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Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild TBI (mTBI) can be difficult to detect using conventional MRI or CT. Injured brain tissues in mTBI patients generate abnormal slow-waves (1-4Hz) that can be measured and localized by resting-state magnetoencephalography (MEG). In this study, we develop a voxel-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mTBI on a single-subject basis. A normative database of resting-state MEG source magnitude images (1-4Hz) from 79 healthy control subjects was established for all brain voxels. The high-resolution MEG source magnitude images were obtained by our recent Fast-VESTAL method. In 84 mTBI patients with persistent post-concussive symptoms (36 from blasts, and 48 from non-blast causes), our method detected abnormalities at the positive detection rates of 84.5%, 86.1%, and 83.3% for the combined (blast-induced plus with non-blast causes), blast, and non-blast mTBI groups, respectively. We found that prefrontal, posterior parietal, inferior temporal, hippocampus, and cerebella areas were particularly vulnerable to head trauma. The result also showed that MEG slow-wave generation in prefrontal areas positively correlated with personality change, trouble concentrating, affective lability, and depression symptoms. Discussion is provided regarding the neuronal mechanisms of MEG slow-wave generation due to deafferentation caused by axonal injury and/or blockages/limitations of cholinergic transmission in TBI. This study provides an effective way for using MEG slow-wave source imaging to localize affected areas and supports MEG as a tool for assisting the diagnosis of mTBI.

Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics. ; A.L.G. and G.D. were supported by Swiss National Science Foundation Sinergia grant no. 170873. A.P.A. and G.D. received funding from the FLAG-ERA JTC (PCI2018- 092891). G.D., S.L. and G.Z.L. received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3). G.D. acknowledges the Spanish Ministry Project PSI2016-75688-P (AEI/ FEDER), the Catalan Research Group Support 2017 SGR 1545, and AWAKENING (PID2019-105772GB-I00, AEI FEDER EU) funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI) and European Regional Development Funds (FEDER). The study was further supported by the University and University Hospital of Liège, the Belgian National Funds for Scientific Research (FRS-FNRS), the European Space Agency (ESA) and the ...

Abstract
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.

Abstract
Neurons in the hippocampus exhibit spontaneous spiking activity during rest that appears to recapitulate previously experienced events. While this replay activity is frequently linked to memory consolidation and learning, the underlying mechanisms are not well understood. Recent large-scale neural recordings in mice have demonstrated that resting-state spontaneous activity is expressed as quasi-periodic cascades of spiking activity that pervade the forebrain, with each cascade engaging a high proportion of recorded neurons. Hippocampal ripples are known to be coordinated with cortical dynamics; however, less is known about the occurrence of replay activity relative to other brain-wide spontaneous events. Here we analyzed responses across the mouse brain to multiple viewings of natural movies, as well as subsequent patterns of neural activity during rest. We found that hippocampal neurons showed time-selectivity, with individual neurons responding consistently during particular moments of the movie. During rest, the population of time-selective hippocampal neurons showed both forward and time-reversed replay activity that matched the sequence observed in the movie. Importantly, these replay events were strongly time-locked to brain-wide spiking cascades, with forward and time-reversed replay activity associated with distinct cascade types. Thus, intrinsic hippocampal replay activity is temporally structured according to large-scale spontaneous physiology affecting areas throughout the forebrain. These findings shed light on the coordination between hippocampal and cortical circuits thought to be critical for memory consolidation.

The Whole-Brain Child Workbook has a unique, interactive approach that allows readers not only to think more deeply about how the ideas fit their own parenting approach, but also develop specific and practical ways to implement the concepts -- and bring them to life for themselves and for their children.

The article is an examination of the 1930 play Brain. A Play of the Whole Earth, by an obscure early twentieth-century British writer, Lionel Britton, in the light of the writings of Polish Jewish physician and philosopher of science Ludwik Fleck and the sociologist Émile Durkheim. A consideration of the notion of collectivity as depicted in the text, its complex representation of a posthuman existence, and the unusual generic characteristics of the play lead to the suggestion that Brain may be regarded as a utopian bildungsroman whose main focus is the life of an idea in society.

Understanding the underlying mechanisms of the human brain in health and disease will require models with necessary and sufficient details to explain how function emerges from the underlying anatomy and is shaped by neuromodulation. Here, we provide such a detailed causal explanation using a whole-brain model integrating multimodal imaging in healthy human participants undergoing manipulation of the serotonin system. Specifically, we combined anatomical data from diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) with neurotransmitter data obtained with positron emission tomography (PET) of the detailed serotonin 2A receptor (5-HT2AR) density map. This allowed us to model the resting state (with and without concurrent music listening) and mechanistically explain the functional effects of 5-HT2AR stimulation with lysergic acid diethylamide (LSD) on healthy participants. The whole-brain model used a dynamical mean-field quantitative description of populations of excitatory and inhibitory neurons as well as the associated synaptic dynamics, where the neuronal gain function of the model is modulated by the 5-HT2AR density. The model identified the causative mechanisms for the non-linear interactions between the neuronal and neurotransmitter system, which are uniquely linked to (1) the underlying anatomical connectivity, (2) the modulation by the specific brainwide distribution of neurotransmitter receptor density, and (3) the non-linear interactions between the two. Taking neuromodulatory activity into account when modeling global brain dynamics will lead to novel insights into human brain function in health and disease and opens exciting possibilities for drug discovery and design in neuropsychiatric disorders. ; G.D. is supported by the ERC Advanced Grant DYSTRUCTURE (295129), the Spanish Research Project PSI2016-75688-P, and the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). M.L.K. is supported by the ERC Consolidator Grant: CAREGIVING (615539) and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). R.L.C.-H. is supported by the Alex Mosley Charitable Trust, and the study that yielded the empirical LSD data was carried out as part of a Beckley-Imperial research collaboration. J. Cabral is supported under the project NORTE-01-0145-FEDER-000023 from the Northern Portugal Regional Operational Program (NORTE 2020) under the Portugal 2020 Partnership Agreement through the European Regional Development Fund (FEDER). G.M.K. and the Cimbi database were supported by a centre grant from the Lundbeck Foundation (2010-5364).

