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Dissipative systems evolve in the preferred temporal direction indicated by the thermodynamic arrow of time. The fundamental nature of this temporal asymmetry led us to hypothesize its presence in the neural activity evoked by conscious perception of the physical world, and thus its covariance with the level of conscious awareness. We implemented a data-driven deep learning framework to decode the temporal inversion of electrocorticography signals acquired from non-human primates. Brain activity time series recorded during conscious wakefulness could be distinguished from their inverted counterparts with high accuracy, both using frequency and phase information. However, classification accuracy was reduced for data acquired during deep sleep and under ketamine-induced anesthesia; moreover, the predictions obtained from multiple independent neural networks were less consistent for sleep and anesthesia than for conscious wakefulness. Finally, the analysis of feature importance scores highlighted transitions between slow ($\approx$20 Hz) and fast frequencies (>40 Hz) as the main contributors to the temporal asymmetry observed during conscious wakefulness. Our results show that a preferred temporal direction is manifest in the neural activity evoked by conscious mentation and in the phenomenology of the passage of time, establishing common ground to tackle the relationship between brain and subjective experience.

Life is a constant battle against equilibrium. From the cellular level to the macroscopic scale, living organisms as dissipative systems require the violation of their detailed balance, i.e. metabolic enzymatic reactions, in order to survive. We present a framework based on temporal asymmetry as a measure of non-equilibrium. By means of statistical physics, it was discovered that temporal asymmetries establish an arrow of time useful for assessing the reversibility in human brain time series. Previous studies in human and non-human primates have shown that decreased consciousness states such as sleep and anaesthesia result in brain dynamics closer to the equilibrium. Furthermore, there is growing interest in the analysis of brain symmetry based on neuroimaging recordings and since it is a non-invasive technique, it can be extended to different brain imaging modalities and applied at different temporo-spatial scales. In the present study, we provide a detailed description of our methodological approach, paying special attention to the theories that motivated this work. We test, for the first time, the reversibility analysis in human functional magnetic resonance imaging data in patients suffering from disorder of consciousness. We verify that the tendency of a decrease in the asymmetry of the brain signal together with the decrease in non-stationarity are key characteristics of impaired consciousness states. We expect that this work will open the way for assessing biomarkers for patients' improvement and classification, as well as motivating further research on the mechanistic understanding underlying states of impaired consciousness. Authors declare no competing interest. E.G.G. is supported by the Spanish Ministry of Universities for University Teacher Training Aid grant FPU 2020-02470. Y.S.P. is supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant 896354. J.V. is supported by the EU H2020 FET Proactive project Neurotwin grant agreement no. 101017716. A.E. is supported by the HBP SGA3 Human Brain Project Specific Grant Agreement 3 (grant agreement no. 945539), funded by the EU H2020 FET Flagship programme. D.M. received individual funding from Ecole Doctorale Frontières de l’Innovation en Recherche et Education–Programme Bettencourt. B.T. received a PhD grant from the French Ministry of Higher Education and Ecole Normale Superieur. M.K. is supported by the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117), and Centre for Eudaimonia and Human Flourishing at Linacre College funded by the Pettit and Carlsberg Foundations. G.D. is supported by the Spanish National Research project (AEI-PID2019-105772GB I00/AEI/10.13039/501100011033) funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI). J.D.S. and E.T. are funded by a grant from STIC-AmSud [project SILIDOC - 21-STIC-11], ECOS-Sud A20M02, and ECOS-Sud U20S01. J.D.S. and G.D. are supported by the ModelDXConsciousness in the framework of the FLAG-ERA JTC 2021—HBP—2021 programme.

One of the potential and promising adjuvant therapies for Alzheimer’s disease is that of non-invasive transcranial neurostimulation to potentiate cognitive training interventions. Conceptually, this is achieved by driving brain dynamics towards an optimal state for an effective facilitation of cognitive training interventions. However, current neurostimulation protocols rely on experimental trial-and-error approaches that result in variability of symptom improvements and suboptimal progress. Here, we leveraged whole-brain computational modelling by assessing the regional susceptibility towards optimal brain dynamics from Alzheimer’s disease. In practice, we followed the three-part concept of Dynamic Sensitivity Analysis by first understanding empirical differences between healthy controls and patients with mild cognitive impairment and mild dementia due to Alzheimer’s Disease; secondly, by building computational models for all individuals in the mild cognitive impairment and mild dementia cohorts; and thirdly, by perturbing brain regions and assessing the impact on the recovery of brain dynamics to the healthy state (here defined in functional terms, summarised by a measure of metastability for the healthy group). By doing so, we show the importance of key regions, along the anterior-posterior medial line, in driving in-silico improvement of mild dementia and mild cognitive impairment groups. Moreover, this subset consists mainly of regions with high structural nodal degree. Overall, this in-silico perturbational approach could inform the design of stimulation strategies for re-establishing healthy brain dynamics, putatively facilitating effective cognitive interventions targeting the cognitive decline in Alzheimer’s disease.

