Chronobiology of Epilepsy

A fine balance between neuronal excitation and inhibition governs the physiological state of the brain. It has been hypothesized that when this balance is lost as a result of excessive excitation or reduced inhibition, pathological states such as epilepsy emerge. Decades of investigation have shown this to be true in vitro. However, in vivo evidence of the emerging imbalance during the "latent period" between the initiation of injury and the expression of the first spontaneous behavioral seizure has not been demonstrated. Here, we provide the first demonstration of this emerging imbalance between excitation and inhibition in vivo by employing long term, high temporal resolution, and continuous local field recordings from microelectrode arrays implanted in an animal model of limbic epilepsy. We were able to track both the inhibitory and excitatory postsynaptic field activity during the entire latent period, from the time of injury to the occurrence of the first spontaneous epileptic seizure. During this latent period we observe a sustained increase in the firing rate of the excitatory postsynaptic field activity, paired with a subsequent decrease in the firing rate of the inhibitory postsynaptic field activity within the CA1 region of the hippocampus. Firing rates of both excitatory and inhibitory CA1 field activities followed a circadian- like rhythm, which is locked near in-phase in controls and near anti-phase during the latent period. We think that these observed changes are implicated in the occurrence of spontaneous seizure onset following injury

Chaogates: morphing logic gates that exploit dynamical patterns

Chaotic systems can yield a wide variety of patterns. Here we use this feature to generate all possible fundamental logic gate functions. This forms the basis of the design of a dynamical computing device, a chaogate, that can be rapidly morphed to become any desired logic gate. Here we review the basic concepts underlying this and present an extension of the formalism to include asymmetric logic functions

Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on–off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis

Breast Cancer Affects Both the Hippocampus Volume and the Episodic Autobiographical Memory Retrieval

International audienceBACKGROUND: Neuroimaging studies show the hippocampus is a crucial node in the neural network supporting episodic autobiographical memory retrieval. Stress-related psychiatric disorders, namely Major Depression and Post Traumatic Stress Disorder (PTSD), are related to reduced hippocampus volume. However, this is not the case for remitted breast cancer patients with co-morbid stress-related psychiatric disorders. This exception may be due to the fact that, consequently to the cancer experience as such, this population might already be characterized by a reduced hippocampus with an episodic autobiographical memory deficit. METHODOLOGY: We scanned, with a 3T Siemens TRIO, 16 patients who had lived through a "standard experience of breast cancer" (breast cancer and a standard treatment in remission since 18 month) in the absence of any associated stress-related psychiatric or neurological disorder and 21 matched controls. We then assessed their episodic autobiographical memory retrieval ability. PRINCIPAL FINDINGS: Remitted breast cancer patients had both a significantly smaller hippocampus and a significant deficit in episodic autobiographical memory retrieval. The hippocampus atrophy was characterized by a smaller posterior hippocampus. The posterior hippocampus volume was intimately related to the ability to retrieve negative memories and to the past experience of breast cancer or not. CONCLUSIONS/SIGNIFICANCE: These results provide two main findings: (1) we identify a new population with a specific reduction in posterior hippocampus volume that is independent of any psychiatric or neurological pathology; (2) we show the intimate relation of the posterior hippocampus to the ability to retrieve episodic autobiographical memories. These are significant findings as it is the first demonstration that indicates considerable long-term effects of living through the experience of breast cancer and shows very specific hippocampal atrophy with a functional deficit without any presence of psychiatric pathology

Understanding Dwarf Galaxies in order to Understand Dark Matter

Much progress has been made in recent years by the galaxy simulation community in making realistic galaxies, mostly by more accurately capturing the effects of baryons on the structural evolution of dark matter halos at high resolutions. This progress has altered theoretical expectations for galaxy evolution within a Cold Dark Matter (CDM) model, reconciling many earlier discrepancies between theory and observations. Despite this reconciliation, CDM may not be an accurate model for our Universe. Much more work must be done to understand the predictions for galaxy formation within alternative dark matter models.Comment: Refereed contribution to the Proceedings of the Simons Symposium on Illuminating Dark Matter, to be published by Springe

Теоремы сходимости и компактности для уравнений Бельтрами

Доведено ряд теорем збіжності та компактності класів регулярних розв'язків вироджених рівнянь Бельтрамі з обмеженнями інтегрального типу на дилатацію.A number of convergence and compactness theorems for classes of regular solutions of the degenerate Beltrami equations with restrictions of the integral type on a dilatation is proved

Ropinirole and pramipexole promote structural plasticity in human iPSC- derived dopaminergic neurons via BDNF and mTOR signaling

The antiparkinsonian ropinirole and pramipexole are D3 receptor- (D3R-) preferring dopaminergic (DA) agonists used as adjunctive therapeutics for the treatment resistant depression (TRD). While the exact antidepressant mechanism of action remains uncertain, a role for D3R in the restoration of impaired neuroplasticity occurring in TRD has been proposed. Since D3R agonists are highly expressed on DA neurons in humans, we studied the effect of ropinirole and pramipexole on structural plasticity using a translational model of human-inducible pluripotent stem cells (hiPSCs). Two hiPSC clones from healthy donors were differentiated into midbrain DA neurons. Ropinirole and pramipexole produced dose-dependent increases of dendritic arborization and soma size after 3 days of culture, effects antagonized by the selective D3R antagonists SB277011-A and S33084 and by the mTOR pathway kinase inhibitors LY294002 and rapamycin. All treatments were also effective in attenuating the D3R-dependent increase of p70S6-kinase phosphorylation. Immunoneutralisation of BDNF, inhibition of TrkB receptors, and blockade of MEK-ERK signaling likewise prevented ropinirole-induced structural plasticity, suggesting a critical interaction between BDNF and D3R signaling pathways. The highly similar profiles of data acquired with DA neurons derived from two hiPSC clones underpin their reliability for characterization of pharmacological agents acting via dopaminergic mechanisms

Weakening dark-matter cusps by clumpy baryonic infall

We consider the infall of a massive clump into a dark-matter halo as a simple and extreme model for the effect of baryonic physics (neglected in gravity-only simulations of large-scale structure formation) on the dark-matter. We find that such an infalling clump is extremely efficient in altering the structure of the halo and reducing its central density: a clump of 1% the mass of the halo can remove about twice its own mass from the inner halo and transform a cusp into a core or weaker cusp. If the clump is subsequently removed, mimicking a galactic wind, the central halo density is further reduced and the mass removed from the inner halo doubled. Lighter clumps are even more efficient: the ratio of removed mass to clump mass increases slightly towards smaller clump masses. This process is the more efficient the more radially anisotropic the initial dark-matter velocities. While such a clumpy infall may be somewhat unrealistic, it demonstrates that the baryons need to transfer only a small fraction of their initial energy to the dark matter via dynamical friction to explain the discrepancy between predicted dark-matter density profiles and those inferred from observations of dark-matter dominated galaxies.Comment: 17 pages, 13 figures, accepted for publication in MNRA