.39 s, *p* = 0.006), and higher spectro-temporal ratios (2.66 vs. 1.92, *p* < 0.001). Distribution analyses further indicated that prolonged temporal chirps were more prevalent in non-SOZ regions.

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bioRxiv2025

A Software for Identification and Characterization of Theta Rhythms in the Hippocampus

Characterizing theta rhythms in the hippocampus provides a window into understanding memory processing. An inquiry that arises when an animal sustains a pathological state is how theta rhythms are affected. In pathological states like epilepsy or Alzheimers, these rhythms change in specific ways. Statistically robust changes in these rhythms could serve as potential biomarkers, indicating the severity of the animals condition and the effectiveness of a drug. However, this understanding depends on how the data is analyzed. There are currently no standard criteria for recognizing theta dominance in experimental recordings. To address this, we have developed novel MATLAB-based software with an easy-to-use graphical user interface which enables identifying and analyzing theta rhythms in a standard way. We discuss the softwares functionality and its underlying algorithms. The algorithms were developed using previously acquired EEG/LFP data recorded from the hippocampus of a mouse kindling model of epilepsy. Two primary analyses were conducted to test the softwares functionality: first, comparing theta rhythms during the baseline period versus during spontaneous recurrent seizures; second, analyzing the timing of theta rhythms relative to the seizure event. Our illustrative results indicate that our developed software can robustly identify theta events with statistically significant feature differences. Further, the examination presented here with two mice shows that while theta events can occur just before seizures, it takes tens of minutes post-seizure before theta rhythms occur again. Our software thus provides the user with the ability to robustly identify and characterize theta rhythms and their feature changes. Significance StatementTheta rhythms in the hippocampus are fundamental for spatial navigation and memory formation. Their observed changes during several pathological states such as epilepsy and Alzheimers make them highly interesting to be able to serve as biomarkers. However, their variability (in terms of the time of occurrence, duration, and frequency range) makes them challenging to quantify. The absence of available tools for the automatic extraction of these rhythms from extended datasets significantly hampers processing efficiency and reduces accuracy and consistency. Clinicians and researchers often manually inspect their data to identify these rhythms, a process that is not only time-consuming but also inherently subjective. We have thus developed a MATLAB software for precise, automatic analysis of theta rhythms in EEG/LFP recordings.

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bioRxiv2024

Using a Deep Learning Approach for Model-based Control of Deep Brain Stimulation

Deep brain stimulation (DBS) has been developed as a treatment method for various neurological disorders, including Parkinsons disease, essential tremor and depression. Although DBS is effective, it often loses efficacy over sustained periods because a constant stimulation is applied without adapting to the patients current clinical state. In contrast, an adaptive closed-loop DBS system can offer more tailored stimulation in real-time based on a feedback biomarker. In early 2024, we developed a model-based DBS control framework that consists of three main functions: (1) a biophysically reasonable encoding model, (2) a simple decoding model, and (3) a controller. We used a polynomial fit function in the decoding model to approximate the neural-motor relationship, from DBS-induced Vim neural activity to muscle fiber electromyography (EMG). Despite promising results, the polynomial method is inaccurate in capturing the full representation of the neural-motor (EMG) function across different DBS frequencies. In this work, to capture the nonlinear intricate relationship between the neural and EMG patterns, we developed a one-dimensional convolutional neural network (1-D CNN) as a decoding model to predict the EMG signal directly from the DBS-induced Vim neural activity. The 1-D CNN network outputted a high R2 value of 0.997 which significantly outperformed the polynomial method (R2 = 0.277) and a deep learning approach based on long short-term memory (R2 = 0.296). We anticipate that our work highlights the need for a data-driven approach that can reliably map neural activities to symptomatic signals like EMG for better adjusting DBS parameters.

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bioRxiv2024

Ictal-Related Chirp as a Biomarker for Monitoring Seizure Progression

Despite being prevalent, the causes, mechanisms, and progression of epilepsy--a chronic neurological disorder with unprovoked seizures--are not well understood, complicating drug development for treatment. This study used a comprehensive mouse epilepsy kindling model dataset to investigate frequency modulation (chirp) as a potential indicator of distinct states of epilepsy (early evoked discharge, late evoked discharge, spontaneous recurrent seizure, and drug state). Employing time-frequency ridge extraction, chirp identification, and statistical testing, our analyses revealed that chirp patterns occur in the majority of ictal discharges (>81.6%), persisting across evoked and spontaneous seizures. While the focus was on hippocampal recordings, chirps were also detected in the piriform peripheral cortex. Significant frequency and duration changes in chirp patterns during the transition from early to late evoked ictal events suggest their potential as the screening tool for seizure progression. Additionally, detailed analyses illuminate the impact of Lorazepam, a GABAA enhancer, on chirp characteristics, providing insights into how increased inhibitory tone quantifiably influences excitatory-inhibitory balances during seizures.

