Browsing by Author "Narayan, Manjari"

Now showing 1 - 6 of 6
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    An asymptotic minimax analysis of nonlocal means on edges
    (2012) Narayan, Manjari; Baraniuk, Richard G.
    This thesis analyzes the non-local means denoising algorithm using the criterion of minimax optimality from statistical decision theory. We show that nonlocal means is minimax suboptimal on images with smooth discontinuities [1] with a rate of convergence of [Special characters omitted.] ( n -1 ) comparable to that of wavelet thresholding. The suboptimality is a consequence of the isotropic nature of the algorithm, and its inability to adapt to the smoothness of the discontinuity. However, all is not lost for nonlocal methods. We also propose an anisotropic nonlocal means algorithm [2] that can attain the optimal rate of [Special characters omitted.] ( n -4/3 ) as well as deliver superior denoising performance using image gradients on synthetic and empirical images, respectively. Nonlocal means is an instance of exemplar based image processing methods. This result broadly implies that exemplar methods that respect anisotropy can yield superior performance in estimating edges in both theory and practice.
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    Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease
    (Elsevier, 2016) Allen, Genevera I.; Amoroso, Nicola; Anghel, Catalina; Balagurusamy, Venkat; Bare, Christopher J.; Beaton, Derek; Bellotti, Roberto; Bennett, David A.; Boehme, Kevin L.; Boutros, Paul C.; Caberlotto, Laura; Caloian, Cristian; Campbell, Frederick; Neto, Elias Chaibub; Chang, Yu-Chuan; Chen, Beibei; Chen, Chien-Yu; Chien, Ting-Ying; Clark, Tim; Das, Sudeshna; Davatzikos, Christos; Deng, Jieyao; Dillenberger, Donna; Dobson, Richard J.B.; Dong, Qilin; Doshi, Jimit; Duma, Denise; Errico, Rosangela; Erus, Guray; Everett, Evan; Fardo, David W.; Friend, Stephen H.; Frӧhlich, Holger; Gan, Jessica; St George-Hyslop, Peter; Ghosh, Satrajit S.; Glaab, Enrico; Green, Robert C.; Guan, Yuanfang; Hong, Ming-Yi; Huang, Chao; Hwang, Jinseub; Ibrahim, Joseph; Inglese, Paolo; Iyappan, Anandhi; Jiang, Qijia; Katsumata, Yuriko; Kauwe, John S.K.; Klein, Arno; Kong, Dehan; Krause, Roland; Lalonde, Emilie; Lauria, Mario; Lee, Eunjee; Lin, Xihui; Liu, Zhandong; Livingstone, Julie; Logsdon, Benjamin A.; Lovestone, Simon; Ma, Tsung-wei; Malhotra, Ashutosh; Mangravite, Lara M.; Maxwell, Taylor J.; Merrill, Emily; Nagorski, John; Namasivayam, Aishwarya; Narayan, Manjari; Naz, Mufassra; Newhouse, Stephen J.; Norman, Thea C.; Nurtdinov, Ramil N.; Oyang, Yen-Jen; Pawitan, Yudi; Peng, Shengwen; Peters, Mette A.; Piccolo, Stephen R.; Praveen, Paurush; Priami, Corrado; Sabelnykova, Veronica Y.; Senger, Philipp; Shen, Xia; Simmons, Andrew; Sotiras, Aristeidis; Stolovitzky, Gustavo; Tangaro, Sabina; Tateo, Andrea; Tung, Yi-An; Tustison, Nicholas J.; Varol, Erdem; Vradenburg, George; Weiner, Michael W.; Xiao, Guanghua; Xie, Lei; Xie, Yang; Xu, Jia; Yang, Hojin; Zhan, Xiaowei; Zhou, Yunyun; Zhu, Fan; Zhu, Hongtu; Zhu, Shanfeng; Alzheimer’s Disease Neuroimaging Initiative
    Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.
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    Inferential Methods to Find Differences in Population of Graphical Models with Applications to Functional Connectomics
    (2016-03-18) Narayan, Manjari; Baraniuk, Richard G.; Allen, Genevera I.
    In many neuroimaging modalities, scientists observe neural activity at distinct units of brain function but seek to study and manipulate functional connectivity or unobserved latent relationships between these units. Functional connectivity is commonly described using networks where nodes correspond to brain locations or regions, electrodes, circuits or neurons while edges correspond to some notion of statistical dependence. Such net- work models are increasingly used in clinical neuroimaging where scientists seek to find robust network biomarkers to detect specific brain based disorders, explain underlying disease mechanisms and guide personalized treatment regimes. However, functional con- nectivity networks are never observed but estimated from complex and noisy data, and as a result, estimated networks are prone to statistical errors. This dissertation shows that failure to account for such statistical errors compromises subsequent inferential analyses to find differences in functional connectivity and proposes a new statistical framework that ameliorates these problems, thus improving the reproducibility of functional connectivity studies. Formally, this dissertation identifies a new statistical problem, Population Post-Selection Inference or popPSI, that arises in functional neuroimaging when scientists ask inferential questions such as — How do network metrics differ between a population of unhealthy subjects and healthy controls How do individual networks vary with symptom severity To investigate popPSI issues in such questions, we use two level models to study network differences, specifically employing Gaussian graphical models (GGMs) for functional connectivity. Whereas standard test statistics do not adequately control type I and type II errors for such models, R^3, our novel methodological approach, based on resampling, random penalization with random effects test statistics addresses the deficiencies of current test statistics employed in neuroimaging. Our framework is general and can be used to test general linear hypotheses of the network at the edge, node or global level. Using extensive simulation studies for a wide variety of sample sizes and network structures, we show that R3 offers improvements in statistical power and error for various network met- rics. Real data case studies reveal that our methods find meaningful and clinically relevant network differences in synesthesia, neurofibromatosis-1 and autism spectrum disorders.
