Thank you Matt. This is really helpful. Any idea when the new classifier
you mentioned in 1. will be available?

On Mon, Apr 22, 2019 at 8:50 PM Glasser, Matthew <glass...@wustl.edu> wrote:

> I guess I haven’t been in the habit of throwing out data like this.
> Things I would consider would include:
>
>    1. MR+FIX classification accuracy (if runs were poorly classified,
>    they won’t be denoised well).  I’ll note that we are training an improved
>    MR+FIX classifier using a combination of HCP-YA resting state (single run
>    FIX), HCP-YA task (MR+FIX), and HCP Lifespan (MR+FIX) to address
>    classification issues we have observed with very large numbers of
>    components, subject with very large amounts of motion, and other artifacts
>    that were not a part of the HCP-YA original training data.
>    2. Unusually small numbers of signal components (though note we found
>    a recent subtle bug whereby if melodic does not finish mixture modeling
>    components, FIX will fail to classify signal components correctly).  If
>    there are few signal components this means that either the SNR is very bad
>    or the structured noise has overwhelmed the signal and mixed in too much
>    with the signal, making it hard to separate.
>    3. DVARS Spikes above baseline (not dips below baseline) in the
>    cleaned timeseries suggest residual noise.  I prefer DVARS derived measures
>    to movement tracer derived measures because they tell you something about
>    what is actually happening to the intensities inside the data, whereas
>    movement tracers may be inaccurate reflections of signal intensity
>    fluctuations for a variety of reasons (see Glasser et al 2018 Neuroimage:
>    https://www.sciencedirect.com/science/article/pii/S1053811918303963 for
>    examples).
>
> Others in the HCP used different means to identify some of the noise
> components I mentioned above that weren’t being classified correctly by
> regular FIX, and might be able to share their suggestions.
>
> Matt.
>
> From: Yizhou Ma <maxxx...@umn.edu>
> Date: Monday, April 22, 2019 at 8:25 PM
> To: Matt Glasser <glass...@wustl.edu>
> Cc: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org>
> Subject: Re: [HCP-Users] Multi-run ICA-FIX with excessive movement
>
> Thank you Matt. Do you have some suggestions for the metrics to use to
> determine scan quality after ICA FIX?
>
> Thanks,
> Cherry
>
> On Mon, Apr 22, 2019 at 8:15 PM Glasser, Matthew <glass...@wustl.edu>
> wrote:
>
>> I would decide after cleaning with MR ICA+FIX if you actually have to
>> exclude the scans and run with them all.
>>
>> Matt.
>>
>> From: <hcp-users-boun...@humanconnectome.org> on behalf of Yizhou Ma <
>> maxxx...@umn.edu>
>> Date: Monday, April 22, 2019 at 3:47 PM
>> To: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org>
>> Subject: [HCP-Users] Multi-run ICA-FIX with excessive movement
>>
>> Dear HCP experts,
>>
>> I am writing for a question with multi-run ICA-FIX for my dataset. I have
>> 4 resting state scans (TR=0.8, length=6.5min each) and 3 task scans
>> (TR=0.8, length=6min each) that I intend to run multi-run ICA-FIX on. We
>> used Euclidean norm values to threshold volumes with excessive movement and
>> decided that scans with more than 20% volumes with excessive movement are
>> not usable. I wonder with multi-run ICA-FIX, if it would be problematic to
>> include these scans. In other words, I am trying to decide if I should 1)
>> run multi-run ICA-FIX on scans with less motion, therefore each subject may
>> have different number of scans that are included in multi-run ICA-FIX; or
>> 2) run multi-run ICA-FIX on all scans, and throw out scans with excessive
>> motion afterward.
>>
>> Thank you very much,
>> Cherry
>>
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