Cell type-specific excitability probed by optogenetic stimulation depends on the phase of the alpha oscillation

BACKGROUND: Alpha oscillations have been proposed to provide phasic inhibition in the brain. Yet, pinging alpha oscillations with transcranial magnetic stimulation (TMS) to examine phase-dependent network excitability has resulted in conflicting findings. At the cellular level, such gating by the alpha oscillation remains poorly understood. OBJECTIVE: We examine how the excitability of pyramidal cells and presumed fast-spiking inhibitory interneurons depends on the phase of the alpha oscillation. METHODS: Optogenetic stimulation pulses were administered at random phases of the alpha oscillation in the posterior parietal cortex (PPC) of two adult ferrets that expressed channelrhodopsin in pyramidal cells. Post-stimulation firing probability was calculated as a function of the stimulation phase of the alpha oscillation for both verum and sham stimulation. RESULTS: The excitability of pyramidal cells depended on the alpha phase, in anticorrelation with their intrinsic phase preference; pyramidal cells were more responsive to optogenetic stimulation at the alpha phase with intrinsically low firing rates. In contrast, presumed fast-spiking inhibitory interneurons did not show such a phase dependency despite their stronger intrinsic phase preference. CONCLUSIONS: Alpha oscillations gate input to PPC in a phase-dependent manner such that low intrinsic activity was associated with higher responsiveness to input. This finding supports a model of cortical oscillation, in which internal processing and communication are limited to the depolarized half-cycle, whereas the other half-cycle serves as a signal detector for unexpected input. The functional role of different parts of the alpha cycle may vary across the cortex depending on local neuronal firing properties

Closed-loop control of bistable symptom states

Dear Editor, We read with great interest the recent article by Scangos and colleagues on their closed-loop deep brain stimulation (DBS) study that targeted ventral capsule/ventral striatum (VC/VS) for the treatment of major depressive disorder (MDD) in a single participant [1]. The study consisted of an open-loop stage (Stage I) and a closed-loop stage (Stage II). In the open-loop stage, gamma power in bilateral amygdala was identified as a biomarker for the high symptom state using cross-validated logistic regression models. VC/VS was identified as an upstream stimulation target based on sophisticated structural (diffusion-based tractography) and functional mapping (stimulation-evoked potentials). In the closed-loop stage, a DBS system was implanted to detect gamma activity in the right amygdala and to stimulate the right VC/VS in a closed loop to reduce amygdala gamma activity and alleviate symptoms of depression. The participant experienced a precipitous drop in symptom severity in the first week of closed-loop stimulation compared to the week prior and remained in a low symptom state for the majority of the closed-loop stimulation period. The authors suggested that while immediate benefits of DBS to VC/VS have been repeatedly demonstrated, these effects are difficult to sustain. With a closed-loop system, the acute benefit of stimulation can be maximized, and attenuation of its efficacy can be avoided with infrequent stimulation. Overall, the work effectively integrates multiple experimental and engineering techniques, which exemplifies the future of personalized psychiatric treatment using closed-loop DBS. We are impressed with the sustained clinical outcome of closed-loop DBS in this n-of-1 study, and we suggest that the precise mechanism underlying the successful intervention could be further elucidated with a dynamical systems approach and a closer examination of the nonlinear relation between gamma activity and symptom severity

Temporal Mapper: Transition networks in simulated and real neural dynamics

AbstractCharacterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method—Temporal Mapper—built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects’ behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics

Glucose metabolism reprogramming promotes immune escape of hepatocellular carcinoma cells

Hepatocellular carcinoma (HCC) is a complex process that plays an important role in its progression. Abnormal glucose metabolism in HCC cells can meet the nutrients required for the occurrence and development of liver cancer, better adapt to changes in the surrounding microenvironment, and escape the attack of the immune system on the tumor. There is a close relationship between reprogramming of glucose metabolism and immune escape. This article reviews the current status and progress of glucose metabolism reprogramming in promoting immune escape in liver cancer, aiming to provide new strategies for clinical immunotherapy of liver cancer

The Coordination Dynamics of Multiple Agents

A fundamental question in Complexity Science is how numerous dynamic processes coordinate with each other on multiple levels of description to form a complex whole—a multiscale coordinative structure (e.g. a community of interacting people, organs, cells, molecules etc.). This dissertation includes a series of empirical, theoretical and methodological studies of rhythmic coordination between multiple agents to uncover dynamic principles underlying multiscale coordinative structures. First, a new experimental paradigm was developed for studying coordination at multiple levels of description in intermediate-sized (N = 8) ensembles of humans. Based on this paradigm, coordination dynamics in 15 ensembles was examined experimentally, where the diversity of subjects’ movement frequency was manipulated to induce different grouping behavior. Phase coordination between subjects was found to be metastable with inphase and antiphase tendencies. Higher frequency diversity led to segregation between frequency groups, reduced intragroup coordination, and dispersion of dyadic phase relations (i.e. relations at different levels of description). Subsequently, a model was developed, successfully capturing these observations. The model reconciles the Kuramoto and the extended Haken-Kelso-Bunz model (for large- and small-scale coordination respectively) by adding the second-order coupling from the latter to the former. The second order coupling is indispensable in capturing experimental observations and connects behavioral complexity (i.e. multistability) of coordinative structures across scales. Both the experimental and theoretical studies revealed multiagent metastable coordination as a powerful mechanism for generating complex spatiotemporal patterns. Coexistence of multiple phase relations gives rise to many topologically distinct metastable patterns with different degrees of complexity. Finally, a new data-analytic tool was developed to quantify complex metastable patterns based on their topological features. The recurrence of topological features revealed important structures and transitions in high-dimensional dynamic patterns that eluded its non-topological counterparts. Taken together, the work has paved the way for a deeper understanding of multiscale coordinative structures

Simulated data of a theoretical model based on the Human Firefly experiment.

Simulated data of a theoretical model based on the Human Firefly experiment

Critical diversity: Divided or united states of social coordination

<div><p>Much of our knowledge of coordination comes from studies of simple, dyadic systems or systems containing large numbers of components. The huge gap ‘in between’ is seldom addressed, empirically or theoretically. We introduce a new paradigm to study the coordination dynamics of such intermediate-sized ensembles with the goal of identifying key mechanisms of interaction. Rhythmic coordination was studied in ensembles of eight people, with differences in movement frequency (‘diversity’) manipulated within the ensemble. Quantitative change in diversity led to qualitative changes in coordination, a critical value separating régimes of integration and segregation between groups. Metastable and multifrequency coordination between participants enabled communication across segregated groups within the ensemble, without destroying overall order. These novel findings reveal key factors underlying coordination in ensemble sizes previously considered too complicated or 'messy' for systematic study and supply future theoretical/computational models with new empirical checkpoints.</p></div

On Thermodynamics of Charged and Rotating Asymptotically AdS Black Strings

In this paper, we study thermodynamics of cylindrically symmetric black holes and calculate the equation of states and heat capacity of charged and rotating black strings. In the process, we treat the cosmological constant as a thermodynamic pressure and its conjugate quantity as a thermodynamic volume. It is shown that, when taking the equivalence between the thermodynamic quantities of black strings and the ones of general thermodynamic system, the isothermal compressibility and heat capacity of black strings satisfy the stability conditions of thermodynamic equilibrium and no divergence points exist for heat capacity. Thus, we obtain the conclusion that the thermodynamic system relevant to black strings is stable and there is no second-order phase transition for AdS black holes in the cylindrically symmetric spacetime