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

To flexibly respond to a continuously changing environment, the human brain must be able to flexibly switch amongst many demanding cognitive tasks. The flexibility inside the brain is enabled by integrating and segregating information in large-scale functional networks over time. In this study, we used graph theory metrics prior to clustering to identify two brain states, segregated and integrated, in 100 healthy adults selected from the Human Connectome Project (HCP) dataset at rest and during six cognitive tasks. Furthermore, we explored two-dimensional (2D) latent space revealed by a deep autoencoder. In the latent space, the integrated state occupied less space compared with the segregated state. After binning the latent space, we obtained entropy from the probability for each data point of being in the bin. The integrated state showed lower entropy than the segregated state, and the rest modality showed higher entropy in both states compared with tasks. We also found that modularity and global efficiency are good measures for distinguishing between tasks and rest in both states. Overall, the study shows that integration and segregation are present in rest and in task modalities, while integration serves as information compression and segregation as information specialisation. These characteristics ensure the necessary cognitive flexibility to learn new tasks with deep proficiency.

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 unknown. We combined whole-brain modelling, 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. An orderly trajectory from wakefulness to brain injured patients is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, both increasing alongside loss of consciousness. Finally, we investigated the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.

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 (10mg and 25mg, 7 days apart) therapy for depression - to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used whole-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 heathy 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-HT2A and 5-HT1A, 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.

This paper examines different aspects of the development of the brain before birth, and the subsequent plasticity after birth. First, rough outlines of the pre-natal formation of the neural structures are presented: cell identity, neuron migration, axon formation and initial synapse formation. Second, the paper looks at the post-natal plasticity with regard to critical periods. A short introduction to the visual system serves as a starting point for a description of Hubel and Wiesel’s pioneering experiments on cats and monkeys. In addition, Harlow’s experiments with sensory deprivation of monkeys are described. This leads to an attempt to link these results to a computational strategy for the analysis and representation of the connections between sensory input and motor output: feed-forward neural units that are dynamically re-grouped by reciprocal connections. The conclusion attempts to place development and plasticity in a broader context within the brain.

Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.

Scientific theories on the functioning and dysfunction of the human brain require a good understanding of both its development — before and after birth, and through maturation to adulthood — and its evolution from the ancestral primate brain. Adopting a complex-systems approach, here we propose that the apparent uniqueness of humans’ cognitive capacities might best be understood as emerging from multiple nested “virtuous cycles.” In particular, we propose that the intimate link that exists between oxygen metabolic loops, cortical expansion, and ultimately cognitive and social demands is a key driver of genetic developmental programs for the human brain. Overall, our proposed evolutionary model makes explicit mechanistic links between metabolism, molecular and cellular brain heterogeneity, and behaviour that may in time provide a clearer understanding of brain developmental trajectories and their disorders.

Music is ubiquitous across human cultures — as a source of affective and pleasurable experience, moving us both physically and emotionally — and learning to play music shapes both brain structure and brain function. Music processing in the brain — namely, the perception of melody, harmony and rhythm — has traditionally been studied as an auditory phenomenon using passive listening paradigms. However, when listening to music, we actively generate predictions about what is likely to happen next. This enactive aspect has led to a more comprehensive understanding of music processing involving brain structures implicated in action, emotion and learning. Here we review the cognitive neuroscience literature of music perception. We show that music perception, action, emotion and learning all rest on the human brain’s fundamental capacity for prediction — as formulated by the predictive coding of music model. This Review elucidates how this formulation of music perception and expertise in individuals can be extended to account for the dynamics and underlying brain mechanisms of collective music making. This in turn has important implications for human creativity as evinced by music improvisation. These recent advances shed new light on what makes music meaningful from a neuroscientific perspective.

