[Mne_analysis] Comparison of brain responses under three different conditions?

Maria Hakonen maria.hakonen at gmail.com
Sun Dec 21 11:40:09 EST 2014
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Ok, many thanks for your help!

-Maria

2014-12-21 18:30 GMT+02:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:

>
>
> 2014-12-21 17:26 GMT+01:00 Maria Hakonen <maria.hakonen at gmail.com>:
>
>> Hi Denis,
>>
>> I have used Newman-Keuls test in Statistica, but maybe paired t-test
>> would work as well if I correct the alpha.
>> Did you mean that I could simply use stats.ttest_rel and then correct the
>> values with, for example, fdr_correction?
>>
>>
> Yes, or just use our 1samp ttest after computing a paired contrast
>
>
>> -Maria
>>
>> 2014-12-21 17:49 GMT+02:00 Denis-Alexander Engemann <
>> denis.engemann at gmail.com>:
>>
>>> Hi Maria,
>>>
>>> 2014-12-21 16:35 GMT+01:00 Maria Hakonen <maria.hakonen at gmail.com>:
>>>
>>>> Hi Denis,
>>>>
>>>> I tried that and got  the following p values: 0.02993846,  0.47414992
>>>> The corresponding Greenhouse-Geisser corrected values computed with
>>>> Statistica were: 0.026775, 0.462423.
>>>> Huynh-Feldt correction gives 0.023894, 0.473539 and lower-bound
>>>> correction 0.036458, 0.427778
>>>>
>>>> The uncorrected values calculated with Statistica and Python are equal
>>>> but also the corrected values are almost equal. So, maybe f_twoway_rm
>>>> works correctly now?
>>>>
>>>>
>>> There might be some minor implementation-related difference, would be
>>> good to investigate.
>>> Generally the ANOVA function is tested against R and SPSS, event for the
>>> correction:
>>>
>>>
>>> https://github.com/mne-tools/mne-python/blob/master/mne/stats/tests/test_parametric.py#L10
>>>
>>> I would therefore trust the results.
>>>
>>>
>>>> It would also be interesting to do post-hoc comparisons between
>>>> conditions but I think that Python doesn't have function for post-hoc test,
>>>> or at least I didn't found it?
>>>>
>>>>
>>> What do you mean? You simply compute paired t-tests and correct the
>>> alpha.
>>>
>>> -Denis
>>>
>>>
>>>> -Maria
>>>>
>>>> 2014-12-21 16:49 GMT+02:00 Denis-Alexander Engemann <
>>>> denis.engemann at gmail.com>:
>>>>
>>>>> Should be Greenhouse-Geisser,
>>>>> also see source code:
>>>>>
>>>>>
>>>>> https://github.com/mne-tools/mne-python/blob/master/mne/stats/parametric.py#L249
>>>>>
>>>>> 2014-12-21 15:47 GMT+01:00 Denis-Alexander Engemann <
>>>>> denis.engemann at gmail.com>:
>>>>>
>>>>>> [f,p]=f_twoway_rm(data,factor_levels=[2,3], correction=True)
>>>>>>
>>>>>>
>>>>>> 2014-12-21 15:45 GMT+01:00 Maria Hakonen <maria.hakonen at gmail.com>:
>>>>>>
>>>>>>> Hi Denis,
>>>>>>>
>>>>>>> Are you sure that the sphericity correction is implemented by
>>>>>>> f_twoway_rm?
>>>>>>> I tested it with data where the assumption of sphericity is
>>>>>>> violated. I calculated p values with Statistica and corrected them using
>>>>>>> the lower-bound estimate, Greenhouse-Geisser correction and the Huynh-Feldt
>>>>>>> correction. However, the p-values given by f_twoway_rm were equal with the
>>>>>>> uncorrected values I calculated with Statistica. I used f_twoway_rm as:
>>>>>>>
>>>>>>> [f,p]=f_twoway_rm(data,factor_levels=[2,3])
>>>>>>>
>>>>>>> where data is hemisphere*sesion ANOVA table.
>>>>>>>
>>>>>>> -Maria
>>>>>>>
>>>>>>> 2014-12-21 15:08 GMT+02:00 Denis-Alexander Engemann <
>>>>>>> denis.engemann at gmail.com>:
>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> 2014-12-21 14:00 GMT+01:00 Maria Hakonen <maria.hakonen at gmail.com>:
>>>>>>>>
>>>>>>>>> Hi,
