Eyes-open State Research Articles

Posterior cingulate cortex can be a regulatory modulator of the default mode network in task-negative state

In recent years, the regulation of brain networks and interactions between different brain regions have become important issues in neuroscience. Effective connectivity can be employed to understand the modulatory mechanisms of brain networks. Previous studies have used the task-positive mode to examine effective connectivity between brain regions and very few studies have considered the task-negative mode to explore effective connectivity using electroencephalography (EEG). In the present study, high-density EEG experiments were conducted in 85 participants to measure EEG effective connectivity in relevant default mode network (DMN) brain regions (i.e., the medial prefrontal cortex [mPFC], posterior cingulate cortex [PCC], precuneus, and right frontal and left occipital regions) to observe the effects of different task-negative modes (eyes-open/eyes-closed state) and personality traits (introversion/extroversion). The results showed that in the eyes-closed state, the PCC had significantly increased effective connectivity and played a prominent role as a regulatory modulator of outflow to other regions mediated by alpha rhythms. The mPFC was a regulatory modulator of outflow in the eyes-open state mediated by delta rhythms. The introvert group showed stronger co-modulations in the relevant DMN regions than the extrovert group.

EcoMeditation and Emotional Freedom Techniques (EFT) Produce Elevated Brain-wave Patterns and States of Consciousness

Background Stress-reduction techniques can be used in combination with each other. Two such methods are Emotional Freedom Techniques (EFT) and EcoMeditation. EFT is an evidence-based self-help method. Reviews and meta-analyses examining more than 100 studies demonstrate the efficacy of EFT for anxiety, depression, and posttraumatic stress disorder (PTSD). EcoMeditation is a secular meditation technique that combines neurofeedback, mindfulness, and heart coherence. Studies demonstrate that EFT and EcoMeditation can improve cortisol levels, heart rate, and other health markers. Objectives EFT is most commonly used to relieve stress and treat traumatic childhood memories, while EcoMeditation is used to produce calm emotional states. This study sought to elucidate whether the release of traumatic stress facilitated by EFT would enhance entry into meditative states, and secondly whether EcoMeditation prior to EFT might establish a baseline of wellbeing that assists in the resolution of trauma. Methods The Mind Mirror 6 (MM) electroencephalogram (EEG) was used to assess brain states in eight participants attending a weekend workshop. The MM measures changes in three advanced neurophysiological states of consciousness characterized by relative amplitude relationships between brain-wave frequencies: 1) the Awakened Mind pattern of lucid awareness, creativity, insight, intuition, and spiritual connection; 2) the Evolved Mind of nondual unity consciousness; and 3) the Gamma Synchrony pattern of whole-brain synchrony, mental integration, nonlocal awareness, and insight. Assessments included eyes-closed (EC) and eyes-open (EO) states, in order to determine whether changes in consciousness were sustained in waking life. Baselines were collected at the start and end of each day, and after EcoMeditation, which was performed for 20 minutes on day two. Results A statistically significant EO posttest change was found in the Awakened Mind pattern (p = 0.003). Cohen’s d = 0.79 indicated a large treatment effect. Increased brainwave coherence—a measure of efficient brain function—was found in all participants in at least one frequency category, while six increased EO Gamma Synchrony. Coherence analytics showed increased brain-wave coherence primarily in alpha but also in theta and delta and occasionally in low and midrange gamma. During EcoMeditation, all participants generated high-amplitude 45–65 Hz gamma frequencies and Gamma Synchrony values, some at the top of the statistical range, with high synchrony at posttest. Conclusions EcoMeditation produced extraordinarily high levels of Gamma Synchrony. In two days, many participants acquired elevated brain states normally found only after years of meditation practice. EcoMeditation facilitated participants’ ability to induce and sustain the alpha brain waves characteristic of high-level emotional, mental, and spiritual integration. A combination of the two methods produced statistical gains in the EO Awakened Mind, indicating that participants were able to carry elevated mental states into waking consciousness.

