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

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.

Deconstructing the self and reshaping perceptions: An intensive whole-brain 7T MRI case study of the stages of insight during advanced investigative insight meditation

The stages of insight (SoI) are a series of psychological realizations experienced through advanced investigative insight meditation (AIIM). SoI provide a powerful structured framework of AIIM for understanding and evaluating insight-based meditative development through changes in perception, experiences of self, cognition, and emotional processing. Yet, the neurophenomenology of SoI remains unstudied due to methodological difficulties, rarity of suitable advanced meditation practitioners, and dominant research emphasis on attention-based meditative practices. We investigated the neurophenomenology of SoI in an intensively sampled adept meditator case study (4 hr 7T fMRI collected in 26 runs with concurrent phenomenology) who performed SoI and rated specific aspects of experience immediately thereafter. Linear mixed models and correlations were used to examine relations among the cortex, subcortex, brainstem, and cerebellum, and SoI phenomenology. We identified distinctive whole-brain activity patterns associated with specific SoI, and that were different from two non-meditative control states. SoI consistently deactivated regions implicated in self-related processing, including the medial prefrontal cortex and temporal poles, while activating regions associated with awareness and perception, including the parietal and visual cortices, caudate, several brainstem nuclei, and cerebellum. Patterns of brain activity related to affective processing and SoI phenomenology were also identified. Our study presents the first neurophenomenological evidence that SoI shifts and deconstructs self-related perception and conceptualization, and increases general awareness and perceptual sensitivity and acuity. Our study provides SoI as a foundation for investigative, and advanced meditation in particular.

Sleep Deprivation Effects on Brain State Dynamics Are Associated With Dopamine D2 Receptor Availability Via Network Control Theory

BACKGROUND

Sleep deprivation (SD) negatively affects brain function. Most brain imaging studies have investigated the effects of SD on static brain function. SD effects on functional brain dynamics and their relationship with molecular changes remain relatively unexplored.

METHODS

We used functional magnetic resonance imaging to examine resting-brain state dynamics after one night of SD compared with rested wakefulness (N = 41) and assessed the association of brain state dynamics with striatal brain dopamine D2 receptor availability measured by positron emission tomography [11C]raclopride using network control theory.

RESULTS

SD reduced dwell time and persistence probabilities, with the strongest effects in two brain states, one characterized by high default mode network and low dorsal attention network activity and the other by high frontoparietal network and low somatomotor network activity. Using network control theory, we showed that after SD, there was an overall increase in the control energy required for brain state transitions, with effects varying for different brain state transitions. Control energy requirement was negatively associated with transition probabilities under SD and restful wakefulness and accounted for SD-induced changes in transition probabilities. Alteration in the energy landscape was associated with SD-induced changes in striatal D2 receptor distribution.

CONCLUSIONS

These findings demonstrate altered occurrence of internally and externally oriented brain states following acute SD and suggest an association with energy requirements for brain state transitions modulated by striatal D2 receptors.

Psychedelics for the Treatment of Obsessive-Compulsive Disorder: Efficacy and Proposed Mechanisms

Psychedelics are emerging as potential treatments for a range of mental health conditions, including anxiety and depression, treatment-resistant depression, and substance use disorders. Recent studies have also suggested that the psychedelic psilocybin may be able to treat obsessive-compulsive disorder (OCD). Since the 1960s, case studies have reported improvements to obsessive and compulsive behaviors in patients taking psychedelics recreationally. The effects of psilocybin were then systematically assessed in a small, open-label trial in 2006, which found that psilocybin significantly reduced the symptoms of OCD. Reduced compulsive behaviors have also been seen in rodent models of OCD after administration of psilocybin. Nonetheless, the mechanisms underlying the effects of psychedelics for OCD are unclear, with hypotheses including their acute pharmacological effects, changes in neuroplasticity and resting state neural networks, and their psychological effects. This review will evaluate the evidence supporting the theory that psychedelics can be used for the treatment of OCD, as well as the data regarding claims about their mechanisms. It will also discuss issues with the current evidence and the ongoing trials of psilocybin that aim to address these knowledge gaps.

A network control theory pipeline for studying the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Energy Associated With Dynamic Network Changes in Patients With Multiple Sclerosis and Cognitive Impairment

BACKGROUND AND OBJECTIVES

Patients with multiple sclerosis (MS) often experience cognitive impairment, and this is related to structural disconnection and subsequent functional reorganization. It is unclear how specific patterns of functional reorganization might make it harder for cognitively impaired (CI) patients with MS to dynamically adapt how brain regions communicate, which is crucial for normal cognition. We aimed to identify dynamic functional network patterns that are relevant to cognitive impairment in MS and investigate whether these patterns can be explained by altered energy costs.

