Locking of correlated neural activity to ongoing oscillations

Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spike synchrony and in how far these channels can be considered separate. Experiments indeed showed that correlated spiking co-modulates with the static firing rate and is also tightly locked to the phase of beta-oscillations. While the dependence of correlations on the mean rate is well understood in feed-forward networks, it remains unclear why and by which mechanisms correlations tightly lock to an oscillatory cycle. We here demonstrate that such correlated activation of pairs of neurons is qualitatively explained by periodically-driven random networks. We identify the mechanisms by which covariances depend on a driving periodic stimulus. Mean-field theory combined with linear response theory yields closed-form expressions for the cyclostationary mean activities and pairwise zero-time-lag covariances of binary recurrent random networks. Two distinct mechanisms cause time-dependent covariances: the modulation of the susceptibility of single neurons (via the external input and network feedback) and the time-varying variances of single unit activities. For some parameters, the effectively inhibitory recurrent feedback leads to resonant covariances even if mean activities show non-resonant behavior. Our analytical results open the question of time-modulated synchronous activity to a quantitative analysis.

JMJD3 Is Crucial for the Female AVPV RIP-Cre Neuron-Controlled Kisspeptin-Estrogen Feedback Loop and Reproductive Function

The hypothalamic-pituitary-gonadal axis controls development, reproduction, and metabolism. Although most studies have focused on the hierarchy from the brain to the gonad, many questions remain unresolved concerning the feedback from the gonad to the central nervous system, especially regarding the potential epigenetic modifications in hypothalamic neurons. In the present report, we generated genetically modified mice lacking histone H3 lysine 27 (H3K27) demethylase Jumonji domain-containing 3 (JMJD3) in hypothalamic rat-insulin-promoter-expressing neurons (RIP-Cre neurons). The female mutant mice displayed late-onset obesity owing to reduced locomotor activity and decreased energy expenditure. JMJD3 deficiency in RIP-Cre neurons also results in delayed pubertal onset, an irregular estrous cycle, impaired fertility, and accelerated ovarian failure in female mice owing to the dysregulation of the hypothalamic-ovarian axis. We found that JMJD3 directly regulates Kiss1 gene expression by binding to the Kiss1 promoter and triggering H3K27me3 demethylation in the anteroventral periventricular (AVPV) nucleus. Further study confirmed that the aberrations arose from impaired kisspeptin signaling in the hypothalamic AVPV nucleus and subsequent estrogen deficiency. Estrogen replacement therapy can reverse obesity in mutant mice. Moreover, we demonstrated that Jmjd3 is an estrogen target gene in the hypothalamus. These results provide direct genetic and molecular evidence that JMJD3 is a key mediator for the kisspeptin-estrogen feedback loop.

Symmetry-breaking in branching epithelia: cells on micro-patterns under flow challenge the hypothesis of positive feedback by a secreted autocrine inhibitor of motility

Branching morphogenesis of epithelia involves division of cells into leader (tip) and follower (stalk) cells. Published work on cell lines in culture has suggested that symmetry-breaking takes place via a secreted autocrine inhibitor of motility, the inhibitor accumulating more in concave regions of the culture boundary, slowing advance of cells there, and less in convex areas, allowing advance and a further exaggeration of the concave/convex difference. Here we test this hypothesis using a two-dimensional culture system that includes strong flow conditions to remove accumulating diffusible secretions. We find that, while motility does indeed follow boundary curvature in this system, flow makes no difference: this challenges the hypothesis of control by a diffusible secreted autocrine inhibitor.

Estrogen and Progesterone Integration in an in vitro Model of RP3V Kisspeptin Neurons

Positive feedback on gonadotropin release requires not only estrogen but also progesterone to activate neural circuits. In rodents, ovarian estradiol (E2) stimulates progesterone synthesis in hypothalamic astrocytes (neuroP), needed for the luteinizing hormone (LH) surge. Kisspeptin (kiss) neurons are the principal stimulators of gonadotropin-releasing hormone neurons, and disruption of kiss signaling abrogates the LH surge. Similarly, blocking steroid synthesis in the hypothalamus or deleting classical progesterone receptor (PGR) selectively in kiss neurons prevents the LH surge. These results suggest a synergistic action of E2 and progesterone in kiss neurons to affect gonadotropin release. The mHypoA51, immortalized kiss-expressing neuronal cell line derived from adult female mice, is a tractable model for examining integration of steroid signaling underlying estrogen positive feedback. Here, we report that kiss neurons in vitro integrate E2 and progesterone signaling to increase levels of kiss translation and release. mHypoA51 neurons expressed nonclassical membrane progesterone receptors (mPRα and mPRβ) and E2-inducible PGR, required for progesterone-augmentation of E2-induced kiss expression. With astrocyte-conditioned media or in mHypoA51-astrocyte co-culture, neuroP augmented stimulatory effects of E2 on kiss protein. Progesterone activation of classical, membrane-localized PGR led to activation of MAPK and Src kinases. Importantly, progesterone or Src activation induced release of kiss from E2-primed mHypoA51 neurons. Consistent with previous studies, the present results provide compelling evidence that the interaction of E2 and progesterone stimulates kiss expression and release. Further, these results demonstrate a mechanism though which peripheral E2 may prime kiss neurons to respond to neuroP, mediating estrogen positive feedback.

Gonadotropin-Inhibitory Hormone, the Piscine Ortholog of LPXRFa, Participates in 17β-Estradiol Feedback in Female Goldfish Reproduction

Gonadotropin-inhibitory hormone (GnIH) plays a critical role in regulating gonadotropin-releasing hormone, gonadotropin hormone, and steroidogenesis in teleosts. In the present study, we sought to determine whether 17β-estradiol (E2) acts directly on GnIH neurons to regulate reproduction in goldfish, a seasonal breeder, and we investigated the role of estrogen receptors (ERs) in mediating this process. We found that GnIH neurons coexpress three types of ERs. Ovariectomy and letrozole implantation into female goldfish at the vitellogenic stage elicited a substantial decrease in the expression of GnIH messenger RNA (mRNA), and E2 supplementation abolished this effect. In primary cultured hypothalamus cells, E2 increased GnIH mRNA levels; surprisingly, selective ERα and ERβ agonists showed opposite effects in regulating GnIH mRNA levels. Using genome walking, we isolated a 2329-bp section of the GnIH promoter sequence, and 7 half-estrogen response elements (EREs) were found in the promoter region. Luciferase assays and electrophoretic mobility shift assay results show that the half-ERE element at -2203 is the key site for competitive binding between ERα and ERβ. Ovariectomy and letrozole implantation into female goldfish in the maturating stage did not change the GnIH mRNA expression levels. Taken together, these findings suggest that E2 binds to multiple types of ERs, which competitively bind to the same half-ERE binding site of the GnIH promoter to achieve both positive and negative feedback in response to estrogen to regulate goldfish reproduction at different stages of ovarian development.

Evidence for Feedback Regulation Following Cholesterol Lowering Therapy in a Prostate Cancer Xenograft Model

BACKGROUND

Epidemiologic data suggest cholesterol-lowering drugs may prevent the progression of prostate cancer, but not the incidence of the disease. However, the association of combination therapy in cholesterol reduction on prostate or any cancer is unclear. In this study, we compared the effects of the cholesterol lowering drugs simvastatin and ezetimibe alone or in combination on the growth of LAPC-4 prostate cancer in vivo xenografts.

METHODS

Proliferation assays were conducted by MTS solution and assessed by Student's t-test. 90 male nude mice were placed on a high-cholesterol Western-diet for 7 days then injected subcutaneously with 1 × 10⁵ LAPC-4 cells. Two weeks post-injection, mice were randomized to control, 11 mg/kg/day simvastatin, 30 mg/kg ezetimibe, or the combination and sacrificed 42 days post-randomization. We used a generalized linear model with the predictor variables of treatment, time, and treatment by time (i.e., interaction term) with tumor volume as the outcome variable. Total serum and tumor cholesterol were measured. Tumoral RNA was extracted and cDNA synthesized from 1 ug of total RNA for quantitative real-time PCR.

