scholarly journals Intrinsic brain connectivity after partial sleep deprivation in young and older adults: results from the Stockholm Sleepy Brain study

2016 ◽  
Author(s):  
Gustav Nilsonne ◽  
Sandra Tamm ◽  
Johanna Schwarz ◽  
Rita Almeida ◽  
Håkan Fischer ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Resting State ◽  
Brain Connectivity ◽  
Low Frequency ◽  
Posterior Cingulate Cortex ◽  
Regional Homogeneity ◽  
Seed Region ◽  
Global Signal ◽  
Partial Sleep Deprivation ◽  
Signal Variability

AbstractSleep deprivation has been reported to affect intrinsic brain connectivity, notably reducing connectivity in the default mode network. Studies to date have however shown inconsistent effects, in many cases lacked monitoring of wakefulness, and largely included young participants. We investigated effects of sleep deprivation on intrinsic brain connectivity in young and older participants. Participants aged 20–30 (n=30) and 65–75 (n=23) years underwent partial sleep deprivation (3h sleep) in a cross-over design, with two 8-minutes eyes-open resting state functional magnetic resonance imaging (fMRI) runs in each session, monitored by eye-tracking. We assessed intrinsic brain connectivity using independent components analysis (ICA) as well as seed-region analyses of functional connectivity, and also analysed global signal variability, regional homogeneity, and the amplitude of low-frequency fluctuations. Sleep deprivation caused increased global signal variability. In our study, changes in investigated resting state networks and in regional homogeneity were not statistically significant. Younger participants had higher connectivity in most examined networks, as well as higher regional homogeneity in areas including anterior and posterior cingulate cortex. In conclusion, we found that sleep deprivation caused increased global signal variability. We speculate that this may be caused by wake-state instability.

scholarly journals Neural basis underlying the trait of attachment anxiety and avoidance revealed by the amplitude of low-frequency fluctuations and resting-state functional connectivity

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Min Deng ◽  
Xing Zhang ◽  
Xiaoyan Bi ◽  
Chunhai Gao
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Low Frequency ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Attachment Anxiety ◽  
Frequency Fluctuation ◽  
Resting State Functional Connectivity ◽  
Attachment Avoidance ◽  
Posterior Cingulate

Abstract Background Attachment theory demonstrates that early attachment experience shapes internal working models with mental representations of self and close relationships, which affects personality traits and interpersonal relationships in adulthood. Although research has focused on brain structural and functional underpinnings to disentangle attachment styles in healthy individuals, little is known about the spontaneous brain activity associated with self-reported attachment anxiety and avoidance during the resting state. Methods One hundred and nineteen individuals participated in the study, completing the Experience in Close Relationship scale immediately after an 8-min fMRI scanning. We used the resting-state functional magnetic resonance imaging (rs-fMRI) signal of the amplitude of low-frequency fluctuation and resting-state functional connectivity to identify attachment-related regions and networks. Results Consequently, attachment anxiety is closely associated with the amplitude of low-frequency fluctuations in the right posterior cingulate cortex, over-estimating emotional intensity and exaggerating outcomes. Moreover, the functional connectivity between the posterior cingulate cortex and fusiform gyrus increases detection ability for potential threat or separation information, facilitating behavior motivation. The attachment avoidance is positively correlated with the amplitude of low-frequency fluctuation in the bilateral lingual gyrus and right postcentral and negatively correlated with the bilateral orbital frontal cortex and inferior temporal gyrus. Functional connection with attachment avoidance contains critical nodes in the medial temporal lobe memory system, frontal-parietal network, social cognition, and default mode network necessary to deactivate the attachment system and inhibit attachment-related behavior. Conclusion and implications These findings clarify the amplitude of low-frequency fluctuation and resting-state functional connectivity neural signature of attachment style, associated with attachment strategies in attachment anxiety and attachment avoidance individuals. These findings may improve our understanding of the pathophysiology of the attachment-related disorder.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Evaluation of spontaneous regional brain activity in weight-recovered anorexia nervosa

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Maria Seidel ◽  
Daniel Geisler ◽  
Viola Borchardt ◽  
Joseph A. King ◽  
Fabio Bernardoni ◽  
...  
Keyword(s):  
Resting State ◽  
Grey Matter ◽  
Brain Activity ◽  
Low Frequency ◽  
Regional Homogeneity ◽  
Degree Centrality ◽  
Mean Square ◽  
Regional Brain ◽  
Cortical Grey Matter ◽  
Regional Brain Activity

