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BACKGROUND: 22q11.2 Deletion Syndrome (22qDel) is a copy number variant (CNV) associated with psychosis and other neurodevelopmental disorders. Adolescents at clinical high risk for psychosis (CHR) are identified based on the presence of subthreshold psychosis symptoms. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped results to biological pathways. METHODS: We analyzed two large multi-site cohorts with resting-state functional MRI (rs-fMRI): 1) 22qDel (n=164, 47% female) and typically developing (TD) controls (n=134, 56% female); 2) CHR individuals (n=244, 41% female) and TD controls (n=151, 46% female) from the North American Prodrome Longitudinal Study-2. We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions, testing case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation. RESULTS: BSV, LC, and GBC are significantly disrupted in 22qDel compared with TD controls (False Discovery Rate q<0.05). Spatial maps of BSV and LC differences are highly correlated with each other, unlike GBC. In CHR, only LC is significantly altered versus controls, with a different spatial pattern compared to 22qDel. Group differences map onto biological gradients, with 22qDel effects strongest in regions with high predicted blood flow and metabolism. CONCLUSION: 22qDel and CHR exhibit divergent effects on fMRI temporal variability and multi-scale functional connectivity. In 22qDel, strong and convergent disruptions in BSV and LC not seen in CHR individuals suggest distinct functional brain alterations.

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Overlapping symptoms between Alzheimer's disease (AD), behavioral variant of frontotemporal dementia (bvFTD), and schizophrenia (SZ) can lead to misdiagnosis and delays in appropriate treatment, especially in cases of early-onset dementia. To determine the potential of brain signal variability as a diagnostic tool, we assessed the coefficient of variation of the BOLD signal (CVBOLD) in 234 participants spanning bvFTD (n = 53), AD (n = 17), SZ (n = 23), and controls (n = 141). All underwent functional and structural MRI scans. Data unveiled a notable increase in CVBOLD in bvFTD patients across both datasets (local and international, p < 0.05), revealing an association with clinical scores (CDR and MMSE, r = 0.46 and r = -0.48, p < 0.0001). While SZ and control group demonstrated no significant differences, a comparative analysis between AD and bvFTD patients spotlighted elevated CVBOLD in the frontopolar cortices for the latter (p < 0.05). Furthermore, CVBOLD not only presented excellent diagnostic accuracy for bvFTD (AUC 0.78-0.95) but also showcased longitudinal repeatability. During a one-year follow-up, the CVBOLD levels increased by an average of 35% in the bvFTD group, compared to a 2% increase in the control group (p < 0.05). Our findings suggest that CVBOLD holds promise as a biomarker for bvFTD, offering potential for monitoring disease progression and differentiating bvFTD from AD and SZ.

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Neural variability is thought to facilitate survival through flexible adaptation to changing environmental demands. In humans, such capacity for flexible adaptation may manifest as fluid reasoning, inhibition of automatic responses, and mental set-switching-skills falling under the broad domain of executive functions that fluctuate over the life span. Neural variability can be quantified via the BOLD signal in resting-state fMRI. Variability of large-scale brain networks is posited to underpin complex cognitive activities requiring interactions between multiple brain regions. Few studies have examined the extent to which network-level brain signal variability across the life span maps onto high-level processes under the umbrella of executive functions. The present study leveraged a large publicly available neuroimaging dataset to investigate the relationship between signal variability and executive functions across the life span. Associations between brain signal variability and executive functions shifted as a function of age. Limbic-specific variability was consistently associated with greater performance across subcomponents of executive functions. Associations between executive function subcomponents and network-level variability of the default mode and central executive networks, as well as whole-brain variability, varied across the life span. Findings suggest that brain signal variability may help to explain to age-related differences in executive functions across the life span.

