Brain State Transitions Research Articles

Longitudinal association of executive function and structural network controllability in the aging brain

Executive function encompasses effortful cognitive processes that are particularly susceptible to aging. Functional brain networks supporting executive function—such as the frontoparietal control network and the multiple demand system—have been extensively investigated. However, it remains unclear how structural networks facilitate and constrain the dynamics of functional networks to contribute to aging-related executive function declines. We examined whether changes in structural network modal controllability—a network’s ability to facilitate effortful brain state transitions that support cognitive functions—are associated with changes in executive function cross-sectionally and longitudinally. Diffusion-weighted imaging and neuropsychological testing were conducted at two time points (Time 1: ages 56 to 66, N = 172; Time 2: ages 61 to 70, N = 267) in community-dwelling men from the Vietnam Era Twin Study of Aging. An executive function factor score was computed from six neuropsychological tasks. Structural networks constructed from white matter connectivity were used to estimate modal controllability in control network and multiple demand system. We showed that higher modal controllability in control network and multiple demand system was associated with better executive function at Time 2, after controlling for age, young adult general cognitive ability, and physical health status. Moreover, changes in executive function over a period of 5 to 6 years (Time 1-Time 2, N = 105) were associated with changes in modal controllability of the multiple demand system and weakly in the control network over the same time period. These findings suggest that changes in the ability of structural brain networks in facilitating effortful brain state transitions may be a key neural mechanism underlying aging-related executive function declines and cognitive aging.

Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape

Psychedelics including lysergic acid diethylamide (LSD) and psilocybin temporarily alter subjective experience through their neurochemical effects. Serotonin 2a (5-HT2a) receptor agonism by these compounds is associated with more diverse (entropic) brain activity. We postulate that this increase in entropy may arise in part from a flattening of the brain’s control energy landscape, which can be observed using network control theory to quantify the energy required to transition between recurrent brain states. Using brain states derived from existing functional magnetic resonance imaging (fMRI) datasets, we show that LSD and psilocybin reduce control energy required for brain state transitions compared to placebo. Furthermore, across individuals, reduction in control energy correlates with more frequent state transitions and increased entropy of brain state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors (obtained from publicly available positron emission tomography (PET) data under non-drug conditions), we demonstrate an association between the 5-HT2a receptor and reduced control energy. Our findings provide evidence that 5-HT2a receptor agonist compounds allow for more facile state transitions and more temporally diverse brain activity. More broadly, we demonstrate that receptor-informed network control theory can model the impact of neuropharmacological manipulation on brain activity dynamics.

Brain-State Transitions, Responsibility, and Personal Identity.

This article examines the emerging possibility of "brain-state transitioning," in which one brain state is prompted through manipulating the dynamics of the active brain. The technique, still in its infancy, is intended to provide the basis for novel treatments for brain-based disorders. Although a detailed literature exists covering topics around brain-machine interfaces, where targets of brain-based activity include artificial limbs, hardware, and software, there is less concentration on the brain itself as a target for instrumental intervention. This article examines some of the science behind brain-state transitioning, before extending beyond current possibilities in order to explore philosophical and ethical questions about how transitions could be seen to impact on assessment of responsibility and personal identity. It concludes with some thoughts on how best to pursue this nascent approach while accounting for the philosophical and ethical issues.

Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls.

PurposeConventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer’s disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer’s disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls.MethodsWe used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies–Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05.ResultsWe identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04).ConclusionWe show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.

A spatio-temporal decomposition framework for dynamic functional connectivity in the human brain

Much recent attention has been directed toward investigating the spatial and temporal organization of brain dynamics, but the rules which constrain the variation of spatio-temporal organization in functional connectivity under different brain states remain unclear. Here, we developed a novel computational approach based on tensor decomposition and regularization to represent dynamic functional connectivity as a linear combination of dynamic modules and time-varying weights. In this approach, dynamic modules represent co-activating functional connectivity patterns, and time-varying weights represent the temporal expression of dynamic modules. We applied this dynamic decomposition model (DDM) on a resting-state fMRI dataset and found that whole-brain dynamic functional connectivity can be decomposed as a linear combination of eight dynamic modules which we summarize as ‘high order modules’ and ‘primary–high order modules’, according to their spatial attributes and correspondence with existing intrinsic functional brain networks. By clustering the time-varying weights, we identified five brain states including three major states and two minor states. We found that state transitions mainly occurred between the three major states, and that temporal variation of dynamic modules may contribute to brain state transitions. We then conceptualized the variability of weights as the flexibility of the corresponding dynamic modules and found that different dynamic modules exhibit different amounts of flexibility and contribute to different cognitive measures. Finally, we applied DDM to a schizophrenia resting-state fMRI dataset and found that atypical flexibility of dynamic modules correlates with impaired cognitive flexibility in schizophrenia. Overall, this work provides a quantitative framework that characterizes temporal variation in the topology of dynamic functional connectivity.

