[HCP-Users] About interpreting EVs and .dtseries.nii files
Dear HCP-Users, I am pretty new to task fMRI data and I would like to check how to use the files in the EVs folder to extract information from the .dtseries.nii file for the MOTOR dataset. As an example, I chose subject 100307 with the LR phase and I was able to build this table from the files in the EVs folder: *type start time (s)* countdown 0 cue_rightHand 8.05 rightHand 11.009 cue-leftFoot 23.164 leftFoot 38.291 So suppose I would like to extract from a .dtseries.nii file (having 284 rows x 91282 grayordinates) the part of the time series corresponding to the Right-Hand task listed above. Then: Should I pick all the rows covering the time interval [11.009;23.164] ? If so, should I pick rows 15 through 33 from the .dtseries.nii file which cover the time interval starting from 10.8 (=0.72*15) until 23.76(=0.72*33) ? Moreover, does the same interpretation hold for obtaining the .dtseries.nii rows corresponding to the cues and the fixation blocks? Thank you for your help! ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
[HCP-Users] 3 faculty positions at UCSB, Brain Initiative
Dear HCP-users, Following positions maybe of interest to users on this list Best, Sang == Sang-Yun Oh Assistant Professor Statistics and Applied Probability University of California, Santa Barbara http://www.pstat.ucsb.edu/faculty/syoh/ We're hiring! > *3 junior faculty in various areas of neuroscience *(*Science *ad here > <https://jobs.sciencecareers.org/job/462185/three-faculty-positions-in-neuroscience-/>; > individual UC Recruit ads below) > If there is someone you'd like to see at UCSB, please bring these to their > attention. > > Note: The first position can be in any of the 9 MLPS departments so if you > know someone who likes brains but would be most appropriate as a faculty > member in Physics, Chem, Stats, Math, etc now is your opportunity. > > *Assistant Prof - Neuroscience *(apply here > <https://recruit.ap.ucsb.edu/apply/JPF01093>) > Any dept in Mathematical Life & Physical Sciences > - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > - - > *Assistant Prof - Neural Basis of Motivated Behavior *(apply here > <https://recruit.ap.ucsb.edu/apply/JPF01092>) > Dept of Psychological & Brain Science > - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > - - > *Assistant Prof - Vision Science/Visual Neuroscience *(apply here > <https://recruit.ap.ucsb.edu/apply/JPF01038>) > Dept of Psychological & Brain Science > > Thanks for your help bringing more great people into our brain community. > > bnQ > > -- > -- > BN Queenan, PhD > Associate Director, UCSB Brain Initiative > Research Director, UCSB Nanolab > Neuroscience Research Institute; Dept of Mechanical Engineering > University of California Santa Barbara > > ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
Re: [HCP-Users] Functional connectivity gradient map
Thank you! On Fri, Aug 18, 2017 at 2:42 PM Glasser, Matthew <glass...@wustl.edu> wrote: > wb_command -cifti-gradient and wb_command -cifti-correlation-gradient. > There are also -metric-gradient for GIFTI files and -volume-gradient for > NIFTI files. > > Peace, > > Matt. > > From: <hcp-users-boun...@humanconnectome.org> on behalf of Sang-Yun Oh < > s...@pstat.ucsb.edu> > Date: Friday, August 18, 2017 at 4:36 PM > To: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org> > Subject: [HCP-Users] Functional connectivity gradient map > > Dear HCP users, > > In this paper, > https://www.nature.com/nature/journal/v536/n7615/fig_tab/nature18933_F2.html, > I imagine Figure 2g is a row in the dense connectome. > > How were Figure 2, c and d generated? Is there code available to compute > the FC gradients? I searched the github, but nothing caught my eye > > Thank you in advance for any advice on how I can compute the FC gradient > map > > Best, > Sang > > ___ > HCP-Users mailing list > HCP-Users@humanconnectome.org > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
[HCP-Users] Functional connectivity gradient map
Dear HCP users, In this paper, https://www.nature.com/nature/journal/v536/n7615/fig_tab/nature18933_F2.html, I imagine Figure 2g is a row in the dense connectome. How were Figure 2, c and d generated? Is there code available to compute the FC gradients? I searched the github, but nothing caught my eye Thank you in advance for any advice on how I can compute the FC gradient map Best, Sang ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
Re: [HCP-Users] Infinite values in Group average data
This makes more sense! Sorry I missed your mention of fisher-z transform
So I would apply tanh to each element to revert back to regular correlation
coefficients
Thank you for your help!