EEG alpha rhythm detection on a portable device

Portable EEG devices have a great potential to become efficient computer interfaces. However, a necessary step is to accurately describe the EEG signal in order to quantify brain rhythms. This study explored two signal analysis techniques, Matching Pursuit (MP) and Fast Fourier Transform (FFT), for differentiation between two states, eyes open (EO) and eyes closed (EC), through the detection of EEG alpha activity obtained from seven scalp regions, using a portable EEG device. Subjects were ten healthy male volunteers. MP results generally reproduced the results from FFT analysis, and all methods performed well on the occipital region. However, there was better state discrimination with MP atom number, and MP atom number was the only variable that reached statistical significance on all locations under study. When employing EEG alpha rhythm for EO vs. EC state discrimination, it may be useful to compute MP atom number, especially when extra-occipital acquisition is warranted.

Altered Functional Connectivity of Alpha Rhythm in Obsessive-Compulsive Disorder During Rest.

Obsessive-compulsive disorder (OCD) is a common inheritable psychiatric disorder characteristic of repetitive thinking, imagination (obsession), and stereotyped behaviors (compulsive). To explore whether there is an alteration of brain functional connectivity (BFC) in patients with OCD during rest, electroencephalogram (EEG) data of healthy controls (HCs) and patients with OCD were collected during rest in both eyes-closed and eyes-open states. Synchronization likelihood and graph theory were applied to construct and analyze brain functional networks of patients with OCD and HCs. Patients with OCD showed abnormal graph-theoretic parameters and impaired small world features in the alpha and beta bands. In addition, the topological analysis consistently showed that the long-range BFC of alpha rhythm was significantly reduced in the bilateral posterior areas in patients with OCD in comparison with HCs, while the BFC in the beta rhythm was significantly increased only in the eyes-open state. The findings suggest that the BFC of patients with OCD show abnormal small-world features and altered topological structure during rest, mainly in alpha and beta bands, which may provide a new insight for the diagnosis and treatment of OCD.

Differentiating resting brain states using ordinal symbolic analysis.

Symbolic methods of analysis are valuable tools for investigating complex time-dependent signals. In particular, the ordinal method defines sequences of symbols according to the ordering in which values appear in a time series. This method has been shown to yield useful information, even when applied to signals with large noise contamination. Here, we use ordinal analysis to investigate the transition between eyes closed (EC) and eyes open (EO) resting states. We analyze two electroencephalography datasets (with 71 and 109 healthy subjects) with different recording conditions (sampling rates and the number of electrodes in the scalp). Using as diagnostic tools the permutation entropy, the entropy computed from symbolic transition probabilities, and an asymmetry coefficient (that measures the asymmetry of the likelihood of the transitions between symbols), we show that the ordinal analysis applied to the raw data distinguishes the two brain states. In both datasets, we find that, during the EC-EO transition, the EO state is characterized by higher entropies and lower asymmetry coefficient, as compared to the EC state. Our results thus show that these diagnostic tools have the potential for detecting and characterizing changes in time-evolving brain states.

Inattentive Delirium vs. Disorganized Thinking: A New Axis to Subcategorize PACU Delirium.

Background: Assessment of patients for delirium in the Post Anesthesia Care Unit (PACU) is confounded by the residual effects of the varied anesthetic and analgesic regimens employed during surgery and by the physiological consequences of surgery such as pain. Nevertheless, delirium diagnosed at this early stage has been associated with adverse clinical outcomes. The last decade has seen the emergence of the confusion assessment method-intensive care unit (CAM-ICU) score as a quick practical method of detecting delirium in clinical situations. Nonetheless, this tool has not been specifically designed for use in this immediate postoperative setting.Methods: Patients enrolled in a larger observational study were administered the CAM-ICU delirium screening tool 15 min after the latter of return of responsiveness to command or arrival in the post-anesthesia care unit. Numerical pain rating scores were also recorded. In addition, we reviewed additional behavioral observations suggestive of disordered thinking, such as hallucinations, a non-reactive eyes-open state, or an inability to state a pain score.Results: Two-hundred and twenty-nine patients underwent CAM-ICU testing in PACU. 33 patients (14%) were diagnosed with delirium according to CAM-ICU criteria; 25 of these were inattentive with low arousal, seven were inattentive with high arousal, and one was inattentive and calm and with disordered thinking. Using our extended criteria an additional eleven patients showed signs of disordered thinking. CAM-ICU delirium was associated with increased length of operation (p = 0.028), but a positive CAM-PACU designation was associated with both increased operation length and age (p = 0.003 and 0.010 respectively). Two of the CAM-ICU positive patients with inattention and high arousal reported high pain scores and were not classified as CAM-PACU positive.Conclusion: Disordered thinking is correlated with older patients and longer operations. The sensitivity of the existing CAM-ICU score in diagnosing delirium or disordered thinking in PACU patients is improved by the inclusion of a few extra criteria, namely: patients having perceptual hallucinations, in an unreactive eyes-open state, or who cannot state a pain score. We present this alternative screening tool for use in the post-anesthetic period, which we have named CAM-PACU.

