[pymvpa] Noise normalization
raul at lafuentelab.org
Wed Apr 1 22:48:51 BST 2020
Thank you!, this is very helpful and clear :)
On Wed, Apr 1, 2020 at 6:25 PM <yoh at onerussian.com> wrote:
> On Wed, 01 Apr 2020, Raúl Hernández wrote:
> > Dear PyMVPA community,
> > I'm a bit confused with noise normalization and I was hoping someone
> > clarify it for me. Walther et al., (2016) mentions that noise
> > normalization is an important step in multivariate fMRI. For what I
> > understand, if I Z-score each voxel, I'm performing a univariate noise
> > normalization, as I'm transforming the signal and it's variations to a
> > score that accounts for the dispersion of the data (thus, the noise).
> > this correct? Or the noise normalization is something different?
> Hi Raúl,
> zscoring could indeed be considered to be a "poor man" univariate
> noise normalization if you zscore against some condition of no interest
> (e.g. rest), where mean/var would be estimated not across the entire
> time series, but only where there is presumably no signal of interest
> (thus only noise).
> If you take it across the entire time series, then it would indeed be
> hardly "noise normalization", but just "data standardization" (bring it
> to the common mean of 0 and variance of 1).
> In that paper (https://doi.org/10.1016/j.neuroimage.2015.12.012) noise
> normalization seems to not involve mean subtraction (like zscoring does)
> and then in a univariate case would just divide by the noise variance.
> In multivariate case -- would use entire covariance structure, thus
> would decorellate (not necessarily "approximately independent" as
> authors say) remaining noise across voxels.
> Hope this helps
> Yaroslav O. Halchenko
> Center for Open Neuroscience http://centerforopenneuroscience.org
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
> WWW: http://www.linkedin.com/in/yarik
> Pkg-ExpPsy-PyMVPA mailing list
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