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The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the biophysical limitations of direct electrical stimulation (DES) of microcircuits, this opens exciting possibilities for discovering stimulation targets and selecting connectivity patterns that can ensure propagation of DES-induced neural excitation, potentially making it possible to create awakenings from complex cases of brain injury.

•We studied EEG brain states in disorders of consciousness and healthy controls using two independent datasets of resting-state and task-based data.•Brain states are remarkably similar after injury compared to those of healthy controls.•Brain state dynamics are more unstable in higher frequencies in DoC.•Anticorrelation between active states is lower in DoC patients in higher frequencies.•Using combined learning classification, we show that brain state dynamics could serve as biomarkers to assess consciousness levels in DoC. This multi-centric study aimed to explore differences in brain activity patterns in patients with disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Using high-density electroencephalographic (EEG) recordings from 368 DoC patients, 39 who emerged from MCS (eMCS), and 73 healthy controls, we examined instantaneous functional connectivity-based meta-states acting as attractors in a dynamical system, extracted by means of community detection algorithms and recurrence analysis. We analyzed data from two patient cohorts and included resting-state and auditory processing tasks in four frequency bands (delta, theta, alpha, beta) and from three perspectives, namely: (i) discrete activation of dominant states, (ii) a dynamical system composed of attractor states and (iii) the correlation and anticorrelation patterns of the active states. Findings revealed that while the overall structure of brain connectivity remained stable after injury, patients with DoC and those who emerged showed notable differences in the speed and consistency of how their brain states activated. Specifically, in higher frequencies, UWS patients exhibited faster, and less stable dynamics, shorter dwell times and decreased meta-state anticorrelation compared to those in MCS and eMCS. Moreover, a four-way combined learning classification analysis showed that the measures were able to distinguish the UWS and MCS subgroups. These brain state dynamics could serve as valuable markers for assessing states of consciousness. Our results highlight the potential of using high-temporal resolution dynamic brain activity patterns to improve the understanding of altered consciousness and their application to clinical settings.

Itinerant dynamics of the brain generates transient and recurrent spatiotemporal patterns in neuroimaging data. Characterizing metastable functional connectivity (FC) - particularly at rest and using functional magnetic resonance imaging (fMRI) - has shaped the field of dynamic functional connectivity (DFC). Mainstream DFC research relies on (sliding window) correlations to identify recurrent FC patterns. Recently, functional relevance of the (IPS) of fMRI signals has been revealed using imaging studies and computational models. In the present paper, we identify the repertoire of whole-brain inter-network IPS states at rest. Moreover, we uncover a hierarchy in the temporal organization of IPS modes. We hypothesize that connectivity disorder in schizophrenia (SZ) is related to the (deep) temporal arrangement of large-scale IPS modes. Hence, we analyze resting-state fMRI data from 68 healthy controls (HC) and 51 SZ patients. Seven resting-state networks (and their sub-components) are identified using spatial independent component analysis. IPS is computed between subject-specific network time courses, using analytic signals. The resultant phase coupling patterns, across time and subjects, are clustered into eight IPS states. Statistical tests show that the relative expression and mean lifetime of certain IPS states have been altered in SZ. Namely, patients spend (45%) less time in a globally coherent state and a subcortical-centered state, and (40%) more time in states reflecting anticoupling within the cognitive control network, compared to the HC. Moreover, the transition profile (between states) reveals a deep temporal structure, shaping two metastates with distinct phase synchrony profiles. A metastate is a collection of states such that within-metastate transitions are more probable than across. Remarkably, metastate occupation balance is altered in SZ, in favor of the less synchronous metastate that promotes disconnection across networks. Furthermore, the trajectory of IPS patterns is less efficient, less smooth, and more restricted in SZ subjects, compared to the HC. Finally, a regression analysis confirms the diagnostic value of the defined IPS measures for SZ identification, highlighting the distinctive role of metastate proportion. Our results suggest that the proposed IPS features may be used for classification studies and for characterizing phase synchrony modes in other (clinical) populations.

Embryonic stem cells (ESCs) are derived from inner cell mass (ICM) of the blastocyst. In serum/LIF culture condition, they show variable expression of pluripotency genes that mark cell fluctuation between pluripotency and differentiation metastate. The ESCs subpopulation marked by zygotic genome activation gene (ZGA) signature, including , retains a wider differentiation potency than epiblast-derived ESCs. We have recently shown that retinoic acid (RA) significantly enhances Zscan4 cell population. However, it remains unexplored how RA initiates the ESCs to 2-cell like reprogramming. Here we found that RA is decisive for ESCs to 2C-like cell transition, and reconstructed the gene network surrounding . We revealed that RA regulates 2C-like population co-activating and . We provided novel evidence that RA dependent ESCs to 2C-like cell transition is regulated by , and antagonized by . Our suggested mechanism could shed light on the role of RA on ESC reprogramming.

