Detecting static and dynamic differences between eyes-closed and eyes-open resting states using ASL and BOLD fMRI

Abnormalities in static and dynamic intrinsic neural activity and neurotransmitters in first-episode OCD

BACKGROUND

Obsessive-compulsive disorder (OCD) is a disabling disorder in which the temporal variability of regional brain connectivity is not well understood. The aim of this study was to investigate alterations in static and dynamic intrinsic neural activity (INA) in first-episode OCD and whether these changes have the potential to reflect neurotransmitters.

METHODS

A total of 95 first-episode OCD patients and 106 matched healthy controls (HCs) were included in this study. Based on resting-state functional magnetic resonance imaging (rs-fMRI), the static and dynamic local connectivity coherence (calculated by static and dynamic regional homogeneity, sReHo and dReHo) were compared between the two groups. Furthermore, correlations between abnormal INA and PET- and SPECT-derived maps were performed to examine specific neurotransmitter system changes underlying INA abnormalities in OCD.

RESULTS

Compared with HCs, OCD showed decreased sReHo and dReHo values in left superior, middle temporal gyrus (STG/MTG), left Heschl gyrus (HES), left putamen, left insula, bilateral paracentral lobular (PCL), right postcentral gyrus (PoCG), right precentral gyrus (PreCG), left precuneus and right supplementary motor area (SMA). Decreased dReHo values were also found in left PoCG, left PreCG, left SMA and left middle cingulate cortex (MCC). Meanwhile, alterations in INA present in brain regions were correlated with dopamine system (D2, FDOPA), norepinephrine transporter (NAT) and the vesicular acetylcholine transporter (VAChT) maps.

CONCLUSION

Static and dynamic INA abnormalities exist in first-episode OCD, having the potential to reveal the molecular characteristics. The results help to further understand the pathophysiological mechanism and provide alternative therapeutic targets of OCD.

Modular brain network in volitional eyes closing: enhanced integration with a marked impact on hubs

Volitional eyes closing would shift brain's information processing modes from the "exteroceptive" to "interoceptive" state. This transition induced by the eyes closing is underpinned by a large-scale reconfiguration of brain network, which is still not fully comprehended. Here, we investigated the eyes-closing-relevant network reconfiguration by examining the functional integration among intrinsic modules. Our investigation utilized a publicly available dataset with 48 subjects being scanned in both eyes closed and eyes open conditions. It was found that the modular integration was significantly enhanced during the eyes closing, including lower modularity index, higher participation coefficient, less provincial hubs, and more connector hubs. Moreover, the eyes-closing-enhanced integration was particularly noticeable in the hubs of network, mainly located in the default-mode network. Finally, the hub-dominant modular enhancement was positively correlated to the eyes-closing-reduced entropy of BOLD signal, suggesting a close connection to the diminished consciousness of individuals. Collectively, our findings strongly suggested that the enhanced modular integration with substantially reorganized hubs characterized the large-scale cortical underpinning of the volitional eyes closing.

Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data

Introduction

Functional connectivity in the brain is often studied with blood oxygenation level dependent (BOLD) resting state functional magnetic resonance imaging (rsfMRI), but the BOLD signal is several steps removed from neuronal activity. Arterial spin labeling (ASL), particularly pulsed ASL (PASL), has also the capacity to measure the blood-flow changes in response to activity. In this paper, we investigated the feasibility of extracting major brain networks from PASL data, in contrast with rsfMRI analsyis.

Materials and methods

In this retrospective study, we analyzed a cohort dataset that consists of 21 mild traumatic brain injury (mTBI) patients and 29 healthy controls, which was collected in a previous study. By extracting 10 major brain networks from the data of both PASL and rsfMRI, we contrasted their similarities and differences in the 10 networks extracted from both modalities.

Results

Our data demonstrated that PASL could be used to extract all 10 major brain networks. Eight out of 10 networks demonstrated over 60 % similarity to rsfMRI data. Meanwhile, there are similar but not identical changes in networks detected between mTBI patients and healthy controls with both modalities. Notably, the PASL-extracted default mode network (DMN), other than the rsfMRI-extracted DMN, includes some regions known to be associated with the DMN in other studies. It demonstrated that PASL data can be analyzed to identify resting state networks with reasonable reliability, even without rsfMRI data.

Conclusion

Our analysis provides an opportunity to extract functional connectivity information in heritage datasets in which ASL but not BOLD was collected.

Static and dynamic changes of intrinsic brain local connectivity in internet gaming disorder

BACKGROUND

Studies have revealed that intrinsic neural activity varies over time. However, the temporal variability of brain local connectivity in internet gaming disorder (IGD) remains unknown. The purpose of this study was to explore the alterations of static and dynamic intrinsic brain local connectivity in IGD and whether the changes were associated with clinical characteristics of IGD.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 36 individuals with IGD (IGDs) and 44 healthy controls (HCs) matched for age, gender and years of education. The static regional homogeneity (sReHo) and dynamic ReHo (dReHo) were calculated and compared between two groups to detect the alterations of intrinsic brain local connectivity in IGD. The Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the severity of online gaming addiction and sleep quality, respectively. Pearson correlation analysis was used to evaluate the relationship between brain regions with altered sReHo and dReHo and IAT and PSQI scores. Receiver operating characteristic (ROC) curve analysis was used to reveal the potential capacity of the sReHo and dReHo metrics to distinguish IGDs from HCs.

RESULTS

Compared with HCs, IGDs showed both increased static and dynamic intrinsic local connectivity in bilateral medial superior frontal gyrus (mSFG), superior frontal gyrus (SFG), and supplementary motor area (SMA). Increased dReHo in the left putamen, pallidum, caudate nucleus and bilateral thalamus were also observed. ROC curve analysis showed that the brain regions with altered sReHo and dReHo could distinguish individuals with IGD from HCs. Moreover, the sReHo values in the left mSFG and SMA as well as dReHo values in the left SMA were positively correlated with IAT scores. The dReHo values in the left caudate nucleus were negatively correlated with PSQI scores.

CONCLUSIONS

These results showed impaired intrinsic local connectivity in frontostriatothalamic circuitry in individuals with IGD, which may provide new insights into the underlying neuropathological mechanisms of IGD. Besides, dynamic changes of intrinsic local connectivity in caudate nucleus may be a potential neurobiological marker linking IGD and sleep quality.

