<div dir="ltr">I am using the NeuroDebian virtual machine and I have given it 4GB of memory to work with (the maximum it can handle as I understand, since it's a 32-bit OS). <div><br></div><div>The dataset I'm working with has dimensions of 79x95x68 for each functional run, after being pre-processed from an original scan of 96x96x37. There are 2151 functional runs in total across all 5 of my runs. I am actually however, masking my data and so I only end up including about 950 voxels from each functional run.</div>
<div><br></div><div>To me the error message seemed to indicate that I was running out of memory when I was loading the full dataset before any masking had occurred. I wonder, if I load it in a piece meal fashion (1 run at a time) with the mask, after the data is loaded the masking should cut down on the number of voxels (and so overall memory signature) significantly, and so perhaps in the end I won't end up hitting a memory error once the data is loaded?</div>
<div><br></div><div>Thanks in advance for your thoughts</div><div><br></div><div>Cheers,</div><div>Jason</div><div><br></div><div><br></div><div><br></div><div><br></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">
On Mon, Jan 13, 2014 at 7:40 PM, Yaroslav Halchenko <span dir="ltr"><<a href="mailto:debian@onerussian.com" target="_blank">debian@onerussian.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div class="HOEnZb"><div class="h5"><br>
On Mon, 13 Jan 2014, Jason Ozubko wrote:<br>
<br>
> Hello,<br>
> I'm currently trying to setup my first MVPA analysis after working through<br>
> a few tutorials and I'm hitting a snag at the first stage, loading in the<br>
> data! �I've got a rather long experiment (~80 minutes, 2.2 s TR, broken<br>
> into about 5 sessions/runs). �I do some minimal preprocessing in SPM<br>
> before outputting 4D nii files that contain the functional data that I<br>
> want to use in my MVPA analyses.<br>
> My initial idea was to concatenate all the runs into 1 big .nii and then<br>
> load that in as a single dataset using fmri_dataset however when I try<br>
> this, I get a memory error (see the attached txt file for a full output)<br>
> I have since learned that python does not seem to crash if I try loading<br>
> in the runs individually (i.e., if I have 5 separate .nii files, each<br>
> containing functional data from a different run). �I am wondering, if I<br>
> loaded in the runs individually, and then I concatenated the loaded<br>
> datasets into one big dataset and then added the sa.targets and sa.chunks<br>
> properties appropriately, could I then proceed to the next steps of my<br>
> analysis and carry forward normally, or will there be issues with the fact<br>
> that I have concatenated the datasets together?<br>
<br>
</div></div>no -- concatenated datasets are as good. But I have a concern about the<br>
memory limit you have experienced: most probably you would keep hitting<br>
it again. How much memory do you have available, and how big is the<br>
actual dataset? (we still do not know dimensions to do algebra<br>
ourselves here). Also intersting is -- that big .nii, did it have the<br>
same original datatype?<br>
<span class="HOEnZb"><font color="#888888"><br>
--<br>
Yaroslav O. Halchenko, Ph.D.<br>
<a href="http://neuro.debian.net" target="_blank">http://neuro.debian.net</a> <a href="http://www.pymvpa.org" target="_blank">http://www.pymvpa.org</a> <a href="http://www.fail2ban.org" target="_blank">http://www.fail2ban.org</a><br>
Senior Research Associate, Psychological and Brain Sciences Dept.<br>
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755<br>
Phone: <a href="tel:%2B1%20%28603%29%20646-9834" value="+16036469834">+1 (603) 646-9834</a> Fax: <a href="tel:%2B1%20%28603%29%20646-1419" value="+16036461419">+1 (603) 646-1419</a><br>
WWW: <a href="http://www.linkedin.com/in/yarik" target="_blank">http://www.linkedin.com/in/yarik</a><br>
<br>
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