Hi Ali,
Matt pointed out to me that melodic has a hidden --temporal option so that
you may avoid reshaping.
However, it is not entirely clear why you donšt stick to matlab's FastICA.
Cheers,
Andreas
Von: FSL - FMRIB's Software Library <[log in to unmask]> on behalf of Ali
Golestani <[log in to unmask]>
Antworten an: FSL - FMRIB's Software Library <[log in to unmask]>
Datum: Donnerstag, 1. Februar 2018 um 22:39
An: <[log in to unmask]>
Betreff: Re: [FSL] Temporal ICA with MELODIC
Thanks Andreas;
Does it mean reshaping my 4D data from let's say 64x64x15x2000 to
2000x64x15x64? In this case, won't MELODIC think I only have 64 samples?
whereas actually I want to have 64x64x15 samples.
Cheers
Ali
On Tue, Jan 30, 2018 at 4:32 AM, Andreas Bartsch <[log in to unmask]>
wrote:
Hi,
you could - if you want to use melodic - rearrange your data, i.e. by
changing the temporal with your biggest spatial dimension, run melodic and
then rearrange the outputs.
Cheers,
Andreas
Von: FSL - FMRIB's Software Library <[log in to unmask]> on behalf of
Matt Glasser <[log in to unmask]>
Antworten an: FSL - FMRIB's Software Library <[log in to unmask]>
Datum: Montag, 29. Januar 2018 um 23:38
An: <[log in to unmask]>
Betreff: Re: [FSL] Temporal ICA with MELODIC
How do you know the correct dimensionality of a single subject, single run
of temporal ICA? From experience it is much lower than spatial ICA. It
is possible to run temporal ICA with melodic, but it will not magically
work better than matlab FastICA. Also the melodic default is to reduce
the dimensionality with PCA to the same dimensionality as the ICA. I
recommend reading a few papers on temporal ICA:
http://www.pnas.org/content/109/8/3131.short
https://www.biorxiv.org/content/early/2017/12/13/193862
Peace,
Matt.
From: FSL - FMRIB's Software Library <[log in to unmask]> on behalf of
Ali Golestani <[log in to unmask]>
Reply-To: FSL - FMRIB's Software Library <[log in to unmask]>
Date: Monday, January 29, 2018 at 4:04 PM
To: <[log in to unmask]>
Subject: Re: [FSL] Temporal ICA with MELODIC
Using MATLAB FastICA, the only way I can get components with no spike is
to reduce dimension with PCA to 20 or 30 and then run temporal ICA. But 10
or 20 dimension is too low. Running ICA with no PCA does not give
meaningful result, even if I choose 10 components.
I was wondering if somehow I can run temporal ICA with MELODIC?
On Mon, Jan 29, 2018 at 4:51 PM, Matt Glasser <[log in to unmask]> wrote:
What about 10 components? Does that give reasonable results? What is the
spatial covariance of the component maps? Are some almost perfectly
correlated?
Peace,
Matt.
From: FSL - FMRIB's Software Library <[log in to unmask]> on behalf of
Ali Golestani <[log in to unmask]>
Reply-To: FSL - FMRIB's Software Library <[log in to unmask]>
Date: Monday, January 29, 2018 at 3:45 PM
To: <[log in to unmask]>
Subject: Re: [FSL] Temporal ICA with MELODIC
Around 30 to 50 components.
Cheers
Ali
On Mon, Jan 29, 2018 at 4:43 PM, Matt Glasser <[log in to unmask]> wrote:
How many components are you requesting?
Peace,
Matt.
From: FSL - FMRIB's Software Library <[log in to unmask]> on behalf of
Ali Golestani <[log in to unmask]>
Reply-To: FSL - FMRIB's Software Library <[log in to unmask]>
Date: Monday, January 29, 2018 at 3:41 PM
To: <[log in to unmask]>
Subject: Re: [FSL] Temporal ICA with MELODIC
a multi-band resting-state fMRI with around 1900 voxels and 2000 samples
per voxel (TR around 400 ms).
On Mon, Jan 29, 2018 at 4:35 PM, Matt Glasser <[log in to unmask]> wrote:
What kind of data are you trying to run temporal ICA on?
Peace,
Matt.
From: FSL - FMRIB's Software Library <[log in to unmask]> on behalf of
Ali Golestani <[log in to unmask]>
Reply-To: FSL - FMRIB's Software Library <[log in to unmask]>
Date: Monday, January 29, 2018 at 9:36 AM
To: <[log in to unmask]>
Subject: [FSL] Temporal ICA with MELODIC
Hi;
I want to run a single-subject temporal ICA and wondering how I can run
temporal ICA with MELODIC?
I tried FastICA in MATLAB but, for both spatial and temporal ICA, all I
got is lots of spikes and bumps
(https://www.cs.helsinki.fi/u/ahyvarin/papers/ICA99_bumps.pdf).
Dimension reduction with PCA alleviates the problem but does not fix
it. The same dataset gives beautiful results with MELODIC spatial ICA
Thanks
Ali
|