RESULTS

Simvastatin directly reduced in vitro prostate cell proliferation in a dose-dependent, cell line-specific manner, but ezetimibe had no effect. In vivo, low continuous dosing of ezetimibe, delivered by food, or simvastatin, delivered via an osmotic pump had no effect on tumor growth compared to control mice. In contrast, dual treatment of simvastatin and ezetimibe accelerated tumor growth. Ezetimibe significantly lowered serum cholesterol by 15%, while simvastatin had no effect. Ezetimibe treatment resulted in higher tumor cholesterol. A sixfold induction of low density lipoprotein receptor mRNA was observed in ezetimibe and the combination with simvastatin versus control tumors.

CONCLUSIONS

Systemic cholesterol lowering by ezetimibe did not slow tumor growth, nor did the cholesterol independent effects of simvastatin and the combined treatment increased tumor growth. Despite lower serum cholesterol, tumors from ezetimibe treated mice had higher levels of cholesterol. This study suggests that induction of low density lipoprotein receptor is a possible mechanism of resistance that prostate tumors use to counteract the therapeutic effects of lowering serum cholesterol. Prostate 77:446-457, 2017. © 2016 Wiley Periodicals, Inc.

Feedback between motion and sensation provides nonlinear boost in run-and-tumble navigation

Many organisms navigate gradients by alternating straight motions (runs) with random reorientations (tumbles), transiently suppressing tumbles whenever attractant signal increases. This induces a functional coupling between movement and sensation, since tumbling probability is controlled by the internal state of the organism which, in turn, depends on previous signal levels. Although a negative feedback tends to maintain this internal state close to adapted levels, positive feedback can arise when motion up the gradient reduces tumbling probability, further boosting drift up the gradient. Importantly, such positive feedback can drive large fluctuations in the internal state, complicating analytical approaches. Previous studies focused on what happens when the negative feedback dominates the dynamics. By contrast, we show here that there is a large portion of physiologically-relevant parameter space where the positive feedback can dominate, even when gradients are relatively shallow. We demonstrate how large transients emerge because of non-normal dynamics (non-orthogonal eigenvectors near a stable fixed point) inherent in the positive feedback, and further identify a fundamental nonlinearity that strongly amplifies their effect. Most importantly, this amplification is asymmetric, elongating runs in favorable directions and abbreviating others. The result is a “ratchet-like” gradient climbing behavior with drift speeds that can approach half the maximum run speed of the organism. Our results thus show that the classical drawback of run-and-tumble navigation—wasteful runs in the wrong direction—can be mitigated by exploiting the non-normal dynamics implicit in the run-and-tumble strategy.

Countless bacteria, larvae and even larger organisms (and robots) navigate gradients by alternating periods of straight motion (runs) with random reorientation events (tumbles). Control of the tumble probability is based on previously-encountered signals. A drawback of this run-and-tumble strategy is that occasional runs in the wrong direction are wasteful. Here we show that there is an operating regime within the organism’s internal parameter space where run-and-tumble navigation can be extremely efficient. We characterize how the positive feedback between behavior and sensed signal results in a type of non-equilibrium dynamics, with the organism rapidly tumbling after moving in the wrong direction and extending motion in the right ones. For a distant source, then, the organism can find it fast.

Stimulus-specific adaptation in a recurrent network model of primary auditory cortex

Stimulus-specific adaptation (SSA) occurs when neurons decrease their responses to frequently-presented (standard) stimuli but not, or not as much, to other, rare (deviant) stimuli. SSA is present in all mammalian species in which it has been tested as well as in birds. SSA confers short-term memory to neuronal responses, and may lie upstream of the generation of mismatch negativity (MMN), an important human event-related potential. Previously published models of SSA mostly rely on synaptic depression of the feedforward, thalamocortical input. Here we study SSA in a recurrent neural network model of primary auditory cortex. When the recurrent, intracortical synapses display synaptic depression, the network generates population spikes (PSs). SSA occurs in this network when deviants elicit a PS but standards do not, and we demarcate the regions in parameter space that allow SSA. While SSA based on PSs does not require feedforward depression, we identify feedforward depression as a mechanism for expanding the range of parameters that support SSA. We provide predictions for experiments that could help differentiate between SSA due to synaptic depression of feedforward connections and SSA due to synaptic depression of recurrent connections. Similar to experimental data, the magnitude of SSA in the model depends on the frequency difference between deviant and standard, probability of the deviant, inter-stimulus interval and input amplitude. In contrast to models based on feedforward depression, our model shows true deviance sensitivity as found in experiments.

Applied Interventions in the Prevention and Treatment of Obesity Through the Research of Professor Jane Wardle

PURPOSE OF REVIEW

Obesity presents a challenge for practitioners, policy makers, researchers and for those with obesity themselves. This review focuses on psychological approaches to its management and prevention in children and adults.

RECENT FINDINGS

Through exploring the work of the late Professor Jane Wardle, we look at the earliest behavioural treatment approaches and how psychological theory has been used to develop more contemporary approaches, for example incorporating genetic feedback and habit formation theory into interventions. We also explore how Jane has challenged thinking about the causal pathways of obesity in relation to eating behaviour. Beyond academic work, Jane was an advocate of developing interventions which had real-world applications. Therefore, we discuss how she not only developed new interventions but also made these widely available and the charity that she established.

Impact of the feedback provided by a gastric electrical stimulation system on eating behavior and physical activity levels

OBJECTIVE

The closed-loop gastric electrical stimulation (CLGES) abiliti^® system provides tailored gastric electrical stimulation activated by food entry into the stomach and sensor-based data to medical professionals. The aim of this study was to analyze behavior changes using sensor-based food intake and activity data in participants treated with the CLGES system.

METHODS

Food intake and activity data (3D accelerometer) were downloaded at baseline and monthly/bimonthly for 12 months in a subset of patients with obesity (N = 45) participating in a multicenter trial with CLGES. Measured food intake parameters included the number of intakes during allowed and disallowed periods, nighttime intakes, and between-meal snacks (average/d). Activity parameters included time in different levels of physical activity (min/d), sleep/sedentary (h/d), and estimated energy expenditure (EE).

RESULTS

Weight loss at 12 months averaged 15.7 ± 7.7% of the baseline body weight. Stable reduction in the number of disallowed meals and between-meal snacks (P < 0.05), an increase in all levels of physical activity (P < 0.001), and an increase in activity-based EE (303 ± 53 kcal/d on average, P < 0.001) were seen.

CONCLUSIONS

Significant improvement in eating and activity was seen in participants. It is hypothesized that feedback of the sensor-based data induced behavioral changes and contributed to weight loss in patients treated with CLGES.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Computational Account of Spontaneous Activity as a Signature of Predictive Coding

Spontaneous activity is commonly observed in a variety of cortical states. Experimental evidence suggested that neural assemblies undergo slow oscillations with Up ad Down states even when the network is isolated from the rest of the brain. Here we show that these spontaneous events can be generated by the recurrent connections within the network and understood as signatures of neural circuits that are correcting their internal representation. A noiseless spiking neural network can represent its input signals most accurately when excitatory and inhibitory currents are as strong and as tightly balanced as possible. However, in the presence of realistic neural noise and synaptic delays, this may result in prohibitively large spike counts. An optimal working regime can be found by considering terms that control firing rates in the objective function from which the network is derived and then minimizing simultaneously the coding error and the cost of neural activity. In biological terms, this is equivalent to tuning neural thresholds and after-spike hyperpolarization. In suboptimal working regimes, we observe spontaneous activity even in the absence of feed-forward inputs. In an all-to-all randomly connected network, the entire population is involved in Up states. In spatially organized networks with local connectivity, Up states spread through local connections between neurons of similar selectivity and take the form of a traveling wave. Up states are observed for a wide range of parameters and have similar statistical properties in both active and quiescent state. In the optimal working regime, Up states are vanishing, leaving place to asynchronous activity, suggesting that this working regime is a signature of maximally efficient coding. Although they result in a massive increase in the firing activity, the read-out of spontaneous Up states is in fact orthogonal to the stimulus representation, therefore interfering minimally with the network function.

Spontaneous bursts of activity, commonly observed in the brain, can be understood in terms of error-correcting computation within a neural network. Bursts arise automatically in a network that is inefficiently correcting its internal representation.

