Awakening: Predicting external stimulation to force transitions between different brain states

Slow semantic learning in the cerebral cortex, and its relation to the hippocampal episodic memory system

A key question is how new semantic representations are formed in the human brain and how this may benefit from the hippocampal episodic memory system. Here, we describe the major effective connectivity between the hippocampal memory system and the anterior temporal lobe (ATL) semantic memory system in humans. Then, we present and model a theory of how semantic representations may be formed in the human ATL using slow associative learning in semantic attractor networks that receive inputs from the hippocampal episodic memory system. The hypothesis is that if one category of semantic representations is being processed for several seconds, then a slow short-term memory trace associative biologically plausible learning rule will enable all the components during that time to be associated together in a semantic attractor network. This benefits from the binding of components provided by the hippocampal episodic memory system. The theory is modeled in a four-layer network for view-invariant visual object recognition, followed by a semantic attractor network layer that utilizes a temporal trace associative learning rule to form semantic categories based on the inputs that occur close together in time, using inputs from the hippocampal system or from the world.

Smartphone-based Monitoring and cognition Modification Against Recurrence of Depression (SMARD): An RCT of Memory Bias Modification Training vs. Cognitive Control Training vs. Attention Bias Modification Training in remitted recurrently depressed patients with 1.5 year follow-up

BACKGROUND

Major Depressive Disorder (MDD) has a 50-80% recurrence rate highlighting the urgent need for more efficient recurrence prevention programs. Currently, recurrences are often identified too late, while existing preventive strategies may not sufficiently address ethio-patho-physiological mechanisms for recurrence. Negative memory bias (the tendency to better remember negative than positive events), negative attention bias (selective attention favoring mood-congruent information), and cognitive control deficits are important factors involved in the onset, maintenance, and recurrence of depressive episodes.

METHODS

Here we describe the protocol for the Smartphone-based Monitoring and cognition Modification Against Recurrence of Depression (SMARD) study, aiming to investigate different forms of cognitive training programs administered via smartphones, in order to develop a second-generation recurrence prevention program. In addition, we will gather Experience Sampling Method (ESM) assessments during a 6-day period, and during the follow-up period we will obtain behavioral data on (social) activities with BEHAPP, a smartphone-based Mobile Passive Monitoring application for remote behavioral monitoring to identify behavioral changes indicative of an imminent depressive episode. In a randomized controlled trial, SMARD will compare the effects of a smartphone-based Memory Bias Modification Training (MBT), Cognitive Control Training (CCT), and Attention Bias Modification Training (ABT) versus cognitive domain-specific (active-) sham trainings in 120 remitted MDD-patients with recurrent-MDD. Over the course of three weeks, participants receive multiple daily training sessions. Thereafter, participants will be followed up for 1.5 years with 3-monthly interviews to assess recurrences.

DISCUSSION

The SMARD study aims to 1. assess the effects of the cognitive training programs versus their training-specific (active-) sham conditions on changes in memory, cognitive control dysfunction and attention; 2. relate training effects to neural networks previously identified in (recurrence of) MDD (therefore we obtain functional Magnetic Resonance Imaging ((f)MRI) scans before and after the training in a subset of participants); 3. link baseline and change in memory, cognitive control, attention and neural functioning, and ESM data to prospective recurrences; 4. examine whether passive smartphone-use monitoring can be used for prediction of recurrences.

TRIAL REGISTRATION

NL-OMON26184 and NL-OMON27513. Registered 12 August 2021-Retrospectively registered,  https://onderzoekmetmensen.nl/en/trial/26184 en https://onderzoekmetmensen.nl/en/trial/27513 .

Visual cortical networks for "What" and "Where" to the human hippocampus revealed with dynamical graphs

Key questions for understanding hippocampal function in memory and navigation in humans are the type and source of visual information that reaches the human hippocampus. We measured bidirectional pairwise effective connectivity with functional magnetic resonance imaging between 360 cortical regions while 956 Human Connectome Project participants viewed scenes, faces, tools, or body parts. We developed a method using deterministic dynamical graphs to define whole cortical networks and the flow in both directions between their cortical regions over timesteps after signal is applied to V1. We revealed that a ventromedial cortical visual "Where" network from V1 via the retrosplenial and medial parahippocampal scene areas reaches the hippocampus when scenes are viewed. A ventrolateral "What" visual cortical network reaches the hippocampus from V1 via V2-V4, the fusiform face cortex, and lateral parahippocampal region TF when faces/objects are viewed. There are major implications for understanding the computations of the human vs rodent hippocampus in memory and navigation: primates with their fovea and highly developed cortical visual processing networks process information about the location of faces, objects, and landmarks in viewed scenes, whereas in rodents the representations in the hippocampal system are mainly about the place where the individual is located and self-motion between places.

Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan

The development of the human brain is a complex, lifelong process during which collective behaviors of neurons exhibit self-organizing dynamics. Metastable states play a crucial role in understanding the complex dynamical mechanisms of the brain, and analyzing them helps to reveal the mechanisms of functional changes in the brain throughout development and aging. Specifically, global metastable state provides a overall perspective on the flexibility of brain reorganization, while the evolution trajectories of transient functional patterns capture detailed changes in brain activity. The leading eigenvector dynamics analysis (LEiDA) method significantly reduces the dimensionality of data and is widely used to capture the temporal trajectory characteristics of transient functional patterns, i.e., metastable brain states. However, LEiDA's linear dimensionality reduction of high-dimensional raw brain data may overlook non-linear information and lose some relationships between features. We developed a framework based on Phase Coherence Graph Autoencoder (PCGAE) that employs graph autoencoders (GAE) for non-linear dimensionality reduction of phase coherence matrices. This approach clusters to identify more distinct metastable brain states and is applied to the analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data across the human lifespan. This paper investigates age-related differences and continuity changes from different perspectives: metastable state indicators and state trajectory indicators (occurrence probability, lifetime, and state transition metrics). Global metastable state shows a linear decline with age, while both linear and quadratic effects of age-related changes are observed in detailed state metastable and state trajectory indicators. Finally, the proposed feature extraction scheme demonstrates good classification performance for categorizing brain age groups. These findings can help us understand the self-organizing reorganization characteristics associated with aging and their complex dynamic changes, providing new insights into brain development throughout the entire lifespan.

Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders

The Hopf whole-brain model, based on structural connectivity, overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters, quantifying dynamic brain characteristics in healthy and diseased states. Traditional parameter fitting techniques lack precision, restricting broader use. To address this, we validated parameter fitting methods using simulated networks and synthetic models, introducing improvements such as individual-specific initialization and optimized gradient descent, which reduced individual data loss. We also developed an approximate loss function and gradient adjustment mechanism, enhancing parameter fitting accuracy and stability. Applying this refined method to datasets for major depressive disorder (MDD) and autism spectrum disorder (ASD), we identified differences in brain regions between patients and healthy controls, explaining related anomalies. This rigorous validation is crucial for clinical application, paving the way for precise neuropathological identification and novel treatments in neuropsychiatric research, demonstrating substantial potential in clinical neurology.

Impaired brain ability of older adults to transit and persist to latent states with well-organized structures at wakeful rest

The intrinsic brain functional network organization continuously changes with aging. By integrating spatial and temporal information, the process of how brain networks temporally reconfigure and remain well-organized spatial structure largely reflects the brain function, thereby holds the potential to capture its age-related declines. In this study, we examined the spatiotemporal brain dynamics from resting-state functional Magnetic Resonance Imaging (fMRI) data of healthy young and older adults using a Hidden Markov Model (HMM). Six brain states were generated by HMM, with the young group showing higher fractional occupancy and mean dwell time in states 1, 3, and 4 (SY1, SY2 and SY3), and the older group in states 2, 5, and 6 (SO1, SO2 and SO3). Importantly, comparisons of transition probabilities revealed that the older group showed a reduced brain ability to transition into states dominated by the younger group, as well as a diminished capacity to persist in them. Moreover, graph analysis revealed that these young-specific states exhibited higher modularity and k-coreness. Collectively, these findings suggested that the older group showed impaired brain ability of both transition into and sustain well spatially organized states. This emphasized that the temporal changes in brain state organization, rather than its static mode, could be a key biomarker for detecting age-related functional decline. These insights may pave the way for targeted interventions aimed at mitigating cognitive decline in the aging population.

Analyzing the brain's dynamic response to targeted stimulation using generative modeling

Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss (a) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics and (b) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

Dynamic cycles between brain states during creative storytelling

Many theories suggest that creative thinking involves a dynamic transition between different mental states, yet empirical evidence supporting this notion remains scarce. The dual process model proposes that spontaneous thinking and deliberate thinking drive the dwell in and the transitions between different mental states during creative thinking, but there is a debate over whether the two types of thinking operate in parallel or in sequence. To address these gaps, we conducted a functional magnetic resonance imaging (fMRI) study in 41 college students during a creative storytelling task. We then compared the dynamic brain states in creative versus uncreative storytelling to identify key brain states associated with creative thinking. And we further performed correlation analysis between these key brain states with performance of various creative tasks, trying to link the key brain states with different cognitive processes. The results showed that two key brain states are associated with creative thinking, with one involving whole-brain synchronization and the other involving the synchronization of four networks, including the default mode network and the control network. The transition patterns between the key brain states provide tentative evidence for dynamic circulation between different mental states during creative storytelling. Using a deep learning approach, we demonstrate an alternating interaction between spontaneous and deliberate thinking, driving dwelling in and the transitions between different brain states. These findings deepen our understanding of the cognitive and neural mechanisms underlying creative thinking.

Understanding Altered Dynamics in Cocaine Use Disorder Through State Transitions Mediated by Artificial Perturbations

Background/Objectives: Cocaine use disorder (CUD) poses a worldwide health challenge, with severe consequences for brain function. However, the phase dynamics underlying CUD and the transitions between CUD and health remain poorly understood. Methods: Here, we used resting-state functional magnetic resonance imaging (fMRI) data from 43 CUD patients and 45 healthy controls (HCT). We performed empirical analysis to identify phase-coherence states and compared their probabilities of occurrence between conditions. To further explore the underlying mechanism, we employed computational modeling to replicate the observed state probabilities for each condition. These generated whole-brain models enabled us to simulate external perturbations and identify optimal brain regions mediating transitions between HCT and CUD. Results: We found that CUD was associated with a reduced occurrence probability of the state dominated by the default mode network (DMN). Perturbing the nucleus accumbens, thalamus, and specific regions within the default mode, limbic and frontoparietal networks drives transitions from HCT to CUD, while perturbing the hippocampus and specific regions within the visual, dorsal attention, and DMN facilitates a return from CUD to HCT. Conclusions: This study revealed altered DMN-related dynamics in CUD from the phase perspective and provides potential regions critical for state transitions. The results contribute to understanding the pathogenesis of CUD and the development of therapeutic stimulation strategies.