Best,
Sang
On Mon, May 15, 2017 at 5:57 PM Timothy Coalson <tsc...@mst.edu> wrote:
> After the fisher-z transform, you can have values greater than 1, see the
> graph on the right:
>
> https://en.wikipedia.org/wiki/Fisher_transformation
>
> This is why the "correct" answer for the diagonal is infinity for the
> "zcorr" file.
>
> Tim
>
>
> On Mon, May 15, 2017 at 7:51 PM, Sang-Yun Oh <san...@gmail.com> wrote:
>
>> I am also finding that some off-diagonal elements in this matrix are also
>> greater than 1 indicating this matrix is not a correlation matrix.
>>
>> In [5]: img
>> Out[5]:
>> memmap([[ 8.66434002e+00, 1.96847185e-01, 1.66294336e-01, ...,
>> 1.01449557e-01, 7.45474100e-02, 1.15624115e-01],
>>[ 1.96847185e-01, inf, 3.36383432e-01, ...,
>> -5.70017472e-03, -5.49946353e-02, 3.72834280e-02],
>>[ 1.66294336e-01, 3.36383432e-01, inf, ...,
>> -4.45242636e-02, -6.07097335e-02, -1.51601573e-02],
>>...,
>>[ 1.01449557e-01, -5.70017472e-03, -4.45242636e-02, ...,
>> inf, 1.91883039e+00, 9.20160294e-01],
>>[ 7.45474100e-02, -5.49946353e-02, -6.07097335e-02, ...,
>> * 1.91883111e+00*, 8.31776619e+00, 8.82132888e-01],
>>[ 1.15624115e-01, 3.72833721e-02, -1.51601573e-02, ...,
>> 9.20160294e-01, 8.82132888e-01, 8.66434002e+00]],
>> dtype=float32)
>>
>> Any insight would be appreciated
>>
>> Thanks,
>> Sang
>>
>> On Mon, May 15, 2017 at 1:13 PM Sang-Yun Oh <san...@gmail.com> wrote:
>>
>>> Thank you for the response.
>>>
>>> I am, too, confused by some being non-zero finite values, and others
>>> being infinities.
>>>
>>> Before computing a correlation matrix, if standardized by subtracting
>>> the mean and scaling by variance, all diagonal elements should be exactly 1.
>>>
>>> What I am concerned about is how the whole matrix was computed, since a
>>> fundamental characteristic of correlation matrix is not satisfied
>>>
>>> Best,
>>> Sang
>>>
>>> On Mon, May 15, 2017 at 11:33 AM Timothy Coalson <tsc...@mst.edu> wrote:
>>>
>>>> Per the name "zcorr", the correlation values have been z-transformed
>>>> (fisher's small z transform). I am somewhat confused as to why some
>>>> elements in the diagonal are not infinite. The "true" value of applying
>>>> this transform would be infinite on the entire diagonal, as arctanh(1) is
>>>> infinite. I am guessing this result was generated in matlab, as wb_command
>>>> actually prevents infinities when using the z transform, putting a cap on
>>>> the correlation (when not using z-transform, it shows correlations of 1 as
>>>> expected).
>>>>
>>>> Whatever analysis you do with correlation matrices like this should
>>>> ignore the diagonal anyway, since it is correlation to itself.
