No abstract

We study the DC conductivity of iron-based superconductors within the orbital-selective spin fluctuation scenario. Within this approach, the anisotropy of spin fluctuations below the spinnematic transition at Ts is also responsible for the orbital ordering, induced by nematic self-energy corrections to the quasiparticle dispersion. As a consequence, the anisotropy of the DC conductivity below Ts is determined not only by the anisotropy of the scattering rates as expected within a spinnematic scenario, but also by the modification of the Fermi velocity due to the orbital reconstruction. More interestingly, it turns out that these two effects contribute to the DC-conductivity anisotropy with opposite signs. By using realistic band-structure parameters we compute the conductivity anisotropy for both 122 and FeSe compounds, discussing the possible origin of the different dcconductivity anisotropy observed experimentally in these two families of iron-based superconductors. arXiv:1804.07293v4 [cond-mat.supr-con]

We analyze the magnetic excitations and the spin-mediated superconductivity in iron-based superconductors within a low energy model that operates in the band basis, but fully incorporates the orbital character of the spin excitations. We show how the orbital selectivity, encoded in our low energy description, simplifies substantially the analysis and allows for analytical treatments, while retaining all the main features of both spin excitations and gap functions computed using multiorbital models. Importantly, our analysis unveils the orbital matching between the hole and electron pockets as the key parameter to determine the momentum dependence and the hierarchy of the superconducting gaps, instead of the Fermi surface matching, as in the common nesting scenario.

The analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on the visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on (subjective) human criteria. Data-driven approaches enabling both spatial and temporal localization of epileptic spikes would represent a major leap forward in clinical MEG practice. Here, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. Combined with kurtosis mapping, we developed a fully automated identification of epileptiform independent components (ICs) or HMM states. We tested our pipeline on MEG recordings at rest from 10 school-age children with either focal or multifocal epilepsy and compared results with the traditional MEG analysis performed by an experienced clinical magnetoencephalographer. In patients with focal epilepsy, both ICA- and HMM-based pipelines successfully detected visually identified IEDs with high sensitivity, but also revealed low-amplitude IEDs unidentified by the visual detection. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci-an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. Therefore, IED detection based on ICA or HMM represents an efficient way to identify spike localization and timing, with heightened sensitivity to IEDs compared to visual MEG signal inspection and requiring minimal input from clinical practitioners.