AbstractWhereas research using structural magnetic resonance imaging (sMRI) reports sizable grey matter reductions in patients suffering from acute anorexia nervosa (AN) to be largely reversible already after short-term weight gain, many task-based and resting-state functional connectivity (RSFC) studies suggest persistent brain alterations even after long-term weight rehabilitation. First investigations into spontaneous regional brain activity using voxel-wise resting-state measures found widespread abnormalities in acute AN, but no studies have compared intrinsic brain activity properties in weight-recovered individuals with a history of AN (recAN) with healthy controls (HCs). SMRI and RSFC data were analysed from a sample of 130 female volunteers: 65 recAN and 65 pairwise age-matched HC. Cortical grey matter thickness was assessed using FreeSurfer software. Fractional amplitude of low-frequency fluctuations (fALFFs), mean-square successive difference (MSSD), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VHMC), and degree centrality (DC) were calculated. SMRI and RSFC data were analysed from a sample of 130 female volunteers: 65 recAN and 65 pairwise age-matched HCs. Cortical grey matter thickness was assessed using FreeSurfer software. Fractional amplitude of low-frequency fluctuations (fALFF), mean-square successive difference (MSSD), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VHMC), and degree centrality (DC) were calculated. Abnormal regional homogeneity found in acute AN seems to normalize in recAN, supporting assumptions of a state rather than a trait marker. Aberrant fALFF values in the cerebellum and the infertior temporal gyrus could possibly hint towards trait factors or a scar (the latter, e.g., from prolonged periods of undernutrition), warranting further longitudinal research.

scholarly journals Amplitude of fNIRS Resting-State Global Signal Is Related to EEG Vigilance Measures: A Simultaneous fNIRS and EEG Study

2020 ◽  
Vol 14 ◽  
Author(s):  
Yuxuan Chen ◽  
Julia Tang ◽  
Yafen Chen ◽  
Jesse Farrand ◽  
Melissa A. Craft ◽  
...  
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Near Infrared ◽  
Low Frequency ◽  
Temporal Correlation ◽  
Frequency Range ◽  
Functional Near Infrared Spectroscopy ◽  
Physical Constraints ◽  
Global Signal ◽  
Eeg Data

Recently, functional near-infrared spectroscopy (fNIRS) has been utilized to image the hemodynamic activities and connectivity in the human brain. With the advantage of economic efficiency, portability, and fewer physical constraints, fNIRS enables studying of the human brain at versatile environment and various body positions, including at bed side and during exercise, which complements the use of functional magnetic resonance imaging (fMRI). However, like fMRI, fNIRS imaging can be influenced by the presence of a strong global component. Yet, the nature of the global signal in fNIRS has not been established. In this study, we investigated the relationship between fNIRS global signal and electroencephalogram (EEG) vigilance using simultaneous recordings in resting healthy subjects in high-density and whole-head montage. In Experiment 1, data were acquired at supine, sitting, and standing positions. Results found that the factor of body positions significantly affected the amplitude of the resting-state fNIRS global signal, prominently in the frequency range of 0.05–0.1 Hz but not in the very low frequency range of less than 0.05 Hz. As a control, the task-induced fNIRS or EEG responses to auditory stimuli did not differ across body positions. However, EEG vigilance plays a modulatory role in the fNIRS signals in the frequency range of less than 0.05 Hz: resting-state sessions of low EEG vigilance measures are associated with high amplitudes of fNIRS global signals. Moreover, in Experiment 2, we further examined the epoch-to-epoch fluctuations in concurrent fNIRS and EEG data acquired from a separate group of subjects and found a negative temporal correlation between EEG vigilance measures and fNIRS global signal amplitudes. Our study for the first time revealed that vigilance as a neurophysiological factor modulates the resting-state dynamics of fNIRS, which have important implications for understanding and processing the noises in fNIRS signals.

Global signal regression has complex effects on regional homogeneity of resting state fMRI signal

2015 ◽  
Vol 33 (10) ◽  
pp. 1306-1313 ◽  
Author(s):  
Zhao Qing ◽  
Zhangye Dong ◽  
Sufang Li ◽  
Yufeng Zang ◽  
Dongqiang Liu
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Regional Homogeneity ◽  
Fmri Signal ◽  
Global Signal

Cardiovascular effects of partial sleep deprivation in healthy volunteers

2012 ◽  
Vol 113 (2) ◽  
pp. 232-236 ◽  
Author(s):  
Josilene L. Dettoni ◽  
Fernanda Marciano Consolim-Colombo ◽  
Luciano F. Drager ◽  
Marcelo C. Rubira ◽  
Silvia Beatriz P. Cavasin de Souza ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Sympathetic Activity ◽  
Low Frequency ◽  
Cardiovascular Effects ◽  
Epidemiological Studies ◽  
Sleep Diary ◽  
Daily Routine ◽  
Cardiovascular Morbidity And Mortality ◽  
Wrist Actigraphy ◽  
Partial Sleep Deprivation