An Application of Invertibility of Boolean Control Networks to the Control of the Mammalian Cell Cycle

In Fauré et al. (2006), the dynamics of the core network regulating the mammalian cell cycle is formulated as a Boolean control network (BCN) model consisting of nine proteins as state nodes and a tenth protein (protein CycD) as the control input node. In this model, one of the state nodes, protein Cdc20, plays a central role in the separation of sister chromatids. Hence, if any Cdc20 sequence can be obtained, fully controlling the mammalian cell cycle is feasible. Motivated by this fact, we study whether any Cdc20 sequence can be obtained theoretically. We formulate the foregoing problem as the invertibility of BCNs, that is, whether one can obtain any Cdc20 sequence by designing input (i.e., protein CycD) sequences. We give an algorithm to verify the invertibility of any BCN, and find that the BCN model for the core network regulating the mammalian cell cycle is not invertible, that is, one cannot obtain any Cdc20 sequence. We further present another algorithm to test whether a finite Cdc20 sequence can be generated by the BCN model, which leads to a series of periodic infinite Cdc20 sequences with alternately active and inactive Cdc20 segments. States of these sequences are alternated between the two attractors in the proposed model, which reproduces correctly how a cell exits the cell cycle to enter the quiescent state, or the opposite.

The Arcuate Nucleus: A Site of Fast Negative Feedback for Corticosterone Secretion in Male Rats

Variations in circulating corticosterone (Cort) are driven by the paraventricular nucleus of the hypothalamus (PVN), mainly via the sympathetic autonomic nervous system (ANS) directly stimulating Cort release from the adrenal gland and via corticotropin-releasing hormone targeting the adenohypophysis to release adrenocorticotropic hormone (ACTH). Cort feeds back through glucocorticoid receptors (GRs). Here we show in male Wistar rats that PVN neurons projecting to the adrenal gland do not express GRs, leaving the question of how the ANS in the PVN gets information about circulating Cort levels to control the adrenal. Since the arcuate nucleus (ARC) shows a less restrictive blood-brain barrier, expresses GRs, and projects to the PVN, we investigated whether the ARC can detect and produce fast adjustments of circulating Cort. In low Cort conditions (morning), local microdialysis in the ARC with type I GR antagonist produced a fast and sustained increase of Cort. This was not observed with a type II antagonist. At the circadian peak levels of Cort (afternoon), a type II GR antagonist, but not a type I antagonist, increased Cort levels but not ACTH levels. Antagonist infusions in the PVN did not modify circulating Cort levels, demonstrating the specificity of the ARC to give Cort negative feedback. Furthermore, type I and II GR agonists in the ARC prevented the increase of Cort after stress, demonstrating the role of the ARC as sensor to modulate Cort release. Our findings show that the ARC may be essential to sense blood levels of Cort and adapt Cort secretion depending on such conditions as stress or time of day.

[The meteopathic reactions as reflected in the indicators of electropuncture diagnostics. A pilot study]

The present article was designed to report the results of the electropuncture diagnostics (EPD) with the use of the 'Prognoz' electropunctural device obtained during the examination of 12 patients who experienced meteopathic reactions (MPR) during magnetic storms. The most frequently detected changes were deviations affecting the acupuncture channels (AC) of the heart, pericardium and triple energizer. The latter underwent the most pronounced deviation from the 'balanced condition'. In 2 cases out of the three (66,6%), its hypofunction was documented The pericardium channel was characterized by extreme asymmetry of its left and right branches. To a lesser extent, a similar asymmetry was apparent in the acupuncture channels of the heart. The present pilot study revealed the noticeable changed in the functional state of two AC (pericardium and triple energizer) in people suffering from meteopathic reactions. It is concluded that the further studies involving a larger population of the weather-dependent subjects is likely to identify other sensitive acupuncture channels; the analysis of their functional condition may be instrumental in determining the typical MPR profiles based on the results of the evaluation of the effectiveness of electropunctural diagnostics. The newly detected patterns may be useful for the development of the personalized approach to the prediction, prevention, and relief of the meteopathic reactions.

Hypothalamic-Pituitary-Adrenocortical Dysfunction in Elderly, Male Marathon Runners: Feedback Sensitivity, Stress Response, and Effects on Verbal Memory

Animal studies suggest that repeated episodes of elevated glucocorticoids lead to a dysregulation of the hypothalamic-pituitary-adrenal (HPA) system at a suprapituitary level, and to impaired mnemonic function. We compared cognitive tests, as well as feedback integrity and stress responsivity of the HPA system, between 11 elderly, male marathon runners - a model of repeated HPA system activation - and 10 sedentary controls. The marathon runners had significantly increased baseline, stress, and post-stress ACTH - but not cortisol - concentrations. Also, suppression of ACTH by 3 mg dexamethasone was impaired in the athletes compared to the control subjects, while the ACTH and cortisol response to additional CRH did not differ between the 2 groups. Finally, long-term verbal memory was impaired in the athletes compared to the controls. Regarding the HPA system, these findings are in accordance with an acquired suprapituitary feedback disturbance in marathon runners; however, the similar glucocorticoid concentrations in the 2 groups may be due to reduced adrenal sensitivity to ACTH. Together with impaired verbal memory, these data support the assumption that repeated episodes of HPA system activity may exert negative effects at the level of the hippocampus.

Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion

Using a computational model of the Caenorhabditis elegans connectome dynamics, we show that proprioceptive feedback is necessary for sustained dynamic responses to external input. This is consistent with the lack of biophysical evidence for a central pattern generator, and recent experimental evidence that proprioception drives locomotion. The low-dimensional functional response of the Caenorhabditis elegans network of neurons to proprioception-like feedback is optimized by input of specific spatial wavelengths which correspond to the spatial scale of real body shape dynamics. Furthermore, we find that the motor subcircuit of the network is responsible for regulating this response, in agreement with experimental expectations. To explore how the connectomic dynamics produces the observed two-mode, oscillatory limit cycle behavior from a static fixed point, we probe the fixed point's low-dimensional structure using Dynamic Mode Decomposition. This reveals that the nonlinear network dynamics encode six clusters of dynamic modes, with timescales spanning three orders of magnitude. Two of these six dynamic mode clusters correspond to previously-discovered behavioral modes related to locomotion. These dynamic modes and their timescales are encoded by the network's degree distribution and specific connectivity. This suggests that behavioral dynamics are partially encoded within the connectome itself, the connectivity of which facilitates proprioceptive control.

The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics

Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses seem difficult to reconcile with probabilistic inference: neural activity displays prominent oscillations in response to constant input, and large transient changes in response to stimulus onset. Indeed, cortical models of probabilistic inference have typically either concentrated on tuning curve or receptive field properties and remained agnostic as to the underlying circuit dynamics, or had simplistic dynamics that gave neither oscillations nor transients. Here we show that these dynamical behaviours may in fact be understood as hallmarks of the specific representation and algorithm that the cortex employs to perform probabilistic inference. We demonstrate that a particular family of probabilistic inference algorithms, Hamiltonian Monte Carlo (HMC), naturally maps onto the dynamics of excitatory-inhibitory neural networks. Specifically, we constructed a model of an excitatory-inhibitory circuit in primary visual cortex that performed HMC inference, and thus inherently gave rise to oscillations and transients. These oscillations were not mere epiphenomena but served an important functional role: speeding up inference by rapidly spanning a large volume of state space. Inference thus became an order of magnitude more efficient than in a non-oscillatory variant of the model. In addition, the network matched two specific properties of observed neural dynamics that would otherwise be difficult to account for using probabilistic inference. First, the frequency of oscillations as well as the magnitude of transients increased with the contrast of the image stimulus. Second, excitation and inhibition were balanced, and inhibition lagged excitation. These results suggest a new functional role for the separation of cortical populations into excitatory and inhibitory neurons, and for the neural oscillations that emerge in such excitatory-inhibitory networks: enhancing the efficiency of cortical computations.

Our brain operates in the face of substantial uncertainty due to ambiguity in the inputs, and inherent unpredictability in the environment. Behavioural and neural evidence indicates that the brain often uses a close approximation of the optimal strategy, probabilistic inference, to interpret sensory inputs and make decisions under uncertainty. However, the circuit dynamics underlying such probabilistic computations are unknown. In particular, two fundamental properties of cortical responses, the presence of oscillations and transients, are difficult to reconcile with probabilistic inference. We show that excitatory-inhibitory neural networks are naturally suited to implement a particular inference algorithm, Hamiltonian Monte Carlo. Our network showed oscillations and transients like those found in the cortex and took advantage of these dynamical motifs to speed up inference by an order of magnitude. These results suggest a new functional role for the separation of cortical populations into excitatory and inhibitory neurons, and for the neural oscillations that emerge in such excitatory-inhibitory networks: enhancing the efficiency of cortical computations.

