Using MION would be substituting a exogenous contrast agent for the endogenous deoxyhemoglobin, so you would still be subject to the same issues of signal drift over long time blocks (e.g., gradient heating). The reason that this isn't an issue for ASL is that you are continually collecting tag/control pairs, and the subtraction of these when computing CBF removes any long term drift related to the magnet.

cheers,

-MH

Michael Harms, Ph.D.

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Conte Center for the Neuroscience of Mental Disorders

Washington University School of Medicine

Department of Psychiatry, Box 8134

660 South Euclid Ave. Tel: 314-747-6173

St. Louis, MO 63110 Email: [log in to unmask]');" target="_blank">[log in to unmask]

From: David Grayson <[log in to unmask]');" target="_blank">[log in to unmask]>
Reply-To: FSL - FMRIB's Software Library <[log in to unmask]');" target="_blank">[log in to unmask]>
Date: Tuesday, April 8, 2014 2:34 PM
To: FSL - FMRIB's Software Library <[log in to unmask]');" target="_blank">[log in to unmask]>
Subject: Re: [FSL] modeling a shift in baseline activity

Thanks for your quick responses. Comparing RSN's would naturally be a good way to go, but unfortunately it isn't amenable to this project for various technical reasons not worth getting into. Very unfortunate indeed.

However, I'm intrigued by the suggestion to use ASL. And I am wondering how you think this compares against contrast-enhanced fMRI with MION (monocrystalline iron oxide nanoparticles). These experiments will be performed in monkeys. So, we will have a saline control condition to compare against the drug condition, and in addition, there is the possibility of using contrast, which has been proposed as a better way to assess baseline physiology relative to BOLD. Here is a quote from a nice review (Mandeville. Iron fMRI measurements of CBV and implications for BOLD signal. Neuroimage 2012):

"...resting state BOLD relaxation rates, which act as amplification factors for relative changes in hemodynamic and metabolic responses, are difficult to measure accurately and routinely. Conversely, the baseline IRON relaxation rate is determined directly from signal attenuation during injection. Thus, IRON signals can be converted to percentage changes in CBV, whereas it is difficult generally to separate functional reactivity from basal physiology using BOLD signal."

Do you have any thoughts on whether ASL or MION-enhanced fMRI would be superior for this experiment? Any other references or advice would be greatly appreciated!

Thanks very much!

-David

On Tue, Apr 8, 2014 at 10:40 AM, Eugene Duff <[log in to unmask]');" target="_blank">[log in to unmask]> wrote:

Hi David -
No, it's really not possible to separate these effects. People do routinely do task-activation and connectivity FMRI studies assessing before vs after drug effects. Here, you obviously need Placebo conditions to avoid the order confound.

Eugene

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On 8 April 2014 18:14, David Grayson <[log in to unmask]');" target="_blank">[log in to unmask]> wrote:

Hi all,

I am seeking suggestions or advice on how to modify standard preprocessing steps in order to analyze an fMRI experiment that assesses drug-induced deactivation of brain regions. The protocol is to acquire standard whole-brain fMRI volumes for 30 minutes continuously, where the first 15 minutes is a pre-drug baseline, and the following 15 minutes is post-injection of the drug. My hypothesis is the baseline BOLD signal will shift down specifically in my regions of interest. Therefore, I would like to compare the mean BOLD signal for the 15 minutes post-injection versus the 15 minutes pre-injection. According to my lit search, this type of analysis is uncommon though apparently not unprecedented with standard fMRI. Clearly, fMRI is very susceptible to baseline artifacts (i.e. scanner drift, physiology, head position) that are very difficult to remove, and *may* be indiscriminable from the experimental manipulation itself.

So I have two questions:

1) What is the best way to remove scanner drift with this design? I have avoided any highpass voxelwise filtering, as I believe this could remove the "shift" in activity in the regions that I am interested in. My first thought was to remove the global drift component using single-session MELODIC. But, surprisingly, no such component appeared. Rather, the drift was included on most other components. Now I am thinking to remove the average white matter signal using linear regression instead. Any thoughts?

2) What is likely to be more powerful when comparing post-drug versus pre-drug - comparing mean scanner units (i.e. %signal change) or modeling the shift in activity and extracting the beta weight?

Thanks all!

David

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