Contributions of short- and long-range white matter tracts in dynamic compensation with aging

Optimal brain function is shaped by a combination of global information integration, facilitated by long-range connections, and local processing, which relies on short-range connections and underlying biological factors. With aging, anatomical connectivity undergoes significant deterioration, which affects the brain's overall function. Despite the structural loss, previous research has shown that normative patterns of functions remain intact across the lifespan, defined as the compensatory mechanism of the aging brain. However, the crucial components in guiding the compensatory preservation of the dynamical complexity and the underlying mechanisms remain uncovered. Moreover, it remains largely unknown how the brain readjusts its biological parameters to maintain optimal brain dynamics with age; in this work, we provide a parsimonious mechanism using a whole-brain generative model to uncover the role of sub-communities comprised of short-range and long-range connectivity in driving the dynamic compensation process in the aging brain. We utilize two neuroimaging datasets to demonstrate how short- and long-range white matter tracts affect compensatory mechanisms. We unveil their modulation of intrinsic global scaling parameters, such as global coupling strength and conduction delay, via a personalized large-scale brain model. Our key finding suggests that short-range tracts predominantly amplify global coupling strength with age, potentially representing an epiphenomenon of the compensatory mechanism. This mechanistically explains the significance of short-range connections in compensating for the major loss of long-range connections during aging. This insight could help identify alternative avenues to address aging-related diseases where long-range connections are significantly deteriorated.

Turbulence as a framework for brain dynamics in health and disease

Turbulence is a universal principle for fast energy and information transfer. Moving beyond the turbulence of fluid dynamics, turbulence has recently been demonstrated in brain dynamics. Importantly, turbulence can be expressed as the rich variability across spacetime of the local levels of synchronisation of coupled brain signals. In fact, the optimal mixing properties of turbulence is what allows for efficient transfer of energy/information over space and time in the brain. This is especially important for survival given the need to overcome the inherent slowness in neural dynamics. Here, we review the research showing that the turbulence offers a convenient framework for describing brain dynamics and that the scale-free nature of turbulence, reflected in power-laws, provides the necessary mechanisms for time-critical information transfer in the brain. Whole-brain modelling of turbulence as coupled-oscillators has been shown to provide precise signatures of many different brain states. The levels of turbulence change in disease, and careful research of the vortex space could potentially help discover new avenues for a better understanding of this breakdown and offer better control of these highly non-linear, non-equilibrium states. Overall, the framework of the turbulent brain is a highly fertile, fast developing field with great potential.

Driving brain state transitions via Adaptive Local Energy Control Model

The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions. Existing studies have primarily focused on analyzing the energy required for brain state transitions, which are driven either by the single brain region or by all brain regions. However, they often neglect the critical question of how the whole brain responds to external control inputs that are driven by control energy from multiple brain regions, which limits their application value in guiding clinical neurostimulation. In this paper, we proposed the Adaptive Local Energy Control Model (ALECM) to explore brain state transitions, which considers the complex interactions of the whole brain along the white matter network when external control inputs are applied to multiple regions. It not only quantifies the energy required for state transitions but also predicts their outcomes based on local control. Our results indicated that patients with Schizophrenia (SZ) and Bipolar Disorder (BD) required more energy to drive the brain state transitions from the pathological state to the healthy baseline state, which is defined as Hetero-state transition. Importantly, we successfully induced Hetero-state transition in the patients' brains by using the ALECM, with subnetworks or specific brain regions serving as local control sets. Eventually, the network similarity between patients and healthy subjects reached baseline levels. These offer evidence that the ALECM can effectively quantify the cost characteristics of brain state transitions, providing a theoretical foundation for accurately predicting the efficacy of electromagnetic perturbation therapies in the future.

Long-term mindfulness meditation increases occurrence of sensory and attention brain states

Interest has been growing in the use of mindfulness meditation (MM) as a therapeutic practice, as accumulating evidence highlights its potential to effectively address a range of mental conditions. While many fMRI studies focused on neural activation and functional connectivity during meditation, the impact of long-term MM practice on spontaneous brain activity, and on the expression of resting state networks over time, remains unclear. Here, intrinsic functional network dynamics were compared between experienced meditators and meditation-naïve participants during rest. Our analysis revealed that meditators tend to spend more time in two brain states that involve synchrony among cortical regions associated with sensory perception. Conversely, a brain state involving frontal areas associated with higher cognitive functions was detected less frequently in experienced meditators. These findings suggest that, by shifting attention toward enhanced sensory and embodied processing, MM effectively modulates the expression of functional network states at rest. These results support the suggested lasting effect of long-term MM on the modulation of resting-state networks, reinforcing its therapeutic potential for disorders characterized by imbalanced network dynamics. Moreover, this study reinforces the utility of analytic approaches from dynamical systems theory to extend current knowledge regarding brain activity and evaluate its response to interventions.

Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness

Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest. The networks associated with these patterns have been extensively analyzed. However, the overall dynamic organization and how it relates to consciousness remains unclear. We hypothesized that deep learning networks would help to model this relationship. Recent studies have used low-dimensional variational autoencoders (VAE) to learn meaningful representations that can help explaining consciousness. Here, we investigated the complexity of selecting such a generative model to study brain dynamics, and extended the available methods for latent space characterization and modeling. Therefore, our contributions are threefold. First, compared with probabilistic principal component analysis and sparse VAE, we showed that the selected low-dimensional VAE exhibits balanced performance in reconstructing dFCs and classifying brain patterns. We then explored the organization of the obtained low-dimensional dFC latent representations. We showed how these representations stratify the dynamic organization of the brain patterns as well as the experimental conditions. Finally, we proposed to delve into the proposed brain computational model. We first applied a receptive field analysis to identify preferred directions in the latent space to move from one brain pattern to another. Then, an ablation study was achieved where we virtually inactivated specific brain areas. We demonstrated the model's efficiency in summarizing consciousness-specific information encoded in key inter-areal connections, as described in the global neuronal workspace theory of consciousness. The proposed framework advocates the possibility of developing an interpretable computational brain model of interest for disorders of consciousness, paving the way for a dynamic diagnostic support tool.