Improved brain stimulation targeting by optimising image acquisition parameters

Functional connectivity analysis from rs-fMRI data has been used for determining cortical targets in therapeutic applications of non-invasive brain stimulation using transcranial magnetic stimulation (TMS). Reliable connectivity measures are therefore essential for every rs-fMRI-based TMS targeting approach. Here, we examine the effect of echo time (TE) on the reproducibility and spatial variability of resting-state connectivity measures. We acquired multiple runs of single-echo fMRI data with either short (TE = 30 ms) or long (TE = 38 ms) echo time to investigate inter-run spatial reproducibility of a clinically relevant functional connectivity map, i.e., originating from the sgACC. We find that connectivity maps obtained from TE = 38 ms rs-fMRI data are significantly more reliable than those obtained from TE = 30 ms data sets. Our results clearly show that optimizing sequence parameters can be beneficial for ensuring high-reliability resting-state acquisition protocols to be used for TMS targeting. The differences between reliability in connectivity measures for different TEs could inform future clinical research in optimising MR sequences.

Evaluate the efficacy and reliability of functional gradients in within-subject designs

The cerebral cortex is characterized as the integration of distinct functional principles that correspond to basic primary functions, such as vision and movement, and domain-general functions, such as attention and cognition. Diffusion embedding approach is a novel tool to describe transitions between different functional principles, and has been successively applied to investigate pathological conditions in between-group designs. What still lacking and urgently needed is the efficacy of this method to differentiate within-subject circumstances. In this study, we applied the diffusion embedding to eyes closed (EC) and eyes on (EO) resting-state conditions from 145 participants. We found significantly lower within-network dispersion of visual network (VN) (p = 7.3 × 10^-4 ) as well as sensorimotor network (SMN) (p = 1 × 10^-5 ) and between-network dispersion of VN (p = 2.3 × 10^-4 ) under EC than EO, while frontoparietal network (p = 9.2 × 10^-4 ) showed significantly higher between-network dispersion during EC than EO. Test-retest reliability analysis further displayed fair reliability (intraclass correlation coefficient [ICC] < 0.4) of the network dispersions (mean ICC = 0.116 ± 0.143 [standard deviation]) except for the within-network dispersion of SMN under EO (ICC = 0.407). And the reliability under EO was higher but not significantly higher than reliability under EC. Our study demonstrated that the diffusion embedding approach that shows fair reliability is capable of distinguishing EC and EO resting-state conditions, such that this method could be generalized to other within-subject designs.

The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI-EEG study

The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI-ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI-ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG-amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG-ALFF). We observed that the MEG-ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG-amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG-ALFF in the sensorimotor cortex overlapped with the most reliable EC-EO differences observed in fMRI at slow-3 (0.073-0.198 Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI-ALFF may relate to the ALFF in neural activity.

Higher reliability and validity of Wavelet-ALFF of resting-state fMRI: From multicenter database and application to rTMS modulation

Amplitude of low-frequency fluctuation (ALFF) has been widely used for localization of abnormal activity at the single-voxel level in resting-state fMRI (RS-fMRI) studies. However, previous ALFF studies were based on fast Fourier transform (FFT-ALFF). Our recent study found that ALFF based on wavelet transform (Wavelet-ALFF) showed better sensitivity and reproducibility than FFT-ALFF. The current study aimed to test the reliability and validity of Wavelet-ALFF, and apply Wavelet-ALFF to investigate the modulation effect of repetitive transcranial magnetic stimulation (rTMS). The reliability and validity were assessed on multicenter RS-fMRI datasets under eyes closed (EC) and eyes open (EO) conditions (248 healthy participants in total). We then detected the sensitivity of Wavelet-ALFF using a rTMS modulation dataset (24 healthy participants). For each dataset, Wavelet-ALFF based on five mother wavelets (i.e., db2, bior4.4, morl, meyr and sym3) and FFT-ALFF were calculated in the conventional band and five frequency sub-bands. The results showed that the reliability of both inter-scanner and intra-scanner was higher with Wavelet-ALFF than with FFT-ALFF across multiple frequency bands, especially db2-ALFF in the higher frequency band slow-2 (0.1992-0.25 Hz). In terms of validity, the multicenter ECEO datasets showed that the effect sizes of Wavelet-ALFF with all mother wavelets (especially for db2-ALFF) were larger than those of FFT-ALFF across multiple frequency bands. Furthermore, Wavelet-ALFF detected a larger modulation effect than FFT-ALFF. Collectively, Wavelet db2-ALFF showed the best reliability and validity, suggesting that db2-ALFF may offer a powerful metric for inspecting regional spontaneous brain activities in future studies.

Dynamic regional homogeneity alterations and cognitive impairment in patients with moderate and severe obstructive sleep apnea

Background and purpose

Previous studies have found that abnormal local spontaneous brain activity in patients with obstructive sleep apnea (OSA) was associated with cognitive impairment, and dynamic functional connections can capture the time changes of functional connections during magnetic resonance imaging acquisition. The purpose of this study was to investigate the dynamic characteristics of regional brain connectivity and its relationship with cognitive function in patients with OSA and to explore whether the dynamic changes can be used to distinguish them from healthy controls (HCs).

Methods

Seventy-nine moderate and severe male OSA patients without any treatment and 84 HCs with similar age and education were recruited, and clinical data and resting functional magnetic resonance imaging data were collected. The dynamic regional homogeneity (dReHo) was calculated using a sliding window technique, and a double-sample t-test was used to test the difference in the dReHo map between OSA patients and HCs. We explored the relationship between dReHo and clinical and cognitive function in OSA patients using Pearson correlation analysis. A support vector machine was used to classify the OSA patients and HCs based on abnormal dReHo.

Result

Compared with HCs, OSA patients exhibited higher dReHo values in the right medial frontal gyrus and significantly lower dReHo values in the right putamen, right superior temporal gyrus, right cingulate gyrus, left insula and left precuneus. The correlation analysis showed that the abnormal dReHo values in multiple brain regions in patients with OSA were significantly correlated with nadir oxygen saturation, the oxygen depletion index, sleep period time, and Montreal cognitive assessment score. The support vector machine classification accuracy based on the dReHo difference in brain regions was 81.60%, precision was 81.01%, sensitivity was 81.01%, specificity was 82.14%, and area under the curve was 0.89.

Conclusion

The results of this study suggested that there was abnormal dynamic regional spontaneous brain activity in patients with OSA, which was related to clinical and cognitive evaluation and can be used to distinguish OSA patients from HCs. The dReHo is a potential objective neuroimaging marker for patients with OSA that can further the understanding of the neuropathological mechanism of patients with OSA.

