Low-dimensional organization of global brain states of reduced consciousness.

Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unclear. We combine whole-brain modeling, data augmentation, and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states.

Inhibitory neurons control the consolidation of neural assemblies via adaptation to selective stimuli.

Brain circuits display modular architecture at different scales of organization. Such neural assemblies are typically associated to functional specialization but the mechanisms leading to their emergence and consolidation still remain elusive. In this paper we investigate the role of inhibition in structuring new neural assemblies driven by the entrainment to various inputs.

From neurotransmitters to networks: Transcending organisational hierarchies with molecular-informed functional imaging.

The human brain exhibits complex interactions across micro, meso-, and macro-scale organisational principles. Recent synergistic multi-modal approaches have begun to link micro-scale information to systems level dynamics, transcending organisational hierarchies and offering novel perspectives into the brain's function and dysfunction. Specifically, the distribution of micro-scale properties (such as receptor density or gene expression) can be mapped onto macro-scale measures from functional MRI to provide novel neurobiological insights.

Disrupted resting-sate brain network dynamics in children born extremely preterm.

The developing brain has to adapt to environmental and intrinsic insults after extremely preterm (EPT) birth. Ongoing maturational processes maximize their fit to the environment and this can provide a substrate for neurodevelopmental failures. Resting-state functional magnetic resonance imaging was used to scan 33 children born EPT, at < 27 weeks of gestational age, and 26 full-term controls at 10 years of age.

Contextual incongruency triggers memory reinstatement and the disruption of neural stability.

Schemas, or internal representation models of the environment, are thought to be central in organising our everyday life behaviour by giving stability and predictiveness to the structure of the world. However, when an element from an unfolding event mismatches the schema-derived expectations, the coherent narrative is interrupted and an update to the current event model representation is required. Here, we asked whether the perceived incongruence of an item from an unfolding event and its impact on memory relied on the disruption of neural stability patterns preceded by the neural reactivation of the memory representations of the just-encoded event.

Anxiolytic effects of vestibular stimulation: an update.

The vestibular system inhibits both HPA and SAM axis and contributes to the management of anxiety. Both direct and indirect pathways exist in the inhibition of the HPA and SAM axis. In this review article, the authors describe various pathways through which the vestibular system can regulate the HPA and SAM axis activity.

A whole-brain model of the neural entropy increase elicited by psychedelic drugs.

Psychedelic drugs, including lysergic acid diethylamide (LSD) and other agonists of the serotonin 2A receptor (5HT2A-R), induce drastic changes in subjective experience, and provide a unique opportunity to study the neurobiological basis of consciousness. One of the most notable neurophysiological signatures of psychedelics, increased entropy in spontaneous neural activity, is thought to be of relevance to the psychedelic experience, mediating both acute alterations in consciousness and long-term effects. However, no clear mechanistic explanation for this entropy increase has been put forward so far.

Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks.

Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale brain models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from the intricate and multi-scale structure of the brain. Despite substantial progress in the field, many aspects about the mechanisms behind the onset of spatiotemporal neural dynamics are still unknown.

Model-based whole-brain perturbational landscape of neurodegenerative diseases.

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively.

Using in silico perturbational approach to identify critical areas in schizophrenia.

Schizophrenia is a debilitating neuropsychiatric disorder whose underlying correlates remain unclear despite decades of neuroimaging investigation. One contentious topic concerns the role of global signal (GS) fluctuations and how they affect more focal functional changes. Moreover, it has been difficult to pinpoint causal mechanisms of circuit disruption.

Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study.

The relationship between structural connectivity (SC) and functional connectivity (FC) captured from magnetic resonance imaging, as well as its interaction with disability and cognitive impairment, is not well understood in people with multiple sclerosis (pwMS). The Virtual Brain (TVB) is an open-source brain simulator for creating personalized brain models using SC and FC. The aim of this study was to explore SC-FC relationship in MS using TVB.

Seeing inferences: brain dynamics and oculomotor signatures of non-verbal deduction.

We often express our thoughts through words, but thinking goes well beyond language. Here we focus on an elementary but basic thinking process, disjunction elimination, elicited by elementary visual scenes deprived of linguistic content, describing its neural and oculomotor correlates. We track two main components of a nonverbal deductive process: the construction of a logical representation (A or B), and its simplification by deduction (not A, therefore B).

