1. A Cortical Area Selective for Visual Processing of the Human Body by P.E. Downing, Y. Jiang, M. Shuman, N.Kanwisher presented by Ilya Levner

2. OutlineOutline Functional neuro-imagingFunctional neuro-imaging –Issues –fMRI basics Data analysis with SPMData analysis with SPM Experiment SummaryExperiment Summary ConclusionsConclusions Functional neuro-imagingFunctional neuro-imaging –Issues –fMRI basics Data analysis with SPMData analysis with SPM Experiment SummaryExperiment Summary ConclusionsConclusions

3. Integration vs Segregation Integration vs Segregation Are physiological changes elicited by sensorimotor or cognitive challenges best characterized byAre physiological changes elicited by sensorimotor or cognitive challenges best characterized by –Functional Segregation the activation of functionally segregated areasthe activation of functionally segregated areas –Functional Integration Anatomically distributed, but functionally integrated systemAnatomically distributed, but functionally integrated system Are physiological changes elicited by sensorimotor or cognitive challenges best characterized byAre physiological changes elicited by sensorimotor or cognitive challenges best characterized by –Functional Segregation the activation of functionally segregated areasthe activation of functionally segregated areas –Functional Integration Anatomically distributed, but functionally integrated systemAnatomically distributed, but functionally integrated system

4. fMRI Basics Based on BOLD ContrastBased on BOLD Contrast –Blood Oxygenation Level Dependent contrast. –Deoxyhemoglobin is more paramagnetic (magnetizes easier) than oxyhemoglobin –So deoxyhemoglobin is the contrasting agent revealing cortical areas metabolizing oxygen at highter rates (presumably). Based on BOLD ContrastBased on BOLD Contrast –Blood Oxygenation Level Dependent contrast. –Deoxyhemoglobin is more paramagnetic (magnetizes easier) than oxyhemoglobin –So deoxyhemoglobin is the contrasting agent revealing cortical areas metabolizing oxygen at highter rates (presumably).

5. fMRI (Cont) Small contrast difference oxygenated and deoxygenated blood.Small contrast difference oxygenated and deoxygenated blood. –1.5T fMRI produce image intensity changes of no more than 2-4%. –2T fMRI produce image intensity changes of less than 15%. –4T fMRI has produced contrast changes of up to 30% Small contrast difference oxygenated and deoxygenated blood.Small contrast difference oxygenated and deoxygenated blood. –1.5T fMRI produce image intensity changes of no more than 2-4%. –2T fMRI produce image intensity changes of less than 15%. –4T fMRI has produced contrast changes of up to 30%

6. Data Analysis Overview RealignmentSmoothing Normalisation General linear model Statistical parametric map (SPM) Time-series data Parameter estimates Design matrix Template Kernel Gaussian field theory p <0.05 Statisticalinference

7. Reasons for Motion Correction Subjects will always move in the scanner.Subjects will always move in the scanner. –movement may be related to the tasks performed. The sensitivity of the analysis is determined by the amount of residual noise in the image series, so movement that is unrelated to the task will add to this noise and reduce the sensitivity.The sensitivity of the analysis is determined by the amount of residual noise in the image series, so movement that is unrelated to the task will add to this noise and reduce the sensitivity. Subjects will always move in the scanner.Subjects will always move in the scanner. –movement may be related to the tasks performed. The sensitivity of the analysis is determined by the amount of residual noise in the image series, so movement that is unrelated to the task will add to this noise and reduce the sensitivity.The sensitivity of the analysis is determined by the amount of residual noise in the image series, so movement that is unrelated to the task will add to this noise and reduce the sensitivity. registration - i.e. determining the 6 parameters that describe the rigid body transformation between each image and a reference image.registration - i.e. determining the 6 parameters that describe the rigid body transformation between each image and a reference image. transformation - i.e. re- sampling each image according to the determined transformation parameters.transformation - i.e. re- sampling each image according to the determined transformation parameters. registration - i.e. determining the 6 parameters that describe the rigid body transformation between each image and a reference image.registration - i.e. determining the 6 parameters that describe the rigid body transformation between each image and a reference image. transformation - i.e. re- sampling each image according to the determined transformation parameters.transformation - i.e. re- sampling each image according to the determined transformation parameters. The Steps in Motion Correction

