Spatial Special Preprocessing
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1 Spatial Special Preprocessing Methods & Models for fmri Data Analysis Septeber 26, 2014 Lars Kasper, PhD TNU & MR-Technology Group Institute for Bioedical Engineering, UZH & ETHZ Generous slide support: Ged Ridgway Klaas Enno Stephan John Ashburner
2 Signal, Noise and Preprocessing * Methods & Models for fmri Data Analysis Septeber 26, 2014 Lars Kasper, PhD TNU & MR-Technology Group Institute for Bioedical Engineering, UZH & ETHZ Generous slide support: Ged Ridgway *Huettel et al. Klaas Enno Stephan John Ashburner
3 Overview of SPM for fmri Preprocessing Iage tie-series Kernel Design atrix Statistical paraetric ap (SPM) Realignent Soothing General linear odel Noralisation Statistical inference Rando field theory Teplate Paraeter estiates p <0.05 Lars Kasper Signal, Noise and Preprocessing 3
4 fmri = Acquiring Movies! Run/Session: Tie Series of Iages z x y Task! of threediensional Blood Oxygen-Level Dependent (BOLD) contrast iages! typically echo-planar iages (EPI) No Task Task scan 1 tie scan N Lars Kasper Signal, Noise and Preprocessing 4
5 fmri = Acquiring Movies! The Localized Tie-series is the Fundaental Inforation of fmri Signal: Fluctuation through Blood oxygen level dependent (BOLD) contrast Noise: All other fluctuations! Run/Session: Tie Series of Iages scan 1 tie scan N Lars Kasper Signal, Noise and Preprocessing 5
6 fmri Movie: An exaple Lars Kasper Signal, Noise and Preprocessing 6
7 fmri Movie: Subtracting the Mean! interest in fluctuations only Lars Kasper Signal, Noise and Preprocessing 7
8 Introducing the Dataset (MoAE)! Mother of All Experients: Auditory Stiulation! TR 7 seconds! 6 TR rest! 6 TR binaural stiulation (1 bi-syllabic word per second)! Chapter 28 of SPM anual Lars Kasper Signal, Noise and Preprocessing 8
9 The Goal of Preprocessing Before After Preprocessing Lars Kasper Signal, Noise and Preprocessing 9
10 Sources of Noise in fmri! Subject Motion! Acquisition Tiing! Anatoical Identity Spatial Preproc Teporal Preproc Spatial Preproc! Realignent! Slice-Tiing! Co-registration! Inter-subject variability! Theral Noise! Physiological Noise Spatial Preproc Spatial Preproc Noise Modelling! Segentation! Soothing! PhysIO Toolbox Lars Kasper Signal, Noise and Preprocessing 10
11 The SPM Graphical User Interface (GUI) 1.! Preprocessing! Realignent! Slice-Tiing Correction! Co-registration! Unified Segentation & Noralization! Soothing Lars Kasper Signal, Noise and Preprocessing 11
12 Sources of Noise in fmri! Subject Motion! Realignent! Acquisition Tiing! Anatoical Identity Teporal Preproc! Slice-Tiing! Co-registration! Inter-subject variability! Theral Noise! Physiological Noise! Segentation! Soothing! PhysIO Toolbox Lars Kasper Signal, Noise and Preprocessing 12
13 Slice-tiing correction (STC)! Acquisition onset differs between slices! STC: teporal alignent of all voxels in a volue! Via sinc interpolation Sladky et al, NeuroIage 2011 Lars Kasper Signal, Noise and Preprocessing 13
14 Slice-tiing correction (STC): Siulation Sladky et al, NeuroIage 2011 Lars Kasper Signal, Noise and Preprocessing 14
15 Slice-tiing correction (STC): Experient Sladky et al, NeuroIage 2011 Lars Kasper Signal, Noise and Preprocessing 15
16 Sources of Noise in fmri! Subject Motion! Acquisition Tiing! Anatoical Identity! Inter-subject variability! Theral Noise! Physiological Noise Spatial Preproc Spatial Preproc Spatial Preproc Spatial Preproc! Realignent! Slice-Tiing! Co-registration! Segentation! Soothing! PhysIO Toolbox Lars Kasper Signal, Noise and Preprocessing 16
17 Finite Resolution and Voxel Identity! voxel = volue eleent (3D pixel) Lars Kasper Signal, Noise and Preprocessing 17
18 Spatial Preprocessing = Correcting Voxel Misatches Voxel Misatch Between Functional Scans/Runs Functional/Structural Iages Subjects Realignent Coregistration Noralization/ Segentation Lars Kasper Signal, Noise and Preprocessing 18
19 Spatial Preprocessing REALIGN COREG SEGMENT NORM WRITE SMOOTH GLM Lars Kasper Signal, Noise and Preprocessing 19
