The Praeger Handbook of Chiropractic Health Care: Evidence-Based Practices
What does scientific research show about the effectiveness of chiropractic care? How are chiropractors trained and what do they do? When should one turban to chiropractic care, and how does one select a practitioner? This book answers all of these questions and more.

Chiropractic is the most frequently used complementary and alternative medicine (CAM) practice in the United States, with nearly $4 billion spent out-of-pocket annually by chiropractic patients. In fact, as evidence for its effectiveness for common conditions such as back pain continues to mount and acceptance grows in a variety of health care settings, chiropractic could be considered more "mainstream" than many other forms of CAM.

In this information-packed single-volume work, an expert team led by Cheryl Hawk—a well-known chiropractic researcher—explains chiropractic licensure, practice, and effectiveness to general readers researching chiropractic care options and to undergraduate students choosing a major or specialty. Readers will see the range of scientific evidence supporting the use of chiropractic health care for many common conditions, learn about the typical chiropractic clinical encounter and chiropractic procedures, and understand the criteria by which patients and other health professionals can use to select a chiropractic physician. This book also provides health care practitioners in other fields with current information that enables a greater understanding of the training and the roles of chiropractors in health care.

"1144190992"
The Praeger Handbook of Chiropractic Health Care: Evidence-Based Practices
What does scientific research show about the effectiveness of chiropractic care? How are chiropractors trained and what do they do? When should one turban to chiropractic care, and how does one select a practitioner? This book answers all of these questions and more.

Chiropractic is the most frequently used complementary and alternative medicine (CAM) practice in the United States, with nearly $4 billion spent out-of-pocket annually by chiropractic patients. In fact, as evidence for its effectiveness for common conditions such as back pain continues to mount and acceptance grows in a variety of health care settings, chiropractic could be considered more "mainstream" than many other forms of CAM.

In this information-packed single-volume work, an expert team led by Cheryl Hawk—a well-known chiropractic researcher—explains chiropractic licensure, practice, and effectiveness to general readers researching chiropractic care options and to undergraduate students choosing a major or specialty. Readers will see the range of scientific evidence supporting the use of chiropractic health care for many common conditions, learn about the typical chiropractic clinical encounter and chiropractic procedures, and understand the criteria by which patients and other health professionals can use to select a chiropractic physician. This book also provides health care practitioners in other fields with current information that enables a greater understanding of the training and the roles of chiropractors in health care.

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The Praeger Handbook of Chiropractic Health Care: Evidence-Based Practices

The Praeger Handbook of Chiropractic Health Care: Evidence-Based Practices

The Praeger Handbook of Chiropractic Health Care: Evidence-Based Practices

The Praeger Handbook of Chiropractic Health Care: Evidence-Based Practices

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Overview

What does scientific research show about the effectiveness of chiropractic care? How are chiropractors trained and what do they do? When should one turban to chiropractic care, and how does one select a practitioner? This book answers all of these questions and more.

Chiropractic is the most frequently used complementary and alternative medicine (CAM) practice in the United States, with nearly $4 billion spent out-of-pocket annually by chiropractic patients. In fact, as evidence for its effectiveness for common conditions such as back pain continues to mount and acceptance grows in a variety of health care settings, chiropractic could be considered more "mainstream" than many other forms of CAM.

In this information-packed single-volume work, an expert team led by Cheryl Hawk—a well-known chiropractic researcher—explains chiropractic licensure, practice, and effectiveness to general readers researching chiropractic care options and to undergraduate students choosing a major or specialty. Readers will see the range of scientific evidence supporting the use of chiropractic health care for many common conditions, learn about the typical chiropractic clinical encounter and chiropractic procedures, and understand the criteria by which patients and other health professionals can use to select a chiropractic physician. This book also provides health care practitioners in other fields with current information that enables a greater understanding of the training and the roles of chiropractors in health care.


Product Details

ISBN-13: 9781440837463
Publisher: Bloomsbury Academic
Publication date: 03/20/2017
Pages: 344
Product dimensions: 6.14(w) x 9.21(h) x 0.81(d)

About the Author

Cheryl Hawk, DC, PhD, CHES, is author of more than 100 publications in peer-reviewed scientific jourbanals and lead author of the 2013 book Health Promotion and Wellness: An Evidence-Based Guide to Clinical Preventive Services.

