[HCP-Users] BedpostX for 7T Diffusion data

[Shadi, Kamal]

Dear HCP experts,

Is bedpostx data available for 7T dMRI scans?

Regards,
Kamal

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Re: [HCP-Users] Reporting dense analysis results

[Glasser, Matthew]

If you want to make a cluster table, I think percent overlaps with areas is a 
very reasonable way to do it.  I would recommend you follow Tim’s suggestion 
with vertex areas as well.  I would strongly recommend sharing your data as 
well (if it is CIFTI/GIFTI/NIFTI, the balsa.wustl.edu database is designed for 
it).

Matt.

From: 
mailto:hcp-users-boun...@humanconnectome.org>>
 on behalf of Timothy Coalson mailto:tsc...@mst.edu>>
Date: Thursday, April 25, 2019 at 1:55 PM
To: "Stevens, Michael" 
mailto:michael.stev...@hhchealth.org>>
Cc: "hcp-users@humanconnectome.org" 
mailto:hcp-users@humanconnectome.org>>
Subject: Re: [HCP-Users] Reporting dense analysis results

For just finding the overlap of some (positive-only) map with the parcels, the 
script would likely be a lot simpler if you used -cifti-parcellate with the 
"-method SUM" option (when doing so, I would also recommend using vertex areas, 
so that the resulting numbers are surface-area integrals rather than based on 
number of vertices).  You can then use -cifti-stats SUM to get the total, and 
divide by that in -cifti-math to get percentages.

Sharing the data files of the results means that to some extent, tables may not 
be as necessary.  I don't have a strong opinion here.  Personally, I like 
figures, but I haven't done/used meta-analysis.

Tim


On Thu, Apr 25, 2019 at 8:50 AM Stevens, Michael 
mailto:michael.stev...@hhchealth.org>> wrote:
Hi folks,

Yesterday’s question/replies on reporting tables of pscalar results prompted us 
to ask about a related question – I’m wondering what HCP folks recommend in 
terms of the format of tabulating/reporting straightforward “activation 
results” for DENSE data?  I couldn’t find a prior listserv post that exactly 
addressed this question, nor did a couple passes through recently published 
literature using HCP methodology turn up a good example to follow.  Could be 
I’m just missing stuff…

We’re finishing up analyses on a somewhat conceptually novel analysis that we 
think might be received at peer review better if we report the dense results.  
So we sorta envision reporting a table of clusters/cluster peaks where we refer 
to the 2017 parcellation paper for annotations, e.g., “Cluster 1 – Left IFSp 
(72%), Left IFJa (26%), Left IFSa (2%)”.  To get there, I’m picturing a 
do-able, yet somewhat awkward combination of cluster finding calls, label file 
references, ROI definitions, finding peaks/center-of-mass, and then a whole a 
bunch of –cifti-math operations to determine overlap of clusters vs. parcels… 
The number of steps/operations that would go into this is enough that I’m just 
brought up short thinking, “Wait, am I possibly missing something…”

Before I start going down this path in coding something like this up, I thought 
I’d check two things:

A) Is there a different conceptual approach altogether that you’d recommend 
considering for showcasing dense analysis results?  Our goal ultimately is to 
simply reinforce our results are fairly compatible with the demarcations of the 
360-parcel atlas to remove a potential reviewer criticism (this analysis is 
some weird stuff… using spontaneous fluctuations of electrodermal signals as 
event-onsets for fMRI timeseries analyses… amazingly, it seemed to work, with 
pretty interesting results that mirror our connectivity analyses on the same 
data).  But if HCP has an entirely different approach to tabulating/summarizing 
dense results, we’d welcome being brought up-to-speed.

B) The lazy part of me wonders… Has someone already coded up workbench function 
call or even a script for the various wb_commands needed that might already do 
this sort of thing with dense data?  Again, this seems so meat-and-potatoes for 
fMRI that we don’t want to re-invent the wheel here.

Thanks,
Mike


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Re: [HCP-Users] Probabilistic tractography for dense connectome

[Glasser, Matthew]

Not as far as I am aware, but Stam might know.

