The Art and Pitfalls of fmri Preprocessing

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Translational Neuroodeling Unit Institute

1 Translational Neuroodeling Unit The Art and Pitfalls of fmri Preprocessing Lars Kasper June 14 th, An SPM Perspective MR-Technology Group & Translational Neuroodeling Unit Institute for Bioedical Engineering University of Zurich and ETH Zurich

2 Abstract fmri preprocessing has catastrophic consequences on study outcoes - if it fails. However, such failures are hard to detect, due to the coplexity of preprocessing pipelines and the sheer aount of iage data to be reviewed. Thus, an autoatic way of reproducing, docuenting and assessing coplex workflows is key to a successful preprocessing strategy. This talk deonstrates how SPM caters for this need by one of its ain user interface features: the Batch Editor. First, we see how its odular structure and input/output definitions allow for setting up the whole preprocessing pipeline at once in a reproducible fashion that can be easily reviewed, and applied to different subjects. Modules such as realignent, slice-tiing correction, coregistration, and noralization via unified segentation are covered. We introduce one specific preprocessing pipeline with its rationale, and a clear decision tree for when to deviate fro it. Secondly, the Batch Editor greatly facilitates quality assurance by recruiting the versatile (but soewhat hidden) visualization capabilities of SPM, to create graphical reports of each preprocessing step. Finally, we highlight how to integrate nonstandard preprocessing odules into SPM via the Batch Editor, by the exaple of physiological noise correction with a custo-built toolbox (TAPAS PhysIO). The forat of this talk will incorporate the graphical user interface wherever possible, including walk-throughs and screen-shots. 2

3 Tutorial: Code and Data Download exaple code and data presented in this talk: Section: Talk and Lecture Materials SPM12 (Statistical Paraetric Mapping) developed by the Functional Iage Lab, UCL, London TAPAS PhysIO Toolbox (SPM or Matlab standalone) Docuentation & Exaple Data (Philips/Sieens/GE): 3

4 Outline Setting up a Preprocessing Pipeline in SPM: The Batch Editor Monitoring and Coparing Pipelines Multi-subject Pipelines Integrating Own Code and Toolboxes: Physiological Noise Modeling and Evaluation 4

5 Outline Setting up a Preprocessing Pipeline in SPM: The Batch Editor SPM Overview & Vocabulary Preprocessing Theory (SPM-specific) Interface/Philosophy: Modules/Dependencies/Data Monitoring and Coparing Pipelines Multi-subject Pipelines Integrating Own Code and Toolboxes: Physiological Noise Modeling and Evaluation 5

6 Outline Setting up a Preprocessing Pipeline in SPM: The Batch Editor SPM Vocabulary What is SPM? GUI What is Realign/Co-Registration/Noralisation via Unified Segentation Dealing with Headers (Estiate vs Write/Reslice), 3D vs 4D fraes Preprocessing Theory (SPM-specific) Co-Registration Joint Histogra Unified Segentation: Algorith, TPMs, Why does it noralise? Batch Editor Philosophy: Modules/Dependencies/Data History, V. Glauche, sp/batches (Ged), preproc_fri_siplified. Finally: Process Diagra and corresponding pipeline, run! 6

7 Overview of SPM for fmri Iage tie-series Kernel Design atrix Statistical paraetric ap (SPM) Realignent Soothing General linear odel Preprocessing Noralisation Statistical inference Rando field theory Teplate Paraeter estiates p <0.05 7

8 The SPM GUI Preprocessing Realignent Slice-Tiing Correction Co-registration Unified Segentation & Noralisation Soothing Noise Modelling 3. Physiological Confound Regressors The Batch Editor 8

9 Spatial Preprocessing: SPM vocabulary SPM uses different naes for different odes of iage registration depending on input iages and allowed transforations Realignent Intra-odal iage registration e.g. functional iages rigid body transforations translation/rotation Co-Registration Inter-odal registration e.g. T1/T2 contrast functional to structural iage affine transforations rigid body stretching/shearing Unified Segentation Multi-odal registration e.g. T1 and/or T2 structural iage(s) to teplate non-linear transforations constrained deforation fields 9

