In this paper, we describe the details of the experimental setup developed with the objective of ... more In this paper, we describe the details of the experimental setup developed with the objective of demonstrating the principles of tomography using visible light. Most tomographic methods use invisible forms of radiation (e.g., x-rays or ultrasound) and therefore it is not very instructive to see them in operation. The proposed setup consists of a translucent object illuminated by a simple white-light source and imaged with the digital camera at different angles. Collimation is provided by using a readily available telecentric lens to perform the imaging. This eliminates the need for a collimated light source, which can increase the cost of the system, and which usually involves hazardous sources such as lasers or laser diodes. By using visible light, students can observe the whole process directly. The students can control the image acquisition parameters and observe the reconstruction process on a computer. In this paper we focus on providing detailed design information so that the experimental setup can be reproduced by interested educators. A separate paper [3] discusses the educational issues relating to the proposed experiment including assessment results.
Background: The development of therapeutic interventions for Parkinson disease (PD) is challenged... more Background: The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects. Objectives: We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD. Methods: Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms. Results: Both classifiers discriminated HC from PD (p < 0.01, p < 0.03), and classifier scores for age-and gender-matched HC and PD correlated with Hoehn & Yahr stage (R 2 = 0.24, p < 0.015) and UPDRS (R 2 = 0.23, p < 0.018). Metabolic patterns were highly similar, with hypometabolism in parieto-occipital and prefrontal regions and hypermetabolism in cerebellum, pons, thalamus, paracentral gyrus, and lentiform nucleus relative to whole brain, consistent with the PDRP. An additional classifier was developed using only PD subjects, resulting in scores that correlated with UPDRS (R 2 = 0.25, p < 0.02) and Hoehn & Yahr stage (R 2 = 0.16, p < 0.06). Conclusions: Two independent analyses performed in a cohort of mild PD patients replicated key features of the PDRP, confirming that FDG-PET and multivariate classification can provide an objective, sensitive biomarker of disease stage with the potential to detect treatment effects on PD progression.
Background: Adults with Down syndrome (DS) represent an enriched population for the development o... more Background: Adults with Down syndrome (DS) represent an enriched population for the development of Alzheimer's disease (AD), which could aid the study of therapeutic interventions, and in turn, could benefit from discoveries made in other AD populations. Objectives: 1) Understand the relationship between tau pathology and age, amyloid deposition, neurodegeneration (MRI and FDG PET), and cognitive and functional performance; 2) detect and differentiate AD-specific changes from DS-specific brain changes in longitudinal MRI. Methods: Twelve non-demented adults, ages 30 to 60, with DS were enrolled in the Down Syndrome Biomarker Initiative (DSBI), a 3-year, observational, cohort study to demonstrate the feasibility of conducting AD intervention/prevention trials in adults with DS. We collected imaging data with 18 F-AV-1451 tau PET, AV-45 amyloid PET, FDG PET, and volumetric MRI, as well as cognitive and functional measures and additional laboratory measures.
We propose a signal-detection approach for detecting brain activations from PET or fMRI images in... more We propose a signal-detection approach for detecting brain activations from PET or fMRI images in a two-state ("on-off") neuroimaging study. We model the activation pattern as a superposition of an unknown number of circular spatial basis functions of unknown position, size, and amplitude. We determine the number of these functions and their parameters by maximum a posteriori (MAP) estimation. To
We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activa... more We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and 2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. In an on-off design, we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude, and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a Reversible-Jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches. Index Terms-Functional neuroimaging, kernel methods, relevance vector machine (RVM), reversible-jump Markov-chain Monte-Carlo (RJMCMC).
2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Abstract We propose an approach to analyzing functional neuroimages in which:(1) regions of neuro... more Abstract We propose an approach to analyzing functional neuroimages in which:(1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data; and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). In an on-off design we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude and/or size. We employ two Bayesian methods of estimating ...
Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2016
Introduction: Down Syndrome (DS) adults experience accumulation of Alzheimer's disease (AD)like a... more Introduction: Down Syndrome (DS) adults experience accumulation of Alzheimer's disease (AD)like amyloid plaques and tangles and a high incidence of dementia and could provide an enriched population to study AD-targeted treatments. However, to evaluate effects of therapeutic intervention, it is necessary to dissociate the contributions of DS and AD from overall phenotype. Imaging biomarkers offer the potential to characterize and stratify patients who will worsen clinically but have yielded mixed findings in DS subjects. Methods: We evaluated 18F fluorodeoxyglucose positron emission tomography (PET), florbetapir PET, and structural magnetic resonance (sMR) image data from 12 nondemented DS adults using advanced multivariate machine learning methods. Results: Our results showed distinctive patterns of glucose metabolism and brain volume enabling dissociation of DS and AD effects. AD-like pattern expression corresponded to amyloid burden and clinical measures. Discussion: These findings lay groundwork to enable AD clinical trials with characterization and disease-specific tracking of DS adults.
A new signal-detection approach for detecting brain activations from PET or fMRI images in a two-... more A new signal-detection approach for detecting brain activations from PET or fMRI images in a two-state (" on-off") neuroimaging study is proposed. The activation pattern is modeled as a superposition of an unknown number of circular spatial basis functions of unknown position, size, and amplitude. Also, the number of these functions and their parameters is determined by maximum a posteriori (MAP) estimation. To maximize the posterior distribution, a reversible-jump Markov-chain Monte-Carlo (RJMCMC) algorithm ...
A Bayesian approach is proposed for statistical analysis of fMRI data sets in a two state ("... more A Bayesian approach is proposed for statistical analysis of fMRI data sets in a two state ("on-off") activation study. The approach is based on the Relevance Vector Machine (RVM) regression framework. According to this approach the shape of the activations is a superposition of kernel functions, one at each pixel of the image, and a hierarchical Bayesian model is employed
Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis tha... more Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of activation are organized into a spatial network and the variance of the networked, task-related signals is high enough for the signal to be easily detected in the data. We show that reproducibility of activation maps is a metric that captures this switch in intrinsic dimensionality. Except...
2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), 2004
We propose the use of the relevance vector machine (RVM) regression framework for statistical ana... more We propose the use of the relevance vector machine (RVM) regression framework for statistical analysis of PET or fMRI data sets in a two state (&amp;quot;on-off&amp;quot;) activation study. According to this approach the shape of the activations is a superposition of kernel functions, one at each pixel of the image, of unknown amplitude and a hierarchical Bayesian model is employed
2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Abstract We propose an approach to analyzing functional neuroimages in which:(1) regions of neuro... more Abstract We propose an approach to analyzing functional neuroimages in which:(1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data; and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). In an on-off design we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude and/or size. We employ two Bayesian methods of estimating ...
Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been in... more Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been independently associated with reduced risk of Alzheimer's disease (AD). Their association has not been investigated with the use of biomarkers. This study examines whether, among cognitively normal (NL) individuals, those who are less physically active and show lower MeDi adherence have brain biomarker abnormalities consistent with AD. Forty-five NL individuals (age 54 ± 11, 71% women) with complete leisure time physical activity (LTA), dietary information, and cross-sectional 3D T1-weigthed MRI, (11)C-Pittsburgh Compound B (PiB) and (18)F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) scans were examined. Voxel-wise multivariate partial least square (PLS) regression was used to examine the effects of LTA, MeDi and their interaction on brain biomarkers. Age, gender, ethnicity, education, caloric intake, BMI, family history of AD, Apolipoprotein E (APOE) genotype, presence ...
Digital infrared iris photography using a modified digital camera system was performed on approxi... more Digital infrared iris photography using a modified digital camera system was performed on approximately 300 subjects seen during routine clinical care and research at one facility. Because this image database offered an opportunity to gain new insight into the potential utility of infrared iris imaging, it was surveyed for unique image patterns. Then, a selection of photographs was compiled that would illustrate the spectrum of this imaging experience. Potentially informative image patterns were observed in subjects with cataracts, diabetic retinopathy, Posner-Schlossman syndrome, iridociliary cysts, long anterior lens zonules, nevi, oculocutaneous albinism, pigment dispersion syndrome, pseudophakia, suspected vascular anomaly, and trauma. Image patterns were often unanticipated regardless of preexisting information and suggest that infrared iris imaging may have numerous potential clinical and research applications, some of which may still not be recognized. These observations suggest further development and study of this technology.