Abnormal auditory-evoked gamma band oscillations in first-episode schizophrenia during both eye open and eye close states

Abnormal auditory steady state response (ASSR) is a typical finding among schizophrenia patients, which is thought to directly reflect deficient gamma band oscillations in the brain. However, whether these ASSR alterations are state dependent, e.g. during eye-open or eye-closed conditions, has not yet been carefully elucidated in schizophrenia. Our study aimed to explore whether the abnormality of ASSR in patients with first-episode schizophrenia (FEP) is altered under eye-open (EO) and eye-closed (EC) states. ASSR was elicited using 40 Hz click trains under EO and EC states. Twenty-eight healthy control subjects (HC) and thirty-three FEP individuals, 17 of whom were medication-naïve, were recruited. The event-related spectrum perturbation (ERSP) and intertrial coherence (ITC) in response to 40 Hz click sounds were quantified. Compared to HC group, FEP group showed a lower ITC and ERSP during EO state, as well as a decreased ITC during EC state. Our results suggest that abnormalities in gamma band oscillations among first-episode schizophrenia patients are present under both eye open and eye close states. Although differences in gamma band oscillations between EO and EC states within the FEP group were not observed, exploratory results suggest that state-sensitivity may be contingent on medication use.

Analysis of Age-Related Dynamics and Gender-Specific Characteristics of Spontaneous Bioelectrical Activity and Components of Auditory Evoked Potentials in Junior School Students Living in the Arctic Region of the Russian Federation

We studied neurophysiological characteristics of the age-related development in junior school students (7–8 and 10–11 years of age) living in the Arctic region of the Russian Federation. The background electroencephalograms (EEGs) were recorded during quiet wakefulness with the eyes closed and open, and event related potentials (ERP) were recorded during the passive perception of sound stimuli within the oddball paradigm in the group of children (33 subjects, 18 boys and 15 girls). A decrease of the latency period and the spatial rearrangement of mismatch negativity with an increase in the amplitude in the centrofrontal cortex have been revealed in the groups of children aged 10–11 years during the perception of a rare stimulus and a decrease of the latency period of the Р300 component in the central and parietal areas associated with the maturation of mechanisms for involuntary auditory attention. Age-specific differences in the components of auditory ERP (N1 and N2) have been shown during passive perception of rare and frequent sounds, which reflect the processes of the morphofunctional maturation of the brain cortex in healthy Arctic school students (an increase of the N1 component amplitude, a decrese of the amplitude and the latency period of the N2 component). The analysis of the background EEG characteristics has shown both the common features, such as a decrease with age of the EEG power in the Δ and θ bands in the eyes-open state, and the different direction and topographic specificities in the age-dependent reorganization of bioelectrical activity in boys and girls in the α1 and α2 EEG bands.

