<p>I know very little about fmri, but as @strongbad points out, surely it is <a href="http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols" rel="nofollow">Professor Thomas Nichols at Warwick</a>.</p>

<p>I'm not sure what the authoritative reference is, but Luo and Nichols (2003) might be worth a look. They state:</p>

<blockquote>
  <p>We construct a histogram based on all non-tail data (10th to 90th percentile) and use the location of the minimum bin as the antimode estimate. </p>
</blockquote>

<p>Luo, W. L., &amp; Nichols, T. E. (2003). Diagnosis and exploration of massively univariate neuroimaging models. NeuroImage, 19(3), 1014-1032. <a href="http://www-personal.umich.edu/~nichols/Docs/fMRIvis.pdf" rel="nofollow">http://www-personal.umich.edu/~nichols/Docs/fMRIvis.pdf</a></p>

CC BY-SA 3.0
2016-07-27T08:56:37.877Z
Think @jeromy is correct with respect to the identity.
conjectures
CC BY-SA 3.0
2015-04-11T08:36:10.943Z
2015-04-11T08:36:10.943Z
Jeromy Anglim
<p>I am using nilearn and nipy package for python processing FMRI data. When computing mask, it says: </p>

<blockquote>
  <p>Compute and write the mask of an image based on the grey level This is based on an heuristic proposed by T.Nichols: find the least dense point of the histogram, between fractions m and M of the total image histogram.</p>
</blockquote>

<p>and </p>

<blockquote>
  <p>This is based on an heuristic proposed by T.Nichols: find the least dense point of the histogram, between fractions lower_cutoff and upper_cutoff of the total image histogram.</p>
</blockquote>

<p>In both masking functions of nilearn and nipy. Who is T.Nichols? I wasn't able to google him/her out.</p>

<p>here are the links to the functions: </p>

<p><a href="http://nipy.org/nipy/stable/api/generated/nipy.labs.mask.html" rel="noreferrer">http://nipy.org/nipy/stable/api/generated/nipy.labs.mask.html</a></p>

<p><a href="https://nilearn.github.io/building_blocks/generated/nilearn.masking.compute_epi_mask.html" rel="noreferrer">https://nilearn.github.io/building_blocks/generated/nilearn.masking.compute_epi_mask.html</a></p>

CC BY-SA 3.0
2014-11-24T10:35:37.533Z
I think it is potentially this [T Nichols](http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols)
StrongBad
<reference-request><fmri>
Reference request for creator of heuristic for processing fMRI data (T.Nichols)
fast tooth
# Reference request for creator of heuristic for processing fMRI data (T.Nichols)

(Asked by: username_0 on 2014-11-21)

I am using nilearn and nipy package for python processing FMRI data. When computing mask, it says:

> Compute and write the mask of an image based on the grey level This is based on an heuristic proposed by T.Nichols: find the least dense point of the histogram, between fractions m and M of the total image histogram.

and

> This is based on an heuristic proposed by T.Nichols: find the least dense point of the histogram, between fractions lower\_cutoff and upper\_cutoff of the total image histogram.

In both masking functions of nilearn and nipy. Who is T.Nichols? I wasn't able to google him/her out.

here are the links to the functions:

<http://nipy.org/nipy/stable/api/generated/nipy.labs.mask.html>

<https://nilearn.github.io/building_blocks/generated/nilearn.masking.compute_epi_mask.html>

**Tags:** reference-request, fmri

> **Comments:**
> **username_1**: I think it is potentially this [T Nichols](http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols)  

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## 1 answers

### Answer by username_2 on 2015-04-11. Score: 4 | Accepted answer

I know very little about fmri, but as @strongbad points out, surely it is [Professor Thomas Nichols at Warwick](http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols).

I'm not sure what the authoritative reference is, but Luo and Nichols (2003) might be worth a look. They state:

> We construct a histogram based on all non-tail data (10th to 90th percentile) and use the location of the minimum bin as the antimode estimate.

Luo, W. L., & Nichols, T. E. (2003). Diagnosis and exploration of massively univariate neuroimaging models. NeuroImage, 19(3), 1014-1032. <http://www-personal.umich.edu/~nichols/Docs/fMRIvis.pdf>

> **Comments:**
> **username_3**: Think @jeromy is correct with respect to the identity.  

---

