Biology subjects

Nikulin, V. V.

Publications and source records attributed to Nikulin, V. V..

21 records · Page 2Linked to original sources

bioRxiv2019

BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability - identified with either hemodynamic or electrophysiological methods - reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25{+/-}3 years, N=135) and older (67{+/-}4 years, N=54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1-12 Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15-25 Hz) were seen predominantly in temporal brain regions. There were significant sex differences in EEG signal variability in various brain regions while no significant sex differences were observed in BOLD signal variability. Bivariate and multivariate correlation analyses revealed no significant associations between EEG- and fMRI-based variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.

neuroscience↗

bioRxiv2018

Multiple mechanisms link prestimulus neural oscillations to sensory responses

Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentation of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an amplification of late components (after 0.4 s). Whereas functional inhibition of sensory processing underlies the reduction of early ERP responses, we found that the modulation of non-zero-mean oscillations (baseline shift) accounted for the amplification of late responses. Distinguishing between these two mechanisms is crucial for the understanding of how internal brain states modulate the processing of incoming sensory information.

neuroscience↗

bioRxiv2018

Spatial neuronal synchronization and the waveform of oscillations: implications for EEG and MEG

Neuronal oscillations are ubiquitous in the human brain and are implicated in virtually all brain functions. Often they are referred to by their frequency content, i.e., -, {beta}-,{gamma} -oscillations. Although they indeed can be described by a prominent peak in the power spectrum, their waveform is not necessarily sinusoidal and shows a rather complex morphology which needs to be captured with multiple spectral harmonics. Both frequency and temporal descriptions of such non-sinusoidal neuronal oscillations can be utilized. However, in non-invasive EEG/MEG recordings the waveform of oscillations often takes a sinusoidal shape which in turn leads to a rather oversimplified view on oscillatory processes.\n\nIn this study, we show in simulations how spatial synchronization can mask non-sinusoidal features of the underlying rhythmic neuronal processes. Consequently, the degree of non-sinusoidality can serve as a measure of spatial synchronization. To confirm this empirically, we show that a mixture of EEG components is indeed associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component. Using simulations, we also show that the spatial mixing of the non-sinusoidal neuronal signals strongly affects the amplitude ratio of the spectral harmonics constituting the waveform. This in turn has high relevance for the interpretation of the relative strength of spectral peaks, which is commonly used for inferring neuronal signatures corresponding to specific behavioral states.\n\nMoreover, our simulations show how spatial mixing can affect the strength and even the direction of the amplitude coupling between constituent neuronal harmonics. Consistently with these simulations, we also demonstrate these effects in real EEG recordings. Our findings have far reaching implications for the neu-rophysiological interpretation of neuronal oscillations and cross-frequency interactions, as well as for the unequivocal determination of oscillatory phase.

neuroscience↗