Typical resting state activity of the brain requires visual input during an early sensitive period

Sensory deprivation, following a total loss of one sensory modality e.g. vision, has been demonstrated to result in compensatory plasticity. It is yet not known to which extent neural changes, e.g. higher resting-state activity in visual areas (cross-modal plasticity) as a consequence of blindness, reverse, when sight is restored. Here, we used functional MRI to acquire blood oxygen level-dependent resting-state activity during an eyes open and an eyes closed state in congenital cataract-reversal individuals, developmental cataract-reversal individuals, congenitally permanently blind individuals and sighted controls. The amplitude of low frequency fluctuation of the blood oxygen level-dependent signal—a neural marker of spontaneous brain activity during rest—was analyzed. In accordance with previous reports, in normally sighted controls we observed an increase in amplitude of low-frequency fluctuation during rest with the eyes open compared with rest with eyes closed in visual association areas and in parietal cortex but a decrease in auditory and sensorimotor regions. In congenital cataract-reversal individuals, we found an increase of the amplitude of slow blood oxygen level-dependent fluctuations in visual cortex during rest with eyes open compared with rest with eyes closed too but this increase was larger in amplitude than in normally sighted controls. In contrast, congenital cataract-reversal individuals lagged a similar increase in parietal regions and did not show the typical decrease of amplitude of low-frequency fluctuation in auditory cortex. Congenitally blind individuals displayed an overall higher amplitude in slow blood oxygen level-dependent fluctuations in visual cortex compared with sighted individuals and compared with congenital cataract-reversal individuals in the eyes closed condition. Higher amplitude of low-frequency fluctuation in visual cortex of congenital cataract-reversal individuals than in normally sighted controls during eyes open might indicate an altered excitatory–inhibitory balance of visual neural circuits. By contrast, the lower parietal increase and the missing downregulation in auditory regions suggest a reduced influence of the visual system on multisensory and the other sensory systems after restoring sight in congenitally blind individuals. These results demonstrate a crucial dependence of visual and multisensory neural system functioning on visual experience during a sensitive phase in human brain development.

Effect of Acupuncture on the Cognitive Control Network of Patients with Knee Osteoarthritis: Study Protocol for a Randomized Controlled Trial

Purpose

Materials and Methods

Discussion

Study Registration

Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults

Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.

Localizing Spectral Interactions in the Resting State Network Using the Hilbert–Huang Transform

Brain synchronizations are orchestrated from neuronal oscillations through frequency interactions, such as the alpha rhythm during relaxation. Nevertheless, how the intrinsic interaction forges functional integrity across brain segregations remains elusive, thereby motivating recent studies to localize frequency interactions of resting-state fMRI (rs-fMRI). To this point, we aim to unveil the fMRI-based spectral interactions using the time-frequency (TF) analysis; however, Fourier-based TF analyses impose restrictions on revealing frequency interactions given the limited time points in fMRI signals. Instead of using the Fourier-based wavelet analysis to identify the fMRI frequency of interests, we employed the Hilbert–Huang transform (HHT) for probing the specific frequency contribution to the functional integration, called ensemble spectral interaction (ESI). By simulating data with time-variant frequency changes, we demonstrated the Hilbert TF maps with high spectro-temporal resolution and full accessibility in comparison with the wavelet TF maps. By detecting amplitude-to-amplitude frequency couplings (AAC) across brain regions, we elucidated the ESI disparity between the eye-closed (EC) and eye-open (EO) conditions in rs-fMRI. In the visual network, the strength of the spectral interaction within 0.03–0.04 Hz was amplified in EC compared with that in EO condition, whereas a canonical connectivity analysis did not present differences between conditions. Collectively, leveraging from the instantaneous frequency of HHT, we firstly addressed the ESI technique to map the fMRI-based functional connectivity in a brand-new AAC perspective. The ESI possesses potential in elucidating the functional connectivity at specific frequency bins, thereby providing additional diagnostic merits for future clinical neuroscience.

The effect of congenital blindness on resting-state functional connectivity revisited

Lower resting-state functional connectivity (RSFC) between 'visual' and non-'visual' neural circuits has been reported as a hallmark of congenital blindness. In sighted individuals, RSFC between visual and non-visual brain regions has been shown to increase during rest with eyes closed relative to rest with eyes open. To determine the role of visual experience on the modulation of RSFC by resting state condition-as well as to evaluate the effect of resting state condition on group differences in RSFC-, we compared RSFC between visual and somatosensory/auditory regions in congenitally blind individuals (n = 9) and sighted participants (n = 9) during eyes open and eyes closed conditions. In the sighted group, we replicated the increase of RSFC between visual and non-visual areas during rest with eyes closed relative to rest with eyes open. This was not the case in the congenitally blind group, resulting in a lower RSFC between 'visual' and non-'visual' circuits relative to sighted controls only in the eyes closed condition. These results indicate that visual experience is necessary for the modulation of RSFC by resting state condition and highlight the importance of considering whether sighted controls should be tested with eyes open or closed in studies of functional brain reorganization as a consequence of blindness.

Echo Time Dependency of Local Activity Metrics of Resting-State Functional MRI

Local activity metrics of resting-state functional MRI (RS-fMRI), such as the amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC), are widely used to detect brain abnormalities based on signal fluctuations. Although signal changes with echo time (TE) have been widely studied, the effect of TE on local activity metrics has not been investigated. RS-fMRI datasets from 12 healthy subjects with eyes open (EO) and eyes closed (EC) were obtained with a four-echo gradient-echo-planar imaging pulse sequence with the following parameters: repetition time/TE1/TE2/TE3/TE4 = 2,000/13/30.93/48.86/66.79 ms. Six representative regions were selected for simulating the spatial feature of TE dependency of local activity metrics. Moreover, whole-brain local activity metrics were calculated from each echo dataset and compared between EO and EC conditions. Dice overlap coefficient (DOC) was then employed to calculate the overlap between the T maps. We found that all the local activity metrics displayed different TE dependency characteristics, while their overall change patterns were similar: an initial large change followed by a slow variation. The T maps for local activity metrics also varied greatly with TE. For ALFF, fALFF, ReHo, and DC, the DOCs for voxels in four TE datasets were 6.87, 0.73, 5.08, and 0.93%, respectively. Collectively, these findings demonstrate that local metrics are greatly dependent on TE. Therefore, TE should be carefully considered for the optimization of data acquisition and multi-center data analysis in RS-fMRI.

The Relationship Among Glucose Metabolism, Cerebral Blood Flow, and Functional Activity: a Hybrid PET/fMRI Study

¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) estimate brain activities from different aspects, including regional glucose uptake (rGU) by ¹⁸FDG-PET, regional cerebral blood flow (rCBF) by arterial spin labeling, and dynamic changes of deoxyhemoglobin by blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI). However, the relationships between them remain incompletely understood. In the current study, twenty-four subjects (14 males, 10 females) were recruited and investigated the correlation among rGU, rCBF, and BOLD fMRI-derived metrics reflecting the neural activity, including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) by hybrid PET/fMRI. Correlation analyses were performed across subject and across space at both voxel level and region level, considering partial volume effects by adjusting for gray matter volume. Each pair of metrics showed significant across-space correlations. rGU against ReHo showed the highest mean correlation coefficients. rGU had higher correlations with three resting-state (RS) fMRI metrics than did ASL-rCBF. However, the across-subject correlations were not significant among functional modalities (rGU, rCBF, and RS-fMRI BOLD data) at either voxel level or region level even with a liberal threshold, except for significant across-subject correlation between RS-fMRI metrics (ALFF, ReHo, and DC). These comprehensive findings from hybrid PET/MR might provide complementary information to reveal the underlying mechanisms of the brain activity and open new perspective to interpret pathologic conditions.

