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993 Research products

  • Neuroinformatics
  • 2023-2023
  • English

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  • Authors: Kessler, Dan;

    The scale of modern datasets, with more and more variables measured on more and more observations, presents many statistical challenges, but also opportunities to discover and exploit the rich structure that is often present in the data. In neuroimaging studies, multiple kinds of brain imaging are conducted on the same participant, with each modality of imaging having its own further structure, and many associated phenotypic measurements taken on the participants. Understanding the complicated and noisy underlying relationships between all of these measurements holds promise for scientific and treatment breakthroughs in the long term, and requires sophisticated methods designed to uncover this structure. This thesis presents three projects on learning structure in high-dimensional datasets motivated by applications in neuroimaging. The first project considers the setting where many networks are observed on a common node set: each observation comprises edge weights, covariates observed at each node, and a response. In our neuroimaging application, the edge weights correspond to functional connectivity between brain regions, node covariates encode task activations at each brain region, and performance on a behavioral task is the response. The goal is to use the edge weights and node covariates to predict the response and to identify a parsimonious and interpretable set of predictive features. We propose an approach that uses feature groups defined according to a community structure believed to exist in the network (naturally occurring in neuroimaging applications). We propose two schemes for forming feature groups where each group incorporates both edge weights and node covariates, and derive optimization algorithms for both using an overlapping group LASSO penalty. Empirical results on synthetic data show that our method, relative to competing approaches, has similar or improved prediction error along with superior support recovery, enabling a more interpretable and potentially a more accurate understanding of the underlying process. We also apply the method to neuroimaging data. The second project focuses on inference for structure learned using Canonical Correlation Analysis (CCA). CCA is a method for analyzing a sample of pairs of random vectors; it learns a sequence of paired linear transformations of the original variables that are maximally correlated within pairs while uncorrelated across pairs. CCA outputs both canonical correlations as well as the canonical directions which define the transformations. While inference for canonical correlations is well developed, conducting inference for canonical directions is more challenging and not well-studied, but is key to interpretability. We propose a computational bootstrap method for inference on CCA direction (combootcca). We conduct thorough simulation studies that range from simple and well-controlled to complex but realistic and validate the statistical properties of combootcca while comparing it to several competitors. We also apply the combootcca method to a brain imaging dataset and discover linked patterns in brain connectivity and behavioral scores. The third project proposes a new method for matrix CCA (matcca), which works with pairs of random matrices rather than pairs of random vectors, motivated by a neuroimaging application where the brain imaging data takes the form of a high-dimensional covariance matrix. Our matcca method uses a nuclear norm penalty that encourages the canonical directions associated with the matrix-variate data to have low rank structure when arranged into a matrix. Results from both synthetic and neuroimaging data show that matcca is very effective at recovering low rank signals even in noisy cases with few observations.

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  • Authors: Root-Bernstein, Meredith; Bertoni, Filippo; Forssman, Natalie;

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    Authors: Koskinen, Maija-Kreetta; Hovatta, Iiris;

    Anxiety and fear are evolutionarily conserved emotions that increase the likeli-hood of an organism surviving threatening situations. Anxiety and vigilance states are regulated by neural networks involving multiple brain regions. In anx-iety disorders, this intricate regulatory system is disturbed, leading to excessive or prolonged anxiety or fear. Anxiety disorders have both genetic and environ-mental risk factors. Genetic research has the potential to identify specific genetic variants causally associated with specific phenotypes. In recent decades, genome-wide association studies (GWASs) have revealed variants predisposing to neuropsychiatric disorders, suggesting novel neurobiological pathways in the etiology of these disorders. Here, we review recent human GWASs of anxiety dis -orders, and genetic studies of anxiety-like behavior in rodent models. These studies are paving the way for a better understanding of the neurobiological mechanisms underlying anxiety disorders. Peer reviewed

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  • Authors: Valax, Marc; Kazma, Badih;

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    Authors: Abdelrahman, Hiba Abuelgasim Fadlelmoula;
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    Authors: Deakin, Naomi D;

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  • Authors: Peng, Hui;