Traditionally, regional variability in brain signals has been viewed as a source of noise in human neuroimaging research. Our study demonstrates that brain signal variability may contain meaningful information related to psychological processes. We demonstrate that brain signal variability, particularly whole-brain variability, may serve as a reliable indicator of cognitive functions across the life span. Global variability and network-level variability play differing roles in supporting executive functions. Findings suggest that brain signal variability serves as a meaningful indicator of development and cognitive aging.

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The extent to which brain responses are less distinctive across varying cognitive loads in older adults is referred to as neural dedifferentiation. Moment-to-moment brain signal variability, an emerging indicator, reveals not only the adaptability of an individual's brain as an inter-individual trait, but also the allocation of neural resources within an individual due to ever-changing task demands, thus shedding novel insight into the process of neural dedifferentiation. However, how the modulation of intra-individual brain signal variability reflects behavioral differences related to cognitively demanding tasks remains unclear. In this study, we employed functional near-infrared spectroscopy (fNIRS) imaging to capture the variability of brain signals, which was quantified by the standard deviation, during both the resting state and an n-back task (n = 1, 2, 3) in 57 healthy older adults. Using multivariate Partial Least Squares (PLS) analysis, we found that fNIRS signal variability increased from the resting state to the task and increased with working memory load in older adults. We further confirmed that greater fNIRS signal variability generally supported faster and more stable response time in the 2- and 3-back conditions. However, the intra-individual level analysis showed that the greater the up-modulation in fNIRS signal variability with cognitive loads, the more its accuracy decreases and mean response time increases, suggesting that a greater intra-individual brain signal variability up-modulation may reflect decreased efficiency in neural information processing. Taken together, our findings offer new insights into the nature of brain signal variability, suggesting that inter- and intra-individual brain signal variability may index distinct theoretical constructs.

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OBJECTIVE: This study investigated how high-definition transcranial direct current stimulation (HD-tDCS) over the primary motor cortex (M1) affects brain signal variability and functional connectivity in the trigeminal pain pathway, and their association with changes in migraine attacks. METHODS: Twenty-five episodic migraine patients were randomized for ten daily sessions of active or sham M1 HD-tDCS. Resting-state blood-oxygenation-level-dependent (BOLD) signal variability and seed-based functional connectivity were assessed pre- and post-treatment. A mediation analysis was performed to test whether BOLD signal variability mediates the relationship between treatment group and moderate-to-severe headache days. RESULTS: The active M1 HD-tDCS group showed reduced BOLD variability in the spinal trigeminal nucleus (SpV) and thalamus, but increased variability in the rostral anterior cingulate cortex (rACC) compared to the sham group. Connectivity decreased between medial pulvinar-temporal pole, medial dorsal-precuneus, and the ventral posterior medial nucleus-SpV, but increased between the rACC-amygdala, and the periaqueductal gray-parahippocampal gyrus. Changes in medial pulvinar variability mediated the reduction in moderate-to-severe headache days at one-month post-treatment. CONCLUSIONS: M1 HD-tDCS alters BOLD signal variability and connectivity in the trigeminal somatosensory and modulatory pain system, potentially alleviating migraine headache attacks. SIGNIFICANCE: M1 HD-tDCS realigns brain signal variability and connectivity in migraineurs closer to healthy control levels.