Integration of Simultaneous Resting-State Electroencephalography, Functional Magnetic Resonance Imaging, and Eye-Tracker Methods to Determine and Verify Electroencephalography Vigilance Measure

Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects. Impact statement We revealed an association between electroencephalography frontal and occipital beta power (FOBP) and pupil size changes during an eyes-open resting state, which supports the conclusion that FOBP could serve as an objective measure of vigilance in healthy human subjects. The results were validated by using simultaneously recorded heart rate and functional magnetic resonance imaging (fMRI). Interestingly, independently verified heart rate changes can also provide an easy-to-determine measure of vigilance during resting-state fMRI. These findings have important implications for an analysis and interpretation of dynamic resting-state fMRI connectivity studies in health and disease.

Dynamic alterations of spontaneous neural activity in patients with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a multi-system disease featured by movement disorder. Studies on ALS using static neuroimaging indexes demonstrated inconsistent results. However, recent work indicated that the intrinsic brain activity was time-varying, and the abnormal temporal dynamics of brain activity in ALS remains unknown. Resting-state functional magnetic resonance imaging data were first obtained from 54 patients with ALS and 54 healthy controls (HCs). Then the dynamic regional homogeneity (d-ReHo) was calculated and compared between the two groups. Correlation analyses between altered d-ReHo and clinical scores were further performed. Compared with HCs, ALS patients showed higher d-ReHo in the left lingual gyrus while lower d-ReHo in the left rectus gyrus and left parahippocampal gyrus. Moreover, the d-ReHo in the left lingual gyrus exhibited correlation with disease progression rate in ALS at a trend level. Our findings suggested that altered dynamics in intrinsic brain activity might be a potential biomarker for diagnosing of ALS.

Open eyes and closed eyes elicit different temporal properties of brain functional networks

The eyes are our windows to the brain. There are differences in brain activity between people who have their eyes closed (EC) and eyes open (EO). Previous studies focused on differences in brain functional properties between these eyes conditions based on an assumption that brain activity is a static phenomenon. However, the dynamic nature of the brain activity in different eyes conditions is still unclear. In this study, we collected resting-state fMRI data from 21 healthy subjects in the EC and EO conditions. Using a sliding time window approach and a k-means clustering algorithm, we calculated the temporal properties of dynamic functional connectivity (dFC) states in the eyes conditions. We also used graph theory to estimate the dynamic topological properties of functional networks in the two conditions. We detected two dFC states, a hyper-connected State 1 and a hypo-connected State 2. We showed the following results: (i) subjects in the EC condition stayed longer in the hyper-connected State 1 than those in the EO; (ii) subjects in the EO condition stayed longer in the hypo-connected State 2 than those in the EC; and (iii) the dFC state transformed into the other state more frequently during EC than during EO. We also found the variance of the characteristic path length was higher during EC than during EO in the hyper-connected State 1. These results indicate that brain activity may be more active and unstable during EC than during EO. Our findings may provide insights into the dynamic nature of the resting-state brain and could be a useful reference for future rs-fMRI studies.

A toolbox for brain network construction and classification (BrainNetClass)

Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation-based functional network and group-level comparisons. We introduce a "Brain Network Construction and Classification (BrainNetClass)" toolbox to promote more advanced brain network construction methods to the filed, including some state-of-the-art methods that were recently developed to capture complex and high-order interactions among brain regions. The toolbox also integrates a well-accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with both graphical user-friendly interfaces and a command line mode targeting cognitive neuroscientists and clinicians for promoting reliability, reproducibility, and interpretability of connectome-based, computer-aided diagnosis. It generates abundant classification-related results from network presentations to contributing features that have been largely ignored by most studies to grant users the ability of evaluating the disease diagnostic model and its robustness and generalizability. We demonstrate the effectiveness of the toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) is available at https://github.com/zzstefan/BrainNetClass.

Opening or closing eyes at rest modulates the functional connectivity of V1 with default and salience networks

Current evidence suggests that volitional opening or closing of the eyes modulates brain activity and connectivity. However, how the eye state influences the functional connectivity of the primary visual cortex has been poorly investigated. Using the same scanner, fMRI data from two groups of participants similar in age, sex and educational level were acquired. One group (n = 105) performed a resting state with eyes closed, and the other group (n = 63) performed a resting state with eyes open. Seed-based voxel-wise functional connectivity whole-brain analyses were performed to study differences in the connectivity of the primary visual cortex. This region showed higher connectivity with the default mode and sensorimotor networks in the eyes closed group, but higher connectivity with the salience network in the eyes open group. All these findings were replicated using an open source shared dataset. These results suggest that opening or closing the eyes may set brain functional connectivity in an interoceptive or exteroceptive state.

Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI

The fast Fourier transform (FFT) is a widely used algorithm used to depict the amplitude of low-frequency fluctuation (ALFF) of resting-state functional magnetic resonance imaging (RS-fMRI). Wavelet transform (WT) is more effective in representing the complex waveform due to its adaptivity to non-stationary or local features of data and many varieties of wavelet functions with different shapes being available. However, there is a paucity of RS-fMRI studies that systematically compare between the results of FFT versus WT. The present study employed five cohorts of datasets and compared the sensitivity and reproducibility of FFT-ALFF with those of Wavelet-ALFF based on five mother wavelets (namely, db2, bior4.4, morl, meyr, and sym3). In addition to the conventional frequency band of 0.0117-0.0781 Hz, a comparison was performed in sub-bands, namely, Slow-6 (0-0.0117 Hz), Slow-5 (0.0117-0.0273 Hz), Slow-4 (0.0273-0.0742 Hz), Slow-3 (0.0742-0.1992 Hz), and Slow-2 (0.1992-0.25 Hz). The results indicated that the Wavelet-ALFF of all five mother wavelets was generally more sensitive and reproducible than FFT-ALFF in all frequency bands. Specifically, in the higher frequency band Slow-2 (0.1992-0.25 Hz), the mean sensitivity of db2-ALFF results was 1.54 times that of FFT-ALFF, and the reproducibility of db2-ALFF results was 2.95 times that of FFT-ALFF. The findings suggest that wavelet-ALFF can replace FFT-ALFF, especially in the higher frequency band. Future studies should test more mother wavelets on other RS-fMRI metrics and multiple datasets.

