Biology subjects

Nasseroleslami, B.

Publications and source records attributed to Nasseroleslami, B..

6 recordsLinked to original sources

bioRxiv2025

Spinal electrophysiology reveals frequency-specific spatial patterns of neural activity and corticospinal coherence during pincer-grip

BackgroundNeural dynamics within sensory-motor networks involved in motor control exhibit well-established frequency-dependent patterns of cortical activity. In contrast, corresponding neural patterns of spinal cord activity remain poorly understood. As an active functional part of motor control, characterising spinal cord activity is essential for understanding sensorimotor function. High-density electrospinography presents a novel technique to assess spinal neural dynamics by non-invasively recording task-relevant electrical activity. ObjectiveTo non-invasively investigate spatio-spectral patterns of task-related spinal activity and its functional connectivity with cortical regions during isometric pincer-grip contraction. MethodologyHere, we simultaneously recorded 128-channel electroencephalography (EEG), 64-channel electrospinography (ESG), and two bipolar electromyography (EMG) signals during an isometric pincer-grip task. Frequency-specific spatial patterns of spinal activity and cortico-spinal connectivity were evaluated by calculating task-related ESG power and cortico-spinal coherence between ESG and EEG signals during the isometric hold. ResultsDistinct frequency-specific spatial patterns of spinal activity were observed at lower cervical levels during sustained hold, with significant activation over the ipsilateral anterolateral region. In the beta band, task-related spinal activity was significantly ipsilateralised, and exhibited significant cortico-spinal connectivity between contralateral motor cortex and ipsi-anterolateral spinal region. ConclusionsThis first-of-its-kind application of HD non-invasive spinal electrophysiology revealed that the spinal cord exhibits distinct frequency-specific spatial activation and cortical connectivity patterns during pincer-grip sustained hold. Specifically, the ipsi-anterolateral spinal region demonstrated high task-relevance, potentially indicating anterior horn activity and its connectivity with motor cortex. Furthermore, beta-band activation was observed as a key signature during sustained hold, further underpinning its relevance during motor control.

neuroscience↗

bioRxiv2024

SC10X/U: A High-density Electrode System for Non-Invasive Recording of Neural Activity of the Cervical Spinal Cord

ObjectiveTo design and develop a high-density (HD) electrode system that describes the position of surface electrodes for recording electrophysiological signals from the human cervical spinal cord. The system is intended to standardize experimental recordings and facilitate the subsequent analysis of evoked and spontaneous spinal cord neural activity, using high-density electrospinography (HD-ESG). MethodThe proposed system (SC10-X/U) describes the locations of up to 76 channels with a unique nomenclature, where the division of the spinal cord (SC) electrode space was inspired by the EEG 10-10 system. As proof of concept, spinal evoked potentials in response to median nerve stimulation at the wrist were recorded from 10 participants and characterized based on a 64-channel derivation from the SC10-X/U system. ResultsFollowing the design criteria, the SC10-X/U defines 76 electrode positions and its configuration. HD-ESG system was utilized to successfully record evoked spinal responses and significant N13 and P9 potential were observed in response to the stimulation. The spinal N13 potential had a latency of 13.2 +-1.1ms (mean +-SD) after stimulation. A topographic map of the N13 electro-spinal activity using the 64-channel recording system revealed an epicentre at C5-C7 dorsal-vertebral locations (ML4 - ML6 electrodes). ConclusionThe proposed SC10-X/U system will facilitate standardized recording and analysis of high-density ESG signals from the human cervical spinal cord. The system defines electrode locations to promote standardization across different individuals, studies, and clinical and research centres. The HD-ESG evoked potentials recorded using the proposed system were comparable to those observed in previous non-HD studies. The presented topographic maps conform to known neurophysiological and neuroanatomical findings. This served to validate the design and development of the electrode system and patch for future studies.

neuroscience↗

bioRxiv2019

Non-Parametric Rank Statistics for Spectral Power and Coherence

Despite advances in multivariate spectral analysis of neural signals, the statistical inference of measures such as spectral power and coherence in practical and real-life scenarios remains a challenge. The non-normal distribution of the neural signals and presence of artefactual components make it difficult to use the parametric methods for robust estimation of measures or to infer the presence of specific spectral components above the chance level. Furthermore, the bias of the coherence measures and their complex statistical distributions are impediments in robust statistical comparisons between 2 different levels of coherence. Non-parametric methods based on the median of auto-/cross-spectra have shown promise for robust estimation of spectral power and coherence estimates. However, the statistical inference based on these non-parametric estimates remain to be formulated and tested. In this report a set of methods based on non-parametric rank statistics for 1-sample and 2-sample testing of spectral power and coherence is provided. The proposed methods were demonstrated and tested using simulated neural signals in different conditions. The results show that non-parametric methods provide robustness against artefactual components. Moreover, they provide new possibilities for robust 1-sample and 2-sample testing of the complex coherency function, including both the magnitude and phase, where existing methods fall short of functionality. The utility of the methods were further demonstrated by examples on experimental neural data. The proposed approach provides a new framework for non-parametric spectral analysis of digital signals. These methods are especially suited to neuroscience and neural engineering applications, given the attractive properties such as minimal assumption on distributions, statistical robustness, and the diverse testing scenarios afforded.

