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The brain operates at a critical point that is balanced between order and disorder. Even during rest, unstable periods of random behavior are interspersed with stable periods of balanced activity patterns that support optimal information processing. Being born preterm may cause deviations from this normal pattern of development. We compared 33 extremely preterm (EPT) children born at < 27 weeks of gestation and 28 full-term controls. Two approaches were adopted in both groups, when they were 10 years of age, using structural and functional brain magnetic resonance imaging data. The first was using a novel intrinsic ignition analysis to study the ability of the areas of the brain to propagate neural activity. The second was a whole-brain Hopf model, to define the level of stability, desynchronization, or criticality of the brain. EPT-born children exhibited fewer intrinsic ignition events than controls; nodes were related to less sophisticated aspects of cognitive control, and there was a different hierarchy pattern in the propagation of information and suboptimal synchronicity and criticality. The largest differences were found in brain nodes belonging to the rich-club architecture. These results provide important insights into the neural substrates underlying brain reorganization and neurodevelopmental impairments related to prematurity. ; Swedish Medical Research Council (grant numbers 523-2011-3981, 2017-03043); the regional agreement on medical training and clinical research (grant number, ALF SLL 20170243) between Stockholm County Council and the Karolinska Institutet; European Union Seventh Framework Project (grant number 223767); Swedish Order of Freemasons in Stockholm; Swedish Medical Society; Swedish Brain Foundation (grant number FO2017-0131); Sällskapet Barnavård; Linnèa och Josef Carlssons Stifelse' Erik and Edith Fernström Stiftelse; Ishizu Matsumura Foundation.

Brain dynamics depicts an extremely complex energy landscape that changes over time, and its characterisation is a central unsolved problem in neuroscience. We approximate the non-stationary landscape sustained by the human brain through a novel mathematical formalism that allows us characterise the attractor structure, i.e. the stationary points and their connections. Due to its time-varying nature, the structure of the global attractor and the corresponding number of energy levels changes over time. We apply this formalism to distinguish quantitatively between the different human brain states of wakefulness and different stages of sleep, as a step towards future clinical applications. ; J.A.G., I.G. and J.A.L have been partially supported by Spanish Ministerio de Economía y Competitividad and FEDER, projects MTM2015-63723-P, PGC2018-096540-B-I00, Proyecto US-1254251 del Fondo Europeo de Desarrollo Regional (FEDER) y la Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía, dentro del Programa Operativo FEDER 2014-2020 and Proyecto PAIDI 2020 P20_00592. S.S.P. is supported by the Research Project PGC2018-096641-B-I00 (Ministerio de Ciencia, Innovación y Universidades / Agencia Estatal de Investigación /FEDER, UE). G.D. is supported by the Spanish Research Project PSI2016-75688-P (Agencia Estatal de Investigacíon/Fondo Europeo de Desarrollo Regional, European Union); by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreements 720270 (Human Brain Project [HBP] SGA1) and 785907 (HBP SGA2); and by the Catalan Agency for Management of University and Research Grants Programme 2017 SGR 1545. M.L.K. is supported by the European Research Council Consolidator Grant: CAREGIVING (615539) and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). H.L. and data acquisition were supported by the Bundesministerium für Bildung und Forschung (grant no. 01 EV 0703); H.L. and E.T. by the LOEWE Neuronale Koordination ...

Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50–64 years versus older group (n = 310); age range, 65–91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local–global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain. ; A.E. was supported by the Catalan project Imagenoma de L'Envelliment (Aging Imageomics Study). G.D. was supported by the Spanish Ministry of Economy and Competitiveness, Spain (grant agreement number PSI2016- 75688-P, MINECO/AEI/FEDER-EU); European Union's Horizon 2020 FET Flagship Human Brain Project (grant agreement number 785907, HBP SGA2); the Catalan Research Support, Spain (grant agreement number 2017 SGR 1545) and La Marató TV3 2017 (grant agreement 201725.33).

Deep brain stimulation (DBS) for Parkinson's disease is a highly effective treatment in controlling otherwise debilitating symptoms. Yet the underlying brain mechanisms are currently not well understood. Whole-brain computational modeling was used to disclose the effects of DBS during resting-state functional Magnetic Resonance Imaging in ten patients with Parkinson's disease. Specifically, we explored the local and global impact that DBS has in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts global brain dynamics of patients towards a Healthy regime. This effect was more pronounced in very specific brain areas such as the thalamus, globus pallidus and orbitofrontal regions of the right hemisphere (with the left hemisphere not analyzed given artifacts arising from the electrode lead). Global aspects of integration and synchronization were also rebalanced. Empirically, we found higher communicability and coherence brain measures during DBS-ON compared to DBS-OFF. Finally, using our model as a framework, artificial in silico DBS was applied to find potential alternative target areas for stimulation and whole-brain rebalancing. These results offer important insights into the underlying large-scale effects of DBS as well as in finding novel stimulation targets, which may offer a route to more efficacious treatments ; In this work, Gustavo Deco is supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research Project PSI2016-75688-P and by the the European Union's Horizon 2020 research and innovation programme under grant agreement n. 720270 (HBP SGA1). Morten Kringelbach is supported by the ERC Consolidator Grant CAREGIVING (n. 615539) and the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). Victor M Saenger is supported by the Research Personnel Training program PSI2013-42091-P funded by the Spanish Ministry of Economy and Competitiveness. ...