Different whole-brain models constrained by neuroimaging data have been developed during the last years to investigate causal hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is a particularly attractive model, combining a biophysically realistic single-neuron model that is scaled up via a mean-field approach and multimodal imaging data. Despite these favourable features, an important barrier for a widespread usage of the DMF model is that current implementations are computationally expensive – to the extent that the model often becomes unfeasible when no high-performance computing infrastructure is available. Furthermore, even when such computing structure is available, current implementations can only support simulations on brain parcellations that consider less than 100 brain regions. To remove these barriers, here we introduce a user-friendly and computationally-efficient implementation of the DMF model, which we call FastDMF, with the goal of making biophysical whole-brain modelling accessible to neuroscientists worldwide. By leveraging a suit of analytical and numerical advances – including a novel estimation of the feedback inhibition control parameter, and a Bayesian optimisation algorithm – the FastDMF circumvents various computational bottlenecks of previous implementations. An evaluation of the performance of the FastDMF showed that it can attain a significantly faster performance than previous implementations while reducing the memory consumption by several orders of magnitude. Thanks to these computational advances, FastDMF makes it possible to increase the number of simulated regions by one order of magnitude: we found good agreement between empirical and simulated functional MRI data parcellated at two different spatial scales (N=90 and N=1000 brain regions). These advances open the way to the widespread use of biophysically grounded whole-brain models for understanding the interplay among anatomy, function and brain dynamics in health and disease, and to provide mechanistic explanations of recent results obtained from empirical fine-grained neuroimaging data sets, such as turbulence or connectome harmonics.HighlightsWe present the FastDMF, a user-friendly and computationally efficient implementation of the Dynamic Mean Field model for simulations of whole-brain dynamics.Using analytical and numerical tools, we develop a novel estimation of the feedback inhibition control based on the structural connectivity, bypassing an important computational bottleneck.The FastDMF is coupled with a Bayesian Optimization algorithm significantly reducing the number of simulations required to fit the FastDMF to empirical neuroimaging data.Our advances open the possibility of simulating thousands of brain regions in a biophysically grounded whole-brain model.

In this chapter we review an emerging literature concerning the neuroimaging of various subcomponents of imagination. The preliminary conclusions of this review are two-fold. First, acts of imagination recruit similar networks in the brain to those used for the sensory and motor processing during corresponding actions in, or interactions with the real world (with the important exception that imagined movements do not activate the primary motor cortex). That the majority of studies reviewed have been concerned with visual imagery was inevitable since this is the form of imagination for which most neuroimaging experiments have been conducted. It should be noted that this first conclusion is relevant to all forms of imagination, and not just those of veridical imagery, where there is a 'real world' referent for the imaginary content. Second, the selection processes used in subcomponents of imagination such as anticipation, mindedness, and counterfactual thinking rely on widely distributed subcortical and cortical networks within the brain, consisting of important components such as the cingulate cortex, the dorsolateral prefrontal cortex, the cerebellum, and the orbitofrontal cortex. These neural structures play quite different functional roles in the complex interactions of real and imagined acts that constitute human thought and behaviour. Further knowledge of the precise functional roles of the interacting networks can be expected from neuroimaging in the coming years, perhaps through the technical breakthroughs which we imagine in a Coda and which could potentially facilitate and enhance our understanding of imagination in the future. © The British Academy 2007.

Emotion is central to human life and intimately connected with consciousness. Investigating the underlying brain mechanisms can help us understand and potentially treat the serious problems of affective disorders, such as, for example, unipolar depression, bipolar disorder, chronic pain, and the worldwide epidemic of obesity.

Over fifty years ago the discovery that rats would work to electrically stimulate their brains suggested the intriguing possibility that bliss could be achieved through the use of 'pleasure electrodes' implanted deep within the brain. Subsequent research has failed to bring about this brave new world of boundless pleasure, but more recent findings have started to throw new light on the intriguing links between brain mechanisms of pleasure and happiness. We discuss these findings of the underlying neural mechanisms and functional neuroanatomy of pleasure in the brain. In particular we address how they may come to shed light on our understanding of the brain basis of happiness. Beyond sensory pleasures, we examine how higher pleasures may be related to the brain's default networks, especially in orchestrating cognitive aspects of the meaningfulness important to happiness. We also address how understanding of the hedonic brain might help alleviate the suffering caused by the lack of pleasure, anhedonia, which is a central feature of affective disorders such as depression and chronic pain.

As a clinical intervention, deep brain stimulation (DBS) has provided remarkable therapeutic benefits for otherwise treatment-resistant movement and affective disorders including chronic pain. In this review, we concentrate on the experience of using DBS to treat chronic pain in Oxford. We provide a brief historical background as well as details of our methods for patient selection, surgical techniques and assessment. While the precise mechanisms of action for DBS remain uncertain, we describe how DBS can help for treatment-resistant chronic pain and have great potential to advance our general understanding of the human brain. In particular, we show how DBS can be used in conjunction with methods such as local field potentials and magnetoencephalography to map the underlying mechanisms of normal and abnormal oscillatory synchronization in the brain related to the pleasure of pain relief. © 2010 by Nova Science Publishers, Inc. All rights reserved.