>>>>>>>>>
>>>>>>>>> Is it that when I have hemisphere*session ANOVAs, I can use
>>>>>>>>> f_twoway_rm, and this gives the corrected values?
>>>>>>>>>
>>>>>>>>
>>>>>>>> with correction=True
>>>>>>>>
>>>>>>>>
>>>>>>>>> Should one-way ANOVA be used in the cases where I am not
>>>>>>>>> interested in hemisperic asymmetry? Is there any function for one way ANOVA
>>>>>>>>> that gives sphericity corrected values?
>>>>>>>>>
>>>>>>>>>
>>>>>>>> A one-way ANOVA is currently not implemented. But you can just look
>>>>>>>> at the relevant main effect. Should be equivalent.
>>>>>>>>
>>>>>>>>
>>>>>>>>> -Maria
>>>>>>>>>
>>>>>>>>> 2014-12-21 14:21 GMT+02:00 Denis A. Engemann <
>>>>>>>>> denis.engemann at gmail.com>:
>>>>>>>>>
>>>>>>>>>> Hi Maria,
>>>>>>>>>>
>>>>>>>>>> On 21 Dec 2014, at 12:51, Maria Hakonen <maria.hakonen at gmail.com>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>> Hi Denis,
>>>>>>>>>>
>>>>>>>>>> Should I use two-way ANOVA if I also want to compare hemispheres,
>>>>>>>>>> i.e. have a 2*3 (hemisphere*session) ANOVA table?
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Sounds like the repeated measures anova is what you want to do
>>>>>>>>>> here indeed. I just realized the sphericity correction is actually
>>>>>>>>>> implemented by our code. So that should fly. Checkout the correction
>>>>>>>>>> parameter.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I would need to do group level analysis.
>>>>>>>>>>
>>>>>>>>>> -Maria
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> 2014-12-21 12:38 GMT+02:00 Denis-Alexander Engemann <
>>>>>>>>>> denis.engemann at gmail.com>:
>>>>>>>>>>
>>>>>>>>>>> Hi Maria,
>>>>>>>>>>>
>>>>>>>>>>> 2014-12-21 10:18 GMT+01:00 Maria Hakonen <
>>>>>>>>>>> maria.hakonen at gmail.com>:
>>>>>>>>>>>
>>>>>>>>>>>> Hi all,
>>>>>>>>>>>>
>>>>>>>>>>>> I would need to find the time intervals where there are
>>>>>>>>>>>> statistically significant differences between MEG responses measured under
>>>>>>>>>>>> three different conditions.  I study the responses by comparing the mean
>>>>>>>>>>>> values in steps of 200 ms between the range of 400-2400 ms. I would like to
>>>>>>>>>>>> do this for responses from several brain areas. What could be the best way
>>>>>>>>>>>> to compare the mean values of the tree responses?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>> Single subject or group level?
>>>>>>>>>>> For single subject a standard F-test would be appropriate. For
>>>>>>>>>>> groups a oneway repeated measures ANOVA, if I understand your design
>>>>>>>>>>> correctly (simply 3 different conditions).
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> I have planned to try f_twoway_rm in a loop. However, when
>>>>>>>>>>>> rmANOVA is used, sphericity test is needed, and if the assumption of
>>>>>>>>>>>> sphericity is violated, p values need to be corrected. Is there any
>>>>>>>>>>>> function for this in Python (e.g. Mauchly's sphericity test and
>>>>>>>>>>>> Greenhouse-Geisser test)?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>> Note that this only needed if you have more than 2 factors with
>>>>>>>>>>> more than two levels. Currently this is not implemented in our stats code.
>>>>>>>>>>> The other questions is why you need a 2-way test for 3 conditions. I
>>>>>>>>>>> probably did not fully understand your design.
>>>>>>>>>>>
>>>>>>>>>>> Denis
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> Many thanks already in advance!
>>>>>>>>>>>>
>>>>>>>>>>>> Regards,
>>>>>>>>>>>> Maria
>>>>>>>>>>>>
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