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Suicidality involves thoughts (ideations and plans) and actions related to self-inflicted death. To improve management and prevention of suicidality, it is essential to understand the key neural mechanisms underlying suicidal thoughts and actions. Following empirically informed neural framework, we hypothesized that suicidal thoughts would be primarily characterized by alterations in the default mode network indicating disrupted self-related processing, whereas suicidal actions would be characterized by changes in the lateral prefrontal corticostriatal circuitries implicating compromised action control.We analyzed the gray matter volume and resting-state functional connectivity of 113 individuals with late-life depression, including 45 nonsuicidal patients, 33 with suicidal thoughts but no action, and 35 with past suicidal action. Between-group analyses revealed key neural features associated with suicidality. The functional directionality of the identified resting-state functional connectivity was examined using dynamic causal modeling to further elucidate its mechanistic nature. Post hoc classification analysis examined the contribution of the neural measures to suicide classification.As expected, reduced gray matter volumes in the default mode network and lateral prefrontal regions characterized patients with suicidal thoughts and those with past suicidal actions compared with nonsuicidal patients. Furthermore, region-of-interest analyses revealed that the directionality and strength of the ventrolateral prefrontal cortex-caudate resting-state functional connectivity were related to suicidal thoughts and actions. The neural features significantly improved classification of suicidal thoughts and actions over that based on clinical and suicide questionnaire variables.Gray matter reductions in the default mode network and lateral prefrontal regions and the ventrolateral prefrontal cortex-caudate connectivity alterations characterized suicidal thoughts and actions in patients with late-life depression.

Acute sleep deprivation (ASD) is often observed in shift workers and characterized by drowsiness and unrelenting exhaustion. The physiological and psychological effects of ASD include anxiety, depression, cognitive impairment, systemic inflammation, stress responses, and disruptions of gut microbiota. However, the mechanisms involved in the ASD-associated circadian dysregulations with regard to gut dysbiosis, systemic inflammation, physiological modulation, and psychiatry disorders remain unclear. The aim of this study was to investigate whether central nervous system disorders induced by ASD are related to inflammation, barrier dysfunction, and circadian dysregulation. We also assessed impacts on microbiota succession. Male C57BL/6 mice were randomly allocated to the control and sleep deprivation (SD) groups. Mice in the SD group were subjected to 72 h of paradoxical SD using the modified multiple-platform method for ASD induction (72 h rapid eye movement-SD). The effects of ASD on dietary consumption, behaviors, cytokines, microbiota, and functional genes were determined. The appetite of the SD group was significantly higher than that of the control group, but the body weight was significantly lower than that of the control group. The anxiety-like behaviors were found in the SD group. Alpha and beta diversity of microbiota showed significant decrease after ASD induction; the relative abundance of Candidatus_Arthromitus and Enterobacter was increased, whereas that abundance of Lactobacillus, Muribaculum, Monoglobus, Parasutterella, and others was decreased in the SD group. These effects were accompanied by reduction in fecal propionic acid. In the proximal colon, the SD group exhibited significantly higher inflammation (tumor necrosis factor-α [TNF-α]) and dysregulation of the circadian rhythms (brain and muscle ARNT-like 1 [BMAL1] and cryptochrome circadian regulator 1 [CRY1]) and tight junction genes (occludin [OCLN]) than the control group. Gut barrier dysfunction slightly increased the plasma concentration of lipopolysaccharide and significantly elevated TNF-α. Inflammatory signals might be transduced through the brain via TNF receptor superfamily member 1 A (TNFRSF1A), which significantly increased the levels of microglia activation marker (ionized calcium-binding adapter molecule 1 [IBA1]) and chemokine (intercellular adhesion molecule 1 [ICAM1]) in the cerebral cortex. The serotonin receptor (5-hydroxytryptamine 1A receptor [5-HT