Pulse and Surge Profiles of Luteinizing Hormone Secretion in the Mouse

Using a new tail-tip bleeding procedure and a sensitive ELISA, we describe here the patterns of LH secretion throughout the mouse estrous cycle; in ovariectomized mice; in ovariectomized, estradiol-treated mice that model estrogen-negative and -positive feedback; and in transgenic GNR23 mice that exhibit allele-dependent reductions in GnRH neuron number. Pulsatile LH secretion was evident at all stages of the estrous cycle, with LH pulse frequency being approximately one pulse per hour in metestrous, diestrous, and proestrous mice but much less frequent at estrus (less than one pulse per 4 h). Ovariectomy resulted in substantial increases in basal and pulsatile LH secretion with pulses occurring approximately every 21 minutes. Chronic treatment with negative-feedback, estradiol-filled capsules returned LH pulse frequency to intact follicular phase levels, although pulse amplitude remained elevated. On the afternoon of proestrus, the LH surge was found to begin in a highly variable manner over a 4-hour range, lasting for more than 3 hours. In contrast, ovariectomized, estradiol-treated, positive-feedback mice exhibited a relatively uniform surge onset at approximately 0.5 hour prior to lights out. Gonadectomized wild-type and heterozygous GNR23 (∼200 GnRH neurons) male mice exhibited an LH pulse every 60 minutes. Homozygous GNR23 mice (∼80 GnRH neurons) had very low basal LH concentrations but continued to exhibit small amplitude LH pulses every 90 minutes. These studies provide the first characterization in mice of pulse and surge modes of LH secretion across the estrous cycle and demonstrate that very few GnRH neurons are required for pulsatile LH secretion.

Interactions Between Neurokinin B and Kisspeptin in Mediating Estrogen Feedback in Healthy Women

CONTEXT

Kisspeptin and neurokinin B (NKB) are obligate for normal gonadotropin secretion, but their hierarchy is unexplored in normal women.

OBJECTIVE

To investigate the interaction between kisspeptin and NKB on estrogen-regulated LH secretion.

DESIGN

Women were treated with neurokinin-3 receptor (NK3R) antagonist followed by transdermal estradiol to induce LH secretion 48 hours later, with kisspeptin-10 or vehicle infusion during estrogen administration in a 2-way crossover study.

SETTING

Clinical research facility.

PATIENTS OR OTHER PARTICIPANTS

Healthy females with regular menses.

INTERVENTION(S)

NK3R antagonist AZD4901 40 mg twice daily orally was taken from cycle day 4-6 for 6 days (n = 10, with 10 no treatment controls). Transdermal estradiol patches (200 μg/d) were applied after 5 days of NK3R antagonist treatment. At 24-hour estradiol treatment, women were randomized to 7-hour kisspeptin-10 (4 μg/kg/h) or vehicle iv infusion, with the alternate infusion in a subsequent cycle.

MAIN OUTCOME MEASURE(S)

Plasma gonadotropin and estradiol secretion.

RESULTS

After an initial suppression, LH secretion was increased 48 hours after estradiol treatment. Kisspeptin-10 increased LH secretion during the inhibitory phase, and LH remained elevated beyond the discontinuation of kisspeptin-10 infusion. NK3R antagonist decreased LH pulse frequency (0.5 ± 0.2 vs 0.7 ± 0.2 pulses/h, P < .05) and stimulated FSH response to kisspeptin-10 infusion (10.7 ± 11.0 vs 5.0 ± 3.6 IU/L, P < .05) with a nonsignificant rise in LH. The duration of LH response was blunted, with LH being lower at 48 hours (7.5 ± 4.8 vs 15.0 ± 11.4 IU/L, P < .05).

CONCLUSIONS

These data demonstrate that NKB signaling regulates GnRH/LH secretion in normal women, and is predominantly proximal to kisspeptin in mediating estrogenic positive and negative feedback on LH secretion.

Polycomb-Mediated Disruption of an Androgen Receptor Feedback Loop Drives Castration-Resistant Prostate Cancer

The lethal phenotype of castration-resistant prostate cancer (CRPC) is generally caused by augmented signaling from the androgen receptor (AR). Here, we report that the AR-repressed gene CCN3/NOV inhibits AR signaling and acts in a negative feedback loop to block AR function. Mechanistically, a cytoplasmic form of CCN3 interacted with the AR N-terminal domain to sequester AR in the cytoplasm of prostate cancer cells, thereby reducing AR transcriptional activity and inhibiting cell growth. However, constitutive repression of CCN3 by the Polycomb group protein EZH2 disrupted this negative feedback loop in both CRPC and enzalutamide-resistant prostate cancer cells. Notably, restoring CCN3 was sufficient to effectively reduce CPRC cell proliferation in vitro and to abolish xenograft tumor growth in vivo Taken together, our findings establish CCN3 as a pivotal regulator of AR signaling and prostate cancer progression and suggest a functional intersection between Polycomb and AR signaling in CRPC. Cancer Res; 77(2); 412-22. ©2016 AACR.

Disturbance Decoupling of Singular Boolean Control Networks

This paper investigates the controller designing for disturbance decoupling problem (DDP) of singular Boolean control networks (SBCNs). Using semi-tensor product (STP) of matrices and the Implicit Function Theorem, a SBCN is converted into the standard BCN. Based on the redundant variable separation technique, both state feedback and output feedback controllers are designed to solve the DDP of the SBCN. Sufficient conditions are also given to analyze the invariance of controllers concerning the DDP of the SBCN with function perturbation. Two illustrative examples are presented to support the effectiveness of these obtained results.

Impact of time delays on oscillatory dynamics of interlinked positive and negative feedback loops

Interlinking a positive feedback loop (PFL) with a negative feedback loop (NFL) constitutes a typical motif in genetic networks, performing various functions in cell signaling. How time delay in feedback regulation affects the dynamics of such systems still remains unclear. Here, we investigate three systems of interlinked PFL and NFL with time delays: a synthetic genetic oscillator, a three-node circuit, and a simplified single-node model. The stability of steady states and the routes to oscillation in the single-node model are analyzed in detail. The amplitude and period of oscillations vary with a pointwise periodicity over a range of time delay. Larger-amplitude oscillations can be induced when the PFL has an appropriately long delay, in comparison with the PFL with no delay or short delay; this conclusion holds true for all the three systems. We unravel the underlying mechanism for the above effects via analytical derivation under a limiting condition. We also develop a stochastic algorithm for simulating a single reaction with two delays and show that robust oscillations can be maintained by the PFL with a properly long delay in the single-node system. This work presents an effective method for constructing robust large-amplitude oscillators and interprets why similar circuit architectures are engaged in timekeeping systems such as circadian clocks.

Glucose Oscillations Can Activate an Endogenous Oscillator in Pancreatic Islets

Pancreatic islets manage elevations in blood glucose level by secreting insulin into the bloodstream in a pulsatile manner. Pulsatile insulin secretion is governed by islet oscillations such as bursting electrical activity and periodic Ca²⁺ entry in β-cells. In this report, we demonstrate that although islet oscillations are lost by fixing a glucose stimulus at a high concentration, they may be recovered by subsequently converting the glucose stimulus to a sinusoidal wave. We predict with mathematical modeling that the sinusoidal glucose signal’s ability to recover islet oscillations depends on its amplitude and period, and we confirm our predictions by conducting experiments with islets using a microfluidics platform. Our results suggest a mechanism whereby oscillatory blood glucose levels recruit non-oscillating islets to enhance pulsatile insulin output from the pancreas. Our results also provide support for the main hypothesis of the Dual Oscillator Model, that a glycolytic oscillator endogenous to islet β-cells drives pulsatile insulin secretion.

A global shift throughout the last century toward excessive nutrient intake relative to energy expenditure has fueled a dramatic increase in the incidence of diabetes in humans. The epidemic is primarily of type 2 diabetes, a disease characterized by the inability of the body to effectively control blood glucose levels. Insulin plays a key role in regulating blood glucose levels by restraining endogenous glucose output from the liver and promoting blood glucose uptake by tissue throughout the body. It is secreted in pulses by islets of Langerhans, endocrine cell aggregates dispersed throughout the pancreas. Loss of insulin pulsatility is an early event in the development of type 2 diabetes. Here, we demonstrate, with a combined modeling and experimental approach, that the loss of pulsatile insulin release that results from elevated glucose may be recovered by an oscillatory glucose stimulus. Our results have potential implications for enhancing insulin pulsatility and therefore mitigating the development of type 2 diabetes.