10 Spatial Preprocessing: SPM vocabulary Realignent Unified Segentation REALIGN COREG SEGMENT NORM WRITE SMOOTH Co-Registration 10

11 Spatial Preprocessing Input Output fmri tie-series Structural MRI TPMs Segentation Deforation Fields (y_struct.nii) Kernel REALIGN COREG SEGMENT NORM WRITE SMOOTH Motion corrected Mean functional (Headers changed) MNI Space GLM 11

12 Theory: Co-Registration Structural MRI Motion corrected COREG Mean functional (Headers changed) Aligns structural iage to ean functional iage Affine transforation: translations, rotations, scaling, shearing Objective function: utual inforation, since contrast different Optiisation via Powell s ethod: conjugate directions, line seach along paraeters Typically only transforation atrix changed (no reslicing) 12

Theory: Coreg Joint Histogra Joint Histogra Marginal Histogra Mean functional Anatoical MRI Joint and arginal Histogra Quantify how well one iage predicts the other how uch shared inforation Joint

13 Theory: Coreg Joint Histogra Joint Histogra Marginal Histogra Mean functional Anatoical MRI Joint and arginal Histogra Quantify how well one iage predicts the other how uch shared inforation Joint probability distribution estiated fro joint histogra intensity bins structural intensity bins functional Joint Histogra: h(i f,i s ) Count of voxels who have intensity i f in functional and i s in structural iage 13

14 Theory: Good Co-Registration Output Voxels of sae tissue have sae intensity in MR-contrast 2 nd MR contrast, this intensity ay be different, but still the sae aong all voxels of sae tissue type Aligned voxels in 2 iages = crisp peaks in joint histogra 14

15 Theory: Unified Segentation Structural MRI TPMs Segented Iages Deforation Fields SEGMENT NORM WRITE (y_struct.nii) Motion corrected Mean functional (Headers changed) MNI Space 15

16 Theory: Segentation/Noralisation MRI iperfections: No siple siilarity easure, a lot of possible transforations Noise, artefacts, partial volue effects Intensity inhoogeneity (bias field) Geoetric/Contrast differences between sequences Noralisation of segented tissues is ore robust and precise than of original iage Tissue segentation benefits fro spatially aligned tissue probability aps (of prior segentation data) This circularity otivates siultaneous segentation and noralisation in a unified odel Lars Kasper: An SPM Perspective on fmri Preprocessing 16

17 Theory: Unified Model Segentation SPM12 ipleents a generative odel of voxel intensity fro tissue class probabilities Principled Bayesian probabilistic forulation Segentation by inverting a Gaussian ixture odel Deforations of prior tissue probability aps (TPMs, priors) are also part of the odel The inverse of the transforation that aligns the TPMs can be used to noralise the original iage Non-linear deforations are constrained by regularisation factors Bias correction is included within the odel Lars Kasper: An SPM Perspective on fmri Preprocessing 17

Mixture of Gaussians Classification is based on a Mixture of Gaussians odel, which represents the intensity probability density by a nuber of Gaussian distributions.

18 Mixture of Gaussians Classification is based on a Mixture of Gaussians odel, which represents the intensity probability density by a nuber of Gaussian distributions. Multiple Gaussians per tissue class allow non-gaussian intensity distributions to be odelled e.g. partial volue effects Frequency (nuber of pixels) Iage Intensity Lars Kasper: An SPM Perspective on fmri Preprocessing 18

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.