Matt.

From: 
mailto:hcp-users-boun...@humanconnectome.org>>
 on behalf of Aaron C mailto:aaroncr...@outlook.com>>
Date: Thursday, April 25, 2019 at 9:15 AM
To: "hcp-users@humanconnectome.org" 
mailto:hcp-users@humanconnectome.org>>
Subject: [HCP-Users] Probabilistic tractography for dense connectome

Dear HCP experts,

I have a question about the probabilistic tractography command used for 
generating dense connectome 
(https://wustl.app.box.com/s/wna2cu94pqgt8zskg687mj8zlmfj1pq7). Are there any 
shared scripts for generating "pial.L.asc", "white.L.asc", 
"Whole_Brain_Trajectory_ROI_2.nii.gz", and the files such as 
"CIFTI_STRUCTURE_ACCUMBENS_LEFT.nii.gz" used in the probabilistic tractography 
command?

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Re: [HCP-Users] Reporting dense analysis results

[Timothy Coalson]

For just finding the overlap of some (positive-only) map with the parcels,
the script would likely be a lot simpler if you used -cifti-parcellate with
the "-method SUM" option (when doing so, I would also recommend using
vertex areas, so that the resulting numbers are surface-area integrals
rather than based on number of vertices).  You can then use -cifti-stats
SUM to get the total, and divide by that in -cifti-math to get percentages.

Sharing the data files of the results means that to some extent, tables may
not be as necessary.  I don't have a strong opinion here.  Personally, I
like figures, but I haven't done/used meta-analysis.

Tim


On Thu, Apr 25, 2019 at 8:50 AM Stevens, Michael <
michael.stev...@hhchealth.org> wrote:

> Hi folks,
>
>
>
> Yesterday’s question/replies on reporting tables of pscalar results
> prompted us to ask about a related question – I’m wondering what HCP folks
> recommend in terms of the format of tabulating/reporting straightforward
> “activation results” for DENSE data?  I couldn’t find a prior listserv post
> that exactly addressed this question, nor did a couple passes through
> recently published literature using HCP methodology turn up a good example
> to follow.  Could be I’m just missing stuff…
>
>
>
> We’re finishing up analyses on a somewhat conceptually novel analysis that
> we think might be received at peer review better if we report the dense
> results.  So we sorta envision reporting a table of clusters/cluster peaks
> where we refer to the 2017 parcellation paper for annotations, e.g.,
> “Cluster 1 – Left IFSp (72%), Left IFJa (26%), Left IFSa (2%)”.  To get
> there, I’m picturing a do-able, yet somewhat awkward combination of cluster
> finding calls, label file references, ROI definitions, finding
> peaks/center-of-mass, and then a whole a bunch of –cifti-math operations to
> determine overlap of clusters vs. parcels… The number of steps/operations
> that would go into this is enough that I’m just brought up short thinking,
> “Wait, am I possibly missing something…”
>
>
>
> Before I start going down this path in coding something like this up, I
> thought I’d check two things:
>
>
>
> A) Is there a different conceptual approach altogether that you’d
> recommend considering for showcasing dense analysis results?  Our goal
> ultimately is to simply reinforce our results are fairly compatible with
> the demarcations of the 360-parcel atlas to remove a potential reviewer
> criticism (this analysis is some weird stuff… using spontaneous
> fluctuations of electrodermal signals as event-onsets for fMRI timeseries
> analyses… amazingly, it seemed to work, with pretty interesting results
> that mirror our connectivity analyses on the same data).  But if HCP has an
> entirely different approach to tabulating/summarizing dense results, we’d
> welcome being brought up-to-speed.
>
>
>
> B) The lazy part of me wonders… Has someone already coded up workbench
> function call or even a script for the various wb_commands needed that
> might already do this sort of thing with dense data?  Again, this seems so
> meat-and-potatoes for fMRI that we don’t want to re-invent the wheel here.
>
>
>
> Thanks,
>
> Mike
>
>
>
> *This e-mail message, including any attachments, is for the sole use of
> the intended recipient(s) and may contain confidential and privileged
> information. Any unauthorized review, use, disclosure, or distribution is
> prohibited. If you are not the intended recipient, or an employee or agent
> responsible for delivering the message to the intended recipient, please
> contact the sender by reply e-mail and destroy all copies of the original
> message, including any attachments. *
>
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> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
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[HCP-Users] Probabilistic tractography for dense connectome

