Thanks Dr. Harms. This is super helpful!

On Tue, Mar 31, 2015 at 9:10 AM, Harms, Michael <[email protected]> wrote:

>
>  The AR(1) correction is a scaling applied during the r-to-z
> transformation
>
>  This is from nets_netmats.m
>
>  RtoZcorrection=1/std(grotZ);
> netmats=0.5*log((1+netmats)./(1-netmats))*RtoZcorrection;
>
>  So, if you knew the value of RtoZcorrection you could convert the HCP
> distributed netmats back to r-value.  BUT, that value wasn't saved.
>
>  If you want the netmats expressed as correlations (r-values) why not
> just run:
>
>  ts_dir='PATH_TO_TIMESERIES';
> nRuns = 4;
> ts = nets_load(ts_dir,0.72,1,nRuns);
> netmat1 = nets_netmats(ts,0,'corr');  %% Second argument 0, to leave
> netmats as r
>
>  It takes a little bit of time, but not *that* much, and you only need to
> do it once.  If I recall correctly, when I did it on the full set of
> time-series from the HCP 500 release, it took something like an hour or two
> total for the nets_load and nets_netmats steps.
>
>  cheers,
> -MH
>
>   --
> Michael Harms, Ph.D.
>  -----------------------------------------------------------
> Conte Center for the Neuroscience of Mental Disorders
> Washington University School of Medicine
> Department of Psychiatry, Box 8134
> 660 South Euclid Ave. Tel: 314-747-6173
> St. Louis, MO  63110 Email: [email protected]
>
>   From: Yizhou Ma <[email protected]>
> Date: Monday, March 30, 2015 6:54 PM
> To: "Harms, Michael" <[email protected]>
> Cc: Stephen Smith <[email protected]>, "[email protected]"
> <[email protected]>
>
> Subject: Re: [HCP-Users] netmats in HCP
>
>   Dear Dr. Harms,
>
>  Thank you very much for your codes. They are very helpful for me to
> understand how FSLNets works. I am able to reproduce individual netmats
> that are perfectly correlated with HCP distributed netmats.
>
>  I also tried the mean netmat of full correlations netmats from the 4
> runs. They also give me netmats that are perfectly correlated with HCP
> distributed netmats (by perfectly correlated I mean Pearson's
> correlation=1.0000 in matlab). Thus the AR(1) process does not seem to
> affect the result very much.
>
>  Since I still do not understand the scaling of the z values in HCP
> distributed netmats, may I know how I can transform them back to r values,
> so that to get an idea of the size of correlation? (to run nets_netmats
> with z=0 (leave netmats as r)) is too time consuming for this purpose).
>
>  Thanks,
> Cherry
>
> On Mon, Mar 30, 2015 at 1:34 PM, Harms, Michael <[email protected]> wrote:
>
>>
>>  Hi,
>> You'll want to refer to the relevant functions in the FSLnets package.
>>
>>  I did something very similar to what you are trying to do recently.
>> Here is the code that you want to use if you are trying to replicate the
>> distributed netmats (pconn's) from the distributed time-series:
>>
>>  ts_dir='PATH_TO_TIMESERIES';
>> nRuns = 4;
>> ts = nets_load(ts_dir,0.72,1,nRuns);
>> netmat1 = nets_netmats(ts,1,'corr');
>>
>>  Of note, the estimation of the AR(1) correction factor that is used
>> internally in nets_netmats as part of the r-to-z transformation is the
>> median over all subjects, so if you are trying to duplicate the HCP
>> distributed netmats "exactly", you correspondingly need to load the full
>> set of distributed time-series as part of your 'nets_load' command.  Even
>> then, there will be a very small residual difference in the resulting
>> output from nets_netmats due to the random seed component of the AR(1)
>> simulation (i.e., running nets_netmats twice does not itself generate
>> identical results when using its r-to-z conversion option).
>>
>>  cheers,
>> -MH
>>
>>   --
>> Michael Harms, Ph.D.
>>  -----------------------------------------------------------
>> Conte Center for the Neuroscience of Mental Disorders
>> Washington University School of Medicine
>> Department of Psychiatry, Box 8134
>> 660 South Euclid Ave. Tel: 314-747-6173
>> St. Louis, MO  63110 Email: [email protected]
>>
>>   From: Yizhou Ma <[email protected]>
>> Date: Monday, March 30, 2015 12:26 PM
>> To: Stephen Smith <[email protected]>
>>
>> Cc: "[email protected]" <[email protected]>
>> Subject: Re: [HCP-Users] netmats in HCP
>>
>>   Thank you all. I don't see a comment on correction for temporal
>> autocorrelation in the PTN document though. Could someone please point me
>> to a page where this is explained?