Bereavement, the experience of losing a loved one, is one of the most catastrophic but inevitable events in life. It causes grief and intense depression‐like sadness. Recent studies have revealed the effectiveness and proficiency of mindfulness‐based cognitive therapy (MBCT) in emotional regulation among bereavement populations. MBCT improves the well‐being of the bereaved by enhancing cognitive performances. Regarding the neural correlates of bereavement grief, previous studies focused on the alleviation of emotion–cognition interferences at specific brain regions. Here, we hypothesized that the bereavement grief fundamentally triggers global alterations in the resting‐state brain networks and part of the internetwork connectivity could be reformed after MBCT intervention. We recruited 19 bereaved individuals who participated the 8‐week MBCT program. We evaluated (a) the large‐scale changes in brain connectivity affected by the MBCT program; as well as (b) the association between connectivity changes and self‐rated questionnaire. First, after MBCT, the bereaved individuals showed the reduction of the internetwork connectivity in the salience, default‐mode and fronto‐parietal networks in the resting state but not under emotional arousal, implying the alleviated attention to spontaneous mind wandering after MBCT. Second, the alterations of functional connectivity between subcortical (e.g., caudate) and cortical networks (e.g., cingulo‐opercular/sensorimotor) were associated with the changes of the mindfulness scale, the anxiety and the emotion regulation ability. In summary, MBCT could enhance spontaneous emotion regulation among the bereaved individuals through the internetwork reorganizations in the resting state., Among the bereaved participants, the changes of internetwork connectivity between subcortical (e.g., caudate) and cortical networks (e.g., cingulo‐opercular/sensorimotor) were positively correlated with the changes of FFMQ, the mindfulness scale. MBCT could enhance spontaneous emotion regulation among the bereaved individuals through the internetwork reorganizations in the resting state.

Few studies have investigated the measurement of oxygen uptake ($${\dot{\text{V}}}$$ V ˙ O2) in tracheostomized patients undergoing unassisted breathing trials (UBTs) for liberation from mechanical ventilation (MV). Using an open-circuit, breath-to-breath method, we continuously measured $${\dot{\text{V}}}$$ V ˙ O2 and relevant parameters during 120-min UBTs via a T-tube in 49 tracheostomized patients with prolonged MV, and calculated mean values in the first and last 5-min periods. Forty-one (84%) patients successfully completed the UBTs. The median $${\dot{\text{V}}}$$ V ˙ O2 increased significantly (from 235.8 to 298.2 ml/min; P = 0.025) in the failure group, but there was no significant change in the success group (from 223.1 to 221.6 ml/min; P = 0.505). In multivariate logistic regression analysis, an increase in $${\dot{\text{V}}}$$ V ˙ O2 > 17% from the beginning period (odds ratio [OR] 0.084; 95% confidence interval [CI] 0.012–0.600; P = 0.014) and a peak inspiratory pressure greater than − 30 cmH2O (OR 11.083; 95% CI 1.117–109.944; P = 0.04) were significantly associated with the success of 120-min UBT. A refined prediction model combining heart rate, energy expenditure, end-tidal CO2 and oxygen equivalent showed a modest increase in the area under the receiver operating characteristic curve of 0.788 (P = 0.578) and lower Akaike information criterion score of 41.83 compared to the traditional prediction model including heart rate and respiratory rate for achieving 48 h of unassisted breathing. Our findings show the potential of monitoring $${\dot{\text{V}}}$$ V ˙ O2 in the final phase of weaning in tracheostomized patients with prolonged MV.

Sertraline is one of the most commonly prescribed antidepressants. Major depressive disorder (MDD) is characterized by spontaneous thoughts that are laden with negative affect—a “malignant sadness”. Prior neuroimaging studies have identified abnormal resting-state functional connectivity (rsFC) in the spontaneous brain networks of MDD patients. But how antidepressant medication acts to relieve the experience of depression as well as adjust its associated spontaneous networks and mood-regulation circuits remains an open question. In this study, we recruited 22 drug-naive MDD patients along with 35 normal controls and investigated whether the functional integrity of cortical networks associated with spontaneous thoughts is modulated by sertraline treatment. We attempted to predict post-treatment effects based upon what we observed in the pre-treatment rsFC of drug-naive MDD patients. In the result, we demonstrated that (1) after the sertraline treatment, the medial temporal lobe of default network (DNMTL) and mood regulation pathway—the fronto-parietal control network (FPCN), the thalamus, and the salience network (SN)—were restored to normal connectivity, relative to the pre-treatment condition; however, the altered connections of FPCN-core DN (DNCORE), FPCN-SN, and intra-FPCN among MDD patients remained impaired; (2) thalamo-prefrontal connectivity provides moderate predictive power (r2 = 0.63) for the effectiveness of sertraline treatment. In summary, our findings contribute to a body of evidence that suggests salubrious effects of sertraline treatment primarily involve the FPCN-thalamus-SN pathway. The pre-treatment rsFC in this pathway could serve as a predictor of sertraline treatment outcome.