[Aaron C]

Dear HCP experts,

I have a question about the probabilistic tractography command used for 
generating dense connectome 
(https://wustl.app.box.com/s/wna2cu94pqgt8zskg687mj8zlmfj1pq7). Are there any 
shared scripts for generating "pial.L.asc", "white.L.asc", 
"Whole_Brain_Trajectory_ROI_2.nii.gz", and the files such as 
"CIFTI_STRUCTURE_ACCUMBENS_LEFT.nii.gz" used in the probabilistic tractography 
command?

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Re: [HCP-Users] Reporting dense analysis results

[Joseph Orr]

In a recent paper (Orr et al., bioRxiv
) we made tables of dense
activations by overlaying the results on the MMP parcellation and clicking
on the clusters to get labels and surface coordinates - see tables 7-10.
The tables are awkward looking and the coordinates wouldn't really
correspond to MNI coordinates for the reasons Tim describes, so I'd rather
just get rid of them since the .spec and .scene results were all shared
anyway. Based on Matt and Tim's replies to my email, I'm just going to show
the dense results overlaid on the parcellation outlines and annotate the
regions of particular interest in workbench.

Joe
--
Joseph M. Orr, Ph.D.
Assistant Professor
Department of Psychological and Brain Sciences
Texas A Institute for Neuroscience
Texas A University
College Station, TX


On Thu, Apr 25, 2019 at 8:50 AM Stevens, Michael <
michael.stev...@hhchealth.org> wrote:

> Hi folks,
>
>
>
> Yesterday’s question/replies on reporting tables of pscalar results
> prompted us to ask about a related question – I’m wondering what HCP folks
> recommend in terms of the format of tabulating/reporting straightforward
> “activation results” for DENSE data?  I couldn’t find a prior listserv post
> that exactly addressed this question, nor did a couple passes through
> recently published literature using HCP methodology turn up a good example
> to follow.  Could be I’m just missing stuff…
>
>
>
> We’re finishing up analyses on a somewhat conceptually novel analysis that
> we think might be received at peer review better if we report the dense
> results.  So we sorta envision reporting a table of clusters/cluster peaks
> where we refer to the 2017 parcellation paper for annotations, e.g.,
> “Cluster 1 – Left IFSp (72%), Left IFJa (26%), Left IFSa (2%)”.  To get
> there, I’m picturing a do-able, yet somewhat awkward combination of cluster
> finding calls, label file references, ROI definitions, finding
> peaks/center-of-mass, and then a whole a bunch of –cifti-math operations to
> determine overlap of clusters vs. parcels… The number of steps/operations
> that would go into this is enough that I’m just brought up short thinking,
> “Wait, am I possibly missing something…”
>
>
>
> Before I start going down this path in coding something like this up, I
> thought I’d check two things:
>
>
>
> A) Is there a different conceptual approach altogether that you’d
> recommend considering for showcasing dense analysis results?  Our goal
> ultimately is to simply reinforce our results are fairly compatible with
> the demarcations of the 360-parcel atlas to remove a potential reviewer
> criticism (this analysis is some weird stuff… using spontaneous
> fluctuations of electrodermal signals as event-onsets for fMRI timeseries
> analyses… amazingly, it seemed to work, with pretty interesting results
> that mirror our connectivity analyses on the same data).  But if HCP has an
> entirely different approach to tabulating/summarizing dense results, we’d
> welcome being brought up-to-speed.
>
>
>
> B) The lazy part of me wonders… Has someone already coded up workbench
> function call or even a script for the various wb_commands needed that
> might already do this sort of thing with dense data?  Again, this seems so
> meat-and-potatoes for fMRI that we don’t want to re-invent the wheel here.
>
>
>
> Thanks,
>
> Mike
>
>
>
> *This e-mail message, including any attachments, is for the sole use of
> the intended recipient(s) and may contain confidential and privileged
> information. Any unauthorized review, use, disclosure, or distribution is
> prohibited. If you are not the intended recipient, or an employee or agent
> responsible for delivering the message to the intended recipient, please
> contact the sender by reply e-mail and destroy all copies of the original
> message, including any attachments. *
>
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Re: [HCP-Users] "activation" tables for reporting pscalar results

