Background: 47,XYY syndrome (XYY) occurs in ~0.1% of population-based males but is reported in 1%... more Background: 47,XYY syndrome (XYY) occurs in ~0.1% of population-based males but is reported in 1% of males with autism spectrum disorders (ASD). Approximately 19-36% of males with XYY are reported to satisfy ASD diagnostic criteria. Diffusion tensor MRI (DTI) has implicated widespread differences in white matter (WM) microstructure in ASD versus TD children, with the predominant pattern of compromised WM of the frontal and temporal lobes. We compared these WM patterns of results in boys with XYY versus our previous findings in boys with idiopathic ASD (ASD-I). Objectives: Using innovative neuroimaging biomarkers derived from DTI, we compared imaging results from boys with XYY with (XYY+ASD) and without ASD (XYY-ASD) versus age-matched typically developing boys (TD). Methods: We performed ASD, cognitive, social function, and language evaluations and neuroimaging in 12 boys with karyotype-confirmed XYY (mean age 11.5±2.4y), using the Social Communications Questionnaire (SCQ), the Soci...
Background: There is increasing evidence that autism spectrum disorder (ASD) is associated with d... more Background: There is increasing evidence that autism spectrum disorder (ASD) is associated with disruptions in the excitatory/inhibitory balance of neural activity leading to abnormal functional connectivity of the brain, thereby causing inefficient information processing. Examination of oscillatory resting-state MEG activity within specific frequency bands affords insight into these brain abnormalities. Amongst these frequency bands, recent studies have demonstrated delta-band (1–4 Hz) connectivity alterations in ASD, with the connectivity being computed over large time intervals. However, analysis of resting-state brain connectivity dynamics in short-time windows may provide a greater insight into the differential effect of ASD pathology. Objectives: The aim of this work is to investigate difference in temporal connectivity dynamics of the resting-state MEG source-space signal, between ASD and typically developing control (TDC), with connectivity computed in short-time windows in ...
(1)H-magnetic resonance spectroscopy ((1)H-MRS) and spectral editing methods, such as MEGA-PRESS,... more (1)H-magnetic resonance spectroscopy ((1)H-MRS) and spectral editing methods, such as MEGA-PRESS, allow researchers to investigate metabolite and neurotransmitter concentrations in-vivo. Here we address the utilization of (1)H-MRS for the investigation of GABA concentrations in the ASD brain, in three locations; motor, visual and auditory areas. An initial repeatability study (5 subjects, 5 repeated measures separated by~5days on average) indicated no significant effect of reference metabolite choice on GABA quantitation (p>0.6). Coefficients of variation for GABA+/NAA, GABA+/Cr and GABA+/Glx were all of the order of 9-11%. Based on these findings, we investigated creatine-normalized GABA+ratios (GABA+/Cr) in a group of (n=17) children with autism spectrum disorder (ASD) and (n=17) typically developing children (TD) for Motor, Auditory and Visual regions of interest (ROIs). Linear regression analysis of grey matter (GM) volume changes (known to occur with development) revealed a ...
Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insig... more Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insight into brain processes. Their high dimensionality necessitates the development of methods for population-based statistics, in the face of small sample sizes. In this paper, we present such a method applicable to functional connectivity networks, based on identifying the basis of dominant connectivity components that characterize the patterns of brain pathology and population variation. Projection of individual connectivity matrices into this basis allows for dimensionality reduction, facilitating subsequent statistical analysis. We find dominant components for a collection of connectivity matrices by using the projective non-negative component analysis technique which ensures that the components have non-negative elements and are non-negatively combined to obtain individual subject networks, facilitating interpretation. We demonstrate the feasibility of our novel framework by applying i...
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009
We provide a Bayesian framework for measuring similarity in white matter fiber bundles based on G... more We provide a Bayesian framework for measuring similarity in white matter fiber bundles based on Gaussian Processes. This framework does not rely on point-to-point correspondences, it takes into account a priori information about the fiber structure working with three dimensional curves instead of point sequences. Moreover, it spans an inner product space among curves together with its induced metric. Thus, it provides an environment to perform statistics on curves. Finally, we show clustering results to illustrate the utility of this model.