19 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 Lars Kasper: An SPM Perspective on fmri Preprocessing 19

20 Deforing the Tissue Probability Maps Tissue probability ap teplates are warped to atch the subject The inverse transfor warps to the TPMs 20

21 Preprocessing Pipeline Input Output fmri tie-series Structural MRI TPMs Segentation Deforation Fields (y_struct.nii) Kernel REALIGN COREG SEGMENT NORM WRITE SMOOTH Motion corrected Mean functional (Headers changed) MNI Space GLM 21

22 The Batch Editor in SPM 3. fmri tie-series Structural MRI Eleents of a Pipeline 1. Modules 2. Dependencies 3. Data (Subject-specific) Philosophy: Batch = Process Diagra 1. REALIGN COREG Input Output Motion corrected Mean functional 22

23 The Batch Editor in SPM developer: V. Glauche coand line usage: sp_joban 23

24 Deo 1 Siple preprocessing pipeline for fmri (G. Ridgway) Based on sp12/batches/preproc_fri_siplified. Data: Social Learning (A. Diaconescu [1], TNU Zurich, Philips 3T) After download: 1. Unzip archive exaples_physio_short.zip 2. Open Matlab, run code/init_reset_exaple. subject01-folder created, Batches filled with right data (filenaes and path) already 3. Load subject01/batches/deo01_siple_batch_preproc/ batch_sp_preproc_fri_siplified. in Batch Editor Either via GUI or sp_joban( initcfg ); sp_joban( interactive, batch_sp_preproc_fri_siplified.. ); 4. Run Batch (Press Play) [1] Diaconescu (2014), PLoS CB 24

25 Deo 1 - GUI 25

26 Deo 1 - Results Autoatic status plots saved in sp_<date>.ps REALIGN COREG 26

27 Outline Setting up a Preprocessing Pipeline in SPM: The Batch Editor Monitoring and Coparing Pipelines Perforance Measures: Mean/SD/SNR/Diff Iage SPM Plotting Routines and Autoatic Reporting Spotting Failed Pipelines Coparing Alternative Pipelines Multi-subject Pipelines Integrating Own Code and Toolboxes: Physiological Noise Modeling and Evaluation 27

28 Outline Monitoring and Coparing Pipelines Perforance Measures: Mean/SD/SNR/Diff Iages Rationale fro tie series, partial volue effects ean = artifact, SD = sensitivity loss, SNR = cobined, ean(abs(diff)) SPM Plotting Routines and Autoatic Reporting sp_print, sp_check_registration Spotting Failed Pipelines fri.at with 1 vol (or spike in FFT?), anatoy irrored for coreg/segent Coparing Alternative Pipelines slice tiing correction no/before/after realign: preproc_fri. deep or broad folder tree for different pipelines? 28

29 Pipeline Monitoring Pipelines => Autoatisation of Preprocessing When soething goes wrong how do we even notice? Monitoring, but: cubersoe, when lots of data Thus: Autoatise quality onitoring as well via pipelines Required: Suitable perforance easures Single iage: visual inspection geoetry/contrast/noise/snr Tie series Mean => Artifact levels (localization) SD => Fluctuation levels (consistent over whole tie series) SNR = Mean/SD => sensitivity for signal changes of interest, e.g. BOLD Diff iages (ax(abs(diff)) or odd vs even) => outlier detection, iage noise Welvaert (2013), PLoS One Friedan/Glover (2006), 29

Statistical Iages of Tie Series Setup Pipeline Quality Monitoring Mul2- Subject Noise Modeling Structural: visual inspection geoetry/contrast/noise/ SNR Functional Mean => Artifact levels

30 Statistical Iages of Tie Series Setup Pipeline Quality Monitoring Mul2- Subject Noise Modeling Structural: visual inspection geoetry/contrast/noise/ SNR Functional Mean => Artifact levels (localization) SD => Fluctuation levels (consistent over whole tie series) SNR = Mean/SD => sensitivity for signal changes of interest, e.g. BOLD Diff iages (ax(abs(diff)) or odd vs even) => outlier detection, iage noise Lars Kasper: An SPM Perspective on fmri Preprocessing 30

31 Statistical Iages of Tie Series Structural: visual inspection geoetry/contrast/noise/ SNR Functional Mean => Artifact levels (localization) SD => Fluctuation levels (consistent over whole tie series) SNR = Mean/SD => sensitivity for signal changes of interest, e.g. BOLD Diff iages (ax(abs(diff)) or odd vs even) => outlier detection, iage noise 31

through Blood oxygen level dependent (BOLD) contrast Noise:

32 fmri = Acquiring Movies The Localized Tie-series is the Fundaental Inforation Unit 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 32

33 Noise Categories & Reduction Theral Noise teporally uncorrelated reduced SNR è risk of false negatives Reedy: Spatial Soothing Noise: All other fluctuations Structured Noise teporally correlated reduced SNR è risk of false negatives correlated with task è risk of false positives Reedy: Noise Modelling (e.g. GLM) Inference = Signal-To-Noise 33

34 Deo 2 Coplete preprocessing pipeline for fmri Based on sp12/batches/preproc_fri Includes batch_report_quality. to visualize and save quality easures after each preprocessing step Run via 1. Load subject01/batches/deo02_siple_batch_preproc/ batch_preproc_fri_report_quality. 2. Either via GUI or sp_joban( interactive, batch_sp_preproc_fri_report_quality. ); 3. Alternative: Run batch directly fro coand line: sp_joban( run, batch_sp_preproc_fri_report_quality. ); 34

35 Deo 2 - Output Output: subject01/report_quality/report_quality.ps PostScript file with all output plots subject01/00_raw /ean.nii /01_realigned /04_soothed => raw tie series statistics => ean of tie series (per pixel) /sd.nii => standard deviation (per pixel) /snr.nii => ean/sd (per pixel) /diffoddeven.nii => SuOddIages SuEvenIages /axabsdiff => axiu delta iage (vol n vs n+1) => realigned tie series stats => soothed tie series stats 35

36 SPM Plotting Routines Check Reg Matlab coand line: sp_check_registration( iage1.nii, iage2.nii, ) via Batch Editor: SPM => Util => Check Registration Right click reveals aazing features (edge detection, anatoic labeling, header info, windowing, show intensity add blobs) 36

37 Plotting Exaple Teporal Fluctuation (SD) per pixel in functional iage tie series after each preprocessing step 37

38 Quiz What went wrong here? subjecta1 subjecta2 subjecta3 38

39 Outline Setting up a Preprocessing Pipeline in SPM: The Batch Editor Monitoring and Coparing Pipelines Multi-subject Pipelines Staying on Top: Organisation of Data Looping Pipelines over Subjects Data Tips for Efficient Perforance Monitoring Integrating Own Code and Toolboxes: Physiological Noise Modeling and Evaluation 39

40 Outline Multi-subject Pipelines Staying on Top: Organisation of Data Sae folder structure for different subjects, suggested naing Looping Pipelines over Subjects Data Unfilled job dependencies (<-X) as input paraeters Skipping subjects No GUI for cluster (sp_get_defaults( cdline,1)) => howto output? Tips for Efficient Perforance Monitoring Copiling large PS files with abundant plotting Scripts for re-loading quality report iages (sp_check_registration) 40

41 Deo 3 Multi-subject Pipelines Staying on Top: Organisation of Data Sae folder structure for different subjects, suggested naing Looping Pipelines over Subjects Data Unfilled job dependencies (<-X) as input paraeters Skipping subjects No GUI for cluster (sp_get_defaults( cdline,1)) => howto output? Tips for Efficient Perforance Monitoring Copiling large PS files with abundant plotting Scripts for re-loading quality report iages (sp_check_registration) 41

42 Outline Setting up a Preprocessing Pipeline in SPM: The Batch Editor Monitoring and Coparing Pipelines Multi-subject Pipelines Integrating Own Code and Toolboxes: Physiological Noise Modeling and Evaluation Executing Custo Matlab Code within the Pipeline The TAPAS PhysIO Toolbox Autoatic (Noise) Modeling and Contrast Reporting 42

43 Outline Integrating Own Code and Toolboxes: Physiological Noise Modeling and Evaluation Executing Custo Matlab Code within the Pipeline e.g. creation of ultiple_conditions, extra otion regressors (12/24) The TAPAS PhysIO Toolbox Overview which noise odels ipleented Focus on robustness, ulti-vendor copatibility Modules/Paraeters colored and in Batch Editor Autoatic (Noise) Modeling and Contrast Reporting Contrast Report => Glass Brain & Table as.ps tapas_physio_report_contrasts => F-contrast, showing typical distributions and structural underlay 43

44 Iage-based Noise Correction PhysIO Toolbox 44

Physiological Noise Correction 1. Physiological Monitoring 2. Preprocessing of Physiological Data 3. Model tie series physiological noise 4.

