Dataset Information

Cortical source localization of sleep-stage specific oscillatory activity.

ABSTRACT: The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject's anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each non-REM sleep epoch in eight standard frequency bands, spanning the complete spectrum, and computed cortical source reconstructions of the spectral contrasts between each sleep state in comparison to the resting wakefulness. Despite not distinguishing periods of high and low activity within each sleep stage, our results provide new information about relative overall spectral changes in the non-REM sleep stages.

SUBMITTER: Brancaccio A

PROVIDER: S-EPMC7181624 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cortical source localization of sleep-stage specific oscillatory activity.

Brancaccio Arianna A Tabarelli Davide D Bigica Marco M Baldauf Daniel D

Scientific reports 20200424 1

The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject's anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each n ...[more]

PMID: 32332806

Similar Datasets

Project description:Oscillatory neural activity during sleep, such as that in the delta and sigma bands, is important for motor learning consolidation. This activity is reduced with typical aging, and this reduction may contribute to aging-related declines in motor learning consolidation. Evidence suggests that brain regions involved in motor learning contribute to oscillatory neural activity during subsequent sleep. However, aging-related differences in regional contributions to sleep oscillatory activity following motor learning are unclear. To characterize these differences, we estimated the cortical sources of consolidation-related oscillatory activity using individual anatomical information in young and older adults during non-rapid eye movement sleep after motor learning and analyzed them in light of cortical thickness and pre-sleep functional brain activation. High-density electroencephalogram was recorded from young and older adults during a midday nap, following completion of a functional magnetic resonance imaged serial reaction time task as part of a larger experimental protocol. Sleep delta activity was reduced with age in a left-weighted motor cortical network, including premotor cortex, primary motor cortex, supplementary motor area, and pre-supplementary motor area, as well as non-motor regions in parietal, temporal, occipital, and cingulate cortices. Sleep theta activity was reduced with age in a similar left-weighted motor network, and in non-motor prefrontal and middle cingulate regions. Sleep sigma activity was reduced with age in left primary motor cortex, in a non-motor right-weighted prefrontal-temporal network, and in cingulate regions. Cortical thinning mediated aging-related sigma reductions in lateral orbitofrontal cortex and frontal pole, and partially mediated delta reductions in parahippocampal, fusiform, and lingual gyri. Putamen, caudate, and inferior parietal cortex activation prior to sleep predicted frontal and motor cortical contributions to sleep delta and theta activity in an age-moderated fashion, reflecting negative relationships in young adults and positive or absent relationships in older adults. Overall, these results support the local sleep hypothesis that brain regions active during learning contribute to consolidation-related neural activity during subsequent sleep and demonstrate that sleep oscillatory activity in these regions is reduced with aging.

Project description:Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.

Project description:The synchronization of neuronal activity in the sensorimotor cortices is crucial for motor control and learning. This synchrony can be modulated by upstream activity in the cerebello-cortical network. However, many questions remain over the details of how the cerebral cortex and the cerebellum communicate. Therefore, our aim is to study the contribution of the cerebellum to oscillatory brain activity, in particular in the case of dystonia, a severely disabling motor disease associated with altered sensorimotor coupling. We used a kainic-induced dystonia model to evaluate cerebral cortical oscillatory activity and connectivity during dystonic episodes. We performed microinjections of low doses of kainic acid into the cerebellar vermis in mice and examined activities in somatosensory, motor and parietal cortices. We showed that repeated applications of kainic acid into the cerebellar vermis, for five consecutive days, generate reproducible dystonic motor behavior. No epileptiform activity was recorded on electrocorticogram (ECoG) during the dystonic postures or movements. We investigated the ECoG power spectral density and coherence between motor cortex, somatosensory and parietal cortices before and during dystonic attacks. During the baseline condition, we found a phenomenon of permanent adaptation with a change of baseline locomotor activity coupled to an ECoG gamma band increase in all cortices. In addition, after kainate administration, we observed an increase in muscular activity, but less signs of dystonia together with modulations of the ECoG power spectra with an increase in gamma band in motor, parietal and somatosensory cortices. Moreover, we found reduced coherence in all measured frequency bands between the motor cortex and somatosensory or parietal cortices compared to baseline. In conclusion, examination of cortical oscillatory activities in this animal model of chronic dystonia caused by cerebellar dysfunction reveals a disruption in the coordination of neuronal activity across the cortical sensorimotor/parietal network, which may underlie motor skill deficits.

Dataset Information

Cortical source localization of sleep-stage specific oscillatory activity.

Publications

Cortical source localization of sleep-stage specific oscillatory activity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets