Decreased resting-state alpha-band activation and functional connectivity after sleep deprivation

Neuroelectrophysiological alteration associated with cognitive flexibility after 24 h sleep deprivation in adolescents

The cognitive neural mechanisms by which sleep deprivation affects cognitive flexibility are poorly understood. Therefore, the study investigated the neuroelectrophysiological basis of the effect of 24 h sleep deprivation on cognitive flexibility in adolescents. 72 participants (36 females, mean age ± SD=20.46 ± 2.385 years old) participated in the study and were randomly assigned to the sleep deprivation group and control group. They were instructed to complete a task switch paradigm, during which participants' behavioral and electroencephalographic data were recorded. Behaviorally, there were significant between-group differences in accuracy. The results of event-related potential showed that the P2, N2 and P3 components had significant group effects or interaction effects. At the time-frequency level, there were statistically significant differences between the delta and theta bands. These results suggested that 24 h sleep deprivation affected problem-solving effectiveness rather than efficiency, mainly because it systematically impaired cognitive processing associated with cognitive flexibility.

Relationships between peak alpha frequency, age, and autistic traits in young children with and without autism spectrum disorder

Background

Atypical peak alpha frequency (PAF) has been reported in children with autism spectrum disorder (ASD); however, the relationships between PAF, age, and autistic traits remain unclear. This study was conducted to investigate and compare the resting-state PAF of young children with ASD and their typically developing (TD) peers using magnetoencephalography (MEG).

Methods

Nineteen children with ASD and 24 TD children, aged 5-7 years, underwent MEG under resting-state conditions. The PAFs in ten brain regions were calculated, and the associations between these findings, age, and autistic traits, measured using the Social Responsiveness Scale (SRS), were examined.

Results

There were no significant differences in PAF between the children with ASD and the TD children. However, a unique positive association between age and PAF in the cingulate region was observed in the ASD group, suggesting the potential importance of the cingulate regions as a neurophysiological mechanism underlying distinct developmental trajectory of ASD. Furthermore, a higher PAF in the right temporal region was associated with higher SRS scores in TD children, highlighting the potential role of alpha oscillations in social information processing.

Conclusions

This study emphasizes the importance of regional specificity and developmental factors when investigating neurophysiological markers of ASD. The distinct age-related PAF patterns in the cingulate regions of children with ASD and the association between right temporal PAF and autistic traits in TD children provide novel insights into the neurobiological underpinnings of ASD. These findings pave the way for future research on the functional implications of these neurophysiological patterns and their potential as biomarkers of ASD across the lifespan.

Resting-state electroencephalography (EEG) microstates of healthy individuals following mild sleep deprivation

Mild sleep deprivation is widespread in many societies worldwide. Electroencephalography (EEG) microstate analysis provides information on spatial and temporal characteristics of resting brain network, serving as an indicator of neurophysiological activities at rest. This study seeks to investigate potential neural markers in EEG following mild sleep deprivation of a single night using EEG microstate analysis. Six-minute resting EEG was conducted on thirty healthy adults within 6 hours of waking in the morning and after at least 18 h of sleep deprivation. Translated and validated Malay language Karolinska Sleepiness Scale was used to assess the participants' degree of sleepiness. Microstate characteristics analysis was conducted on the final 24 subjects based on four standard microstate maps. Microstate C shows a significant increase in mean duration, coverage and occurrence, while microstate D has significantly higher occurrence after sleep deprivation. This study demonstrates notable changes in resting state EEG microstates following mild sleep deprivation. Present findings deepen our understanding of the brain's spatiotemporal dynamics under this condition and suggest the potential utility of neural markers in this domain as components of composite markers for sleep deprivation.

Minimum spanning tree analysis of EEG resting-state functional networks in schizophrenia

Schizophrenia is a serious and complex mental disease, known to be associated with various subtle structural and functional deviations in the brain. Recently, increased attention is given to the analysis of brain-wide, global mechanisms, strongly altering the communication of long-distance brain areas in schizophrenia. Data of 32 patients with schizophrenia and 28 matched healthy control subjects were analyzed. Two minutes long 64-channel EEG recordings were registered during resting, eyes closed condition. Average connectivity strength was estimated with Weighted Phase Lag Index (wPLI) in lower frequencies: delta and theta, and Amplitude Envelope Correlation with leakage correction (AEC-c) in higher frequencies: alpha, beta, lower gamma and higher gamma. To analyze functional network topology Minimum Spanning Tree (MST) algorithms were applied. Results show that patients have weaker functional connectivity in delta and alpha frequency bands. Concerning network differences, the result of lower diameter, higher leaf number, and also higher maximum degree and maximum betweenness centrality in patients suggest a star-like, and more random network topology in patients with schizophrenia. Our findings are in accordance with some previous findings based on resting-state EEG (and fMRI) data, suggesting that MST network structure in schizophrenia is biased towards a less optimal, more centralized organization.

Sleep deprivation changes frequency-specific functional organization of the resting human brain

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Olfactory Epithelium Stimulation Using Rhythmic Nasal Air-Puffs Improves the Cognitive Performance of Individuals with Acute Sleep Deprivation

This study aimed to investigate the effects of intranasal air-puffing on cognitive impairments and brain cortical activity following one night of partial sleep deprivation (PSD) in adults. A total of 26 healthy adults underwent the numerical Stroop test (NST) and electroencephalography (EEG) before and after one night of PSD. Following PSD, subjects in the treatment group (n = 13) received nasal air-puffs (5 Hz, 3 min) before beginning the NST and EEG recording. Administration of nasal air-puffs in the treatment group restored the PSD-induced increase in error rate and decrease in reaction time and missing rate in the NST. Intranasal air-puffs recovered the PSD-induced augmentation of delta and theta power and the reduction of beta and gamma power in the EEG, particularly in the frontal lobes. Intranasal air-puffing also almost reversed the PSD-induced decrease in EEG signal complexity. Furthermore, it had a restorative effect on PSD-induced alteration in intra-default mode network functional connectivity in the beta and gamma frequency bands. Rhythmic nasal air-puffing can mitigate acute PSD-induced impairments in cognitive functions. It exerts part of its ameliorating effect by restoring neuronal activity in cortical brain areas involved in cognitive processing.

The Effect of Sleep Deprivation on Brain Fingerprint Stability: A Magnetoencephalography Validation Study

This study examined the stability of the functional connectome (FC) over time using fingerprint analysis in healthy subjects. Additionally, it investigated how a specific stressor, namely sleep deprivation, affects individuals' differentiation. To this aim, 23 healthy young adults underwent magnetoencephalography (MEG) recording at three equally spaced time points within 24 h: 9 a.m., 9 p.m., and 9 a.m. of the following day after a night of sleep deprivation. The findings indicate that the differentiation was stable from morning to evening in all frequency bands, except in the delta band. However, after a night of sleep deprivation, the stability of the FCs was reduced. Consistent with this observation, the reduced differentiation following sleep deprivation was found to be negatively correlated with the effort perceived by participants in completing the cognitive task during sleep deprivation. This correlation suggests that individuals with less stable connectomes following sleep deprivation experienced greater difficulty in performing cognitive tasks, reflecting increased effort.

Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder

Background. Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method. The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction.

Mental fatigue assessment by an arbitrary channel EEG based on morphological features and LSTM-CNN

In order to achieve more sensitive mental fatigue assessment (MFA) based on an arbitrary channel EEG, this study proposed a series of feature extraction methods that combine mathematical morphology (MM), as well as an LSTM-CNN architecture. Firstly, 37 subjects had their resting-state EEGs collected at rested wakefulness (RW) and after 24 h of sleep deprivation (SD) using a 30-channel EEG acquisition device, the RW and SD groups were regarded as the negative and positive groups of mental fatigue, respectively, and the EEG collection were further categorized into two conditions: eye-opened state (EO) and eye-closed state (EC). Then, since MM can reflect the morphological characteristics of EEG rhythms and their potentials relatively independently of the time-frequency analysis and phase calculation, the MM methods were found to better reflect the mental fatigue after SD statistically, whether for single features (ANOVA: p<0.000001), multiple features (clustering by K-means, t-test: p<0.01), or time series feature spaces (calculating CD, t-test: p<0.01) of a single channel. Finally, the LSTM-CNN enhanced the generalization ability when dealing with different single-channel EEG by combining GRUs with convolutional layers: comparing the AUCs of different architectures for MFA based on an arbitrary channel, LSTM-CNN (0.992) > LSTM network (0.94) > CNN (0.831) > MLP (0.754). Moreover, the use of MM also improved the accuracy of analyzed architectures, and the true/false positive rate (TPR/FPR) of the LSTM-CNN architecture for MFA based on an arbitrary channel reached 97.024 %/3.497 %, which provided a feasible solution for the arbitrary channel EEG-based MFA.