METHODS

Resting-state functional and diffusion MRI was acquired in a cross-sectional design, as part of the Amsterdam MS cohort. Patients with clinically definitive MS (relapse-free) were classified as CI (≥2/7 domains Z < -2), mildly CI (MCI) (≥2/7 domains Z < -1.5), or cognitively preserved (CP) based on an expanded Brief Repeatable Battery of Neuropsychological Tests. Functional connectivity states were determined using k-means clustering of moment-to-moment cofluctuations (i.e., edge time series), and the resulting state sequence was used to characterize the frequency of transitions. Control energy of the state transitions was calculated using the structural network with network control theory.

RESULTS

Imaging and cognitive data were available for 95 controls and 330 patients (disease duration: 15 years; 179 CP, 65 MCI, and 86 CI). We identified a "visual network state," "sensorimotor network state," "ventral attention network state," and "default mode network state." CI patients transitioned less frequently between connectivity states compared with CP (β = -5.78; p = 0.038). Relative to the time spent in a state, CI patients transitioned less from a "default mode network state" to a "visual network state" (β = -0.02; p = 0.004). The CI patients required more control energy to transition between states (β = 0.32; p = 0.007), particularly for the same transition (β = 0.34; p = 0.049).

DISCUSSION

This study showed that it costs more energy for MS patients with cognitive impairment to dynamically change the functional network, possibly explaining why these transitions occur less frequently. In particular, transitions from a default mode network state to a visual network state were relevant for cognition in these patients. To further study the order of events leading to these network disturbances, future work should include longitudinal data across different disease stages.

Release the Krakencoder: A unified brain connectome translation and fusion tool

Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.

Shaping dynamical neural computations using spatiotemporal constraints

Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

Altered Structural Connectivity and Functional Brain Dynamics in Individuals With Heavy Alcohol Use Elucidated via Network Control Theory

BACKGROUND

Heavy alcohol use and its associated conditions, such as alcohol use disorder, impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models that incorporate whole-brain neuroanatomical, functional, and pharmacological information under one framework.

METHODS

Here, we utilized diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in 130 individuals with a high amount of current alcohol use. We compared these alcohol-using individuals to 308 individuals with minimal use of any substances.

RESULTS

We found that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Furthermore, using separately acquired positron emission tomography data, we deployed an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with alcohol use disorder, may relate to our observed findings.

CONCLUSIONS

This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of heavy alcohol use.

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.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Large-scale brain connectivity changes following the administration of lysergic acid diethylamide, d-amphetamine, and 3,4-methylenedioxyamphetamine

Psychedelics have recently attracted significant attention for their potential to mitigate symptoms associated with various psychiatric disorders. However, the precise neurobiological mechanisms responsible for these effects remain incompletely understood. A valuable approach to gaining insights into the specific mechanisms of action involves comparing psychedelics with substances that have partially overlapping neurophysiological effects, i.e., modulating the same neurotransmitter systems. Imaging data were obtained from the clinical trial NCT03019822, which explored the acute effects of lysergic acid diethylamide (LSD), d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers. The clinical trial employed a double-blind, placebo-controlled, crossover design. Herein, various resting-state connectivity measures were examined, including within-network connectivity (integrity), between-network connectivity (segregation), seed-based connectivity of resting-state networks, and global connectivity. Differences between placebo and the active conditions were assessed using repeated-measures ANOVA, followed by post-hoc pairwise t-tests. Changes in voxel-wise seed-based connectivity were correlated with serotonin 2 A receptor density maps. Compared to placebo, all substances reduced integrity in several networks, indicating both common and unique effects. While LSD uniquely reduced integrity in the default-mode network (DMN), the amphetamines, in contrast to our expectations, reduced integrity in more networks than LSD. However, LSD exhibited more pronounced segregation effects, characterized solely by decreases, in contrast to the amphetamines, which also induced increases. Across all substances, seed-based connectivity mostly increased between networks, with LSD demonstrating more pronounced effects than both amphetamines. Finally, while all substances decreased global connectivity in visual areas, compared to placebo, LSD specifically increased global connectivity in the basal ganglia and thalamus. These findings advance our understanding of the distinctive neurobiological effects of psychedelics, prompting further exploration of their therapeutic potential.

Whole-brain turbulent dynamics predict responsiveness to pharmacological treatment in major depressive disorder

Depression is a multifactorial clinical syndrome with a low pharmacological treatment response rate. Therefore, identifying predictors of treatment response capable of providing the basis for future developments of individualized therapies is crucial. Here, we applied model-free and model-based measures of whole-brain turbulent dynamics in resting-state functional magnetic resonance imaging (fMRI) in healthy controls and unmedicated depressed patients. After eight weeks of treatment with selective serotonin reuptake inhibitors (SSRIs), patients were classified as responders and non-responders according to the Hamilton Depression Rating Scale 6 (HAMD6). Using the model-free approach, we found that compared to healthy controls and responder patients, non-responder patients presented disruption of the information transmission across spacetime scales. Furthermore, our results revealed that baseline turbulence level is positively correlated with beneficial pharmacological treatment outcomes. Importantly, our model-free approach enabled prediction of which patients would turn out to be non-responders. Finally, our model-based approach provides mechanistic evidence that non-responder patients are less sensitive to stimulation and, consequently, less prone to respond to treatment. Overall, we demonstrated that different levels of turbulent dynamics are suitable for predicting response to SSRIs treatment in depression.

Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder

An individual's risk of substance use disorder (SUD) is shaped by a complex interplay of potent biosocial factors. Current neurodevelopmental models posit vulnerability to SUD in youth is due to an overreactive reward system and reduced inhibitory control. Having a family history of SUD is a particularly strong risk factor, yet few studies have explored its impact on brain function and structure prior to substance exposure. Herein, we utilized a network control theory approach to quantify sex-specific differences in brain activity dynamics in youth with and without a family history of SUD, drawn from a large cohort of substance-naïve youth from the Adolescent Brain Cognitive Development Study. We summarize brain dynamics by calculating transition energy, which probes the ease with which a whole brain, region or network drives the brain towards a specific spatial pattern of activation (i.e., brain state). Our findings reveal that a family history of SUD is associated with alterations in the brain's dynamics wherein: i) independent of sex, certain regions' transition energies are higher in those with a family history of SUD and ii) there exist sex-specific differences in SUD family history groups at multiple levels of transition energy (global, network, and regional). Family history-by-sex effects reveal that energetic demand is increased in females with a family history of SUD and decreased in males with a family history of SUD, compared to their same-sex counterparts with no SUD family history. Specifically, we localize these effects to higher energetic demands of the default mode network in females with a family history of SUD and lower energetic demands of attention networks in males with a family history of SUD. These results suggest a family history of SUD may increase reward saliency in males and decrease efficiency of top-down inhibitory control in females. This work could be used to inform personalized intervention strategies that may target differing cognitive mechanisms that predispose individuals to the development of SUD.

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

Propofol anesthesia destabilizes neural dynamics across cortex

Every day, hundreds of thousands of people undergo general anesthesia. One hypothesis is that anesthesia disrupts dynamic stability-the ability of the brain to balance excitability with the need to be stable and controllable. To test this hypothesis, we developed a method for quantifying changes in population-level dynamic stability in complex systems: delayed linear analysis for stability estimation (DeLASE). Propofol was used to transition animals between the awake state and anesthetized unconsciousness. DeLASE was applied to macaque cortex local field potentials (LFPs). We found that neural dynamics were more unstable in unconsciousness compared with the awake state. Cortical trajectories mirrored predictions from destabilized linear systems. We mimicked the effect of propofol in simulated neural networks by increasing inhibitory tone. This in turn destabilized the networks, as observed in the neural data. Our results suggest that anesthesia disrupts dynamical stability that is required for consciousness.

Clinical utility of fMRI in evaluating of LSD effect on pain-related brain networks in healthy subjects

Objective

We aimed to evaluate the effect of Lysergic acid diethylamide (LSD) on the pain neural network (PNN) in healthy subjects using functional magnetic resonance imaging (fMRI).

Methods

Twenty healthy volunteers participated in a balanced-order crossover study, receiving intravenous administration of LSD and placebo in two fMRI scanning sessions. Brain regions associated with pain processing were analyzed by amplitude of low-frequency fluctuation (ALFF), independent component analysis (ICA), functional connectivity and dynamic casual modeling (DCM).

Results

ALFF analysis demonstrated that LSD effectively relieves pain due to modulation in the neural network associated with pain processing. ICA analysis showed more active voxels in anterior cingulate cortex (ACC), thalamus (THL)-left, THL-right, insula cortex (IC)-right, parietal operculum (PO)-left, PO-right and frontal pole (FP)-right in the placebo session than the LSD session. There were more active voxels in FP-left and IC-left in the LSD session compared to the placebo session. Functional brain connectivity was observed between THL-left and PO-right and between PO-left with FP-left, FP-right and IC-left in the placebo session. In the LSD session, functional connectivity of PO-left with FP-left and FP-right was observed. The effective connectivity between left anterior insula cortex (lAIC)-lAIC, lAIC-dorsolateral prefrontal cortex (dlPFC) and secondary somatosensory cortex (SII)-dlPFC were significantly different. Finally, the correlation between fMRI biomarkers and clinical pain criteria was calculated.

Conclusion

This study enhances our understanding of the LSD effect on the architecture and neural behavior of pain in healthy subjects and provides great promise for future research in the field of cognitive science and pharmacology.

Unravelling consciousness and brain function through the lens of time, space, and information

Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.

