Title: Multivariate methods for identifying multi-task/multimodal brain imaging biomarkers
Funding Source: NIH/NIBIB: 1R01EB006841
PI: Calhoun
Abstract:
Each brain imaging modality reports on a different aspect of the brain (e.g. gray matter integrity, blood flow changes, electrical activity) and each has strengths and weaknesses. Combining multimodal imaging data is not easy since, among other reasons, each modality requires specialized expertise, and thus it is typical to analyze each imaging modality separately and interpret the results independently of one another. Many mental illnesses, such as schizophrenia, bipolar disorder, depression, and others, currently lack definitive biological markers and rely primarily on symptom assessments for diagnosis. One area which can benefit greatly from the combination of multimodal data is the study of schizophrenia. The brain imaging findings in schizophrenia are widespread and heterogeneous and have limited replicability. We show evidence that, in part, the lack of consistent findings is because most models do not combine imaging modalities in an integrated manner and miss important changes which are partially detected by each modality separately. We propose to develop multivariate methods based upon independent component analysis (ICA) to enable research on healthy versus diseased brain by identifying associations between different data types. The successful completion of this research will 1) provide a powerful set of tools [stand alone toolbox and database] for identifying relationships between multi-modal data, 2) provide a set of reliable brain imaging biomarkers for differentiating schizophrenia patients, bipolar patients (who share many symptoms with schizophrenia), and healthy controls, and 3) lay the groundwork for future work towards using imaging biomarkers for clinical purposes. In addition, the algorithms, model selection methods and anonymized data we provide will enable other investigators to use our tools and to compare their own methods with our own as well as to apply them to a large variety of brain disorders.
Title: Informed Data-Driven Fusion of Behvaior, Brain Function, and Genes (formerly Spatiotemporal Fusion of fMRI, EEG, and Genetic Data)
Funding Source: NIH/NIBIB: R01EB005846
PI: Calhoun
Abstract:
The brain is an incredibly complex highly inter-connected organ. Each existing modality for imaging the living brain can only report upon a limited domain. For example, functional imaging provides information about dynamic blood flow changes in response to a stimulus, whereas electroencephalography (EEG) provides information about the electrical activity of the brain with centimeter spatial and millisecond temporal resolution. Finally, gene array imaging can assess specific differences at the chromosomal level that are present in individuals, some of which have functional consequences. Even though all three of these modalities can easily be collected on the same set of individuals, methods for effectively combining these different types of information are still in their infancy. All of these modalities typically involve thousands of data points per subject, and thus simple correlative approaches are of very limited nature for uncovering hidden patterns and associations in these data and can easily be computationally overwhelming. INTELLECTUAL MERIT: The goal of this project is to examine associations among fMRI, EEG, and genetic variations related to healthy and abnormal brain function. We propose to develop a set of tools based on independent component analysis (ICA) that can effectively fuse the information provided by multiple imaging modalities to span a vast range of spatial and temporal scales. The tools we develop will thus maximally exploit the information provided by multiple images and thus enable significant advances in the challenging problem of the study of brain function. We will develop these tools and apply to a large data set of healthy individuals in order to explore healthy brain function. In the last two years of the grant, we will apply our approaches to data collected from patients with schizophrenia as an example of impaired brain connectivity, and examine where the fMRl/EEG/genetic relationships have deviated from that of the healthy brain. We have recently introduced an ICA-based framework to jointly analyze data from multiple imaging sources and showed the richness of information conveyed by such a joint optimization approach. We have also introduced a number of approaches to effectively incorporate prior information into the ICA estimation in order to improve the performance. In the proposed study, we will bring these two research areas together, develop an ICA based fusion framework that enables the incorporation of prior information, specific for each data type, and will demonstrate the power of joint data analysis both between pairwise data types, and then for more than two data types for joint fusion. We will also extend the fusion ICA framework to one that incorporates prior information depending on each image type to improve the performance of joint information extraction. We will focus upon three image types, fMRI, EEG and genetic array imaging of single nucleotide polymorphisms. These three image types provide complementary information about brain function, and all can benefit from the incorporation of prior information. BROADER IMPACT: The broad impacts of the proposed work lie in its potential to substantially impact science and information technology. It also has the potential to impact public health by providing new information about healthy and abnormal brain which can then lead to new strategies for protecting and improving health. The study of human brain function is a very challenging and rich problem. The ICA-based fusion approach we believe is the key for achieving significant advances in the field. A significant broader impact of our proposal is to stimulate research at the interface between medical imaging and information processing by making the tools for the study of brain function widely available. We plan to develop a toolbox that enables fusion and analysis of various types of imaging data and allows the incorporation of prior information and make it available to the research community though a website.
