[Neuroimaging] Nifti File

Eric Larson larson.eric.d at gmail.com
Wed Jun 3 16:55:48 EDT 2020

Looking at the docs for `cwt`:


It just wants an `ndarray` of the right shape. `img.get_fdata()` in nibabel
followed by `np.reshape` to get it to be 2D of shape (n_signals, n_times)
and you should be good to go (and reshape back to the 4D image shape if you

But this is going to be quite slow because there are so many signals,
you're probably better off computing the CWT of the MEG sensor data,
keeping it complex, then using linearity of the inverse to get it to source
space (including an MRI image, if you want) then taking the magnitude,
assuming you want that. Also, assuming that the MRI data are the
*magnitude* (abs) of the response at the given voxel -- I haven't worked
with HCP MRI images so I don't know -- doing the CWT of this is going to be
problematic because of the nonlinearity of the magnitude operation... if on
the other hand they are signed (e.g., the result of a max-power-ori LCMV or
something) then doing it in source or sensor space (then projecting to
source space) should be equivalent, but the former will be much slower than
the latter.


On Tue, Jun 2, 2020 at 2:59 PM Winter, Matthew <mattwint at iu.edu> wrote:

> Hi,
> I am trying to do a time-frequency continuous wave transform on the Nifti
> files provided by the final pipeline in the Human Connectome Project’s MEG
> N-back task. I am working in MNE and trying to use their mne.time_frequency.tfr.cwt().
> I have loaded the Nifti files into MNE, but am unaware of how to get the
> Nifti’s into the right format to be used by MNE functions that typically
> use .fif files. Is there a nibable function that could help with this
> process? Thank you for any advice. Best, Matt
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
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