    Young’s double slit (and Arago spot) experiments revived the wave theory of light. The standard interpretation is that the light behaves as waves before and after passing through the double slit. Feynman called the double slit experiment the only mystery in quantum mechanics. In this article, we postulate and experimentally confirm the precise rules of convex lenses. Based on those Postulates, we study the evolution of the patterns of single slit, double slit, triple slit, cross-double slit, disc ring, 1D-grating and 2D-grating experiments. The experiments show, for the first time, the novel universal phenomena: (1) Within a certain macroscopic distance from the diaphragm (Zone-1), light behaves as photons and produces a non-interference pattern, while near the screen (the far field, Zone-3), the photons produce an interference pattern, while between Zone-1 and Zone-3 (Zone-2), the photons produce an non-interference pattern; namely the nature and characteristics of the pattern are distance dependent; (2) The non-interference pattern evolves to the orthogonal interference pattern gradually in the same experiment; (3) The convex lens stops the evolution of the patterns at where it is placed; (4) The experiments violate the existing wave theories that describe the wave experiments, and violate Bohr’s complementarity principle/wave particle duality. The experiments provide profound phenomena/data for further theoretical development of optics and for understanding the nature of light. We suggest that a complete theory should be able to interpret the non-interference pattern in Zone-1, the non-interference pattern in Zone-2, and the interference pattern in Zone-3; and the evolution from one pattern to another.

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  • Authors: Guerra, Maria; Lemasson, Quentin; Moignard, Brice; Pacheco, Claire; +1 Authors

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    Authors: Ghosal, Shubhayan;

    In this paper we try to prove that ζ(2n+1) is irrational for all natural number n.Also,in our attempt, we construct an upper bound tothe Zeta values at odd integers.It is interesting to see how the irrational-ity of Zeta values at even positive integers mixed up with Dirichletsirrationality criterion and this bound accelerates our proof further,caseby case.

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    Other literature type . 2023
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      Other literature type . 2023
  • Authors: Manceau, Mathilde; Farre, Carole; Lagarde, Florence; Mathey, Raphaël; +4 Authors

    International audience; Bacillus cereus is a food poisoning bacterium present in dairy products. To detect it, antibody based biosensors are efficient but available antibodies are expensive and may cross-react with other Bacillus species. Aptamers are a good alternative to these issues. Herein, seven aptamer sequences derived from the literature (1,2) are compared and their specificity to B. cereus is assessed versus Escherichia coli and Bacillus subtilis by Surface Plasmon Resonance imaging (SPRi).

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993 Research products
  • Authors: Kessler, Dan;

    The scale of modern datasets, with more and more variables measured on more and more observations, presents many statistical challenges, but also opportunities to discover and exploit the rich structure that is often present in the data. In neuroimaging studies, multiple kinds of brain imaging are conducted on the same participant, with each modality of imaging having its own further structure, and many associated phenotypic measurements taken on the participants. Understanding the complicated and noisy underlying relationships between all of these measurements holds promise for scientific and treatment breakthroughs in the long term, and requires sophisticated methods designed to uncover this structure. This thesis presents three projects on learning structure in high-dimensional datasets motivated by applications in neuroimaging. The first project considers the setting where many networks are observed on a common node set: each observation comprises edge weights, covariates observed at each node, and a response. In our neuroimaging application, the edge weights correspond to functional connectivity between brain regions, node covariates encode task activations at each brain region, and performance on a behavioral task is the response. The goal is to use the edge weights and node covariates to predict the response and to identify a parsimonious and interpretable set of predictive features. We propose an approach that uses feature groups defined according to a community structure believed to exist in the network (naturally occurring in neuroimaging applications). We propose two schemes for forming feature groups where each group incorporates both edge weights and node covariates, and derive optimization algorithms for both using an overlapping group LASSO penalty. Empirical results on synthetic data show that our method, relative to competing approaches, has similar or improved prediction error along with superior support recovery, enabling a more interpretable and potentially a more accurate understanding of the underlying process. We also apply the method to neuroimaging data. The second project focuses on inference for structure learned using Canonical Correlation Analysis (CCA). CCA is a method for analyzing a sample of pairs of random vectors; it learns a sequence of paired linear transformations of the original variables that are maximally correlated within pairs while uncorrelated across pairs. CCA outputs both canonical correlations as well as the canonical directions which define the transformations. While inference for canonical correlations is well developed, conducting inference for canonical directions is more challenging and not well-studied, but is key to interpretability. We propose a computational bootstrap method for inference on CCA direction (combootcca). We conduct thorough simulation studies that range from simple and well-controlled to complex but realistic and validate the statistical properties of combootcca while comparing it to several competitors. We also apply the combootcca method to a brain imaging dataset and discover linked patterns in brain connectivity and behavioral scores. The third project proposes a new method for matrix CCA (matcca), which works with pairs of random matrices rather than pairs of random vectors, motivated by a neuroimaging application where the brain imaging data takes the form of a high-dimensional covariance matrix. Our matcca method uses a nuclear norm penalty that encourages the canonical directions associated with the matrix-variate data to have low rank structure when arranged into a matrix. Results from both synthetic and neuroimaging data show that matcca is very effective at recovering low rank signals even in noisy cases with few observations.