FNIRS-based brain state transition features to signify functional degeneration after Parkinson’s disease

Objective. Parkinson’s disease (PD) is a common neurodegenerative brain disorder, and early diagnosis is of vital importance for treatment. Existing methods are mainly focused on behavior examination, while the functional neurodegeneration after PD has not been well explored. This paper aims to investigate the brain functional variation of PD patients in comparison with healthy controls. Approach. In this work, we propose brain hemodynamic states and state transition features to signify functional degeneration after PD. Firstly, a functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation during dual-task walking from PD patients and healthy controls. Then, three brain states, named expansion, contraction, and intermediate states, were defined with respect to the oxyhemoglobin and deoxyhemoglobin responses. After that, two features were designed from a constructed transition factor and concurrent variations of oxy- and deoxy-hemoglobin over time, to quantify the transitions of brain states. Further, a support vector machine classifier was trained with the proposed features to distinguish PD patients and healthy controls. Main results. Experimental results showed that our method with the proposed brain state transition features achieved classification accuracy of 0.8200 and F score of 0.9091, and outperformed existing fNIRS-based methods. Compared with healthy controls, PD patients had significantly smaller transition acceleration and transition angle. Significance. The proposed brain state transition features well signify functional degeneration of PD patients and may serve as promising functional biomarkers for PD diagnosis.

Driving brain state transitions in major depressive disorder through external stimulation.

Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.

Arousal impacts distributed hubs modulating the integration of brain functional connectivity

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.

EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics.

The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.

Inferring Brain State Dynamics Underlying Naturalistic Stimuli Evoked Emotion Changes With dHA-HMM.

The brain functional mechanisms underlying emotional changes have been primarily studied based on the traditional task design with discrete and simple stimuli. However, the brain state transitions when exposed to continuous and naturalistic stimuli with rich affection variations remain poorly understood. This study proposes a dynamic hyperalignment algorithm (dHA) to functionally align the inter-subject neural activity. The hidden Markov model (HMM) was used to study how the brain dynamics responds to emotion during long-time movie-viewing activity. The results showed that dHA significantly improved inter-subject consistency and allowed more consistent temporal HMM states across participants. Afterward, grouping the emotions in a clustering dendrogram revealed a hierarchical grouping of the HMM states. Further emotional sensitivity and specificity analyses of ordered states revealed the most significant differences in happiness and sadness. We then compared the activation map in HMM states during happiness and sadness and found significant differences in the whole brain, but strong activation was observed during both in the superior temporal gyrus, which is related to the early process of emotional prosody processing. A comparison of the inter-network functional connections indicates unique functional connections of the memory retrieval and cognitive network with the cerebellum network during happiness. Moreover, the persistent bilateral connections among salience, cognitive, and sensorimotor networks during sadness may reflect the interaction between high-level cognitive networks and low-level sensory networks. The main results were verified by the second session of the dataset. All these findings enrich our understanding of the brain states related to emotional variation during naturalistic stimuli.

Quantifying brain state transition cost via Schrödinger Bridge.

Quantifying brain state transition cost is a fundamental problem in systems neuroscience. Previous studies utilized network control theory to measure the cost by considering a neural system as a deterministic dynamical system. However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost. Here, we propose a novel framework based on optimal control in stochastic systems. In our framework, we quantify the transition cost as the Kullback-Leibler divergence from an uncontrolled transition path to the optimally controlled path, which is known as Schrödinger Bridge. To test its utility, we applied this framework to functional magnetic resonance imaging data from the Human Connectome Project and computed the brain state transition cost in cognitive tasks. We demonstrate correspondence between brain state transition cost and the difficulty of tasks. The results suggest that our framework provides a general theoretical tool for investigating cognitive functions from the viewpoint of transition cost.

Neuron-Glia Interactions and Brain Circuits.

