Atjaunināt sīkdatņu piekrišanu

E-grāmata: MEG-EEG Primer

(Eleanor Cox Riggs Professor, Department of Psychological and Brain Sciences, Indiana University), (Professor Emerita of Systems Neuroscience and Neuroimaging, Department of Art, Aalto University)
  • Formāts: 304 pages
  • Izdošanas datums: 28-Mar-2017
  • Izdevniecība: Oxford University Press Inc
  • Valoda: eng
  • ISBN-13: 9780190497781
  • Formāts - PDF+DRM
  • Cena: 83,27 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: 304 pages
  • Izdošanas datums: 28-Mar-2017
  • Izdevniecība: Oxford University Press Inc
  • Valoda: eng
  • ISBN-13: 9780190497781

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

Magnetoencephalography (MEG) and electroencephalography (EEG) provide complementary views to the neurodynamics of healthy and diseased human brains. Both methods are totally noninvasive and can track with millisecond temporal resolution spontaneous brain activity, evoked responses to various sensory stimuli, as well as signals associated with the performance of motor, cognitive and affective tasks.

MEG records the magnetic fields, and EEG the potentials associated with the same neuronal currents, which however are differentially weighted due to the physical and physiological differences between the methods. MEG is rather selective to activity in the walls of cortical folds, whereas EEG senses currents from the cortex (and brain) more widely, making it harder to pinpoint the locations of the source currents in the brain. Another important difference between the methods is that skull and scalp dampen and smear EEG signals, but do not affect MEG. Hence, to fully understand brain function, information from MEG and EEG should be combined. Additionally, the excellent neurodynamical information these two methods provide can be merged with data from other brain-imaging methods, especially functional magnetic resonance imaging where spatial resolution is a major strength.

MEG-EEG Primer is the first-ever volume to introduce and discuss MEG and EEG in a balanced manner side-by-side, starting from their physical and physiological bases and then advancing to methods of data acquisition, analysis, visualization, and interpretation. The authors pay special attention to careful experimentation, guiding readers to differentiate brain signals from various artifacts and to assure that the collected data are reliable. The book weighs the strengths and weaknesses of MEG and EEG relative to one another and to other methods used in systems, cognitive, and social neuroscience. The authors also discuss the role of MEG and EEG in the assessment of brain function in various clinical disorders. The book aims to bring members of multidisciplinary research teams onto equal footing so that they can contribute to different aspects of MEG and EEG research and to be able to participate in future developments in the field.

Recenzijas

This Primer fulfills a gap in the human neurophysiology literature where no book exists dealing with MEG and EEG in equal terms. This is important since both methodologies are, in essence, complementary and jointly can be used to answer specific scientific questions with respect to a variety of brain functions. Special attention is paid to comparisons of findings obtained using both MEG and EEG modalities, what can yield important new insights, particularly in the growing field of cognitive neuroscience. Written in an appealing style, this Primer embraces the whole field of human neurophysiology." -Fernando Lopes Da Silva, MD, PhD, Emeritus Professor, Center of Neuroscience, Swammerdam Institute for Life Sciences, Amsterdam, The Netherlands

