Perfect! Thank you very much.
Regards,
Hari
On Fri, November 13, 2009 9:27 am, Chris Watson wrote:
> This is what you're looking for:
> Chumbley & Friston. _False discovery rate revisited: FDR and topological
> inference using Gaussian random fields._ NeuroImage. 44(1):62-70 (2009)
> <http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%236968%232009%23999559998%23701157%23FLA%23&_cdi=6968&_pubType=J&view=c&_auth=y&_acct=C000057638&_version=1&_urlVersion=0&_userid=2503305&md5=933125709e50f7c83f8a328d6d9827f3>
> Hari Bharadwaj wrote:
>> Dear experts,
>>
>> As much as I appreciate that FDR control is a conceptual breakthrough in
>> the theory of multiple comparisons, I have what I think are fundamental
>> questions (which are probably already addressed in the literature):
>>
>> When I am searching a continuous space (such as a time-frequency map)
>> for
>> significant effects, how do I interpret small patches (spanning say 2-3
>> elements/pixels/voxels/vertices in each dimension) of significant
>> effects
>> that show up? As discoveries, they are as valid as a big blob that might
>> show up. When the FDR control is done element-wise, it is clear that the
>> cluster-wise FDR could be high. Since we are actually going after
>> clusters
>> or rather peaks in a functional map of some kind, what does voxelwise or
>> element-wise FDR control actually mean? It doesn't seem appropriate to
>> take the mass-univariate approach for a time-frequency map without any
>> consideration of the topology. What does it even mean to assign
>> activations to time-frequency elements when the underlying quantity is
>> inherently continuous? Am I missing something? FDR control seems to be
>> done routinely with functional imaging data.
>>
>> Thanks and Regards,
>> Hari
>>
>>
>>
>
>
>
--
Hari Bharadwaj
|