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