Background: A disruption in the excitatory/inhibitory balance of neural activity is increasingly ... more Background: A disruption in the excitatory/inhibitory balance of neural activity is increasingly thought to characterize autism spectrum disorders (ASD). Despite cellular and molecular evidence (Rubenstein and Merzenich 2003), very few studies to date have investigated an excitatory/inhibitory imbalance in ASD at a macroscopic cortical circuit scale. This putative imbalance can be assessed noninvasively during a resting-state (RS) exam via patterns of neural oscillations, as oscillatory activity reflects the synchronous firing of large populations of neurons mediated by excitatory/inhibitory interactions. In a previous study, our laboratory observed increased parietal-occipital RS alpha activity in non-medicated children with ASD, with increased alpha power associated with higher scores on the Social Responsiveness Scale (Cornew et al. 2012). Objectives: The present studies sought to replicate and extend our previous findings, examining a new and larger sample of ASD and typically d...
2007 IEEE 9th Workshop on Multimedia Signal Processing, 2007
Abstract The development of novel methodologies that utilize various non-invasive imaging modalit... more Abstract The development of novel methodologies that utilize various non-invasive imaging modalities has resulted in their increased use and relevance in the pie-clinical phase of pharmaceutical compound development. Scientific questions that may benefit from the ...
Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition, 2014
Connectivity analysis of resting state brain has provided a novel means of investigating brain ne... more Connectivity analysis of resting state brain has provided a novel means of investigating brain networks in the study of neurodevelpmental disorders. The study of functional networks, often represented by high dimensional graphs, predicates on the ability of methods in succinctly extracting meaningful representative connectivity information at the subject and population level. This need motivates the development of techniques that can extract underlying network modules that characterize the connectivity in a population, while capturing variations of these modules at the individual level. In this paper, we propose a multi-layer raph clustering technique that fuses the information from a collection of connectivity networks of a population to extract the underlying common network modules that serve as network hubs for the population. These hubs form a functional network atlas. In addition, our technique provides subject-specific factors designed to characterize and quantify the degree of intra- and inter- connectivity between hubs, thereby providing a representation that is amenable to group level statistical analyses. We demonstrate the utility of the technique by creating a population network atlas of connectivity by examining MEG based functional connectivity in typically developing children, and using this to describe the individualized variation in those diagnosed with autism spectrum disorder.
The paper presents a method for creating abnormality classifiers from high angular resolution dif... more The paper presents a method for creating abnormality classifiers from high angular resolution diffusion imaging (HARDI) data. We utilized the fiber orientation distribution (FOD) diffusion model to represent the local WM architecture of each subject. The FOD images are then spatially normalized to a common template using a non-linear registration technique. Regions of homogeneous white matter architecture (ROIs) are determined by applying a parcellation algorithm to the population average FOD image. Orientation invariant features of each ROI's mean FOD are determined and concatenated into a feature vector to represent each subject. Principal component analysis (PCA) was used for dimensionality reduction and a linear support vector machine (SVM) classifier is trained on the PCA coefficients. The classifier assigns each test subject a probabilistic score indicating the likelihood of belonging to the patient group. The method was validated using a 5 fold validation scheme on a population containing autism spectrum disorder (ASD) patients and typically developing (TD) controls. A clear distinction between ASD patients and controls was obtained with a 77% accuracy.