Transcranial ultrasound stimulation effect in the redundant and synergistic networks consistent across macaques

Low-intensity transcranial ultrasound stimulation (TUS) is a noninvasive technique that safely alters neural activity, reaching deep brain areas with good spatial accuracy. We investigated the effects of TUS in macaques using a recent metric, the synergy minus redundancy rank gradient, which quantifies different kinds of neural information processing. We analyzed this high-order quantity on the fMRI data after TUS in two targets: the supplementary motor area (SMA-TUS) and the frontal polar cortex (FPC-TUS). The TUS produced specific changes at the limbic network at FPC-TUS and the motor network at SMA-TUS and altered the sensorimotor, temporal, and frontal networks in both targets, mostly consistent across macaques. Moreover, there was a reduction in the structural and functional coupling after both stimulations. Finally, the TUS changed the intrinsic high-order network topology, decreasing the modular organization of the redundancy at SMA-TUS and increasing the synergistic integration at FPC-TUS.

Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF

Different whole-brain computational models have been recently developed to investigate hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is particularly attractive, combining a biophysically realistic model that is scaled up via a mean-field approach and multimodal imaging data. However, an important barrier to the widespread usage of the DMF model is that current implementations are computationally expensive, supporting only simulations on brain parcellations that consider less than 100 brain regions. Here, we introduce an efficient and accessible implementation of the DMF model: the FastDMF. By leveraging analytical and numerical advances-including a novel estimation of the feedback inhibition control parameter and a Bayesian optimization algorithm-the FastDMF circumvents various computational bottlenecks of previous implementations, improving interpretability, performance, and memory use. Furthermore, these advances allow the FastDMF to increase the number of simulated regions by one order of magnitude, as confirmed by the good fit to fMRI data parcellated at 90 and 1,000 regions. These advances open the way to the widespread use of biophysically grounded whole-brain models for investigating the interplay between anatomy, function, and brain dynamics and to identify mechanistic explanations of recent results obtained from fine-grained neuroimaging recordings.

Synergistic, multi-level understanding of psychedelics: three systematic reviews and meta-analyses of their pharmacology, neuroimaging and phenomenology

Serotonergic psychedelics induce altered states of consciousness and have shown potential for treating a variety of neuropsychiatric disorders, including depression and addiction. Yet their modes of action are not fully understood. Here, we provide a novel, synergistic understanding of psychedelics arising from systematic reviews and meta-analyses of three hierarchical levels of analysis: (1) subjective experience (phenomenology), (2) neuroimaging and (3) molecular pharmacology. Phenomenologically, medium and high doses of LSD yield significantly higher ratings of visionary restructuralisation than psilocybin on the 5-dimensional Altered States of Consciousness Scale. Our neuroimaging results reveal that, in general, psychedelics significantly strengthen between-network functional connectivity (FC) while significantly diminishing within-network FC. Pharmacologically, LSD induces significantly more inositol phosphate formation at the 5-HT2A receptor than DMT and psilocin, yet there are no significant between-drug differences in the selectivity of psychedelics for the 5-HT2A, 5-HT2C, or D2 receptors, relative to the 5-HT1A receptor. Our meta-analyses link DMT, LSD, and psilocybin to specific neural fingerprints at each level of analysis. The results show a highly non-linear relationship between these fingerprints. Overall, our analysis highlighted the high heterogeneity and risk of bias in the literature. This suggests an urgent need for standardising experimental procedures and analysis techniques, as well as for more research on the emergence between different levels of psychedelic effects.

Beyond Focal Lesions: Dynamical Network Effects of White Matter Hyperintensities

White matter (WM) tracts shape the brain's dynamical activity and their damage (e.g., white matter hyperintensities, WMH) yields relevant functional alterations, ultimately leading to cognitive symptoms. The mechanisms linking the structural damage caused by WMH to the arising alterations of brain dynamics is currently unknown. To estimate the impact of WMH on brain dynamics, we combine neural-mass whole-brain modeling with a virtual-lesioning (disconnectome) approach informed by empirical data. We account for the heterogeneous effects of WMH either on inter-regional communication (i.e., edges) or on dynamics (i.e., nodes) and create models of their local versus global, and edge versus nodal effects using a large fMRI dataset comprising 188 non-demented individuals (120 cognitively normal, 68 with mild cognitive impairment) with varying degrees of WMH. We show that, although WMH mainly determine local damage to specific WM tracts, these lesions yield relevant global dynamical effects by reducing the overall synchronization of the brain through a reduction of global coupling. Alterations of local nodal dynamics through disconnections are less relevant and present only at later stages of WMH damage. Exploratory analyses suggest that education might play a beneficial role in counteracting the reduction in global coupling associated with WMH. This study provides generative models linking the structural damage caused by WMH to alterations in brain dynamics. These models might be used to evaluate the detrimental effects of WMH on brain dynamics in a subject-specific manner. Furthermore, it validates the use of whole-brain modeling for hypothesis-testing of structure-function relationships in diseased states characterized by empirical disconnections.

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors-including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.

The localization of coma

Coma and disorders of consciousness (DoC) are common manifestations of acute severe brain injuries. Research into their neuroanatomical basis can be traced from Hippocrates to the present day. Lesions causing DoC have traditionally been conceptualized as decreasing "alertness" from damage to the ascending arousal system, and/or, reducing level of "awareness" due to structural or functional impairment of large-scale brain networks. Within this framework, pharmacological and neuromodulatory interventions to promote recovery from DoC have hitherto met with limited success. This is partly due to inter-individual heterogeneity of brain injury patterns, and an incomplete understanding of brain network properties that characterize consciousness. Advances in multiscale computational modelling of brain dynamics have opened a unique opportunity to explore the causal mechanisms of brain activity at the biophysical level. These models can provide a novel approach for selection and optimization of potential interventions by simulation of brain network dynamics individualized for each patient.