Dynamic Functional Hyperconnectivity After Psilocybin Intake Is Primarily Associated With Oceanic Boundlessness

BACKGROUND

Psilocybin is a widely studied psychedelic substance that leads to the psychedelic state, a specific altered state of consciousness. To date, the relationship between the psychedelic state's neurobiological and experiential patterns remains undercharacterized because they are often analyzed separately. We investigated the relationship between neurobiological and experiential patterns after psilocybin by focusing on the link between dynamic cerebral connectivity and retrospective questionnaire assessment.

METHODS

Healthy participants were randomized to receive either psilocybin (n = 22) or placebo (n = 27) and scanned for 6 minutes in an eyes-open resting state during the peak subjective drug effect (102 minutes posttreatment) in ultrahigh field 7T magnetic resonance imaging. The 5-Dimensional Altered States of Consciousness Rating Scale was administered 360 minutes after drug intake.

RESULTS

Under psilocybin, there were alterations across all dimensions of the 5-Dimensional Altered States of Consciousness Rating Scale and widespread increases in averaged brain functional connectivity. Time-varying functional connectivity analysis unveiled a recurrent hyperconnected pattern characterized by low blood oxygen level-dependent signal amplitude, suggesting heightened cortical arousal. In terms of neuroexperiential links, canonical correlation analysis showed higher transition probabilities to the hyperconnected pattern with feelings of oceanic boundlessness and secondly with visionary restructuralization.

CONCLUSIONS

Psilocybin generates profound alterations at both the brain and the experiential levels. We suggest that the brain's tendency to enter a hyperconnected-hyperarousal pattern under psilocybin represents the potential to entertain variant mental associations. These findings illuminate the intricate interplay between brain dynamics and subjective experience under psilocybin, thereby providing insights into the neurophysiology and neuroexperiential qualities of the psychedelic state.

A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia

Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.

Psychedelics and disorders of consciousness: the current landscape and the path forward

Modern medicine has been shaken by the surge of psychedelic science that proposes a new approach to mitigate mental disorders, such as depression and post-traumatic stress disorder. Clinical trials to investigate whether psychedelic substances can treat psychiatric conditions are now underway, yet less discussion gravitates around their use in neurological disorders due to brain injury. One suggested implementation of brain-complexity enhancing psychedelics is to treat people with post-comatose disorders of consciousness (DoC). In this article, we discuss the rationale of this endeavour, examining possible outcomes of such experiments by postulating the existence of an optimal level of complexity. We consider the possible counterintuitive effects of both psychedelics and DoC on the functional connectivity of the default mode network and its possible impact on selfhood. We also elaborate on the role of computational modelling in providing complementary information to experimental studies, both contributing to our understanding of the treatment mechanisms and providing a path towards personalized medicine. Finally, we update the discourse surrounding the ethical considerations, encompassing clinical and scientific values.

Psychedelics and the 'inner healer': Myth or mechanism?

BACKGROUND

Reference to an intrinsic healing mechanism or an 'inner healer' is commonplace amongst psychedelic drug-using cultures. The 'inner healer' refers to the belief that psychedelic compounds, plants or concoctions have an intrinsically regenerative action on the mind and brain, analogous to intrinsic healing mechanisms within the physical body, for example, after sickness or injury.

AIMS

Here, we sought to test and critique this idea by devising a single subjective rating item pertaining to perceived 'inner healing' effects.

METHODS

The item was issued to 59 patients after a single high (25 mg, n = 30) or 'placebo' (1 mg, n = 29) dose of psilocybin in a double-blind randomised controlled trial of psilocybin for depression.

RESULTS

Inner healer scores were higher after the high versus placebo dose of psilocybin (t = 3.88, p < 0.001). Within the high-dose sub-sample only, inner healer scores predicted improved depressive symptomatology at 2 weeks post-dosing.

CONCLUSIONS

The principle of activating inner healing mechanisms via psychedelics is scientifically nascent; however, this study takes a positivist and pragmatic step forward, asking whether it warrants further examination.

Exploring psychedelic-assisted therapy in the treatment of functional seizures: A review of underlying mechanisms and associated brain networks

Functional seizures (FS), the most common subtype of functional neurological disorder (FND), cause serious neurological disability and significantly impact quality of life. Characterized by episodic disturbances of functioning that resemble epileptic seizures, FS coincide with multiple comorbidities and are treated poorly by existing approaches. Novel treatment approaches are sorely needed. Notably, mounting evidence supports the safety and efficacy of psychedelic-assisted therapy (PAT) for several psychiatric conditions, motivating investigations into whether this efficacy also extends to neurological disorders. Here, we synthesize past empirical findings and frameworks to construct a biopsychosocial mechanistic argument for the potential of PAT as a treatment for FS. In doing so, we highlight FS as a well-defined cohort to further understand the large-scale neural mechanisms underpinning PAT. Our synthesis is guided by a complexity science perspective which we contend can afford unique mechanistic insight into both FS and PAT, as well as help bridge these two domains. We also leverage this perspective to propose a novel analytic roadmap to identify markers of FS diagnostic specificity and treatment success. This endeavor continues the effort to bridge clinical neurology with psychedelic medicine and helps pave the way for a new field of psychedelic neurology.