This study explored the effects of exercise with either high cognitive load or low cognitive load on cognitive performance and neuroplasticity in healthy elderly. Twenty-eight sedentary community-dwelling seniors participated in this study. Participants were assigned to one of three groups: the control group (C), low-cognitive load exercise group (LE), or high-cognitive load exercise group (HE). Individuals in both exercise groups engaged in moderate-intensity aerobic exercise for 4 months. Resting-state functional MRI and diffusion MRI were used to investigate the effects of intervention on functional and structural connectivity, respectively. Analysis of covariance with baseline, age and the education year as covariates was used to determine the effects of intervention. We found 4 months of exercise with high-cognitive load, but not exercise with low-cognitive load, improved the overall cognitive function of healthy elderly. Additionally, the HE group showed increased resting-state functional connectivity of superior frontal gyrus and anterior cingulate cortex and decreased functional connectivity of middle occipital gyrus and postcentral gyrus. The age-related alterations in local efficiency and betweenness were protected by exercise. Our findings might suggest that exercise with greater cognitive load likely results in greater training effects on cognition and brain connectivity than exercise requiring lower cognitive loads for healthy elderly.

Neurovascular coupling (NVC), the transient regional hyperemia following the evoked neuronal responses, is the basis of blood oxygenation level-dependent techniques and is generally adopted across physiological conditions, including the intrinsic resting state. However, the possibility of neurovascular dissociations across physiological alterations is indicated in the literature. To examine the NVC stability across sleep–wake states, we used electroencephalography (EEG) as the index of neural activity and functional magnetic resonance imaging (fMRI) as the measure of cerebrovascular response. Eight healthy adults were recruited for simultaneous EEG-fMRI recordings in nocturnal sleep. We compared the cross-modality (EEG vs. fMRI) consistency of functional indices (spectral amplitude and functional connectivity) among five states of wakefulness and sleep (state effect). We also segregated the brain into three main partitions (anterior, middle and posterior) for spatial assessments (regional effect). Significant state effects were found on δ, α and fMRI indices and regional effects on the α and fMRI indices. However, the cross-state EEG changes in spectral amplitude and functional connectivity did not consistently match the changes in the fMRI indices across sleep–wake states. In spectral amplitude, the δ band peaked at the N3 stage for all brain regions, while the fMRI fluctuation amplitude peaked at the N2 stage in the central and posterior regions. In regional connectivity, the inter-hemispheric connectivity of the δ band peaked at the N3 stage for all regions, but the bilateral fMRI connectivity showed the state changes in the anterior and central regions. The cross-modality inconsistencies across sleep–wake states provided preliminary evidence that the neurovascular relationship may not change in a linear consistency during NREM sleep. Thus, caution shall be exercised when applying the NVC presumption to investigating sleep/wake transitions, even among healthy young adults.

