1. Group Analyses in fMRI http://www.fmri4newbies.com/ Last Update: November 9, 2014 Last Course: Psychology 9223, F2014, Western University Jody Culham Brain and Mind Institute Department of Psychology Western University

2. Group Analyses 1. Get all the subjects’ brains into a common space –Talairach space –MNI space –cortex-based alignment 2A. Do group statistics –Random Effects GLM and/or 2B. Use a Region of Interest Approach

3. Combining Group Data

4. Brains are Heterogeneous Slide from Duke course

5. Talairach Coordinate System Note: That’s TalAIRach, not TAILarach! Talairach & Tournoux, 1988 made an atlas based on one brain any brain can be squished or stretched to fit hers and locations can be described using a 3D coordinate system (x, y, z) … from an alcoholic old lady

6. Rotate brain into ACPC plane Find posterior commisure (PC) Find anterior commisure (AC) ACPC line = horizontal axis Corpus Callosum Fornix Pineal Body “bent asparagus” Note: official Tal says to use top of AC and bottom of PC but I suspect few people actually do this Source: Duvernoy, 1999

7. AC = Origin x = 0 y = 0 z = 0 + - z - x + + - y LR

8. Left is what?!!! Neurologic (i.e. sensible) convention left is left, right is right LR Radiologic (i.e. stupid) convention left is right, right is left RL Note: Make sure you know what your magnet and software are doing before publishing left/right info! x = 0 - + +-

9. Tip Put a vitamin E capsule on the one side of the subject’s head or coil.

10. Squish or stretch brain to fit in “shoebox” of Tal system Deform brain into Talairach space y AC=0y>0y<0 ACPC=0 y>0 y<0 z x Extract 3 coordinates Mark 8 points in the brain: anterior commisure posterior commisure front back top bottom (of temporal lobe) left right

11. Talairach Tables Talairach coordinates can be useful for other people to check whether their activation foci are similar to yours Often it’s easiest to just put coordinates in a table to avoid cluttering text Source: Culham et al., 2003, Exp. Brain Res.

12. Do We need a “Tarailach Atras”? Variability between Japanese and European brains, both male (red > yellow > green > blue) Variability between male and female brains, both European (red > yellow > green > blue) Source: Zilles et al., 2001, NeuroImage

13. Talairach Pros and Cons Advantages widespread system allows averaging of fMRI data between subjects allows researchers to compare activation foci relatively easy to use Disadvantages does better for central regions of cortex, but not great for most of cortex not appropriate for all brains (e.g., group variability, patients may not fit well) ignores left- vs. right-hemisphere asymmetries activation foci can vary considerably – other landmarks like sulci may be more reliable

14. MNI Space Researchers at the Montreal Neurological Institute (MNI) created a better template based on a morphed average of hundreds of brains –many different versions http://en.wikibooks.org/wiki/MINC/Atlases/Atlases/History

15. MNI Space Benefits –MNI Space is based on many subjects not just one brain like Talairach space –MNI transformations use nonlinear warping, which leads to better intersubject alignment Image Source

16. Converting Between MNI and Tal space You many want to convert between the systems –Only Tal provides Brodmann’s areas –Need to convert for meta-analyses The MNI and Talairach coordinates are similar but not identical –e.g., temporal lobes extend 10 mm lower in MNI brain Caveat: careful comparison requires a transformation -- converters can be found online http://www.brainmap.org/icbm2tal/ Source: http://www.mrc-cbu.cam.ac.uk/personal/matthew.brett/abstracts/MNITal/mniposter.pdf

17. Anatomical Localization Sulci and Gyri gray matter (dendrites & synapses) white matter (axons) FUNDUS BANK CROWN GYRUS SULCUS gray/white border pial surface FISSURE Source: Ludwig & Klingler, 1956, in Tamraz & Comair, 2000 neuron

18. Variability of Sulci Source: Szikla et al., 1977, in Tamraz & Comair, 2000

19. Effects of Sulcal Variability Source: Frost & Goebel, 2012, NeuroImage

20. Variability of Functional Areas Watson et al., 1995 - motion-selective area, MT+ (=V5) is quite variable in stereotaxic space - however, the area is quite consistent in its location relative to sulci - junction of inferior temporal sulcus and lateral occipital sulcus - see also Dumoulin et al., 2000