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks

Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem’s gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally–e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the “state” and “control” in the model refer to its own (internal) and another subsystem’s (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model’s parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with evolutionarily ancient functions (e.g. the ribosomal proteins), in contrast to those with more recently evolved functions (e.g., cell-cell communication). This suggests that despite striking morphological differences, some fundamental embryonic-developmental processes are still controlled by ancient regulatory systems.

The dynamics of a biological system can be controlled by its own internal mechanisms and external perturbations. To gain intuition on this, we may draw a comparison with a mass hanging from a spring. The mass will move naturally by itself but its dynamics is also affected by one’s pulling it. That is, the dynamics of the mass is governed by the effect of the external perturbations superimposed on the internal mechanism of the spring (i.e. Hooke’s law). Similarly, given a group of genes, their temporal gene expression dynamics can be controlled by both transcription factors inside the group and external regulatory factors. Therefore, it is useful to identify the expression dynamics that are exclusively controlled by internal or external factors and compare them across various systems. While state-space models have been widely used to decouple the internal and external effects in physical systems, such as the mass and spring, typical biological systems do not have enough time samples to infer all the model’s parameters, and applications of state-space models were not very effective in these instances. Hence, we developed a general-purpose computational method by integrating state-space models and dimensionality reduction to identify temporal gene expression patterns driven by internal and external regulatory networks. We applied our method to the embryonic developmental datasets in the worm and fly (and also in a human cancer context). We successfully identified the temporal expression dynamics of cross-species conserved genes that were driven by conserved and species-specific regulatory networks.

Genetic Redundancies Enhance Information Transfer in Noisy Regulatory Circuits

Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies. The result is due to the contribution of noise to the generation of autonomous responses by each copy, which are altogether associated with a common decision. Moreover, factors that correlate the responses of the redundant units (extrinsic noise or regulatory cross-talk) contribute to reduce fidelity, while those that further uncouple them (heterogeneity within the copies) can lead to stronger information gain. Overall, our study emphasizes how the interplay of signal thresholding, redundancy, and noise influences the accuracy of cellular decision making. Understanding this interplay provides a basis to explain collective cell signaling mechanisms, and to engineer robust decisions with noisy genetic circuits.

There is increasing evidence that the presence of molecular noise greatly influences function in biological systems. This could imply, for instance, that genetic circuits adopt particular architectures in order to reduce noise. On the other hand, noise can be beneficial. Here, we show that this could be the case for the functioning of analog to digital genetic devices, which are commonly found in cellular decision making situations. We use the framework of information theory to illustrate first how noise can enhance information transfer in these devices. In those regimes in which noise is detrimental, we discuss how genetic redundancies allow information to be maximized, and how this effect depends on the specifics of the devices, and the interdependence among them. These results provide overall an additional rationale for genetic redundancies in genomic systems.

Theta-Gamma Coding Meets Communication-through-Coherence: Neuronal Oscillatory Multiplexing Theories Reconciled

Several theories have been advanced to explain how cross-frequency coupling, the interaction of neuronal oscillations at different frequencies, could enable item multiplexing in neural systems. The communication-through-coherence theory proposes that phase-matching of gamma oscillations between areas enables selective processing of a single item at a time, and a later refinement of the theory includes a theta-frequency oscillation that provides a periodic reset of the system. Alternatively, the theta-gamma neural code theory proposes that a sequence of items is processed, one per gamma cycle, and that this sequence is repeated or updated across theta cycles. In short, both theories serve to segregate representations via the temporal domain, but differ on the number of objects concurrently represented. In this study, we set out to test whether each of these theories is actually physiologically plausible, by implementing them within a single model inspired by physiological data. Using a spiking network model of visual processing, we show that each of these theories is physiologically plausible and computationally useful. Both theories were implemented within a single network architecture, with two areas connected in a feedforward manner, and gamma oscillations generated by feedback inhibition within areas. Simply increasing the amplitude of global inhibition in the lower area, equivalent to an increase in the spatial scope of the gamma oscillation, yielded a switch from one mode to the other. Thus, these different processing modes may co-exist in the brain, enabling dynamic switching between exploratory and selective modes of attention.

There is a growing consensus that neuronal oscillations constitute a fundamental computational mechanism in the brain. Beyond this, recent experimental evidence has highlighted interactions between oscillations at high and low frequencies (e.g. gamma oscillations, 40–80 Hz, are modulated by theta oscillations, 4–10 Hz), and two major theories have developed regarding the functional role of this kind of cross-frequency coupling. Here, we present a computational modelling study of these theories with strong implications for biological studies. Firstly, we demonstrate for the first time that each of these theories is physiologically plausible, in that they can be implemented in a spiking network model with parameters guided by experimental data. Secondly, we show that they are each computationally useful, able to overcome a feature-binding ambiguity in a presented stimulus. Finally, we implement both theories within a single network model, and find that only a single parameter change is required to switch between the two processing states. This leads to the exciting new proposal that both theories may be correct, both implemented in the brain, with dynamic switching between modes according to processing and attentional requirements.

Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit

Oscillations are omnipresent in neural population signals, like multi-unit recordings, EEG/MEG, and the local field potential. They have been linked to the population firing rate of neurons, with individual neurons firing in a close-to-irregular fashion at low rates. Using a combination of mean-field and linear response theory we predict the spectra generated in a layered microcircuit model of V1, composed of leaky integrate-and-fire neurons and based on connectivity compiled from anatomical and electrophysiological studies. The model exhibits low- and high-γ oscillations visible in all populations. Since locally generated frequencies are imposed onto other populations, the origin of the oscillations cannot be deduced from the spectra. We develop an universally applicable systematic approach that identifies the anatomical circuits underlying the generation of oscillations in a given network. Based on a theoretical reduction of the dynamics, we derive a sensitivity measure resulting in a frequency-dependent connectivity map that reveals connections crucial for the peak amplitude and frequency of the observed oscillations and identifies the minimal circuit generating a given frequency. The low-γ peak turns out to be generated in a sub-circuit located in layer 2/3 and 4, while the high-γ peak emerges from the inter-neurons in layer 4. Connections within and onto layer 5 are found to regulate slow rate fluctuations. We further demonstrate how small perturbations of the crucial connections have significant impact on the population spectra, while the impairment of other connections leaves the dynamics on the population level unaltered. The study uncovers connections where mechanisms controlling the spectra of the cortical microcircuit are most effective.

Recordings of brain activity show multiple coexisting oscillations. The generation of these oscillations has so far only been investigated in generic one- and two-population networks, neglecting their embedment into larger systems. We introduce a method that determines the mechanisms and sub-circuits generating oscillations in structured spiking networks. Analyzing a multi-layered model of the cortical microcircuit, we trace back characteristic oscillations to experimentally observed connectivity patterns. The approach exposes the influence of individual connections on frequency and amplitude of these oscillations and therefore reveals locations, where biological mechanisms controlling oscillations and experimental manipulations have the largest impact. The new analytical tool replaces parameter scans in computationally expensive models, guides circuit design, and can be employed to validate connectivity data.

APC/C and SCF(cyclin F) Constitute a Reciprocal Feedback Circuit Controlling S-Phase Entry

The anaphase promoting complex/cyclosome (APC/C) is an ubiquitin ligase and core component of the cell-cycle oscillator. During G1 phase, APC/C binds to its substrate receptor Cdh1 and APC/C(Cdh1) plays an important role in restricting S-phase entry and maintaining genome integrity. We describe a reciprocal feedback circuit between APC/C and a second ubiquitin ligase, the SCF (Skp1-Cul1-F box). We show that cyclin F, a cell-cycle-regulated substrate receptor (F-box protein) for the SCF, is targeted for degradation by APC/C. Furthermore, we establish that Cdh1 is itself a substrate of SCF(cyclin F). Cyclin F loss impairs Cdh1 degradation and delays S-phase entry, and this delay is reversed by simultaneous removal of Cdh1. These data indicate that the coordinated, temporal ordering of cyclin F and Cdh1 degradation, organized in a double-negative feedback loop, represents a fundamental aspect of cell-cycle control. This mutual antagonism could be a feature of other oscillating systems.

Processing Oscillatory Signals by Incoherent Feedforward Loops

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose.