Structural connectivity models hold great promise for expanding what is known about the ways info... more Structural connectivity models hold great promise for expanding what is known about the ways information travels throughout the brain. The physiologic interpretability of structural connectivity models depends heavily on how the connections between regions are quantified. This article presents an integrated structural connectivity framework designed around such an interpretation. The framework provides three measures to characterize the structural connectivity of a subject: (1) the structural connectivity matrix describing the proportion of connections between pairs of nodes, (2) the nodal connection distribution (nCD) characterizing the proportion of connections that terminate in each node, and (3) the connection density image, which presents the density of connections as they traverse through white matter (WM). Individually, each possesses different information concerning the structural connectivity of the individual and could potentially be useful for a variety of tasks, ranging from characterizing and localizing group differences to identifying novel parcellations of the cortex. The efficiency of the proposed framework allows the determination of large structural connectivity networks, consisting of many small nodal regions, providing a more detailed description of a subject's connectivity. The nCD provides a gray matter contrast that can potentially aid in investigating local cytoarchitecture and connectivity. Similarly, the connection density images offer insight into the WM pathways, potentially identifying focal differences that affect a number of pathways. The reliability of these measures was established through a test/retest paradigm performed on nine subjects, while the utility of the method was evaluated through its applications to 20 diffusion datasets acquired from typically developing adolescents.
Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insig... more Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insight into brain processes. Their high dimensionality necessitates the development of methods for population-based statistics, in the face of small sample sizes. In this paper, we present such a method applicable to functional connectivity networks, based on identifying the basis of dominant connectivity components that characterize the patterns of brain pathology and population variation. Projection of individual connectivity matrices into this basis allows for dimensionality reduction, facilitating subsequent statistical analysis. We find dominant components for a collection of connectivity matrices by using the projective non-negative component analysis technique which ensures that the components have non-negative elements and are non-negatively combined to obtain individual subject networks, facilitating interpretation. We demonstrate the feasibility of our novel framework by applying it to simulated connectivity matrices as well as to a clinical study using connectivity matrices derived from resting state magnetoencephalography (MEG) data in a population of subjects diagnosed with autism spectrum disorder (ASD).
In this work, a novel method for determining the principal directions (maxima) of the diffusion o... more In this work, a novel method for determining the principal directions (maxima) of the diffusion orientation distribution function (ODF) is proposed. We represent the ODF as a symmetric high-order Cartesian tensor restricted to the unit sphere and show that the extrema of the ODF are solutions to a system of polynomial equations whose coefficients are polynomial functions of the tensor elements. In addition to demonstrating the ability of our methods to identify the principal directions in real data, we show that this method correctly identifies the principal directions under a range of noise levels. We also propose the use of the principal curvatures of the graph of the ODF function as a measure of the degree of diffusion anisotropy in that direction. We present simulated results illustrating the relationship between the mean principal curvature, measured at the maxima, and the fractional anisotropy of the underlying diffusion tensor.
IEEE International Symposium on Biomedical Imaging, 2010
In this work we present a method for non-rigid registration of high angular resolution diffusion ... more In this work we present a method for non-rigid registration of high angular resolution diffusion imaging (HARDI) datasets that are modeled by a field of antipodally symmetric spherical functions, represented by their expansion in the real spherical harmonic (RSH) basis. We use a multichannel demons algorithm which utilizes a computationally simple, rotationally invariant similarity function defined in the RSH space.
Adult studies have shown that a basic property of resting-state (RS) brain activity is the coupli... more Adult studies have shown that a basic property of resting-state (RS) brain activity is the coupling of posterior alpha oscillations (alpha phase) to posterior gamma oscillations (gamma amplitude). The present study examined whether this basic RS process is present in children. Given reports of abnormal parietal-occipital RS alpha in children with autism spectrum disorder (ASD), the present study examined whether RS alpha-to-gamma phase-amplitude coupling (PAC) is disrupted in ASD. Simulations presented in this study showed limitations with traditional PAC analyses. In particular, to avoid false-positive PAC findings, simulations showed the need to use a unilateral passband to filter the upper frequency band as well as the need for longer epochs of data. For the human study, eyes-closed RS magnetoencephalography data were analyzed from 25 children with ASD and 18 typically developing (TD) children with at least 60 sec of artifact-free data. Source modeling provided continuous time co...