Alterations of functional connectivity in young people with depression mediate the relationship between sleep quality and cognitive function

BACKGROUND

Sleep disturbances is common in young people with depression, and poor sleep quality affects the ability to learn. In this study, we examined possible resting-state functional connectivity abnormalities between regions of interest, and clarified the relationship with depressive symptoms, sleep quality, and cognitive function.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) was collected on 42 healthy controls (HCs), 82 youth depressive patients (44 without sleep disturbances (NSD), and 38 with sleep disturbances (SD)). Regions of interest were defined by using Brainnetome Atlas. Functional connectivity was calculated, and its associations with depressive symptoms, sleep quality, and cognitive function were examined using correlation analysis and mediation analysis.

RESULTS

The left and right caudal of cingulate gyrus, tongue and larynx region of postcentral gyrus were significant brain regions in NSD versus SD. The average functional connectivity between these regions was associated with poor sleep quality (r = 0.368, p = 0.001) and worse working memory (r = -0.256, p = 0.023) and mediated the relationship between sleep quality and working memory (c = -0.738, c' = -0.500).

LIMITATION

Data consistency in this study was not good enough. This study did not monitor sleep rhythms to provide objective sleep-related data.

CONCLUSION

The functional connectivity between the left and right caudal of cingulate gyrus with tongue and larynx region of postcentral gyrus may be the neural mechanism by which sleep disturbances affect working memory. This provides an intervention target for clinically improving cognitive function in young people with depression.

The nucleus accumbens functional connectivity in patients with insomnia using resting-state fMRI

Background

The aim of this study was to investigate the functional abnormalities between the nucleus accumbens (NAc) and the whole brain in individuals with Insomnia Disorder (ID) using resting-state functional magnetic resonance imaging (fMRI). Additionally, the study aimed to explore the underlying neural mechanisms of ID.

Methods

We enrolled 18 participants with ID and 16 normal controls (NC). Resting-state functional connectivity (FC) between the NAc and the whole brain voxels was calculated and compared between the two groups to identify differential brain region. Receiver operating characteristic (ROC) curve analysis was employed to assess the ability of differential features to distinguish between groups. Furthermore, Pearson correlation analysis was performed to examine the relationship between neurocognitive scores and differential features.

Results

The ID group exhibited significantly reduced FC values in several brain regions, including the right supplementary motor area, the bilateral middle frontal gyrus, the bilateral median cingulate and paracingulate gyri and the left precuneus. The area under the curve (AUC) of the classification model based on FC in these brain regions was 83.3%. Additionally, the abnormal functional changes observed in ID patients were positively correlated with the Fatigue Severity Scale (R = 0.650, p = 0.004).

Conclusion

These findings suggest that the NAc may play a crucial role in the diagnosis of ID and could serve as a potential imaging biomarker, providing insights into the underlying neural mechanisms of the disorder.

Sleep quality differentially modulates neural oscillations and proteinopathy in Alzheimer's disease

BACKGROUND

Alterations in resting-state neural activity have been reported in people with sleep disruptions and in patients with Alzheimer's disease, but the direct impact of sleep quality on Alzheimer's disease-related neurophysiological aberrations is unclear.

METHODS

We collected cross-sectional resting-state magnetoencephalography and extensive neuropsychological and clinical data from 38 biomarker-confirmed patients on the Alzheimer's disease spectrum and 20 cognitively normal older control participants. Sleep efficiency was quantified using the Pittsburgh Sleep Quality Index.

