Biology subjects

LeVan, P.

Publications and source records attributed to LeVan, P..

3 recordsLinked to original sources

bioRxiv2023

Probing hippocampal stimulation in experimental temporal lobe epilepsy with functional MRI

BackgroundElectrical neurostimulation is a potentially effective therapy in epilepsy but the optimal approach is not yet clear. The parameter space is wide and the effects of different stimulations are not immediately obvious. Functional MRI (fMRI) can reveal which brain areas are affected by stimulation and help understand the induced effects. However, simultaneous deep brain stimulation (DBS)-fMRI examinations in patients are rare and the possibility to investigate multiple stimulation protocols is limited. Preclinical stimulation-fMRI studies can provide predictive value and help identify optimal neurostimulation parameters. ObjectiveTo systematically investigate the brain-wide responses to hippocampal electrical stimulations in a mouse model of mesial temporal lobe epilepsy (mTLE) using fMRI. MethodsWe applied electrical stimulation in the intrahippocampal kainate mouse model of mTLE and assessed the effect of different stimulation amplitudes (80-230 {micro}A) and frequencies (1-100 Hz). In addition, the effect of prolonged 1 Hz stimulation was explored. Saline-injected mice served as controls. ResultsVarying the stimulation amplitudes had little effect on the resulting activation patterns. Low frequency stimulation led to a local response at the stimulation site only, whereas high frequency resulted in a spread of activation from the hippocampal formation into cortical and frontal areas. Prolonged low frequency stimulation reduced excitability. ConclusionsWhile the amplitude parameter offers little opportunity to vary the outcome, the frequency represented the key parameter and determined whether the induced activation remained local or spread across the brain. This is in line with the few DBS-fMRI results obtained in epilepsy patients demonstrating the translational value of fMRI.

neuroscience↗

bioRxiv2021

Holo-Hilbert Spectral-based Removal of Gamma-Band Noise from Simultaneous EEG-fMRI Recordings

Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (> 30 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention, and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to issues such as MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a noise removal method for the gamma band of EEG that is based on the Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively) by averaging over frequencies with high and significant powers. Our method examines gamma band by applying two layers of EMD to the FM and AM components, removing components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method efficiently reduces artifacts while preserving the original gamma signal which is especially critical for simultaneous EEG/fMRI studies.

neuroscience↗

bioRxiv2020

The neuronal associations of respiratory-volume variability in the resting state

The desire to enhance the sensitivity and specificity of resting-state (rs-fMRI) measures has prompted substantial recent research into removing noise components. Chief among contributions to noise in rs-fMRI are physiological processes, and the neuronal implications of respiratory-volume variability (RVT), a main rs-fMRI-relevant physiological process, is incompletely understood. The potential implications of RVT in modulating and being modulated by autonomic nervous regulation, has yet to be fully understood by the rs-fMRI community. In this work, we use high-density electroencephalography (EEG) along with simultaneously acquired RVT recordings to help address this question. We hypothesize that (1) there is a significant relationship between EEG and RVT in multiple EEG bands, and (2) that this relationship varies by brain region. Our results confirm our first hypothesis, although all brain regions are shown to be equally implicated in RVT-related EEG-signal fluctuations. The lag between RVT and EEG is consistent with previously reported values. However, an interesting finding is related to the polarity of the correlation between RVT and EEG. Our results reveal potentially two main regimes of EEG-RVT association, one in which EEG leads RVT with a positive association between the two, and one in which RVT leads EEG but with a positive association between the two. We propose that these two patterns can be interpreted differently in terms of the involvement of higher cognition. These results further suggest that treating RVT simply as noise is likely a questionable practice, and that more work is needed to avoid discarding cognitively relevant information when performing physiological correction rs-fMRI.

biophysics↗