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  • Authors: Root-Bernstein, Meredith; Bertoni, Filippo; Forssman, Natalie;

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    Authors: Koskinen, Maija-Kreetta; Hovatta, Iiris;

    Anxiety and fear are evolutionarily conserved emotions that increase the likeli-hood of an organism surviving threatening situations. Anxiety and vigilance states are regulated by neural networks involving multiple brain regions. In anx-iety disorders, this intricate regulatory system is disturbed, leading to excessive or prolonged anxiety or fear. Anxiety disorders have both genetic and environ-mental risk factors. Genetic research has the potential to identify specific genetic variants causally associated with specific phenotypes. In recent decades, genome-wide association studies (GWASs) have revealed variants predisposing to neuropsychiatric disorders, suggesting novel neurobiological pathways in the etiology of these disorders. Here, we review recent human GWASs of anxiety dis -orders, and genetic studies of anxiety-like behavior in rodent models. These studies are paving the way for a better understanding of the neurobiological mechanisms underlying anxiety disorders. Peer reviewed

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HELDA - Digital Repo...arrow_drop_down
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  • Authors: Valax, Marc; Kazma, Badih;

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    Authors: Abdelrahman, Hiba Abuelgasim Fadlelmoula;
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ JAIROarrow_drop_down
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    JAIRO
    Doctoral thesis . 2023
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    Authors: Deakin, Naomi D;

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  • Authors: Peng, Hui;

    Young’s double slit (and Arago spot) experiments revived the wave theory of light. The standard interpretation is that the light behaves as waves before and after passing through the double slit. Feynman called the double slit experiment the only mystery in quantum mechanics. In this article, we postulate and experimentally confirm the precise rules of convex lenses. Based on those Postulates, we study the evolution of the patterns of single slit, double slit, triple slit, cross-double slit, disc ring, 1D-grating and 2D-grating experiments. The experiments show, for the first time, the novel universal phenomena: (1) Within a certain macroscopic distance from the diaphragm (Zone-1), light behaves as photons and produces a non-interference pattern, while near the screen (the far field, Zone-3), the photons produce an interference pattern, while between Zone-1 and Zone-3 (Zone-2), the photons produce an non-interference pattern; namely the nature and characteristics of the pattern are distance dependent; (2) The non-interference pattern evolves to the orthogonal interference pattern gradually in the same experiment; (3) The convex lens stops the evolution of the patterns at where it is placed; (4) The experiments violate the existing wave theories that describe the wave experiments, and violate Bohr’s complementarity principle/wave particle duality. The experiments provide profound phenomena/data for further theoretical development of optics and for understanding the nature of light. We suggest that a complete theory should be able to interpret the non-interference pattern in Zone-1, the non-interference pattern in Zone-2, and the interference pattern in Zone-3; and the evolution from one pattern to another.

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  • Authors: Guerra, Maria; Lemasson, Quentin; Moignard, Brice; Pacheco, Claire; +1 Authors

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    Authors: Ghosal, Shubhayan;

    In this paper we try to prove that ζ(2n+1) is irrational for all natural number n.Also,in our attempt, we construct an upper bound tothe Zeta values at odd integers.It is interesting to see how the irrational-ity of Zeta values at even positive integers mixed up with Dirichletsirrationality criterion and this bound accelerates our proof further,caseby case.

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