Nonrapid eye movement (NREM) sleep is associated with fading consciousness in humans. Recent neuroimaging studies have demonstrated the spatiotemporal alterations of the brain functional connectivity (FC) in NREM sleep, suggesting the changes of information integration in the sleeping brain. However, the common stationarity assumption in FC does not satisfactorily explain the dynamic process of information integration during sleep. The dynamic FC (dFC) across brain networks is speculated to better reflect the time‐varying information propagation during sleep. Accordingly, we conducted simultaneous EEG‐fMRI recordings involving 12 healthy men during sleep and observed dFC across sleep stages using the sliding‐window approach. We divided dFC into two aspects: mean dFC (dFC(mean)) and variance dFC (dFC(var)). A high dFC(mean) indicates stable brain network integrity, whereas a high dFC(var) indicates instability of information transfer within and between functional networks. For the network‐based dFC, the dFC(var) were negatively correlated with the dFC(mean) across the waking and three NREM sleep stages. As sleep deepened, the dFC(mean) decreased (N0~N1 > N2 > N3), whereas the dFC(var) peaked during the N2 stage (N0~N1

Mindfulness-based stress reduction (MBSR) has been proven to improve mental health and quality of life. This study examined how mindfulness training and various types of mindfulness practices altered brain activity. Specifically, the spectral powers of scalp electroencephalography (EEG) of the MBSR group (n = 17) who underwent an 8-week MBSR training-including mindful breathing and body-scan-were evaluated and compared with those of the waitlist controls (n = 14). Empirical results indicated that the long-term intervention effect of MBSR significantly elevated the resting-state beta powers and reduced resting-state delta powers in both practices; such changes were not observed in the waitlist control. Compared with mindful breathing, body-scanning resulted in an overall decline in EEG spectral powers at both delta and gamma bands among trained participants. Together with our preliminary data of expert mediators, the aforementioned spectral changes were salient after intervention, but mitigated along with expertise. Additionally, after receiving training, the MBSR group9s Five Facet Mindfulness Questionnaire (FFMQ) and Difficulties in Emotion Regulation Scale (DERS) scores improved significantly, which were correlated with the EEG spectral changes in the theta, alpha, and low-beta bands. This study elaborated the neurophysiological correlates of mindfulness practices, suggesting that MBSR might function as a unique internal processing tool that involves increased vigilant capability and induces alterations similar to other cognitive training. Additionally, the EEG signatures of mindfulness training assessed by this study may serve as objective indicators for evaluating mindfulness practices.

The involvement of neural substrates in respiratory sensory gating remained unclear. This study aimed to investigate cortical and subcortical activations associated with respiratory sensory gating by using functional magnetic resonance imaging. First, we hypothesized that paired occlusions would induce neural activation in cortical and subcortical areas, including the thalamus and sensorimotor cortices. Secondly, we hypothesized that, in terms of parameter estimates in the general linear model, the activation effect size β ratios (β

The neural correlates of consciousness, defined as the minimum neuronal mechanisms sufficient for any conscious percept, are usually subject to different interpretations depending on whether one uses measures of local or global brain activities. We argue that the local regions may support consciousness by serving as hubs within the brain’s global network. We adopt a unique functional magnetic resonance imaging resting state dataset that encompasses various conscious states, including non-rapid eye movement (NREM)-sleep, rapid eye movement (REM)-sleep, anesthesia, and brain injury patients. Using a graph-theoretical measure for detecting local hubs within the brain’s global network, we identify various higher-order sensory and motor regions as hubs with significantly reduced degree centrality during unconsciousness. Additionally, these regions form a sensorimotor circuit which correlates with levels of consciousness. Our findings suggest that integration of higher-order sensorimotor function may be a key mechanism of consciousness. This opens novel perspectives for therapeutic modulation of unconsciousness.

Late-life depression (LLD) is associated with greater risk of suicide and white matter hyperintensities (WMH), which are also found in suicide attempters regardless of age. Greater periventricular WMH are related to worse cognitive function. We investigated the spatial distribution of WMH in suicide attempters with LLD and its association with cognitive function. We recruited 114 participants with LLD (34 with history of suicide attempt and 80 without) and 47 older adult controls (individuals without LLD or history of suicide attempt). WMH were quantified by an automated segmentation algorithm and were classified into different regions. Suicide attempters with LLD had significantly higher global WMH (F3, 150 = 2.856, p = 0.039) and periventricular WMH (F3, 150 = 3.635, p = 0.014) compared to other groups. Suicide attempters with high WMH had significantly lower executive function, which could be an underlying mechanism for cognitive decline in older adults with suicidality.