>>>>
>>>> Tim
>>>>
>>>>
>>>> On Mon, May 15, 2017 at 3:57 AM, Sang-Yun Oh <san...@gmail.com> wrote:
>>>>
>>>>> I downloaded group average functional correlation
>>>>> file: HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii
>>>>>
>>>>> Some diagonal elements of the square matrix (91282x91282) are infinite
>>>>> (Please see below).
>>>>>
>>>>> I want to use this matrix in ananalysis; however, I am not sure how to
>>>>> understand or deal with infinite diagonal values.
>>>>>
>>>>> I appreciate any insight
>>>>>
>>>>> Thanks,
>>>>> Sang
>>>>>
>>>>> ==
>>>>>
>>>>> In [1]: import nibabel
>>>>>
>>>>> In [2]: asdf =
>>>>> nibabel.load('HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii')
>>>>>
>>>>> In [3]: img = asdf.get_data()
>>>>>
>>>>> In [4]: img.shape
>>>>> Out[4]: (1, 1, 1, 1, 91282, 91282)
>>>>>
>>>>> In [5]: S = img[0,0,0,0,:,:]
>>>>>
>>>>> In [6]: S
>>
Re: [HCP-Users] Infinite values in Group average data
I am also finding that some off-diagonal elements in this matrix are also
greater than 1 indicating this matrix is not a correlation matrix.
In [5]: img
Out[5]:
memmap([[ 8.66434002e+00, 1.96847185e-01, 1.66294336e-01, ...,
1.01449557e-01, 7.45474100e-02, 1.15624115e-01],
[ 1.96847185e-01, inf, 3.36383432e-01, ...,
-5.70017472e-03, -5.49946353e-02, 3.72834280e-02],
[ 1.66294336e-01, 3.36383432e-01, inf, ...,
-4.45242636e-02, -6.07097335e-02, -1.51601573e-02],
...,
[ 1.01449557e-01, -5.70017472e-03, -4.45242636e-02, ...,
inf, 1.91883039e+00, 9.20160294e-01],
[ 7.45474100e-02, -5.49946353e-02, -6.07097335e-02, ...,
* 1.91883111e+00*, 8.31776619e+00, 8.82132888e-01],
[ 1.15624115e-01, 3.72833721e-02, -1.51601573e-02, ...,
9.20160294e-01, 8.82132888e-01, 8.66434002e+00]],
dtype=float32)
Any insight would be appreciated
Thanks,
Sang
On Mon, May 15, 2017 at 1:13 PM Sang-Yun Oh <san...@gmail.com> wrote:
> Thank you for the response.
>
> I am, too, confused by some being non-zero finite values, and others being
> infinities.
>
> Before computing a correlation matrix, if standardized by subtracting the
> mean and scaling by variance, all diagonal elements should be exactly 1.
>
> What I am concerned about is how the whole matrix was computed, since a
> fundamental characteristic of correlation matrix is not satisfied
>
> Best,
> Sang
>
> On Mon, May 15, 2017 at 11:33 AM Timothy Coalson <tsc...@mst.edu> wrote:
>
>> Per the name "zcorr", the correlation values have been z-transformed
>> (fisher's small z transform). I am somewhat confused as to why some
>> elements in the diagonal are not infinite. The "true" value of applying
>> this transform would be infinite on the entire diagonal, as arctanh(1) is
>> infinite. I am guessing this result was generated in matlab, as wb_command
>> actually prevents infinities when using the z transform, putting a cap on
>> the correlation (when not using z-transform, it shows correlations of 1 as
>> expected).
>>
>> Whatever analysis you do with correlation matrices like this should
>> ignore the diagonal anyway, since it is correlation to itself.
>>
>> Tim
>>
>>
>> On Mon, May 15, 2017 at 3:57 AM, Sang-Yun Oh <san...@gmail.com> wrote:
>>
>>> I downloaded group average functional correlation
>>> file: HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii
>>>
>>> Some diagonal elements of the square matrix (91282x91282) are infinite
>>> (Please see below).
>>>
>>> I want to use this matrix in ananalysis; however, I am not sure how to
>>> understand or deal with infinite diagonal values.