21. Cortical Surfaces segment gray-white matter boundary inflate cortical surface sulci = concave = dark gray gyri = convex = light gray render cortical surface

22. Cortical Inflation Movie Movie: unfoldorig.mpeg http://cogsci.ucsd.edu/~sereno/unfoldorig.mpg Source: Marty Sereno’s web pageMarty Sereno’s web page

23. Cortical Flattening Source: Brain Voyager Getting Started Guide 2) make cuts along the medial surface (Note, one cut typically goes along the fundus of the calcarine sulcus though in this example the cut was placed below) 1) inflate the brain 3) unfold the medial surface so the cortical surface lies flat 4) correct for the distortions so that the true cortical distances are preseved

24. Spherical Averaging Source: Fischl et al., 1999 Future directions of fMRI: Use cortical surface mapping coordinates Inflate the brain into a sphere Use sulci and/or functional areas to match subject’s data to template Cite “latitude” & “longitude” of spherical coordinates

25. Spherical Averaging Source: Fischl et al., 1999 Movie: brain2ellipse.mpeg http://cogsci.ucsd.edu/~sereno/coord1.mpg Source: Marty Sereno’s web pageMarty Sereno’s web page Movie: morph-curv1.mpg http://www.cogsci.ucsd.edu/~sereno/morph-curv1.mpg Source: Marty Sereno’s web pageMarty Sereno’s web page

26. Before and After CBA Source: Frost & Goebel, 2012, NeuroImage

27. Before and After CBA hand motor area (M1) hand somatosensory area (S1) Source: Frost & Goebel, 2012, NeuroImage

28. Gains in Overlap Source: Frost & Goebel, 2012, NeuroImage

29. Voxelwise Group Analyses Fixed vs. Random Effects

30. Example Three subjects Three conditions: Baseline, Faces, Objects For simplicity, just consider one voxel in FFA

31. Stupid Way: Concatenated Fixed Effects (FFX) Concatenate data, pretend you have one subject who did three runsConcatenate data, pretend you have one subject who did three runs

32. Make one predictor for Faces and one for Objects (2 df) Scale predictors (by beta weights) Note why this is stupid –Assumes all subject show same magnitude of activation –Errors in this assumption   residuals

33. Better Way: Separate Subjects FFX Don’t concatenate Separate predictors for 3 subjects x 2 conditions –6 df

34. Problem: Separate Subjects FFX We could do business-as-usual GLM and see if predictors account for significant variance considering noise Effectively, we are asking how confident we are that this effect is true (not due to chance) in these three subjects (and only these three subjects) BUT usually, we want to generalize to the population from which we sampled

35. Best Way: Random Effects (RFX)

36. Second-level analysis Nothing too complicated… it’s effectively just a paired t-test Subjectβ Faces β Objects Difference β Faces - β Objects S10.5520.1050.447 S22.0611.1210.940 S31.0190.2470.772 Mean0.719 SD0.250 SEM [=SD/sqrt(N)]0.145 t crit (df=3)4.3 95%CI lower (= mean – (t*SEM))0.097 95%CI lower (= mean + (t*SEM))1.34 Estimated Distribution of Differences does not include zero  significant (p<.05) 0.7190.0971.34 95% CI 0

37. Repeat for the other 60,000 voxels… Huettel, Song & McCarthy, 2008 First-level analysis Second-level analysis

38. If you really want to be correct… We often refer to this type of analysis as random effects (RFX) Since subjects is a random effect but other aspects (e.g., stimulus categories) are fixed effects, technically the proper term is Mixed Effects Analysis Other common jargon = Second-level Analysis

39. Take-home Message RFX enables us to generalize to the population from which we sampled subjects Degrees of freedom comes from number of subjects, not number of time points –No need to worry about correction for serial correlations for most fMRI studies, this means underpaid graduate students in need of a few bucks!

