1st Edition

Statistical Techniques for Neuroscientists

Edited By Young K. Truong, Mechelle M. Lewis Copyright 2016
    445 Pages 61 B/W Illustrations
    by CRC Press

    Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein.

    The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods.

    The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful.

    STATISTICAL ANALYSIS OF NEURAL SPIKE TRAIN DATA

    Statistical Modeling of Neural Spike Train Data
    Ruiwen Zhang, S. Lin, H. Shen, and Y. Truong

    Introduction
    Point Process and Conditional Intensity Function
    The Likelihood Function of a Point Process Model
    Continuous State-Space Model
    M-Files for Simulation
    M-Files for Real Data
    R Code for Real Data

    Regression Spline
    Ruiwen Zhang, S. Lin, H. Shen, and Y. Truong

    Introduction
    Linear Models for the Conditional Log-Intensity Function
    Maximum Likelihood Estimation
    Simulation Studies
    Data Analysis
    Conclusion
    R Code for Real Data Analysis
    R Code for Simulation

    STATISTICAL ANALYSIS OF FMRI DATA

    Hypothesis Testing Approach
    Wenjie Chen, H. Shen, and Y. Truong

    Introduction
    Hypothesis Testing
    Simulation
    Real Data Analysis
    Discussion
    Software: R

    An Efficient Estimate of HRF
    Wenjie Chen, H. Shen, and Y. Truong

    Introduction
    TFE Method: WLS Estimate
    Simulation
    Real Data Analysis
    Software: R

    Independent Component Analysis
    D. Wang, S. Lee, H. Shen, and Y. Truong

    Introduction
    Neuroimaging Data Analysis
    Single Subject ICA and the Group Structure Assumptions
    Homogeneous in Space
    Homogeneous in Both Space and Time
    Homogeneous in Both Space and Time but with Subject-Specific Weights
    Inhomogeneous in Space
    Approaches with Multiple Group Structures
    Software
    Conclusion

    Instantaneous Independent Component Analysis
    A. Kawaguchi and Y. Truong

    Introduction
    Method
    Simulation Study
    Application
    Discussions and Conclusions
    Logspline Density Estimation
    Stochastic EM Algorithm
    Software: R

    Colored Independent Component Analysis
    S. Lee, H. Shen, and Y. Truong

    Introduction
    Colored Independent Component Analysis
    Stationary Time Series Models
    Stationary Colored Source Models
    Maximum Likelihood Estimation
    coloredICA R-package
    Resting State EEG Data Analysis
    Software: M-Files

    Group Blind Source Separation (GBSS)
    D. Wang, H. Shen, and Y. Truong

    Introduction
    Background on ICA and PICS
    Group Parametric Independent Colored Sources (GPICS)
    Simulations
    Real Data Analysis
    Discussions and Conclusions
    Software: M-Files

    Diagnostic Probability Modeling
    A. Kawaguchi

    Introduction
    Methods
    Application
    ROC Analysis
    Summary and Conclusion
    Software Implementation

    Supervised SVD
    A. Halevy and Y. Truong

    Introduction
    Independent Component Analysis (ICA)
    Supervised SVD
    Extension to Time Varying Frequency
    Simulation Studies
    Conclusion
    Software: M-Files

    Appendices:
    A: Discrete Fourier Transform
    B: R Software Package
    C: Matrix Computation
    D: Singular Value Decomposition

    Biography

    Young K. Truong, PhD, is a professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill, USA. He earned his BS in mathematics with Baccalaureate Honors at the University of Washington, Seattle, in 1978 and his MA (1980) and PhD (1985) degrees in statistics from the University of California, Berkeley, USA. He has published extensively, is the recipient of many prestigious awards, and is an often-invited professional speaker and presenter.