Browsing by Author "Young, Joseph"

Now showing 1 - 5 of 5
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    Addressing indirect frequency coupling via partial generalized coherence
    (Springer Nature, 2021) Young, Joseph; Homma, Ryota; Aazhang, Behnaam
    Distinguishing between direct and indirect frequency coupling is an important aspect of functional connectivity analyses because this distinction can determine if two brain regions are directly connected. Although partial coherence quantifies partial frequency coupling in the linear Gaussian case, we introduce a general framework that can address even the nonlinear and non-Gaussian case. Our technique, partial generalized coherence (PGC), expands prior work by allowing pairwise frequency coupling analyses to be conditioned on other processes, enabling model-free partial frequency coupling results. By taking advantage of recent advances in conditional mutual information estimation, we are able to implement our technique in a way that scales well with dimensionality, making it possible to condition on many processes and produce a partial frequency coupling graph. We analyzed both linear Gaussian and nonlinear simulated networks. We then performed PGC analysis of calcium recordings from mouse olfactory bulb glomeruli under anesthesia and quantified the dominant influence of breathing-related activity on the pairwise relationships between glomeruli for breathing-related frequencies. Overall, we introduce a technique capable of eliminating indirect frequency coupling in a model-free way, empowering future research to correct for potentially misleading frequency interactions in functional connectivity analyses.
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    Addressing Indirect Functional Connectivity in Neuroscience via Graphical Information Theory: Causality and Coherence
    (2020-12-03) Young, Joseph; Aazhang, Behnaam
    Accurate inference of functional connectivity, i.e. statistical relationships between brain regions, is critical for understanding brain function. Distinguishing between direct and indirect relationships is particularly important because this corresponds to identifying if brain regions are directly connected. Although solutions exist for linear Gaussian cases, we introduce a general framework that can address nonlinear and non-Gaussian cases, which are more relevant for neural data. Previous model-free methods have limited ability to identify indirect connections because of inadequate scaling with dimensionality. This poor scaling performance reduces the number of nodes, e.g. brain regions, that can be included in conditioning. By contrast, we develop model-free techniques quantifying (1) causality in the time domain and (2) coherence in the frequency domain that scale markedly better and thereby enable minimization of indirect functional connectivity. Our first model-free framework, graphical directed information (GDI), enables pairwise directed functional connections to be conditioned on substantially more processes, producing a more accurate graph of direct causal functional connectivity in the time domain. GDI correctly inferred the circuitry of simulated arbitrary Gaussian, nonlinear, and conductance-based networks. Furthermore, GDI inferred many connections of a model of a central pattern generator (CPG) circuit in Aplysia, while also reducing many indirect connections. GDI can be used on a variety of scales and data types to provide accurate direct causal connectivity graphs. Our second model-free framework, partial generalized coherence (PGC), expands prior work by allowing pairwise frequency coupling analyses to be conditioned on other processes, enabling model-free partial frequency coupling results. Our technique scales well with dimensionality, making it possible to condition on many processes and to even produce a partial frequency coupling graph. We analyzed both linear Gaussian and nonlinear simulated networks containing indirect frequency coupling which was correctly eliminated by PGC. We then performed PGC analysis of calcium recordings from the rodent olfactory bulb and quantified the dominant influence of breathing-related activity on the pairwise relationships between glomeruli for low frequencies. Overall, we introduce a technique capable of eliminating indirect frequency coupling in a model-free way.
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    Complexed Multifunctional Metallic and Chalcogenide Nanostructures as Theranostic Agents
    (2013-12-03) Young, Joseph; Drezek, Rebekah A.; Hicks, Illya V.; Kono, Junichiro
    Nanostructures have attracted substantial attention due to their distinctive properties and various applications. Nanostructures consisting of multiple morphologies and/or materials have recently become the focus of intense study with particular attention being paid to their optical and magnetic properties and the enhanced role of the interface between materials. Of particular interest are metallic-based plasmonic nanostructures, structures that support surface plasmon resonances that are sensitive to the environment, and ferrimagnetic-based nanostructures, structures that exhibit strong magnetic properties when exposed to an external field. These nanostructures provide theranostic potential in the context of cancer photothermal therapies, diagnostics and imaging. Additionally, chalcogenide based nanostructure complexes are particularly interesting. Metallic chalcogenides offer the ability to combine different types of linear and nonlinear optical properties, enable design of nanostructure complexes with surface plasmon resonance effects in new wavelength ranges, and act as photo-emitting agents for novel theranostic applications. In this thesis an in depth analysis of plasmonic, magnetic and photo-emitting nanostructures as theranostic agents is presented. We have created several multifunctional nanostructures and the factors contributing to the functional properties of these nanostructures are explored systematically through experimentation, theory, and simulations. Both in vivo and in vitro testing demonstrates the applicability of these nanostructures as theranostic agents.
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    Inferring functional connectivity through graphical directed information
    (IOP Publishing, 2021) Young, Joseph; Neveu, Curtis L.; Byrne, John H.; Aazhang, Behnaam
    Objective. Accurate inference of functional connectivity is critical for understanding brain function. Previous methods have limited ability distinguishing between direct and indirect connections because of inadequate scaling with dimensionality. This poor scaling performance reduces the number of nodes that can be included in conditioning. Our goal was to provide a technique that scales better and thereby enables minimization of indirect connections. Approach. Our major contribution is a powerful model-free framework, graphical directed information (GDI), that enables pairwise directed functional connections to be conditioned on the activity of substantially more nodes in a network, producing a more accurate graph of functional connectivity that reduces indirect connections. The key technology enabling this advancement is a recent advance in the estimation of mutual information (MI), which relies on multilayer perceptrons and exploiting an alternative representation of the Kullback–Leibler divergence definition of MI. Our second major contribution is the application of this technique to both discretely valued and continuously valued time series. Main results. GDI correctly inferred the circuitry of arbitrary Gaussian, nonlinear, and conductance-based networks. Furthermore, GDI inferred many of the connections of a model of a central pattern generator circuit in Aplysia, while also reducing many indirect connections. Significance. GDI is a general and model-free technique that can be used on a variety of scales and data types to provide accurate direct connectivity graphs and addresses the critical issue of indirect connections in neural data analysis.
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    Precise measurement of correlations between frequency coupling and visual task performance
    (Springer Nature, 2020) Young, Joseph; Dragoi, Valentin; Aazhang, Behnaam
    Functional connectivity analyses focused on frequency-domain relationships, i.e. frequency coupling, powerfully reveal neurophysiology. Coherence is commonly used but neural activity does not follow its Gaussian assumption. The recently introduced mutual information in frequency (MIF) technique makes no model assumptions and measures non-Gaussian and nonlinear relationships. We develop a powerful MIF estimator optimized for correlating frequency coupling with task performance and other relevant task phenomena. In light of variance reduction afforded by multitaper spectral estimation, which is critical to precisely measuring such correlations, we propose a multitaper approach for MIF and compare its performance with coherence in simulations. Additionally, multitaper MIF and coherence are computed between macaque visual cortical recordings and their correlation with task performance is analyzed. Our multitaper MIF estimator produces low variance and performs better than all other estimators in simulated correlation analyses. Simulations further suggest that multitaper MIF captures more information than coherence. For the macaque data set, coherence and our new MIF estimator largely agree. Overall, we provide a new way to precisely estimate frequency coupling that sheds light on task performance and helps neuroscientists accurately capture correlations between coupling and task phenomena in general. Additionally, we make an MIF toolbox available for the first time.