Percent amplitude of fluctuation: A simple measure for resting-state fMRI signal at single voxel level

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel's ALFF by the global mean ALFF, named mALFF. Although fractional ALFF (fALFF), a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. The current study borrowed the percent signal change in task fMRI studies and proposed percent amplitude of fluctuation (PerAF) for RS-fMRI. We firstly applied PerAF and mPerAF (i.e., divided by global mean PerAF) to eyes open (EO) vs. eyes closed (EC) RS-fMRI data. PerAF and mPerAF yielded prominently difference between EO and EC, being well consistent with previous studies. We secondly performed test-retest reliability analysis and found that (PerAF ≈ mPerAF ≈ mALFF) > (fALFF ≈ mfALFF). Head motion regression (Friston-24) increased the reliability of PerAF, but decreased all other metrics (e.g. mPerAF, mALFF, fALFF, and mfALFF). The above results suggest that mPerAF is a valid, more reliable, more straightforward, and hence a promising metric for voxel-level RS-fMRI studies. Future study could use both PerAF and mPerAF metrics. For prompting future application of PerAF, we implemented PerAF in a new version of REST package named RESTplus.

Associations between cerebral blood flow and structural and functional brain imaging measures in individuals with neuropsychologically defined mild cognitive impairment

Reduced cerebral blood flow (CBF), an indicator of neurovascular processes and metabolic demands, is a common finding in Alzheimer's disease. However, little is known about what contributes to CBF deficits in individuals with mild cognitive impairment (MCI). We examine regional CBF differences in 17 MCI compared with 21 age-matched cognitively healthy older adults. Next, we examined associations between CBF, white matter lesion (WML) volume, amplitude of low-frequency fluctuations, and cortical thickness to better understand whether altered CBF was detectable before other markers and the potential mechanistic underpinnings of CBF deficits in MCI. MCI had significantly reduced CBF, whereas cortical thickness and amplitude of low-frequency fluctuation were not affected. Reduced CBF was associated with the WML volume but not associated with other measures. Given the presumed vascular etiology of WML and relative worsening of vascular health in MCI, it may suggest CBF deficits result from early vascular as opposed to metabolic deficits in MCI. These findings may support vascular mechanisms as an underlying component of cognitive impairment.

Links Between the Amplitude Modulation of Low-Frequency Spontaneous Fluctuation Across Resting State Conditions and Thalamic Functional Connectivity

A comparison of the different types of resting state reveals some interesting characteristics of spontaneous brain activity that cannot be found in a single condition. Differences in the amplitude of low-frequency fluctuation (ALFF) between the eyes open (EO) and the eyes closed (EC) almost have a spatially distinct pattern with traditional EO-EC activation within sensory systems, suggesting the divergent functional roles of ALFF and activation. However, the underlying mechanism is far from clear. Since the thalamus plays an essential role in sensory processing, one critical step toward understanding the divergences is to depict the relationships between the thalamus and the ALFF modulation in sensory regions. In this preliminary study, we examined the association between the changes of ALFF and the changes of thalamic functional connectivity (FC) between EO and EC. We focused on two visual thalamic nuclei, the lateral geniculate nucleus (LGN) and the pulvinar (Pu). FC results showed that LGN had stronger synchronization with regions in lateral but not in medial visual networks, while Pu had a weaker synchronization with auditory and sensorimotor areas during EO compared with EC. Moreover, the patterns of FC modulation exhibited considerable overlaps with the ALFF modulation, and there were significant correlations between them across subjects. Our findings support the crucial role of the thalamus in amplitude modulation of low-frequency spontaneous activity in sensory systems, and may pave the way to elucidate the mechanisms governing distinction between evoked activation and modulation of low-frequency spontaneous brain activity.

Alterations of cerebral perfusion and functional brain connectivity in medication-naïve male adults with attention-deficit/hyperactivity disorder

Aims

Functional brain abnormalities, including altered cerebral perfusion and functional connectivities, have been illustrated in adults with attention‐deficit/hyperactivity disorder (aADHD). The present study attempted to explore the alterations of cerebral blood flow (CBF) and resting‐state functional connectivity (RSFC) simultaneously to understand the neural mechanisms for adults with ADHD comprehensively.

Methods

Resting‐state arterial spin labeling (ASL) and blood oxygenation level‐dependent (BOLD) magnetic resonance imaging (MRI) data were acquired for 69 male aADHD and 69 matched healthy controls (HCs). The altered CBFs associated with aADHD were explored based on both categorical (aADHD vs HCs) and dimensional (correlation with aADHD core symptoms) perspectives. Then, the seed‐based RSFC analyses were developed for the regions showing significant alterations of CBF.

Results

Significantly decreased CBF in the large‐scale resting‐state networks regions (eg, ventral attentional network, somatomotor network, limbic network) and subcortical regions was indicated in aADHD compared with HCs. The correlation analyses indicated that the hypoperfusion in left putamen/global pallidum and left amygdala/hippocampus was correlated with ADHD inattentive and total symptoms, respectively. Further, weaker negative functional connectivity between left amygdala and bilateral supplementary motor area, bilateral superior frontal gyrus, and left medial frontal gyrus was found in adults with ADHD.

Conclusion

The present findings suggested alterations of both cerebral perfusion and functional connectivity for the left amygdala in aADHD. The combination of CBF and RSFCs may help to interpret the neuropathogenesis of ADHD more comprehensively.

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective

Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique.

Characteristic Fluctuations Around Stable Attractor Dynamics Extracted from Highly Nonstationary Electroencephalographic Recordings

Since the discovery of electrical activity of the brain, electroencephalographic (EEG) recordings constitute one of the most popular techniques of brain research. However, EEG signals are highly nonstationary and one should expect that averages of the cross-correlation coefficient, which may take positive and negative values with equal probability, (almost) vanish when estimated over long data segments. Instead, we found that the average zero-lag cross-correlation matrix estimated with a running window over the whole night of sleep EEGs, or of resting state during eyes-open and eyes-closed conditions of healthy subjects shows a characteristic correlation pattern containing pronounced nonzero values. A similar correlation structure has already been encountered in scalp EEG signals containing focal onset seizures. Therefore, we conclude that this structure is independent of the physiological state. Because of its pronounced similarity across subjects, we believe that it depicts a generic feature of the brain dynamics. Namely, we interpret this pattern as a manifestation of a dynamical ground state of the brain activity, necessary to preserve an efficient operational mode, or, expressed in terms of dynamical system theory, we interpret it as a "shadow" of the evolution on (or close to) an attractor in phase space. Nonstationary dynamical aspects of higher cerebral processes should manifest in deviations from this stable pattern. We confirm this hypothesis through a correlation analysis of EEG recordings of 10 healthy subjects during night sleep, 20 recordings of 9 epilepsy patients, and 42 recordings of 21 healthy subjects in resting state during eyes-open and eyes-closed conditions. In particular, we show that the estimation of deviations from the stationary correlation structures provides a more significant differentiation of physiological states and more homogeneous results across subjects.

