Co-activation Patterns Research Articles

Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study.

The resting‐state human brain is a dynamic system that shows frequency‐dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub‐bands from slow‐5 (0.01–0.027 Hz), slow‐4 (0.027–0.073 Hz), slow‐3 (0.073–0.198 Hz), to slow‐2 (0.198–0.25 Hz), in addition to the typical low‐frequency range (0.01–0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow‐5, 4, and 3, but not in slow‐2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between‐state transitions. Specifically, from slow‐5 to slow‐4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow‐5. Using leave‐one‐pair‐out, hold‐out and resampling validations, the highest classification accuracy (84%) was achieved by slow‐4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.

The inferior frontal sulcus: Cortical segregation, molecular architecture and function

The inferior frontal sulcus is conceptualized as the landmark delineating ventro-from dorsolateral prefrontal cortex. Functional imaging studies report activations within the sulcus during tasks addressing cognitive control and verbal working memory, while their microstructural correlates are not well defined. Existing microstructural maps, e.g., Brodmann's map, do not distinguish separate areas within the sulcus. We identified six new areas in the inferior frontal sulcus and its junction to the precentral sulcus, ifs1-4, ifj1-ifj2, by combined cytoarchitectonic analysis and receptor autoradiography. A hierarchical cluster analysis of receptor densities of these and neighbouring prefrontal areas revealed that they form a distinct cluster within the prefrontal cortex. Major interhemispheric differences were found in both cyto- and receptorarchitecture. The function of cytoarchitectonically identified areas was explored by comparing probabilistic maps of the areas in stereotaxic space with their functions and co-activation patterns as analysed by means of a coordinate-based meta-analysis. We found a bilateral involvement in working memory, as well as a lateralization of different language-related processes to the left hemisphere, and of music processing and attention to the right-hemispheric areas. Particularly ifj2 might act as a functional hub between the networks. The cytoarchitectonic maps and receptor densities provide a powerful tool to further elucidate the function of these areas. The maps are available through the Human Brain Atlas of the Human Brain Project and serve in combination with the information on the cyto- and receptor architecture of the areas as a resource for brain models and simulations.

Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression.

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCNwere associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response

BackgroundDelivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response. MethodsThe Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study randomized adults with major depressive disorder to 8 weeks of either sertraline or placebo, and depression severity was evaluated longitudinally. Participants completed resting-state neuroimaging pretreatment and again after 1 week of treatment (n = 259 eligible for analyses). Coactivation pattern analyses identified recurrent whole-brain states of spatial coactivation, and computed time spent in each state for each participant was the main dynamic measure. Multilevel modeling estimated the associations between pretreatment network dynamics and sertraline response and between early (pretreatment to 1 week) changes in network dynamics and sertraline response. ResultsDynamic network markers of early sertraline response included increased time in network states consistent with canonical default and salience networks, together with decreased time in network states characterized by coactivation of cingulate and ventral limbic or temporal regions. The effect of sertraline on depression recovery was mediated by these dynamic network changes. In contrast, early changes in dynamic functioning of corticolimbic and frontoinsular-default networks were related to patterns of symptom recovery common across treatment groups. ConclusionsDynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.

Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.

Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.

Equalization of Brain State Occupancy Accompanies Cognitive Impairment in Cerebral Small Vessel Disease

BackgroundCognitive impairment is a hallmark of cerebral small vessel disease (cSVD). Functional magnetic resonance imaging has highlighted connections between patterns of brain activity and variability in behavior. We aimed to characterize the associations between imaging markers of cSVD, dynamic connectivity, and cognitive impairment. MethodsWe obtained magnetic resonance imaging and clinical data from the population-based Hamburg City Health Study. cSVD was quantified by white matter hyperintensities and peak-width of skeletonized mean diffusivity (PSMD). Resting-state blood oxygen level–dependent signals were clustered into discrete brain states, for which fractional occupancies (%) and dwell times (seconds) were computed. Cognition in multiple domains was assessed using validated tests. Regression analysis was used to quantify associations between white matter damage, spatial coactivation patterns, and cognitive function. ResultsData were available for 979 participants (ages 45–74 years, median white matter hyperintensity volume 0.96 mL). Clustering identified five brain states with the most time spent in states characterized by activation (+) or suppression (−) of the default mode network (DMN) (fractional occupancy: DMN+ = 25.1 ± 7.2%, DMN− = 25.5 ± 7.2%). Every 4.7-fold increase in white matter hyperintensity volume was associated with a 0.95-times reduction of the odds of occupying DMN+ or DMN−. Time spent in DMN-related brain states was associated with executive function. ConclusionsAssociations between white matter damage, whole-brain spatial coactivation patterns, and cognition suggest equalization of time spent in different brain states as a marker for cSVD-associated cognitive decline. Reduced gradients between brain states in association with brain damage and cognitive impairment reflect the dedifferentiation hypothesis of neurocognitive aging in a network-theoretical context.

