Statistical parametric mapping : the analysis of functional brain images / / editors, Karl Friston [et al.]

• Record Nr.: UNINA9910784658203321
• Pubbl/distr/stampa: London : , : Academic, , 2007
• ISBN: 1-280-72899-X; 9786610728992; 0-08-046650-8
• Descrizione fisica: 1 online resource (689 pages) : illustrations
• Altri autori (Persone): FristonK. J (Karl J.)
• Disciplina: 611.810222
• Soggetti: Brain - Imaging - Mathematical models; Brain mapping; Statistics - Graphic methods
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Front Cover; Statistical Parametric Mapping; Copyright Page; Table of Contents; Acknowledgements; Part 1 Introduction; Chapter 1 A short history of SPM; INTRODUCTION; THE PET YEARS; THE fMRI YEARS; THE MEG-EEG YEARS; REFERENCES; Chapter 2 Statistical parametric mapping; INTRODUCTION; SPATIAL TRANSFORMS AND COMPUTATIONAL ANATOMY; STATISTICAL PARAMETRIC MAPPING AND THE GENERAL LINEAR MODEL; TOPOLOGICAL INFERENCE AND THE THEORY OF RANDOM FIELDS; EXPERIMENTAL AND MODEL DESIGN; INFERENCE IN HIERARCHICAL MODELS; CONCLUSION; REFERENCES; Chapter 3 Modelling brain responses; INTRODUCTION; ANATOMICAL MODELS; STATISTICAL MODELS; MODELS OF FUNCTIONAL INTEGRATION; CONCLUSION; REFERENCES; Part 2 Computational anatomy; Chapter 4 Rigid Body Registration; INTRODUCTION; RE-SAMPLING IMAGES; RIGID BODY TRANSFORMATIONS; WITHIN-MODALITY RIGID REGISTRATION; BETWEEN-MODALITY RIGID REGISTRATION; REFERENCES; Chapter 5 Non-linear Registration; INTRODUCTION; OBJECTIVE FUNCTIONS; LARGE DEFORMATION APPROACHES; ESTIMATING THE MAPPINGS; SPATIAL NORMALIZATION IN THE SPM SOFTWARE; EVALUATION STRATEGIES; REFERENCES; Chapter 6 ; Segmentation; INTRODUCTION; THE OBJECTIVE FUNCTION; OPTIMIZATION; REFERENCES; Chapter 7 Voxel-Based Morphometry; INTRODUCTION; PREPARING THE DATA; STATISTICAL MODELLING AND INFERENCE; REFERENCES; Part 3 General linear models; Chapter 8 The General Linear Model; INTRODUCTION; THE GENERAL LINEAR MODEL; INFERENCE; PET AND BASIC MODELS; fMRI MODELS; APPENDIX 8.1 THE AUTOREGRESSIVE MODEL OF ORDER 1 PLUS WHITE NOISE; APPENDIX 8.2 THE SATTERTHWAITE APPROXIMATION; REFERENCES; Chapter 9 Contrasts and Classical Inference; INTRODUCTION; CONSTRUCTING MODELS What should be included in the model?; CONSTRUCTING AND TESTING CONTRASTS; CONSTRUCTING AND TESTING F-CONTRASTS; CORRELATION BETWEEN REGRESSORS; DESIGN COMPLEXITY; SUMMARY; APPENDIX 9.1 NOTATION; APPENDIX 9.2 SUBSPACES; APPENDIX 9.3 ORTHOGONAL PROJECTION; REFERENCES; Chaper 10 Covariance Components; INTRODUCTION; SOME MATHEMATICAL EQUIVALENCES; ESTIMATING COVARIANCE COMPONENTS; CONCLUSION; REFERENCES; Chapter 11 Hierarchical Models; INTRODUCTION; TWO-LEVEL MODELS; PARAMETRIC EMPIRICAL BAYES; NUMERICAL EXAMPLE; BELIEF PROPAGATION; DISCUSSION; REFERENCES; Chapter 12 Random Effects Analysis; INTRODUCTION; RANDOM EFFECTS ANALYSIS; FIXED EFFECTS ANALYSIS; PARAMETRIC EMPIRICAL BAYES; PET DATA EXAMPLE; fMRI DATA EXAMPLE; DISCUSSION; APPENDIX 12.1 EXPECTATIONS AND TRANSFORMATIONS; REFERENCES; Chapter 13 Analysis of Variance; INTRODUCTION; ONE-WAY BETWEEN-SUBJECT ANOVA; ONE-WAY WITHIN-SUBJECT ANOVA; TWO-WAY WITHIN-SUBJECT ANOVAs; GENERALIZATION TO M-WAY ANOVAs; fMRI BASIS FUNCTIONS; DISCUSSION; APPENDIX 13.1 THE KRONECKER PRODUCT; APPENDIX 13.2 WITHIN-SUBJECT MODELS; REFERENCES; Chapter 14 Convolution Models for fMRI; INTRODUCTION; THE HAEMODYNAMIC RESPONSE FUNCTION (HRF); TEMPORAL BASIS FUNCTIONS; TEMPORAL FILTERING AND AUTOCORRELATION; NON-LINEAR CONVOLUTION MODELS; A WORKED EXAMPLE; REFERENCES
• Sommario/riassunto: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to m