bioinformatics↗

bioRxiv2018

Visuomotor Control of Ankle Joint using Position vs. Force

Ankle joint plays a critical role in daily activities involving interactions with environment using force and position control. Neuromechanical dysfunctions (e.g. due to stroke or brain injury), therefore, have a major impact on individuals quality of life. The effective design of neurorehabilitation protocols for robotic rehabilitation platforms, relies on understanding the control characteristics of the ankle joint in interaction with external environment using force and position. This is particularly of interest since the findings in upper-limb may not be generalizable to the lower-limb. This study aimed to characterize the skilled performance of ankle joint in visuomotor position and force control. A 2-degree of freedom (DOF) robotic footplate was used to measure individuals force and position. Healthy individuals (n = 27) used ankle force or position for point-to-point and tracking control tasks in 1-DOF and 2-DOF virtual game environments. Subjects performance was quantified as a function of accuracy and completion time. While the performance measures in 1-DOF control tasks were comparable, the subjects performance in 2-DOF tasks was significantly better with position control. Subjective questionnaires on the perceived difficulty matched the objective experimental results; suggesting that the poor performance in force control was not due to experimental setup or fatigue but can be attributed to the different levels of challenge needed in neural control. It is inferred that in visuomotor coordination, the neuromuscular specialization of ankle provides better control over position rather than force. These findings can inform the design of neuro-rehabilitation platforms, selection of effective tasks, and therapeutic protocols.

neuroscience↗

bioRxiv2018

An Implementation of Empirical Bayesian Inference and Non-Null Bootstrapping for Threshold Selection and Power Estimation in Multiple and Single Statistical Testing

The majority of conclusions and interpretations in quantitative sciences such as neuroscience are based on statistical tests. However, the statistical inferences, especially in multivariate analyses, commonly rely on the p-values, but not on more expressive measures such as posterior probabilities, false discovery rates (FDR) and statistical power (1 - {beta}). The aim of this report is to make these statistical measures further accessible in single and multiple statistical testing. For multiple testing, the Empirical Bayesian Inference (Efron et al., 2001; Efron, 2007) was implemented using non-parametric test statistics (e.g. the Area Under the Curve of the Receiving Operator Characteristics Curve or Spearmans rank correlation) and Gaussian Mixture Model estimation of the probability density function of the original and bootstrapped data. For single statistical tests, the same test statistics were used to construct and estimate the null and non-null probability density functions using bootstrapping under null and non-null grouping assumptions. Simulations were used to test the reliability of the results under a wide range of conditions. The results show conformity to the real truth in the simulated conditions, which is held under various conditions imposed on the simulated data. The open-source MATLAB codes are provided and the utility of the approach has been exemplified and discussed for real-world electroencephalographic signals. This implementation of Empirical Bayesian Inference and informed selection of statistical thresholds are expected to facilitate more realistic scientific deductions in versatile fields, especially in neuroscience, neural signal analysis and neuro-imaging.

neuroscience↗

bioRxiv2017

EyeBallGUI: A Tool For Visual Inspection And Binary Marking of Multi-Channel Bio-Signals

A wide range of studies in human neuroscience rely on the analysis of electrophysiological bio-signals such as electroencephalogram (EEG) where customized data analysis may require supervised artefact rejection, binary marking through visual inspection, selection of noise and artefact samples for pre-processing algorithms, and selection of clinically-relevant signal segments in neurological conditions. Nevertheless, the existing preprocessing tools do not provide the needed flexibility to handle such tasks efficiently. We therefore developed a free open-source Graphical User Interface (GUI), EyeBallGUI, that allows visualization and flexible, manual marking (binary classification) of multi-channel bio-signal data. EyeBallGUI, developed for MATLAB(R), allows the user to interactively and accurately inspect and mark multi-channel digitized data with no restriction on marking periods of data in subsets of channels (a restriction in place in existing tools). The new tool facilitates precise, manual marking of bio-signals by allowing any desired segment of data to be marked in any subset of channels. It is therefore of utility in circumstances where such flexibility is essential. The developed GUI is an auxiliary analysis tool that shall facilitate neural signal (pre-)processing applications where it is desirable to perform accurate supervised artefact rejection, flexible data marking for increased data retention yield, extraction of specific signal segments by expert users from sample data, or labeling of data for clinical and scientific research purposes.

neuroscience↗