Signal integration and integral feedback control with biochemical reaction networks

Biochemical reaction networks perform a variety of signal processing functions, one of which is computing the integrals of signal values. This is often used in integral feedback control, where it enables a system's output to respond to changing inputs, but to then return exactly back to some pre-determined setpoint value afterward. To gain a deeper understanding of how biochemical networks are able to both integrate signals and perform integral feedback control, we investigated these abilities for several simple reaction networks. We found imperfect overlap between these categories, with some networks able to perform both tasks, some able to perform integration but not integral feedback control, and some the other way around. Nevertheless, networks that could either integrate or perform integral feedback control shared key elements. In particular, they included a chemical species that was neutrally stable in the open loop system (no feedback), meaning that this species does not have a unique stable steady-state concentration. Neutral stability could arise from zeroth order decay reactions, binding to a partner that was produced at a constant rate (which occurs in antithetic control), or through a long chain of covalent cycles. Mathematically, it arose from rate equations for the reaction network that were underdetermined when evaluated at steady-state.

Review of Psilocybin Use for Depression among Cancer Patients after Approval in Oregon

Despite the legalization of psilocybin therapy for depression in terminal illnesses such as advanced cancer through Oregon's Measure 109 in 2020, significant challenges have impeded its implementation. This review synthesizes the empirical data supporting the utilization of psilocybin therapy for addressing cancer-related depression, including an evaluation of its purported benefits and potential adverse effects. It provides a comprehensive examination of therapeutic strategies, dosing regimens, and barriers to ensuring responsible and equitable access. Salient issues explored include the development of ethical protocols, integration within healthcare systems, ensuring statewide availability, resolving legal ambiguities, and defining clinical standards. Oregon's pioneering role serves as a case study, highlighting the necessity of addressing regulatory, logistical, and ethical obstacles to ensure the establishment of rigorous and equitable psilocybin care models.

The 'PSILAUT' protocol: an experimental medicine study of autistic differences in the function of brain serotonin targets of psilocybin

BACKGROUND

The underlying neurobiology of the complex autism phenotype remains obscure, although accumulating evidence implicates the serotonin system and especially the 5HT2A receptor. However, previous research has largely relied upon association or correlation studies to link differences in serotonin targets to autism. To directly establish that serotonergic signalling is involved in a candidate brain function our approach is to change it and observe a shift in that function. We will use psilocybin as a pharmacological probe of the serotonin system in vivo. We will directly test the hypothesis that serotonergic targets of psilocybin - principally, but not exclusively, 5HT2A receptor pathways-function differently in autistic and non-autistic adults.

METHODS

The 'PSILAUT' "shiftability" study is a case-control study autistic and non-autistic adults. How neural responses 'shift' in response to low doses (2 mg and 5 mg) of psilocybin compared to placebo will be examined using multimodal techniques including functional MRI and EEG. Each participant will attend on up to three separate visits with drug or placebo administration in a double-blind and randomized order.

RESULTS

This study will provide the first direct evidence that the serotonin targets of psilocybin function differently in the autistic and non-autistic brain. We will also examine individual differences in serotonin system function.

CONCLUSIONS

This work will inform our understanding of the neurobiology of autism as well as decisions about future clinical trials of psilocybin and/or related compounds including stratification approaches.

TRIAL REGISTRATION

NCT05651126.

Disrupted brain state dynamics in opioid and alcohol use disorder: attenuation by nicotine use

Substance use disorder (SUD) is a chronic relapsing disorder with long-lasting changes in brain intrinsic networks. While most research to date has focused on static functional connectivity, less is known about the effect of chronic drug use on dynamics of brain networks. Here we investigated brain state dynamics in individuals with opioid use (OUD) and alcohol use disorder (AUD) and assessed how concomitant nicotine use, which is frequent among individuals with OUD and AUD, affects brain dynamics. Resting-state functional magnetic resonance imaging data of 27 OUD, 107 AUD, and 137 healthy participants were included in the analyses. To identify recurrent brain states and their dynamics, we applied a data-driven clustering approach that determines brain states at a single time frame. We found that OUD and AUD non-smokers displayed similar changes in brain state dynamics including decreased fractional occupancy or dwell time in default mode network (DMN)-dominated brain states and increased appearance rate in visual network (VIS)-dominated brain states, which were also reflected in transition probabilities of related brain states. Interestingly, co-use of nicotine affected brain states in an opposite manner by lowering VIS-dominated and enhancing DMN-dominated brain states in both OUD and AUD participants. Our finding revealed a similar pattern of brain state dynamics in OUD and AUD participants that differed from controls, with an opposite effect for nicotine use suggesting distinct effects of various drugs on brain state dynamics. Different strategies for treating SUD may need to be implemented based on patterns of co-morbid drug use.