Deep brain stimulation (DBS) for Parkinson's disease is a highly effective treatment in controlling otherwise debilitating symptoms. Yet the underlying brain mechanisms are currently not well understood. Whole-brain computational modeling was used to disclose the effects of DBS during resting-state functional Magnetic Resonance Imaging in ten patients with Parkinson's disease. Specifically, we explored the local and global impact that DBS has in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts global brain dynamics of patients towards a Healthy regime. This effect was more pronounced in very specific brain areas such as the thalamus, globus pallidus and orbitofrontal regions of the right hemisphere (with the left hemisphere not analyzed given artifacts arising from the electrode lead). Global aspects of integration and synchronization were also rebalanced. Empirically, we found higher communicability and coherence brain measures during DBS-ON compared to DBS-OFF. Finally, using our model as a framework, artificial in silico DBS was applied to find potential alternative target areas for stimulation and whole-brain rebalancing. These results offer important insights into the underlying large-scale effects of DBS as well as in finding novel stimulation targets, which may offer a route to more efficacious treatments. ; In this work, Gustavo Deco is supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research Project PSI2016-75688-P and by the the European Union's Horizon 2020 research and innovation programme under grant agreement n. 720270 (HBP SGA1). Morten Kringelbach is supported by the ERC Consolidator Grant CAREGIVING (n. 615539) and the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). Victor M Saenger is supported by the Research Personnel Training program PSI2013-42091-P funded by the Spanish Ministry of Economy and Competitiveness.

Previous studies suggest that the brain operates at a critical point in which phases of order and disorder coexist, producing emergent patterned dynamics at all scales and optimizing several brain functions. Here, we combined light-sheet microscopy with GCaMP zebrafish larvae to study whole-brain dynamics in vivo at near single-cell resolution. We show that spontaneous activity propagates in the brain's three-dimensional space, generating scaleinvariant neuronal avalanches with time courses and recurrence times that exhibit statistical self-similarity at different magnitude, temporal, and frequency scales. This suggests that the nervous system operates close to a non-equilibrium phase transition, where a large repertoire of spatial, temporal, and interactive modes can be supported. Finally, we show that gap junctions contribute to the maintenance of criticality and that, during interactions with the environment (sensory inputs and self-generated behaviors), the system is transiently displaced to a more ordered regime, conceivably to limit the potential sensory representations and motor outcomes. ; A.P.-A. was supported by a Juan de la Cierva fellowship (IJCI-2014-21066) from the Spanish Ministry of Economy and Competitiveness. A.J. was supported by the Fondation pour la Recherche Medicale (FRM:FDT20140930915) and the ENS Cachan. M.P. was supported by the ENS Lyon. G.D. was funded by the European Research Council (ERC) Advanced Grant DYSTRUCTURE (No. 295129), by the Spanish Research Project PSI2016-75688-P (AEI/FEDER), and by the European Union's Horizon 2020 research and innovation program under grant agreement No. 720270 (HBP SGA1). G.S. was supported by ERC StG 243106, ERC CoG 726280, ANR-10-LABX-54 MEMO LIFE, and ANR-11-IDEX-0001-02 PSL Research University. We thank J. Boulanger-Weill for technical assistance and discussions, Patricia Gongal for editorial assistance, and David Hildebrand for providing GCaMP6f line.

Remarkable progress has come from whole-brain models linking anatomy and function. Paradoxically, it is not clear how a neuronal dynamical system running in the fixed human anatomical connectome can give rise to the rich changes in the functional repertoire associated with human brain function, which is impossible to explain through long-term plasticity. Neuromodulation evolved to allow for such flexibility by dynamically updating the effectivity of the fixed anatomical connectivity. Here, we introduce a theoretical framework modeling the dynamical mutual coupling between the neuronal and neurotransmitter systems. We demonstrate that this framework is crucial to advance our understanding of whole-brain dynamics by bidirectional coupling of the two systems through combining multimodal neuroimaging data (diffusion magnetic resonance imaging [dMRI], functional magnetic resonance imaging [fMRI], and positron electron tomography [PET]) to explain the functional effects of specific serotoninergic receptor (5-HT2AR) stimulation with psilocybin in healthy humans. This advance provides an understanding of why psilocybin is showing considerable promise as a therapeutic intervention for neuropsychiatric disorders including depression, anxiety, and addiction. Overall, these insights demonstrate that the whole-brain mutual coupling between the neuronal and the neurotransmission systems is essential for understanding the remarkable flexibility of human brain function despite having to rely on fixed anatomical connectivity. ; G.D. is supported by the Spanish Research Project PSI2016-75688-P (Agencia Estatal de Investigación/Fondo Europeo de Desarrollo Regional, European Union); by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreements 720270 (Human Brain Project [HBP] SGA1) and 785907 (HBP SGA2); and by the Catalan Agencia de Gestión de Ayudas Universitarias Programme 2017 SGR 1545. M.L.K. is supported by the European Research Council Consolidator Grant CAREGIVING (615539); Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117); and Centre for Eudaimonia and Human Flourishing, funded by the Pettit and Carlsberg Foundations. J. Cabral is supported by the Portuguese Foundation for Science and Technology (CEECIND/03325/2017), Portugal.

