State-Dependent Variability of Neuronal Responses to Transcranial Magnetic Stimulation of the Visual Cortex

SummaryElectrical brain stimulation is a promising tool for both experimental and clinical applications. However, the effects of stimulation on neuronal activity are highly variable and poorly understood. To investigate the basis of this variability, we performed extracellular recordings in the visual cortex following application of transcranial magnetic stimulation (TMS). Our measurements of spiking and local field potential activity exhibit two types of response patterns which are characterized by the presence or absence of spontaneous discharge following stimulation. This variability can be partially explained by state-dependent effects, in which higher pre-TMS activity predicts larger post-TMS responses. These results reveal the possibility that variability in the neural response to TMS can be exploited to optimize the effects of stimulation. It is conceivable that this feature could be utilized in real time during the treatment of clinical disorders

Deep Learning for Neuroimaging: a Validation Study

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager’s toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data

Exploring the Psychosis Functional Connectome: Aberrant Intrinsic Networks in Schizophrenia and Bipolar Disorder

Intrinsic functional brain networks (INs) are regions showing temporal coherence with one another. These INs are present in the context of a task (as opposed to an undirected task such as rest), albeit modulated to a degree both spatially and temporally. Prominent networks include the default mode, attentional fronto-parietal, executive control, bilateral temporal lobe, and motor networks. The characterization of INs has recently gained considerable momentum, however; most previous studies evaluate only a small subset of the INs (e.g., default mode). In this paper we use independent component analysis to study INs decomposed from functional magnetic resonance imaging data collected in a large group of schizophrenia patients, healthy controls, and individuals with bipolar disorder, while performing an auditory oddball task. Schizophrenia and bipolar disorder share significant overlap in clinical symptoms, brain characteristics, and risk genes which motivates our goal of identifying whether functional imaging data can differentiate the two disorders. We tested for group differences in properties of all identified INs including spatial maps, spectra, and functional network connectivity. A small set of default mode, temporal lobe, and frontal networks with default mode regions appearing to play a key role in all comparisons. Bipolar subjects showed more prominent changes in ventromedial and prefrontal default mode regions whereas schizophrenia patients showed changes in posterior default mode regions. Anti-correlations between left parietal areas and dorsolateral prefrontal cortical areas were different in bipolar and schizophrenia patients and amplitude was significantly different from healthy controls in both patient groups. Patients exhibited similar frequency behavior across multiple networks with decreased low frequency power. In summary, a comprehensive analysis of INs reveals a key role for the default mode in both schizophrenia and bipolar disorder

Tribenzoatobismuth(III): a new ­polymorph

A new polymorph (β) was obtained for an active pharmaceutical ingredient, bis­muth tribenzoate, [Bi(C6H5CO2)3]. The new β-polymorph is 1.05 times denser than the previously known polymorph [Rae et al. (1998 ▶). Acta Cryst. B54, 438–442]. In the β-polymorph, the Bi atom is linked with three benzoate anions, each of them acting as a bidentate ligand, and these assemblies with C 3 point symmetry can be considered as ‘mol­ecules’. The structure of the β-polymorph has no polymeric chains, in contrast to the previously known polymorph. The ‘mol­ecules’ in the β-polymorph are stacked along [001], so that the phenyl rings of the neighbouring mol­ecules are parallel to each other. Based on the pronounced difference in the crystal structures, one can suppose that two polymorphs should differ in the dissolution kinetics and bioavailability

Supramolecular Magnetic Brushes: The Impact of Dipolar Interactions on the Equilibrium Structure