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bioRxiv2023

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Closed-loop control of deep brain stimulation (DBS) is crucial for effective and automatic treatments of various neurological disorders like Parkinsons disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists expertise and patients experience. The continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system utilizes a feedback biomarker/signal to track worsening (or improving) patients symptoms and offers several advantages compared to open-loop DBS. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying the DBS or symptoms, for example how DBS modulates dynamics of synaptic plasticity. In this work, we proposed a computational framework for development of a model-based DBS controller where a biophysically-reasonable model can describe the relationship between DBS and neural activity, and a polynomial-based approximation can estimate the relationship between the neural and behavioral activity. A controller is utilized in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. These DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given EMG recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed a biophysically-reasonable simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By utilizing a PID controller, we showed that a closed-loop system can track EMG signals and adjusts the stimulation frequency of Vim-DBS so that the power of EMG in [2, 200] Hz reaches a desired target. We demonstrated that our model-based closed-loop control system of Vim-DBS finds an appropriate DBS frequency that aligns well with clinical studies. Our model-based closed-loop system is adaptable to different control targets, highlighting its potential usability for different diseases and personalized systems.

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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

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bioRxiv2023

Graph representations of iEEG data for seizure detection with graph neural networks

Epilepsy is a neurological disorder that affects over 50 million individuals worldwide. Today, the gold-standard treatment for those who are drug resistant, meaning that symptoms cannot be controlled with medication, is to surgically remove the seizure onset zone (SOZ), the area of the brain believed to cause seizures: the main symptom of epilepsy. Unfortunately, around 50% of drug resistant patients are not resective candidates, which can be attributed in part to poor SOZ localization. SOZ localization is a complex and lengthy procedure, requiring visual inspection and manual processing by human experts that first need to localize and isolate seizure events. The intracranial electroencephalography (iEEG) is a tool that records electrophysiological activity of the inner brain at different regions and depths, and provides critical information on the SOZ. However, iEEG data processing methodologies are not standardized, and practice and resources vary across hospitals and clinics. To assist human experts with systematic processing of iEEG data, we propose a data processing pipeline that generates graph representations of iEEG data. We evaluate 9 different graph representations of publicly available iEEG data from 25 patients with epilepsy with a graph neural network model trained to detect seizures. Our results suggest that graph representations of iEEG data that leverage electrode and functional connectivity features are powerful data structures to analyze and interpret iEEG data in the context of epilepsy. We anticipate that our data pipeline that provides a systematic processing of neural data with graphs can integrate other data modalities like neuroimaging data. Moreover, methods used in the data pipeline have potentials to apply to other neurological disorders such as Parkinsons disease or major depression disorder.

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bioRxiv2023

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings using SVD-based Adaptive Filtering

Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on how a burst of DBS pulses, usually delivered at clinical frequency, i.e., 130 Hz, perturb participants choices. It was observed that neural activities recorded during DBS were contaminated with stereotype large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal ([~]1-2 Hz) that can persist for hundreds of milliseconds. While the focus of the most of methods for removing DBS artifact was on the former, the artifact removal of the slow signal has not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) Normalized least mean square adaptive filtering, (2) Optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) Moving average. The algorithms capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

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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

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bioRxiv2022

Reduced oriens-lacunosum/moleculare (OLM)cell model identifies biophysical currentbalances for in vivo theta frequency spikingresonance

Conductance-based models have played an important role in the development of modern neuroscience. These mathematical models are powerful "tools" that enable theoretical explorations in experimentally untenable situations, and can lead to the development of novel hypotheses and predictions. With advances in cell imaging and computational power, multi-compartment models with morphological accuracy are becoming common practice. However, as more biological details are added, they make extensive explorations and analyses more challenging largely due to their huge computational expense. Here, we focus on oriens-lacunosum/moleculare (OLM) cell models. OLM cells can contribute to functionally relevant theta rhythms in the hippocampus by virtue of their ability to express spiking resonance at theta frequencies, but what characteristics underlie this is far from clear. We converted a previously developed detailed multi-compartment OLM cell model into a reduced single compartment model that retained biophysical fidelity with its underlying ion currents. We showed that the reduced OLM cell model can capture complex output that includes spiking resonance in in vivo-like scenarios as previously obtained with the multi-compartment model. Using the reduced model, we were able to greatly expand our in vivo-like scenarios. Applying spike-triggered average analyses, we were thus able to to determine that it is a combination of hyperpolarization-activated cation and muscarinic type potassium currents that specifically allow OLM cells to exhibit spiking resonance at theta frequencies. Further, we developed a robust Kalman Filtering (KF) method to estimate parameters of the reduced model in real-time. We showed that it may be possible to directly estimate conductance parameters from experiments since this KF method can reliably extract parameter values from model voltage recordings. Overall, our work showcases how the contribution of cellular biophysical current details could be determined and assessed for spiking resonance. As well, our work shows that it may be possible to directly extract these parameters from current clamp voltage recordings.

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