PPT-1 st level analysis: Design matrix, contrasts, and inference

Roy Harris amp Caroline Charpentier Outline What is 1st level analysis The Design matrix What are we testing for What do all the black lines mean What do we need to include Contrasts. Reviews for later topics. Model parameterization (. estimability. ). Contrasts (power analysis). Analysis with contrasts. Orthogonal polynomial contrasts. Polynomial goodness-of-fit. Completely Randomized Design. Zurich, February 2012. Statistical Inference. Guillaume Flandin. Wellcome Trust Centre for Neuroimaging. University College London. Normalisation. Statistical Parametric Map. Image time-series. Parameter estimates. Introduction. Course Information. Your instructor: . Hyunseung. (pronounced Hun-Sung). Or HK (not Hong Kong . ). E-mail. : khyuns@wharton.upenn.edu . Lecture:. Time: Mon/Tues/Wed/. Thur. . at 10:45AM-12:15PM. fMRI. Guillaume Flandin. Wellcome. Trust Centre for Neuroimaging. University College London. SPM Course. Chicago, 22-23 Oct 2015. Brief. Stimulus. Undershoot. Peak. BOLD . r. esponse. Early event-related fMRI studies . . og. . læring. – . får. vi . det. . til. ?. Assessment . and . Learning – Fields Apart. ?. Summary and Comments. Jan-Eric Gustafsson. Professor II. Purposes of assessment. o. f – for – as learning at individual – institutional – system levels. Kherif. . Ferath. Wellcome. Trust Centre for Neuroimaging. University College London. SPM Course. L. ondon, Oct 2010. Normalisation. Statistical Parametric Map. Image time-series. Parameter estimates. Finding arrays (dimensions) and chunks. Multidimensional scaling. MDS is . a multivariate . data-reduction technique. . Like factor analysis, it is used to tease out underlying relations among a set of observations. . London, . May . 2014. Contrasts &. Statistical Inference. Will Penny. Normalisation. Statistical Parametric Map. Image time-series. Parameter estimates. General Linear Model. Realignment. Smoothing. Chunxu Tang. The Mystery Machine: End-to-end performance analysis of large-scale Internet services. Introduction. Complexity comes from. Scale. Heterogeneity. Introduction (Cont.). End-to-end:. From a user initiates a page load in a client Web browser,. Contrasts & Inference - EEG & MEG Himn Sabir 1 Topics 1 st level analysis 2 nd level analysis Space-Time SPMs Time-frequency analysis Conclusion 2 Voxel Space 3 (revisited) 2D scalp projection Contrasts & Inference - EEG & MEG Himn Sabir 1 Topics 1 st level analysis 2 nd level analysis Space-Time SPMs Time-frequency analysis Conclusion 2 Voxel Space 3 (revisited) 2D scalp projection Benjamin Chew and Rani Moran. 16 November 2016. Overview. Introduction. GLM. Design Matrix. Contrasts. Inference. Methodology. Overview. Introduction. GLM. Design Matrix. Contrasts. Inference. Methodology. Hikaru. . Tsujimura. and . Hsuan. -Chen Wu. Design . Matrix. . , . Contrasts. & . Inference. Motion. correction. Smoothing. kernel. Spatial. normalisation. Standard. template. fMRI. time-series. in characters . The big question: “Aunty Ifeoma is a deliberate foil to Mama in Purple Hibiscus” Explore this with reference to examples of how she contrasts with the behaviour of the mother.. Success Criteria: .