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BACKGROUND CONTEXT: The moment-to-moment variability of resting-state brain activity has been suggested to play an active role in chronic pain. PURPOSE: To investigate preoperative alterations in regional blood-oxygen-level-dependent signal variability (BOLDsv) and inter-regional dynamic functional connectivity (dFC) in individuals with degenerative cervical myelopathy (DCM), and their potential association with postoperative axial pain severity. STUDY DESIGN: Cross-sectional study. PATIENT SAMPLE: Resting-state functional magnetic resonance imaging was obtained in 42 migraine individuals and 40 healthy controls (HCs). OUTCOME MEASURES: We calculated the standard deviation (SD) of the BOLD time-series at each voxel and the SD and mean of the dynamic conditional correlation between the brain regions which showed significant group differences in BOLDsv. METHODS: A group comparison was conducted using whole-brain voxel-wise analysis of the standard deviation (SD) of the BOLD time-series which was a measure of the BOLDsv. The brain areas displaying notable group discrepancies in BOLDsv were utilized to outline regions of interest (ROIs). To determine the strength/variability of the dFC, the mean and SD of the dynamic conditional correlation were calculated within these ROIs. Moreover, the postoperative axial pain (PAP) severity of patients was evaluated. RESULTS: Our results revealed that DCM patients with postoperative axial pain (PAP) demonstrated considerably increased BOLDsv in the bilateral thalamus and right insular, but significantly lower BOLDsv in the right S1. By applying dynamic functional connectivity (dFC) analysis, we found that DCM patients with PAP exhibited greater fluctuation of dFC in the thalamo-cortical pathway (specifically, thalamus-S1), when compared to HCs and patients without PAP (nPAP). Lastly, we established that dysfunctional BOLDsv and dFC in the ascending pain pathway were positively associated with the severity of PAP in DCM patients. CONCLUSION: Our results indicate a potential correlation between impaired pain ascending pathway and postoperative axial pain in DCM patients. These findings could potentially spark novel treatment approaches for individuals experiencing preoperative axial pain.

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A large body of evidence suggests that brain signal complexity (BSC) may be an important indicator of healthy brain functioning or alternately, a harbinger of disease and dysfunction. However, despite recent progress our current understanding of how BSC emerges and evolves in large-scale networks, and the factors that shape these dynamics, remains limited. Here, we utilized resting-state functional near-infrared spectroscopy (rs-fNIRS) to capture and characterize the nature and time course of BSC dynamics within large-scale functional networks in 107 healthy participants ranging from 6-13 years of age. Age-dependent increases in spontaneous BSC were observed predominantly in higher-order association areas including the default mode (DMN) and attentional (ATN) networks. Our results also revealed asymmetrical developmental patterns in BSC that were specific to the dorsal and ventral ATN networks, with the former showing a left-lateralized and the latter demonstrating a right-lateralized increase in BSC. These age-dependent laterality shifts appeared to be more pronounced in females compared to males. Lastly, using a machine-learning model, we showed that BSC is a reliable predictor of chronological age. Higher-order association networks such as the DMN and dorsal ATN demonstrated the most robust prognostic power for predicting ages of previously unseen individuals. Taken together, our findings offer new insights into the spatiotemporal patterns of BSC dynamics in large-scale intrinsic networks that evolve over the course of childhood and adolescence, suggesting that a network-based measure of BSC represents a promising approach for tracking normative brain development and may potentially aid in the early detection of atypical developmental trajectories.

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OBJECTIVES: Although primary motor cortex (M1) transcranial direct current stimulation (tDCS) has an analgesic effect in fibromyalgia (FM), its neural mechanism remains elusive. We investigated whether M1-tDCS modulates a regional temporal variability of blood-oxygenation-level-dependent (BOLD) signals, an indicator of the brain's flexibility and efficiency and if this change is associated with pain improvement. MATERIALS AND METHODS: In a within-subjects cross-over design, 12 female FM patients underwent sham and active tDCS on five consecutive days, respectively. Each session was performed with an anode placed on the left M1 and a cathode on the contralateral supraorbital region. The subjects also participated in resting-state functional magnetic resonance imaging (fMRI) at baseline and after sham and active tDCS. We compared the BOLD signal variability (SDBOLD), defined as the standard deviation of the BOLD time-series, between the tDCS conditions. Baseline SDBOLD was compared to 15 healthy female controls. RESULTS: At baseline, FM patients showed reduced SDBOLD in the ventromedial prefrontal cortex (vmPFC), lateral PFC, and anterior insula and increased SDBOLD in the posterior insula compared to healthy controls. After active tDCS, compared to sham, we found an increased SDBOLD in the left rostral anterior cingulate cortex (rACC), lateral PFC, and thalamus. After sham tDCS, compared to baseline, we found a decreased SDBOLD in the dorsomedial PFC and posterior cingulate cortex/precuneus. Interestingly, after active tDCS compared to sham, pain reduction was correlated with an increased SDBOLD in the rACC/vmPFC but with a decreased SDBOLD in the posterior insula. CONCLUSION: Our findings suggest that M1-tDCS might revert temporal variability of fMRI signals in the rACC/vmPFC and posterior insula linked to FM pain. Changes in neural variability would be part of the mechanisms underlying repetitive M1-tDCS analgesia in FM.