Understanding the underlying mechanisms of the human brain in health and disease will require models with necessary and sufficient details to explain how function emerges from the underlying anatomy and is shaped by neuromodulation. Here, we provide such a detailed causal explanation using a whole-brain model integrating multimodal imaging in healthy human participants undergoing manipulation of the serotonin system. Specifically, we combined anatomical data from diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) with neurotransmitter data obtained with positron emission tomography (PET) of the detailed serotonin 2A receptor (5-HT2AR) density map. This allowed us to model the resting state (with and without concurrent music listening) and mechanistically explain the functional effects of 5-HT2AR stimulation with lysergic acid diethylamide (LSD) on healthy participants. The whole-brain model used a dynamical mean-field quantitative description of populations of excitatory and inhibitory neurons as well as the associated synaptic dynamics, where the neuronal gain function of the model is modulated by the 5-HT2AR density. The model identified the causative mechanisms for the non-linear interactions between the neuronal and neurotransmitter system, which are uniquely linked to (1) the underlying anatomical connectivity, (2) the modulation by the specific brainwide distribution of neurotransmitter receptor density, and (3) the non-linear interactions between the two. Taking neuromodulatory activity into account when modeling global brain dynamics will lead to novel insights into human brain function in health and disease and opens exciting possibilities for drug discovery and design in neuropsychiatric disorders. ; ERC Advanced Grant DYSTRUCTURE (295129), the Spanish Research ProjectPSI2016-75688-P, and the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). ERC Consolidator ...

The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aims to define a reliable standard for the extraction of multiple signatures from fMRI data, while verifying that they do not mix information about the different modalities (e.g., subjects and conditions such as tasks performed by them). In particular, condition-specific signatures should not be contaminated by subjectrelated information, since they aim to generalize over subjects. Practically, signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key for robust prediction is using effective connectivity instead of functional connectivity. Our method demonstrates excellent generalization capabilities for subject identification in two datasets, using only a few sessions per subject as reference. Using another dataset with resting state and movie viewing, we show that the two signatures related to subjects and tasks correspond to distinct subnetworks, which are thus topologically orthogonal. Our results set solid foundations for applications tailored to individual subjects, such as clinical diagnostic. ; This work was supported by the European Union's Horizon 2020 research and innovation programme/Human Brain Project (grant FP7-FET-ICT-604102 to MG and GD; H2020-720270 HBP SGA1 to GD) and the Marie Sklodowska-Curie Action (grant H2020-MSCA-656547 to MG). A.I. is supported by the Spanish Ministry of Economy and Competitiveness Flag-ERA APCIN project CHAMPMouse (PCIN-2015-127). GD also acknowledges funding from the ERC Advanced Grant DYSTRUCTURE (#295129), the Spanish Research Project PSI2016-75688-P and the Catalan Research Group Support 2017 SGR 1545. DM was supported by the KU Leuven Special Research Fund (grant C16/15/070). SK has been funded by a Heisenberg grant from the German Science Foundation (DFG KU 3322/1-1), the European Union (ERC-2016-StG-Self-Control-677804) and a Fellowship from the Jacobs Foundation (JRF 2016-2018). This work has in part been funded by the German Science Foundation (SFB 936/C7).

Experimental and modeling work of neural activity has described recurrent and attractor dynamic patterns in cerebral microcircuits. However, it is still poorly understood whether similar dynamic principles exist or can be generalizable to the large-scale level. Here, we applied dynamic graph theory-based analyses to evaluate the dynamic streams of whole-brain functional connectivity over time across cognitive states. Dynamic connectivity in local networks is located in attentional areas during tasks and primary sensory areas during rest states, and dynamic connectivity in distributed networks converges in the default mode network (DMN) in both task and rest states. Importantly, we find that distinctive dynamic connectivity patterns are spatially associated with Allen Human Brain Atlas genetic transcription levels of synaptic long-term potentiation and long-term depression-related genes. Our findings support the neurobiological basis of large-scale attractor-like dynamics in the heteromodal cortex within the DMN, irrespective of cognitive state. ; This work has been partially supported by the National Institutes of Health (NIH) grant K23EB019023 (to J.S.), Postdoctoral Fellowship Program from the Basque Country Government and Bizkaia Talent (to I.D.).

In the book <em>The Whole-Brain Child: 12 Revolutionary Strategies to Nurture Your Child's Developing Mind, </em>Siegel and Payne Bryson share tools for parents to use to make parenting an easier process as they help their children be more healthy and happy.