FINDINGS

Neural activity in the delta frequency range was differentially affected by poor sleep in patients on the Alzheimer's disease spectrum. Such neural changes were related to processing speed abilities and regional amyloid accumulation, and these associations were mediated and moderated, respectively, by sleep quality.

INTERPRETATION

Together, our results point to a mechanistic role for sleep disturbances in the widely reported neurophysiological aberrations seen in patients on the Alzheimer's disease spectrum, with implications for basic research and clinical intervention.

FUNDING

National Institutes of Health, USA.

Suprachiasmatic nucleus functional connectivity related to insomnia symptoms in adolescents with major depressive disorder

Background

Insomnia is a commonly seen symptom in adolescents with major depressive disorder (MDD). The suprachiasmatic nucleus (SCN), which is the circadian rhythm regulation center, plays a crucial role in the regulation of sleep-wake circulation. Nevertheless, how SCN function contributes to the exact neural mechanisms underlying the associations between insomnia and depressive symptoms has not been explored in adolescents. In the current study, we aimed to explore the relationship between SCN functional connectivity (FC) and insomnia symptoms in adolescents with MDD using a seed-based FC method.

Methods

In the current study, we recruited sixty-eight first-episode drug-naïve adolescents with MDD and classified them into high insomnia (MDD-HI) and low insomnia (MDD-LI) groups according to the sleep disturbance subscale of the Hamilton Depression Rating Scale (HAMD-S). Forty-three age/gender-matched healthy controls (HCs) were also recruited. SCN FC maps were generally for all subjects and compared among three groups using one-way ANOVA with age, gender and adjusted HAMD score as covariates. We used partial correlations to explore associations between altered FC and clinical symptoms, including sleep quality scores.

Results

Adolescents with MDD showed worse sleep quality, which positively correlated with the severity of depression. Compared to MDD-LI and HCs, MDD-HI adolescents demonstrated significantly decreased FC between the right SCN and bilateral precuneus, and there was no significant difference between the MDD-LI and HC groups. The HAMD-S scores were negatively correlated with bilateral SCN-precuneus connectivity, and the retardation factor score of HAMD was negatively correlated with right SCN-precuneus connectivity.

Conclusion

The altered FC between the SCN and precuneus may underline the neural mechanism of sleep-related symptoms in depressive adolescents and provide potential targets for personalized treatment strategies.

Sleep Deprivation-Induced Changes in Baseline Brain Activity and Vigilant Attention Performance

Sleep deprivation (SD) negatively affects several aspects of cognitive performance, and one of the most widely-used tools to evaluate these effects is the Psychomotor Vigilance Test (PVT). The present study investigated the possibility of predicting changes induced by SD in vigilant attention performance by evaluating the baseline electroencephalographic (EEG) activity immediately preceding the PVT stimuli onset. All participants (n = 10) underwent EEG recordings during 10 min of PVT before and after a night of SD. For each participant, the root mean square (RMS) of the baseline EEG signal was evaluated for each 1 s time window, and the respective average value was computed. After SD, participants showed slower (and less accurate) performance in the PVT task. Moreover, a close relationship between the changes in the baseline activity with those in cognitive performance was identified at several electrodes (Fp2, F7, F8, P3, T6, O1, Oz, O2), with the highest predictive power at the occipital derivations. These results indicate that vigilant attention impairments induced by SD can be predicted by the pre-stimulus baseline activity changes.

Regular cannabis use modulates the impact of HIV on the neural dynamics serving cognitive control

BACKGROUND

Cannabis use and HIV are independently associated with decrements in cognitive control. However, the combined effects of HIV and regular cannabis use on the brain circuitry serving higher-order cognition are unclear.

AIMS

Investigate the interaction between cannabis and HIV on neural interference effects during the flanker task and spontaneous activity in regions underlying higher-order cognition.

METHODS

The sample consisted of 100 participants, including people with HIV (PWH) who use cannabis, PWH who do not use cannabis, uninfected cannabis users, and uninfected nonusers. Participants underwent an interview regarding their substance use history and completed the Eriksen flanker task during magnetoencephalography (MEG). MEG data were imaged in the time-frequency domain and oscillatory maps depicting the neural flanker interference effect were probed for group differences. Voxel time series were then assessed for group-level differences in spontaneous activity.