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    Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity
    (Frontiers, 2016) Narayan, Manjari; Allen, Genevera I.
    Many complex brain disorders, such as autism spectrum disorders, exhibit a wide range of symptoms and disability. To understand how brain communication is impaired in such conditions, functional connectivity studies seek to understand individual differences in brain network structure in terms of covariates that measure symptom severity. In practice, however, functional connectivity is not observed but estimated from complex and noisy neural activity measurements. Imperfect subject network estimates can compromise subsequent efforts to detect covariate effects on network structure. We address this problem in the case of Gaussian graphical models of functional connectivity, by proposing novel two-level models that treat both subject level networks and population level covariate effects as unknown parameters. To account for imperfectly estimated subject level networks when fitting these models, we propose two related approaches-R (2) based on resampling and random effects test statistics, and R (3) that additionally employs random adaptive penalization. Simulation studies using realistic graph structures reveal that R (2) and R (3) have superior statistical power to detect covariate effects compared to existing approaches, particularly when the number of within subject observations is comparable to the size of subject networks. Using our novel models and methods to study parts of the ABIDE dataset, we find evidence of hypoconnectivity associated with symptom severity in autism spectrum disorders, in frontoparietal and limbic systems as well as in anterior and posterior cingulate cortices.
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    Neural Networks of Colored Sequence Synesthesia
    (Society for Neuroscience, 2013) Tomson, Steffie N.; Narayan, Manjari; Allen, Genevera I.; Eagleman, David M.
    Synesthesia is a condition in which normal stimuli can trigger anomalous associations. In this study,weexploit synesthesia to understand how the synesthetic experience can be explained by subtle changes in network properties. Of the many forms of synesthesia, we focus on colored sequence synesthesia, a form in which colors are associated with overlearned sequences, such as numbers and letters (graphemes). Previous studies have characterized synesthesia using resting-state connectivity or stimulus-driven analyses, but it remains unclear how network properties change as synesthetes move from one condition to another. To address this gap, we used functional MRI in humans to identify grapheme-specific brain regions, thereby constructing a functional “synesthetic” network. We then explored functional connectivity of color and grapheme regions during a synesthesia-inducing fMRI paradigm involving rest, auditory grapheme stimulation, and audiovisual grapheme stimulation. Using Markov networks to represent direct relationships between regions, we found that synesthetes had more connections during rest and auditory conditions. We then expanded the network space to include 90 anatomical regions, revealing that synesthetes tightly cluster in visual regions, whereas controls cluster in parietal and frontal regions. Together, these results suggest that synesthetes have increased connectivity between grapheme and color regions, and that synesthetes use visual regions to a greater extent than controls when presented with dynamic grapheme stimulation. These data suggest that synesthesia is better characterized by studying global network dynamics than by individual properties of a single brain region.
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    Resting state functional MRI reveals abnormal network connectivity in neurofibromatosis 1
    (Wiley, 2015) Tomson, Steffie N.; Schreiner, Matthew J.; Narayan, Manjari; Rosser, Tena; Enrique, Nicole; Silva, Alcino J.; Allen, Genevera I.; Bookheimer, Susan Y.; Bearden, Carrie E.
    Neurofibromatosis type I (NF1) is a genetic disorder caused by mutations in the neurofibromin 1 gene at locus 17q11.2. Individuals with NF1 have an increased incidence of learning disabilities, attention deficits, and autism spectrum disorders. As a single-gene disorder, NF1 represents a valuable model for understanding gene–brain–behavior relationships. While mouse models have elucidated molecular and cellular mechanisms underlying learning deficits associated with this mutation, little is known about functional brain architecture in human subjects with NF1. To address this question, we used resting state functional connectivity magnetic resonance imaging (rs-fcMRI) to elucidate the intrinsic network structure of 30 NF1 participants compared with 30 healthy demographically matched controls during an eyes-open rs-fcMRI scan. Novel statistical methods were employed to quantify differences in local connectivity (edge strength) and modularity structure, in combination with traditional global graph theory applications. Our findings suggest that individuals with NF1 have reduced anterior–posterior connectivity, weaker bilateral edges, and altered modularity clustering relative to healthy controls. Further, edge strength and modular clustering indices were correlated with IQ and internalizing symptoms. These findings suggest that Ras signaling disruption may lead to abnormal functional brain connectivity; further investigation into the functional consequences of these alterations in both humans and in animal models is warranted.