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Haskins fMRI WorkshopPart III:

Across Subjects Analysis - Univariate, Multivariate, Connectivity

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Across-subjects “Composite” MapsRecall: two-stage analysis• Stage 1: extract subject maps for effects of interest (B weights)• Stage 2: at each voxel, test the values across-subjects versus zero

• t-test, ANOVA with planned comparisons or contrasts• each new composite map shows p-values for one subject-level

effect

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[ single-subject maps ] [ composite map ]

S1 S2 S3 S4 average

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data matrix layout for across-subjects analysis

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CRM study:“new” words

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CRM study:“old” words

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CRM study:contrast ofold-new words

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ThresholdsWhat is the appropriate threshold?• 902,629 voxels in standard MNI space image;

258,370 actually in-brain• Type I and Type II error

Approaches:• assume real activations are large (reduce number of actual tests)

control Family-Wise Error Rate “chance of any false positives”• alternatively: control False Discovery Rate

“proportion of false positives among rejected tests”• employ a priori regions-of-interest (ROIs)• multivariate analysis

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Correlational Analysis

• across subjects: at each voxel, correlate the activation level to some external subject variable like age:

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Correlational Analysis

• across subjects: at each voxel, correlate the activation level to some external subject variable like age...

or behavioral skill:

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Multivariate Analysis• PCA/SVD/Eigenimage analysis/ICA

• within subject: identify set of (1) spatial patterns with (2) associated timecourse

• across subject: identify spatial patterns with associated subject loadings

• data driven, work only on the input image data(not classified by condition, subject group, etc.)

• PLS (Partial Least Squares)• across subject: identify spatial patterns that change from task to

task• also data driven, but optimized to identify task-related changes• identifies the strongest possible contrasts among conditions

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Multivariate Analysis

Calhoun et al., Human Brain Mapping, 2001

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ConnectivityFunctional Connectivity:• df: correlations between spatially remote neurophysiological events• does not imply causality, but identifies covariation• subject to “third variable” explanations

Effective Connectivity:• df: the influence one neuronal system exerts on another• implies causality; requires something beyond correlations and

correlational analysis such as• tests of temporal relations (e.g. lagged autocorrelation analysis)• SEM - model testing

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Within- vs. Between-Subjects ConnectivityWithin-subject Connectivity:• df: correlations over time-course of a single study

activations by time point

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Within- vs. Between-Subjects ConnectivityWithin-subject Connectivity:• difficulties...

• low signal-to-noise• primarily reported in low frequencies <20sec/cycle• HRF response dissimilar across regions

Hampson et al., Human Brain Mapping, 2002

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Within- vs. Between-Subjects ConnectivityBetween-subject Connectivity:• df: correlations over subjects within a single task• cf Horwitz et al., 1984 (!)

activations by subject number

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Pugh et al., 2000;also Horwitz et al., 1992

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Functional Connectivity

activations in Shaywitz et al. 2002

older good readers

74 good readers 7-18 yrs70 dyslexic readers 7-18 yrs

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Functional Connectivity

seed voxel correlations

older good readers

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Functional Connectivity

selected univariate correlations

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Functional Connectivity

Older Non-Impaired

univariate correlations

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Older Non-Impaired

Older Dyslexics Younger Dyslexics

Younger Non-Impaired

Functional Connectivity

univariate correlations

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Functional Connectivity

First Component

Second Component

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Connecticut Longitudinal Study: ConnectivityConnecticut Longitudinal Study: Connectivity

Shaywitz et al., 2003

24Worsely et al., 2005

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Dynamic System…