Frequency specific resting state functional abnormalities in psychosis

Resting state functional magnetic resonance imaging studies of psychosis have focused primarily on the amplitude of low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal ranging from .01 to 0.1 Hz. Few studies, however, have investigated the amplitude of frequency fluctuations within discrete frequency bands and higher than 0.1 Hz in patients with psychosis at different illness stages. We investigated BOLD signal within three frequency ranges including slow-4 (.027-.073 Hz), slow-3 (.074-0.198 Hz) and slow-2 (0.199-0.25 Hz) in 89 patients with either first-episode or chronic psychosis and 119 healthy volunteers. We investigated the amplitude of frequency fluctuations within three frequency bands using 47 regions-of-interest placed within 14 known resting state networks derived using group independent component analysis. There were significant group x frequency interactions for the visual and motor cortex networks, with the largest significant group differences (patients < healthy volunteers) evident in slow-4 and slow-3, respectively. Also, healthy volunteers had an overall higher amplitude of frequency fluctuations compared to patients across the three frequency ranges in the visual cortex, dorsal attention and motor cortex networks with the opposite effect (patients > healthy volunteers) evident within the salience and frontal gyrus networks. Subsequent analyses indicated that these effects were evident in both first-episode and chronic patients. Our study provides new data regarding the importance of BOLD signal fluctuations within different frequency bands in the neurobiology of psychosis.

Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study

Dynamic causal modelling (DCM) for resting state fMRI – namely spectral DCM – is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures – and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns.

•

Across datasets, subjects’ effective connectivity patterns in the core default mode network showed hemispheric asymmetry.

•

Differences in hemispheric asymmetry was found to be a major source of between-subject variability.

•

In contrast, most subjects showed reliable within-subject hemispheric asymmetry.

•

Differences in preprocessing methods had little effect on connectivity estimates.

•

Bayesian model reduction increased the within- and between-subject consistency of connectivity patterns.

Distinction Between Variability-Based Modulation and Mean-Based Activation Revealed by BOLD-fMRI and Eyes-Open/Eyes-Closed Contrast

Recent BOLD-fMRI studies have revealed spatial distinction between variability- and mean-based between-condition differences, suggesting that BOLD variability could offer complementary and even orthogonal views of brain function with traditional activation. However, these findings were mainly observed in block-designed fMRI studies. As block design may not be appreciate for characterizing the low-frequency dynamics of BOLD signal, the evidences suggesting the distinction between BOLD variability and mean are less convincing. Based on the high reproducibility of signal variability modulation between continuous eyes-open (EO) and eyes-closed (EC) states, here we employed EO/EC paradigm and BOLD-fMRI to compare variability- and mean-based EO/EC differences while the subjects were in light. The comparisons were made both on block-designed and continuous EO/EC data. Our results demonstrated that the spatial patterns of variability- and mean-based EO/EC differences were largely distinct with each other, both for block-designed and continuous data. For continuous data, increases of BOLD variability were found in secondary visual cortex and decreases were mainly in primary auditory cortex, primary sensorimotor cortex and medial nuclei of thalamus, whereas no significant mean-based differences were observed. For the block-designed data, the pattern of increased variability resembled that of continuous data and the negative regions were restricted to medial thalamus and a few clusters in auditory and sensorimotor networks, whereas activation regions were mainly located in primary visual cortex and lateral nuclei of thalamus. Furthermore, with the expanding window analyses we found variability results of continuous data exhibited a rather slower dynamical process than typically considered for task activation, suggesting block design is less optimal than continuous design in characterizing BOLD variability. In sum, we provided more solid evidences that variability-based modulation could represent orthogonal views of brain function with traditional mean-based activation.

Intra- and Inter-scanner Reliability of Scaled Subprofile Model of Principal Component Analysis on ALFF in Resting-State fMRI Under Eyes Open and Closed Conditions

Scaled Subprofile Model of Principal Component Analysis (SSM-PCA) is a multivariate statistical method and has been widely used in Positron Emission Tomography (PET). Recently, SSM-PCA has been applied to discriminate patients with Parkinson's disease and healthy controls with Amplitude of Low Frequency Fluctuation (ALFF) from Resting-State Functional Magnetic Resonance Imaging (RS-fMRI). As RS-fMRI scans are more readily available than PET scans, it is important to investigate the intra- and inter-scanner reliability of SSM-PCA in RS-fMRI. A RS-fMRI dataset with Eyes Open (EO) and Eyes Closed (EC) conditions was obtained in 21 healthy subjects (21.8 ± 1.8 years old, 11 females) on 3 visits (V1, V2, and V3), with V1 and V2 (mean interval of 14 days apart) on one scanner and V3 (about 8 months from V2) on a different scanner. To simulate between-group analysis in conventional SSM-PCA studies, 21 subjects were randomly divided into two groups, i.e., EC-EO group (EC ALFF map minus EO ALFF map, n = 11) and EO-EC group (n = 10). A series of covariance patterns and their expressions were derived for each visit. Only the expression of the first pattern showed significant differences between the two groups for all the visits (p = 0.012, 0.0044, and 0.00062 for V1, V2, and V3, respectively). This pattern, referred to as EOEC-pattern, mainly involved the sensorimotor cortex, superior temporal gyrus, frontal pole, and visual cortex. EOEC-pattern's expression showed fair intra-scanner reliability (ICC = 0.49) and good inter-scanner reliability (ICC = 0.65 for V1 vs. V2 and ICC = 0.66 for V2 vs. V3). While the EOEC-pattern was similar with the pattern of conventional unpaired T-test map, the two patterns also showed method-specific regions, indicating that SSM-PCA and conventional T-test are complementary for neuroimaging studies.