Closed-loop neuromodulation for studying spontaneous activity and causality.

Having established that spontaneous brain activity follows meaningful coactivation patterns and correlates with behavior, researchers have turned their attention to understanding its function and behavioral significance. We suggest closed-loop neuromodulation as a neural perturbation tool uniquely well suited for this task. Closed-loop neuromodulation has primarily been viewed as an interventionist tool to teach subjects to directly control their own brain activity. We examine an alternative operant conditioning model of closed-loop neuromodulation which, through implicit feedback, can manipulate spontaneous activity at the network level, without violating the spontaneous or endogenous nature of the signal, thereby providing a direct test of network causality.

Functional Connectivity of the Brain Across Rodents and Humans.

Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain’s physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.

Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder.

Organized patterns of system-wide neural activity adapt fluently within the brain to adjust behavioral performance to environmental demands. In major depressive disorder (MD), markedly different co-activation patterns across the brain emerge from a rather similar structural substrate. Despite the application of advanced methods to describe the functional architecture, e.g., between intrinsic brain networks (IBNs), the underlying mechanisms mediating these differences remain elusive. Here we propose a novel complementary approach for quantifying the functional relations between IBNs based on the Kuramoto model. We directly estimate the Kuramoto coupling parameters (K) from IBN time courses derived from empirical fMRI data in 24 MD patients and 24 healthy controls. We find a large pattern with a significant number of Ks depending on the disease severity score Hamilton D, as assessed by permutation testing. We successfully reproduced the dependency in an independent test data set of 44 MD patients and 37 healthy controls. Comparing the results to functional connectivity from partial correlations (FC), to phase synchrony (PS) as well as to first order auto-regressive measures (AR) between the same IBNs did not show similar correlations. In subsequent validation experiments with artificial data we find that a ground truth of parametric dependencies on artificial regressors can be recovered. The results indicate that the calculation of Ks can be a useful addition to standard methods of quantifying the brain's functional architecture.

Network-specific differences in transient brain activity at rest are associated with age-related reductions in motor performance

Multiple functional changes occur in the brain with increasing age. Among those, older adults typically display more restricted fluctuations of brain activity, both during resting-state and task execution. These altered dynamic patterns have been linked to reduced task performance across multiple behavioral domains. Windowed functional connectivity, which is typically employed in the study of connectivity dynamics, however, might not be able to properly characterize moment-to-moment variations of individual networks. In the present study, we used innovation-driven co-activation patterns (ICAP) to overcome this limitation and investigate the length (duration) and frequency (innovation) in which various brain networks emerged across the adult lifespan (N= 92) during a resting-state period. We identified a link between increasing age and a tendency to engage brain areas with distinct functional associations simultaneously as a single network. The emergence of isolated and spatially well-defined visual, motor, frontoparietal, and posterior networks decreased with increased age. This reduction in dynamics of specialized networks mediated age-related performance decreases (i.e., increases in interlimb interference) in a bimanual motor task. Altogether, our findings demonstrated that older compared to younger adults tend to activate fewer network configurations, which include multiple functionally distinct brain areas. The reduction in independent emergence of functionally well-defined and task-relevant networks may reflect an expression of brain dedifferentiation and is likely associated with functional modulatory deficits, negatively impacting motor behavior.