RESULTS

Group differences in behavioral performance were identified along with group differences in theta and alpha neural interference responses in higher-order regions across the cortex, with nonusers with HIV generally exhibiting the most aberrant responses. Likewise, time series analyses indicated that nonusers with HIV also had significantly elevated spontaneous alpha activity in the left inferior frontal and dorsolateral prefrontal cortices (dlPFC). Finally, we found that spontaneous and oscillatory alpha activity were significantly coupled in the inferior frontal cortex and dlPFC among cannabis users, but not nonusers.

CONCLUSIONS

Regular cannabis use appears to suppress the impact of HIV on spontaneous and oscillatory alpha deficits in the left inferior frontal cortex and dlPFC.

Resting-state functional connectivity of the sensory/somatomotor network associated with sleep quality: evidence from 202 young male samples

Previous neuroimaging studies have demonstrated that sleep is associated with brain functional changes in some specific brain regions. However, few studies have examined the relationship between all possible functional connectivities (FCs) within the sensory/somatomotor network (SSN) and the sleep quality of young male samples. The SSN consists of two motor cortices and is known to play a critical role in sleep. Poor sleep quality may be associated with increased sensory/somatomotor functional connectivity during rest. Hence, 202 young male participants underwent a resting-state functional magnetic resonance imaging (fMRI) scan and completed the Pittsburgh Sleep Quality Index (PSQI). Results indicated that increased functional connectivity within the SSN was associated with poor sleep quality. Specifically, the total PSQI score was positively correlated with the increased functional connectivity of the left paracentral lobule (PCL), bilateral precentral gyrus (PreCG), supplementary motor area (SMA) and bilateral postcentral gyrus (PoCG). Additionally, our findings also exhibited that (a) the subjective sleep quality factor of PSQI was positively correlated with FC between the bilateral PoCG and the bilateral PCL as well as between the left PreCG and the right SMA; (b) the sleep latency factor of PSQI was positively correlated with FC between the left PoCG and the right precuneus (PCUN); (c) the sleep disturbances factor of PSQI was positively correlated with FC between the left PCL and the right PoCG, and (d) the daytime dysfunction factor of PSQI was positively correlated with FC between the bilateral PoCG and the left PCL as well as between the bilateral PreCG and the SMA. In short, our findings can be comprehensively understood as neural mechanisms of intrinsic SSN connectivity are associated with sleep quality of man. Meanwhile, it may expand our knowledge and provide new insight into a deeper understanding of the neurobiological mechanisms of sleep or sleep problems.

Total Sleep Deprivation Triggers Greater Activation in the Parietal Brain in the Visual Working Memory Updating Processes: An Event-Related Potentials Study

Working memory functions are known to be altered after total sleep deprivation (TSD). However, few studies have explored the deficits of working memory updating (WMU) after TSD, or the underlying electrophysiological mechanisms of these alterations. In the current exploratory study, we enrolled 14 young male volunteers who performed two kinds of WMU tasks—spatial and object two-back tasks—with simultaneous electroencephalography recordings under two sleep conditions: a normal sleep baseline condition and after 36 h of TSD. Repeated-measures analysis of variance showed that compared with those at baseline, the rates of correct responses in the WMU tasks decreased significantly after TSD. Analysis of event-related potentials revealed that the average amplitude of P3 components decreased significantly in the frontal and central brain regions and increased significantly in the parietal brain regions. Our findings suggest that TSD damages WMU behavior, impairs cognitive functions in the frontal and central brain regions, and triggers greater activation in the parietal brain regions. This is the first study to report the existence of event-related compensatory neural activity. This event-related compensatory effect may provide a new perspective for understanding the mechanisms underlying the influences triggered by sleep loss.

