Thanks for the advice everyone. I've tried loading the data in run-wise and combining it and things seem to work fine!<div><br><br>On Tuesday, January 14, 2014, Nick Oosterhof wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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On Jan 14, 2014, at 4:53 PM, Jason Ozubko wrote:<br>
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> 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.<br>
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> 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?<br>
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That sounds plausible, as indeed first the full image is loaded and then the mask is applied - which reduces the required memory significantly after garbage collection.<br>
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Thus indeed it may be better in your case to load the images run-wise, and then manually vstack them. Just be careful that the volume dimensions/positions match across the different runs, as with the manually stacking approach there is by default no check for this, unless you would use {h,v}stack with 'unique' as the second argument.<br>
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