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The current limited understanding of tinnitus neurophysiology is one of the major obstacles in developing effective treatments for chronic tinnitus. As such, there is an urgent need for knowledge on underlying neural and/or neurobehavioral correlates that might function as potential biomarkers for tinnitus. We aimed to develop a model for the detection of tinnitus cases based on such potential biomarkers. In a first step, data from twenty patients suffering from chronic tinnitus, but no concurrent hearing loss or psychological complaints, were compared to data from twenty matched controls. Cortical auditory evoked potentials (CAEP) were elicited using a standard oddball paradigm. Source estimation and brain signal variability were analyzed to investigate putative differences between tinnitus patients and controls. Other examinations included standard audiometry, speech understanding in quiet and noisy conditions, and cognitive testing using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The P300 component, a response to unexpected but relevant stimuli, was significantly reduced in the tinnitus group. Source estimation revealed that the response of tinnitus patients was characterized by a decreased activity in temporal cortex, parahippocampus and insula. Brain signal variability on fine time scales was significantly higher in the tinnitus group, suggesting that tinnitus patients rely more strongly on local information processing. Furthermore, tinnitus was associated with a decreased cognitive performance, especially on tasks measuring delayed memory. In a second step, a logistic regression model was constructed based on CAEP activity, brain signal variability and RBANS scores. This model performed significantly above chance level when detecting tinnitus cases in an unseen dataset (accuracy of 75%, area under the ROC curve of 0.86). The successful classification between tinnitus cases and controls demonstrates the potential value of the proposed combination of biomarkers. Moreover, the identified associations between tinnitus, auditory evoked activity and cognitive performance point towards a significant contribution of top-down information processing in the perception of tinnitus.

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Neurons in the brain are seldom perfectly quiet. They continually receive input and generate output, resulting in highly variable patterns of ongoing activity. Yet the functional significance of this variability is not well understood. If brain signal variability is functionally relevant and serves as an important indicator of cognitive function, then it should be highly sensitive to the precise manner in which a cognitive system is engaged and/or relate strongly to differences in behavioral performance. To test this, we examined EEG activity in younger adults as they performed a cognitive skill learning task and during rest. Several measures of EEG variability and signal strength were calculated in overlapping time windows that spanned the trial interval. We performed a systematic examination of the factors that most strongly influenced the variability and strength of EEG activity. First, we examined the relative sensitivity of each measure to across-subject variation (within blocks) and across-block variation (within subjects). We found that the across-subject variation in EEG variability and signal strength was much stronger than the across-block variation. Second, we examined the sensitivity of each measure to different sources of across-block variation during skill acquisition. We found that key task-driven changes in EEG activity were best reflected in changes in the strength, rather than the variability, of EEG activity. Lastly, we examined across-subject variation in each measure and its relationship with behavior. We found that individual differences in response time measures were best reflected in individual differences in the variability, rather than the strength, of EEG activity. Importantly, we found that individual differences in EEG variability related strongly to stable indicators of subject identity rather than dynamic indicators of subject performance. We therefore suggest that EEG variability may provide a more sensitive subject-driven measure of individual differences than task-driven signal of interest.