Competitive interactions shape brain dynamics and computation across species

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

The temporal asymmetry of cortical dynamics as a signature of brain states

The brain is a complex non-equilibrium system capable of expressing many different dynamics as well as the transitions between them. We hypothesized that the level of non-equilibrium can serve as a signature of a given brain state, which was quantified using the arrow of time (the level of irreversibility). Using this thermodynamic framework, the irreversibility of emergent cortical activity was quantified from local field potential recordings in male Lister-hooded rats at different anesthesia levels and during the sleep-wake cycle. This measure was carried out on five distinct brain states: slow-wave sleep, awake, deep anesthesia-slow waves, light anesthesia-slow waves, and microarousals. Low levels of irreversibility were associated with synchronous activity found both in deep anesthesia and slow-wave sleep states, suggesting that slow waves were the state closest to the thermodynamic equilibrium (maximum symmetry), thus requiring minimum energy. Higher levels of irreversibility were found when brain dynamics became more asynchronous, for example, in wakefulness. These changes were also reflected in the hierarchy of cortical dynamics across different cortical areas. The neural dynamics associated with different brain states were characterized by different degrees of irreversibility and hierarchy, also acting as markers of brain state transitions. This could open new routes to monitoring, controlling, and even changing brain states in health and disease.

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework

This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain's functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient's brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

EEG microstate transition cost correlates with task demands

The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task, a cognitive test in which participants respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the framework's effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain.

A ventromedial visual cortical 'Where' stream to the human hippocampus for spatial scenes revealed with magnetoencephalography

The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1-V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1-3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical 'Where' stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T.

Sleep-like cortical dynamics during wakefulness and their network effects following brain injury

By connecting old and recent notions, different spatial scales, and research domains, we introduce a novel framework on the consequences of brain injury focusing on a key role of slow waves. We argue that the long-standing finding of EEG slow waves after brain injury reflects the intrusion of sleep-like cortical dynamics during wakefulness; we illustrate how these dynamics are generated and how they can lead to functional network disruption and behavioral impairment. Finally, we outline a scenario whereby post-injury slow waves can be modulated to reawaken parts of the brain that have fallen asleep to optimize rehabilitation strategies and promote recovery.

The Nested States Model: A Phenomenologically-Grounded Model of the Mind

BACKGROUND

Subjective experience is central to the nature of mental illness, yet it has not played a central role in most empirical approaches to psychopathology. While phenomenological perspectives in psychiatry have seen a recent resurgence, there remains a need for more detailed models of psychopathological processes based on explicit phenomenological and enactive foundations.

SUMMARY

We present a framework derived from the Nested States Model (NSM) through which such phenomenologically-grounded models might be constructed. The NSM describes the dynamic structure of subjective experience as a system of nested states that reciprocally influence one another across hierarchical layers. Here, we show how the NSM provides a scheme for characterizing patterns of experience that comprise various psychopathological processes. We demonstrate the utility of this scheme both for clinical practice and for building our knowledge of psychopathological processes more broadly.

KEY MESSAGES

The NSM can advance three aims that we see as critical for the lasting integration of phenomenological approaches to psychopathology within psychiatry. First, we show that the NSM provides a means for constructing clinical formulations and treatment considerations that center squarely on an individual's subjective experiences. Second, the NSM supplies a framework for organizing findings from clinical-phenomenological research that can guide the construction of broader phenomenologically-grounded models of psychopathological processes. Lastly, the NSM aligns our perspective on subjective experience with emerging perspectives on brain dynamics, helping to bridge phenomenological work with ongoing neurophysiological research.

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient's lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient's data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain.

Time-varying phase synchronization of resting-state functional magnetic resonance imaging reveals a shift toward self-referential processes during sustained pain

ABSTRACT

Growing evidence has suggested that time-varying functional connectivity between different brain regions might underlie the dynamic experience of pain. This study used a novel, data-driven framework to characterize the dynamic interactions of large-scale brain networks during sustained pain by estimating recurrent patterns of phase-synchronization. Resting-state functional magnetic resonance imaging signals were collected from 50 healthy participants before (once) and after (twice) the onset of sustained pain that was induced by topical application of capsaicin cream. We first decoded the instantaneous phase of neural activity and then applied leading eigenvector dynamic analysis on the time-varying phase-synchronization. We identified 3 recurrent brain states that show distinctive phase-synchronization. The presence of state 1, characterized by phase-synchronization between the default mode network and auditory, visual, and sensorimotor networks, together with transitions towards this brain state, increased during sustained pain. These changes can account for the perceived pain intensity and reported unpleasantness induced by capsaicin application. In contrast, state 3, characterized by phase-synchronization between the cognitive control network and sensory networks, decreased after the onset of sustained pain. These results are indicative of a shift toward internally directed self-referential processes (state 1) and away from externally directed cognitive control processes (state 3) during sustained pain.

The model of the brain as a complex system: Interactions of physical, neural and mental states with neurocognitive functions

The isolated approaching of physical, neural and mental states and the binary classification into stable traits and fluctuating states previously lead to a limited understanding concerning underlying processes and possibilities to explain, measure and regulate neural and mental performance along with the interaction of mental states and neurocognitive traits. In this article these states are integrated by i) differentiating the model of the brain as a complex, self-organizing system, ii) showing possibilities to measure this model, iii) offering a classification of mental states and iv) presenting a holistic operationalization of state regulations and trait trainings to enhance neural and mental high-performance on a macro- and micro scale. This model integrates current findings from the theory of constructed emotions, the theory of thousand brains and complex systems theory and yields several testable hypotheses to provide an integrated reference frame for future research and applied target points to regulate and enhance performance.

Inducing a meditative state by artificial perturbations: A mechanistic understanding of brain dynamics underlying meditation

Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest, and controls during rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each condition, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Moreover, we implemented a model-based approach by adjusting the PMS of each condition to a whole-brain model, which enabled us to explore in silico perturbations to transition from resting-state to meditation and vice versa. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how transitions can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.