20 Spatial Preprocessing fmri tie-series Anatoical MRI TPMs Input Output Segentation Transforation (seg_sn.at) Kernel REALIGN COREG SEGMENT NORM WRITE SMOOTH & $ $ $ $ $ $ % #!!!!!! " Motion corrected Mean functional (Headers changed) MNI Space GLM Lars Kasper Signal, Noise and Preprocessing 20
21 Realignent fmri tie-series! Aligns all volues of all runs spatially! Rigid-body transforation: three translations, three rotations REALIGN! Objective function: ean squared error of corresponding voxel intensities! Voxel correspondence via Interpolation Motion corrected Mean functional Lars Kasper Signal, Noise and Preprocessing 21
22 Realignent Output: Paraeters Lars Kasper Signal, Noise and Preprocessing 22
23 B-spline Interpolation Lars Kasper Signal, Noise and Preprocessing 23
24 Reslicing Reoriented Resliced (1x1x3 voxel size) (to 2 cubic) Lars Kasper Signal, Noise and Preprocessing 24
25 fmri Run after Realignent Lars Kasper Signal, Noise and Preprocessing 25
26 Co-Registration Anatoical MRI! Aligns structural iage to ean functional iage COREG & $ $ $ $ $ $ % ! Affine transforation: translations, rotations, scaling, shearing! Objective function: utual inforation, since contrast different! Typically only transforation atrix ( header ) changed (no reslicing) #!!!!!! " Motion corrected Mean functional (Headers changed) Lars Kasper Signal, Noise and Preprocessing 26
27 Co-Registration: Output Mean functional Anatoical MRI! Joint and arginal Histogra! Quantify how well one iage predicts the other = how uch shared info! Joint probability distribution estiated fro joint histogra Lars Kasper Signal, Noise and Preprocessing 27
28 Co-Registration: Output Lars Kasper Signal, Noise and Preprocessing 28
29 Sources of Noise in fmri! Subject Motion! Acquisition Tiing! Anatoical Identity! Realignent! Slice-Tiing! Co-registration! Inter-subject variability! Theral Noise! Physiological Noise Spatial Preproc! Segentation! Soothing! PhysIO Toolbox Lars Kasper Signal, Noise and Preprocessing 29
30 Inter-subject Variability Lars Kasper Signal, Noise and Preprocessing 30
31 Spatial Noralisation - Reasons! Inter-subject averaging! Increase sensitivity with ore subjects! Fixed-effects analysis! Extrapolate findings to the population as a whole! Mixed-effects analysis! Make results fro different studies coparable by aligning the to standard space! e.g. The Talairach&Tournoux (T&T) convention, using the Montreal Neurological Institute (MNI) teplate (152/305 subjects) Signal, Noise and Preprocessing 31
32 Unified Segentation Anatoical MRI TPMs Segentation Transforation (seg_sn.at) SEGMENT NORM WRITE & $ $ $ $ $ $ % #!!!!!! " Motion corrected Mean functional (Headers changed) MNI Space Lars Kasper Signal, Noise and Preprocessing 32
33 Standard spaces The Talairach Atlas The MNI/ICBM AVG152 Teplate The MNI teplate follows the convention of T&T, but doesn t atch the particular brain Recoended reading: Lars Kasper Signal, Noise and Preprocessing 33
34 Noralisation via unified segentation! MRI iperfections ake noralisation harder! Noise, artefacts, partial volue effect! Intensity inhoogeneity or bias field! Differences between sequences! Noralising segented tissue aps should be ore robust and precise than using the original iages...! Tissue segentation benefits fro spatially-aligned prior tissue probability aps (fro other segentations)! This circularity otivates siultaneous segentation and noralisation in a unified odel Signal, Noise and Preprocessing 34
35 Suary of the unified odel! SPM8 ipleents a generative odel! Principled Bayesian probabilistic forulation! Gaussian ixture odel segentation with deforable tissue probability aps (TPMs, priors)! The inverse of the transforation that aligns the TPMs can be used to noralise the original iage! Bias correction is included within the odel Signal, Noise and Preprocessing 35
36 Mixture of Gaussians (MOG)! Classification is based on a Mixture of Gaussians odel (MOG), which represents the intensity probability density by a nuber of Gaussian distributions. Frequency (nuber of pixels) Iage Intensity Signal, Noise and Preprocessing 36
37 Tissue intensity distributions (T1-w MRI) Lars Kasper Signal, Noise and Preprocessing 37