Preface xiii
About the Authors xv
Preamble xvii
Section 1
1 Introduction
3(10)
MEG and EEG Set-Ups
3(3)
Comparison of MEG and EEG
6(5)
Structure of This Primer
11(1)
References
12(1)
2 Insights into the Human Brain
13(12)
Overview of the Human Brain
13(1)
How to Obtain Information about Brain Function
14(1)
Timing in Human Behavior
14(1)
Functional Structure of the Human Cerebral Cortex
15(2)
Sample Exercise
16(1)
Communication Between Brain Areas
17(1)
Thalamocortical Connections
17(1)
Intrabrain Connectivity
17(1)
Sample Exercise
18(1)
Electric Signaling in Neurons
18(5)
Membrane Potentials
19(2)
Action Potentials
21(1)
Postsynaptic Potentials
22(1)
References
23(2)
3 Basic Physics and Physiology of MEG and EEG
25(13)
An Overview of MEG and EEG Signal Generation
25(1)
Charges and Electric Current
25(2)
Ohm's and Kirchoff's Laws
27(1)
Relationship between Current and Magnetic Field
28(1)
Superconductivity
29(1)
Inverse Problem
30(1)
Source Currents
31(4)
Primary Current
31(1)
Layers, Open Fields, and Closed Fields
32(1)
Intracortical Cancellation
33(1)
Volume Conduction
33(2)
Spherical Head Model
35(1)
Some General Points about Source Localization
35(2)
References
37(1)
4 An Overview of EEG and MEG
38(9)
Historical Aspects
38(1)
Early EEG Recordings
38(2)
Early MEG Recordings
40(1)
Brain Rhythms
41(2)
Evoked and Event-Related Responses
43(1)
References
44(3)
Section 2
5 Instrumentation for MEG and EEG
47(30)
EEG Instrumentation
47(14)
Electrodes
47(2)
EEG Amplifiers
49(1)
Differential Amplifiers, Common Mode Rejection, and Amplifier Input Impedances
49(2)
Standard Electrode Positions
51(3)
Effects of Reference Electrode on Potential Distribution
54(5)
Re-Referencing Relative to an Average Reference
59(2)
MEG Instrumentation
61(3)
SQUIDS and SQUID Electronics
61(2)
Flux Transformers and Their Configuration
63(1)
Shielding
64(1)
Other Ways to Maintain a Noise-Free Environment
65(1)
Stimulators and Monitoring Devices
66(6)
Auditory Stimulators
66(1)
Visual Stimulators
67(1)
Somatosensory Stimulators
68(2)
Stimulators for Inducing Acute Pain
70(1)
Passive-Movement Stimulators
71(1)
Monitoring Devices
72(1)
Future Developments of EEG and MEG Instrumentation
72(2)
Developments in EEG
72(1)
Developments in MEG
73(1)
References
74(3)
6 Practicalities of Data Collection
77(12)
General Principles of Good Experimentation
77(1)
Replicability Checks
78(1)
EEG Recordings: The Practice
78(3)
Electrodes, Skin Preparation, and Electrode-Impedance Measurement
78(2)
Postrecording Infection Control
80(1)
MEG Recordings: The Practice
81(2)
Measurement of MEG Sensor and EEG Electrode Positions
83(3)
Electrical Safety
86(1)
References
87(2)
7 Data Acquisition and Preprocessing
89(9)
Filtering
89(6)
Data Sampling Rate
95(2)
References
97(1)
8 Artifacts
98(30)
Introduction
98(1)
Artifact-Removal Methods
99(4)
Blind Source Separation and Independent-Component Analysis
99(2)
Signal-Space Projection and Temporal Signal-Space Separation for MEG
101(2)
Eye-Related Artifacts
103(9)
Eye Movements and Eye Blinks
103(4)
Saccades and Microsaccades
107(2)
Electroretinogram and Magnetoretinogram
109(1)
Removal
109(3)
Muscle Artifacts
112(4)
Generation and Recognition
112(3)
Removal
115(1)
Cardiac Artifacts
116(4)
Generation and Recognition
116(3)
Removal
119(1)
Respiration-Related Artifacts
120(1)
Generation and Recognition
120(1)
Removal
120(1)
Sweating
120(1)
Generation and Recognition
120(1)
Removal
120(1)
Nonphysiological Artifacts
120(5)
Power-Line Noise and Its Removal
121(2)
Response-Box Artifacts
123(1)
EEG Electrode- and MEG Sensor-Related Artifacts
123(1)
How to Ensure the Signals Come from the Brain
124(1)
References
125(3)
9 Analyzing the Data
128(37)
Introduction
128(1)
Data Inspection and Preprocessing
128(1)
Signal Averaging of MEG/EEG Data
129(4)
Signal-to-Noise Considerations
129(1)
Segmentation
130(1)
Amplitude and Latency Measures
130(1)
Across-Group Averaging and Assessment of Group Differences
131(1)
Topographic Maps of EEG and MEG Activity
132(1)
Whole-Head Statistical Analysis of EEG Data
133(1)
Analysis of Spontaneous Activity and Single-Trial Data
133(12)
Overview
133(2)
Evoked versus Induced Activity
135(1)
MEG/EEG Signal Level and Power
135(1)
Event-Related Desynchronization/Synchronization and Temporal Spectral Evolution
136(1)
Time-Frequency Analyses
137(2)
Phase Resetting and Models of Evoked Activity
139(1)
Coherence and Other Measures of Association
140(1)
Some Issues with Coherence Calculations
141(1)
Cross-Frequency Coupling
141(3)
Global Field Power, Dissimilarity, and Brain Microstates
144(1)
Source Modeling
145(11)
Forward and Inverse Problems in MEG and EEG
145(1)
Head Models
146(1)
Single-Dipole Model and Model Validity
146(1)
Goodness-of-Fit and Confidence Limits of the Model
147(3)
Spatial Resolution
150(1)
Source Extent