To investigate near infrared iris transillumination (NIRit) imaging as a new method to quantify p... more To investigate near infrared iris transillumination (NIRit) imaging as a new method to quantify pupil shape, size, and position because the imaging modality can uniquely provide simultaneous information regarding iris structural details that influence pupil characteristics and because exploration of related techniques could promote discovery helpful to clinical research and care. Digital NIRit images of normal and diseased eyes were used along with computer-assisted techniques to quantify four primary pupil parameters, including pupil roundness (PR), pupil ovalness (PO), pupil size (PS), and pupil eccentricity (PE). A combined measure of PR and PO was also developed (the pupil circularity index [PCI]). Repeatability of the measures was studied and example analyses were performed. Pupil measures could be calculated for right eyes of 307 subjects (164 normal, 143 other), with fewer than 0.5% exclusions due to image quality. Repeatability study did not show significant bias (P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .05) for any of the four primary measures. Example analyses could show age-associated differences in pupil shape (≥ 50 year olds had less regular pupils than &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; 50 year olds: median PCI = 0.009 vs 0.006; P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .01) and that a group of pigment dispersion syndrome subjects (n = 27) had less regular pupils than a group of matched controls (PO = 0.9966 vs 0.9990; P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .05). Digital NIRit imaging can provide novel, reliable, and informative methods to quantify pupil characteristics while providing simultaneous information about iris structure that may influence these parameters.
Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis tha... more Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of activation are organized into a spatial network and the variance of the networked, task-related signals is high enough for the signal to be easily detected in the data. We show that reproducibility of activation maps is a metric that captures this switch in intrinsic dimensionality. Except for reproducibility, all of the methods of dimensionality estimation we considered failed to capture this transition: optimization of Bayesian evidence, minimum description length, supervised and unsupervised LDA prediction, and Stein's unbiased risk estimator. This failure results in sub-optimal ROC performance of LDA in the presence of a spatially distributed network, and may have caused LDA to underperform in many of the reported comparisons in the literature. Using real fMRI data sets, including multi-subject group and withinsubject longitudinal analysis we demonstrate the existence of these dimensionality transitions in real data.
In this paper, we describe the details of the experimental setup developed with the objective of ... more In this paper, we describe the details of the experimental setup developed with the objective of demonstrating the principles of tomography using visible light. Most tomographic methods use invisible forms of radiation (e.g., x-rays or ultrasound) and therefore it is not very instructive to see them in operation. The proposed setup consists of a translucent object illuminated by a simple white-light source and imaged with the digital camera at different angles. Collimation is provided by using a readily available telecentric lens to perform the imaging. This eliminates the need for a collimated light source, which can increase the cost of the system, and which usually involves hazardous sources such as lasers or laser diodes. By using visible light, students can observe the whole process directly. The students can control the image acquisition parameters and observe the reconstruction process on a computer. In this paper we focus on providing detailed design information so that the experimental setup can be reproduced by interested educators. A separate paper [3] discusses the educational issues relating to the proposed experiment including assessment results.
Background: The development of therapeutic interventions for Parkinson disease (PD) is challenged... more Background: The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects. Objectives: We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD. Methods: Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms. Results: Both classifiers discriminated HC from PD (p < 0.01, p < 0.03), and classifier scores for age-and gender-matched HC and PD correlated with Hoehn & Yahr stage (R 2 = 0.24, p < 0.015) and UPDRS (R 2 = 0.23, p < 0.018). Metabolic patterns were highly similar, with hypometabolism in parieto-occipital and prefrontal regions and hypermetabolism in cerebellum, pons, thalamus, paracentral gyrus, and lentiform nucleus relative to whole brain, consistent with the PDRP. An additional classifier was developed using only PD subjects, resulting in scores that correlated with UPDRS (R 2 = 0.25, p < 0.02) and Hoehn & Yahr stage (R 2 = 0.16, p < 0.06). Conclusions: Two independent analyses performed in a cohort of mild PD patients replicated key features of the PDRP, confirming that FDG-PET and multivariate classification can provide an objective, sensitive biomarker of disease stage with the potential to detect treatment effects on PD progression.
