Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization.

Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy

The Automatic Neuroscientist: automated experimental design with real-time fMRI

A standard approach in functional neuroimaging explores how a particular cognitive task activates a set of brain regions (one task-to-many regions mapping). Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system (many tasks-to-region mapping). In our work, presented here we propose an alternative framework, the Automatic Neuroscientist, which turns the typical fMRI approach on its head. We use real-time fMRI in combination with state-of-the-art optimisation techniques to automatically design the optimal experiment to evoke a desired target brain state. Here, we present two proof-of-principle studies involving visual and auditory stimuli. The data demonstrate this closed-loop approach to be very powerful, hugely speeding up fMRI and providing an accurate estimation of the underlying relationship between stimuli and neural responses across an extensive experimental parameter space. Finally, we detail four scenarios where our approach can be applied, suggesting how it provides a novel description of how cognition and the brain interrelate.Comment: 22 pages, 7 figures, work presented at OHBM 201

Universality of the excess number of clusters and the crossing probability function in three-dimensional percolation

Extensive Monte-Carlo simulations were performed to evaluate the excess number of clusters and the crossing probability function for three-dimensional percolation on the simple cubic (s.c.), face-centered cubic (f.c.c.), and body-centered cubic (b.c.c.) lattices. Systems L x L x L' with L' >> L were studied for both bond (s.c., f.c.c., b.c.c.) and site (f.c.c.) percolation. The excess number of clusters $\tilde {b}$ per unit length was confirmed to be a universal quantity with a value $\tilde {b} \approx 0.412$. Likewise, the critical crossing probability in the L' direction, with periodic boundary conditions in the L x L plane, was found to follow a universal exponential decay as a function of r = L'/L for large r. Simulations were also carried out to find new precise values of the critical thresholds for site percolation on the f.c.c. and b.c.c. lattices, yielding $p_c(f.c.c.)= 0.199 236 5 \pm 0.000 001 0$, $p_c(b.c.c.)= 0.245 961 5\pm 0.000 001 0$.Comment: 14 pages, 7 figures, LaTeX, submitted to J. Phys. A: Math. Gen, added references, corrected typo

Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization.

BACKGROUND: Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation. OBJECTIVE: We demonstrate that Bayesian optimization can rapidly search through large parameter spaces and identify subject-level stimulation parameters in real-time. METHODS: To validate the method, Bayesian optimization was employed using participants' binary judgements about the intensity of phosphenes elicited through tACS. RESULTS: We demonstrate the efficiency of Bayesian optimization in identifying parameters that maximize phosphene intensity in a short timeframe (5 min for >190 possibilities). Our results replicate frequency-dependent effects across three montages and show phase-dependent effects of phosphene perception. Computational modelling explains that these phase effects result from constructive/destructive interference of the current reaching the retinas. Simulation analyses demonstrate the method's versatility for complex response functions, even when accounting for noisy observations. CONCLUSION: Alongside subjective ratings, this method can be used to optimize tACS parameters based on behavioral and neural measures and has the potential to be used for tailoring stimulation protocols to individuals

Percolation Threshold, Fisher Exponent, and Shortest Path Exponent for 4 and 5 Dimensions

We develop a method of constructing percolation clusters that allows us to build very large clusters using very little computer memory by limiting the maximum number of sites for which we maintain state information to a number of the order of the number of sites in the largest chemical shell of the cluster being created. The memory required to grow a cluster of mass s is of the order of $s^\theta$ bytes where $\theta$ ranges from 0.4 for 2-dimensional lattices to 0.5 for 6- (or higher)-dimensional lattices. We use this method to estimate $d_{\scriptsize min}$, the exponent relating the minimum path $\ell$ to the Euclidean distance r, for 4D and 5D hypercubic lattices. Analyzing both site and bond percolation, we find $d_{\scriptsize min}=1.607\pm 0.005$ (4D) and $d_{\scriptsize min}=1.812\pm 0.006$ (5D). In order to determine $d_{\scriptsize min}$ to high precision, and without bias, it was necessary to first find precise values for the percolation threshold, $p_c$: $p_c=0.196889\pm 0.000003$ (4D) and $p_c=0.14081\pm 0.00001$ (5D) for site and $p_c=0.160130\pm 0.000003$ (4D) and $p_c=0.118174\pm 0.000004$ (5D) for bond percolation. We also calculate the Fisher exponent, $\tau$, determined in the course of calculating the values of $p_c$: $\tau=2.313\pm 0.003$ (4D) and $\tau=2.412\pm 0.004$ (5D)

The Rewiring of Ubiquitination Targets in a Pathogenic Yeast Promotes Metabolic Flexibility, Host Colonization and Virulence

Funding: This work was funded by the European Research Council [http://erc.europa.eu/], AJPB (STRIFE Advanced Grant; C-2009-AdG-249793). The work was also supported by: the Wellcome Trust [www.wellcome.ac.uk], AJPB (080088, 097377); the UK Biotechnology and Biological Research Council [www.bbsrc.ac.uk], AJPB (BB/F00513X/1, BB/K017365/1); the CNPq-Brazil [http://cnpq.br], GMA (Science without Borders fellowship 202976/2014-9); and the National Centre for the Replacement, Refinement and Reduction of Animals in Research [www.nc3rs.org.uk], DMM (NC/K000306/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments We thank Dr. Elizabeth Johnson (Mycology Reference Laboratory, Bristol) for providing strains, and the Aberdeen Proteomics facility for the biotyping of S. cerevisiae clinical isolates, and to Euroscarf for providing S. cerevisiae strains and plasmids. We are grateful to our Microscopy Facility in the Institute of Medical Sciences for their expert help with the electron microscopy, and to our friends in the Aberdeen Fungal Group for insightful discussions.Peer reviewedPublisher PD

Implementation of the Random Forest Method for the Imaging Atmospheric Cherenkov Telescope MAGIC

The paper describes an application of the tree classification method Random Forest (RF), as used in the analysis of data from the ground-based gamma telescope MAGIC. In such telescopes, cosmic gamma-rays are observed and have to be discriminated against a dominating background of hadronic cosmic-ray particles. We describe the application of RF for this gamma/hadron separation. The RF method often shows superior performance in comparison with traditional semi-empirical techniques. Critical issues of the method and its implementation are discussed. An application of the RF method for estimation of a continuous parameter from related variables, rather than discrete classes, is also discussed.Comment: 16 pages, 8 figure

Exact Hypersurface-Homogeneous Solutions in Cosmology and Astrophysics

A framework is introduced which explains the existence and similarities of most exact solutions of the Einstein equations with a wide range of sources for the class of hypersurface-homogeneous spacetimes which admit a Hamiltonian formulation. This class includes the spatially homogeneous cosmological models and the astrophysically interesting static spherically symmetric models as well as the stationary cylindrically symmetric models. The framework involves methods for finding and exploiting hidden symmetries and invariant submanifolds of the Hamiltonian formulation of the field equations. It unifies, simplifies and extends most known work on hypersurface-homogeneous exact solutions. It is shown that the same framework is also relevant to gravitational theories with a similar structure, like Brans-Dicke or higher-dimensional theories.Comment: 41 pages, REVTEX/LaTeX 2.09 file (don't use LaTeX2e !!!) Accepted for publication in Phys. Rev.