LEADER 05456nam 2200649Ia 450 001 9910784658203321 005 20230329180035.0 010 $a1-280-72899-X 010 $a9786610728992 010 $a0-08-046650-8 035 $a(CKB)1000000000364049 035 $a(EBL)282095 035 $a(OCoLC)437175620 035 $a(SSID)ssj0000310315 035 $a(PQKBManifestationID)11235353 035 $a(PQKBTitleCode)TC0000310315 035 $a(PQKBWorkID)10288635 035 $a(PQKB)10478305 035 $a(Au-PeEL)EBL282095 035 $a(CaPaEBR)ebr10155854 035 $a(CaONFJC)MIL72899 035 $a(MiAaPQ)EBC282095 035 $a(EXLCZ)991000000000364049 100 $a20060905d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aStatistical parametric mapping $ethe analysis of functional brain images /$feditors, Karl Friston [et al.] 210 1$aLondon :$cAcademic,$d2007. 215 $a1 online resource (689 pages) $cillustrations 300 $aDescription based upon print version of record. 311 0 $a1-4933-0095-4 311 0 $a0-12-372560-7 320 $aIncludes bibliographical references and index. 327 $aFront 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 327 $aANATOMICAL 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 327 $aChapter 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 327 $aCORRELATION 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 327 $afMRI 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 330 $aIn 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 606 $aBrain$xImaging$xMathematical models 606 $aBrain mapping 606 $aStatistics$xGraphic methods 615 0$aBrain$xImaging$xMathematical models. 615 0$aBrain mapping. 615 0$aStatistics$xGraphic methods. 676 $a611.810222 701 $aFriston$b K. J$g(Karl J.)$01497187 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784658203321 996 $aStatistical parametric mapping$93722237 997 $aUNINA