Background: Adults with Down syndrome (DS) represent an enriched population for the development o... more Background: Adults with Down syndrome (DS) represent an enriched population for the development of Alzheimer's disease (AD), which could aid the study of therapeutic interventions, and in turn, could benefit from discoveries made in other AD populations. Objectives: 1) Understand the relationship between tau pathology and age, amyloid deposition, neurodegeneration (MRI and FDG PET), and cognitive and functional performance; 2) detect and differentiate AD-specific changes from DS-specific brain changes in longitudinal MRI. Methods: Twelve non-demented adults, ages 30 to 60, with DS were enrolled in the Down Syndrome Biomarker Initiative (DSBI), a 3-year, observational, cohort study to demonstrate the feasibility of conducting AD intervention/prevention trials in adults with DS. We collected imaging data with 18 F-AV-1451 tau PET, AV-45 amyloid PET, FDG PET, and volumetric MRI, as well as cognitive and functional measures and additional laboratory measures.
We propose a signal-detection approach for detecting brain activations from PET or fMRI images in... more We propose a signal-detection approach for detecting brain activations from PET or fMRI images in a two-state ("on-off") neuroimaging study. We model the activation pattern as a superposition of an unknown number of circular spatial basis functions of unknown position, size, and amplitude. We determine the number of these functions and their parameters by maximum a posteriori (MAP) estimation. To
We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activa... more We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and 2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. In an on-off design, we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude, and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a Reversible-Jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches. Index Terms-Functional neuroimaging, kernel methods, relevance vector machine (RVM), reversible-jump Markov-chain Monte-Carlo (RJMCMC).
2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Abstract We propose an approach to analyzing functional neuroimages in which:(1) regions of neuro... more Abstract We propose an approach to analyzing functional neuroimages in which:(1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data; and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). In an on-off design we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude and/or size. We employ two Bayesian methods of estimating ...
Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2016
Introduction: Down Syndrome (DS) adults experience accumulation of Alzheimer's disease (AD)like a... more Introduction: Down Syndrome (DS) adults experience accumulation of Alzheimer's disease (AD)like amyloid plaques and tangles and a high incidence of dementia and could provide an enriched population to study AD-targeted treatments. However, to evaluate effects of therapeutic intervention, it is necessary to dissociate the contributions of DS and AD from overall phenotype. Imaging biomarkers offer the potential to characterize and stratify patients who will worsen clinically but have yielded mixed findings in DS subjects. Methods: We evaluated 18F fluorodeoxyglucose positron emission tomography (PET), florbetapir PET, and structural magnetic resonance (sMR) image data from 12 nondemented DS adults using advanced multivariate machine learning methods. Results: Our results showed distinctive patterns of glucose metabolism and brain volume enabling dissociation of DS and AD effects. AD-like pattern expression corresponded to amyloid burden and clinical measures. Discussion: These findings lay groundwork to enable AD clinical trials with characterization and disease-specific tracking of DS adults.
A new signal-detection approach for detecting brain activations from PET or fMRI images in a two-... more A new signal-detection approach for detecting brain activations from PET or fMRI images in a two-state (" on-off") neuroimaging study is proposed. The activation pattern is modeled as a superposition of an unknown number of circular spatial basis functions of unknown position, size, and amplitude. Also, the number of these functions and their parameters is determined by maximum a posteriori (MAP) estimation. To maximize the posterior distribution, a reversible-jump Markov-chain Monte-Carlo (RJMCMC) algorithm ...
A Bayesian approach is proposed for statistical analysis of fMRI data sets in a two state ("... more A Bayesian approach is proposed for statistical analysis of fMRI data sets in a two state ("on-off") activation study. The approach is based on the Relevance Vector Machine (RVM) regression framework. According to this approach the shape of the activations is a superposition of kernel functions, one at each pixel of the image, and a hierarchical Bayesian model is employed
Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis tha... more Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of activation are organized into a spatial network and the variance of the networked, task-related signals is high enough for the signal to be easily detected in the data. We show that reproducibility of activation maps is a metric that captures this switch in intrinsic dimensionality. Except...