>>
>>  Thanks,
>> Cherry
>>
>> On Mon, Mar 30, 2015 at 12:18 PM, Stephen Smith <[email protected]>
>> wrote:
>>
>>> yup - that also affects the scaling - see FSLNets doc and the PTN
>>> release doc.
>>> Cheers.
>>>
>>>
>>>  On 30 Mar 2015, at 18:17, Glasser, Matthew <[email protected]>
>>> wrote:
>>>
>>>  Hi Steve,
>>>
>>>  What about the correction for temporal autocorrelation?
>>>
>>>  Matt.
>>>
>>>   From: Stephen Smith <[email protected]>
>>> Date: Monday, March 30, 2015 at 12:15 PM
>>> To: Yizhou Ma <[email protected]>
>>> Cc: "[email protected]" <[email protected]>
>>> Subject: Re: [HCP-Users] netmats in HCP
>>>
>>>  Hi - one thing is that we estimate (z versions of) netmats separately
>>> for each 15min run and then average the 4 netmats to give a single netmat
>>> per subject.
>>> Cheers.
>>>
>>>
>>>  On 30 Mar 2015, at 18:14, Yizhou Ma <[email protected]> wrote:
>>>
>>>  Hi Timothy,
>>>
>>>  Thank you for pointing that out. I typed that wrong but I used the
>>> correct transformation in matlab. As I said my transformed z scores are
>>> actually almost perfectly linearly correlated with HCP netmats, though the
>>> latter is much larger. I want to understand why the latter is larger and
>>> why the two are not exactly correlated.
>>>
>>>  Thanks,
>>> Cherry
>>>
>>> On Mon, Mar 30, 2015 at 12:08 PM, Timothy Coalson <[email protected]>
>>> wrote:
>>>
>>>> A quick possibility: if you have pasted in the formula you used, I see
>>>> an order of operations problem: .5*ln(1+r)-ln(1-r) means 
>>>> (.5*ln(1+r))-ln(1-r),
>>>> where the usual formula is .5*ln((1+r)/(1-r)), which after some log
>>>> identities becomes .5*(ln(1+r)-ln(1-r)).
>>>>
>>>>  Tim
>>>>
>>>>
>>>>  On Mon, Mar 30, 2015 at 11:02 AM, Yizhou Ma <[email protected]> wrote:
>>>>
>>>>>  Dear HCP experts,
>>>>>
>>>>>  I am trying to reproduce the individual netmats from HCP500-PTN so
>>>>> that I am sure where the numbers come from. I used individual node
>>>>> timeseries in /ts2/subjID and did correlation in matlab. I then used
>>>>> fisher's z transformation: .5*ln(1+r)-ln(1-r). The resulting netmat is
>>>>> different from what is provided in *_netmat1/. However, they almost have a
>>>>> linear relationship. It seems to me that HCP is not using the same z
>>>>> transformation I have used. The transformation seems more like
>>>>> 7*ln(1+r)-ln(1-r).
>>>>>
>>>>>  I have downloaded FSLNets but could not identify which function was
>>>>> used to generate individual netmats in the first place. The example script
>>>>> seems to be about group-level netmats only.
>>>>>
>>>>>  Could you please share with me how exactly the numbers in individual
>>>>> netmats were generated?
>>>>>
>>>>>  Thanks,
>>>>> Cherry
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>>>>
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>>>
>>> ---------------------------------------------------------------------------
>>> Stephen M. Smith, Professor of Biomedical Engineering
>>> Associate Director,  Oxford University FMRIB Centre
>>>
>>> FMRIB, JR Hospital, Headington, Oxford  OX3 9DU, UK
>>> +44 (0) 1865 222726  (fax 222717)
>>> [email protected]    http://www.fmrib.ox.ac.uk/~steve
>>>
>>> ---------------------------------------------------------------------------
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>>>
>>>
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>>>
>>>
>>> ---------------------------------------------------------------------------
>>> Stephen M. Smith, Professor of Biomedical Engineering
>>> Associate Director,  Oxford University FMRIB Centre
>>>
>>> FMRIB, JR Hospital, Headington, Oxford  OX3 9DU, UK
>>> +44 (0) 1865 222726  (fax 222717)
>>> [email protected]    http://www.fmrib.ox.ac.uk/~steve
>>>
>>> ---------------------------------------------------------------------------
>>>
>>>  Stop the cultural destruction of Tibet <http://smithinks.net>
>>>
>>>
>>>
>>>
>>>
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> Healthcare Information or other information of a sensitive nature. If you
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> of this information is strictly prohibited. If you have received this email
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