Body Mass Index Moderates Brain Dynamics and Executive Function: A Structural Equation Modeling Approach

Obesity is associated with negative physical and mental health outcomes. Being overweight/obese is also associated with executive functioning impairments and structural changes in the brain. However, the impact of body mass index (BMI) on the relationship between brain dynamics and executive function (EF) is unknown. The goal of the study was to assess the modulatory effects of BMI on brain dynamics and EF. A large sample of publicly available neuroimaging and neuropsychological assessment data collected from 253 adults (18–45 years; mean BMI 26.95 kg/m2 ± 5.90 SD) from the Nathan Kline Institute (NKI) were included (http://fcon_1000.projects.nitrc.org/indi/enhanced/). Participants underwent resting-state functional MRI and completed the Delis-Kaplan Executive Function System (D-KEFS) test battery (1). Time series were extracted from 400 brain nodes and used in a co-activation pattern (CAP) analysis. Dynamic CAP metrics including dwell time (DT), frequency of occurrence, and transitions were computed. Multiple measurement models were compared based on model fit with indicators from the D-KEFS assigned a priori (shifting, inhibition, and fluency). Multiple structural equation models were computed with interactions between BMI and the dynamic CAP metrics predicting the three latent factors of shifting, inhibition, and fluency while controlling for age, sex, and head motion. Models were assessed for the main effects of BMI and CAP metrics predicting the latent factors. A three-factor model (shifting, inhibition, and fluency) resulted in the best model fit. Significant interactions were present between BMI and CAP 2 (lateral frontoparietal (L-FPN), medial frontoparietal (M-FPN), and limbic nodes) and CAP 5 (dorsal frontoparietal (D-FPN), midcingulo-insular (M-CIN), somatosensory motor, and visual network nodes) DTs associated with shifting. A higher BMI was associated with a positive relationship between CAP DTs and shifting. Conversely, in average and low BMI participants, a negative relationship was seen between CAP DTs and shifting. Our findings indicate that BMI moderates the relationship between brain dynamics of networks important for cognitive control and shifting, an index of cognitive flexibility. Furthermore, higher BMI is linked with altered brain dynamic patterns associated with shifting.

Inter-Person Differences in Isometric Coactivations of Triceps Surae and Tibialis Anterior Decrease in Young, but Not in Older Adults After 14 Days of Bed Rest

We examined activation patterns of the gastrocnemius medialis (GM), gastrocnemius lateralis (GL), soleus (SO), and tibialis anterior (TA) muscles in eight older (58.4 ± 3.3 years) and seven young (23.1 ± 2.9 years) participants, before and after 14 days of horizontal bed rest. Visual feedback on the exerted muscle torque was provided to the participants. The discharge patterns of individual motor units (MUs) were studied in three repetitions of isometric plantar flexion at 30 and 60% of Maximum Voluntary Contraction (MVC), before, and 1 day after the 14-day bed rest, respectively. In the GL and GM muscles, the older participants demonstrated higher MU discharge rates than the young, regardless of the contraction level, both before and after the bed rest. In the TA and SO muscles, the differences between the older and young participants were less consistent. Detailed analysis revealed person-specific changes in the MU discharge rates after the bed rest. To quantify the coactivation patterns we calculated the correlation coefficients between the cumulative spike trains of identified MUs from each muscle, and measured the root mean square difference of the correlation coefficients between the trials of the same session (intra-session variability) and between different sessions (inter-session variability) in each participant (intra-person comparison) and across participants (inter-person comparison). In the intra-person comparison, the inter-session variability was higher than the intra-session variability, either before or after the bed rest. At 60% MVC torque, the young demonstrated higher inter-person variability of coactivation than the older participants, but this variability decreased significantly after the bed rest. In older participants, inter-person variability was consistently lower at 60% than at 30% MVC torque. In young participants, inter-person variability became lower at 60% than at 30% MVC torque only after the bed rest. Precaution is required when analyzing the MU discharge and coactivation patterns, as individual persons demonstrate individual adaptations to aging or bed rest.

