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<div class="moz-cite-prefix">Hi Lewis,</div>
<div class="moz-cite-prefix"><br>
</div>
<div class="moz-cite-prefix">This kind of question would better go
to Neurostars.</div>
<div class="moz-cite-prefix">IIUC, you have put in the dataframe
both the target variable (the class), often denoted "y", and the
voxel data ("X"). So you don't need to apply a Masker any more.</div>
<div class="moz-cite-prefix">The only thing is to extract X and y
from your dataFrame, and give that to scikit-learn, together with
a correct cross-validation scheme.</div>
<div class="moz-cite-prefix">Does that make sense ?</div>
<div class="moz-cite-prefix">Best,</div>
<div class="moz-cite-prefix">Bertrand<br>
</div>
<div class="moz-cite-prefix"><br>
</div>
<div class="moz-cite-prefix"><br>
</div>
<div class="moz-cite-prefix">On 20/05/2020 10:49,
<a class="moz-txt-link-abbreviated" href="mailto:lewis.dunne00@gmail.com">lewis.dunne00@gmail.com</a> wrote:<br>
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<p class="MsoNormal">Hello. I'm trying to follow the <a
href="https://nilearn.github.io/auto_examples/plot_decoding_tutorial.html#sphx-glr-auto-examples-plot-decoding-tutorial-py"
moz-do-not-send="true">tutorial on Nilearn for decoding</a>,
only using my own data which is event related. I used SPM to
create unique beta images for each trial in my dataset as
recommended. But since this is a different approach from the
Haxby dataset I'm a little unclear on how to follow along now.
I wonder if I could get some feedback on what I have done so
far.</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I created a dataframe with the trial event
labels and paired each trial with its respective beta image. I
did this by reading in each beta file and masking it with a
previously made 6mm ROI (not quite sure how to make an
anatomical mask yet), which left me with a 1x120 array where
120 is the number of voxels for that trial event within the
mask. I don't know if this is correct... Since I have 82
events in this task, I created an 82x120 matrix containing all
of these masked beta images as rows, voxel data as columns,
and concatenated this onto my dataframe containing
trial-by-trial task info. My plan was to then use these 120
voxels as features and my experimental condition column (it's
just binary data) as the labels.</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">The problem is I get to the NiftiMasker and
receive this warning: </p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">`UserWarning: Standardization of 3D signal
has been requested but would lead to zero values. Skipping.</p>
<p class="MsoNormal"> warnings.warn('Standardization of 3D
signal has been requested but '` <span
style="font-family:Wingdings">ß</span> not a typo</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I think this has to do with the fact that
these are beta images for events rather than an entire block,
so there is no time dimension? But I'm not sure because I am
quite unfamiliar with this. For example in the tutorial it
says that the variable "fmri_masked" is an array with shape
`time x voxels`. The array that I get is of course 1 x 120,
which makes sense given the event-related design, but the
tutorial doesn't mention anything about this.</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I think I've misunderstood something. What
should I really be doing differently in the case of an
event-related design with trial-wise betas? Feedback would be
greatly appreciated!</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Best</p>
<p class="MsoNormal">L</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Sent from <a
href="https://go.microsoft.com/fwlink/?LinkId=550986"
moz-do-not-send="true">Mail</a> for Windows 10</p>
<p class="MsoNormal"><o:p> </o:p></p>
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