Hi,
thanks for this clarification.
wolf
Mark Jenkinson wrote:
> Hi,
>
> It is a simple method and does not take account of autocorrelation
> except for the lower limit which is put there precisely for reasons
> of prewhitening.
>
> I imagine that there may be ways of incorporating autocorrelation
> estimation as well, but I suspect that the differences would be small
> and that it is probably not worth the effort to do.
>
> All the best,
> Mark
>
>
> On 6 Oct 2009, at 10:41, wolf zinke wrote:
>
>> Hi,
>>
>> Thanks for the explanations. Just a follow up. It is just based on
>> the temporal structure of the model, but does not take the noise and
>> autocorrelation of the data into account. The paper by Smith et al.
>> ('Meaningful design and contrast estimability in FMRI, NeuroImage
>> 2007) did comment on the interaction between high-pass filtering and
>> the autocorrelation. Is there a simple method to use both
>> informations (retained power and sufficient data to do a valid
>> prewhitening) for the determination of an optimal HP cutoff.
>> Otherwise it is most likely that for event related designs the lower
>> limit is always given by the shortest period which one dares to apply
>> without affecting the AR modelling to much. Is there a comparable
>> method available to obtain a cutoff values based on AR modelling?
>>
>> thanks,
>> wolf
>>
>> On 10/06/2009 09:55 AM, Mark Jenkinson wrote:
>>> Hi,
>>>
>>> It is quite a simple tool so doesn't really have documentation yet.
>>> Essentially it tried different filter cutoffs and calculates the
>>> percentage
>>> of retained power in the EVs of the design matrix. Once this goes
>>> below
>>> a threshold (default is 99%) then it chooses the last value for
>>> which it
>>> passed the threshold. However, it also has a lower limit on the period
>>> of the cutoff (default 90s) to avoid it being too aggressive and
>>> removing
>>> information that the pre-whitening needs to work.
>>> Note that the output is in seconds - just paste it into the FEAT GUI.
>>>
>>> You are right about running it on design matrices with confound
>>> regressors
>>> in. It does not know about this and so doesn't do any adjustment.
>>> You need
>>> to save a design without these (no need to run anything in FEAT -
>>> just save
>>> the design from the GUI and use the *.mat file as input). Running
>>> it on such
>>> a subset (no confounds) is the best approach to use.
>>>
>>> All the best,
>>> Mark
>>>
>>>
>>> On 5 Oct 2009, at 16:46, wolf zinke wrote:
>>>
>>>> Hi,
>>>>
>>>> I came across the recently added and quiet handy tool cutoffcalc.
>>>> Is there any documentation/reference how it determines the minimal
>>>> period for the high pass filter? If I understand it correctly, it
>>>> uses all regressors of the design matrix for the calculation.
>>>> Hence, if I run it on the 'pure' design matrix I'll get different
>>>> results than running it on the feat design matrix with motion and
>>>> confound regressors added. I guess it is reasonable to apply
>>>> cutoffcalc only on the regressors of interest. Do I need to
>>>> generate a reduced model for this purpose or can I tell cutoffcalc
>>>> to use only specified regressors for the calculation (or is it
>>>> completely stupid to use a regressor subset)
>>>>
>>>> Thanks for all the help,
>>>> wolf?
>>>>
>>
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