Cognate facilitation effect during auditory comprehension of a second language: A visual world eye-tracking study

Aims and Objectives: The cognate facilitation effect (CFE) is a robust effect in language production and visual word comprehension, but evidence for CFE during auditory comprehension is still scarce. This study aimed to explore the CFE during auditory comprehension of a second language (L2) while manipulating proficiency in the L2 and cognate type. These two variables are known to influence the CFE. Methodology: Low and highly proficient Spanish–English bilinguals listened to individual words in their L2, English, that shared high, low, and no phonological overlap (PO) with their native language Spanish. We designed a visual world paradigm task that consisted of selecting an image shown as a spoken word unfolded in time while eye movements were recorded. Data and Analysis: Response times revealed a clear CFE in low proficiency bilinguals, while this effect was absent in highly proficient bilinguals. The eye-tracking (ET) data showed late coactivation of low-PO words and, surprisingly, no coactivation of high-PO words in low proficiency bilinguals. Highly proficient bilinguals showed no clear pattern of language coactivation in the ET data. The English monolingual control group showed no effects during the critical time window. Conclusions: These results are interpreted within the framework of L2 processing models. At low levels of proficiency, the PO between translations facilitates access to meaning. On the other hand, highly proficient bilinguals no longer benefit from the PO between translations, at least for concrete and simple nouns. Originality: The findings demonstrate a clear CFE in auditory comprehension. Proficiency in L2 and PO modulated the effect, as shown in both the response time and in the ET data, respectively. Implications: These findings suggest that at low levels of L2 proficiency, learners more easily access the conceptual information if the auditory input is similar to their native language. Nevertheless, as proficiency increases, this facilitation disappears.

On co-activation pattern analysis and non-stationarity of resting brain activity

The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the dynamic characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, the statistical nature of the dynamics captured by these techniques needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity.

Dynamic reconfiguration of frequency-specific cortical coactivation patterns during psychedelic and anesthetized states induced by ketamine

Recent neuroimaging studies have demonstrated that spontaneous brain activity exhibits rich spatiotemporal structure that can be characterized as the exploration of a repertoire of spatially distributed patterns that recur over time. The repertoire of brain states may reflect the capacity for consciousness, since general anesthetics suppress and psychedelic drugs enhance such dynamics. However, the modulation of brain activity repertoire across varying states of consciousness has not yet been studied in a systematic and unified framework. As a unique drug that has both psychedelic and anesthetic properties depending on the dose, ketamine offers an opportunity to examine brain reconfiguration dynamics along a continuum of consciousness. Here we investigated the dynamic organization of cortical activity during wakefulness and during altered states of consciousness induced by different doses of ketamine. Through k-means clustering analysis of the envelope data of source-localized electroencephalographic (EEG) signals, we identified a set of recurring states that represent frequency-specific spatial coactivation patterns. We quantified the effect of ketamine on individual brain states in terms of fractional occupancy and transition probabilities and found that ketamine anesthesia tends to shift the configuration toward brain states with low spatial variability. Furthermore, by assessing the temporal dynamics of the occurrence and transitions of brain states, we showed that subanesthetic ketamine is associated with a richer repertoire, while anesthetic ketamine induces dynamic changes in brain state organization, with the repertoire richness evolving from a reduced level to one comparable to that of normal wakefulness before recovery of consciousness. These results provide a novel description of ketamine's modulation of the dynamic configuration of cortical activity and advance understanding of the neurophysiological mechanism of ketamine in terms of the spatial, temporal, and spectral structures of underlying whole-brain dynamics.

Unique spatiotemporal fMRI dynamics in the awake mouse brain

SummaryHuman imaging studies have shown that spontaneous brain activity exhibits stereotypic spatiotemporal reorganization in awake, conscious conditions with respect to minimally conscious states. However, whether and how this phenomenon can be generalized to lower mammalian species remains unclear. Leveraging a robust protocol for resting-state fMRI (rsfMRI) mapping in non-anesthetized, head-fixed mice, we investigated functional network topography and dynamic structure of spontaneous brain activity in wakeful animals. We found that rsfMRI networks in the awake state, while anatomically comparable to those observed under anesthesia, are topologically configured to maximize interregional communication, departing from the underlying community structure of the mouse axonal connectome. We further report that rsfMRI activity in wakeful animals exhibits unique spatiotemporal dynamics characterized by a state-dependent, dominant occurrence of coactivation patterns encompassing a prominent participation of arousal-related forebrain nuclei and functional anti-coordination between visual-auditory and polymodal cortical areas. We finally show that rsfMRI dynamics in awake mice exhibits a stereotypical temporal structure, in which state-dominant coactivation patterns are configured as network attractors. These findings suggest that spontaneous brain activity in awake mice is critically shaped by state-specific involvement of basal forebrain arousal systems and document that its dynamic structure recapitulates distinctive, evolutionarily relevant principles that are predictive of conscious states in higher mammalian species.

