bioRxiv Science⌕ Search

Biology subjects

Varone, G.

Publications and source records attributed to Varone, G..

2 recordsLinked to original sources

Signatures of Brain Network Alteration in Psychogenic Non-Epileptic Seizures: A Rest-EEG Study Based on Power Spectral Density and Phase Lag Index

The main challenge in the clinical assessment of Psychogenic Non-Epileptic Seizures (PNES) is the lack of an electroencephalographic marker in the electroencephalography (EEG) readout. Although decades of EEG studies have focused on detecting cortical brain function underlying PNES, the principle of PNES remains poorly understood. To address this problem, electric potentials generated by large populations of neurons were collected during the resting state to be processed after that by Power Spectrum Density (PSD) for possible analysis of PNES signatures. Additionally, the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas has been observed using functional connectivity tools like Phase Lag Index (PLI) and graph-derived metrics. A cohort study of 20 PNES and 19 Healthy Control subjects (HC) were enrolled. The major finding is that PNES patients exhibited significant differences in alpha-power spectrum in brain regions related to cognitive operations, attention, working memory, and movement regulation. Noticeably, we observed that there exists an altered oscillatory activity and a widespread inter-regional phase desynchronization. This indicates changes in global efficiency, node betweenness, shortest path length, and small worldness in the delta, theta, alpha, and beta frequency bands. Finally, our findings look into new evidence of the intrinsic organization of functional brain networks that reflects a dysfunctional level of integration of local activity across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES.

neuroscience↗

The rt-TEP tool: real-time visualization of TMS-evoked potentials to maximize cortical activation and minimize artifacts

BackgroundThe impact of transcranial magnetic stimulation (TMS) on cortical neurons is currently hard to predict based on a priori biophysical and anatomical knowledge alone. This problem can hamper the reliability and reproducibility of protocols aimed at measuring electroencephalographic (EEG) responses to TMS. New MethodWe introduce and release a novel software tool to facilitate and standardize the acquisition of TMS-evoked potentials (TEPs). The tool, rt-TEP (real-time TEP), interfaces with different EEG amplifiers and offers a series of informative visualization modes to assess in real time the immediate impact of TMS on the underlying neuronal circuits. ResultsWe show that rt-TEP can be used to abolish or minimize magnetic and muscle artifacts contaminating the post-stimulus period thus affording a clear visualization and quantification of the amplitude of the early (<50 ms) EEG response after averaging a limited number of trials. This real-time readout can then be used to adjust TMS parameters (e.g. site, orientation, intensity) and experimental settings (e.g. loudness and/or spectral features of the noise masking) to ultimately maximize direct cortical effects over the undesired sensory effects of the coils discharge. Comparison with Existing MethodsThe ensemble of real-time visualization modes of rt-TEP are not implemented in any current commercial software and provide a key readout to titrate TMS parameters beyond the a priori information provided by anatomical models. ConclusionsReal-time optimization of stimulation parameters with rt-TEP can facilitate the acquisition of reliable TEPs with a high signal-to-noise ratio and improve the standardization and reproducibility of data collection across laboratories. HighlightsO_LIrt-TEP provides an immediate EEG readout to assess the quality of TEPs in real time C_LIO_LIrt-TEP interfaces with most commercial EEG systems C_LIO_LIInformative, real time visualization allows maximising the cortical impact of TMS while minimizing artifacts C_LIO_LIrt-TEP facilitates the acquisition of reliable TEPs with a high signal-to-noise ratio C_LI

neuroscience↗