The equilibrium structure of supramolecular magnetic filament brushes is analyzed at two different scales. First, we study the density and height distributions for brushes with various grafting densities and chain lengths. We use Langevin dynamics simulations with a bead-spring model that takes into account the cross-links between the surface of the ferromagnetic particles, whose magnetization is characterized by a point dipole. Magnetic filament brushes are shown to be more compact near the substrate than nonmagnetic ones, with a bimodal height distribution for large grafting densities. This latter feature makes them also different from brushes with electric dipoles. Next, in order to explain the observed behavior at the filament scale, we introduce a graph theory analysis to elucidate for the first time the structure of the brush at the scale of individual beads. It turns out that, in contrast to nonmagnetic brushes, in which the internal structure is determined by random density fluctuations, magnetic forces introduce a certain order in the system. Because of their highly directional nature, magnetic dipolar interactions prevent some of the random connections to be formed. On the other hand, they favor a higher connectivity of the chains' free and grafted ends. We show that this complex dipolar brush microstructure has a strong impact on the magnetic response of the brush, as any weak applied field has to compete with the dipole-dipole interactions within the crowded environment.This research has been partially supported by the Austrian Research Fund (FWF): START-Projekt Y 627-N27. The authors are grateful to the Ural Federal University stimulating programme. S.S.K, E.S.P., and E.V.N. are supported by RFBR mol-a-ved 15-32-20549. The work of E.V.N. was partially supported by the President of RF, Grant NO MK-5216.2015.2. S.S.K. is supported by the Ministry of Education and Science of the Russian Federation (Contract 02.A03.21.000, Project 3.12.2014/K) and EU-Project 642774 ETN-Colldense. P.A.S. acknowledges financial support from the Universitat de les Illes Balears within its Programa de foment de la recerca. T.S. and J.J.C. were supported by the project FIS2012-30634 (funded by the Spanish Mineco). J.J.C. and T.S. also acknowledge funding from a grant awarded by the Conselleria d’Educació, Cultura i Universitats del Govern de les Illes Balears and the European Social Fund (ESF).Peer Reviewe

On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors

In this paper we address both to the problem of identifying the noise Power Spectral Density of interferometric detectors by parametric techniques and to the problem of the whitening procedure of the sequence of data. We will concentrate the study on a Power Spectral Density like the one of the Italian-French detector VIRGO and we show that with a reasonable finite number of parameters we succeed in modeling a spectrum like the theoretical one of VIRGO, reproducing all its features. We propose also the use of adaptive techniques to identify and to whiten on line the data of interferometric detectors. We analyze the behavior of the adaptive techniques in the field of stochastic gradient and in the Least Squares ones.Comment: 28 pages, 21 figures, uses iopart.cls accepted for pubblication on Classical and Quantum Gravit

Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State

Neuroimaging studies have shown that functional brain networks composed from select regions of interest have a modular community structure. However, the organization of functional network connectivity (FNC), comprising a purely data-driven network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain networks in this mental illness

Correspondence between structure and function in the human brain at rest

To further understanding of basic and complex cognitive functions, previous connectome research has identified functional and structural connections of the human brain. Functional connectivity is often measured by using resting-state functional magnetic resonance imaging (rs-fMRI) and is generally interpreted as an indirect measure of neuronal activity. Gray matter (GM) primarily consists of neuronal and glia cell bodies; therefore, it is surprising that the majority of connectome research has excluded GM measures. Therefore, we propose that by exploring where GM corresponds to function would aid in the understanding of both structural and functional connectivity and in turn the human connectome. A cohort of 603 healthy participants underwent structural and functional scanning on the same 3 T scanner at the Mind Research Network. To investigate the spatial correspondence between structure and function, spatial independent component analysis (ICA) was applied separately to both GM density (GMD) maps and to rs-fMRI data. ICA of GM delineates structural components based on the covariation of GMD regions among subjects. For the rs-fMRI data, ICA identified spatial patterns with common temporal features. These decomposed structural and functional components were then compared by spatial correlation. Basal ganglia components exhibited the highest structural to resting-state functional spatial correlation (r = 0.59). Cortical components generally show correspondence between a single structural component and several resting-state functional components. We also studied relationships between the weights of different structural components and identified the precuneus as a hub in GMD structural network correlations. In addition, we analyzed relationships between component weights, age, and gender; concluding that age has a significant effect on structural components

(E)-Benzaldehyde O-{[3-(pyridin-3-yl)isoxazol-5-yl]meth­yl}oxime

The asymmetric unit of the title compound, C16H13N3O2, contains two independent mol­ecules in which the pyridine and benzene rings form dihedral angles of 81.7 (2) and 79.8 (2)°, indicating the twist in the mol­ecules. In the crystal, weak C—H⋯N inter­actions link mol­ecules into chains along [100]