8. Spatial Normalisation Original image Template image Spatially normalised Deformation field Enable reporting of activations as co- ordinates within a known standard spaceEnable reporting of activations as co- ordinates within a known standard space –e.g. the space described by Talairach & Tournoux Inter-subject averagingInter-subject averaging –extrapolate findings to the population as a whole –increase activation signal above that obtained from single subject Enable reporting of activations as co- ordinates within a known standard spaceEnable reporting of activations as co- ordinates within a known standard space –e.g. the space described by Talairach & Tournoux Inter-subject averagingInter-subject averaging –extrapolate findings to the population as a whole –increase activation signal above that obtained from single subject

9. Why Smooth?Why Smooth? –Potentially increase signal to noise. –Inter-subject averaging. –Increase validity of SPM. In SPM, smoothing is a convolution with a Gaussian kernel.In SPM, smoothing is a convolution with a Gaussian kernel. Kernel defined in terms of FWHM (full width at half maximum).Kernel defined in terms of FWHM (full width at half maximum). Why Smooth?Why Smooth? –Potentially increase signal to noise. –Inter-subject averaging. –Increase validity of SPM. In SPM, smoothing is a convolution with a Gaussian kernel.In SPM, smoothing is a convolution with a Gaussian kernel. Kernel defined in terms of FWHM (full width at half maximum).Kernel defined in terms of FWHM (full width at half maximum). SmoothingSmoothing Gaussian smoothing kernel Before convolutionAfter convolution

10. Statistical Parametric Maps (SPM) Construction of statistical processes to test hypotheses about regionally specific effects. (functional segregation hypothesis)Construction of statistical processes to test hypotheses about regionally specific effects. (functional segregation hypothesis) Analysis of each voxel done using any standard (univariate) statistical test.Analysis of each voxel done using any standard (univariate) statistical test. –Voxel is a 3-D volumetric pixel. Construction of statistical processes to test hypotheses about regionally specific effects. (functional segregation hypothesis)Construction of statistical processes to test hypotheses about regionally specific effects. (functional segregation hypothesis) Analysis of each voxel done using any standard (univariate) statistical test.Analysis of each voxel done using any standard (univariate) statistical test. –Voxel is a 3-D volumetric pixel.

11. model specification f MRI time series statistic image or SPM Voxel by voxel statistics… voxel time series parameter estimation hypothesis statistic

12. True signal Correlation between regressors Fitting (blue : global fit) Noise Model (green and red)

13. …fitted…fitted raw fMRI time series scaled for global changes adjusted for global & low Hz effects residuals fitted “high-pass filter” fitted box-car

14. Data Analysis Overview Motion correction smoothing Spatial normalisation General Linear Model Statistical Parametric Map fMRI time-series Parameter Estimates Design matrix anatomical reference kernel

15. p = 0.05 p = 0.0000001 p = 0.0001 Simple threshold tests… 5mm 10mm 15mm

16. Experimental Results

17. Results (cont)

18. ResultsResults Response of the extrastriate body area Ex-1) Human Body Parts 1.3%, face parts 1.0% vs object parts 0.5% Ex-2) hands 1.4% = body parts 1.4% Ex-3) Whole body 1.9% vs body parts 1.4% Ex-4) Ex-5) objects = object parts = 0.5% Ex-6) Stick figure 1.7% Response of the extrastriate body area Ex-1) Human Body Parts 1.3%, face parts 1.0% vs object parts 0.5% Ex-2) hands 1.4% = body parts 1.4% Ex-3) Whole body 1.9% vs body parts 1.4% Ex-4) Ex-5) objects = object parts = 0.5% Ex-6) Stick figure 1.7%