From circadian clocks to ultradian rhythms, oscillatory signals are found ubiquitously in nature. These oscillations are crucial in the regulation of cellular processes. While the fundamental design principles underlying the generation of these oscillations are extensively studied, the mechanisms for decoding these signals are underappreciated. With implications in both the basic understanding of how cells process temporal signals and the design of synthetic systems, we use quantitative modeling to probe one mechanism, the counting of pulses. We demonstrate the capability of an Incoherent Feedforward Loop motif for the differentiation between sustained and oscillatory input signals.

Dynamical Robustness against Multiple Mutations in Signaling Networks

It has been known that the robust behavior of a cellular signaling network is strongly related to the structural characteristics of the network, such as connectivity, the number of feedback loops, and the number of feed-forward loops. Previous studies proved such relationships through dynamical simulations of various random network models. Most of them, however, focused on robustness against a single node mutation. Considering that complex diseases such as cancer are mostly caused by simultaneous dysfunction of multiple genes, it is needed to investigate the robustness of a network against multiple node mutations. In this paper, we investigated the robustness of a network against multiple node mutations through extensive simulations on the basis of Boolean network models. We found that the robustness against multiple mutations is, in most cases, weaker than the robustness against a single node mutation on average. Moreover, we found that the robustness against multiple mutations is strongly positively correlated with the robustness against single mutation. The difference between the multiple- and single-mutation robustness became larger as the number of mutated nodes increased or the number of nodes that are robust to single-mutation decreased. We further found that a node of relatively large connectivity or being involved with many feedback loops tends to be non-robust against multiple mutations. This finding is supported by the observation that poly-genic disease genes have high connectivity and are involved with a large number of feedback loops than mono-genic disease genes in a human signaling network. Together, our study shows that previous studies for a single node mutation can be extended to understand the network dynamics for multiple node mutations.

Central neural control of the cardiovascular system: current perspectives

This brief review, which is based on a lecture presented at the American Physiological Society Teaching Refresher Course on the Brain and Systems Control as part of the Experimental Biology meeting in 2015, aims to summarize current concepts of the principal mechanisms in the brain that regulate the autonomic outflow to the cardiovascular system. Such cardiovascular regulatory mechanisms do not operate in isolation but are closely coordinated with respiratory and other regulatory mechanisms to maintain homeostasis. The brain regulates the cardiovascular system by two general means: 1) feedforward regulation, often referred to as "central command," and 2) feedback or reflex regulation. In most situations (e.g., during exercise, defensive behavior, sleep, etc.), both of these general mechanisms contribute to overall cardiovascular homeostasis. The review first describes the mechanisms and central circuitry subserving the baroreceptor, chemoreceptor, and other reflexes that work together to regulate an appropriate level of blood pressure and blood oxygenation and then considers the brain mechanisms that defend the body against more complex environmental challenges, using dehydration and cold and heat stress as examples. The last section of the review considers the central mechanisms regulating cardiovascular function associated with different behaviors, with a specific focus on defensive behavior and exercise.

Are Nonlinear Model-Free Conditional Entropy Approaches for the Assessment of Cardiac Control Complexity Superior to the Linear Model-Based One?

OBJECTIVE

We test the hypothesis that the linear model-based (MB) approach for the estimation of conditional entropy (CE) can be utilized to assess the complexity of the cardiac control in healthy individuals.

METHODS

An MB estimate of CE was tested in an experimental protocol (i.e., the graded head-up tilt) known to produce a gradual decrease of cardiac control complexity as a result of the progressive vagal withdrawal and concomitant sympathetic activation. The MB approach was compared with traditionally exploited nonlinear model-free (MF) techniques such as corrected approximate entropy, sample entropy, corrected CE, two k -nearest-neighbor CE procedures and permutation CE. Electrocardiogram was recorded in 17 healthy subjects at rest in supine position and during head-up tilt with table angles of 15°, 30°, 45°, 60°, and 75°. Heart period (HP) was derived as the temporal distance between two consecutive R-wave peaks and analysis was carried out over stationary sequences of 256 successive HPs.

RESULTS

The performance of the MB method in following the progressive decrease of HP complexity with tilt table angles was in line with those of MF approaches and the MB index was remarkably correlated with the MF ones.

CONCLUSION

The MB approach can be utilized to monitor the changes of the complexity of the cardiac control, thus speeding up dramatically the CE calculation.

SIGNIFICANCE

The remarkable performance of the MB approach challenges the notion, generally assumed in cardiac control complexity analysis based on CE, about the need of MF techniques and could allow real-time applications.

Cardiovascular control during whole body exercise

It has been considered whether during whole body exercise the increase in cardiac output is large enough to support skeletal muscle blood flow. This review addresses four lines of evidence for a flow limitation to skeletal muscles during whole body exercise. First, even though during exercise the blood flow achieved by the arms is lower than that achieved by the legs (∼160 vs. ∼385 ml·min(-1)·100 g(-1)), the muscle mass that can be perfused with such flow is limited by the capacity to increase cardiac output (42 l/min, highest recorded value). Secondly, activation of the exercise pressor reflex during fatiguing work with one muscle group limits flow to other muscle groups. Another line of evidence comes from evaluation of regional blood flow during exercise where there is a discrepancy between flow to a muscle group when it is working exclusively and when it works together with other muscles. Finally, regulation of peripheral resistance by sympathetic vasoconstriction in active muscles by the arterial baroreflex is critical for blood pressure regulation during exercise. Together, these findings indicate that during whole body exercise muscle blood flow is subordinate to the control of blood pressure.

Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface

Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nervous system. In BMIs with kinematically redundant actuators, the combination of the task goals and the system under neural control can allow for many equally optimal task solutions. The extent to which kinematically redundant degrees of freedom (DOFs) in a BMI system may be under direct neural control is unknown. To address this question, a Kalman filter was used to decode single- and multi-unit cortical neural activity of two macaque monkeys into the joint velocities of a virtual four-link kinematic chain. Subjects completed movements of the chain's endpoint to instructed target locations within a two-dimensional plane. This system was kinematically redundant for an endpoint movement task, as four DOFs were used to manipulate the 2-D endpoint position. Both subjects successfully performed the task and improved with practice by producing faster endpoint velocity control signals. Kinematic redundancy allowed null movements whereby the individual links of the chain could move in a way that cancels out and does not result in endpoint movement. As the subjects became more proficient at controlling the chain, the amount of null movement also increased. Task performance suffered when the links of the kinematic chain were hidden and only the endpoint was visible. Furthermore, all four DOFs of the joint-velocity control space exhibited task-relevant modulation. The relative usage of each DOF depended on the configuration of the chain, and trials in which the less-prominent DOFs were utilized also had better task performance. Overall, these results indicate that the subjects incorporated the redundant components of the control space into their control strategy. Future BMI systems with kinematic redundancy, such as exoskeletal systems or anthropomorphic robotic arms, may benefit from allowing neural control over redundant configuration dimensions as well as the end-effector.

Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection

The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes’ equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called ‘prototypic’ and ‘arkypallidal’ neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions.

Choosing an appropriate action as quickly and accurately as possible in a given situation is critical for the survival of animals and humans. One of the brain regions involved in action selection is a set of subcortical nuclei known as the basal ganglia. The importance of understanding information processing in the basal ganglia is further emphasised by the fact that their disturbed interactions in Parkinson’s disease results in profound difficulties in movement. Computational models have suggested how the basal ganglia could select actions in the fastest possible way for the required accuracy level. These models further predict that a part of basal ganglia, called the external globus pallidus (GPe), needs to calculate a particular function of its inputs. This paper proposes how this function could be computed in a mathematical model of a network within GPe. Furthermore, it shows that the experimentally observed connectivity and response properties of GPe neurons fulfil the requirements necessary to support optimal action selection. This suggests the GPe neurons have properties that allow them to contribute to optimal action selection in the whole basal ganglia.