>>>
>>> I appreciate any insight
>>>
>>> Thanks,
>>> Sang
>>>
>>> ==
>>>
>>> In [1]: import nibabel
>>>
>>> In [2]: asdf =
>>> nibabel.load('HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii')
>>>
>>> In [3]: img = asdf.get_data()
>>>
>>> In [4]: img.shape
>>> Out[4]: (1, 1, 1, 1, 91282, 91282)
>>>
>>> In [5]: S = img[0,0,0,0,:,:]
>>>
>>> In [6]: S
>>> Out[6]:
>>> memmap([[ 8.66434002e+00, 1.96847185e-01, 1.66294336e-01, ...,
>>> 1.01449557e-01, 7.45474100e-02, 1.15624115e-01],
>>>[ 1.96847185e-01, inf, 3.36383432e-01, ...,
>>> -5.70017472e-03, -5.49946353e-02, 3.72834280e-02],
>>>[ 1.66294336e-01, 3.36383432e-01, inf, ...,
>>> -4.45242636e-02, -6.07097335e-02, -1.51601573e-02],
>>>...,
>>>[ 1.01449557e-01, -5.70017472e-03, -4.45242636e-02, ...,
>>> inf, 1.91883039e+00, 9.20160294e-01],
>>>[ 7.45474100e-02, -5.49946353e-02, -6.07097335e-02, ...,
>>> 1.91883111e+00, 8.31776619e+00, 8.82132888e-01],
>>>[ 1.15624115e-01, 3.72833721e-02, -1.51601573e-02, ...,
>>> 9.20160294e-01, 8.82132888e-01, 8.66434002e+00]],
>>> dtype=float32)
>>>
>>> In [7]: S.diagonal()
>>> Out[7]:
>>> memmap([ 8.66434002, inf, inf, ..., inf,
>>> 8.31776619, 8.66434002], dtype=float32)
>>>
>>>
>>> ___
>>> HCP-Users mailing list
>>> HCP-Users@humanconnectome.org
>>> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>>>
>>
>>
___
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Re: [HCP-Users] Infinite values in Group average data
Thank you for the response.
I am, too, confused by some being non-zero finite values, and others being
infinities.
Before computing a correlation matrix, if standardized by subtracting the
mean and scaling by variance, all diagonal elements should be exactly 1.
What I am concerned about is how the whole matrix was computed, since a
fundamental characteristic of correlation matrix is not satisfied
Best,
Sang
On Mon, May 15, 2017 at 11:33 AM Timothy Coalson <tsc...@mst.edu> wrote:
> Per the name "zcorr", the correlation values have been z-transformed
> (fisher's small z transform). I am somewhat confused as to why some
> elements in the diagonal are not infinite. The "true" value of applying
> this transform would be infinite on the entire diagonal, as arctanh(1) is
> infinite. I am guessing this result was generated in matlab, as wb_command
> actually prevents infinities when using the z transform, putting a cap on
> the correlation (when not using z-transform, it shows correlations of 1 as
> expected).
>
> Whatever analysis you do with correlation matrices like this should ignore
> the diagonal anyway, since it is correlation to itself.
>
> Tim
>
>
> On Mon, May 15, 2017 at 3:57 AM, Sang-Yun Oh <san...@gmail.com> wrote:
>
>> I downloaded group average functional correlation
>> file: HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii
>>
>> Some diagonal elements of the square matrix (91282x91282) are infinite
>> (Please see below).
>>
>> I want to use this matrix in ananalysis; however, I am not sure how to
>> understand or deal with infinite diagonal values.