Resting state brain dynamics: Associations with childhood sexual abuse and major depressive disorder

Early life stress (ELS) and major depressive disorder (MDD) share neural network abnormalities. However, it is unclear how ELS and MDD may separately and/or jointly relate to brain networks, and whether neural differences exist between depressed individuals with vs without ELS. Moreover, prior work evaluated static versus dynamic network properties, a critical gap considering brain networks show changes in coordinated activity over time. Seventy-one unmedicated females with and without childhood sexual abuse (CSA) histories and/or MDD completed a resting state scan and a stress task in which cortisol and affective ratings were collected. Recurring functional network co-activation patterns (CAPs) were examined and time in CAP (number of times each CAP is expressed) and transition frequencies (transitioning between different CAPs) were computed. The effects of MDD and CSA on CAP metrics were examined and CAP metrics were correlated with depression and stress-related variables. Results showed that MDD, but not CSA, related to CAP metrics. Specifically, individuals with MDD (N = 35) relative to HCs (N = 36), spent more time in a posterior default mode (DMN)-frontoparietal network (FPN) CAP and transitioned more frequently between posterior DMN-FPN and prototypical DMN CAPs. Across groups, more time spent in a posterior DMN-FPN CAP and greater DMN-FPN and prototypical DMN CAP transition frequencies were linked to higher rumination. Imbalances between the DMN and the FPN appear central to MDD and might contribute to MDD-related cognitive dysfunction, including rumination. Unexpectedly, CSA did not modulate such dysfunctions, a finding that needs to be replicated by future studies with larger sample sizes.

Task-Based Co-Activation Patterns Reliably Predict Resting State Canonical Network Engagement During Development

Task-Based Co-Activation Patterns Reliably Predict Resting State Canonical Network Engagement During Development

Robotically-induced hallucination triggers subtle changes in brain network transitions

The perception that someone is nearby, although nobody can be seen or heard, is called presence hallucination (PH). Being a frequent hallucination in patients with Parkinson's disease, it has been argued to be indicative of a more severe and rapidly advancing form of the disease, associated with psychosis and cognitive decline. PH may also occur in healthy individuals and has recently been experimentally induced, in a controlled manner during fMRI, using MR-compatible robotics and sensorimotor stimulation. Previous neuroimaging correlates of such robot-induced PH, based on conventional time-averaged fMRI analysis, identified altered activity in the posterior superior temporal sulcus and inferior frontal gyrus in healthy individuals. However, no link with the strength of the robot-induced PH was observed, and such activations were also associated with other sensations induced by robotic stimulation. Here we leverage recent advances in dynamic functional connectivity, which have been applied to different psychiatric conditions, to decompose fMRI data during PH-induction into a set of co-activation patterns that are tracked over time, as to characterize their occupancies, durations, and transitions. Our results reveal that, when PH is induced, the identified brain patterns significantly and selectively increase their transition probabilities towards a specific brain pattern, centred on the posterior superior temporal sulcus, angular gyrus, dorso-lateral prefrontal cortex, and middle prefrontal cortex. This change is not observed in any other control conditions, nor is it observed in association with other sensations induced by robotic stimulation. The present findings describe the neural mechanisms of PH in healthy individuals and identify a specific disruption of the dynamics of network interactions, extending previously reported network dysfunctions in psychotic patients with hallucinations to an induced robot-controlled specific hallucination in healthy individuals.

Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain.

Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network’s internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4−12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli–such as default mode, dorsal attentional, and frontoparietal networks–are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain’s capability for continuous switching between two modes, which is crucial for the emergence of consciousness.