Table of Contents

Preface xvii

Editors xxi

Contributors xxiii

List of Figures xxv

List of Tables xxix

Part I Statistical Analysis of Neural Spike Train Data

Chapter 1 Statistical Modeling of Neural Spike Train Data Ruiwen Zhang Shih-Chieh Lin Haipeng Shen Young K. Truong 3

1.1 Introduction 4

1.2 Point Process and Conditional Intensity Function 4

1.3 The Likelihood Function of a Point Process Model 9

1.4 Continuous State-Space Model 10

1.4.1 Kernel Smoothing 11

1.4.2 Adaptive Kernel Smoothing 13

1.4.3 Kernel Bandwidth Optimization 14

1.4.4 Smoothing Splines 15

1.4.5 Real Data Analysis 16

1.5 M-Files for Simulation 20

1.6 M-Files for Real Data 41

1.7 R Files for Real Data 49

Bibliography 51

Chapter 2 Regression Spline Model for Neural Spike Train Data Ruiwen Zhang Shih-Chieh Lin Haipeng Shen Young K. Truong 57

2.1 Introduction 58

2.2 Linear Models for the Conditional Log-Intensity Function 59

2.3 Maximum Likelihood Estimation 60

2.4 Simulation Studies 61

2.4.1 Case 1: Fixed Number of Basis Functions 61

2.4.2 Case 2: Adaptive Knots Selection 64

2.5 Data Analysis 64

2.6 Conclusion 68

2.6.1 A Parametric Model for Interacting Neurons 68

2.7 R Code for Real Data Analysis 73

2.8 R Code for Simulation 83

Bibliography 99

Part II Statistical Analysis of fMRI Data

Chapter 3 A Hypothesis Testing Approach for Brain Activation Wenjie Chen Haipeng Shen Young K. Truong 103

3.1 Introduction 104

3.1.1 Model 106

3.1.2 Ordinary Least Square Estimate 107

3.1.2.1 Window Estimate 108

3.2 Hypothesis Testing 109

3.2.1 Key Concepts 109

3.2.2 Testing the Linearity 110

3.2.3 Testing the Effect from a Specific Stimulus 111

3.2.4 Detecting the Activation 111

3.2.5 Testing the Difference between HRE 112

3.2.6 Remarks 113

3.3 Simulation 115

3.4 Real Data Analysis 118

3.4.1 Auditory Data 118

3.4.2 Event-Related Visual Data 121

3.5 Discussion 128

3.6 Software: R 129

Bibliography 134

Chapter 4 An Efficient Estimate of HRF Wenjie Chen Haipeng Shen Young K. Truong 137

4.1 Introduction 138

4.1.1 Experiment Design for Detecting HRF 138

4.1.2 General Guideline for Estimating HRF 139

4.1.3 The General Linear Model Framework 140

4.1.4 HRF Modeling 141

4.1.4.1 Time-Domain Methods 141

4.1.4.2 Frequency-Domain Methods 142

4.1.4.3 Comparison of the Current Methods 142

4.2 TFE Method: WLS Estimate 143

4.3 Simulation 144

4.3.1 Simulation 1: WLS 145

4.3.2 Simulation 2: Comparison 147

4.4 Real Data Analysis 152

4.5 Software: R 153

Bibliography 211

Chapter 5 Independent Component Analysis: An Overview Dong Wang Seonjoo Lee Haipeng Shen Young K. Truong 215

5.1 Introduction 215

5.2 Neuroimaging Data Analysis 217

5.3 Single-Subject and the Group Structure Assumptions 218

5.4 Homogeneous in Space 219

5.5 Homogeneous in Both Space and Time 221

5.6 Homogeneous in Both Space and Time with Subject-Specific Weights 221

5.7 Inhornogcneous in Space 222

5.8 Approaches with Multiple Group Structures 223

5.9 Software 223

5.10 Conclusion 223

Bibliography 224

Chapter 6 Polynomial Spline Independent Component Analysis with Application to fMRI Data Atsushi Kawaguchi Young K. Truong 227

6.1 Introduction 228

6.2 Method 229

6.3 Simulation Study 231

6.4 Application 233

6.5 Discussions and Conclusions 236

6.6 Logspline Density Estimation 238

6.6.1 Methodology 238

6.6.2 Numerical Results 239

6.7 Stochastic EM Algorithm 243

6.8 Software: R 244

6.8.1 Example.R 244

6.8.2 MLICA.r 247

6.8.3 a24random.r 251

6.8.4 amarimetric.r 254

6.8.5 logsplinederivative. r 255

6.8.6 pdfunction.r 256

6.8.7 UniOrthoMat4multi.r 259

Bibliography 260

Chapter 7 Colored Independent Component Analysis Seonjoo Lee Haipeng Shen Young K. Truong 263