MECHANISMS IN ENDOCRINOLOGY: New concepts to further unravel adrenal insufficiency during critical illness

The concept of 'relative' adrenal insufficiency during critical illness remains a highly debated disease entity. Several studies have addressed how to diagnose or treat this condition but have often yielded conflicting results, which further fuelled the controversy. The main reason for the controversy is the fact that the pathophysiology is not completely understood. Recently, new insights in the pathophysiology of the hypothalamic-pituitary-adrenal axis response to critical illness were generated. It was revealed that high circulating levels of cortisol during critical illness are explained more by reduced cortisol breakdown than by elevated cortisol production. Cortisol production rate during critical illness is less than doubled during the day but lower than in healthy subjects during the night. High plasma cortisol concentrations due to reduced breakdown in turn reduce plasma ACTH concentrations via feedback inhibition, which with time may lead to an understimulation and hereby a dysfunction of the adrenal cortex. This could explain the high incidence of adrenal insufficiency in the prolonged phase of critical illness. These novel insights have created a new framework for the diagnosis and treatment of adrenal failure during critical illness that has redirected future research.

Movement Performance of Human-Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot

Although the merits of electromyography (EMG)-based control of powered assistive systems have been certified, the factors that affect the performance of EMG-based human-robot cooperation, which are very important, have received little attention. This study investigates whether a more physiologically appropriate model could improve the performance of human-robot cooperation control for an ankle power-assist exoskeleton robot. To achieve the goal, an EMG-driven Hill-type neuromusculoskeletal model (HNM) and a linear proportional model (LPM) were developed and calibrated through maximum isometric voluntary dorsiflexion (MIVD). The two control models could estimate the real-time ankle joint torque, and HNM is more accurate and can account for the change of the joint angle and muscle dynamics. Then, eight healthy volunteers were recruited to wear the ankle exoskeleton robot and complete a series of sinusoidal tracking tasks in the vertical plane. With the various levels of assist based on the two calibrated models, the subjects were instructed to track the target displayed on the screen as accurately as possible by performing ankle dorsiflexion and plantarflexion. Two measurements, the root mean square error (RMSE) and root mean square jerk (RMSJ), were derived from the assistant torque and kinematic signals to characterize the movement performances, whereas the amplitudes of the recorded EMG signals from the tibialis anterior (TA) and the gastrocnemius (GAS) were obtained to reflect the muscular efforts. The results demonstrated that the muscular effort and smoothness of tracking movements decreased with an increase in the assistant ratio. Compared with LPM, subjects made lower physical efforts and generated smoother movements when using HNM, which implied that a more physiologically appropriate model could enable more natural and human-like human-robot cooperation and has potential value for improvement of human-exoskeleton interaction in future applications.

Filopodial-Tension Model of Convergent-Extension of Tissues

In convergent-extension (CE), a planar-polarized epithelial tissue elongates (extends) in-plane in one direction while shortening (converging) in the perpendicular in-plane direction, with the cells both elongating and intercalating along the converging axis. CE occurs during the development of most multicellular organisms. Current CE models assume cell or tissue asymmetry, but neglect the preferential filopodial activity along the convergent axis observed in many tissues. We propose a cell-based CE model based on asymmetric filopodial tension forces between cells and investigate how cell-level filopodial interactions drive tissue-level CE. The final tissue geometry depends on the balance between external rounding forces and cell-intercalation traction. Filopodial-tension CE is robust to relatively high levels of planar cell polarity misalignment and to the presence of non-active cells. Addition of a simple mechanical feedback between cells fully rescues and even improves CE of tissues with high levels of polarity misalignments. Our model extends easily to three dimensions, with either one converging and two extending axes, or two converging and one extending axes, producing distinct tissue morphologies, as observed in vivo.

The development of an embryo from a fertilized egg to an adult organism requires not only cell proliferation and differentiation, but also numerous types of tissue restructuring. The development of a relatively round initial embryo into one elongated along its rostral-caudal axis involves coordinated tissue elongation and cell reorganization in one or more groups of cells or tissues. Counterintuitively, in many organisms, cells in elongating tissues elongate and increase their protrusive activity in the direction perpendicular to the axis of elongation (convergent extension). Experimental and theoretical studies have not determined how this cell-level oriented protrusive activity leads to observed tissue-level changes in morphology. We propose a filopodial-tension model that shows how tension from oriented cell protrusions leads to observed patterns of tissue CE.

The Neural Mechanism Exploration of Adaptive Motor Control: Dynamical Economic Cell Allocation in the Primary Motor Cortex

Adaptive flexibility is of significance for the smooth and efficient movements in goal attainment. However, the underlying work mechanism of the cerebral cortex in adaptive motor control still remains unclear. How does the cerebral cortex organize and coordinate the activity of a large population of cells in the implementation of various motor strategies? To explore this issue, single-unit activities from the M1 region and kinematic data were recorded simultaneously in monkeys performing 3D reach-to-grasp tasks with different perturbations. Varying motor control strategies were employed and achieved in different perturbed tasks, via the dynamic allocation of cells to modulate specific movement parameters. An economic principle was proposed for the first time to describe a basic rule for cell allocation in the primary motor cortex. This principle, defined as the Dynamic Economic Cell Allocation Mechanism (DECAM), guarantees benefit maximization in cell allocation under limited neuronal resources, and avoids committing resources to uneconomic investments for unreliable factors with no or little revenue. That is to say, the cells recruited are always preferentially allocated to those factors with reliable return; otherwise, the cells are dispatched to respond to other factors about task. The findings of this study might partially reveal the working mechanisms underlying the role of the cerebral cortex in adaptive motor control, wherein is also of significance for the design of future intelligent brain-machine interfaces and rehabilitation device.

Non-linear feedback control of the p53 protein-mdm2 inhibitor system using the derivative-free non-linear Kalman filter

It is proven that the model of the p53-mdm2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p53 protein at the desirable levels. To estimate the non-measurable elements of the state vector describing the p53-mdm2 system dynamics, the derivative-free non-linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p53-mdm2 system, the derivative-free non-linear Kalman filter is re-designed as a disturbance observer. The derivative-free non-linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non-linear model. The proposed non-linear feedback control and perturbations compensation method for the p53-mdm2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.

Hypothalamic roles of mTOR complex I: integration of nutrient and hormone signals to regulate energy homeostasis

Mammalian or mechanistic target of rapamycin (mTOR) senses nutrient, energy, and hormone signals to regulate metabolism and energy homeostasis. mTOR activity in the hypothalamus, which is associated with changes in energy status, plays a critical role in the regulation of food intake and body weight. mTOR integrates signals from a variety of "energy balancing" hormones such as leptin, insulin, and ghrelin, although its action varies in response to these distinct hormonal stimuli as well as across different neuronal populations. In this review, we summarize and highlight recent findings regarding the functional roles of mTOR complex 1 (mTORC1) in the hypothalamus specifically in its regulation of body weight, energy expenditure, and glucose/lipid homeostasis. Understanding the role and underlying mechanisms behind mTOR-related signaling in the brain will undoubtedly pave new avenues for future therapeutics and interventions that can combat obesity, insulin resistance, and diabetes.

A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints

The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain's motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human-robot interactions.

Modeling the Male Reproductive Endocrine Axis: Potential Role for a Delay Mechanism in the Inhibitory Action of Gonadal Steroids on GnRH Pulse Frequency

We developed a compartmental model so we could test mechanistic concepts in the control of the male reproductive endocrine axis. Using SAAM II computer software and a bank of experimental data from male sheep, we began by modeling GnRH-LH feed-forward and LH-T feedback. A key assumption was that the primary control signal comes from a hypothetical neural network (the PULSAR) that emits a digital (pulsatile) signal of variable frequency that drives GnRH secretion in square wave-like pulses. This model produced endocrine profiles that matched experimental observations for the testis-intact animal and for changes in GnRH pulse frequency after castration and T replacement. In the second stage of the model development, we introduced a delay in the negative feedback caused by the aromatization of T to estradiol at the brain level, a concept supported by empirical observations. The simulations showed how changes in the process of aromatization could affect the response of the pulsatile signal to inhibition by steroid feedback. The sensitivity of the PULSAR to estradiol was a critical factor, but the most striking observation was the effect of time delays. With longer delays, there was a reduction in the rate of aromatization and therefore a decrease in local estradiol concentrations, and the outcome was multiple-pulse events in the secretion of GnRH/LH, reflecting experimental observations. In conclusion, our model successfully emulates the GnRH-LH-T-GnRH loop, accommodates a pivotal role for central aromatization in negative feedback, and suggests that time delays in negative feedback are an important aspect of the control of GnRH pulse frequency.