19. References P.E. Downing et al (1995): A Cortical Area for Visual Processing of the Human Body. SCIENCE, vol 293, pp 2470-2473. Friston et al (1997): SPM Short Course. Course Notes, 1997. Ashburner et al (1999): Nonlinear spatial normalisation using basis functions. Human Brain Mapping 7(4):254-266 Ashburner & Friston (2000): Voxel- based morphometry - the methods. NeuroImage 11:805-821

20. fMRI Basics (Pros & Cons) ProsPros –Spatio-temporal scale of 1-3mm at 1-2 seconds compared to PET with 6mm and 30 seconds. ConsCons –Blood occupies only 2% of cortical mass. –Precision can’t get any finer. –Increase in blood flow extends well beyond the area of activation. –A cortical region can activate in 10ms while a change in blood flow can take up to 1s. ProsPros –Spatio-temporal scale of 1-3mm at 1-2 seconds compared to PET with 6mm and 30 seconds. ConsCons –Blood occupies only 2% of cortical mass. –Precision can’t get any finer. –Increase in blood flow extends well beyond the area of activation. –A cortical region can activate in 10ms while a change in blood flow can take up to 1s.

21. Residual Errors from fMRI Gaps between slices can cause aliasing artefactsGaps between slices can cause aliasing artefacts Re-sampling can introduce errorsRe-sampling can introduce errors –especially tri-linear interpolation Ghosts (and other artefacts) in the imagesGhosts (and other artefacts) in the images –do not move according to the same rigid body rules as the subject Slices are not acquired simultaneouslySlices are not acquired simultaneously –rapid movements not accounted for by rigid body model fMRI images are distortedfMRI images are distorted –rigid body model does not model these types of distortion Spin excitation history effectsSpin excitation history effects –variations in residual magnetisation Functions of the estimated motion parameters can be used as confounds in subsequent analyses. Gaps between slices can cause aliasing artefactsGaps between slices can cause aliasing artefacts Re-sampling can introduce errorsRe-sampling can introduce errors –especially tri-linear interpolation Ghosts (and other artefacts) in the imagesGhosts (and other artefacts) in the images –do not move according to the same rigid body rules as the subject Slices are not acquired simultaneouslySlices are not acquired simultaneously –rapid movements not accounted for by rigid body model fMRI images are distortedfMRI images are distorted –rigid body model does not model these types of distortion Spin excitation history effectsSpin excitation history effects –variations in residual magnetisation Functions of the estimated motion parameters can be used as confounds in subsequent analyses.

22. Spatial normalisation Inter-subject averagingInter-subject averaging –extrapolate findings to the population as a whole –increase activation signal above that obtained from single subject –increase number of possible degrees of freedom allowed in statistical model Enable reporting of activations as co-ordinates within a known standard spaceEnable reporting of activations as co-ordinates within a known standard space –e.g. the space described by Talairach & Tournoux Inter-subject averagingInter-subject averaging –extrapolate findings to the population as a whole –increase activation signal above that obtained from single subject –increase number of possible degrees of freedom allowed in statistical model Enable reporting of activations as co-ordinates within a known standard spaceEnable reporting of activations as co-ordinates within a known standard space –e.g. the space described by Talairach & Tournoux Warp the images such that functionally homologous regions from the different subjects are as close together as possibleWarp the images such that functionally homologous regions from the different subjects are as close together as possible –Problems: no exact match between structure and functionno exact match between structure and function different brains are organised differentlydifferent brains are organised differently computational problems (local minima, not enough information in the images, computationally expensive)computational problems (local minima, not enough information in the images, computationally expensive) Compromise by correcting for gross differences followed by smoothing of normalised imagesCompromise by correcting for gross differences followed by smoothing of normalised images Warp the images such that functionally homologous regions from the different subjects are as close together as possibleWarp the images such that functionally homologous regions from the different subjects are as close together as possible –Problems: no exact match between structure and functionno exact match between structure and function different brains are organised differentlydifferent brains are organised differently computational problems (local minima, not enough information in the images, computationally expensive)computational problems (local minima, not enough information in the images, computationally expensive) Compromise by correcting for gross differences followed by smoothing of normalised imagesCompromise by correcting for gross differences followed by smoothing of normalised images