Controlling the human connectome with spatially diffuse input signals

The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.

Recent advances in oxidative phenol coupling for the total synthesis of natural products

Covering: 2008 to 2023This review will describe oxidative phenol coupling as applied in the total synthesis of natural products. This review covers catalytic and electrochemical methods with a brief comparison to stoichiometric and enzymatic systems assessing their practicality, atom economy, and other measures. Natural products forged by C-C and C-O oxidative phenol couplings as well as from alkenyl phenol couplings will be addressed. Additionally, exploration into catalytic oxidative coupling of phenols and other related species (carbazoles, indoles, aryl ethers, etc.) will be surveyed. Future directions of this particular area of research will also be assessed.

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.

A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex

Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signalling is an evolutionary and developmental driver and potent modulator of the macroscale functional organization of the human cerebral cortex. A wealth of evidence indicates that the anatomical and functional organization of the cortex follows a unimodal-to-transmodal gradient. Situated at the apex of this processing hierarchy-where it plays a central role in the integrative processes underpinning complex, human-defining cognition-the transmodal cortex has disproportionately expanded across human development and evolution. Notably, the adult human transmodal cortex is especially rich in 5-HT2AR expression and recent evidence suggests that, during early brain development, 5-HT2AR signalling on neural progenitor cells stimulates their proliferation-a critical process for evolutionarily-relevant cortical expansion. Drawing on multimodal neuroimaging and cross-species investigations, we argue that, by contributing to the expansion of the human cortex and being prevalent at the apex of its hierarchy in the adult brain, 5-HT2AR signalling plays a major role in both human cortical expansion and functioning. Owing to its unique excitatory and downstream cellular effects, neuronal 5-HT2AR agonism promotes neuroplasticity, learning and cognitive and psychological flexibility in a context-(hyper)sensitive manner with therapeutic potential. Overall, we delineate a dual role of 5-HT2ARs in enabling both the expansion and modulation of the human transmodal cortex.

Shared functional connectome fingerprints following ritualistic ayahuasca intake

The knowledge that brain functional connectomes are unique and reliable has enabled behaviourally relevant inferences at a subject level. However, whether such "fingerprints" persist under altered states of consciousness is unknown. Ayahuasca is a potent serotonergic psychedelic which produces a widespread dysregulation of functional connectivity. Used communally in religious ceremonies, its shared use may highlight relevant novel interactions between mental state and functional connectome (FC) idiosyncrasy. Using 7T fMRI, we assessed resting-state static and dynamic FCs for 21 Santo Daime members after collective ayahuasca intake in an acute, within-subject study. Here, connectome fingerprinting revealed FCs showed reduced idiosyncrasy, accompanied by a spatiotemporal reallocation of keypoint edges. Importantly, we show that interindividual differences in higher-order FC motifs are relevant to experiential phenotypes, given that they can predict perceptual drug effects. Collectively, our findings offer an example of how individualised connectivity markers can be used to trace a subject's FC across altered states of consciousness.

Psychedelic concentrations of nitrous oxide reduce functional differentiation in frontoparietal and somatomotor cortical networks

Despite the longstanding use of nitrous oxide and descriptions of its psychological effects more than a century ago, there is a paucity of neurobiological investigation of associated psychedelic experiences. We measure the brain's functional geometry (through analysis of cortical gradients) and temporal dynamics (through analysis of co-activation patterns) using human resting-state functional magnetic resonance imaging data acquired before and during administration of 35% nitrous oxide. Both analyses demonstrate that nitrous oxide reduces functional differentiation in frontoparietal and somatomotor networks. Importantly, the subjective psychedelic experience induced by nitrous oxide is inversely correlated with the degree of functional differentiation. Thus, like classical psychedelics acting on serotonin receptors, nitrous oxide flattens the functional geometry of the cortex and disrupts temporal dynamics in association with psychoactive effects.

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use

Heavy alcohol use and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of AUD has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in N = 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals to N = 308 individuals with minimal use of any substances. We find that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of AUD.

Shaping dynamical neural computations using spatiotemporal constraints

Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.

With great power comes great vulnerability: an ethical analysis of psychedelics' therapeutic mechanisms proposed by the REBUS hypothesis

Psychedelics are experiencing a renaissance in mental healthcare. In recent years, more and more early phase trials on psychedelic-assisted therapy have been conducted, with promising results overall. However, ethical analyses of this rediscovered form of treatment remain rare. The present paper contributes to the ethical inquiry of psychedelic-assisted therapy by analysing the ethical implications of its therapeutic mechanisms proposed by the relaxed beliefs under psychedelics (REBUS) hypothesis. In short, the REBUS hypothesis states that psychedelics make rigid beliefs revisable by increasing the influence of bottom-up input. Put differently, patients become highly suggestible and sensitive to context during a psychedelic session, amplifying therapeutic influence and effects. Due to that, patients are more vulnerable in psychedelic-assisted therapy than in other therapeutic interventions; they lose control during a psychedelic session and become dependent on the therapeutic setting (including the therapist). This enhanced vulnerability is ethically relevant and has been exploited by some therapists in the past. Therefore, patients in current research settings and starting mainstream medical settings need to be well informed about psychedelics' mechanisms and their implications to give valid informed consent to treatment. Furthermore, other security measures are warranted to protect patients from the vulnerability coming with psychedelic-assisted therapy.