Gaming expertise induces meso‑scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling

Video games are a valuable tool for studying the effects of training and neural plasticity on the brain. However, the underlying mechanisms related to plasticity-associated brain structural changes and their impact on brain dynamics are unknown. Here, we used a semi-empirical whole-brain model to study structural neural plasticity mechanisms linked to video game expertise. We hypothesized that video game expertise is associated with neural plasticity-mediated changes in structural connectivity that manifest at the meso‑scale level, resulting in a more segregated functional network topology. To test this hypothesis, we combined structural connectivity data of StarCraft II video game players (VGPs, n = 31) and non-players (NVGPs, n = 31), with generic fMRI data from the Human Connectome Project and computational models, to generate simulated fMRI recordings. Graph theory analysis on simulated data was performed during both resting-state conditions and external stimulation. VGPs' simulated functional connectivity was characterized by a meso‑scale integration, with increased local connectivity in frontal, parietal, and occipital brain regions. The same analyses at the level of structural connectivity showed no differences between VGPs and NVGPs. Regions that increased their connectivity strength in VGPs are known to be involved in cognitive processes crucial for task performance such as attention, reasoning, and inference. In-silico stimulation suggested that differences in FC between VGPs and NVGPs emerge in noisy contexts, specifically when the noisy level of stimulation is increased. This indicates that the connectomes of VGPs may facilitate the filtering of noise from stimuli. These structural alterations drive the meso‑scale functional changes observed in individuals with gaming expertise. Overall, our work sheds light on the mechanisms underlying structural neural plasticity triggered by video game experiences.

Insomnia disorder characterized by probabilistic metastable substates using blood-oxygenation-level-dependent (BOLD) phase signals

PURPOSE

From a clinical point of view, how to force a transition from insomnia brain state to healthy brain state by external driven stimulation is of great interest. This needs to define brain state of insomnia disorder as metastable substates. The current study was to identify recurrent substates of insomnia disorder in terms of probability of occurrence, lifetime, and alternation profiles by using leading eigenvector dynamics analysis (LEiDA) method.

METHODS

We enrolled 32 patients with insomnia disorder and 30 healthy subjects. We firstly obtained the BOLD phase coherence matrix from Hilbert transform of BOLD signals and then extracted all the leading eigenvectors from the BOLD phase coherence matrix for all subjects across all time points. Lastly, we clustered the leading eigenvectors using a k-means clustering algorithm to find the probabilistic metastable substates (PMS) and calculate the probability of occurrence and associated lifetime for substates.

RESULTS

The resulting 3 clusters were optimal for brain state of insomnia disorder and healthy brain state, respectively. The occurred probabilities of the PMS were significantly different between the patients with insomnia disorder and healthy subjects, with 0.51 versus 0.44 for PMS-1 (p < 0.001), 0.25 versus 0.27 for PMS-2 (p = 0.051), and 0.24 versus 0.29 for PMS-3 (p < 0.001), as well as the lifetime (in TR) of 36.65 versus 33.15 for PMS-1 (p = 0.068), 14.36 versus 15.43 for PMS-2 (p = 0.117), and 14.80 versus 16.34 for PMS-3 (p = 0.042). The values of the diagonal of the transition matrix were much higher than the probabilities of switching states, indicating the metastable nature of substates.

CONCLUSION

The resulted probabilistic metastable substates hint the characteristic brain dynamics of insomnia disorder. The results may lay a foundation to help determine how to force a transition from insomnia brain state to healthy brain state by external driven stimulation.

Revealing trends and persistent cycles of non-autonomous systems with autonomous operator-theoretic techniques

An important problem in modern applied science is to characterize the behavior of systems with complex internal dynamics subjected to external forcings. Many existing approaches rely on ensembles to generate information from the external forcings, making them unsuitable to study natural systems where only a single realization is observed. A prominent example is climate dynamics, where an objective identification of signals in the observational record attributable to natural variability and climate change is crucial for making climate projections for the coming decades. Here, we show that operator-theoretic techniques previously developed to identify slowly decorrelating observables of autonomous dynamical systems provide a powerful means for identifying nonlinear trends and persistent cycles of non-autonomous systems using data from a single trajectory of the system. We apply our framework to real-world examples from climate dynamics: Variability of sea surface temperature over the industrial era and the mid-Pleistocene transition of Quaternary glaciation cycles.

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling

INTRODUCTION

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results.

METHODS

We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls).

RESULTS

Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings.

DISCUSSION

The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.

Two what, two where, visual cortical streams in humans

ROLLS, E. T. Two What, Two Where, Visual Cortical Streams in Humans. NEUROSCI BIOBEHAV REV 2024. Recent cortical connectivity investigations lead to new concepts about 'What' and 'Where' visual cortical streams in humans, and how they connect to other cortical systems. A ventrolateral 'What' visual stream leads to the inferior temporal visual cortex for object and face identity, and provides 'What' information to the hippocampal episodic memory system, the anterior temporal lobe semantic system, and the orbitofrontal cortex emotion system. A superior temporal sulcus (STS) 'What' visual stream utilising connectivity from the temporal and parietal visual cortex responds to moving objects and faces, and face expression, and connects to the orbitofrontal cortex for emotion and social behaviour. A ventromedial 'Where' visual stream builds feature combinations for scenes, and provides 'Where' inputs via the parahippocampal scene area to the hippocampal episodic memory system that are also useful for landmark-based navigation. The dorsal 'Where' visual pathway to the parietal cortex provides for actions in space, but also provides coordinate transforms to provide inputs to the parahippocampal scene area for self-motion update of locations in scenes in the dark or when the view is obscured.

Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation

A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.