Title: Independent Component Analysis of Complex-Valued Brain Imaging Data
Funding Source: NSF: 0715022
PI: Calhoun
Abstract:
Independent component analysis (ICA) has emerged as an attractive analysis tool for discovering hidden factors in observed data and has been successfully applied for data analysis in a wide array of applications such as biomedicine, communications, finance, and remote sensing. In a good number of these application domains, the data are typically complex valued. This is also the case in biomedical image analysis where ICA has been recognized as a promising tool for studying the brain function. Most biomedical image analysis techniques, however, use only the magnitude information and discard the phase, resulting in an unnecessary loss of information. Moreover, most brain imaging studies collect multiple data types where each existing modality for imaging the brain reports upon a limited domain and provides complementary information. Thus processing of imaging data in its native, complex form and by utilizing multiple modality images promises significant advances in our understanding of the brain function. We propose to develop a class of complex ICA algorithms, in particular for analysis of biomedical imaging data and demonstrate the power of joint data analysis as well as performing the analysis on the complete set of data, i.e., by utilizing both the magnitude and the phase information. We focus upon three image types, functional magnetic resonance imaging (fMRI), structural MRI (sMRI) and diffusion tensor imaging (DTI). These three imaging data provide complementary information about brain connectivity, and all can benefit from the incorporation of a complex-valued data processing approach. The broad impact of the proposed work lies in its potential to substantially impact science and information technology as well as in its educational features. Study of human brain connectivity is a very challenging and rich problem. The ICA-based fusion approach as well as the use of imaging data in its native, complex form, we believe is the key for achieving significant advances in the field. Successful demonstration of our approach for medical imaging data will also benefit other areas of science and technology where data from multiple sources and/or data in complex form need to be jointly analyzed for inferences. A significant broader impact of our proposal is to stimulate research at the interface between medical imaging and information processing by making the tools for the study of brain connectivity widely available through a toolbox and a medical imaging database.
Title: A unified framework for flexible brain image analysis (formally Multi-group Semi-blind ICA of fMRI)
Funding Source: NIH/NIBIB: 2R01 EB000840-06
PI: Calhoun
Abstract:
Data driven methods are being increasingly used to analyze brain imaging data. FMRI analyses can be put on an analytic spectrum with heavily model-based approaches (like the general linear model (GLM) implemented in the SPM software) on one end and flexible data-driven approaches like independent component analysis (ICA), principal component analysis (PCA), or clustering on the other end. In between there is a gap, which we and others have been trying to fill. In particular, methods such as ICA are particularly useful for reducing the multivariate fMRI problem down to one that is both tractable and also enables the incorporation of prior information. In the first period of this competing renewal, we focused our efforts upon developing ICA of fMRI methods which would be suitable for making group inferences, and which would allow the incorporation of prior information, hence moving from a 'blind' ICA approach to a semi-blind ICA approach. Despite the progress we have made, there is still considerable work to be done in the analysis of fMRI data with ICA. In this competing renewal, we propose to continue and significantly expand this work. First, we will extend our semi-blind ICA (sbICA) framework to provide a general framework for incorporating prior information from multiple spatial and temporal sources. In the second aim we will focus upon statistical inference and develop a framework for integrating the relevant functional components. In the third aim, we will validate the algorithms in aims 1 and 2, including using fMRI data collected on multiple days from a variety of paradigms. In this aim we develop a decision mechanism for selecting the best combination of methods given a particular problem. For the fourth aim, we will apply our methods to data collected during four well-studied paradigms in healthy controls and patients with schizophrenia. Our final aim involves the continuing development of our GIFT toolbox, and incorporation of the above algorithms, constraint selection mechanisms, and visual interfaces into the software. The successful completion of this research will provide a powerful set of tools for the research community to increase the sensitivity and specificity of BOLD analysis methods by drawing upon the strengths of both model-based and data-driven approaches. These tools will also provide a way to study the inter-relationship among functional networks in a flexible manner. This has application not only in schizophrenia but in many other diseases such as Alzheimer's, attention deficit hyperactivity, and psychopathy.
