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Research Methods for Cognitive Neuroscience

by Aaron Newman SAGE Publications Ltd
Pub Date:
Pbk 640 pages
AU$126.00 NZ$132.17
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This fresh, new textbook provides a thorough and student-friendly guide to the different techniques used in cognitive neuroscience. Given the breadth of neuroimaging techniques available today, this text is invaluable, serving as an approachable text for students, researchers, and writers. This text provides the right level of detail for those who wish to understand the basics of neuroimaging and also provides more advanced material in order to learn further about particular techniques. With a conversational, student-friendly writing style, Aaron Newman introduces the key principles of neuroimaging techniques, the relevant theory and the recent changes in the field.

PART 1: Introductory and Background Material
CHAPTER 1: Introduction
CHAPTER 1.1: The Organization of the Brain
CHAPTER 2: Fundamentals of Experimental Design (optional background material
CHAPTER 2.1: Experimental Control
CHAPTER 2.2: Basic Experimental Designs
CHAPTER 2.2.1: Additive Factors Designs
CHAPTER 2.2.2: Subtractive Designs
CHAPTER 2.2.3: Factorial Designs
CHAPTER 2.2.4: Correlational Designs
CHAPTER 2.3: Research Ethics
CHAPTER 3: Behavioural and Psychophysical Methods (optional background material)
CHAPTER 3.1: Reaction Time Studies
CHAPTER 3.2: Accuracy
CHAPTER 3.3: Psychophysics
CHAPTER 3.4: Eye tracking
PART 2: Methods
CHAPTER 4: Electroencephalography (EEG) and Event-Related Potentials (ERP)
CHAPTER 4.1: What are we measuring?
CHAPTER 4.1.1: What are we measuring?
CHAPTER 4.2: How do we measure it?
CHAPTER 4.2.1: .Data Acquisition
CHAPTER 4.2.2: Filtering
CHAPTER 4.3: Common Experimental Designs
CHAPTER 4.4: Source Localization
CHAPTER 4.5: ERP Components
CHAPTER 4.5.1: Wavelet Analysis
CHAPTER 4.6: EEG Coherence
CHAPTER 5: Magnetoencephalography (MEG)
CHAPTER 5.1: What are we measuring?
CHAPTER 5.2: How do we measure it?
CHAPTER 5.2.1:: Data Acquisition
CHAPTER 5.3: Common Experimental Designs
CHAPTER 5.4: Source Localization
CHAPTER 6: MRI — Basics
CHAPTER 6.1: What are we measuring? Quantum Physics and Other Things You Never Thought You'd Study
CHAPTER 6.2: How do we measure it?
CHAPTER 6.2.1: The Physics of MRI
CHAPTER 6.2.2: 3-Dimensional Encoding of MRI Images
CHAPTER 6.2.3: MRI Pulse Sequences
CHAPTER 7: Functional MRI
CHAPTER 7.1: What are we measuring?
CHAPTER 7.1.1: The BOLD Signal and its Physiological Basis
CHAPTER 7.2: Basic fMRI Experimental Designs
CHAPTER 7.2.1: Block designs
CHAPTER 7.2.2: Event-Related Designs
CHAPTER 7.3: The Baseline and Problems of Interpretation
CHAPTER 7.4: Functional Connectivity (fcMRI)
CHAPTER 7.4.1: Resting State fMRI (rsMRI)
CHAPTER 8: Structural MRI Methods
CHAPTER 8.1: Diffusion MRI Tractography
CHAPTER 8.1.1: What are we measuring?
CHAPTER 8.1.2: How do we measure it?
CHAPTER 8.1.3: Common Experimental Designs
CHAPTER 8.1.4: Applications
CHAPTER 8.2: Volumetric MRI and Voxel-Based Morphometry
CHAPTER 8.2.1: What are we measuring?
CHAPTER 8.2.2: Common Experimental Designs
CHAPTER 8.2.3: Applications
CHAPTER 9: Noninvasive Neuro-Pharmacology
CHAPTER 9.1: Positron Emission Tomography (PET
CHAPTER 9.1.1: What are we measuring?
CHAPTER 9.1.2: How do we measure it?
CHAPTER 9.1.3: Radiation Safety
CHAPTER 9.1.4: Common Experimental Designs
CHAPTER 9.1.5: Applications
CHAPTER 9.2:Pharmaco-MRI (phMRI)
CHAPTER 9.2.1: What are we measuring?
