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fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Co

fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Components Analysis. Calhoun, Adali, McGinty, Pekar, Watson, & Pearlson (2001). Geneviève Desmarais - November 5, 2002. MVPT-R. Motor-free Visual Perception Task (Revised)

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fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Co

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  1. fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Components Analysis Calhoun, Adali, McGinty, Pekar, Watson, & Pearlson (2001). Geneviève Desmarais - November 5, 2002

  2. MVPT-R • Motor-free Visual Perception Task (Revised) • Probes: • spatial relationships • visual discrimination • figure-ground perception • visual closure • etc...

  3. Basic Paradigm

  4. Participants • 2 females, 8 males • mean age 27 • screened with physical and neurological exam • no Axis 1 disorders (clinical) • good visual acuity without correction

  5. Anatomic Scan T-1 weighted TR = 500 msec TE = 30 msec slice thickness = 5 mm gap = 0.5 mm 18 slices through entire brain Functional Scan single-shot echo-planar TR = 1 s TE = 39 msec 5 min = 300 scans 10 dummy scans at beginning Imaging Parameters

  6. Data Analysis - Preprocessing(fudging?) • Corrected for timing differences • Motion corrected • Spatially smoothed • Normalized to Talairach space

  7. Data AnalysisGeneral Linear Model 1. Fixed-effect group analysis • stimulus function • times when figures were presented to the participants • Filters: • High-pass • Low pass • trends verified in each individual data set 2. Random effect analysis on individual data

  8. Data AnalysisIndependent Component Analysis • Decomposes data into signals that are maximally independent • individual preprocessed data arranged into 2D matrix of space and time • 20 components estimated, grouped into: • motor, visual, cerebellar, frontoparietal, orbitofrontal, and basal ganglia

  9. Data AnalysisIndependent Component Analysis • Components normalized • Components within each area averaged across participants • Each group image converted to Z scores • threshold = 2.5

  10. Results • 85% average correct response • range 67 - 100 (…) • Incorrect and correct responses grouped together

  11. ActivationGLM analysis * visual areas * visual association * frontal eye fields * dorsolateral prefrontal * supplemental motor * no positive parietal * extensive cerebellar

  12. ActivationICA analysis * colours = diff component * mostly same regions * superior parietal and prefrontal in same as FEF

  13. Discussion • Expected activation in: • large network of areas involved in visual and spatial perception • all primary visual areas and many visual association areas

  14. Event-averaged time courses

  15. Parietal regions • Decreasing signal following figural presentation • attributed to • eye movement • working memory • both eye movement and attention

  16. No Primary Motor Region? • ICA finds it… • Why not GLM? • Because participants were responding with both hands… • GLM = averaged over trials for each subject, and is only active some of the times • ICA = looks for independent activation, and only one hand will be active at a time

  17. Cerebellum • Surprised at the extensive involvement of the cerebellum • maybe because button box was vertically configured

  18. Conclusions • MVPT-R battery activates a large network of areas… • Both methods selected similar but not identical regions • GLM: more selective and sensitive • especially primary visual and cerebellar • ICA: detected motor components not detected via SPM

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