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Aging is associated with cognitive impairment, but there are large individual differences in these declines. One neural measure that is lower in older adults and predicts these individual differences is moment-to-moment brain signal variability. Testing the assumption that GABA should heighten neural variability, we examined whether reduced brain signal variability in older, poorer performing adults could be boosted by increasing GABA pharmacologically. Brain signal variability was estimated using fMRI in 20 young and 24 older healthy human adults during placebo and GABA agonist sessions. As expected, older adults exhibited lower signal variability at placebo, and, crucially, GABA agonism boosted older adults' variability to the levels of young adults. Furthermore, poorer performing older adults experienced a greater increase in variability on drug, suggesting that those with more to gain benefit the most from GABA system potentiation. GABA may thus serve as a core neurochemical target in future work on aging- and cognition-related human brain dynamics.SIGNIFICANCE STATEMENT Prior research indicates that moment-to-moment brain signal variability is lower in older, poorer performing adults. We found that this reduced brain signal variability could be boosted through GABA agonism in older adults to the levels of young adults and that this boost was largest in the poorer performing older adults. These results provide the first evidence that brain signal variability can be restored by increasing GABAergic activity and suggest the promise of developing interventions targeting inhibitory systems to help slow cognitive declines in healthy aging.

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Reduced moment-to-moment blood oxygen level-dependent (BOLD) signal variability has been consistently linked to advanced age and poorer cognitive performance, showing potential as a functional marker of brain aging. To date, however, this promise has rested exclusively on cross-sectional comparisons. In a sample of 74 healthy adults, we provide the first longitudinal evidence linking individual differences in BOLD variability, age, and performance across multiple cognitive domains over an average period of 2.5 years. As expected, those expressing greater loss of BOLD variability also exhibited greater decline in cognition. The fronto-striato-thalamic system emerged as a core neural substrate for these change-change associations. Preservation of signal variability within regions of the fronto-striato-thalamic system also cohered with preservation of functional integration across regions of this system, suggesting that longitudinal maintenance of "local" dynamics may require across-region communication. We therefore propose this neural system as a primary target in future longitudinal studies on the neural substrates of cognitive aging. Given that longitudinal change-change associations between brain and cognition are notoriously difficult to detect, the presence of such an association within a relatively short follow-up period bolsters the promise of brain signal variability as a viable, experimentally sensitive probe for studying individual differences in human cognitive aging.

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BACKGROUND: The moment-to-moment variability of resting-state brain activity has been suggested to play an active role in chronic pain. Here, we investigated the regional blood-oxygen-level-dependent signal variability (BOLDSV) and inter-regional dynamic functional connectivity (dFC) in the interictal phase of migraine and its relationship with the attack severity. METHODS: We acquired resting-state functional magnetic resonance imaging from 20 migraine patients and 26 healthy controls (HC). We calculated the standard deviation (SD) of the BOLD time-series at each voxel as a measure of the BOLD signal variability (BOLDSV) and performed a whole-brain voxel-wise group comparison. The brain regions showing significant group differences in BOLDSV were used to define the regions of interest (ROIs). The SD and mean of the dynamic conditional correlation between those ROIs were calculated to measure the variability and strength of the dFC. Furthermore, patients' experimental pain thresholds and headache pain area/intensity levels during the migraine ictal-phase were assessed for clinical correlations. RESULTS: We found that migraineurs, compared to HCs, displayed greater BOLDSV in the ascending trigeminal spinal-thalamo-cortical pathways, including the spinal trigeminal nucleus, pulvinar/ventral posteromedial (VPM) nuclei of the thalamus, primary somatosensory cortex (S1), and posterior insula. Conversely, migraine patients exhibited lower BOLDSV in the top-down modulatory pathways, including the dorsolateral prefrontal (dlPFC) and inferior parietal (IPC) cortices compared to HCs. Importantly, abnormal interictal BOLDSV in the ascending trigeminal spinal-thalamo-cortical and frontoparietal pathways were associated with the patient's headache severity and thermal pain sensitivity during the migraine attack. Migraineurs also had significantly lower variability and greater strength of dFC within the thalamo-cortical pathway (VPM-S1) than HCs. In contrast, migraine patients showed greater variability and lower strength of dFC within the frontoparietal pathway (dlPFC-IPC). CONCLUSIONS: Migraine is associated with alterations in temporal signal variability in the ascending trigeminal somatosensory and top-down modulatory pathways, which may explain migraine-related pain and allodynia. Contrasting patterns of time-varying connectivity within the thalamo-cortical and frontoparietal pathways could be linked to abnormal network integrity and instability for pain transmission and modulation.