Recent evidence suggests that glial cells take an active role in a number of brain functions that were previously attributed solely to neurons. For example, astrocytes, one type of glial cells, have been shown to promote coordinated activation of neuronal networks, modulate sensory-evoked neuronal network activity, and influence brain state transitions during development. This reinforces the idea that astrocytes not only provide the "housekeeping" for the neurons, but that they also play a vital role in supporting and expanding the functions of brain circuits and networks. Despite this accumulated knowledge, the field of computational neuroscience has mostly focused on modeling neuronal functions, ignoring the glial cells and the interactions they have with the neurons. In this chapter, we introduce the biology of neuron-glia interactions, summarize the existing computational models and tools, and emphasize the glial properties that may be important in modeling brain functions in the future.

Anesthesia in mice activates discrete populations of neurons throughout the brain

The brain undergoes rapid, dramatic, and reversible transitioning between states of wakefulness and unconsciousness during natural sleep and in pathological conditions such as hypoxia, hypotension, and concussion. Transitioning can also be induced pharmacologically using general anesthetic agents. The effect is selective. Mobility, sensory perception, memory formation, and awareness are lost while numerous housekeeping functions persist. How is selective transitioning accomplished? Classically a handful of brainstem and diencephalic "arousal nuclei" have been implicated in driving brain-state transitions on the grounds that their net activity systematically varies with brain state. Here we used transgenic targeted recombination in active populations mice to label neurons active during wakefulness with one reporter and neurons active during pentobarbital-induced general anesthesia with a second, contrasting reporter. We found 'wake-on' and 'anesthesia-on' neurons in widely distributed regions-of-interest, but rarely encountered neurons labeled with both reporters. Nearly all labeled neurons were either wake-on or anesthesia-on. Thus, anesthesia-on neurons are not unique to the few nuclei discovered to date whose activity appears to increase during anesthesia. Rather neuronal populations selectively active during anesthesia are located throughout the brain where they likely play a causative role in transitioning between wakefulness and anesthesia. The widespread neuronal suppression reported in prior comparisons of the awake and anesthetized brain in animal models and noninvasive imaging in humans reflects only net differences. It misses the ubiquitous presence of neurons whose activity increases during anesthesia. The balance in recruitment of anesthesia-on versus wake-on neuronal populations throughout the brain may be a key driver of regional and global vigilance states. [Correction added on September 22, 2021, after first online publication: Due to a typesetting error, the abstract text was cut off. This has been corrected now.].

A nodal point for brain-state transitions: the mesopontine tegmental anesthesia area (MPTA) in mice.

The mesopontine tegmental anesthesia area (MPTA) was identified in rats as a singular brainstem locus at which microinjection of minute quantities of GABAergic agents rapidly and reversibly induces loss-of-consciousness and a state of general anesthesia, while lesioning renders animals insensitive to anesthetics at normal systemic doses. Obtaining similar results in mice has been challenging, however, slowing research progress on how anesthetics trigger brain-state transitions. We have identified roadblocks that impeded translation from rat to mouse and tentatively located the MPTA equivalent in this second species. We describe here a series of modifications to the rat protocol that allowed us to document pro-anesthetic changes in mice following localized stereotactic delivery of minute quantities (20 nL) of the GABAA-receptor agonist muscimol into the brainstem mesopontine tegmentum. The optimal locus identified proved to be homologous to the MPTA in rats, and local neuronal populations in rats and mice were similar in size and shape. This outcome should facilitate application of the many innovative gene-based methodologies available primarily in mice to the study of how activity in brainstem MPTA neurons brings about anesthetic loss-of-consciousness and permits pain-free surgery.

Macroscopic Quantities of Collective Brain Activity during Wakefulness and Anesthesia.

The study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. We showed that the coupling between each brain region and the rest of the cortex provides an efficient statistic to classify the two brain states. Based on this and other statistics, we estimated maximum entropy models to derive collective, macroscopic properties that quantify the system's capabilities to produce work, to contain information, and to transmit it, which were all maximized in the awake state. The differences in these properties were consistent with a phase transition from critical dynamics in the awake state to supercritical dynamics in the anesthetized state. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in the state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

Cortical Responses to Vagus Nerve Stimulation Are Modulated by Brain State in Nonhuman Primates.