[Joseph Orr]

Thanks for all the input on this, its been helpful.
--
Joseph M. Orr, Ph.D.
Assistant Professor
Department of Psychological and Brain Sciences
Texas A Institute for Neuroscience
Texas A University
College Station, TX


On Wed, Apr 24, 2019 at 7:34 PM Glasser, Matthew  wrote:

> I do not report MNI coordinates for any studies because I don’t think they
> add a lot of value for the reasons described in Tim’s PNAS paper.  Studies
> that provide actual results on the surface are much more useful, and I have
> extensively used such studies to make incisive neuroanatomical
> comparisons.  I’ve also yet to be asked to provide MNI coordinates by a
> peer reviewer.  I think if you share the actual data, MNI coordinates are
> superfluous and if you use a well defined neuroanatomical parcellation such
> as the HCP’s multi-modal parcellation, it is fine to talk about findings in
> particular brain areas (if you actually check to see that your findings
> overlap with the brain areas you name—i.e. don’t just eyeball vs a picture
> on the wall).
>
> Matt.
>
> From:  on behalf of Timothy
> Coalson 
> Date: Wednesday, April 24, 2019 at 1:47 PM
> To: Joseph Orr 
> Cc: HCP Users 
> Subject: Re: [HCP-Users] "activation" tables for reporting pscalar results
>
> We recommend sharing the results as data files (as mentioned, this is the
> intent of BALSA), even if you choose to report MNI coordinates in the
> text.  Something to keep in mind is that group average surfaces do not
> behave like group average volume data, the surface gets smoothed out
> wherever folding patterns aren't fully aligned, resulting in a surface that
> does not approach gyral crowns or sulcal fundi (most notably with
> functional alignment such as MSMAll - freesurfer-aligned surfaces will
> average to something with more folding preserved, at the cost of functional
> locality, but there are still locations with high variability in folding
> patterns across subjects that will still get smoothed out on a group
> average surface).  See supplementary material, figure S1, and figure S9
> panel B2, from our paper on the effects of volume-based methods:
>
> https://www.ncbi.nlm.nih.gov/pubmed/29925602
> 
>
> If meta analysis of this sort is only intended to give a very rough idea
> of location, even this may not be a deal breaker.  You can use wb_command
> -surface-coordinates-to-metric to get the coordinates as data, use
> -cifti-create-dense-from-template to convert that to cifti, and then use
> -cifti-parcellate on that to get center of gravity coordinates of the
> vertices used.  Note that these center of gravity coordinates could be a
> distance away from the surface, due to curvature.
>
> Tim
>
>
> On Wed, Apr 24, 2019 at 11:06 AM Joseph Orr  wrote:
>
>> True - these kind of tools generally assume certain degrees of smoothing,
>> which isn't the case with surface-based. And activation based meta-analysis
>> will apply a kernel that will likely extend outside the brain for a surface
>> activation that is not within a sulcus. I'd be curious to hear what those
>> more familiar with meta-analytic methods think about how surface-based
>> results can be incorporated with volumetric results.
>> --
>> Joseph M. Orr, Ph.D.
>> Assistant Professor
>> Department of Psychological and Brain Sciences
>> Texas A Institute for Neuroscience
>> Texas A University
>> College Station, TX
>>
>>
>> On Wed, Apr 24, 2019 at 11:00 AM Harms, Michael  wrote:
>>
>>>
>>>
>>> Well, that raises the question if surface-based results should just be
>>> automatically “lumped in” with volume-based results by tools such as
>>> neurosynth to begin with…
>>>
>>>
>>>
>>> --
>>>
>>> Michael Harms, Ph.D.
>>>
>>> ---
>>>
>>> Associate Professor of Psychiatry
>>>
>>> Washington University School of Medicine
>>>
>>> Department of Psychiatry, Box 8134
>>>
>>> 660 South Euclid Ave.Tel: 314-747-6173
>>>
>>> St. Louis, MO  63110  Email: mha...@wustl.edu
>>>
>>>
>>>
>>> *From: *Joseph Orr 
>>> *Date: *Wednesday, April 24, 2019 at 10:51 AM
>>> *To: *"Harms, Michael" 
>>> *Cc: *HCP Users 
>>> *Subject: *Re: [HCP-Users] "activation" tables for reporting pscalar
>>> results
>>>
>>>
>>>
>>> Well I am planning on doing that, but that doesn't necessarily help with
>>> automated meta-analytic tools like neurosynth that mine for tables.
>>>
>>> --
>>>
>>> Joseph M. Orr, Ph.D.
>>>
>>> Assistant Professor
>>>
>>> Department of Psychological and Brain Sciences
>>>
>>> Texas A Institute for Neuroscience
>>>
>>> Texas A University
>>>
>>> College Station, TX
>>>
>>>
>>>
>>>
>>>
>>> On Wed, Apr 24, 2019 at 10:36 AM Harms, Michael 
>>> wrote:
>>>
>>>
>>>
>>> Why not 