Vascular-metabolic and GABAergic Inhibitory Correlates of Neural Variability Modulation. A Combined fMRI and PET Study

Neural activity varies continually from moment to moment. Such temporal variability (TV) has been highlighted as a functionally specific brain property playing a fundamental role in cognition. We sought to investigate the mechanisms involved in TV changes between two basic behavioral states, namely having the eyes open (EO) or eyes closed (EC) in vivo in humans. To these ends we acquired BOLD fMRI, ASL, and [¹⁸F]-fluoro-deoxyglucose PET in a group of healthy participants (n = 15), along with BOLD fMRI and [¹⁸F]-flumazenil PET in a separate group (n = 19). Focusing on an EO- vs EC-sensitive region in the occipital cortex (identified in an independent sample), we show that TV is constrained in the EO condition compared to EC. This reduction is correlated with an increase in energy consumption and with regional GABA_A receptor density. This suggests that the modulation of TV by behavioral state involves an increase in overall neural activity that is related to an increased effect from GABAergic inhibition in addition to any excitatory changes. These findings contribute to our understanding of the mechanisms underlying activity variability in the human brain and its control.

Visual Occlusion Decreases Motion Sickness in a Flight Simulator

Sensory conflict theories of motion sickness (MS) assert that symptoms may result when incoming sensory inputs (e.g., visual and vestibular) contradict each other. Logic suggests that attenuating input from one sense may reduce conflict and hence lessen MS symptoms. In the current study, it was hypothesized that attenuating visual input by blocking light entering the eye would reduce MS symptoms in a motion provocative environment. Participants sat inside an aircraft cockpit mounted onto a motion platform that simultaneously pitched, rolled, and heaved in two conditions. In the occluded condition, participants wore "blackout" goggles and closed their eyes to block light. In the control condition, participants opened their eyes and had full view of the cockpit's interior. Participants completed separate Simulator Sickness Questionnaires before and after each condition. The posttreatment total Simulator Sickness Questionnaires and subscores for nausea, oculomotor, and disorientation in the control condition were significantly higher than those in the occluded condition. These results suggest that under some conditions attenuating visual input may delay the onset of MS or weaken the severity of symptoms. Eliminating visual input may reduce visual/nonvisual sensory conflict by weakening the influence of the visual channel, which is consistent with the sensory conflict theory of MS.

Effects of resting state condition on reliability, trait specificity, and network connectivity of brain function measured with arterial spin labeled perfusion MRI

Resting state fMRI (rs-fMRI) provides imaging biomarkers of task-independent brain function that can be associated with clinical variables or modulated by interventions such as behavioral training or pharmacological manipulations. These biomarkers include time-averaged regional brain function as manifested by regional cerebral blood flow (CBF) measured using arterial spin labeled (ASL) perfusion MRI and correlated temporal fluctuations of function across brain networks with either ASL or blood oxygenation level dependent (BOLD) fMRI. Resting-state studies are typically carried out using just one of several prescribed state conditions such as eyes closed (EC), eyes open (EO), or visual fixation on a cross-hair (FIX), which may affect the reliability and specificity of rs-fMRI. In this study, we collected test-retest ASL MRI data during 4 resting-state task conditions: EC, EO, FIX and PVT (low-frequency psychomotor vigilance task), and examined the effects of these task conditions on reliability and reproducibility as well as trait specificity of regional brain function. We also acquired resting-state BOLD fMRI under FIX and compared the network connectivity reliabilities between the four ASL conditions and the BOLD FIX condition. For resting-state ASL data, EC provided the highest CBF reliability, reproducibility, trait specificity, and network connectivity reliability, followed by EO, while FIX was lowest on all of these measures. PVT demonstrated lower CBF reliability, reproducibility and trait specificity than EO and EC. Overall network connectivity reliability was comparable between ASL and BOLD. Our findings confirm ASL CBF as a reliable, stable, and consistent measure of resting-state regional brain function and support the use of EC or EO over FIX and PVT as the resting-state condition.

PAIR Comparison between Two Within-Group Conditions of Resting-State fMRI Improves Classification Accuracy

Classification approaches have been increasingly applied to differentiate patients and normal controls using resting-state functional magnetic resonance imaging data (RS-fMRI). Although most previous classification studies have reported promising accuracy within individual datasets, achieving high levels of accuracy with multiple datasets remains challenging for two main reasons: high dimensionality, and high variability across subjects. We used two independent RS-fMRI datasets (n = 31, 46, respectively) both with eyes closed (EC) and eyes open (EO) conditions. For each dataset, we first reduced the number of features to a small number of brain regions with paired t-tests, using the amplitude of low frequency fluctuation (ALFF) as a metric. Second, we employed a new method for feature extraction, named the PAIR method, examining EC and EO as paired conditions rather than independent conditions. Specifically, for each dataset, we obtained EC minus EO (EC—EO) maps of ALFF from half of subjects (n = 15 for dataset-1, n = 23 for dataset-2) and obtained EO—EC maps from the other half (n = 16 for dataset-1, n = 23 for dataset-2). A support vector machine (SVM) method was used for classification of EC RS-fMRI mapping and EO mapping. The mean classification accuracy of the PAIR method was 91.40% for dataset-1, and 92.75% for dataset-2 in the conventional frequency band of 0.01–0.08 Hz. For cross-dataset validation, we applied the classifier from dataset-1 directly to dataset-2, and vice versa. The mean accuracy of cross-dataset validation was 94.93% for dataset-1 to dataset-2 and 90.32% for dataset-2 to dataset-1 in the 0.01–0.08 Hz range. For the UNPAIR method, classification accuracy was substantially lower (mean 69.89% for dataset-1 and 82.97% for dataset-2), and was much lower for cross-dataset validation (64.69% for dataset-1 to dataset-2 and 64.98% for dataset-2 to dataset-1) in the 0.01–0.08 Hz range. In conclusion, for within-group design studies (e.g., paired conditions or follow-up studies), we recommend the PAIR method for feature extraction. In addition, dimensionality reduction with strong prior knowledge of specific brain regions should also be considered for feature selection in neuroimaging studies.

Resting-state BOLD oscillation frequency predicts vigilance task performance at both normal and high environmental temperatures

Hyperthermia may impair vigilance functions and lead to slower reaction times (RTs) in the psychomotor vigilance task (PVT) and possibly disturbing cerebral hemodynamic rhythms. To test these hypotheses, we acquired the resting-state BOLD and cerebral blood flow (CBF) data, as well as PVTRTs from 15 participants in two simulated environmental thermal conditions (50 °C/25 °C). We adopted a data-driven method, frequency component analysis, to quantify the mean frequency of the BOLD series of each voxel. Across-subject correlation analysis was employed to detect the brain areas whose BOLD oscillation frequency was correlated with the RTs. Significant changes of BOLD frequency and CBF within these areas were compared between hyperthermia and normothermia conditions. Spatial correlations between BOLD frequency and CBF were calculated within different brain areas for each subject under both thermal conditions. Results showed that, under both thermal conditions, the RTs correlated with the BOLD frequency positively in the default mode network (DMN) and negatively in the sensorimotor network (SMN). The increase of BOLD frequency in the thalamus and ventral medial prefrontal cortex was correlated with the increase of RTs in hyperthermia compared with normothermia. Hyperthermia decreased BOLD frequency and CBF in the SMN, while it increased CBF in the thalamus and posterior cingulate. In both thermal conditions, the spatial distribution of CBF negatively correlated with the spatial distribution of BOLD oscillation frequency in most cortical areas, especially in cingulate cortices, precuneus, and primary visual cortex. These results suggest that hyperthermia might deteriorate task performance by interfering with the resting-state CBF, and with BOLD rhythms. The overlapping of the thermoregulatory and vigilance functions in the SMN and DMN might underlie the neural mechanisms of the cognitive-behavioral impairments induced by hyperthermia.

Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study

The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.

Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity

The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test-retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies.

Influences of Head Motion Regression on High-Frequency Oscillation Amplitudes of Resting-State fMRI Signals

High-frequency oscillations (HFOs, >0.1 Hz) of resting-state fMRI (rs-fMRI) signals have received much attention in recent years. Denoising is critical for HFO studies. Previous work indicated that head motion (HM) has remarkable influences on a variety of rs-fMRI metrics, but its influences on rs-fMRI HFOs are still unknown. In this study, we investigated the impacts of HM regression (HMR) on HFO results using a fast sampling rs-fMRI dataset. We demonstrated that apparent high-frequency (∼0.2–0.4 Hz) components existed in the HM trajectories in almost all subjects. In addition, we found that individual-level HMR could robustly reveal more between-condition (eye-open vs. eye-closed) amplitude differences in high-frequency bands. Although regression of mean framewise displacement (FD) at the group level had little impact on the results, mean FD could significantly account for inter-subject variance of HFOs even after individual-level HMR. Our findings suggest that HM artifacts should not be ignored in HFO studies, and HMR is necessary for detecting HFO between-condition differences.

Early- and late-latency gamma auditory steady-state response in schizophrenia during closed eyes: Does hallucination status matter?

OBJECTIVES

Auditory steady-state responses are larger in patients experiencing auditory verbal hallucinations (AVH) than in never hallucinating subjects (NH) when recorded with open eyes. Compensatory effects were shown for schizophrenic patients when recorded with closed eyes. This effect has not been evaluated in respect to hallucination status.

METHODS

Gamma responses to 40Hz stimulation were recorded in 15AVH patients, 25 healthy controls and 11NH patients with closed eyes. Mean and peak evoked amplitude and phase-locking index, peak time and maximal frequency were extracted for early- and late-latency responses and compared between groups.

RESULTS

Phase-locking of early, but not late-latency gamma was diminished in schizophrenic patients independently on hallucination status. Peak entrainment time was delayed in hallucinating patients. Magnitude and frequency of early-latency response correlated to negative symptoms.

CONCLUSIONS

In AVH patients, entrainment at gamma frequency was "normal" when eyes were closed. In contrast to never hallucinating subjects, entrainment to stimulation was delayed in AVH. The early-latency gamma response, standing for early sensory stimulus processing, on the contrary, was impaired in SZ irrespective of prevalence of hallucinations and was not modulated by subjects' general state; however its magnitude might be related to the expression of negative symptomatology.

SIGNIFICANCE

Evaluation of auditory entrainment in both open eyes and closed eyes conditions is informative. Frequency and timing information of both early-latency and late-latency responses helps to uncover different aspects of impairment in schizophrenia patients.

Frequency-Dependent Modulation of Regional Synchrony in the Human Brain by Eyes Open and Eyes Closed Resting-States

The eyes-open (EO) and eyes-closed (EC) states have differential effects on BOLD-fMRI signal dynamics, affecting both the BOLD oscillation frequency of a single voxel and the regional homogeneity (ReHo) of several neighboring voxels. To explore how the two resting-states modulate the local synchrony through different frequency bands, we decomposed the time series of each voxel into several components that fell into distinct frequency bands. The ReHo in each of the bands was calculated and compared between the EO and EC conditions. The cross-voxel correlations between the mean frequency and the overall ReHo of each voxel’s original BOLD series in different brain areas were also calculated and compared between the two states. Compared with the EC state, ReHo decreased with EO in a wide frequency band of 0.01–0.25 Hz in the bilateral thalamus, sensorimotor network, and superior temporal gyrus, while ReHo increased significantly in the band of 0–0.01 Hz in the primary visual cortex, and in a higher frequency band of 0.02–0.1 Hz in the higher order visual areas. The cross-voxel correlations between the frequency and overall ReHo were negative in all the brain areas but varied from region to region. These correlations were stronger with EO in the visual network and the default mode network. Our results suggested that different frequency bands of ReHo showed different sensitivity to the modulation of EO-EC states. The better spatial consistency between the frequency and overall ReHo maps indicated that the brain might adopt a stricter frequency-dependent configuration with EO than with EC.

Investigating the Temporal Patterns within and between Intrinsic Connectivity Networks under Eyes-Open and Eyes-Closed Resting States: A Dynamical Functional Connectivity Study Based on Phase Synchronization

The brain active patterns were organized differently under resting states of eyes open (EO) and eyes closed (EC). The altered voxel-wise and regional-wise resting state active patterns under EO/EC were found by static analysis. More importantly, dynamical spontaneous functional connectivity has been observed in the resting brain. To the best of our knowledge, the dynamical mechanisms of intrinsic connectivity networks (ICNs) under EO/EC remain largely unexplored. The goals of this paper were twofold: 1) investigating the dynamical intra-ICN and inter-ICN temporal patterns during resting state; 2) analyzing the altered dynamical temporal patterns of ICNs under EO/EC. To this end, a cohort of healthy subjects with scan conditions of EO/EC were recruited from 1000 Functional Connectomes Project. Through Hilbert transform, time-varying phase synchronization (PS) was applied to evaluate the inter-ICN synchrony. Meanwhile, time-varying amplitude was analyzed as dynamical intra-ICN temporal patterns. The results found six micro-states of inter-ICN synchrony. The medial visual network (MVN) showed decreased intra-ICN amplitude during EC relative to EO. The sensory-motor network (SMN) and auditory network (AN) exhibited enhanced intra-ICN amplitude during EC relative to EO. Altered inter-ICN PS was found between certain ICNs. Particularly, the SMN and AN exhibited enhanced PS to other ICNs during EC relative to EO. In addition, the intra-ICN amplitude might influence the inter-ICN synchrony. Moreover, default mode network (DMN) might play an important role in information processing during EO/EC. Together, the dynamical temporal patterns within and between ICNs were altered during different scan conditions of EO/EC. Overall, the dynamical intra-ICN and inter-ICN temporal patterns could benefit resting state fMRI-related research, and could be potential biomarkers for human functional connectome.