Decision-making ability of Physarum polycephalum enhanced by its coordinated spatiotemporal oscillatory dynamics

An amoeboid unicellular organism, a plasmodium of the true slime mold Physarum polycephalum, exhibits complex spatiotemporal oscillatory dynamics and sophisticated information processing capabilities while deforming its amorphous body. We previously devised an 'amoeba-based computer (ABC),' that implemented optical feedback control to lead this amoeboid organism to search for a solution to the traveling salesman problem (TSP). In the ABC, the shortest TSP route (the optimal solution) is represented by the shape of the organism in which the body area (nutrient absorption) is maximized while the risk of being exposed to aversive light stimuli is minimized. The shortness of the TSP route found by ABC, therefore, serves as a quantitative measure of the optimality of the decision made by the organism. However, it remains unclear how the decision-making ability of the organism originates from the oscillatory dynamics of the organism. We investigated the number of coexisting traveling waves in the spatiotemporal patterns of the oscillatory dynamics of the organism. We show that a shorter TSP route can be found when the organism exhibits a lower number of traveling waves. The results imply that the oscillatory dynamics are highly coordinated throughout the global body. Based on the results, we discuss the fact that the decision-making ability of the organism can be enhanced not by uncorrelated random fluctuations, but by its highly coordinated oscillatory dynamics.

Altered somatotroph feedback regulation improves metabolic efficiency and limits adipose deposition in male mice

Several transgenic mouse models with disruption in the growth hormone (GH) axis support the role of GH in augmenting metabolic homeostasis. Specifically, interest has focused on GH's lipolytic properties and ability to affect adipose deposition. Furthermore, both GH and insulin growth factor 1 (IGF-1) may also play a direct or indirect role in adipose development. The somatotroph insulin-like growth factor-1 receptor knockout (SIGFRKO) mouse with only a modest increase in serum GH and IGF-1 demonstrates less adipose tissue than controls. In order to characterize the metabolic phenotype of SIGFRKO mice, histologic analysis of fat depots confirmed a smaller average diameter of adipocytes in the SIGFRKO mice compared to controls. These changes were accompanied by an increase in lipolytic gene expression in fat depots. Indirect calorimetry performed on 6-8week old male mice and again at 25weeks of age demonstrated that SIGFRKO mice, at both ages, had a higher VO2 and increased energy expenditure when compared with controls. The calculated respiratory exchange ratio (RER) was lower in the younger SIGFRKO mice compared to controls. No differences in food consumption or in either ambulatory or total activity were seen between SIGFRKO and control mice in either age group. These studies highlight the role of GH in adipose deposition and its influence on the expression of lipolytic genes resulting in an altered metabolic state, thus providing a mechanism for the decrease in weight gain seen in the SIGFRKO mouse model.

Feedback, Lineages and Self-Organizing Morphogenesis

Feedback regulation of cell lineage progression plays an important role in tissue size homeostasis, but whether such feedback also plays an important role in tissue morphogenesis has yet to be explored. Here we use mathematical modeling to show that a particular feedback architecture in which both positive and negative diffusible signals act on stem and/or progenitor cells leads to the appearance of bistable or bi-modal growth behaviors, ultrasensitivity to external growth cues, local growth-driven budding, self-sustaining elongation, and the triggering of self-organization in the form of lamellar fingers. Such behaviors arise not through regulation of cell cycle speeds, but through the control of stem or progenitor self-renewal. Even though the spatial patterns that arise in this setting are the result of interactions between diffusible factors with antagonistic effects, morphogenesis is not the consequence of Turing-type instabilities.

Increased Thyroid Hormone Activation Accompanies the Formation of Thyroid Hormone-Dependent Negative Feedback in Developing Chicken Hypothalamus

The hypothalamic-pituitary-thyroid axis is governed by hypophysiotropic TRH-synthesizing neurons located in the hypothalamic paraventricular nucleus under control of the negative feedback of thyroid hormones. The mechanisms underlying the ontogeny of this phenomenon are poorly understood. We aimed to determine the onset of thyroid hormone-mediated hypothalamic-negative feedback and studied how local hypothalamic metabolism of thyroid hormones could contribute to this process in developing chicken. In situ hybridization revealed that whereas exogenous T4 did not induce a statistically significant inhibition of TRH expression in the paraventricular nucleus at embryonic day (E)19, T4 treatment was effective at 2 days after hatching (P2). In contrast, TRH expression responded to T3 treatment in both age groups. TSHβ mRNA expression in the pituitary responded to T4 in a similar age-dependent manner. Type 2 deiodinase (D2) was expressed from E13 in tanycytes of the mediobasal hypothalamus, and its activity increased between E15 and P2 both in the mediobasal hypothalamus and in tanycyte-lacking hypothalamic regions. Nkx2.1 was coexpressed with D2 in E13 and P2 tanycytes and transcription of the cdio2 gene responded to Nkx2.1 in U87 glioma cells, indicating its potential role in the developmental regulation of D2 activity. The T3-degrading D3 enzyme was also detected in tanycytes, but its level was not markedly changed before and after the period of negative feedback acquisition. These findings suggest that increasing the D2-mediated T3 generation during E18-P2 could provide the sufficient local T3 concentration required for the onset of T3-dependent negative feedback in the developing chicken hypothalamus.

Feedbacks, Bifurcations, and Cell Fate Decision-Making in the p53 System

The p53 transcription factor is a regulator of key cellular processes including DNA repair, cell cycle arrest, and apoptosis. In this theoretical study, we investigate how the complex circuitry of the p53 network allows for stochastic yet unambiguous cell fate decision-making. The proposed Markov chain model consists of the regulatory core and two subordinated bistable modules responsible for cell cycle arrest and apoptosis. The regulatory core is controlled by two negative feedback loops (regulated by Mdm2 and Wip1) responsible for oscillations, and two antagonistic positive feedback loops (regulated by phosphatases Wip1 and PTEN) responsible for bistability. By means of bifurcation analysis of the deterministic approximation we capture the recurrent solutions (i.e., steady states and limit cycles) that delineate temporal responses of the stochastic system. Direct switching from the limit-cycle oscillations to the “apoptotic” steady state is enabled by the existence of a subcritical Neimark—Sacker bifurcation in which the limit cycle loses its stability by merging with an unstable invariant torus. Our analysis provides an explanation why cancer cell lines known to have vastly diverse expression levels of Wip1 and PTEN exhibit a broad spectrum of responses to DNA damage: from a fast transition to a high level of p53 killer (a p53 phosphoform which promotes commitment to apoptosis) in cells characterized by high PTEN and low Wip1 levels to long-lasting p53 level oscillations in cells having PTEN promoter methylated (as in, e.g., MCF-7 cell line).

Cancers are diseases of signaling networks. Transcription factor p53 is a pivotal node of a network that integrates a variety of stress signals and governs critical processes of DNA repair, cell cycle arrest, and apoptosis. Somewhat paradoxically, despite the fact that carcinogenesis is prevalently caused by p53 network malfunction, most of our knowledge about p53 signaling is based on cancer or immortalized cell lines. In this paper, we construct a mathematical model of intact p53 network to understand dynamics of non-cancerous cells and then dynamics of cancerous cells by introducing perturbations to the regulatory system. Cell fate decisions are enabled by the presence of interlinked feedback loops which give rise to a rich repertoire of behaviors. We explain and analyze by means of numerical simulations how the dynamical structure of the regulatory system allows for generating unambiguous single-cell fate decisions, also in the case when the cell population splits into an apoptotic and a surviving subpopulation. Perturbation analysis provides an explanation why cancer cell lines known to have vastly diverse expression levels of p53 regulators can exhibit a broad spectrum of responses to DNA damage.

Forests and ozone: productivity, carbon storage, and feedbacks

Tropospheric ozone is a serious air-pollutant, with large impacts on plant function. This study demonstrates that tropospheric ozone, although it damages plant metabolism, does not necessarily reduce ecosystem processes such as productivity or carbon sequestration because of diversity change and compensatory processes at the community scale ameliorate negative impacts at the individual level. This study assesses the impact of ozone on forest composition and ecosystem dynamics with an individual-based gap model that includes basic physiology as well as species-specific metabolic properties. Elevated tropospheric ozone leads to no reduction of forest productivity and carbon stock and to increased isoprene emissions, which result from enhanced dominance by isoprene-emitting species (which tolerate ozone stress better than non-emitters). This study suggests that tropospheric ozone may not diminish forest carbon sequestration capacity. This study also suggests that, because of the often positive relationship between isoprene emission and ozone formation, there is a positive feedback loop between forest communities and ozone, which further aggravates ozone pollution.