Sertraline is one of the most commonly prescribed antidepressants. Major depressive disorder (MDD) is characterized by spontaneous thoughts that are laden with negative affect-a "malignant sadness". Prior neuroimaging studies have identified abnormal resting-state functional connectivity (rsFC) in the spontaneous brain networks of MDD patients. But how antidepressant medication acts to relieve the experience of depression as well as adjust its associated spontaneous networks and mood-regulation circuits remains an open question. In this study, we recruited 22 drug-naïve MDD patients along with 35 normal controls and investigated whether the functional integrity of cortical networks associated with spontaneous thoughts is modulated by sertraline treatment. We attempted to predict post-treatment effects based upon what we observed in the pre-treatment rsFC of drug-naïve MDD patients. In the result, we demonstrated that (1) after the sertraline treatment, the medial temporal lobe of default network (DN

Objective: Late-life depression (LLD) is associated with greater risk of suicide and white matter hyperintensity (WMH), which is also found in suicide attempter (SA) regardless of age. Greater periventricular WMH is related to worse cognitive function. We investigated the spatial distribution of WMH in suicide attempters with LLD and its association with cognitive function. Methods: We recruited 114 participants with LLD (34 with history of suicide attempt and 80 without) and 47 elderly controls (individuals without LLD or history of suicide attempt). Suicide attempters were also classified as early and late life SA (using 55 as median age). WMH was quantified by an automated segmentation algorithm and was classified into different regions including juxtaventricular, periventricular, deep, and juxtacortical WMH. We used analysis of covariance and generalized linear model to compare group differences in WMH (global and regional) and its association with neurocognitive function. Results: Suicide attempters with LLD had significantly higher global WMH (F3, 150 = 2.856, p = 0.039) and periventricular WMH (F3, 150 = 3.635, p=0.014) compared to other groups. This difference was primarily in early-life SAs compared to late-life. There was a significant interaction between global WMH and suicide attempt history on executive function, where SAs with high WMH had lower executive function. Conclusion: Individuals with LLD and a history of suicide attempt had greater WMH (primarily in periventricular WMH) compared to others without. WMH in SAs was associated with reduced executive function and may be an underlying mechanism for cognitive decline in older adults with suicidality. Funding Statement: This study was supported by funding from Supported by (1) medical research grants CMRPG3C0041/42 from Chang Gung Memorial Hospital and NRRPG2H0031 from Ministry of Science and Technology, Taiwan to Chemin Lin (2) NMRPG3G6031/32 and NMRPG3J0121 from Ministry of Science and Technology, Taiwan to Shwu-Hua, Lee (3) the KKHo International Charitable Foundation to Tatia Lee. Declaration of Interests: All authors declare that no support, financial or otherwise, has been received from any organization that may have an interest in the submitted work; and (ii) there are no other relationships or activities that could appear to have influenced the submitted work. Ethics Approval Statement: This project was approved by the ethics committee of the Chang Gung Memorial Hospital Institutional Review Board (IRB: 201601753B0C102).

PURPOSE Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping. METHODS Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI. RESULTS With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P < .05) than that of canonical and task-based approaches within putative language regions. CONCLUSION The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.

Consciousness is a mental characteristic of the human mind, whose exact neural features remain unclear. We aimed to identify the critical nodes within the brain's global functional network that support consciousness. To that end, we collected a large fMRI resting state dataset with subjects in at least one of the following three consciousness states: preserved (including the healthy awake state, and patients with a brain injury history (BI) that is fully conscious), reduced (including the N1-sleep state, and minimally conscious state), and lost (including the N3-sleep state, anesthesia, and unresponsive wakefulness state). We also included a unique dataset of subjects in rapid eye movement sleep state (REM-sleep) to test for the presence of consciousness with minimum movements and sensory input. To identify critical nodes, i.e., hubs, within the brain's global functional network, we used a graph-theoretical measure of degree centrality conjoined with ROI-based functional connectivity. Using these methods, we identified various higher-order sensory and motor regions including the supplementary motor area, bilateral supramarginal gyrus (part of inferior parietal lobule), supragenual/dorsal anterior cingulate cortex, and left middle temporal gyrus, that could be important hubs whose degree centrality was significantly reduced when consciousness was reduced or absent. Additionally, we identified a sensorimotor circuit, in which the functional connectivity among these regions was significantly correlated with levels of consciousness across the different groups, and remained present in the REM-sleep group. Taken together, we demonstrated that regions forming a higher-order sensorimotor integration circuit are involved in supporting consciousness within the brain's global functional network. That offers novel and more mechanism-guided treatment targets for disorders of consciousness.