38 Non-Gaussian Intensity Distributions! Multiple Gaussians per tissue class allow non-gaussian intensity distributions to be odelled.! E.g. accounting for partial volue effects Signal, Noise and Preprocessing 38
39 Modelling inhoogeneity! A"ultiplicative"bias"field"is"odelled"as"a" linear"cobina0on"of"basis"func0ons." Corrupted iage Bias Field Corrected iage Signal, Noise and Preprocessing 39
40 Tissue Probability Maps! Tissue probability aps (TPMs) are used as the prior, instead of the proportion of voxels in each class ICBM Tissue Probabilistic Atlases. These tissue probability aps were kindly provided by the International Consortiu for Brain Mapping Signal, Noise and Preprocessing 40
41 Deforing the Tissue Probability Maps! Tissue probability aps iages are warped to atch the subject! The inverse transfor warps to the TPMs Lars Kasper Signal, Noise and Preprocessing 41
42 Segentation results Spatially noralised BrainWeb phantos (T1, T2, PD) Tissue probability aps of GM and WM! segentation works irrespective of iage contrast Cocosco, Kollokian, Kwan & Evans. BrainWeb: Online Interface to a 3D MRI Siulated Brain Database. NeuroIage 5(4):S425 (1997) Lars Kasper Signal, Noise and Preprocessing 42
43 Benefits of Unified Segentation Affine registration Non-linear registration Lars Kasper Signal, Noise and Preprocessing 43
44 Spatial noralisation Overfitting Non-linear spatial noralisation can introduce unwanted deforation Teplate iage Non-linear registration using regularisation (error = 302.7) Affine registration (error = 472.1) Non-linear registration without regularisation (error = 287.3) Lars Kasper Signal, Noise and Preprocessing 44
45 Spatial noralisation regularisation! The best paraeters according to the objective function ay not be realistic! In addition to siilarity, regularisation ters or constraints are often needed to ensure a reasonable solution is found! Also helps avoid poor local optia! Can be considered as priors in a Bayesian fraework, e.g. converting ML to MAP:! log(posterior) = log(likelihood) + log(prior) + c Signal, Noise and Preprocessing 45
46 Spatial noralisation Liitations! Seek to atch functionally hoologous regions, but...! Challenging high-diensional optiisation, any local optia! Different cortices can have different folding patterns! No exact atch between structure and function! [Interesting recent paper Aiez et al. (2013), PMID: ]! Coproise! Correct relatively large-scale variability (sizes of structures)! Sooth over finer-scale residual differences Signal, Noise and Preprocessing 46
47 Soothing! Why blurring the data?! Intra-subject signal quality! Suppresses theral noise (averaging)! Increases sensitivity to effects of siilar scale to kernel (atched filter theore)! Single-subject statistical analysis! Makes data ore norally distributed (central liit theore)! Reduces the effective nuber of ultiple coparisons Kernel SMOOTH! Second-level (Inter-subject) statistical analysis! Iproves spatial overlap by blurring over anatoical differences MNI Space GLM Lars Kasper Signal, Noise and Preprocessing 47
48 Soothing! How is it ipleented?! Convolution with a 3D Gaussian kernel, of specified full-width at halfaxiu (FWHM) in! Gaussian kernel is separable, and we can sooth 2D data with 2 separate 1D convolutions! Generalisation is siple and efficient A 2D Gaussian Kernel Signal, Noise and Preprocessing 48
49 fmri Run after Soothing Lars Kasper Signal, Noise and Preprocessing 49
50 Spatial Preprocessing fmri tie-series Anatoical MRI TPMs Input Output Segentation Transforation (seg_sn.at) Kernel REALIGN COREG SEGMENT NORM WRITE SMOOTH & $ $ $ $ $ $ % #!!!!!! " Motion corrected Mean functional (Headers changed) MNI Space GLM Lars Kasper Signal, Noise and Preprocessing 50
51 Sources of Noise in fmri! Subject Motion! Acquisition Tiing! Anatoical Identity! Inter-subject variability! Theral Noise! Realignent! Slice-Tiing! Co-registration! Segentation! Soothing! Physiological Noise Noise Modelling! PhysIO Toolbox Lars Kasper Signal, Noise and Preprocessing 51
52 The Proble: Physiological Noise! Cardiac effects! Respiratory effects Lars Kasper Signal, Noise and Preprocessing 52