Background: 47,XYY syndrome (XYY) occurs in ~0.1% of population-based males but is reported in 1%... more Background: 47,XYY syndrome (XYY) occurs in ~0.1% of population-based males but is reported in 1% of males with autism spectrum disorders (ASD). Approximately 19-36% of males with XYY are reported to satisfy ASD diagnostic criteria. Diffusion tensor MRI (DTI) has implicated widespread differences in white matter (WM) microstructure in ASD versus TD children, with the predominant pattern of compromised WM of the frontal and temporal lobes. We compared these WM patterns of results in boys with XYY versus our previous findings in boys with idiopathic ASD (ASD-I). Objectives: Using innovative neuroimaging biomarkers derived from DTI, we compared imaging results from boys with XYY with (XYY+ASD) and without ASD (XYY-ASD) versus age-matched typically developing boys (TD). Methods: We performed ASD, cognitive, social function, and language evaluations and neuroimaging in 12 boys with karyotype-confirmed XYY (mean age 11.5±2.4y), using the Social Communications Questionnaire (SCQ), the Soci...
Background: There is increasing evidence that autism spectrum disorder (ASD) is associated with d... more Background: There is increasing evidence that autism spectrum disorder (ASD) is associated with disruptions in the excitatory/inhibitory balance of neural activity leading to abnormal functional connectivity of the brain, thereby causing inefficient information processing. Examination of oscillatory resting-state MEG activity within specific frequency bands affords insight into these brain abnormalities. Amongst these frequency bands, recent studies have demonstrated delta-band (1–4 Hz) connectivity alterations in ASD, with the connectivity being computed over large time intervals. However, analysis of resting-state brain connectivity dynamics in short-time windows may provide a greater insight into the differential effect of ASD pathology. Objectives: The aim of this work is to investigate difference in temporal connectivity dynamics of the resting-state MEG source-space signal, between ASD and typically developing control (TDC), with connectivity computed in short-time windows in ...
(1)H-magnetic resonance spectroscopy ((1)H-MRS) and spectral editing methods, such as MEGA-PRESS,... more (1)H-magnetic resonance spectroscopy ((1)H-MRS) and spectral editing methods, such as MEGA-PRESS, allow researchers to investigate metabolite and neurotransmitter concentrations in-vivo. Here we address the utilization of (1)H-MRS for the investigation of GABA concentrations in the ASD brain, in three locations; motor, visual and auditory areas. An initial repeatability study (5 subjects, 5 repeated measures separated by~5days on average) indicated no significant effect of reference metabolite choice on GABA quantitation (p>0.6). Coefficients of variation for GABA+/NAA, GABA+/Cr and GABA+/Glx were all of the order of 9-11%. Based on these findings, we investigated creatine-normalized GABA+ratios (GABA+/Cr) in a group of (n=17) children with autism spectrum disorder (ASD) and (n=17) typically developing children (TD) for Motor, Auditory and Visual regions of interest (ROIs). Linear regression analysis of grey matter (GM) volume changes (known to occur with development) revealed a ...
Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insig... more Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insight into brain processes. Their high dimensionality necessitates the development of methods for population-based statistics, in the face of small sample sizes. In this paper, we present such a method applicable to functional connectivity networks, based on identifying the basis of dominant connectivity components that characterize the patterns of brain pathology and population variation. Projection of individual connectivity matrices into this basis allows for dimensionality reduction, facilitating subsequent statistical analysis. We find dominant components for a collection of connectivity matrices by using the projective non-negative component analysis technique which ensures that the components have non-negative elements and are non-negatively combined to obtain individual subject networks, facilitating interpretation. We demonstrate the feasibility of our novel framework by applying i...
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009
We provide a Bayesian framework for measuring similarity in white matter fiber bundles based on G... more We provide a Bayesian framework for measuring similarity in white matter fiber bundles based on Gaussian Processes. This framework does not rely on point-to-point correspondences, it takes into account a priori information about the fiber structure working with three dimensional curves instead of point sequences. Moreover, it spans an inner product space among curves together with its induced metric. Thus, it provides an environment to perform statistics on curves. Finally, we show clustering results to illustrate the utility of this model.