45 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 Lars Kasper: An SPM Perspective on fmri Preprocessing 45

pulses RETROICOR Respiratory Volue Heart Rate Multiple_regressors file for SPM Finally: Check Influence of Physiological

46 Workflow PhysIO Toolbox Read logfiles Preprocess physiological data Model tie series physiological noise Include confound regressors (GLM) Of peripheral physiological data Vendor-specific Filter noisy ECG & detect cardiac pulses Hand-pick issing pulses RETROICOR Respiratory Volue Heart Rate Multiple_regressors file for SPM Finally: Check Influence of Physiological Noise (Correction) on Data SPM F-contrast on 1st and second level Lars Kasper: An SPM Perspective on fmri Preprocessing 46

PhysIO: SPM Batch Interface 2015-06-14 Lars

47 PhysIO: SPM Batch Interface Lars Kasper: An SPM Perspective on fmri Preprocessing 47

48 Model Check: SPM F-contrasts Finally: Check Influence of Physiological Noise (Correction) on Data SPM F-contrast on 1st and second level Lars Kasper: An SPM Perspective on fmri Preprocessing 48

49 Conclusion Setting up a Preprocessing Pipeline in SPM: The Batch Editor Monitoring and Coparing Pipelines Multi-subject Pipelines Integrating Own Code and Toolboxes: Physiological Noise Modeling and Evaluation 49

50 Thank you for your attention and these people for their support in the preparation: Ged Ridgway Guillaue Flandin Klaas Enno Stephan John Ashburner Questions: Now J Poster #3736 (PhysIO): Wed, 12:45p kasper@bioed.ee.ethz.ch 50

51 Preprocessing: Iage Registration B. Allowed Transforations Rigid-Body Affine Non-linear REALIGN COREG SEGMENT NORM WRITE C. Siilarity Measure Mean-squared Difference Exact Linearized Solution Mutual Inforation Conjugate Direction Line Search D. Optiisation Tissue Class Probability Iterated Conditional Modes (EM/Levenberg-Marquardt) 51

52 Headers (Map Voxel-to-World) Data: 3D iages are ade up of voxels. Voxel intensities are stored on disk as lists of nubers. Header: Meta-inforation about the data: iage diensions (list to 3D) voxel-to-world apping (affine) fro: data coordinates (voxel colun i, row j, slice k) to: a real-world position (x,y,z ) in a coordinate syste ( e.g. Scanner or MNI) SPM changes headers only where possible to avoid interpolation in realign and co-register (estiate) enforce rewriting the data (by interpolation) by reslice / write 52

paraeters, data is resapled onto sae grid of voxels as

53 E. Reslicing/Interpolation Iages have to be saved as voxel intensity list on disk After applying transforation paraeters, data is resapled onto sae grid of voxels as reference iage Reoriented Resliced 1x1x3 voxel size 2x2x2 voxel size 53

54 SPM 3D vs 4D Always take care whether you specify a nifti-file (containing a 4D dataset, including different volues) or a nifti-frae (one 3D volue) file: frae: subject01/fri/fri.nii subject01/fri/fri.nii,1 (or,15 etc.) The Batch Editor is not always consistent in what it needs, i.e. just the file nae or a list of all individual fraes This is one the ost coon error sources using preprocessing pipelines 54

fmri Preprocessing & Noise Modeling

Translational Neuromodeling Unit fmri Preprocessing & Noise Modeling Lars Kasper September 25 th / October 17 th, 2015 MR-Technology Group & Translational Neuromodeling Unit An SPM Tutorial Institute for