NREM sleep brain networks modulate cognitive recovery from sleep deprivation.

Decrease in cognitive performance after sleep deprivation followed by recovery after sleep suggests its key role, and especially non-rapid eye movement (NREM) sleep, in the maintenance of cognition. It remains unknown whether brain network reorganization in NREM sleep stages N2 and N3 can uniquely be mapped onto individual differences in cognitive performance after a recovery nap following sleep deprivation. Using resting state functional magnetic resonance imaging (fMRI), we quantified the integration and segregation of brain networks during NREM sleep stages N2 and N3 while participants took a 1-hour nap following 24-hour sleep deprivation, compared to well-rested wakefulness. Here, we advance a new analytic framework called the hierarchical segregation index (HSI) to quantify network segregation across spatial scales, from whole-brain to the voxel level, by identifying spatio-temporally overlapping large-scale networks and the corresponding voxel-to-region hierarchy. Our results show that network segregation increased in the default mode, dorsal attention and somatomotor networks during NREM sleep compared to wakefulness. Segregation within the visual, limbic, and executive control networks exhibited N2 versus N3 sleep-specific voxel-level patterns. More segregation during N3 was associated with worse recovery of working memory, executive attention, and psychomotor vigilance after the nap. The level of spatial resolution of network segregation varied among brain regions and was associated with the recovery of performance in distinct cognitive tasks. We demonstrated the sensitivity and reliability of voxel-level HSI to provide key insights into within-region variation, suggesting a mechanistic understanding of how NREM sleep replenishes cognition after sleep deprivation.

An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation.

Sleep deprivation (SD) leads to impairments in cognitive function. Here, we tested the hypothesis that cognitive changes in the sleep-deprived brain can be explained by information processing within and between large-scale cortical networks. We acquired functional magnetic resonance imaging (fMRI) scans of 20 healthy volunteers during attention and executive tasks following a regular night of sleep, a night of SD, and a recovery nap containing nonrapid eye movement (NREM) sleep. Overall, SD was associated with increased cortex-wide functional integration, driven by a rise of integration within cortical networks. The ratio of within versus between network integration in the cortex increased further in the recovery nap, suggesting that prolonged wakefulness drives the cortex towards a state resembling sleep. This balance of integration and segregation in the sleep-deprived state was tightly associated with deficits in cognitive performance. This was a distinct and better marker of cognitive impairment than conventional indicators of homeostatic sleep pressure, as well as the pronounced thalamocortical connectivity changes that occurs towards falling asleep. Importantly, restoration of the balance between segregation and integration of cortical activity was also related to performance recovery after the nap, demonstrating a bidirectional effect. These results demonstrate that intra- and interindividual differences in cortical network integration and segregation during task performance may play a critical role in vulnerability to cognitive impairment in the sleep-deprived state.

A study of event related potential frequency domain coherency using multichannel electroencephalogram subspace analysis.

BACKGROUND: Event related potentials (ERP) are time-locked electrical activities of the brain in direct response to a specific sensory, cognitive, or motor stimulus. ERP components, such as the P300 wave, which are involved in the process of decision-making, help scientists diagnose specific cognitive disabilities. NEW METHOD: In this study, we utilize the angles between multichannel electroencephalogram (EEG) subspaces in different frequency bands, as a similarity factor for studying the spatial coherency between ERP frequency responses. A matched filter is used to enhance the ERP from background EEG. RESULTS: While previous researches have focused on frequencies below 10 Hz, as the major frequency band of ERP, it is shown that by using the proposed method, significant ERP-related information can also be found in the 25-40 Hz band. These frequency bands are selected by calculating the correlation coefficient between P300 response segments and synthetic EEG, and ERP segments without P300 waves, and by rejecting the bands having the most association with background EEG and non-P300 components. COMPARISON WITH EXISTING METHODS: The significance of the results is assessed by real EEG acquired in brain computer interface experiments versus synthetic EEG produced by existing methods in the literature, to assure that the results are not systematic side effects of the proposed framework. CONCLUSIONS: The overall results show that the equivalent dipoles corresponding to narrow-band events in the brain are spatially coherent within different (not necessarily adjacent) frequency bands. The results of this study can lead into novel perspectives in ERP studies.

A general framework for extracting fetal magnetoencephalogram and audio-evoked responses.

The analysis of auditory evoked cortical responses in fetal magnetoencephalography (fMEG) can be used as an early marker of functional cerebral development. A major obstacle for this objective is the very low signal-to-noise ratio of the fMEG recordings in presence of other biological contaminants (mainly maternal and fetal cardiac activities). Due to the fMEG nonstationarity and noise, the purpose of the present study is to improve the detection of the fetal auditory evoked response (fAER) by proposing a multi-stage framework for removing maternal and fetal artifacts using quasi-periodicity of cardiac activities, semi-blind source separation methods and detection of fAER using an ad hoc matched filter. The validation stage is performed using synchronous averaging, energy ratio comparison, statistical analysis of signal distribution, and the geometric localization of the fetal head and heart. The validation results show that the method can be effectively used in high precision fMEG and fAER applications.