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Statistical parametric mapping : the analysis of funtional brain images / 1st ed

Statistical parametric mapping : the analysis of funtional brain images / 1st ed (7회 대출)

자료유형
단행본
개인저자
Friston, K. J. (Karl J.).
서명 / 저자사항
Statistical parametric mapping : the analysis of funtional brain images / edited by Karl Friston ... [et al.].
판사항
1st ed.
발행사항
Amsterdam ;   Boston :   Elsevier/Academic Press,   2007.  
형태사항
vii, 647 p. : ill. (some col.) ; 29 cm.
기타형태 저록
Online version:   Statistical parametric mapping.   1st ed.   Amsterdam ; Boston : Elsevier/Academic Press, 2007   9780080466507   (211009) 000045946664  
ISBN
9780123725608 0123725607
일반주기
Online version: Statistical parametric mapping. 1st ed. Amsterdam ; Boston : Elsevier/Academic Press, 2007 9780080466507
서지주기
Includes bibliographical references and index.
일반주제명
Brain mapping --Statistical methods. Brain --Imaging --Statistical methods.
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245 0 0 ▼a Statistical parametric mapping : ▼b the analysis of funtional brain images / ▼c edited by Karl Friston ... [et al.].
250 ▼a 1st ed.
260 ▼a Amsterdam ; ▼a Boston : ▼b Elsevier/Academic Press, ▼c 2007.
300 ▼a vii, 647 p. : ▼b ill. (some col.) ; ▼c 29 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Brain mapping ▼x Statistical methods.
650 0 ▼a Brain ▼x Imaging ▼x Statistical methods.
700 1 ▼a Friston, K. J. ▼q (Karl J.).
776 0 8 ▼i Online version: ▼t Statistical parametric mapping. ▼b 1st ed. ▼d Amsterdam ; Boston : Elsevier/Academic Press, 2007 ▼z 9780080466507 ▼w (211009) 000045946664
945 ▼a KINS

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고7층/ 청구기호 616.804754 S797 등록번호 111449970 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

Part 1: Introduction

Chapter 1: A short history of SPM

Chapter 2: Statistical parametric mapping

Chapter 3: Modelling brain responses

Part 2: Computational anatomy

Chapter 4: Rigid Body Registration

Chapter 5: Non-linear Registration

Chapter 6: Segmentation

Chapter 7: Voxel-Based Morphometry

Part 3: General linear models

Chapter 8: The General Linear Model

Chapter 9: Contrasts and Classical Inference

Chapter 10: Covariance Components

Chapter 11: Hierarchical Models

Chapter 12: Random Effects Analysis

Chapter 13: Analysis of Variance

Chapter 14: Convolution Models for fMRI

Chapter 15: Efficient Experimental Design for fMRI

Chapter 16: Hierarchical models for EEG and MEG

Part 4: Classical inference

Chapter 17: Parametric procedures

Chapter 18: Random Field Theory

Chapter 19: Topological Inference

Chapter 20: False Discovery Rate procedures

Chapter 21: Non-parametric procedures

Part 5: Bayesian inference

Chapter 22: Empirical Bayes and hierarchical models

Chapter 23: Posterior probability maps

Chapter 24: Variational Bayes

Chapter 25: Spatio-temporal models for fMRI

Chapter 26: Spatio-temporal models for EEG

Part 6: Biophysical models

Chapter 27: Forward models for fMRI

Chapter 28: Forward models for EEG

Chapter 29: Bayesian inversion of EEG models

Chapter 30: Bayesian inversion for induced responses

Chapter 31: Neuronal models of ensemble dynamics

Chapter 32: Neuronal models of energetics

Chapter 33: Neuronal models of EEG and MEG

Chapter 34: Bayesian inversion of dynamic models

Chapter 35: Bayesian model selection and averaging

Part 7: Connectivity

Chapter 36: Functional integration

Chapter 37: Functional connectivity: eigenimages and multivariate analyses

Chapter 38: Effective Connectivity

Chapter 39: Non-linear coupling and kernels

Chapter 40: Multivariate autoregressive models

Chapter 41: Dynamic Causal Models for fMRI

Chapter 42: Dynamic causal models for EEG

Chapter 43: Dynamic Causal Models and Bayesian selection

Appendices

Linear models and inference

Dynamical systems

Expectation maximization

Variational Bayes under the Laplace approximation

Kalman filtering

Random field theory

Index

Color Plates


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