40. Examples from a real data set

41. Concatenated FFX Example 17 Subjects x 2 runs with Faces & Houses df = 17 Ss x 2 runs/S x 264 vols/run - 1 one predictor per condition

42. FFX Separate Subjects … If you’re looking at pilot data from a few Ss, with RFX it will be hard to see any effects. You can do FFX with separate subjects. It has the same problems as basic FFX but at least enables you to examine the consistency between Ss

43. RFX … S1 S2 S3 S17 df = 17 Ss - 1 Now that our df no longer depends on # volumes, we don’t have to worry about correction for serial correlations with RFX This contrast is just like doing a paired t-test between Faces and Objects with 17 Ss

44. Smoothing and Averaging anatomical variability of activation for 3 Ss without spatial smoothing each subject shows an effect but there’s not enough spatial overlap to find any voxels in an RFX analysis anatomical variability of activation for 3 Ss with spatial smoothing now there’s enough overlap between Ss that some voxels will be found with RFX analysis

45. Random Effects Analysis Brain Voyager recommends you don’t even toy with random effects unless you’ve got 10 or more subjects (and 50+ is best) Random effects analyses can really squash your data, especially if you don’t have many subjects. Though standards were lower in the early days of fMRI, today it’s virtually impossible to publish any group voxelwise data without RFX analysis

46. Strategies for Exploration vs. Publication Deductive approach –Have a specific hypothesis/contrast planned –Run all your subjects –Run the stats as planned –Publish Inductive approach –Run a few subjects to see if you’re on the right track –Spend a lot of time exploring the pilot data for interesting patterns –“Find the story” in the data –You may even change the experiment, run additional subjects, or run a follow-up experiment to chase the story While you need to use rigorous corrections for publication, do not be overly conservative when exploring pilot data or you might miss interesting trends Random effects analyses can be quite conservative so you may want to do exploratory analyses with fixed effects (and then run more subjects if needed so you can publish random effects)

47. How can we identify activation foci? Talairach coordinates Example: The FFA is at x = 40, y = -55, z = -10 Anatomical localization Example: The FFA is in the right fusiform gyrus at the level of the occipitotemporal junction Functional localization Example: The FFA includes all voxels around the fusiform gyrus that are activated by the comparison between faces and objects Kanwisher, McDermott & Chun, 1997, J Neurosci

48. Talairach Daemon http://www.talairach.org

49. Brodmann’s Areas Brodmann (1905): Based on cytoarchitectonics: study of differences in cortical layers between areas Most common delineation of cortical areas More recent schemes subdivide Brodmann’s areas into many smaller regions Monkey and human Brodmann’s areas not necessarily homologous

50. Definition of an “Area” Neuroimager’s definition of an area: Some blob vaguely in the vicinity (+/- ~3 cm) of where I think it ought to be that lights up for something I think it ought to light up for Neuroanatomist’s definition of an area: A circumscribed region of the cerebral cortex in which neurons together serve a specific function, receive connections from the same regions, have a common structural arrangement, and in some cases show a topographic arrangement may also be called a cortical field 50

51. 51 Cortical Fields: Multiple Criteria 1.Function –an area has a unique pattern of responses to different stimuli 2.Architecture –different brain areas show differences between cortical properties (e.g., thickness of different layers, sensitivity to various dyes) 3.Connectivity –Different areas have different patterns of connections with other areas 4.Topography –many sensory areas show topography (retinotopy, somatotopy, tonotopy) –boundaries between topographic maps can indicate boundaries between areas (e.g., separate maps of visual space in visual areas V1 and V2)

52. 52 Can We Use Multiple Criteria in Human Imaging? 1.Function –this is often the only criterion in fMRI 2.Architecture –there are now probabilistic maps of human brain areas available (Zilles lab) 3.Connectivity –DTI and functional connectivity now give us options here 4.Topography –topography is useful in imaging, especially for early and mid- level sensory areas

53. Brodmann Area 17 53

54. Brodmann Area 17 Meets 21 st Century 54 Logothetis fMRI data: image from http://www.bruker-biospin.com/imaging_neuroanatomy.htmlhttp://www.bruker-biospin.com/imaging_neuroanatomy.html Anatomical MRIFunctional MRI Goense, Zappe & Logothetis, 2007, MRI Layer 4 fMRI activation (0.3 x 0.3 x 2 mm spin echo) Layer 4

