1. Zen and the art of nuisance variable maintenance
Timothy Verstynen
Keck Center for Integrative Neuroscience
Neurosciences Imaging Center, UCSF
Jörn Diedrichsen
Department of Psychology
University of Wales, Bangor Wales
Vibhas Deshpande
Siemens and Neuroscience Imaging Center, UCSF

2. Why use nuisance variables in your fMRI analysis?
The BOLD signal is inherently noisy, even after accounting for task related trends.
Two types of extraneous factors:
Movement Artifacts (common)
Physiological (new)

3. Movement artifacts vs. your signal
Fluctuations in voxel position influence the detected signal.
If movement is correlated with your task, then the mechanical fluctuations in voxel signal can get mistaken for task-related BOLD responses.
Luckily, most analysis packages already account for movement signals GLM-based analyses
Movement Direction
voxel
blood vessel

4. Physiology parameters vs. your signal
The BOLD response itself is a physiological signal!
Related to breathing (oxygenation) and vascular performance (blood flow)
Particularly important in areas with high blood vessel to tissue ratio.
Heeger and Ress (2002)

5. Question: Why should I worry about physiological signals if they are often much faster than my sampling rate?
Answer:
Because of aliasing, high frequency signals may emerge as slowly fluctuating trends which may appear to be relevant signals.

6. Identifying Physiological Artifacts

7. Event Identification and phase information

8. Identifying the phase of the physiological signal
Distance = 2p
Event Mark
time
Distance = 2*2p
Distance = 3*2p
For all events in the scan, the “unwarped” phase distance for each inter-event interval was calculated as:
Distance = Nintervals * 2p
The sine and cosine of this series approximates the first Fourier expansion of this series.

9. Phase-locked Waveforms
Respiration phase centered on the peak of the pressure wave.
- i.e. peak expansion of lungs
ECG phase centered on the R-component of the QRS waveform.
- i.e. contraction of lower ventricles.

10. Identifying heart-rate related voxels
Circle of Willis
(F’s > 20)

11. What does a true physiological artifact look like in the BOLD signal?
Voxels in the Circle of Willis
Predicted = bphysio * Xphysio
Residual = bphysio * Xphysio + e

12. What covariates are important to use in your analysis?

13. PhLEM: Physiological Log Extraction for Modeling
What: Transforms physiologcal event markers into regressors to be used in your GLM analysis (optimized for SPM5).
How: Using the phase unwarping method described above and downsampling to the sampling rate of your TR.
Where: PhLEM can be downloaded with loader functions for NIC data, examples and help files at

14. The Experiment
500ms TR
1000ms TR
2000ms TR
Task: Event-related visual-motor task (variable ITI). Move your hands when you see a flashing checkerboard.
Recorded Parameters:
Movement using SPM realign routines
ECG and Respiration using the Siemen’s physio recording system

15. The Models
Simple
Physio
Movement

16. Spatial Statistics
Simple
Full

17. Spatial Statistics

18. The Region of Interest The primary visual cortex
Active voxels in the Calcarine Fissure
Identified as task related voxels in the 500ms experiment with full covariate model (t > 3.5)

19. Comparing models with different numbers of parameters
Deviance: D(y,u) = -2*log([L(u)/L(y)]) = 2*(l(u)-l(y))
A residual measure on the likelihoods
Hypothesis testing with Deviance:
Given two models f(x), g(x) with Nf and Ng number of parameter respectively (where Ng > Nf), the difference in deviance follows a c,2 distribution.
e.g.- Df(x) – Dg(x) ~ c2 Ng - Nf

20. Results: Model Comparisons
Simple
Vs.
Physio

21. Results: When do physiological parameters help?
Better improvement of model fits the closer the average physiological signal matches the sampling rate (e.g. the TR).

22. Results: When do physiological parameters help?
This improved model fit increases the power of task-related statistics as well.

23. Results: Model Comparisons
Simple
Vs.
Move

24. Results: When do movement parameters help?
The greater the movement error, the more likely incorporating it in the model will improve the model fits; however it does not appear to improve overall task-related statistics.

25. Does adding in nuisance terms like movement and physiological parameters really help improve your signal?
Yes… but it depends on your imaging parameters
At faster TRs, both ECG and Respiration phases help to improve the signal in task relevant voxels. This happens because the sampling rate approaches the inherent signal rate.
Incorporating movement error in the GLM, however, will improve overall model fits but still impairs the strength of task-relevant contrasts.