Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivi... more Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivity aspects of diseases such as schizophrenia and bipolar. Methods that are developed to measure functional connectivity are based on the underlying hypotheses regarding the actual nature of RS-connectivity including evidence of temporally dynamic versus static RS-connectivity and evidence of frequency-specific versus hemodynamically-driven connectivity over a wide frequency range. This study is derived by these observations of variation of RS-connectivity in temporal and frequency domains and evaluates such characteristics of RS-connectivity in clinical population and jointly in temporal and frequency domains (the spectro-temporal domain). We base this study on the hypothesis that by studying functional connectivity of schizophrenia patients and comparing it to the one of healthy controls in the spectro-temporal domain we might be able to make new observations regarding the differences a...
Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivi... more Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivity aspects of diseases such as schizophrenia and bipolar. Methods that are developed to measure functional connectivity are based on the underlying hypotheses regarding the actual nature of RS-connectivity including evidence of temporally dynamic versus static RS-connectivity and evidence of frequency-specific versus hemodynamically-driven connectivity over a wide frequency range. This study is derived by these observations of variation of RS-connectivity in temporal and frequency domains and evaluates such characteristics of RS-connectivity in clinical population and jointly in temporal and frequency domains (the spectro-temporal domain). We base this study on the hypothesis that by studying functional connectivity of schizophrenia patients and comparing it to the one of healthy controls in the spectro-temporal domain we might be able to make new observations regarding the differences a...
Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in ... more Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and vis...
Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted withi... more Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender-among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing t...
We provide a self-contained, accessible introduction to Ratner's Equidistribution Theorem in ... more We provide a self-contained, accessible introduction to Ratner's Equidistribution Theorem in the special case of horocyclic flow on a complete hyperbolic surface of finite area. This equidistribution result was first obtained in the early 1980s by Dani and Smillie and later reappeared as an illustrative special case of Ratner's work on the equidistribution of unipotent flows in homogeneous spaces. We
2014 International Workshop on Pattern Recognition in Neuroimaging, 2014
Spatial variability in intrinsic brain networks has not been well studied in fMRI. Independent ve... more Spatial variability in intrinsic brain networks has not been well studied in fMRI. Independent vector analysis (IVA), is a blind source separation approach that can be used for segregating fMRI data into temporally coherent, maximally spatially independent networks enabling comparison among subjects similar to group independent component analysis (GICA). Using simulated and small sample real data, it has been shown that spatial independence in IVA is achieved while jointly maximizing the dependence across subjects. This study was motivated by the fact that IVA has not yet been applied to a large sample size or to analyze multi-group data for spatial differences. We introduce several new ways to quantify differences in variability of IVA-derived connectivity networks between schizophrenia patients (SZ = 82) from healthy controls (HC = 89) in a large (N=171) data set. Results show that IVA identified significant group differences in the auditory cortex, the basal ganglia, the sensorimotor network and medial visual cortex. Variance maps of the spatial networks showed that there is greater variability in the patients primarily in sensory networks whereas the default mode network showed more variability in the controls. In summary, IVA enables the study of spatial variation in intrinsic brain networks, an area that has not been in focus.
Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance im... more Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance imaging (fMRI) beyond simple scan-length averages and into approaches that capture time-varying properties of connectivity. In this Perspective we use the term "chronnectome" to describe metrics that allow a dynamic view of coupling. In the chronnectome, coupling refers to possibly time-varying levels of correlated or mutually informed activity between brain regions whose spatial properties may also be temporally evolving. We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis. We also discuss the potential these approaches offer to improve characterization and understanding of brain function. There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great...
Functional connectivity analysis of the human brain is an active area in fMRI research. It focuse... more Functional connectivity analysis of the human brain is an active area in fMRI research. It focuses on identifying meaningful brain networks that have coherent activity either during a task or in the resting state. These networks are generally identified either as collections of voxels whose time series correlate strongly with a pre-selected region or voxel, or using data-driven methodologies such as independent component analysis (ICA) that compute sets of maximally spatially independent voxel weightings (component spatial maps (SMs)), each associated with a single time course (TC). Studies have shown that regardless of the way these networks are defined, the activity coherence among them has a dynamic nature which is hard to estimate with global coherence analysis such as correlation or mutual information. Sliding window analyses in which functional network connectivity (FNC) is estimated separately at each time window is one of the more widely employed approaches to studying the d...
Independent component analysis (ICA) is a widely applied technique to derive functionally connect... more Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variabili...
Preliminary report of the results of a project 1 to introduce peer instruction into a multi-secti... more Preliminary report of the results of a project 1 to introduce peer instruction into a multi-section first semester calculus course taught largely by novice instructors. This paper summarizes the instructional approaches instructors chose to use, and the subsequent results of student performance on common exams throughout the course of the term.
Graph theory-based analysis has been widely employed in brain imaging studies, and altered topolo... more Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity...
Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivi... more Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivity aspects of diseases such as schizophrenia and bipolar. Methods that are developed to measure functional connectivity are based on the underlying hypotheses regarding the actual nature of RS-connectivity including evidence of temporally dynamic versus static RS-connectivity and evidence of frequency-specific versus hemodynamically-driven connectivity over a wide frequency range. This study is derived by these observations of variation of RS-connectivity in temporal and frequency domains and evaluates such characteristics of RS-connectivity in clinical population and jointly in temporal and frequency domains (the spectro-temporal domain). We base this study on the hypothesis that by studying functional connectivity of schizophrenia patients and comparing it to the one of healthy controls in the spectro-temporal domain we might be able to make new observations regarding the differences a...
Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivi... more Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivity aspects of diseases such as schizophrenia and bipolar. Methods that are developed to measure functional connectivity are based on the underlying hypotheses regarding the actual nature of RS-connectivity including evidence of temporally dynamic versus static RS-connectivity and evidence of frequency-specific versus hemodynamically-driven connectivity over a wide frequency range. This study is derived by these observations of variation of RS-connectivity in temporal and frequency domains and evaluates such characteristics of RS-connectivity in clinical population and jointly in temporal and frequency domains (the spectro-temporal domain). We base this study on the hypothesis that by studying functional connectivity of schizophrenia patients and comparing it to the one of healthy controls in the spectro-temporal domain we might be able to make new observations regarding the differences a...
Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in ... more Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and vis...
Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted withi... more Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender-among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing t...
We provide a self-contained, accessible introduction to Ratner's Equidistribution Theorem in ... more We provide a self-contained, accessible introduction to Ratner's Equidistribution Theorem in the special case of horocyclic flow on a complete hyperbolic surface of finite area. This equidistribution result was first obtained in the early 1980s by Dani and Smillie and later reappeared as an illustrative special case of Ratner's work on the equidistribution of unipotent flows in homogeneous spaces. We
2014 International Workshop on Pattern Recognition in Neuroimaging, 2014
Spatial variability in intrinsic brain networks has not been well studied in fMRI. Independent ve... more Spatial variability in intrinsic brain networks has not been well studied in fMRI. Independent vector analysis (IVA), is a blind source separation approach that can be used for segregating fMRI data into temporally coherent, maximally spatially independent networks enabling comparison among subjects similar to group independent component analysis (GICA). Using simulated and small sample real data, it has been shown that spatial independence in IVA is achieved while jointly maximizing the dependence across subjects. This study was motivated by the fact that IVA has not yet been applied to a large sample size or to analyze multi-group data for spatial differences. We introduce several new ways to quantify differences in variability of IVA-derived connectivity networks between schizophrenia patients (SZ = 82) from healthy controls (HC = 89) in a large (N=171) data set. Results show that IVA identified significant group differences in the auditory cortex, the basal ganglia, the sensorimotor network and medial visual cortex. Variance maps of the spatial networks showed that there is greater variability in the patients primarily in sensory networks whereas the default mode network showed more variability in the controls. In summary, IVA enables the study of spatial variation in intrinsic brain networks, an area that has not been in focus.
Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance im... more Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance imaging (fMRI) beyond simple scan-length averages and into approaches that capture time-varying properties of connectivity. In this Perspective we use the term "chronnectome" to describe metrics that allow a dynamic view of coupling. In the chronnectome, coupling refers to possibly time-varying levels of correlated or mutually informed activity between brain regions whose spatial properties may also be temporally evolving. We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis. We also discuss the potential these approaches offer to improve characterization and understanding of brain function. There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great...
Functional connectivity analysis of the human brain is an active area in fMRI research. It focuse... more Functional connectivity analysis of the human brain is an active area in fMRI research. It focuses on identifying meaningful brain networks that have coherent activity either during a task or in the resting state. These networks are generally identified either as collections of voxels whose time series correlate strongly with a pre-selected region or voxel, or using data-driven methodologies such as independent component analysis (ICA) that compute sets of maximally spatially independent voxel weightings (component spatial maps (SMs)), each associated with a single time course (TC). Studies have shown that regardless of the way these networks are defined, the activity coherence among them has a dynamic nature which is hard to estimate with global coherence analysis such as correlation or mutual information. Sliding window analyses in which functional network connectivity (FNC) is estimated separately at each time window is one of the more widely employed approaches to studying the d...
Independent component analysis (ICA) is a widely applied technique to derive functionally connect... more Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variabili...
Preliminary report of the results of a project 1 to introduce peer instruction into a multi-secti... more Preliminary report of the results of a project 1 to introduce peer instruction into a multi-section first semester calculus course taught largely by novice instructors. This paper summarizes the instructional approaches instructors chose to use, and the subsequent results of student performance on common exams throughout the course of the term.
Graph theory-based analysis has been widely employed in brain imaging studies, and altered topolo... more Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity...
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Papers by Robyn Miller