[HCP-Users] Reporting dense analysis results

[Stevens, Michael]

Hi folks,

Yesterday's question/replies on reporting tables of pscalar results prompted us 
to ask about a related question - I'm wondering what HCP folks recommend in 
terms of the format of tabulating/reporting straightforward "activation 
results" for DENSE data?  I couldn't find a prior listserv post that exactly 
addressed this question, nor did a couple passes through recently published 
literature using HCP methodology turn up a good example to follow.  Could be 
I'm just missing stuff...

We're finishing up analyses on a somewhat conceptually novel analysis that we 
think might be received at peer review better if we report the dense results.  
So we sorta envision reporting a table of clusters/cluster peaks where we refer 
to the 2017 parcellation paper for annotations, e.g., "Cluster 1 - Left IFSp 
(72%), Left IFJa (26%), Left IFSa (2%)".  To get there, I'm picturing a 
do-able, yet somewhat awkward combination of cluster finding calls, label file 
references, ROI definitions, finding peaks/center-of-mass, and then a whole a 
bunch of -cifti-math operations to determine overlap of clusters vs. parcels... 
The number of steps/operations that would go into this is enough that I'm just 
brought up short thinking, "Wait, am I possibly missing something..."

Before I start going down this path in coding something like this up, I thought 
I'd check two things:

A) Is there a different conceptual approach altogether that you'd recommend 
considering for showcasing dense analysis results?  Our goal ultimately is to 
simply reinforce our results are fairly compatible with the demarcations of the 
360-parcel atlas to remove a potential reviewer criticism (this analysis is 
some weird stuff... using spontaneous fluctuations of electrodermal signals as 
event-onsets for fMRI timeseries analyses... amazingly, it seemed to work, with 
pretty interesting results that mirror our connectivity analyses on the same 
data).  But if HCP has an entirely different approach to tabulating/summarizing 
dense results, we'd welcome being brought up-to-speed.

B) The lazy part of me wonders... Has someone already coded up workbench 
function call or even a script for the various wb_commands needed that might 
already do this sort of thing with dense data?  Again, this seems so 
meat-and-potatoes for fMRI that we don't want to re-invent the wheel here.

Thanks,
Mike


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intended recipient(s) and may contain confidential and privileged information. 
Any unauthorized review, use, disclosure, or distribution is prohibited. If you 
are not the intended recipient, or an employee or agent responsible for 
delivering the message to the intended recipient, please contact the sender by 
reply e-mail and destroy all copies of the original message, including any 
attachments.

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