Preface and acknowledgements -- Welcome to the world -- Challenges -- Face reality -- Whole brain thinking -- Nbi -- L1, the rational -- L2, the practical -- R2, the relational -- R1, the experimental -- Whole brain thinking -- Lead as a coach -- A different mindset -- Integrate coaching into your leadership -- Opening -- Communication -- Exploration -- Activation -- New insights -- Transformation -- Transformation -- Become the whole brain leader -- Appendix 360 self-assessment -- References -- About the authors

Notwithstanding these wise pronouncements, my project here
is to characterize the biological phenomenon of death of the
higher animal species, such as vertebrates. My claim is that
the formulation of "whole-brain death" provides
the most congruent map for our correct understanding of the
concept of death. This essay builds upon the foundation my
colleagues and I have laid since 1981 to characterize the concept
of death and refine when this event occurs. Although our society's
well-accepted program of multiple organ procurement for
transplantation requires the organ donor first to be dead, the
concept of brain death is not merely a social contrivance to
permit us to obtain the benefits of organ procurement. Rather,
the concept of whole-brain death stands independently as the most
accurate biological representation of the demise of the human organism.

The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep. ; Spanish Research Project PSI2016-75688-P (AEI/FEDER) and by the European Union's Horizon 2020 research and innovation programme under Grant agreement no. 720270 (HBP SGA1). M.L.K. was supported by the ERC Consolidator Grant CAREGIVING (615539) and the Center for Music in the Brain, funded by the Danish National Research Foundation Grant DNRF117. J.C. was supported under the project ...

The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep. ; G.D. was supported by the Spanish Research Project PSI2016-75688-P (AEI/FEDER) and by the European Union's Horizon 2020 research and innovation programme under Grant agreement no. 720270 (HBP SGA1). M.L.K. was supported by the ERC Consolidator Grant CAREGIVING (615539) and the Center for Music in the Brain, funded by the Danish National Research Foundation Grant DNRF117. J.C. was supported under the project NORTE-01-0145-FEDER-000023.

[EN] Brain dynamics, neuron activity, information transfer in brains, etc., are a vast field where a large number of questions remain unsolved. Nowadays, computer simulation is playing a key role in the study of such an immense variety of problems. In this work, we explored the possibility of studying brain dynamics using cellular automata, more precisely the famous Game of Life (GoL). The model has some important features (i.e., pseudo-criticality, 1/f noise, universal computing), which represent good reasons for its use in brain dynamics modelling. We have also considered that the model maintains sufficient flexibility. For instance, the timestep is arbitrary, as are the spatial dimensions. As first steps in our study, we used the GoL to simulate the evolution of several neurons (i.e., a statistically significant set, typically a million neurons) and their interactions with the surrounding ones, as well as signal transfer in some simple scenarios. The way that signals (or life) propagate across the grid was described, along with a discussion on how this model could be compared with brain dynamics. Further work and variations of the model were also examined. ; This work was partially supported by the European Union's Seventh Framework Programme (FP7-REGPOT-2012-2013-1) under grant agreement no 316165. This work was done with the support of the Czech Science Foundation, project 17-17921S. ; Fraile, A.; Panagiotakis, E.; Christakis, N.; Acedo Rodríguez, L. (2018). Cellular automata and artificial brain dynamics. Mathematical and Computational Applications (Online). 23(4):1-23. https://doi.org/10.3390/mca23040075 ; S ; 1 ; 23 ; 23 ; 4 ; TURING, A. M. (1950). I.—COMPUTING MACHINERY AND INTELLIGENCE. Mind, LIX(236), 433-460. doi:10.1093/mind/lix.236.433 ; Sarkar, P. (2000). A brief history of cellular automata. ACM Computing Surveys, 32(1), 80-107. doi:10.1145/349194.349202 ; Ermentrout, G. B., & Edelstein-Keshet, L. (1993). Cellular Automata Approaches to Biological Modeling. Journal of Theoretical Biology, ...

Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. beim Graben and Hutt (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al. (2015), and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation—BOLD—signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence toward an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively. ; SR would like to acknowledge Ikerbasque (The Basque Foundation for Science) and moreover, this research is supported by the Basque Government through the BERC 2018-2021 program and by the Spanish State Research Agency through BCAM Severo Ochoa excellence accreditation SEV2017-0718 and through project RTI2018-093860-B- C21 funded by (AEI/FEDER, UE) and acronym MathNEURO. JC acknowledges financial support from Ikerbasque, Ministerio Economia, Industria y Competitividad (Spain) and FEDER (grant DPI2016-79874-R) and the Department of Economical Development and Infrastructure of the Basque Country (Elkartek Program, KK-2018/00032). Finally, PG acknowledges BCAM's hospitality during a visiting fellowship in fall 2017.