Background: A disruption in the excitatory/inhibitory balance of neural activity is increasingly ... more Background: A disruption in the excitatory/inhibitory balance of neural activity is increasingly thought to characterize autism spectrum disorders (ASD). Despite cellular and molecular evidence (Rubenstein and Merzenich 2003), very few studies to date have investigated an excitatory/inhibitory imbalance in ASD at a macroscopic cortical circuit scale. This putative imbalance can be assessed noninvasively during a resting-state (RS) exam via patterns of neural oscillations, as oscillatory activity reflects the synchronous firing of large populations of neurons mediated by excitatory/inhibitory interactions. In a previous study, our laboratory observed increased parietal-occipital RS alpha activity in non-medicated children with ASD, with increased alpha power associated with higher scores on the Social Responsiveness Scale (Cornew et al. 2012). Objectives: The present studies sought to replicate and extend our previous findings, examining a new and larger sample of ASD and typically d...
2007 IEEE 9th Workshop on Multimedia Signal Processing, 2007
Abstract The development of novel methodologies that utilize various non-invasive imaging modalit... more Abstract The development of novel methodologies that utilize various non-invasive imaging modalities has resulted in their increased use and relevance in the pie-clinical phase of pharmaceutical compound development. Scientific questions that may benefit from the ...
Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition, 2014
Connectivity analysis of resting state brain has provided a novel means of investigating brain ne... more Connectivity analysis of resting state brain has provided a novel means of investigating brain networks in the study of neurodevelpmental disorders. The study of functional networks, often represented by high dimensional graphs, predicates on the ability of methods in succinctly extracting meaningful representative connectivity information at the subject and population level. This need motivates the development of techniques that can extract underlying network modules that characterize the connectivity in a population, while capturing variations of these modules at the individual level. In this paper, we propose a multi-layer raph clustering technique that fuses the information from a collection of connectivity networks of a population to extract the underlying common network modules that serve as network hubs for the population. These hubs form a functional network atlas. In addition, our technique provides subject-specific factors designed to characterize and quantify the degree of intra- and inter- connectivity between hubs, thereby providing a representation that is amenable to group level statistical analyses. We demonstrate the utility of the technique by creating a population network atlas of connectivity by examining MEG based functional connectivity in typically developing children, and using this to describe the individualized variation in those diagnosed with autism spectrum disorder.
The paper presents a method for creating abnormality classifiers from high angular resolution dif... more The paper presents a method for creating abnormality classifiers from high angular resolution diffusion imaging (HARDI) data. We utilized the fiber orientation distribution (FOD) diffusion model to represent the local WM architecture of each subject. The FOD images are then spatially normalized to a common template using a non-linear registration technique. Regions of homogeneous white matter architecture (ROIs) are determined by applying a parcellation algorithm to the population average FOD image. Orientation invariant features of each ROI's mean FOD are determined and concatenated into a feature vector to represent each subject. Principal component analysis (PCA) was used for dimensionality reduction and a linear support vector machine (SVM) classifier is trained on the PCA coefficients. The classifier assigns each test subject a probabilistic score indicating the likelihood of belonging to the patient group. The method was validated using a 5 fold validation scheme on a population containing autism spectrum disorder (ASD) patients and typically developing (TD) controls. A clear distinction between ASD patients and controls was obtained with a 77% accuracy.