Background Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience. New method To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise. Results We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD. Comparison with existing method(s) Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t -score distribution. Conclusions We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.

Task-evoked and resting-state (rs) functional magnetic resonance imaging (fMRI) techniques have been applied to the clinical management of neurological diseases, exemplified by presurgical localization of eloquent cortex, to assist neurosurgeons in maximizing resection while preserving brain functions. In addition, recent studies have recommended incorporating cerebrovascular reactivity (CVR) imaging into clinical fMRI to evaluate the risk of lesion-induced neurovascular uncoupling (NVU). Although each of these imaging techniques possesses its own advantage for presurgical mapping, a specialized clinical software that integrates the three complementary techniques and promptly outputs the analyzed results to radiology and surgical navigation systems in a clinical format is still lacking. We developed the Integrated fMRI for Clinical Research (IClinfMRI) software to facilitate these needs. Beyond the independent processing of task-fMRI, rs-fMRI, and CVR mapping, IClinfMRI encompasses three unique functions: (1) supporting the interactive rs-fMRI mapping while visualizing task-fMRI results (or results from published meta-analysis) as a guidance map, (2) indicating/visualizing the NVU potential on analyzed fMRI maps, and (3) exporting these advanced mapping results in a Digital Imaging and Communications in Medicine (DICOM) format that are ready to export to a picture archiving and communication system (PACS) and a surgical navigation system. In summary, IClinfMRI has the merits of efficiently translating and integrating state-of-the-art imaging techniques for presurgical functional mapping and clinical fMRI studies.

Rapid eye movement (REM) sleep has been frequently associated with dreaming. However, mounting evidence obtained from behavioral, pharmacological, and brain imaging studies suggests that REM sleep is not indicative of the dream report and may originate from diverse neural substrates in brain functionality. The aim of the current study was to investigate the functional systems associated with inter-individual differences in dream recall and REM sleep through assessments of the resting-state functional connectivity. We collected resting-state functional magnetic resonance imaging (fMRI) data for functional connectivity evaluations from 43 healthy adult volunteers (23 men) before and after sleep. For assessment of the dream recall frequency, a 2-week sleep diary was maintained by all volunteers. In addition, whole-night polysomnography was performed for measuring the REM sleep percentage. Voxel-wise correlation analyses of 12 functional connectivity networks of interest with the dream recall frequency and REM sleep percentage were conducted using general linear model analysis. Both the dream recall frequency and REM sleep percentage showed negative associations with multiple brain functional networks. However, the dream recall frequency was mainly related to functional connectivity within the lateral visual network and thalamus, whereas the REM sleep percentage was mainly associated with connectivity within the frontoparietal networks and cerebellum. In addition, the dream recall frequency showed stronger coupling with the lateral visual network connectivity at night, whereas the coupling between the REM sleep percentage and cerebellum was higher in the morning. This indicated a significant time of day effect. Our results provide neuroimaging evidence that the functional system associated with the dream recall frequency is different from that associated with the REM sleep percentage.

