Directionality of large-scale resting-state brain networks during eyes open and eyes closed conditions

Perceived stress and brain connectivity in subthreshold depression: Insights from eyes-closed and eyes-open states

Perceived stress is an acknowledged risk factor for subthreshold depression (StD), and fluctuations in perceived stress are thought to disrupt the harmony of brain networks essential for emotional and cognitive functioning. This study aimed to elucidate the relationship between eye-open (EO) and eye-closed (EC) states, perceived stress, and StD. We recruited 27 individuals with StD and 33 healthy controls, collecting resting state fMRI data under both EC and EO conditions. We combined intrinsic connectivity and seed-based functional connectivity analyses to construct the functional network and explore differences between EC and EO conditions. Graph theory analysis revealed weakened connectivity strength in the right superior frontal gyrus (SFG) and right median cingulate and paracingulate gyrus (MCC) among participants with StD, suggesting an important role for these regions in the stress-related emotions dysregulation. Notably, altered SFG connectivity was observed to significantly relate to perceived stress levels in StD, and the SFG connection emerges as a neural mediator potentially influencing the relationship between perceived stress and StD. These findings highlight the role of SFG and MCC in perceived stress and suggest that understanding EC and EO states in relation to these regions is important in the neurobiological framework of StD. This may offer valuable perspectives for early prevention and intervention strategies in mental health disorders.

Brain state dynamics differ between eyes open and eyes closed rest

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

EEG alpha reactivity on eyes opening discriminates patients with schizophrenia and schizoaffective disorder

OBJECTIVE

Alpha activity in the electroencephalogram (EEG) is typically dominant during rest with closed eyes but suppressed by visual stimulation. Previous research has shown that alpha-blockade is less pronounced in schizophrenia patients compared to healthy individuals, but no studies have examined it in schizoaffective disorder.

METHODS

A resting state EEG was used for the analysis of the alpha-reactivity between the eyes closed and the eyes opened conditions in overall (8 - 13 Hz), low (8 - 10 Hz) and high (10 - 13 Hz) alpha bands in three groups: schizophrenia patients (SC, n = 30), schizoaffective disorder (SA, n = 30), and healthy controls (HC, n = 36). All patients had their first psychotic episode and were receiving antipsychotic therapy.

RESULTS

A significant decrease in alpha power was noted across all subjects from the eyes-closed to eyes-open condition, spanning all regions. Alpha reactivity over the posterior regions was lower in SC compared to HC within overall and high alpha. SA showed a trend towards reduced alpha reactivity compared to HC, especially evident over the left posterior region within the overall alpha. Alpha reactivity was more pronounced over the middle and right posterior regions of SA as compared to SC, particularly in the high alpha. Alpha reactivity in SC and SA patients was associated with various negative symptoms.

CONCLUSIONS

Our findings imply distinct alterations in arousal mechanisms in SC and SA and their relation to negative symptomatology. Arousal is more preserved in SA.

SIGNIFICANCE

This study is the first to compare the EEG features of arousal in SC and SA.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Eyes-Open and Eyes-Closed Resting State Network Connectivity Differences

Resting state networks comprise several brain regions that exhibit complex patterns of interaction. Switching from eyes closed (EC) to eyes open (EO) during the resting state modifies these patterns of connectivity, but precisely how these change remains unclear. Here we use functional magnetic resonance imaging to scan healthy participants in two resting conditions (viz., EC and EO). Seven resting state networks were chosen for this study: salience network (SN), default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), visual network (VN), motor network (MN) and auditory network (AN). We performed functional connectivity (FC) analysis for each network, comparing the FC maps for both EC and EO. Our results show increased connectivity between most networks during EC relative to EO, thereby suggesting enhanced integration during EC and greater modularity or specialization during EO. Among these networks, SN is distinctive: during the transition from EO to EC it evinces increased connectivity with DMN and decreased connectivity with VN. This change might imply that SN functions in a manner analogous to a circuit switch, modulating resting state relations with DMN and VN, when transitioning between EO and EC.

Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback

There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deoxygenated blood in the brain, regulates all of the classic brainwave bands (i.e. alpha, theta, delta, beta, gamma). The success of ILF neurofeedback suggests that all forms of neurofeedback may work through a similar mechanism that does not fit the operant conditioning paradigm. This chapter focuses on the possible mechanisms of action for ILF neurofeedback, which may be generalized, based on current evidence.

The manifestation of individual differences in sensitivity to punishment during resting state is modulated by eye state

Structural and functional neuroimaging studies have shown that brain areas associated with fear and anxiety (defensive system areas) are modulated by individual differences in sensitivity to punishment (SP). However, little is known about how SP is related to brain functional connectivity and the factors that modulate this relationship. In this study, we investigated whether a simple methodological manipulation, such as performing a resting state with eyes open or eyes closed, can modulate the manifestation of individual differences in SP. To this end, we performed an exploratory fMRI resting state study in which a group of participants (n = 88) performed a resting state with eyes closed and another group (n = 56) performed a resting state with eyes open. All participants completed the Sensitivity to Punishment and Sensitivity to Reward Questionnaire. Seed-based functional connectivity analyses were performed in the amygdala, hippocampus, and periaqueductal gray (PAG). Our results showed that the relationship between SP and left amygdala-precuneus and left hippocampus-precuneus functional connectivity was modulated by eye state. Moreover, in the eyes open group, SP was negatively related to the functional connectivity between the PAG and amygdala and between the PAG and left hippocampus, and it was positively related to the functional connectivity between the amygdala and hippocampus. Together, our results may suggest underlying differences in the connectivity between anxiety-related areas based on eye state, which in turn would affect the manifestation of individual differences in SP.

Resting-State Activity in High-Order Visual Areas as a Window into Natural Human Brain Activations

A major limitation of conventional human brain research has been its basis in highly artificial laboratory experiments. Due to technical constraints, little is known about the nature of cortical activations during ecological real life. We have previously proposed the "spontaneous trait reactivation (STR)" hypothesis arguing that resting-state patterns, which emerge spontaneously in the absence of external stimulus, reflect the statistics of habitual cortical activations during real life. Therefore, these patterns can serve as a window into daily life cortical activity. A straightforward prediction of this hypothesis is that spontaneous patterns should preferentially correlate to patterns generated by naturalistic stimuli compared with artificial ones. Here we targeted high-level category-selective visual areas and tested this prediction by comparing BOLD functional connectivity patterns formed during rest to patterns formed in response to naturalistic stimuli, as well as to more artificial category-selective, dynamic stimuli. Our results revealed a significant correlation between the resting-state patterns and functional connectivity patterns generated by naturalistic stimuli. Furthermore, the correlations to naturalistic stimuli were significantly higher than those found between resting-state patterns and those generated by artificial control stimuli. These findings provide evidence of a stringent link between spontaneous patterns and the activation patterns during natural vision.

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.

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.

The power of language: Functional brain network topology of deaf and hearing in relation to sign language experience

Prolonged auditory sensory deprivation leads to brain reorganization. This is indicated by functional enhancement in remaining sensory systems and known as cross-modal plasticity. In this study we investigated differences in functional brain network topology between deaf and hearing individuals. We also studied altered functional network responses between deaf and hearing individuals with a recording paradigm containing an eyes-closed and eyes-open condition. Electroencephalography activity was recorded in a group of sign language-trained deaf (N = 71) and hearing people (N = 122) living in rural Africa. Functional brain networks were constructed from the functional connectivity between fourteen electrodes distributed over the scalp. Functional connectivity was quantified with the phase lag index based on bandpass filtered epochs of brain signal. We studied the functional connectivity between the auditory, somatosensory and visual cortex and performed whole-brain minimum spanning tree analysis to capture network backbone characteristics. Functional connectivity between different regions involved in sensory information processing tended to be stronger in deaf people during the eyes-closed condition in both the alpha and beta frequency band. Furthermore, we found differences in functional backbone topology between deaf and hearing individuals. The backbone topology altered during transition from the eyes-closed to eyes-open condition irrespective of deafness, but was more pronounced in deaf individuals. The transition of backbone strength was different between individuals with congenital, pre-lingual or post-lingual deafness. Functional backbone characteristics correlated with the experience of sign language. Overall, our study revealed more insights in functional network reorganization caused by auditory deprivation and cross-modal plasticity. It further supports the idea of a brain plasticity potential in deaf and hearing people. The association between network organization and acquired sign language experience reflects the ability of ongoing brain adaptation in people with hearing disabilities.

