Study of Brain Function

GIFT The brain is an incredibly complex organ of interrelated structural and functional connectivity. We use exploratory methods such as independent component analysis (ICA) to try to understand the brain function in both the healthy and the diseased brain.

Our work is in collaboration with
  • The MIND Institute, Albuquerque, NM
  • Hartford Hospital, Hartford, CT
  • F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Center, Baltimore, MD
  • Johns Hopkins University School of Medicine, Baltimore, MD
  • University of Maryland, Baltimore, Medical School and the VA Hospital, Baltimore, MD
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    References for introduction to the area:

    • V. D. Calhoun and T. Adali, Unmixing fMRI with independent component analysis, IEEE Engineering in Medicine and Biology Magazine, vol. 25, no 2, pp. 79–90, March/April 2006.
      • In this overview article, we present an overview of the application of ICA to fMRI data analysis and discuss current directions and challenges.

    • V. D. Calhoun and T. Adali, "Feature-based fusion of medical imaging data," IEEE Trans. Info. Tech. and Biomedicine, in press.
      • We present an overview of feature-based approaches for fusion of medical data with emphasis on an approach that uses independent component analysis for the task.

    • Functional MRI: Our Window onto the Brain, Theme Issue, IEEE Engineering in Medicine and Biology Magazine, T. Adali, and V. D. Calhoun (editors), vol. 25, no. 2, March/April 2006.
      • This theme issue of the EMB magazine we have edited presents an overview of functional MRI data and the techniques for its analysis.

    Active Projects:

      A Unified Framework for Flexible Brain Image Analysis
      Funded by NIH-NIBIB (Grant Number: R01 EB 000840)
      Independent component analysis has found a fruitful application in the analysis of brain imaging data, in particular for functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. This project focuses on the development of data-driven methods for analysis of fMRI data by incorporating prior information into the estimation.

      CRCNS: Informed Data-Driven Fusion of Behavior, Brain Function, and Genes
      Funded by NIH-NIBIB (Grant Number: R01 EB 005846)
      As imaging scanners improve and are able to collect images faster, more studies collecting multiple types of imaging information from the same participants are being used to study connectivity in healthy controls and pathological states. However, each existing modality for imaging the living brain can only report upon a limited domain. The goal of the second project CRCNS: Collaborative Research: Spatiotemporal Fusion of fMRI, EEG, and Genetic Data is to examine associations among fMRI, EEG, and genetic variations related to healthy and abnormal brain function. As part of this project, we develop a set of tools based on ICA that can effectively fuse the information provided by multiple imaging modalities to span a vast range of spatial and temporal scales.

      Collaborative Research: SEI: Independent Component Analysis of Complex-Valued Brain Imaging Data
      Funded by NSF-IIS (Award no: 0612076)
      We 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.

    Project team:

    Selected recent publications:

    • N. Correa, Y.-O. Li, T. Adali, and V. D. Calhoun, "Canonical correlation analysis for feature-based fusion of biomedical imaging modalities and its application to detection of associative networks in schizophrenia ," IEEE Journal of Selected Topics in Signal Processing, Special Issue on: fMRI Analysis for Human Brain Mapping, vol. 2, no. 6, pp. 998-1007, Dec. 2008.
      • We introduce a data fusion scheme at the feature level using canonical correlation analysis (CCA) to determine inter-subject covariations across modalities. We show both with simulation results and application to two real datasets (an fMRI and EEG, and an fMRI and sMRI dataset, both collected from patients diagnosed with schizophrenia and healthy controls) that multimodal CCA (mCCA) proves to be a flexible and powerful method for discovering associations among various data types.
    • Y.-O. Li, T. Adali, and V. D. Calhoun, "Estimating the number of independent components for fMRI data," Human Brain Mapping, vol. 28, pp. 1251-66, 2007.
      • This work proposes an i.i.d sampling scheme based on an entropy rate measure to correct sample dependence effect on order selection by information theoretical criteria. The method is applied to the true fMRI data and the results are validated by studying the stability of the subsequent independent component analysis on the fMRI data. The order selection scheme is implemented in the Group ICA of fMRI Toolbox (GIFT).
    • N. Correa, T. Adali, and V. D. Calhoun, "Performance of blind source separation algorithms for fMRI analysis using a group ICA method," Magnetic Resonance Imaging, vol. 25, no. 5, pp. 684-694, June 2007.
      • We study the performance of four major classes of algorithms for spatial ICA, namely information maximization, maximization of non-gaussianity, joint diagonalization of cross-cumulant matrices, and second-order correlation based methods when they are applied to fMRI data from subjects performing a visio-motor task. We use a group ICA technique implemented in the GIFT toolbox to study variability among different algorithms and propose analysis techniques to evaluate their performance.
    • Y.-O. Li, T. Adali, and V. Calhoun, "Feature-selective ICA and its convergence properties," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Philadelphia, PA, March 2005, (best student paper award) .
      • We introduce a feature-selective mechanism consisting of a linear filtering to enhance the feature in the sample space of the independent components and a least squares projection to impose the feature enhancement effect onto the estimated demixing vector(s). We prove that the convergence rate of the demixing vector(s) is increased when the feature-selective mechanism is applied.

    Resources:

    • Group ICA Toolbox (Includes GIFT and EEGIFT): GIFT is a Matlab toolbox which implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) functional magnetic resonance imaging data and electro encephalogram data.

    • Fusion ICA Toolbox (FIT): FIT is a MATLAB toolbox which implements the joint ICA and parallel ICA methods. It is used to examine the shared information between the features (SPM contrast image, EEG signal or SNP data).

    • Simulated fMRI dataset: We generate a simulated fMRI-like set of components and mix them with a set of time courses to obtain a simulated fMRI-like dataset.