Alternations in cortical thickness and functional connectivity in patients with generalized anxiety disorder

Objective To investigate the changes of cortical thickness and relative resting state functional connectivity in patients with generalized anxiety disorder (GAD). Methods The present study analyzed structural and eyes-open resting state functional MRI were performed in 21 patients with GAD and 22 matched healthy controls. Cortical thickness was estimated with FreeSurfer. The structurally altered regions were defined as region of interest (ROI) to analyze functional connectivity (FC) using resting state functional MRI data by DPABI. Results Cortical thickness of patients with GAD were increased in right rostral middle frontal gyrus (rMFG; MNI: x=27.9, y=53.4, z=-11.1; size: 241.93 mm2; FDR corrected, P<0.1) and right inferior temporal gyrus (IGT; MNI: x=49.7, y=-57.8, z=-8.7; size: 138.93 mm2; FDR corrected, P<0.1) compared with healthy controls. FC between right rMFG and right superior/middle occipital gyrus as well as well as FC between rMFG and right precentral gyrus showed decreased in patients with GAD compared with healthy controls(AlphaSim corrected, P<0.05). FC between right rMFG and right angular gyrus showed increased in patients with GAD compared with healthy control (AlphaSim corrected, P<0.05). Conclusion The rMFG may play an important role in the pathophysiology of GAD, which can be used as an stimuli target in physicotherapeutics to improve anxiety symptoms. Key words: Generalized anxiety disorder; Cortical thickness; Functional connectivity; Rostral middle frontal gyrus; Magnetic resonance imaging

Symbolic joint entropy reveals the coupling of various brain regions

The convergence and divergence of oscillatory behavior of different brain regions are very important for the procedure of information processing. Measurements of coupling or correlation are very useful to study the difference of brain activities. In this study, EEG signals were collected from ten subjects under two conditions, i.e. eyes closed state and idle with eyes open. We propose a nonlinear algorithm, symbolic joint entropy, to compare the coupling strength among the frontal, temporal, parietal and occipital lobes and between two different states. Instead of decomposing the EEG into different frequency bands (theta, alpha, beta, gamma etc.), the novel algorithm is to investigate the coupling from the entire spectrum of brain wave activities above 4Hz. The coupling coefficients in two states with different time delay steps are compared and the group statistics are presented as well. We find that the coupling coefficient of eyes open state with delay consistently lower than that of eyes close state across the group except for one subject, whereas the results without delay are not consistent. The differences between two brain states with non-zero delay can reveal the intrinsic inter-region coupling better. We also use the well-known Hénon map data to validate the algorithm proposed in this paper. The result shows that the method is robust and has a great potential for other physiologic time series.

EEG-Based Biometric Authentication Using Gamma Band Power During Rest State

Electroencephalography (EEG), one of the most effective noninvasive methods for recording brain’s electrical activity, has widely been employed in the diagnosis of brain diseases for a few decades. Recently, the promising biometric potential of EEG, for developing person identification and authentication systems, has also been explored. This paper presents the superior performance of power spectral density (PSD) features of gamma band (30–50 Hz) in biometric authentication, compared to delta, theta, alpha and beta band of EEG signals during rest state. The proposed authentication technique based on simple cross-correlation values of PSD features extracted from 19 EEG channels during eyes closed and eyes open rest state conditions among 109 subjects offers an equal error rate (EER) of 0.0196 which is better than the state-of-the-art method employing eigenvector centrality features extracted from gamma band of 64 EEG channels of the same dataset. The obtained results are promising, but further investigation is essential for exploring the subject-specific neural dynamics and stability of gamma waves and for optimizing the results.

Levenberg-Marquardt Neural Network for Eye States Detection Based on Electroencephalography Data

The eye state detection is one of various task toward Brain Computer Interface system. The eye state can be read in brain signals. In this paper use EEG Eye State dataset (Rosler, 2013) from UCI Machine Learning Repository Database. Dataset is consisting of continuous 14 EEG measurements in 117 seconds. The eye states were marked as “1” or “0”. “1” indicates the eye-closed and “0” the eye-open state. The proposed schemes use Multi Layer Neural Network with Levenberg Marquardt optimization learning algorithm, as classification method. Levenberg Marquardt method used to optimize the learning algorithm of neural network, because the standard algorithm has a weak convergence rate. It is need many iterations to have minimum error. Based on the analysis towards the experiment on the EEG dataset, it can be concluded that the proposed scheme can be implemented to detect the Eye State. The best accuracy gained from combination variable sigmoid function, data normalization and number of neurons are 31 (95.71%) for one hidden layer, and 98.912% for two hidden layers with number of neurons are 39 and 47 neurons and linear function.

