# Statistical Techniques for Neuroscientists

## Truong, Y.

1ª Edición Agosto 2016

Inglés

Tapa dura

415 pags

1000 gr

21 x 28 x cm

### ISBN 9781466566149

### Editorial CRC PRESS

Recíbelo en un plazo De 2 a 3 semanas

### 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 fMRIand 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.

**Features**

- Provides practical data analysis and new methods for modeling multichannel spike train data
- Presents spatiotemporal modeling for spike trains, EEG, and fMRI
- Brings together some of the current statistical methods for solving emerging problems in neuroscience
- Uses color throughoutthebook

### 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

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

### Editor(s) Bio

**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.

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