Vagus nerve stimulation (VNS) has been tested as therapy for several brain disorders and as a means to modulate cortical excitability and brain plasticity. Cortical effects of VNS, manifesting as vagal-evoked potentials (VEPs), are thought to arise from activation of ascending cholinergic and noradrenergic systems. However, it is unknown whether those effects are modulated by brain state at the time of stimulation. In 2 freely behaving macaque monkeys, we delivered short trains of 5 pulses to the left cervical vagus nerve at different frequencies (5-300 Hz) while recording local field potentials (LFPs) from sites in contralateral prefrontal, sensorimotor and parietal cortical areas. Brain states were inferred from spectral components of LFPs and the presence of overt movement: active awake, resting awake, REM sleep and NREM sleep. VNS elicited VEPs in all sampled cortical areas. VEPs comprised early (<70 ms), intermediate (70-250 ms) and late (>250 ms) components. The magnitude of the intermediate and late components was largest during NREM sleep and smallest during wakefulness, whereas that of the early component was not modulated by brain state. VEPs during NREM were larger for stimuli delivered at the depolarized phase of ongoing delta oscillations. Higher pulsing frequencies generated larger VEPs. These short VNS trains did not affect brain state transitions during wakefulness or sleep. Our findings suggest that ongoing brain state modulates the evoked effects of VNS on cortical activity. This has implications for the role of ongoing cortical activity and brain state in shaping cortical responses to peripheral stimuli, for the modulation of vagal interoceptive signaling by cortical activity, and for the dose calibration of VNS therapies.

Brain Dynamics Underlying Cognitive Flexibility Across the Lifespan.

The neural mechanisms contributing to flexible cognition and behavior and how they change with development and aging are incompletely understood. The current study explored intrinsic brain dynamics across the lifespan using resting-state fMRI data (n = 601, 6-85years) and examined the interactions between age and brain dynamics among three neurocognitive networks (midcingulo-insular network, M-CIN; medial frontoparietal network, M-FPN; and lateral frontoparietal network, L-FPN) in relation to behavioral measures of cognitive flexibility. Hierarchical multiple regression analysis revealed brain dynamics among a brain state characterized by co-activation of the L-FPN and M-FPN, and brain state transitions, moderated the relationship between quadratic effects of age and cognitive flexibility as measured by scores on the Delis-Kaplan Executive Function System (D-KEFS) test. Furthermore, simple slope analyses of significant interactions revealed children and older adults were more likely to exhibit brain dynamic patterns associated with poorer cognitive flexibility compared with younger adults. Our findings link changes in cognitive flexibility observed with age with the underlying brain dynamics supporting these changes. Preventative and intervention measures should prioritize targeting these networks with cognitive flexibility training to promote optimal outcomes across the lifespan.

Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology.

Synergistic Positive Feedback Loops Underlying a Critical Brain State Transition

Brain state transitions have been modeled as tipping points within the phase space1,2, separating two states that are stabilized either by recurrent network activity 3,4 or cellular mechanisms5-9. The critical forces underlying brain state transitions, however, remain unknown. Seizure onset is an extreme, although thankfully rare, type of brain state transition. We show here that this ictal transition is associated with an all-or-nothing change in the response to an optogenetic stimulation indicative of a latent change in dendritic excitability. Our data show how the precipitous nature of the transition can be understood in terms of multiple, synergistic positive feedback mechanisms: raised intracellular Cl- and extracellular K+, coupled to a reduced threshold for dendritic plateau potentials, and which in turn leads to a switch to pyramidal burst firing. These interlinked feedback loops draw together several major lines of epilepsy research and provide a framework for understanding other brain state transitions.

Control of Brain State Transitions with a Photoswitchable Muscarinic Agonist.

The ability to control neural activity is essential for research not only in basic neuroscience, as spatiotemporal control of activity is a fundamental experimental tool, but also in clinical neurology for therapeutic brain interventions. Transcranial‐magnetic, ultrasound, and alternating/direct current (AC/DC) stimulation are some available means of spatiotemporal controlled neuromodulation. There is also light‐mediated control, such as optogenetics, which has revolutionized neuroscience research, yet its clinical translation is hampered by the need for gene manipulation. As a drug‐based light‐mediated control, the effect of a photoswitchable muscarinic agonist (Phthalimide‐Azo‐Iper (PAI)) on a brain network is evaluated in this study. First, the conditions to manipulate M2 muscarinic receptors with light in the experimental setup are determined. Next, physiological synchronous emergent cortical activity consisting of slow oscillations—as in slow wave sleep—is transformed into a higher frequency pattern in the cerebral cortex, both in vitro and in vivo, as a consequence of PAI activation with light. These results open the way to study cholinergic neuromodulation and to control spatiotemporal patterns of activity in different brain states, their transitions, and their links to cognition and behavior. The approach can be applied to different organisms and does not require genetic manipulation, which would make it translational to humans.