Structural connectivity models hold great promise for expanding what is known about the ways info... more Structural connectivity models hold great promise for expanding what is known about the ways information travels throughout the brain. The physiologic interpretability of structural connectivity models depends heavily on how the connections between regions are quantified. This article presents an integrated structural connectivity framework designed around such an interpretation. The framework provides three measures to characterize the structural connectivity of a subject: (1) the structural connectivity matrix describing the proportion of connections between pairs of nodes, (2) the nodal connection distribution (nCD) characterizing the proportion of connections that terminate in each node, and (3) the connection density image, which presents the density of connections as they traverse through white matter (WM). Individually, each possesses different information concerning the structural connectivity of the individual and could potentially be useful for a variety of tasks, ranging from characterizing and localizing group differences to identifying novel parcellations of the cortex. The efficiency of the proposed framework allows the determination of large structural connectivity networks, consisting of many small nodal regions, providing a more detailed description of a subject's connectivity. The nCD provides a gray matter contrast that can potentially aid in investigating local cytoarchitecture and connectivity. Similarly, the connection density images offer insight into the WM pathways, potentially identifying focal differences that affect a number of pathways. The reliability of these measures was established through a test/retest paradigm performed on nine subjects, while the utility of the method was evaluated through its applications to 20 diffusion datasets acquired from typically developing adolescents.
Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insig... more Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insight into brain processes. Their high dimensionality necessitates the development of methods for population-based statistics, in the face of small sample sizes. In this paper, we present such a method applicable to functional connectivity networks, based on identifying the basis of dominant connectivity components that characterize the patterns of brain pathology and population variation. Projection of individual connectivity matrices into this basis allows for dimensionality reduction, facilitating subsequent statistical analysis. We find dominant components for a collection of connectivity matrices by using the projective non-negative component analysis technique which ensures that the components have non-negative elements and are non-negatively combined to obtain individual subject networks, facilitating interpretation. We demonstrate the feasibility of our novel framework by applying it to simulated connectivity matrices as well as to a clinical study using connectivity matrices derived from resting state magnetoencephalography (MEG) data in a population of subjects diagnosed with autism spectrum disorder (ASD).
In this work, a novel method for determining the principal directions (maxima) of the diffusion o... more In this work, a novel method for determining the principal directions (maxima) of the diffusion orientation distribution function (ODF) is proposed. We represent the ODF as a symmetric high-order Cartesian tensor restricted to the unit sphere and show that the extrema of the ODF are solutions to a system of polynomial equations whose coefficients are polynomial functions of the tensor elements. In addition to demonstrating the ability of our methods to identify the principal directions in real data, we show that this method correctly identifies the principal directions under a range of noise levels. We also propose the use of the principal curvatures of the graph of the ODF function as a measure of the degree of diffusion anisotropy in that direction. We present simulated results illustrating the relationship between the mean principal curvature, measured at the maxima, and the fractional anisotropy of the underlying diffusion tensor.
IEEE International Symposium on Biomedical Imaging, 2010
In this work we present a method for non-rigid registration of high angular resolution diffusion ... more In this work we present a method for non-rigid registration of high angular resolution diffusion imaging (HARDI) datasets that are modeled by a field of antipodally symmetric spherical functions, represented by their expansion in the real spherical harmonic (RSH) basis. We use a multichannel demons algorithm which utilizes a computationally simple, rotationally invariant similarity function defined in the RSH space.
Adult studies have shown that a basic property of resting-state (RS) brain activity is the coupli... more Adult studies have shown that a basic property of resting-state (RS) brain activity is the coupling of posterior alpha oscillations (alpha phase) to posterior gamma oscillations (gamma amplitude). The present study examined whether this basic RS process is present in children. Given reports of abnormal parietal-occipital RS alpha in children with autism spectrum disorder (ASD), the present study examined whether RS alpha-to-gamma phase-amplitude coupling (PAC) is disrupted in ASD. Simulations presented in this study showed limitations with traditional PAC analyses. In particular, to avoid false-positive PAC findings, simulations showed the need to use a unilateral passband to filter the upper frequency band as well as the need for longer epochs of data. For the human study, eyes-closed RS magnetoencephalography data were analyzed from 25 children with ASD and 18 typically developing (TD) children with at least 60 sec of artifact-free data. Source modeling provided continuous time co...
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Papers by Luke Bloy