Induction of excitatory brain state governs plastic functional changes in visual cortical topology

Adult visual plasticity underlying local remodeling of the cortical circuitry in vivo appears to be associated with a spatiotemporal pattern of strongly increased spontaneous and evoked activity of populations of cells. Here we review and discuss pioneering work by us and others about principles of plasticity in the adult visual cortex, starting with our study which showed that a confined lesion in the cat retina causes increased excitability in the affected region in the primary visual cortex accompanied by fine-tuned restructuring of neuronal function. The underlying remodeling processes was further visualized with voltage-sensitive dye (VSD) imaging that allowed a direct tracking of retinal lesion-induced reorganization across horizontal cortical circuitries. Nowadays, application of noninvasive stimulation methods pursues the idea further of increased cortical excitability along with decreased inhibition as key factors for the induction of adult cortical plasticity. We used high-frequency transcranial magnetic stimulation (TMS), for the first time in combination with VSD optical imaging, and provided evidence that TMS-amplified excitability across large pools of neurons forms the basis for noninvasively targeting reorganization of orientation maps in the visual cortex. Our review has been compiled on the basis of these four own studies, which we discuss in the context of historical developments in the field of visual cortical plasticity and the current state of the literature. Overall, we suggest markers of LTP-like cortical changes at mesoscopic population level as a main driving force for the induction of visual plasticity in the adult. Elevations in excitability that predispose towards cortical plasticity are most likely a common property of all cortical modalities. Thus, interventions that increase cortical excitability are a promising starting point to drive perceptual and potentially motor learning in therapeutic applications.

A synergetic turn in cognitive neuroscience of brain diseases

Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.

The evolution of whole-brain turbulent dynamics during recovery from traumatic brain injury

It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting-state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using an rsfMRI dataset from a cohort of moderate to severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3, 6, and 12 months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6 months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information-encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.

EiDA: A lossless approach for dynamic functional connectivity; application to fMRI data of a model of ageing

Dynamic Functional Connectivity (dFC) is the study of the dynamic patterns of interaction that characterise brain function. Numerous numerical methods are available to compute and analyse dFC from high-dimensional data. In fMRI, a number of them rely on the computation of the instantaneous Phase Alignment (iPA) matrix (also known as instantaneous Phase Locking). Their limitations are the high computational cost and the concomitant need to introduce approximations with ensuing information loss. Here, we introduce the analytical decomposition of the iPA. This has two advantages. Firstly, we achieve an up to 1000-fold reduction in computing time without information loss. Secondly, we can formally introduce two alternative approaches to the analysis of the resulting time-varying instantaneous connectivity patterns, Discrete and Continuous EiDA (Eigenvector Dynamic Analysis), and a related set of metrics to quantify the total amount of instantaneous connectivity, drawn from dynamical systems and information theory. We applied EiDA to a dataset from 48 rats that underwent functional magnetic resonance imaging (fMRI) at four stages during a longitudinal study of ageing. Using EiDA, we found that the metrics we introduce provided robust markers of ageing with decreases in total connectivity and metastability, and an increase in informational complexity over the life span. This suggests that ageing reduces the available functional repertoire that is postulated to support cognitive functions and overt behaviours, slows down the exploration of this reduced repertoire, and decreases the coherence of its structure. In summary, EiDA is a method to extract lossless connectivity information that requires significantly less computational time, and provides robust and analytically principled metrics for brain dynamics. These metrics are interpretable and promising for studies on neurodevelopmental and neurodegenerative disorders.

Dynamic nonreversibility view of intrinsic brain organization and brain dynamic analysis of repetitive transcranial magnitude stimulation

Intrinsic neural activities are characterized as endless spontaneous fluctuation over multiple time scales. However, how the intrinsic brain organization changes over time under local perturbation remains an open question. By means of statistical physics, we proposed an approach to capture whole-brain dynamics based on estimating time-varying nonreversibility and k-means clustering of dynamic varying nonreversibility patterns. We first used synthetic fMRI to investigate the effects of window parameters on the temporal variability of varying nonreversibility. Second, using real test-retest fMRI data, we examined the reproducibility, reliability, biological, and physiological correlation of the varying nonreversibility substates. Finally, using repetitive transcranial magnetic stimulation-fMRI data, we investigated the modulation effects of repetitive transcranial magnetic stimulation on varying nonreversibility substate dynamics. The results show that: (i) as window length increased, the varying nonreversibility variance decreased, while the sliding step almost did not alter it; (ii) the global high varying nonreversibility states and low varying nonreversibility states were reproducible across multiple datasets and different window lengths; and (iii) there were increased low varying nonreversibility states and decreased high varying nonreversibility states when the left frontal lobe was stimulated, but not the occipital lobe. Taken together, these results provide a thermodynamic equilibrium perspective of intrinsic brain organization and reorganization under local perturbation.

Brain dynamics predictive of response to psilocybin for treatment-resistant depression

Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here, we leveraged the differential outcome in responders and non-responders to psilocybin (10 and 25 mg, 7 days apart) therapy for depression-to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used large-scale brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a healthy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-hydroxytryptamine 2a and 5-hydroxytryptamine 1a, which psilocin, the active metabolite of psilocybin, has an appreciable affinity for, and where it acts as a full-to-partial agonist. Serotonergic transmission has long been associated with depression, and our findings provide causal mechanistic evidence for the role of brain regions in the recovery from depression via psilocybin.

The altered temporal properties of dynamic functional connectivity associated with suicide attempt in bipolar disorders

OBJECTIVE

The suicide risk in bipolar disorder (BD) is the highest among psychiatric disorders, and the neurobiological mechanism of suicide in BD remains unclear. The study aimed to investigate the underlying relevance between the implicated abnormalities of dynamic functional connectivity (FC) and suicide attempt (SA) in BD.

