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Research course on functional magnetic resonance imaging Lecture 2

Research course on functional magnetic resonance imaging Lecture 2. Juha Salmitaival. Today’s lecture. Preprocessing Motion correction Slice timing correction Spatial filtering Temporal filtering ICA denoising Global intensity correction Registration FSL demo

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Research course on functional magnetic resonance imaging Lecture 2

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  1. Research course on functional magnetic resonance imagingLecture 2 Juha Salmitaival

  2. Today’slecture • Preprocessing • Motioncorrection • Slicetimingcorrection • Spatialfiltering • Temporalfiltering • ICA denoising • Globalintensitycorrection • Registration • FSL demo • Thingswehavelearnedsofar

  3. Preprocessing – general things • Signalchanges in BOLD aretypicallysomewherebetween 0.1% and 5% • To enhance the signal and reduce the noise • To prepare the data for statisticalanalysis • Learn to knowyour data!

  4. Preprocessing – motioncorrection • Paddingaround the head to avoidmovement! • Headmovements -> differenttissue in samevoxel and artefactualsignalchanges

  5. Preprocessing – motioncorrection • Howmuchmotion is toomuch? • Largejumpsaremoreseriousthanslowdrifts • Exclusion: outlier?, 1mm? • Ifyouhave stimulus correlatedmotion, youprobablyneedothermethods (e.g., INRIAlign)

  6. Preprocessing – slicetimingcorrection • Slicesarescanned at a slightlydifferenttime (0,2,4,…1,3,5…)

  7. Preprocessing – spatialfiltering • How big areyourblobs? • -> increases SNR • -> Gaussiandistribution (thresholding) • Typically 3-10 mm

  8. Preprocessing – temporalfiltering • Scanner-related and physiologicaldrifts • HP filter - usually, LP filterifneeded (MELODIC?) • Cyclelength x 1.5

  9. Preprocessing – ICA denoising • Need to knowwhat the signalshould look! • Nongray-matter?, weirdtime-series/frequencyspectrum? • Individual/groupanalysis?

  10. Preprocessing – globalintensitynormalization

  11. Registration of images – wholebrain • Standard spaces: MNI space, Talairachspace/atlas (www.talairach.org) • fMRIspace -> performanalysishereifpossible • fMRI to structural -> anatomicallocalization • fMRI to standard -> comparison of results • (betweensubjects and datasets) • Step 1 estimatingtransformation (transformationmatrix) • Step 2 resampling (modifiedimage)

  12. Registration of images – parameters FNIRT - Samemodality - Highquality • DOFS • Costfunction • correlationratio (same session T1) • mutual info (T2 anatomical) • Interpolation

  13. Registration of images • Alwayscheck the resultsvisually! • Twostageregistration • Fieldmapcorrection

  14. Registration of gyri and sulci • Individualdifferences in corticalfoldingarehuge!

  15. Preprocessing & Registration demo • 1. Motioncorrection (fMRIimage) • 2. Brainextraction (manualcheck!) • 3. FEAT preprocessing • (4. fMRImodeling/statistics (nextweekstopic)) • 5. FLIRT registration (manualcheck!)

  16. Groups • 1 GLM and ICA: music vs. speech, audiovisualinteraction • Jussi, Onerva, Hanna, Olli-Pekka • 2 artifacts and signals (ICA/GLM) • Dinos, Jari T., Juha P., Eero K, Timo • 3 cross-sensorycoherence (ISC) • Alexander, Anne, Jonathan, Jaakko • Passwords / Computers

  17. About the dataset • The data is notonly for thiscourse, butalso for scientificpurposes • Originalplan is not to useany of yourwork in publication • Ifyouthinkthatyourcontribution is enough to beauthor in the publication, pleasediscusswith me! • Ifyouwant to publishsomething out of the data, come to discusswith me!

  18. References & Images • FSL-course • http://www.fmrib.ox.ac.uk/fslcourse/ • SPM-course • http://www.fil.ion.ucl.ac.uk/spm/course/

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