Subjective perception of sleep is not necessarily consistent with electroencephalography (EEG) indications of sleep. The mismatch between subjective reports and objective measures is often referred to as "sleep state misperception." Previous studies evince that this mismatch is found in both patients with insomnia and in normal sleepers, but the neurophysiological mechanism remains unclear. The aim of the study is to explore the neurophysiological basis of this mechanism, from the perspective of both EEG power and functional magnetic resonance imaging (fMRI) fluctuations. Thirty-six healthy young adults participated in the study. Simultaneous EEG and fMRI recordings were conducted while the participants were trying to fall asleep in an MRI scanner at approximately 9:00 pm. They were awakened after achieving stable N1 or N2 sleep, or after 90 min without falling into stable sleep. Next they were asked to recall their conscious experiences from the moment immediately prior to awakening. Sixty-one instances of scheduled awakenings were collected: 21 of these after having achieved stable stage N2 sleep; 12, during stage N1 sleep; and, 20 during the waking state. Relative to those awakenings without subjective-objective discrepancy (n = 27), these awakenings with discrepancy (n = 14) were associated with lower θ power, as well as higher α, β, and γ power. Moreover, we found that participants who exhibited the discrepancy, compared with those who did not, evinced a higher amplitude of low-frequency fluctuation levels in the prefrontal cortex. These results lend support to the conjecture that the subjective-objective discrepancy is associated with central nervous system hyperarousal.

Brain functions express rhythmic fluctuations accompanied by sleep and wakefulness each day, but how sleep regulates brain rhythms remains unclear. Following the dose-dependent local sleep concept, two succeeding questions emerge: (1) is the sleep regulation a network-specific process; and (2) is the awakening state dependent on the previous sleep stages? To answer the questions, we conducted simultaneous EEG and fMRI recordings over 22 healthy male participants, along pre-sleep, nocturnal sleep and awakening. Using paired comparisons between awakening and pre-sleep conditions, three scenarios of the regional specificity were demonstrated on awakening: (1) the default-mode and hippocampal networks maintained similar connectivity and spectral power; (2) the sensorimotor network presented reduced connectivity and spectral power; and (3) the thalamus demonstrated substantially enhanced connectivity to the neo-cortex with decreased spectral power. With regard to the stage effect, the deep sleep group had significant changes in both functional connectivity and spectral power on awakening, whereas the indices of light sleep group remained relatively quiescent after sleep. The phenomena implied that slow-wave sleep could be key to rebooting the BOLD fluctuations after sleep. In conclusion, the regional specificity and the stage effect were verified in support of the local awakening concept, indicating that sleep regulation leads to the reorganization of brain networks upon awakening.

Functional magnetic resonance imaging (fMRI) commonly uses gradient-recalled echo (GRE) signals to detect regional hemodynamic variations originating from neural activities. While the spatial localization of activation shows promising applications, indexing temporal response remains a poor mechanism for detecting the timing of neural activity. Particularly, the hemodynamic response may fail to resolve sub-second temporal differences between brain regions because of its signal origin or noise in data, or both. This study aimed at evaluating the performance of latency estimation using different fMRI techniques, with two event-related experiments at 3T. Experiment I evaluated latency variations within the visual cortex and their relationship with contrast-to-noise ratios (CNRs) for GRE, spin echo (SE), and diffusion-weighted SE (DWSE). Experiment II used delayed visual stimuli between two hemifields (delay time = 0, 250, and 500 ms, respectively) to assess the temporal resolving power of three protocols: GRETR1000, GRETR500, and SETR1000. The results of experiment I showed the earliest latency with DWSE, followed by SE, and then GRE. Latency variations decreased as CNR increased. However, similar variations were found between GRE and SE, when the latter had lower CNR. In experiment II, measured stimulus delays from all conditions were significantly correlated with preset stimulus delays. Inter-subject variation in the measured delay was found to be greatest with GRETR1000, followed by GRETR500, and the least with SETR1000. Conclusively, blood oxygenation level-dependent responses obtained from GRE exhibit greater CNR but no compromised latency variations in the visual cortex. SE is potentially capable of improving the performance of latency estimation, especially for group analysis. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 215–221, 2013