Sleep deprivation is common in Western societies and is associated with increased cardiovascular morbidity and mortality in epidemiological studies. However, the effects of partial sleep deprivation on the cardiovascular system are poorly understood. In the present study, we evaluated 13 healthy male volunteers (age: 31 ± 2 yr) monitoring sleep diary and wrist actigraphy during their daily routine for 12 nights. The subjects were randomized and crossover to 5 nights of control sleep (>7 h) or 5 nights of partial sleep deprivation (<5 h), interposed by 2 nights of unrestricted sleep. At the end of control and partial sleep deprivation periods, heart rate variability (HRV), blood pressure variability (BPV), serum norepinephrine, and venous endothelial function (dorsal hand vein technique) were measured at rest in a supine position. The subjects slept 8.0 ± 0.5 and 4.5 ± 0.3 h during control and partial sleep deprivation periods, respectively ( P < 0.01). Compared with control, sleep deprivation caused significant increase in sympathetic activity as evidenced by increase in percent low-frequency (50 ± 15 vs. 59 ± 8) and a decrease in percent high-frequency (50 ± 10 vs. 41 ± 8) components of HRV, increase in low-frequency band of BPV, and increase in serum norepinephrine (119 ± 46 vs. 162 ± 58 ng/ml), as well as a reduction in maximum endothelial dependent venodilatation (100 ± 22 vs. 41 ± 20%; P < 0.05 for all comparisons). In conclusion, 5 nights of partial sleep deprivation is sufficient to cause significant increase in sympathetic activity and venous endothelial dysfunction. These results may help to explain the association between short sleep and increased cardiovascular risk in epidemiological studies.

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

2020 ◽  
Author(s):  
Jintao Wu ◽  
Qianxiang Zhou ◽  
Jiaxuan Li ◽  
Yang Chen ◽  
Shuyu Shao ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Power Spectrum ◽  
Sleep Deprivation ◽  
Resting State ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Alpha Band ◽  
Posterior Cingulate ◽  
Recovery Sleep ◽  
Normal Sleep

Abstract BackgroundCognitive abilities are impaired by sleep deprivation and can be recovered when sufficient sleep is obtained. Changes in alpha-band oscillations are considered to be highly related to sleep deprivation. The effect of sleep deprivation on brain activation and functional connectivity in the resting-state alpha band remains unclear. The purpose of this study was to investigate how sleep deprivation and recovery sleep could change resting-state alpha-band neural oscillations.MethodsIn this study, thirty young, healthy participants obtained approximately 8 h of normal sleep, followed by 36 h of sleep deprivation. On the following recovery night, subjects underwent recovery sleep. Resting-state EEG after normal sleep, sleep deprivation and recovery sleep was recorded. Power spectrum, source localization and functional connectivity analyses were used to investigate the changes in resting-state alpha-band activity after normal sleep, sleep deprivation and recovery sleep.ResultsThe results showed that the global alpha power spectrum decreased and source activation was notably reduced in the precuneus, posterior cingulate cortex, cingulate gyrus, and paracentral lobule after sleep deprivation. Functional connectivity analysis after sleep deprivation showed a weakened functional connectivity pattern in a widespread network with the precuneus and posterior cingulate cortex as the key nodes. Furthermore, the changes caused by sleep deprivation were reversed to a certain extent but not significantly after one night of sleep recovery, which may be due to inadequate time for recovery sleep.ConclusionsIn conclusion, large-scale resting-state alpha-band activation and functional connectivity were weakened after sleep deprivation, and the inhibition of default mode network function with the precuneus and posterior cingulate cortex as the pivotal nodes may be an important cause of cognitive impairment. These findings provide new insight into the physiological response of sleep deprivation and determine how sleep deprivation disrupts brain alpha-band oscillations.

scholarly journals Template-based prediction of vigilance fluctuations in resting-state fMRI

2017 ◽  
Author(s):  
Maryam Falahpour ◽  
Catie Chang ◽  
Chi Wah Wong ◽  
Thomas T. Liu
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Eyes Closed ◽  
Vigilance State ◽  
Global Signal ◽  
Significant Difference ◽  
Eyes Open ◽  
Positive Correlations

AbstractChanges in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as “templates” for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject’s vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.

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