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Adopting particular decision biases allows organisms to tailor their choices to environmental demands. For example, a liberal response strategy pays off when target detection is crucial, whereas a conservative strategy is optimal for avoiding false alarms. Using conventional time-frequency analysis of human electroencephalographic (EEG) activity, we previously showed that bias setting entails adjustment of evidence accumulation in sensory regions (Kloosterman et al., 2019), but the presumed prefrontal signature of a conservative-to-liberal bias shift has remained elusive. Here, we show that a liberal bias shift is reflected in a more unconstrained neural regime (boosted entropy) in frontal regions that is suited to the detection of unpredictable events. Overall EEG variation, spectral power and event-related potentials could not explain this relationship, highlighting that moment-to-moment neural variability uniquely tracks bias shifts. Neural variability modulation through prefrontal cortex appears instrumental for permitting an organism to adapt its biases to environmental demands.

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Definitions of what constitutes the 'signal of interest' in neuroscience can be controversial, due in part to continuously evolving notions regarding the significance of spontaneous neural activity. This review highlights how the challenge of separating brain signal from noise has led to new conceptualizations of brain functional organization at both the micro- and macroscopic level. Recent debates in the functional neuroimaging community surrounding artifact removal processes have revived earlier discussions surrounding how to appropriately isolate and measure neuronal signals against a background of noise from various sources. Insights from electrophysiological studies and computational modeling can inform current theory and data analytic practices in human functional neuroimaging, given that signal and noise may be inextricably linked in the brain.

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Research suggests that the aging relates to variability of resting-state fMRI (rs-fMRI) signal and the functional connectivity. However, the association between the spatial and temporal activity of resting-state fMRI signal was less documented. We recruited 477 healthy Han Chinese participants, who were separated into young, middle and old groups to investigate the relationship between the variability and global functional connectivity (gFC) in different age ranges using standard deviation (SD) of time series and gFC, respectively. Our analysis revealed the changing patterns during healthy aging: 1) 17 brain regions(Olfactory_L, Orbital_L etc.) were identified to have significant association of age with both SD and gFC respectively by linear regression analysis; 2) Two typical associations could be observed between SD and gFC: positive and negative correlations; 3) The variation ratio of SD to gFC was changing with age at the voxel level by using unsupervised clustering method. It is the first time to combine voxel-wise variability and gFC together for the study of age-related changes with rs-fMRI signal. This study may provide a new clue for understanding the synchronization of human brain based on SD and gFC due to the effect of aging.

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Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability - identified with either hemodynamic or electrophysiological methods - reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25 â€‹± â€‹3 years, N â€‹= â€‹135) and older (67 â€‹± â€‹4 years, N â€‹= â€‹54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1-12 â€‹Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15-25 â€‹Hz) were seen predominantly in temporal brain regions. There were significant sex differences in EEG signal variability in various brain regions while no significant sex differences were observed in BOLD signal variability. Bivariate and multivariate correlation analyses revealed no significant associations between EEG- and fMRI-based variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.