2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), 2004
We propose the use of the relevance vector machine (RVM) regression framework for statistical ana... more We propose the use of the relevance vector machine (RVM) regression framework for statistical analysis of PET or fMRI data sets in a two state (&amp;quot;on-off&amp;quot;) activation study. According to this approach the shape of the activations is a superposition of kernel functions, one at each pixel of the image, of unknown amplitude and a hierarchical Bayesian model is employed
2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Abstract We propose an approach to analyzing functional neuroimages in which:(1) regions of neuro... more Abstract We propose an approach to analyzing functional neuroimages in which:(1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data; and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). In an on-off design we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude and/or size. We employ two Bayesian methods of estimating ...
Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been in... more Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been independently associated with reduced risk of Alzheimer's disease (AD). Their association has not been investigated with the use of biomarkers. This study examines whether, among cognitively normal (NL) individuals, those who are less physically active and show lower MeDi adherence have brain biomarker abnormalities consistent with AD. Forty-five NL individuals (age 54 ± 11, 71% women) with complete leisure time physical activity (LTA), dietary information, and cross-sectional 3D T1-weigthed MRI, (11)C-Pittsburgh Compound B (PiB) and (18)F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) scans were examined. Voxel-wise multivariate partial least square (PLS) regression was used to examine the effects of LTA, MeDi and their interaction on brain biomarkers. Age, gender, ethnicity, education, caloric intake, BMI, family history of AD, Apolipoprotein E (APOE) genotype, presence ...
Digital infrared iris photography using a modified digital camera system was performed on approxi... more Digital infrared iris photography using a modified digital camera system was performed on approximately 300 subjects seen during routine clinical care and research at one facility. Because this image database offered an opportunity to gain new insight into the potential utility of infrared iris imaging, it was surveyed for unique image patterns. Then, a selection of photographs was compiled that would illustrate the spectrum of this imaging experience. Potentially informative image patterns were observed in subjects with cataracts, diabetic retinopathy, Posner-Schlossman syndrome, iridociliary cysts, long anterior lens zonules, nevi, oculocutaneous albinism, pigment dispersion syndrome, pseudophakia, suspected vascular anomaly, and trauma. Image patterns were often unanticipated regardless of preexisting information and suggest that infrared iris imaging may have numerous potential clinical and research applications, some of which may still not be recognized. These observations suggest further development and study of this technology.
To investigate near infrared iris transillumination (NIRit) imaging as a new method to quantify p... more To investigate near infrared iris transillumination (NIRit) imaging as a new method to quantify pupil shape, size, and position because the imaging modality can uniquely provide simultaneous information regarding iris structural details that influence pupil characteristics and because exploration of related techniques could promote discovery helpful to clinical research and care. Digital NIRit images of normal and diseased eyes were used along with computer-assisted techniques to quantify four primary pupil parameters, including pupil roundness (PR), pupil ovalness (PO), pupil size (PS), and pupil eccentricity (PE). A combined measure of PR and PO was also developed (the pupil circularity index [PCI]). Repeatability of the measures was studied and example analyses were performed. Pupil measures could be calculated for right eyes of 307 subjects (164 normal, 143 other), with fewer than 0.5% exclusions due to image quality. Repeatability study did not show significant bias (P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .05) for any of the four primary measures. Example analyses could show age-associated differences in pupil shape (≥ 50 year olds had less regular pupils than &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; 50 year olds: median PCI = 0.009 vs 0.006; P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .01) and that a group of pigment dispersion syndrome subjects (n = 27) had less regular pupils than a group of matched controls (PO = 0.9966 vs 0.9990; P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .05). Digital NIRit imaging can provide novel, reliable, and informative methods to quantify pupil characteristics while providing simultaneous information about iris structure that may influence these parameters.
Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis tha... more Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of activation are organized into a spatial network and the variance of the networked, task-related signals is high enough for the signal to be easily detected in the data. We show that reproducibility of activation maps is a metric that captures this switch in intrinsic dimensionality. Except for reproducibility, all of the methods of dimensionality estimation we considered failed to capture this transition: optimization of Bayesian evidence, minimum description length, supervised and unsupervised LDA prediction, and Stein's unbiased risk estimator. This failure results in sub-optimal ROC performance of LDA in the presence of a spatially distributed network, and may have caused LDA to underperform in many of the reported comparisons in the literature. Using real fMRI data sets, including multi-subject group and withinsubject longitudinal analysis we demonstrate the existence of these dimensionality transitions in real data.
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Papers by Ana Lukic