168,365 research outputs found

    Tensor Analysis and Fusion of Multimodal Brain Images

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    Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

    Spectroscopic monitoring of the Herbig Ae star HD 104237. II. Non-radial pulsations, mode analysis and fundamental stellar parameters

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    Herbig Ae/Be stars are intermediate-mass pre-main sequence (PMS) stars showing signs of intense activity and strong stellar winds, whose origin is not yet understood in the frame of current theoretical models of stellar evolution for young stars. The evolutionary tracks of the earlier Herbig Ae stars cross a recently discovered PMS instability strip. Many of these stars exhibit pulsations of delta Scuti type. HD 104237 is a well-known pulsating Herbig Ae star. In this article, we reinvestigated an extensive high-resolution quasi-continuous spectroscopic data set in order to search for very faint indications of non-radial pulsations in the line profile. To do this, we worked on dynamical spectra of equivalent photospheric (LSD) profiles of HD 104237. A 2D Fourier analysis (F2D) was performed of the entire profile and the temporal variation of the central depth of the line was studied with the time-series analysis tools Period04 and SigSpec. We present a mode identification corresponding to the detected dominant frequency. We perform a new accurate determination of the fundamental stellar parameters in view of a forthcoming asteroseismic modeling. Following the previous studies on this star, our analysis of the dynamical spectrum of recentered LSD profiles corresponding to the 22nd -25th of April 1999 nights spectra has confirmed the presence of multiple oscillation modes of low-degree l in HD 104237 and led to the first direct detection of a non-radial pulsation mode in this star: the dominant mode F1 was identified by the Fourier 2D method having a degree l value comprised between 1 and 2, the symmetry of the pattern variation indicating an azimuthal order of +1 or -1. The detailed study of the fundamental stellar parameters has provided a Teff, log g and iron abundance of 8550 +/- 150K, 3.9 +/- 0.3 and -4.38 +/- 0.19 (i.e. [Fe/H]=+0.16 +/- 0.19), respectively

    Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

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    The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior

    Methods to assess binocular rivalry with periodic stimuli

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    This is the final version. Available on open access from SpringerOpen via the DOI in this recordAvailability of data and materials: Source code for the model is available in the GitHub repository farzaneh-darki/Darki2020_methods: https://github.com/farzaneh-darki/Darki2020_methods.Binocular rivalry occurs when the two eyes are presented with incompatible stimuli and perception alternates between these two stimuli. This phenomenon has been investigated in two types of experiments: (1) Traditional experiments where the stimulus is fixed, (2) eye-swap experiments in which the stimulus periodically swaps between eyes many times per second (Logothetis et al. in Nature 380(6575):621–624, 1996). In spite of the rapid swapping between eyes, perception can be stable for many seconds with specific stimulus parameter configurations. Wilson introduced a two-stage, hierarchical model to explain both types of experiments (Wilson in Proc. Natl. Acad. Sci. 100(24):14499–14503, 2003). Wilson’s model and other rivalry models have been only studied with bifurcation analysis for fixed inputs and different types of dynamical behavior that can occur with periodically forcing inputs have not been investigated. Here we report (1) a more complete description of the complex dynamics in the unforced Wilson model, (2) a bifurcation analysis with periodic forcing. Previously, bifurcation analysis of the Wilson model with fixed inputs has revealed three main types of dynamical behaviors: Winner-takes-all (WTA), Rivalry oscillations (RIV), Simultaneous activity (SIM). Our results have revealed richer dynamics including mixed-mode oscillations (MMOs) and a period-doubling cascade, which corresponds to low-amplitude WTA (LAWTA) oscillations. On the other hand, studying rivalry models with numerical continuation shows that periodic forcing with high frequency (e.g. 18 Hz, known as flicker) modulates the three main types of behaviors that occur with fixed inputs with forcing frequency (WTA-Mod, RIV-Mod, SIM-Mod). However, dynamical behavior will be different with low frequency periodic forcing (around 1.5 Hz, so-called swap). In addition to WTA-Mod and SIM-Mod, cycle skipping, multi-cycle skipping and chaotic dynamics are found. This research provides a framework for either assessing binocular rivalry models to check consistency with empirical results, or for better understanding neural dynamics and mechanisms necessary to implement a minimal binocular rivalry model.Engineering and Physical Sciences Research Council (EPSRC
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