150(1)
Multidipole Models, Distributed Models, and Beamformers
151(3)
Hypothesis Testing with Predetermined Source Locations
154(1)
Effect of Synchrony
154(1)
Changes in Orientation/Tilting
154(1)
Assessments of Effective Connectivity
154(2)
Common Pitfalls in Data Analysis and Interpretation
156(1)
References
157(8)
Section 3
10 Brain Rhythms
165(24)
Introduction
165(2)
Alpha Rhythm of the Posterior Cortex
167(3)
Mu Rhythm of the Sensorimotor Cortex
170(2)
Tau Rhythm of the Auditory Cortex
172(2)
Beta Rhythms
174(1)
Theta Rhythms
175(1)
Gamma Rhythms
175(2)
Delta-Band Activity and Ultra-Slow Oscillations
177(2)
Coupling between Different Brain Rhythms
179(1)
Changes in Brain Rhythms During Sleep
179(2)
Effects of Anesthesia and Other Drugs on EEG/MEG
181(3)
References
184(5)
11 Evoked and Event-Related Responses
189(11)
Introduction
189(1)
An Initial Example
190(3)
Nomenclature of Evoked Responses and Brain Rhythms
193(2)
Effects of Interstimulus Interval and Stimulus Timing
195(2)
Effects of Other Stimulus Parameters
197(1)
References
197(3)
12 Auditory Responses
200(14)
Aspects of Auditory Stimulation
200(1)
Hearing Threshold
200(1)
Stimulus Type, Duration, Temporal Frequency, and Other Characteristics
201(1)
Auditory Brainstem Responses
201(3)
Middle-Latency Auditory-Evoked Responses
204(1)
Long-Latency Auditory-Evoked Responses
205(2)
Auditory Steady-State Responses
207(4)
Frequency Tagging
210(1)
References
211(3)
13 Visual Responses
214(13)
Visual Stimuli
214(2)
Visual Acuity
214(1)
Distance and Visual Angle of the Stimulus
215(1)
Foveal, Parafoveal, and Extrafoveal Stimulation
215(1)
Luminance and Contrast
215(1)
Spatial Frequency
216(1)
Transient Visual Responses
216(6)
Assessing the Ventral Visual Stream
219(2)
Assessing the Dorsal Visual Stream
221(1)
Steady-State Visual Responses
222(2)
References
224(3)
14 Somatosensory Responses
227(15)
Compound Action Potentials and Fields of Peripheral Nerves
227(3)
Responses from the SI Cortex
230(5)
Responses from the Posterior Parietal Cortex
235(1)
Responses from the SII Cortex
235(1)
Somatosensory Steady-State Responses
236(1)
High-Frequency Oscillations in the SI Cortex
236(1)
Pain and Nociceptive Responses
237(1)
References
238(4)
15 Other Sensory Responses and Multisensory Interactions
242(10)
Visceral Responses
242(1)
Olfactory and Gustatory Responses
242(1)
Multisensory Interaction
243(7)
Overview
243(2)
Audiotactile Interaction: An MEG Case Study
245(1)
Multisensory Integration of Human Communication
246(4)
Multisensory Integration Reflected in Spontaneous MEG/EEG Activity
250(1)
References
250(2)
16 Motor Function
252(10)
Movement-Related Readiness Potentials and Fields
252(4)
Coherence Between Brain Activity and Movements/Muscles
256(4)
Overview
256(1)
Cortex-Muscle Coherence
256(1)
Corticokinematic Coherence
256(3)
Corticovocal Coherence
259(1)
References
260(2)
17 Brain Signals Related to Change Detection
262(15)
Introduction
262(1)
Contingent Negative Variation
263(2)
Mismatch Negativity and Mismatch Field
265(2)
P300 Responses
267(2)
N400 Responses
269(3)
Error-Related Negativity
272(1)
References
273(4)
18 The Social Brain
277(17)
Theoretical Framework
277(3)
Responses to Emotions Depicted by Faces and Bodies
280(4)
Action Viewing and Mirroring
284(3)
Hyperscanning
287(2)
Verbal Communication
289(1)
References
290(4)
19 Brain Disorders
294(10)
Introduction
294(1)
Epilepsy
294(3)
Preoperative Mapping
297(3)
Functional Identification of the Central Sulcus
297(1)
Anatomical Identification of the Central Sulcus
298(1)
Hemispheric Dominance for Speech and Language
299(1)
Stroke
300(1)
Critically Ill Patients
301(1)
Coma
301(1)
Brain Death
301(1)
References
301(3)
20 MEG/EEG in the Study of Brain Function
304(7)
Advantages of MEG and EEG
304(1)
Disadvantages of MEG and EEG
305(1)
Combining MEG and EEG
305(1)
Combining MEG/EEG with MRI/fMRI
306(2)
EEG During Noninvasive Brain Stimulation
308(2)
References
310(1)
21 Looking to the Future
311(8)
Decoding of Brain States
311(2)
Traveling Light
313(1)
Data Governance
313(1)
Better Analysis of Behavior
314(1)
How MEG and EEG Can Make an Impact on Neuroscience
314(1)
References
315(4)
Index 319
Riitta Hari MD, PhD researches systems neuroscience and neuroimaging. She has pioneered the use of MEG in the study of the spatiotemporal dynamics of sensory, motor, cognitive and social brain functions both in health and disease. Her background training is in medicine, with specialization in clinical neurophysiology.



Aina Puce, PhD is a social neuroscientist with research interests in the brain bases of human non-verbal communication. Her studies in basic and clinical human neuroscience have used scalp and intracranial EEG, and functional MRI methods. Her formal training was in biophysics and functional brain mapping/neurophysiology.