23. EPI T2 T1Transm PDPET 305T1 PD T2 SS Template Images“Canonical” images Spatial normalisation can be weighted so that non-brain voxels do not influence the result. Similar weighting masks can be used for normalising lesioned brains.

24. Original image Spatially normalised Partitioned grey matter Smoothed Preparation of images for each subject Voxel-Based Morphometry A voxel by voxel statistical analysis is used to detect regional differences in the amount of grey matter between populations.

25. General Linear Model… fMRI time series: Y 1,…,Y s,…,Y NfMRI time series: Y 1,…,Y s,…,Y N –acquired at times t 1,…,t s,…,t N Model: Linear combination of basis functionsModel: Linear combination of basis functions Y s =  1 f 1 (t s ) + …+  l f l (t s ) + … +  L f L (t s ) +  s f l (.): basis functionsf l (.): basis functions –“reference waveforms” –dummy variables  l : parameters (fixed effects)  l : parameters (fixed effects) –amplitudes of basis functions (regression slopes)  s : residual errors:  s ~ N(0,  2 )  s : residual errors:  s ~ N(0,  2 ) –identically distributed –independent, or serially autocorrelation (Generalised Linear Model  GLM) fMRI time series: Y 1,…,Y s,…,Y NfMRI time series: Y 1,…,Y s,…,Y N –acquired at times t 1,…,t s,…,t N Model: Linear combination of basis functionsModel: Linear combination of basis functions Y s =  1 f 1 (t s ) + …+  l f l (t s ) + … +  L f L (t s ) +  s f l (.): basis functionsf l (.): basis functions –“reference waveforms” –dummy variables  l : parameters (fixed effects)  l : parameters (fixed effects) –amplitudes of basis functions (regression slopes)  s : residual errors:  s ~ N(0,  2 )  s : residual errors:  s ~ N(0,  2 ) –identically distributed –independent, or serially autocorrelation (Generalised Linear Model  GLM)

26. Example: a line through 3 points… Y i =  x i +  +  i i = 1,2,3 Y 1 =  x 1 +  × 1 +  1 Y 2 =  x 2 +  × 1 +  2 Y 3 =  x 3 +  × 1 +  3 Y 1 x 1 1  1 Y 2 =x 2 1+  2 Y 3 x 3 1  3 Y= X  +  simple linear regression parameter estimates  &  fitted valuesY 1, Y 2, Y 3 residualse 1, e 2, e 3 parameter estimates  &  fitted valuesY 1, Y 2, Y 3 residualse 1, e 2, e 3 x Y  (x 1, Y 1 )   (x 2, Y 2 ) (x 3, Y 3 )  ^ ^  1 ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ dummy variables

27. fMRI box car example… =  +  =  +YX data vector design matrix parameters error vector

28. References Friston et al (1995): Spatial registration and normalisation of images. Human Brain Mapping 3(3):165-189 Ashburner & Friston (1997): Multimodal image coregistration and partitioning - a unified framework. NeuroImage 6(3):209-217 Collignon et al (1995): Automated multi-modality image registration based on information theory. IPMI’95 pp 263-274 Ashburner et al (1997): Incorporating prior knowledge into image registration. NeuroImage 6(4):344-352 Ashburner et al (1999): Nonlinear spatial normalisation using basis functions. Human Brain Mapping 7(4):254-266 Ashburner & Friston (2000): Voxel-based morphometry - the methods. NeuroImage 11:805-821