Title: Neural mechanism of schizophrenia: use of multiple nuroimaging tools to examine
Funding Source: NIH/NCRR: 5P20RR021938-03
PI: Calhoun
Abstract:
This Center for Biomedical Research Excellence (COBRE) will examine the neural mechanisms of schizophrenia by integrating multiple neuroimaging methods with psychiatric and neuropsychological testing, and incorporating genetic testing. Its overarching theme is the study of schizophrenia as a disorder characterized by abnormalities in structural, functional, and effective connectivity between cortical and subcortical brain regions producing abnormalities in the integration of information across distributed brain circuits. The program is composed of four tightly integrated projects conceptualized as a hierarchy in which each independently investigates a major cognitive domain of dysfunction in schizophrenia, as identified by a panel of experts in a recent NIH-sponsored study. This dysfunction ranges from basic sensory to higher-order deficits, with attention, memory, concept formation and problem solving abilities (i.e., intelligence) listed among the top cognitive deficits that detrimentally effected patients with schizophrenia. The plan begins at a basic level of sensory processing (auditory sensory gating; Project 1), followed by multi-sensory integration (auditory and visual; Project 2), to working memory and relational memory integration (transverse patterning; Project 3), and, finally, generalized higher cognitive functioning (intelligence; Project 4). Plans provide for data collection on up to 100 of the same patients with schizophrenia (schizophrenia) and 100 healthy normal volunteers (HNV) and a centralized data processing stream that has been implemented and is already in use. Project 1 quantifies brain function and clinical pathology through multimodal imaging of sensory gating. Project 2 studies the neural mechanisms underlying auditory and visual integration in schizophrenia and HNV using magnetoencephalography (MEG), electroencephalography (EEG) combined with anatomical magnetic resonance imaging (MRI), and functional MRI (fMRI). Project 3 tests the fronto-temporal disconnection hypothesis in schizophrenia by addressing basic clinical and translational research questions. Project 4 addresses whether general cognitive functioning in schizophrenia is related to particular white matter, metabolic, and volumetric changes in subcortical gray- and white-matter regions suggestive of frontosubcortical disconnection. These projects will produce a wealth of information about the nature of antomic and functional misconnections in schizophrenia and how they relate to the manifestation of the illness. Overall, the committee recommended this outstanding application for five years of support with the budget as requested.
Title: Genetic markers of white matter integrity in schizophrenia
Funding Source: NIH/NIMH: 1RC1MH089257-01
PI: Calhoun
Abstract:
Specifically, we plan to use a combination of genetic and neuroimaging tools to identify novel biomarkers of clinical severity in patients with schizophrenia. Schizophrenia is a chronic and severely debilitating mental disorder affecting approximately 1% of the world's population. Multiple neurotransmitter systems have been implicated, as well as both gray and white matter abnormalities. These structural alterations are thought to underlie both synaptic miscommunication at local neuronal circuits and functional disconnectivity among distributed brain regions. Given the role of myelin in sub-serving rapid long-distance communication, it has been proposed that a disruption of oligodendrocyte function and myelin integrity may contribute to some of the symptoms of this illness. Supporting this idea, an increasing number of neuropathological, neuroimaging and molecular genetic studies demonstrate the presence of white matter pathology in patients with schizophrenia. Although schizophrenia is not a dysmyelinating disorder, it is important to note that: a) the onset of symptoms usually coincides with the peak of myelination in the frontal and temporal lobes, b) patients with schizophrenia often show an impaired age- related increase in white matter volumes in the these brain regions and c) specific disruption of myelin structure during this critical period is often associated with schizophrenia-like symptoms. Over 90 articles in the past 5 years have used diffusion tensor imaging (DTI) to characterize white matter abnormalities in chronic and first episode patients. The studies demonstrated myelin integrity defects in the subcortical white matter, particularly in the frontal and temporal lobes. However given that these studies were performed using a small number of patients, and there were some discrepancies about the location and extent of white matter pathology identified, several questions remain about the prevalence of myelin pathology in the patient population. Furthermore, the genetic contributions to these alterations and the significance of white matter pathology to clinical severity remain to be established. As shown in the diagram above, in this challenge grant, we propose to assess influence of white matter alterations and genetic variation to the different symptoms of schizophrenia. The contributions of specific gene polymorphisms to measurements of white matter integrity will be evaluated using 500 patients and control subjects. These measurements will be correlated with disease severity using multivariate statistical methods developed by the PI. Specifically we plan to: Aim 1: Employ available DTI data and DNA samples, collected in 250 well-characterized schizophrenia patients and healthy controls, to identify the putative genetic underpinnings of white matter tract abnormalities and correlate these with several measurements of clinical severity. The data and samples have been collected as part of previously and currently funded studies at two sites: The Mind Research Network (MRN) and the Olin Neuropsychiatry Research Center (ONRC). Aim 2: Use additional data collected at both sites (N=250) to perform a confirmatory analysis validating the observations made under Aim 1. We will also release a set of software tools to the community. Why is a Challenge grant mechanism ideal for the proposed research? 