Long-Range α-Synchronization as Control Signal for BCI: A Feasibility Study.

Shifts in spatial attention are associated with variations in α band (α, 8-14 Hz) activity, specifically in interhemispheric imbalance. The underlying mechanism is attributed to local α-synchronization, which regulates local inhibition of neural excitability, and frontoparietal synchronization reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCIs).

LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics.

A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e.

Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer's Disease.

Healthy brain dynamics can be understood as the emergence of a complex system far from thermodynamic equilibrium. Brain dynamics are temporally irreversible and thus establish a preferred direction in time (i.e.

Distributed harmonic patterns of structure-function dependence orchestrate human consciousness.

A central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function. Here we decompose functional MRI signals from pathological and pharmacologically-induced perturbations of consciousness into distributed patterns of structure-function dependence across scales: the harmonic modes of the human structural connectome. We show that structure-function coupling is a generalisable indicator of consciousness that is under bi-directional neuromodulatory control.

Toward naturalistic neuroscience: Mechanisms underlying the flattening of brain hierarchy in movie-watching compared to rest and task.

Identifying the functional specialization of the brain has moved from using cognitive tasks and resting state to using ecological relevant, naturalistic movies. We leveraged a large-scale neuroimaging dataset to directly investigate the hierarchical reorganization of functional brain activity when watching naturalistic films compared to performing seven cognitive tasks and resting. A thermodynamics-inspired whole-brain model paradigm revealed the generative underlying mechanisms for changing the balance in causal interactions between brain regions in different conditions.

Auditory cortical connectivity in humans.

To understand auditory cortical processing, the effective connectivity between 15 auditory cortical regions and 360 cortical regions was measured in 171 Human Connectome Project participants, and complemented with functional connectivity and diffusion tractography. 1. A hierarchy of auditory cortical processing was identified from Core regions (including A1) to Belt regions LBelt, MBelt, and 52; then to PBelt; and then to HCP A4.

An integrated resource for functional and structural connectivity of the marmoset brain.

Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets.

Advancing brain network models to reconcile functional neuroimaging and clinical research.

Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses.

Human amygdala compared to orbitofrontal cortex connectivity, and emotion.

The amygdala and orbitofrontal cortex have been implicated in emotion. To understand these regions better in humans, their effective connectivity with 360 cortical regions was measured in 171 humans from the Human Connectome Project, and complemented with functional connectivity and diffusion tractography. The human amygdala has effective connectivity from few cortical regions compared to the orbitofrontal cortex: primarily from auditory cortex A5 and the related superior temporal gyrus and temporal pole regions; the piriform (olfactory) cortex; the lateral orbitofrontal cortex 47m; somatosensory cortex; the hippocampus, entorhinal cortex, perirhinal cortex, and parahippocampal TF; and from the cholinergic nucleus basalis.

Assessing the coupling between local neural activity and global connectivity fluctuations: Application to human intracranial electroencephalography during a cognitive task.

Cognitive-relevant information is processed by different brain areas that cooperate to eventually produce a response. The relationship between local activity and global brain states during such processes, however, remains for the most part unexplored. To address this question, we designed a simple face-recognition task performed in patients with drug-resistant epilepsy and monitored with intracranial electroencephalography (EEG).

Brain recognition of previously learned versus novel temporal sequences: a differential simultaneous processing.

Memory for sequences is a central topic in neuroscience, and decades of studies have investigated the neural mechanisms underlying the coding of a wide array of sequences extended over time. Yet, little is known on the brain mechanisms underlying the recognition of previously memorized versus novel temporal sequences. Moreover, the differential brain processing of single items in an auditory temporal sequence compared to the whole superordinate sequence is not fully understood.

Strength-dependent perturbation of whole-brain model working in different regimes reveals the role of fluctuations in brain dynamics.

Despite decades of research, there is still a lack of understanding of the role and generating mechanisms of the ubiquitous fluctuations and oscillations found in recordings of brain dynamics. Here, we used whole-brain computational models capable of presenting different dynamical regimes to reproduce empirical data's turbulence level. We showed that the model's fluctuations regime fitted to turbulence more faithfully reproduces the empirical functional connectivity compared to oscillatory and noise regimes.