The Whole-Brain "Global" Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism.

The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI "nuisance signals" were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a "nuisance signal," also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states.

Lexical tonal discrimination in Zapotec children. A study of the theta rhythm

BackgroundZapotec is a language used mainly in the state of Oaxaca in Mexico of tonal characteristic; homophone words with difference in fundamental frequency with different meanings. Our objective was to analyze changes in the electroencephalographic (EEG) theta rhythm during word discrimination of lexical tonal bi-syllabic homophone word samples of Zapotec. MethodsWe employed electroencephalography analysis during lexical tonal discrimination in 12 healthy subjects 9-16 years of age. ResultsWe observed an increase in theta relative power between lexical discrimination and at rest eyes-open state in right temporal site. We also observed several significant intra- and inter-hemispheric correlations in several scalp sites, mainly in left fronto-temporal and right temporal areas when subjects were performing lexical discrimination. ConclusionsOur data suggest more engagement of neural networks of the right hemisphere are involved in Zapotec language discrimination.

Experiencing affective music in eyes-closed and eyes-open states: an electroencephalography study.

In real life, listening to music may be associated with an eyes-closed or eyes-open state. The effect of eye state on listeners’ reaction to music has attracted some attention, but its influence on brain activity has not been fully investigated. The present study aimed to evaluate the electroencephalographic (EEG) markers for the emotional valence of music in different eye states. Thirty participants listened to musical excerpts with different emotional content in the eyes-closed and eyes-open states. The results showed that participants rated the music as more pleasant or with more positive valence under an eyes-open state. In addition, we found that the alpha asymmetry indices calculated on the parietal and temporal sites reflected emotion valence in the eyes-closed and eyes-open states, respectively. The theta power in the frontal area significantly increased while listening to emotional-positive music compared to emotional-negative music under the eyes-closed condition. These effects of eye states on EEG markers are discussed in terms of brain mechanisms underlying attention and emotion.

Reliability comparison of spontaneous brain activities between BOLD and CBF contrasts in eyes-open and eyes-closed resting states

Blood oxygenation level dependent (BOLD) and arterial spin labeling (ASL) are two predominant resting-state fMRI techniques in mapping spontaneous brain activities. At single voxel level, cerebral blood flow (CBF) measured by ASL and amplitude of low frequency fluctuations (ALFF) of BOLD have been recognized as useful indices to characterize brain function in health and disease. However, no study has directly compared the test–retest reliability between BOLD and CBF contrasts on the same group of subjects at single voxel level. Moreover, both eyes-open and eyes-closed conditions have been employed as resting states, but it is still not clear which state is more reliable. Here we collected BOLD and ASL data during eyes-open and eyes-closed states across three scanning visits on twenty-two healthy young subjects. CBF-mean, BOLD- and CBF-ALFF were computed to characterize corresponding brain activities at single voxel level. Seed-based functional connectivity (FC) with the posterior cingulate cortex (PCC) was further calculated for both BOLD and CBF data. Intra-class correlation was used as the index of long-term reliability between visits 1 and 2 (two months apart) and short-term reliability between visits 2 and 3 (on the same day). Both short- and long-term reliabilities for CBF-mean and BOLD-ALFF were high, but were lower for CBF-ALFF, BOLD- and CBF-FC in both eyes-open and eyes-closed states. Direct comparisons showed that brain regions with the highest reliability of CBF-mean were mainly in the gray matter. The reliability of CBF-ALFF and BOLD-FC was lower than that of BOLD-ALFF, and the reliability of CBF-FC was lower than those of both CBF-ALFF and BOLD-FC. Furthermore, we observed that reliabilities of the eyes-open state were higher than those of the eyes-closed state for both imaging contrasts, though the effect size was small. Voxel-wise comparisons demonstrated that the long-term reliability of BOLD-ALFF was significantly higher with eyes open in the visual system, and both the short- and long-term reliability of BOLD-FC was slightly higher with eyes open in the default mode network. Moreover, we showed that denoising decreased the reliability of both ALFF and FC of both BOLD and ASL contrasts. In conclusion, our results indicated that CBF-mean and BOLD-ALFF could both be used as reliable indices for characterizing resting-state brain activities at single voxel level and recommended the eyes-open state for resting-state studies, especially for those targeting the visual system and default mode network.