METHODS

We used the sliding window method to analyze the dynamic FC patterns from resting-state functional MRI data in 81 healthy controls (HC) and 114 BD patients (50 with SA and 64 with none SA). Then, the temporal properties of dynamic FC and the relationship between altered measures and clinical variables were explored.

RESULTS

We found that one of the five captured brain functional states was more associated with SA. The SA patients showed significantly increased fractional window and dwell time in the suicide-related state, along with increased number of state transitions compared with none SA (NSA). In addition, the connections within subcortical network-subcortical network (SubC-SubC), default mode network-subcortical network (DMN-SubC), and attention network-subcortical network (AN-SubC) were significantly changed in SA patients relative to NSA and HC in the suicide-related state. Crucially, the above-altered measures were significantly correlated with suicide risk.

CONCLUSIONS

Our findings suggested that the impaired dynamic FC within SubC-SubC, DMN-SubC, and AN-SubC were the important underlying mechanism in understanding SA for BD patients. It highlights the temporal properties of whole-brain dynamic FC could serve as the valuable biomarker for suicide risk assessment in BD.

Neural Geometrodynamics, Complexity, and Plasticity: A Psychedelics Perspective

We explore the intersection of neural dynamics and the effects of psychedelics in light of distinct timescales in a framework integrating concepts from dynamics, complexity, and plasticity. We call this framework neural geometrodynamics for its parallels with general relativity's description of the interplay of spacetime and matter. The geometry of trajectories within the dynamical landscape of "fast time" dynamics are shaped by the structure of a differential equation and its connectivity parameters, which themselves evolve over "slow time" driven by state-dependent and state-independent plasticity mechanisms. Finally, the adjustment of plasticity processes (metaplasticity) takes place in an "ultraslow" time scale. Psychedelics flatten the neural landscape, leading to heightened entropy and complexity of neural dynamics, as observed in neuroimaging and modeling studies linking increases in complexity with a disruption of functional integration. We highlight the relationship between criticality, the complexity of fast neural dynamics, and synaptic plasticity. Pathological, rigid, or "canalized" neural dynamics result in an ultrastable confined repertoire, allowing slower plastic changes to consolidate them further. However, under the influence of psychedelics, the destabilizing emergence of complex dynamics leads to a more fluid and adaptable neural state in a process that is amplified by the plasticity-enhancing effects of psychedelics. This shift manifests as an acute systemic increase of disorder and a possibly longer-lasting increase in complexity affecting both short-term dynamics and long-term plastic processes. Our framework offers a holistic perspective on the acute effects of these substances and their potential long-term impacts on neural structure and function.

The connectivity of the human frontal pole cortex, and a theory of its involvement in exploit versus explore

The frontal pole is implicated in humans in whether to exploit resources versus explore alternatives. Effective connectivity, functional connectivity, and tractography were measured between six human frontal pole regions and for comparison 13 dorsolateral and dorsal prefrontal cortex regions, and the 360 cortical regions in the Human Connectome Project Multi-modal-parcellation atlas in 171 HCP participants. The frontal pole regions have effective connectivity with Dorsolateral Prefrontal Cortex regions, the Dorsal Prefrontal Cortex, both implicated in working memory; and with the orbitofrontal and anterior cingulate cortex reward/non-reward system. There is also connectivity with temporal lobe, inferior parietal, and posterior cingulate regions. Given this new connectivity evidence, and evidence from activations and damage, it is proposed that the frontal pole cortex contains autoassociation attractor networks that are normally stable in a short-term memory state, and maintain stability in the other prefrontal networks during stable exploitation of goals and strategies. However, if an input from the orbitofrontal or anterior cingulate cortex that expected reward, non-reward, or punishment is received, this destabilizes the frontal pole and thereby other prefrontal networks to enable exploration of competing alternative goals and strategies. The frontal pole connectivity with reward systems may be key in exploit versus explore.

Controlling target brain regions by optimal selection of input nodes

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (μECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.

Aberrant brain dynamics in individuals with clinical high risk of psychosis

Background

Resting-state network (RSN) functional connectivity analyses have profoundly influenced our understanding of the pathophysiology of psychoses and their clinical high risk (CHR) states. However, conventional RSN analyses address the static nature of large-scale brain networks. In contrast, novel methodological approaches aim to assess the momentum state and temporal dynamics of brain network interactions.

Methods

Fifty CHR individuals and 33 healthy controls (HC) completed a resting-state functional MRI scan. We performed an Energy Landscape analysis, a data-driven method using the pairwise maximum entropy model, to describe large-scale brain network dynamics such as duration and frequency of, and transition between, different brain states. We compared those measures between CHR and HC, and examined the association between neuropsychological measures and neural dynamics in CHR.

Results

Our main finding is a significantly increased duration, frequency, and higher transition rates to an infrequent brain state with coactivation of the salience, limbic, default mode and somatomotor RSNs in CHR as compared to HC. Transition of brain dynamics from this brain state was significantly correlated with processing speed in CHR.

Conclusion

In CHR, temporal brain dynamics are attracted to an infrequent brain state, reflecting more frequent and longer occurrence of aberrant interactions of default mode, salience, and limbic networks. Concurrently, more frequent and longer occurrence of the brain state is associated with core cognitive dysfunctions, predictors of future onset of full-blown psychosis.

Violations of the fluctuation-dissipation theorem reveal distinct nonequilibrium dynamics of brain states

The brain is a nonequilibrium system whose dynamics change in different brain states, such as wakefulness and deep sleep. Thermodynamics provides the tools for revealing these nonequilibrium dynamics. We used violations of the fluctuation-dissipation theorem to describe the hierarchy of nonequilibrium dynamics associated with different brain states. Together with a whole-brain model fitted to empirical human neuroimaging data, and deriving the appropriate analytical expressions, we were able to capture the deviation from equilibrium in different brain states that arises from asymmetric interactions and hierarchical organization.