55. Retinotopic Maps EXPANDING RINGS ROTATING WEDGES

56. DTI in V1 56 Saentz & Fine, 2010, NeuroImage

57. Maps, Maps, Maps 57 Wandell et al., 2007, Neuron Hagler & Sereno, 2006, NeuroImage … even in parietal lobe …… even in frontal lobe …

58. Other Sensory “-topies” Touch: Somatotopy Servos et al., 1998 red = wrist; orange = shoulder Audition: Tonotopy cochlea Sylvian fissure temporal lobe Movie: tonotopy.mpeg http://cogsci.ucsd.edu/~sereno/downsweep2.mpg Source: Marty Sereno’s web page

59. Learning Brain Anatomy Duvernoy, 1999, The Human Brain: Surface, Blood Supply, and Three-Dimensional Sectional Anatomy beautiful pictures good schematic diagrams clear anatomy slices of real brain Springer, US$439 DISCONTINUED Ono, 1990, Atlas of the Cerebral Sulci great for showing intersubject variability gives probabilities of configurations and stats on sulci Theime, US$199 Damasio,2005, Human Brain Anatomy in Computerized Images, 2nd edition good for showing sulci across wide range of slice planes 2nd edition much better than 1st edition Oxford University Press, US$100 Tamraz & Comair, 2000, Atlas of Regional Anatomy of the Brain Using MRI with Functional Correlations good overview Springer, US$203 Talairach & Tournoux, 1988. Co-Planar Stereotaxic Atlas of the Human Brain just because it’s the standard doesn’t mean it’s good Theime, US$240

60. Brain Tutor Mac/PC: free iOS App: $1.99

61. Proposal Guidelines http://www.fmri4newbies.com/ Last Update: November 9, 2014 Last Course: Psychology 9223, F2014, Western University Jody Culham Brain and Mind Institute Department of Psychology Western University

62. Research Proposal Due December 8, 2014 Goals –give students an opportunity to demonstrate what they’ve learned and apply ideas to their research area –give students practice in writing grants/papers 16 double-spaced pages + figures must be original –not thesis –not something your advisor totally worked out for you can get some suggestions from advisor but core of proposal should be your work

63. Research Proposal partially like a grant –proposal for experiment –make case for why experiment should be done “hasn’t been done before” is not good enough –clear question, hypotheses –conclusion: so what? –immunization against potential criticism partially like a paper –just one experiment, not 5 years of experiments –in-depth methods be clear about design (e.g., protocol) and analyses be clear about contrasts Appendix with budget and time line don’t worry about formatting – spend your time on content not formatting

64. Range of Approaches Standard univariate fMRI with hypothesis-driven GLM –Block or Event-related Advanced designs –e.g., MVPA Data-driven fMRI –e.g., ICA on resting state data Approaches we’ve touched on in class –e.g., intersubject correlations Anatomical approaches –DTI For the more computationally inclined –better ways to analyze data –if you must use equations, explain them intuitively in text (and consider putting them in an appendix) Be aware that we won’t discuss these in too much detail in class; therefore you would need to have some prior exposure or to do some extra reading

65. Two questions to consider whenever you write a paper or give a talk Who is my audience? –a math-phobic professor who will be checking whether you understood the core ideas of fMRI What is my goal? –show professor that you can find a way to use neuroimaging in your research –show professor that you understand jargon and concepts Bonus –be clever and creative –write clearly and concisely –solidify your understanding of neuroimaging approaches –think more deeply about how to apply neuroimaging

66. Sections Introduction –Give enough information to put the research in context and lead the reader to the conclusion that the experiment you’re proposing is a reasonable next step –You don’t need to cite every paper in the history of neuroimaging –Do enough of a lit search to be fairly certain proposal hasn’t been done –Replication attempts discouraged (but may be considered with sufficient justification) Methods –Include enough detail to demonstrate that you understand jargon and key concepts –Be clear about specific contrasts Results –How could it turn out? –May want to include graph of hypotheses Conclusions –What would it mean if the results turned out one way or another? –Are there any caveats that should be acknowledged? –What is the broader significance of the research? References –whatever format you like –don’t go overboard Figures (optional) Appendix –How much will it cost? –How long will it take?

67. Example of Hypothesis Figure