1) Our goal of using innovative approaches to identify candidate biomarkers for mental disorders that are suitable for subsequent validation efforts matches the goals of this RFA. The proposed use of genetic, neuroimaging and statistical tools also matches the technological approaches described in the RFA and represents a new direction in the field. There are currently no reliable biomarkers for schizophrenia, so the proposed search for biomarkers that can predict disease severity is of high impact. 2) A two year grant award is ideal for the proposed work. We have already acquired most of the MRI data and collected saliva samples as part of other NIH funded studies that used different imaging modalities (fMRI and EEG) in the same groups of patients. Therefore, the project will focus on the genotyping and DTI analyses and the statistical methods to search for specific biomarkers. 3) We have assembled a team of investigators with unique expertise and an excellent record of effective past collaborations to pursue these studies. Our plan to hire and train new personnel, and to employ unique genome wide and bioinformatics technologies using US-based companies such as Illumina, Inc., will have the added benefit of stimulating the economy. PUBLIC HEALTH RELEVANCE: The goal of this Challenge grant application is to identify novel biomarkers of clinical severity in patients with schizophrenia. There are currently no reliable biomarkers for schizophrenia, so the proposed use of sophisticated genotyping, neuroimaging and biostatistical tools for searching biomarkers that can predict disease severity in two large cohorts of patient has a high clinical impact. The identification of such biomarkers will not only increase our knowledge of the pathophysiology of schizophrenia but also, and most importantly, may help predict an increased risk for this illness even before the onset of symptoms.
Title: Complex-Valued Signal Processing and its Application to Analysis of Brain Imaging Data
Funding Source: NSF: 0840895
PI: Calhoun
Abstract:
Complex-Valued Signal Processing and its Application to Analysis of Brain Imaging Data Complex-valued signals arise frequently in applications as diverse as communications, radar, and biomedicine, as most practical modulation formats are of complex type and applications such as radar and magnetic resonance imaging lead to data that are inherently complex valued. The complex domain not only provides a convenient representation for these signals but also a natural way to preserve the physical characteristics of the signals and the transformations they go though. The complex domain, however, also presents a number of challenges in the derivation and analysis of signal processing algorithms, and as a result, the vast majority of algorithms developed for the complex domain have taken shortcuts limiting their usefulness. This research establishes a framework for complex-valued signal processing such that the full potential of complex-valued signal processing can be realized. It allows for all computations to be carried out in the complex domain eliminating the need for many simplifying assumptions, such as the circularity of signal, both in the derivation and the analysis of the algorithms. It also allows for the use of fully complex functions rather than the more commonly utilized bounded but non-analytic functions. These functions provide attractive alternatives for performing independent component analysis (ICA) by efficiently generating higher-order statistical information. Using this framework, a new class of efficient algorithms are derived for performing ICA in the complex domain, in particular, for studying brain function using the medical imaging data in its native, complex form.
Title: Canonical Dependence Analysis for Multi-modal Data Fusion and Source Separation
Funding Source: NSF: 1016619
PI: Calhoun
Abstract:
In this proposal, the main aim is twofold. First, a number of powerful methods are developed for multi-subject (multi-set) data analysis and multi-modal data fusion based on canonical dependence analysis by significantly extending the power and flexibility of MCCA. Then, the successful application of the methods are demonstrated on a unique problem that demands these properties, namely the study of brain function and functional associations during simulated driving, a naturalistic task where data-driven methods have proven very useful. The data used in the project are complementary in nature but of very different nature: functional magnetic resonance imaging (fMRI), electroencephalography (EEG), structural MRI (sMRI), genetic array data--single nucleotide polymorphism (SNP)--and behavioral variables. The rich characteristics of the data and the problem at hand thus provide a special challenge for the methods developed and a unique testbed for the evaluation of their performance.
|
