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Statistical Techniques for Neuroscientists




ISBN 9781466566149
Published August 12, 2016 by CRC Press
415 Pages - 61 B/W Illustrations

 
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Book Description

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.

Table of Contents

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

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Editor(s)

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.