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.

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

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

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

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Differences in preprocessing methods had little effect on connectivity estimates.

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

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.

From static to temporal network theory: Applications to functional brain connectivity

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain's network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Neural Correlates of Response to Pharmacotherapy in Obsessive-Compulsive Disorder: Individualized Cortical Morphology-Based Structural Covariance

BACKGROUND

Primary pharmacotherapy regimen for obsessive-compulsive disorder (OCD) named Serotonin reuptake inhibitors (SRIs) does not attain sufficient symptom improvement in 40-60% of OCD. We aimed to decode the differential profile of OCD-related brain pathology per subject in the context of cortical surface area (CSA) or thickness (CT)-based individualized structural covariance (ISC) and to demonstrate the potential of which as a biomarker of treatment response to SRI-based pharmacotherapy in OCD using the support vector machine (SVM).

METHODS

T1-weighted magnetic resonance imaging was obtained at 3T from 56 unmedicated OCD subjects and 75 healthy controls (HCs) at baseline. After 4months of SRI-based pharmacotherapy, the OCD subjects were classified as responders (OCD-R,N=25; ≥35% improvement) or nonresponders (OCD-NR,N=31; <35% improvement) according to the percentage change in the Yale-Brown Obsessive Compulsive Scale total score. Cortical ISCs sustaining between-group difference (p<.001) for every run of leave-one-out group-comparison were packaged as feature set for group classification using the SVM.

RESULTS

An optimal feature set of the top 12 ISCs including a CT-ISC between the dorsolateral prefrontal cortex versus precuneus, a CSA-ISC between the anterior insula versus intraparietal sulcus, as well as perisylvian area-related ISCs predicted the initial prognosis of OCD as OCD-R or OCD-NR with an accuracy of 89.0% (sensitivity 88.4%, specificity 90.1%). Extended sets of ISCs distinguished the OCD subjects from the HCs with 90.7-95.6% accuracy (sensitivity 90.8-96.2%, specificity 91.1-95.0%).

CONCLUSION

We showed the potential of cortical morphology-based ISCs, which reflect dysfunctional cortical maturation process, as a possible biomarker that predicts the clinical treatment response to SRI-based pharmacotherapy in OCD.

Negative childhood experiences alter a prefrontal-insular-motor cortical network in healthy adults: A preliminary multimodal rsfMRI-fMRI-MRS-dMRI study

Research in humans and animals has shown that negative childhood experiences (NCE) can have long-term effects on the structure and function of the brain. Alterations have been noted in grey and white matter, in the brain's resting state, on the glutamatergic system, and on neural and behavioural responses to aversive stimuli. These effects can be linked to psychiatric disorder such as depression and anxiety disorders that are influenced by excessive exposure to early life stressors. The aim of the current study was to investigate the effect of NCEs on these systems. Resting state functional MRI (rsfMRI), aversion task fMRI, glutamate magnetic resonance spectroscopy (MRS), and diffusion magnetic resonance imaging (dMRI) were combined with the Childhood Trauma Questionnaire (CTQ) in healthy subjects to examine the impact of NCEs on the brain. Low CTQ scores, a measure of NCEs, were related to higher resting state glutamate levels and higher resting state entropy in the medial prefrontal cortex (mPFC). CTQ scores, mPFC glutamate and entropy, correlated with neural BOLD responses to the anticipation of aversive stimuli in regions throughout the aversion-related network, with strong correlations between all measures in the motor cortex and left insula. Structural connectivity strength, measured using mean fractional anisotropy, between the mPFC and left insula correlated to aversion-related signal changes in the motor cortex. These findings highlight the impact of NCEs on multiple inter-related brain systems. In particular, they highlight the role of a prefrontal-insular-motor cortical network in the processing and responsivity to aversive stimuli and its potential adaptability by NCEs.

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.