Resting-state fMRI (rs-fMRI) is receiving substantial attention for its sensitivity to functional abnormality in the brain networks of people with psychiatric and neurological disorders. However, because of the variety of rs-fMRI processing methods, the necessity of rs-fMRI quality assurance is increasing. Conventionally, the temporal signal-to-noise ratio (tSNR) is generally adopted for quality examination, but the tSNR does not guarantee reliable functional connectivity (FC) outcomes. Theoretically, intrinsic FC is supposed to reflect the spontaneous synchronization of neuronal basis, rather than that from thermal noise or non-neuronal physiological noise. Therefore, we proposed a new quality-assurance index for rs-fMRI to estimate the physiological contributions in spontaneous oscillations (PICSO). The PICSO index was designed as a voxel-wise measure for facilitating practical applications to all existing rs-fMRI data sets on the basis of two assumptions: Gaussian distributions in temporal fluctuations and ultra-slow changes of neural-based physiological fluctuations. To thoroughly validate the sensitivity of the proposed PICSO index to FC, we calibrated the preprocessing steps according to phantom data and verified the relationship between the PICSO and factors that are considered to affect FC in healthy participants (n = 12). Our results demonstrated that FC showed a significantly positive correlation with the PICSO. Moreover, for generating robust FC outcomes, directly acquiring data at a relatively large voxel size was more effective than performing smoothness on high-resolution data sets. In conclusion, compared with tSNR, the PICSO index is more sensitive to the resulting FC, providing a practical quality-assurance indicator for all existing rs-fMRI data sets.

The function of sleep in humans has been investigated using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging recordings to provide accurate sleep scores with spatial precision. Recent studies have demonstrated that spontaneous brain oscillations and functional connectivity dissociate during nonrapid eye movement (NREM) sleep; this leads to spontaneous cognitive processes, such as memory consolidation and emotional modulation. However, variations in network connectivity across the sleep stages or between sleep/wake transitions require further elucidation. We observed changes in the connectivity of the sensorimotor and default-mode networks (DMN) mediated by midnight sleep among 18 healthy participants. The results indicated that (1) functional connectivity in both networks showed increasing dissociation as NREM sleep deepened, whereas hyperconnectivity occurred during rapid eye movement (REM) sleep; and (2) compared with connectivity before sleep, the DMN presented a comparable connectivity pattern immediately after awakening, whereas the connectivity of the sensorimotor network remained disrupted. These findings showed that connectivity patterns dissociate and reconnect coherently in both cortical networks during NREM and REM sleep, respectively. After the person awakened, the DMN connectivity was re-established before the sensorimotor reconnection. These dynamic sleep-related dissociations and reconnections between sleep/wake conditions might provide the key to understanding cognitive modulations in sleep. If so, connectivity changes might serve as an alternative indicator beyond the EEG signature to unveil the spontaneous processes that occur during sleep.

Previous studies suggest that spontaneous fluctuations in the resting-state fMRI (RS-fMRI) signal may reflect fluctuations in transverse relaxation time (T(2)(*)) rather than spin density (S(0)). However, such S(0) and T(2)(*) features have not been well characterized. In this study, spatial and spectral characteristics of functional connectivity on sensorimotor, default-mode, dorsal attention, and primary visual systems were examined using a multiple gradient-echo sequence at 3T. In the spatial domain, we found broad, local correlations at short echo times (TE ≤ 14 ms) due to dominant S(0) contribution, whereas long-range connections mediated by T(2)(*) became explicit at TEs longer than 22 ms. In the frequency domain, compared with the flat spectrum of S(0), spectral power of the T(2)(*)-weighted signal elevated significantly with increasing TE, particularly in the frequency ranges of 0.008-0.023 Hz and 0.037-0.043 Hz. Using the S(0) spectrum as a reference, we propose two indices to measure spectral signal change (SSC) and spectral contrast-to-noise ratio (SCNR), respectively, for quantifying the RS-fMRI signal. These indices demonstrated TE dependency of connectivity-related fluctuation strength, resembling functional contrasts in activation-based fMRI. These findings further confirm that large-scale functional circuit connectivity based on BOLD contrast may be constrained within specific frequency ranges in every brain network, and the spectral features of S(0) and T(2)(*) could be valuable for interpreting and quantifying RS-fMRI data.