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Over the past few decades, the quest for discovering the brain substrates of the affect to understand the underlying neural basis of the human's emotions has resulted in substantial and yet contrasting results. Whereas some point at distinct and independent brain systems for the Positive and Negative affects, others propose the presence of flexible brain regions. In this respect, there are two factors that are common among these previous studies. First, they all focused on the change in brain activation, thereby neglecting the findings that indicate that the stimuli with equivalent sensory and behavioral processing demands may not necessarily result in differential brain activation. Second, they did not take into consideration the brain regional interactivity and the findings that identify that the signals from individual cortical neurons are shared across multiple areas and thus concurrently contribute to multiple functional pathways. To address these limitations, we performed Granger causal analysis on the electroencephalography (EEG) recordings of the human subjects who watched movie clips that elicited Negative, Neutral, and Positive affects. This allowed us to look beyond the brain regional activation in isolation to investigate whether the brain regional interactivity can provide further insights for understanding the neural substrates of the affect. Our results indicated that the differential affect states emerged from subtle variation in information flow of the brain cortical regions that were in both hemispheres. They also showed that these regions that were rather common between affect states than distinct to a specific affect were characterized with both short- as well as long-range information flow. This provided evidence for the presence of simultaneous integration and differentiation in the brain functioning that leads to the emergence of different affects. These results are in line with the findings on the presence of intrinsic large-scale interacting brain networks that underlie the production of psychological events. These findings can help advance our understanding of the neural basis of the human's emotions by identifying the signatures of differential affect in subtle variation that occurs in the whole-brain cortical flow of information.

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Brain signaling occurs across a wide range of spatial and temporal scales, and analysis of brain signal variability and synchrony has attracted recent attention as markers of intelligence, cognitive states, and brain disorders. However, current technologies to measure brain signals in humans have limited resolutions either in space or in time and cannot fully capture spatiotemporal variability, leaving it untested whether temporal variability and spatiotemporal synchrony are valid and reliable proxy of spatiotemporal variability in vivo. Here we used optical voltage imaging in mice under anesthesia and wakefulness to monitor cortical voltage activity at both high spatial and temporal resolutions to investigate functional connectivity (FC, a measure of spatiotemporal synchronization), Multi-Scale Entropy (MSE, a measure of temporal variability), and their relationships to Regional Entropy (RE, a measure of spatiotemporal variability). We observed that across cortical space, MSE pattern can largely explain RE pattern at small and large temporal scales with high positive and negative correlation respectively, while FC pattern strongly negatively associated with RE pattern. The time course of FC and small scale MSE tightly followed that of RE, while large scale MSE was more loosely coupled to RE. fMRI and EEG data simulated by reducing spatiotemporal resolution of the voltage imaging data or considering hemodynamics yielded MSE and FC measures that still contained information about RE based on the high resolution voltage imaging data. This suggested that MSE and FC could still be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. Our results support the notion that FC and MSE are effective biomarkers for brain states, and provide a promising viewpoint to unify these two principal domains in human brain data analysis.

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Generalized anxiety disorder (GAD) is characterized by a chronic, continuous symptom of worry and exaggerated startle response. Although functional abnormality in GAD has been widely studied using functional magnetic resonance imaging (fMRI), the dynamic signatures of GAD are not fully understood. As a vital index of brain function, brain signal variability (BSV) reflects the capacity of state transition of neural activities. In this study, we recruited 47 patients with GAD and 38 healthy controls (HCs) to investigate whether or not BSV is altered in patients with GAD by measuring the standard deviation of fMRI signal of each voxel. We found that patients with GAD exhibited decreased BSV in widespread regions including the visual network, sensorimotor network, frontoparietal network, limbic system, and thalamus, indicating an inflexible brain state transfer pattern in these systems. Furthermore, the correlation between BSV and trait anxiety score was prone to be positive in patients with GAD but negative in HCs. The opposite relationships between BSV and anxiety level in the two groups indicate that the brain with moderate anxiety level may stay in the most stable rather than in the flexible state. As the first study of BSV in GAD, we revealed extensively decreased BSV in patients with GAD similar to that in other mental disorders but with a non-linear relationship between BSV and anxiety level indicating a novel neurodynamic mechanism of the anxious brain.