Roles for Sleep in Neural and Behavioral Plasticity: Reviewing Variation in the Consequences of Sleep Loss

Sleep is a vital physiological state that has been broadly conserved across the evolution of animal species. While the precise functions of sleep remain poorly understood, a large body of research has examined the negative consequences of sleep loss on neural and behavioral plasticity. While sleep disruption generally results in degraded neural plasticity and cognitive function, the impact of sleep loss can vary widely with age, between individuals, and across physiological contexts. Additionally, several recent studies indicate that sleep loss differentially impacts distinct neuronal populations within memory-encoding circuitry. These findings indicate that the negative consequences of sleep loss are not universally shared, and that identifying conditions that influence the resilience of an organism (or neuron type) to sleep loss might open future opportunities to examine sleep's core functions in the brain. Here, we discuss the functional roles for sleep in adaptive plasticity and review factors that can contribute to individual variations in sleep behavior and responses to sleep loss.

Neural correlates of sleep quality in children: Sex-specific associations shown by brain diffusion tractography

BACKGROUND AND PURPOSE

Sleep quality is important for healthy growth and development of children. We aimed to identify associations between sleep disturbances in healthy children without clinical diagnosis of sleep disorders and brain white matter (WM) microstructure using an advanced diffusion-weighted magnetic resonance imaging (DW-MRI) based tractography analysis, and to explore whether there are sex differences in these associations.

METHODS

Brain DW-MRI data were collected from sixty-two 8-year-old children (28 boys, 34 girls) whose parents also completed Children's Sleep Habits Questionnaire (CSHQ). Track-weighted imaging (TWI) measures were computed from the DW-MRI data for 37 WM tracts in each subject. Sex-specific partial correlation analyses were performed to evaluate correlations between TWI measures and a set of sleep disturbance scores derived from the CSHQ.

RESULTS

Significant correlations (P < .05, FDR-corrected; r: .48-.67) were identified in 13 WM tracts between TWI and sleep disturbance scores. Sexually dimorphic differences in correlations between sleep disturbance scores and WM microstructure measurements were observed. Specifically, in boys, daytime sleepiness positively correlated with track-weighted mean or radial diffusivity in 10 WM tracts (bilateral arcuate fasciculus, left cingulum, right middle longitudinal fasciculus, and three bilateral segments of superior longitudinal fasciculus). In girls, total CSHQ score, night walking, or sleep onset delay negatively correlated with track-weighted fractional anisotropy or axial diffusivity in 4 WM tracts (bilateral inferior longitudinal fasciculus and uncinate fasciculus).

CONCLUSIONS

The findings suggest that sleep disturbances without clinical diagnosis of sleep disorders are associated with lower WM microstructural integrity in children. Additionally, the associations possess unique patterns in boys and girls.

Sleep deprivation results in diverse patterns of synaptic scaling across the Drosophila mushroom bodies

Sleep is essential for a variety of plastic processes, including learning and memory. However, the consequences of insufficient sleep on circuit connectivity remain poorly understood. To better appreciate the effects of sleep loss on synaptic connectivity across a memory-encoding circuit, we examined changes in the distribution of synaptic markers in the Drosophila mushroom body (MB). Protein-trap tags for active zone components indicate that recent sleep time is inversely correlated with Bruchpilot (BRP) abundance in the MB lobes; sleep loss elevates BRP while sleep induction reduces BRP across the MB. Overnight sleep deprivation also elevated levels of dSyd-1 and Cacophony, but not other pre-synaptic proteins. Cell-type-specific genetic reporters show that MB-intrinsic Kenyon cells (KCs) exhibit increased pre-synaptic BRP throughout the axonal lobes after sleep deprivation; similar increases were not detected in projections from large interneurons or dopaminergic neurons that innervate the MB. These results indicate that pre-synaptic plasticity in KCs is responsible for elevated levels of BRP in the MB lobes of sleep-deprived flies. Because KCs provide synaptic inputs to several classes of post-synaptic partners, we next used a fluorescent reporter for synaptic contacts to test whether each class of KC output connections is scaled uniformly by sleep loss. The KC output synapses that we observed here can be divided into three classes: KCs to MB interneurons; KCs to dopaminergic neurons; and KCs to MB output neurons. No single class showed uniform scaling across each constituent member, indicating that different rules may govern plasticity during sleep loss across cell types.