>>
>> I appreciate any insight
>>
>> Thanks,
>> Sang
>>
>> ==
>>
>> In [1]: import nibabel
>>
>> In [2]: asdf =
>> nibabel.load('HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii')
>>
>> In [3]: img = asdf.get_data()
>>
>> In [4]: img.shape
>> Out[4]: (1, 1, 1, 1, 91282, 91282)
>>
>> In [5]: S = img[0,0,0,0,:,:]
>>
>> In [6]: S
>> Out[6]:
>> memmap([[ 8.66434002e+00, 1.96847185e-01, 1.66294336e-01, ...,
>> 1.01449557e-01, 7.45474100e-02, 1.15624115e-01],
>>[ 1.96847185e-01, inf, 3.36383432e-01, ...,
>> -5.70017472e-03, -5.49946353e-02, 3.72834280e-02],
>>[ 1.66294336e-01, 3.36383432e-01, inf, ...,
>> -4.45242636e-02, -6.07097335e-02, -1.51601573e-02],
>>...,
>>[ 1.01449557e-01, -5.70017472e-03, -4.45242636e-02, ...,
>> inf, 1.91883039e+00, 9.20160294e-01],
>>[ 7.45474100e-02, -5.49946353e-02, -6.07097335e-02, ...,
>> 1.91883111e+00, 8.31776619e+00, 8.82132888e-01],
>>[ 1.15624115e-01, 3.72833721e-02, -1.51601573e-02, ...,
>> 9.20160294e-01, 8.82132888e-01, 8.66434002e+00]],
>> dtype=float32)
>>
>> In [7]: S.diagonal()
>> Out[7]:
>> memmap([ 8.66434002, inf, inf, ..., inf,
>> 8.31776619, 8.66434002], dtype=float32)
>>
>>
>> ___
>> HCP-Users mailing list
>> HCP-Users@humanconnectome.org
>> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>>
>
>
___
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[HCP-Users] Infinite values in Group average data
I downloaded group average functional correlation
file: HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii
Some diagonal elements of the square matrix (91282x91282) are infinite
(Please see below).
I want to use this matrix in ananalysis; however, I am not sure how to
understand or deal with infinite diagonal values.
I appreciate any insight
Thanks,
Sang
==
In [1]: import nibabel
In [2]: asdf =
nibabel.load('HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii')
In [3]: img = asdf.get_data()
In [4]: img.shape
Out[4]: (1, 1, 1, 1, 91282, 91282)
In [5]: S = img[0,0,0,0,:,:]
In [6]: S
Out[6]:
memmap([[ 8.66434002e+00, 1.96847185e-01, 1.66294336e-01, ...,
1.01449557e-01, 7.45474100e-02, 1.15624115e-01],
[ 1.96847185e-01, inf, 3.36383432e-01, ...,
-5.70017472e-03, -5.49946353e-02, 3.72834280e-02],
[ 1.66294336e-01, 3.36383432e-01, inf, ...,
-4.45242636e-02, -6.07097335e-02, -1.51601573e-02],
...,
[ 1.01449557e-01, -5.70017472e-03, -4.45242636e-02, ...,
inf, 1.91883039e+00, 9.20160294e-01],
[ 7.45474100e-02, -5.49946353e-02, -6.07097335e-02, ...,
1.91883111e+00, 8.31776619e+00, 8.82132888e-01],
[ 1.15624115e-01, 3.72833721e-02, -1.51601573e-02, ...,
9.20160294e-01, 8.82132888e-01, 8.66434002e+00]],
dtype=float32)
In [7]: S.diagonal()
Out[7]:
memmap([ 8.66434002, inf, inf, ..., inf,
8.31776619, 8.66434002], dtype=float32)
___
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[HCP-Users] Replicating "Resting-state fMRI in the Human Connectome Project"
Dear HCP users, I am new to this area and I would like to do a replication exercise to learn more about HCP dataset and neuroscience tools. What would be the simplest way to get my hands on matrix G (Figure 2) in this paper? https://doi.org/10.1016/j.neuroimage.2013.05.039 I would imagine G is after regressing out any motion and white matter signals (is this true?) I tried to read the supplemental material; however, it was very complex and way over my head. Is there a script available for computing G from HCP released data that a newbie like me can use? I would appreciate any guidance Best, Sang ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