CHAPTER 9.2.2: How do we measure it?
CHAPTER 9.2.3: Common Experimental Designs
CHAPTER 9.2.4: Applications
CHAPTER 10: Noninvasive Optical Imaging
CHAPTER 10.1: What are we measuring?
CHAPTER 10.2: How do we measure it?
CHAPTER 10.3: Source Localization
CHAPTER 10.4: Common Experimental Designs
CHAPTER 11: Intraoperative Recording
CHAPTER 11.1: Electrical recordings
CHAPTER 11.1.1: What are we measuring?
CHAPTER 11.1.2: How do we measure it?
CHAPTER 11.1.3: Common Experimental Designs
CHAPTER 11.2: Intraoperative Optical Imaging of Intrinsic Signals (OIS)
CHAPTER 11.2.1: What are we measuring?
CHAPTER 11.2.2: How do we measure it?
CHAPTER 11.2.3: Common Experimental Designs
CHAPTER 12: Brain Stimulation
CHAPTER 12.1: Intraoperative Stimulation
CHAPTER 12.1.1: What are we measuring?
CHAPTER 12.1.2: How do we measure it?
CHAPTER 12.1.3: Common Experimental Designs
CHAPTER 12.2: Deep Brain Stimulation
CHAPTER 12.3: Transcranial Magnetic Stimulation (TMS)
CHAPTER 12.3.1: How Does It Work?
CHAPTER 12.3.2: Common Experimental Designs
CHAPTER 12.3.3: TMS for Neuro-Rehabilitation
CHAPTER 12.4: Transcranial Direct Current Stimulation (TDCS)
CHAPTER 12.4.1: How Does It Work?
CHAPTER 12.4.2: Common Experimental Designs
CHAPTER 12.4.3: TDCS for Neuro-Rehabilitation
CHAPTER 13: Neural Network Modelling (Optional Chapter)
CHAPTER 13.1: Network Models of Cognitive Processes
CHAPTER 13.2: Realistic Neural
CHAPTER 14: Neuroimaging and Genetics (Optional Chapter)
CHAPTER 14.1: What are we measuring
CHAPTER 14.2: Common Experimental Designs
CHAPTER 14.3: Current limitations of genetic approaches in neuroimaging
PART 3: Applications
CHAPTER 15: Brain Reading and Brain-Computer Interfaces (BCI)
CHAPTER 15.1: Brain Reading
CHAPTER 15.2: Basic Principles of BCIs
CHAPTER 15.3: Assistive BCIs
CHAPTER 15.4: Neuro-Marketing
CHAPTER 15.5: Deception Detection
CHAPTER 16: Working With Special Populations
CHAPTER 16.1: Children
CHAPTER 16.2: Older Adults
CHAPTER 16.2: Older Adults
CHAPTER 17: Neuroethics
CHAPTER 17.1: Special Considerations for Research Ethics in Neuroimaging
CHAPTER 17.2: Can We Read People's Minds?
CHAPTER 17.3: Can We Control People's Minds?

Overall, this is an outstanding textbook for several reasons.

First, the author has an elegant and yet simple writing. Second, the comprehensive scope of this textbook is simply amazing, such is the complexity of each of the research techniques in focus (i.e., it is extremely difficult to handle all those with expertise). Third, the book structure presents a logic increment in complexity. Aaron Newman integrates seemingly “hard to grasp” computations in a concise style, while providing unique suggestions on experimental design features and data acquisition and analysis. Such methodological concerns tapping the reader’s hands-on-skills are highly meritorious and should appeal to most researchers in the field.

Finally, the textbook layout is impeccable. The “box inlets” at the beginning of each chapter (learning objectives) are quite useful since they help the reader throughout the chapter, eventually pinpointing contents that may merit further study. In addition, the broad summary and “further readings” suggestions at the end of each chapter help to provide awareness of the learning process. A stylish artwork and excellent figures both contribute to top up this edition.

I believe this textbook will soon become an authoritative resource for both graduate students and experienced researchers in the field.

Aaron Newman is a Professor at Dalhousie University, Canada.