Understanding psychosis

ABSTRACT

Psychotic behavior is often unpredictable; thus, there can be an increased risk of violence toward others and oneself. This article details the etiology and diagnosis of psychosis and nursing interventions to provide appropriate care.

Using network control theory to study the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).

Psychedelics and neonihilism: connectedness in a meaningless world

The resurgence of psychedelic research explicitly targets treating mental health conditions largely through psychedelics-assisted psychotherapy. Current theories about mechanisms of change in psychedelics-assisted psychotherapy focus on mystical experiences as the main driver of symptom improvement. During these mystical experiences, participants report an enhanced sense of salience, connectedness, and meaning. Simultaneously, a growing psychedelic culture is also cultivating the use of psychedelics as medicine for relieving symptoms of anxiety and depression and promoting cognitive functions. We argue that an integral part of the excitement around the resurgence in psychedelics is in response to a meaning and alienation crisis that correlates with rising rates of anxiety and depression. Framing the absence of meaning as neonihilism, a contemporary correlate to the 19th-century phenomenon with unique features present in a neoliberal cultural context, we explore whether psychedelics combined with group therapy can provide answers to modern experiences of meaninglessness. Based on this exploration, we suggest concrete next steps both in the theory and practice of psychedelic psychotherapy toward what we are calling neonihilistic psychedelic group psychotherapy.

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.

Spatial Correspondence of LSD-Induced Variations on Brain Functioning at Rest With Serotonin Receptor Expression

BACKGROUND

Lysergic acid diethylamide (LSD) is an atypical psychedelic compound that exerts its effects through pleiotropic actions, mainly involving 1A/2A serotoninergic (5-HT) receptor subtypes. However, the mechanisms by which LSD promotes a reorganization of the brain's functional activity and connectivity are still partially unknown.

METHODS

Our study analyzed resting-state functional magnetic resonance imaging data acquired from 15 healthy volunteers undergoing LSD single-dose intake. A voxelwise analysis investigated the alterations of the brain's intrinsic functional connectivity and local signal amplitude induced by LSD or by a placebo. Quantitative comparisons assessed the spatial overlap between these 2 indices of functional reorganization and the topography of receptor expression obtained from a publicly available collection of in vivo, whole-brain atlases. Finally, linear regression models explored the relationships between changes in resting-state functional magnetic resonance imaging and behavioral aspects of the psychedelic experience.

RESULTS

LSD elicited modifications of the cortical functional architecture that spatially overlapped with the distribution of serotoninergic receptors. Local signal amplitude and functional connectivity increased in regions belonging to the default mode and attention networks associated with high expression of 5-HT2A receptors. These functional changes correlate with the occurrence of simple and complex visual hallucinations. At the same time, a decrease in local signal amplitude and intrinsic connectivity was observed in limbic areas, which are dense with 5-HT1A receptors.

CONCLUSIONS

This study provides new insights into the neural processes underlying the brain network reconfiguration induced by LSD. It also identifies a topographical relationship between opposite effects on brain functioning and the spatial distribution of different 5-HT receptors.

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity

Quantifying the relationship between the brain's functional activity patterns and its structural backbone is crucial when relating the severity of brain pathology to disability in multiple sclerosis (MS). Network control theory (NCT) characterizes the brain's energetic landscape using the structural connectome and patterns of brain activity over time. We applied NCT to investigate brain-state dynamics and energy landscapes in controls and people with MS (pwMS). We also computed entropy of brain activity and investigated its association with the dynamic landscape's transition energy and lesion volume. Brain states were identified by clustering regional brain activity vectors, and NCT was applied to compute the energy required to transition between these brain states. We found that entropy was negatively correlated with lesion volume and transition energy, and that larger transition energies were associated with pwMS with disability. This work supports the notion that shifts in the pattern of brain activity in pwMS without disability results in decreased transition energies compared to controls, but, as this shift evolves over the disease, transition energies increase beyond controls and disability occurs. Our results provide the first evidence in pwMS that larger lesion volumes result in greater transition energy between brain states and decreased entropy of brain activity.