7.1 Introduction 264

7.2 Colored Independent Component Analysis 265

7.3 Stationary Time Series Models 266

7.3.1 White Noise 266

7.3.2 Moving-Average Processes 267

7.3.3 Autoregressive Processes 267

7.3.4 Autoregressive and Moving-Average Processes 268

7.3.5 Harmonic Processes 269

7.4 Stationary Colored Source Models 269

7.5 Maximum Likelihood Estimation 269

7.6 ColoredICA R-Package 270

7.7 Resting-State EEG Data Analysis 271

7.8 M-Files 277

7.8.1 aic unit 277

7.8.2 aic unit2 279

7.8.3 amari distanceW 280

7.8.4 assignspec.m 281

7.8.5 cICA-xarma2 281

7.8.6 calculate AIC.m 286

7.8.7 colorIC A-pmm 286

7.8.8 colorICA-pmm4 292

7.8.9 dexprand.m 296

7.8.10 estimateAR.m 297

7.8.11 getspec2.m 297

7.8.12 model-select-MM 298

7.8.13 specAR.m 300

7.8.14 whiteICA.m 301

Bibliography 303

Chapter 8 Group Blind Source Separation (GBSS) Dong Wang Haipeng Shen Young K. Truong 307

8.1 Introduction 308

8.2 Background on ICA and PICS 310

8.2.1 Preliminaries 310

8.2.2 Independent Component Analysis (ICA) 311

5.2.1 Parametric Independent Colored Sources (PICS) 312

8.3 Group Parametric Independent Colored Sources (GPICS) 312

8.4 Simulations 313

8.4.1 Blind Source Separation 313

8.5 Real Data Analysis 315

5.5 Discussions and Conclusions 316

8.5 M-Files 317

8.7.1 GCICA H10 317

8.7.2 GCICA H01 331

8.7.3 GCICA H00 343

8.7.4 GCICA H11 348

Bibliography 358

Chapter 9 Diagnostic Probability Modeling for Longitudinal Structural Brain MRI Data Analysis Atsushi Kawaguchi 361

9.1 Introduction 362

9.2 Methods 363

9.2.1 Notation and Conceptual Model 363

9.2.2 Spatial Modeling 363

9.2.3 Temporal Modeling 365

9.2.4 Final Model 367

9.3 Application 368

9.4 ROC Analysis 368

9.5 Summary and Conclusion 370

9.6 Software Implementation 371

Bibliography 372

Chapter 10 Supervised SVD of 1TVIRI Data with Time-Varying Frequency Components Avner Halevy Young K. Truong 375

10.1 Introduction 375

10.2 Independent Component Analysis (ICA) 377

10.2.1 Overview of ICA 377

10.2.2 ICA for fMRI 377

10.2.3 Dimension Reduction 378

10.3 Supervised SVD 379

10.3.1 Low Rank Approximation 379

10.3.2 Supervised SVD 379

10.4 Extension to Time-Varying Frequency 380

10.4.1 Supervised SVD with Localized Sinusoids 380

10.4.2 Wavelets 380

10.4.3 Supervised SVD with Wavelets 380

10.5 Simulation Studies 382

10.5.1 Data Description 382

10.5.2 Analysis 382

10.5.3 Results 382

10.6 Conclusion 383

10.7 M-Files 384

Bibliography 394

Appendix A Discrete Fourier Transform 395

A.1 Discrete Fourier Transform (DFT) 395

A.2 Multivariate Normal Distribution 395

A.3 Complex Normal Distribution 396

Bibliography 396

Appendix B The R Software Package 397

B.1 Software Information 397

B.2 Installation 397

B.2.1 Windows Installation 398

B.2.2 Linux-Ubuntu Installation 398

B.2.3 Linux-RHEL/CenlOS Installation 398

B.2.4 Mac OS X Installation 398

B.2.5 Installing RStudio 399

B.2.6 Installing R Packages, Task Views 399

B.3 Documentation 399

B.4 Tutorials 400

Bibliography 402

Appendix C Matrix Computation 403

C.1 Getting Started 403

C.2 Tutorials 404

Bibliography 406

Appendix D Singular Value Decomposition 407

D.1 Singular Value Decomposition (SVD) 407

D.2 Computing SVD 408

D.3 SVD in R 409

Bibliography 409

Index 411

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