53 The Proble: Physiological Noise! Cardiac effects A Cardiac Cycle in the Brain! Systole:! Blood puped into brain, vessel volue increases: pulsatile vessels! CSF pushed down: pulsatile CSF! Diastole:! Vessel volue decreases! CSF flows back into void brain volue Lars Kasper Signal, Noise and Preprocessing 53
54 The Proble: Physiological Noise! Cardiac effects Vessel Anatoy Locations of Fluctuations Lars Kasper Signal, Noise and Preprocessing 54
55 The Proble: Physiological Noise! Cardiac effects! Respiratory effects! Chest (&head) oves with respiratory cycle! Changes in lung volue change encoding agnetic field for MR! Geoetric distortion/scaling! Respiratory-sinus arrythia! Heart beats faster during inhalation Lars Kasper Signal, Noise and Preprocessing 55
56 Iage-based Physiological Noise Correction! Post-hoc via peripheral easures & odelling:! RETROICOR, cardiac response function, PhysIO Toolbox! Post-Hoc via data-driven ethods:! GM/WM/Global seed signal regression (Conn Toolbox)! ICA/PCA Lars Kasper Signal, Noise and Preprocessing 56
57 Iage-based Physiological Noise Correction PhysIO Toolbox Lars Kasper Signal, Noise and Preprocessing 57
58 The Solution: Physiological Noise Correction 1. Physiological Monitoring 2. Preprocessing of Physiological Data 3. Model tie series physiological noise 4. Noise Reduction and Assessent Peripheral Devices PhysIO Toolbox SPM ECG, PPU Cardiac cycle Breathing belt Respiratory cycle Confound regressors Capnograph CO 2 concentration Lars Kasper 58 Signal, Noise and Preprocessing Signal, Noise and Preprocessing 58
59 When? Literature Evidence! Resting-state:! Birn, R. M. The Role of Physiological Noise in Resting-state Functional Connectivity. NeuroIage 62, 2012! Birn, R. M., et al. Separating Respiratory-variation-related Fluctuations fro Neuronal-activityrelated Fluctuations in fmri. NeuroIage 31, 2006! Task-based:! Hutton, C., et al. The Ipact of Physiological Noise Correction on fmri at 7 T. NeuroIage 57, 2011: Lars Kasper Signal, Noise and Preprocessing 59 59
60 When? PhysIO experience! Andreea Diaconescu (TNU): Social Learning Diaconescu, et al., PLoS Coput Biol 10 Lars Kasper Signal, Noise and Preprocessing 60
61 When? PhysIO experience! Andreea Diaconescu (TNU): Social Learning! Higher sensitivity for group effects (N=35)! Prediction of advice reliability: dpfc, bilateral FFA! Prediction error: dpfc! Less false/abiguous positives:! Brainste (Substantia Nigra) Lars Kasper Signal, Noise and Preprocessing 61
62 When? PhysIO experience! Andreea Diaconescu (TNU): Social Learning! Higher sensitivity for group effects (N=35)! Prediction of advice reliability: dpfc, bilateral FFA! Prediction error: dpfc! Less false/abiguous positives:! Brainste (Substantia Nigra) Lars Kasper Signal, Noise and Preprocessing 62
63 When? PhysIO experience! Andreea Diaconescu (TNU): Social Learning! Higher sensitivity for group effects (N=35)! Prediction of advice reliability: dpfc, bilateral FFA! Prediction error: dpfc! Less false/abiguous positives:! Brainste (Substantia Nigra) Lars Kasper Signal, Noise and Preprocessing 63
64 When? PhysIO experience! Andreea Diaconescu (TNU): Social Learning! Higher sensitivity for group effects (N=35)! Prediction of advice reliability: dpfc, bilateral FFA! Prediction error: dpfc! Less false/abiguous positives:! Brainste (Substantia Nigra) Lars Kasper Signal, Noise and Preprocessing 64
65 Sources of Noise in fmri! Subject Motion! Acquisition Tiing! Anatoical Identity Spatial Preproc Teporal Preproc Spatial Preproc! Realignent! Slice-Tiing! Co-registration! Inter-subject variability! Theral Noise! Physiological Noise Spatial Preproc Spatial Preproc Noise Modelling! Segentation! Soothing! PhysIO Toolbox Lars Kasper Signal, Noise and Preprocessing 65
66 Spatial Preprocessing fmri tie-series Anatoical MRI TPMs Input Output Segentation Transforation (seg_sn.at) Kernel REALIGN COREG SEGMENT NORM WRITE SMOOTH & $ $ $ $ $ $ % #!!!!!! " Motion corrected Mean functional (Headers changed) MNI Space GLM Lars Kasper Signal, Noise and Preprocessing 66
67 Thank you! and:! TNU Zurich, in particular: Klaas! MR-technology Group IBT, in particular: Klaas! Everyone I borrowed slides fro Lars Kasper Signal, Noise and Preprocessing 67
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