Clinical Applications of Magnetoencephalography 1st edition by Shozo Tobimatsu, Ryusuke Kakigi – Ebook PDF Instant Download/Delivery: 4431557296, 9784431557296
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ISBN 10: 4431557296
ISBN 13: 9784431557296
Author: Shozo Tobimatsu, Ryusuke Kakigi
This book presents an overview of the recent advances in clinical applications of magnetoencephalography (MEG). With the expansion of MEG to neuroscience, its clinical applications have also been actively pursued. Featuring contributions from prominent experts in the fields, the book focuses on the current status of the application of MEG, not only to each nervous system but also to various diseases such as epilepsy, neurological disorders, and psychiatric disorders, while also examining the feasibility of using MEG for these diseases. Clinical Applications of Magnetoencephalography offers an indispensable resource for neurologists, neurosurgeons, pediatricians, and psychiatrists, as well as researchers in the field of neuroscience.
Clinical Applications of Magnetoencephalography 1st Table of contents:
Part I: Introduction
1. Principles of Magnetoencephalography
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Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields generated by neuronal activity in the brain.
1.1 Principles and Computational Analysis
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Origin of Magnetic Fluxes Detected by Neuromagnetometers: MEG detects the magnetic fields generated by synchronized neural activity, typically from pyramidal neurons.
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Pick-Up Coils: Sensors used to detect the magnetic fields.
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Sensor Array: A collection of sensors that detect magnetic activity over various regions of the brain.
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Signal Processing and Programming: Methods for analyzing the data captured by the sensors, involving computational techniques to extract meaningful signals from raw data.
1.2 Forward Solution
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Sarvas Formula: Describes the relationship between current distribution in the brain and the magnetic fields measured by the sensors.
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Lead Field Matrix: A matrix that relates the neuronal current sources to the recorded magnetic field.
1.3 Inverse Problem
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The inverse problem involves estimating the sources of neural activity from the measured magnetic fields.
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Equivalent Current Dipole (ECD) Estimation: A method for estimating the location and strength of neural sources.
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Minimum Norm Estimation (MNE): A technique for solving the inverse problem by minimizing the discrepancy between observed data and model predictions.
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What is Norm?: A mathematical function that measures the size of a solution.
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Inverse Matrix: Used in solving the inverse problem by reversing the forward model.
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Minimum L2 Norm Estimation: A specific approach to minimize the error in solutions.
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Minimum L1 Norm Estimation: Another variation aiming for sparsity in the solution.
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Adaptive Beam Former: A method to localize sources of activity in noisy data.
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1.4 Limitations of Inverse Solution: Challenges include the non-uniqueness of solutions and the complexity of accurately modeling neural activity.
Part II: Motor System
2. Basic Functions and Clinical Applications
2.1 Basic Principles and Physiology
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Movement and Posture: Central to motor control, including voluntary and involuntary movements.
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Voluntary vs. Involuntary Movements: Differentiating between movements under conscious control and reflexive or automatic actions.
2.2 Clinical Applications
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Investigation of Movement Control: How the brain coordinates movement and posture.
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Generator Sources for Involuntary Movements: Identifying neural sources of spontaneous or pathological movements.
2.3 Principal Analysis Methods
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Recording and Analysis of Self-Paced Movement: Techniques to analyze brain activity related to voluntary movements.
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Trigger Point and Averaging Window: Key parameters for analyzing self-paced movements.
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Source Analysis and Spatial Filters: Used to isolate specific brain regions involved in motor control.
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2.4 Advanced Techniques
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Event-Related Desynchronization/Synchronization (ERD/ERS): Monitoring brain activity changes related to motor tasks.
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Corticomuscular Coherence (CMC) and Corticokinematic Coherence (CKC): Studying the coupling between brain signals and muscle activity.
Part III: Somatosensory System
3. Basic Function
3.1 The Tactile System
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A-Beta Mechanoreceptor Signals: Involved in detecting touch and pressure.
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Bilateral SII Regions: Important areas of the brain for processing sensory input.
3.2 Pain and Itch Systems
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Processing of Nociceptor Signals: Involves the sensory pathways for pain detection.
Part IV: Auditory System
5. Basic Function
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The auditory system is essential for processing sound stimuli, including different paradigms such as repetition and oddball tasks to understand auditory responses.
Part VI: Epilepsy
9. Childhood Epilepsy
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Methods: Using MEG to study epilepsy in children, focusing on short-time Fourier transforms and dynamic statistical parametric mapping (dSPM).
10. Adult Epilepsy
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Temporal Lobe Epilepsy (TLE): Specific patterns observed in the MEG for adult epileptic patients.
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Ictal MEG: Capturing brain activity during seizures to help with diagnosis and presurgical planning.
Part VII: Neurological Disorders
11. Cerebrovascular Diseases
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Cerebral Ischemia Diagnosis: MEG is used to detect ischemic events and assess functional recovery.
12. Neurodegenerative Disorders
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Parkinson’s Disease, ALS, Multiple Sclerosis: MEG can aid in understanding pathophysiological changes in these diseases.
Part VIII: Psychiatric Disorders
13. Autism Spectrum Disorder (ASD)
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MEG studies help in understanding sensory processing and cognitive functioning in ASD, including altered oscillatory activity.
14. Schizophrenia
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Auditory Event-Related Responses: Schizophrenia patients show altered neural responses to auditory stimuli, which can be detected with MEG.
Part IX: Cognition and Brain-Machine Interface
16. ECoG-Based BCI for BCI-MEG Research
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Electrocorticography (ECoG) and MEG Integration: This combination helps in understanding brain-machine interfaces for motor control, including robot arms and humanoid movements.
17. Oscillation and Cross-Frequency Coupling
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Cerebral Oscillation: Studying the changes in neural oscillations for various brain functions like motor and language processing.
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Shozo Tobimatsu,Ryusuke Kakigi,Clinical Applications,Magnetoencephalography