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

The present study examined directional connections in the brain among resting-state networks (RSNs) when the participant had their eyes open (EO) or had their eyes closed (EC). The resting-state fMRI data were collected from 20 healthy participants (9 males, 20.17 ± 2.74 years) under the EO and EC states. Independent component analysis (ICA) was applied to identify the separated RSNs (i.e., the primary/high-level visual, primary sensory-motor, ventral motor, salience/dorsal attention, and anterior/posterior default-mode networks), and the Gaussian Bayesian network (BN) learning approach was then used to explore the conditional dependencies among these RSNs. The network-to-network directional connections related to EO and EC were depicted, and a support vector machine (SVM) was further employed to identify the directional connection patterns that could effectively discriminate between the two states. The results indicated that the connections among RSNs are directionally connected within a BN during the EO and EC states. The directional connections from the salience network (SN) to the anterior/posterior default-mode networks and the high-level to primary-level visual network were the obvious characteristics of both the EO and EC resting-state BNs. Of the directional connections in BN, the directional connections of the salience and dorsal attention network (DAN) were observed to be discriminative between the EO and EC states. In particular, we noted that the properties of the salience and DANs were in opposite directions. Overall, the present study described the directional connections of RSNs using a BN learning approach during the EO and EC states, and the results suggested that the directionality of the attention systems (i.e., mainly for the salience and the DAN) in resting state might have important roles in switching between the EO and EC conditions.

Scale-free brain ensemble modulated by phase synchronization

To listen to brain activity as a piece of music, we proposed the scale-free brainwave music (SFBM) technology, which could translate the scalp electroencephalogram (EEG) into music notes according to the power law of both EEG and music. In the current study, this methodology was further extended to a musical ensemble of two channels. First, EEG data from two selected channels are translated into musical instrument digital interface (MIDI) sequences, where the EEG parameters modulate the pitch, duration, and volume of each musical note. The phase synchronization index of the two channels is computed by a Hilbert transform. Then the two MIDI sequences are integrated into a chorus according to the phase synchronization index. The EEG with a high synchronization index is represented by more consonant musical intervals, while the low index is expressed by inconsonant musical intervals. The brain ensemble derived from real EEG segments illustrates differences in harmony and pitch distribution during the eyes-closed and eyes-open states. Furthermore, the scale-free phenomena exist in the brainwave ensemble. Therefore, the scale-free brain ensemble modulated by phase synchronization is a new attempt to express the EEG through an auditory and musical way, and it can be used for EEG monitoring and bio-feedback.

Difference between eyes-closed and eyes-open resting state alpha power is an indicator of susceptibility to the rubber hand illusion

Difference between eyes-closed and eyes-open resting state alpha power is an indicator of susceptibility to the rubber hand illusion

Brain spontaneous fluctuations in sensorimotor regions were directly related to eyes open and eyes closed: evidences from a machine learning approach.

Previous studies have demonstrated that the difference between resting-state brain activations depends on whether the subject was eyes open (EO) or eyes closed (EC). However, whether the spontaneous fluctuations are directly related to these two different resting states are still largely unclear. In the present study, we acquired resting-state functional magnetic resonance imaging data from 24 healthy subjects (11 males, 20.17 ± 2.74 years) under the EO and EC states. The amplitude of the spontaneous brain activity in low-frequency band was subsequently investigated by using the metric of fractional amplitude of low frequency fluctuation (fALFF) for each subject under each state. A support vector machine (SVM) analysis was then applied to evaluate whether the category of resting states could be determined from the brain spontaneous fluctuations. We demonstrated that these two resting states could be decoded from the identified pattern of brain spontaneous fluctuations, predominantly based on fALFF in the sensorimotor module. Specifically, we observed prominent relationships between increased fALFF for EC and decreased fALFF for EO in sensorimotor regions. Overall, the present results indicate that a SVM performs well in the discrimination between the brain spontaneous fluctuations of distinct resting states and provide new insight into the neural substrate of the resting states during EC and EO.