The spontaneous dynamic characteristics of resting-state functional networks contain much internal brain physiological or pathological information. The metastate analysis of brain functional networks is an effective technique to quantify the essence of brain functional connectome dynamics. However, the widely used functional connectivity-based metastate analysis ignored the topological structure, which could be locally reflected by node centrality. In this study, 23 healthy young volunteers (21-26 years) were recruited and scanned twice with a 1-week interval. Based on the time sequences of node centrality, we promoted a node centrality-based clustering method to find metastates of functional connectome and conducted a test-retest experiment to assess the stability of those identified metastates using the described method. The hub regions of metastates were further compared with the structural networks' organization to depict its potential relationship with brain structure. Results of extracted metastates showed repeatable dynamic features between repeated scans and high overlapping rate of hub regions with brain intrinsic sub-networks. These identified hub patterns from metastates further highly overlapped with the structural hub regions. These findings indicated that the proposed node centrality-based metastates detection method could reveal reliable and meaningful metastates of spontaneous dynamics and indicate the underlying nature of brain dynamics as well as the potential relationship between these dynamics and the organization of the brain connectome.

Unlike with gases, for liquids and solids the pressure of a system can be not only positive, but also negative, or even zero. Upon isobaric heat exchange (heating or cooling) at p = 0, the volume work (p-V) should be zero, assuming the general validity of traditional δW = dWp = −pdV equality. This means that at zero pressure, a special process can be realized; a macroscopic change of volume achieved by isobaric heating/cooling without any work done by the system on its surroundings or by the surroundings on the system. A neologism is proposed for these dWp = 0 (and in general, also for non-trivial δW = 0 and W = 0) processes: “aergiatic” (from Greek: Ἀεργία, “inactivity”). In this way, two phenomenologically similar processes—adiabatic without any heat exchange, and aergiatic without any work—would have matching, but well-distinguishable terms.

Background Altered expression of specific microRNAs (miRNA) is known to occur during colorectal carcinogenesis. However, little is known about the genome-wide alterations in miRNA expression during the neoplastic progression of primary colorectal cancers. Methods Using a miRNA array platform, we evaluated the expression of 668 miRNA in primary colonic adenocarcinomas. Biological functions of selected miRNA were evaluated with in vitro invasion assays. Results RNA was extracted for miRNA analysis from 65 primary colon cancers. We identified a seven-miRNA expression signature that differentiated stage I and stage IV primary colon cancers. We then demonstrated this signature was able to discriminate between stage II and III primary colon cancers. Six differentially expressed miRNA were downregulated in association with the development of metastases, and all 7 miRNA were complementary strand miRNA. We transfected HCT-116, a highly invasive colon cancer cell line, with corresponding downregulated miRNA and demonstrated that overexpression of three miRNA (miR200c*, miR143*, and miR424*) significantly abrogated invasive potential. Conclusion We have identified a seven-miRNA signature that is associated with metastatic potential in the primary tumor. Forced overexpression of three downregulated miRNA resulted in attenuation of in vitro invasion, suggesting direct tumor suppressive function and further supporting the biological importance of complementary strand miRNA.

We study ground states of Ising models with random ferromagnetic couplings, proving the triviality of all zero-temperature metastates. This result sheds a new light on the properties of these systems, putting strong restrictions on their possible ground state structure. Open problems related to existence of interface-supporting ground states are stated and an interpretation of the main result in terms of first-passage and random surface models in a random environment is presented.

Properties of Random Overlap Structures (ROSt)’s constructed from the Edwards-Anderson (EA) Spin Glass model on ℤd with periodic boundary conditions are studied. ROSt’s are ℕ×ℕ random matrices whose entries are the overlaps of spin configurations sampled from the Gibbs measure. Since the ROSt construction is the same for mean-field models (like the Sherrington-Kirkpatrick model) as for short-range ones (like the EA model), the setup is a good common ground to study the effect of dimensionality on the properties of the Gibbs measure. In this spirit, it is shown, using translation invariance, that the ROSt of the EA model possesses a local stability that is stronger than stochastic stability, a property known to hold at almost all temperatures in many spin glass models with Gaussian couplings. This fact is used to prove stochastic stability for the EA spin glass at all temperatures and for a wide range of coupling distributions. On the way, a theorem of Newman and Stein about the pure state decomposition of the EA model is recovered and extended.