In vivo mapping of pharmacologically induced functional reorganization onto the human brain's neurotransmitter landscape

To understand how pharmacological interventions can exert their powerful effects on brain function, we need to understand how they engage the brain's rich neurotransmitter landscape. Here, we bridge microscale molecular chemoarchitecture and pharmacologically induced macroscale functional reorganization, by relating the regional distribution of 19 neurotransmitter receptors and transporters obtained from positron emission tomography, and the regional changes in functional magnetic resonance imaging connectivity induced by 10 different mind-altering drugs: propofol, sevoflurane, ketamine, lysergic acid diethylamide (LSD), psilocybin, N,N-Dimethyltryptamine (DMT), ayahuasca, 3,4-methylenedioxymethamphetamine (MDMA), modafinil, and methylphenidate. Our results reveal a many-to-many mapping between psychoactive drugs' effects on brain function and multiple neurotransmitter systems. The effects of both anesthetics and psychedelics on brain function are organized along hierarchical gradients of brain structure and function. Last, we show that regional co-susceptibility to pharmacological interventions recapitulates co-susceptibility to disorder-induced structural alterations. Collectively, these results highlight rich statistical patterns relating molecular chemoarchitecture and drug-induced reorganization of the brain's functional architecture.

Effect of LSD and music on the time-varying brain dynamics

RATIONALE

Psychedelics are getting closer to being widely used in clinical treatment. Music is known as a key element of psychedelic-assisted therapy due to its psychological effects, specifically on the emotion, meaning-making, and sensory processing. However, there is still a lack of understanding in how psychedelics influence brain activity in experimental settings involving music listening.

OBJECTIVES

The main goal of our research was to investigate the effect of music, as a part of "setting," on the brain states dynamics after lysergic acid diethylamide (LSD) intake.

METHODS

We used an open dataset, where a group of 15 participants underwent two functional MRI scanning sessions under LSD and placebo influence. Every scanning session contained three runs: two resting-state runs separated by one run with music listening. We applied K-Means clustering to identify the repetitive patterns of brain activity, so-called brain states. For further analysis, we calculated states' dwell time, fractional occupancy and transition probability.

RESULTS

The interaction effect of music and psychedelics led to change in the time-varying brain activity of the task-positive state. LSD, regardless of the music, affected the dynamics of the state of combined activity of DMN, SOM, and VIS networks. Crucially, we observed that the music itself could potentially have a long-term influence on the resting-state, in particular on states involving task-positive networks.

CONCLUSIONS

This study indicates that music, as a crucial element of "setting," can potentially have an influence on the subject's resting-state during psychedelic experience. Further studies should replicate these results on a larger sample size.

Neuromodulatory control of complex adaptive dynamics in the brain

How is the massive dimensionality and complexity of the microscopic constituents of the nervous system brought under sufficiently tight control so as to coordinate adaptive behaviour? A powerful means for striking this balance is to poise neurons close to the critical point of a phase transition, at which a small change in neuronal excitability can manifest a nonlinear augmentation in neuronal activity. How the brain could mediate this critical transition is a key open question in neuroscience. Here, I propose that the different arms of the ascending arousal system provide the brain with a diverse set of heterogeneous control parameters that can be used to modulate the excitability and receptivity of target neurons-in other words, to act as control parameters for mediating critical neuronal order. Through a series of worked examples, I demonstrate how the neuromodulatory arousal system can interact with the inherent topological complexity of neuronal subsystems in the brain to mediate complex adaptive behaviour.

Time-resolved network control analysis links reduced control energy under DMT with the serotonin 2a receptor, signal diversity, and subjective experience

Psychedelics offer a profound window into the functioning of the human brain and mind through their robust acute effects on perception, subjective experience, and brain activity patterns. In recent work using a receptor-informed network control theory framework, we demonstrated that the serotonergic psychedelics lysergic acid diethylamide (LSD) and psilocybin flatten the brain's control energy landscape in a manner that covaries with more dynamic and entropic brain activity. Contrary to LSD and psilocybin, whose effects last for hours, the serotonergic psychedelic N,N-dimethyltryptamine (DMT) rapidly induces a profoundly immersive altered state of consciousness lasting less than 20 minutes, allowing for the entirety of the drug experience to be captured during a single resting-state fMRI scan. Using network control theory, which quantifies the amount of input necessary to drive transitions between functional brain states, we integrate brain structure and function to map the energy trajectories of 14 individuals undergoing fMRI during DMT and placebo. Consistent with previous work, we find that global control energy is reduced following injection with DMT compared to placebo. We additionally show longitudinal trajectories of global control energy correlate with longitudinal trajectories of EEG signal diversity (a measure of entropy) and subjective ratings of drug intensity. We interrogate these same relationships on a regional level and find that the spatial patterns of DMT's effects on these metrics are correlated with serotonin 2a receptor density (obtained from separately acquired PET data). Using receptor distribution and pharmacokinetic information, we were able to successfully recapitulate the effects of DMT on global control energy trajectories, demonstrating a proof-of-concept for the use of control models in predicting pharmacological intervention effects on brain dynamics.

Reduced emergent character of neural dynamics in patients with a disrupted connectome

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.