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RE: Can I save preprocessing files elsewhere

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[color=#000000]Hi [/color]

[color=#000000]In these scenarios (multiple users working on same source data on different CONN projects) I typically recommend using CONN's option to copy the raw data into local BIDS folders. You may do so using any of the two following procedures:[/color]

[color=#000000]A) if using the GUI to import your raw data (in CONN's [i]Setup.Functional[/i] or [i]Setup.Structural[/i] tabs) simply change the option that reads "import selected files" to "copy to local BIDS folder and import".[/color]

[color=#000000]or B) if using batch scripts to import your raw simply add a "batch.Setup.localcopy=true" field to your batch structure. [/color]

That will create one local copy of your raw data separately for each CONN project that uses that same data, so that all preprocessed files will be stored in those project-specific folders. If (e.g. for space-saving reasons) you prefer to avoid having multiple copies of the same raw data, one undocumented option to do so would be to edit in the file conn_importvol2bids.m the line that reads "[i]SOFTLINK=false[/i];" and change that to "[i]SOFTLINK=true[/i];". That will create soft links to your raw data files in the project-specific folders instead of actually copying the raw data when importing it (so that preprocessed files will still be stored in these folders but the original data does not need to be replicated too many times). This option has not been thoroughly tested (and it only works on linux or mac systems) so please let me know if you run into any issues

Hope this helps
Alfonso
[i]Originally posted by tkarsten:[/i][quote]Hello,

I'm wondering if there is any way to save the preprocessing files Conn outputs in a directory other than where the raw data came from? We have multiple users in our lab using the same dataset and running different analyses (and preprocessing pipelines). To prevent overwriting one another's data, we've been copying the raw data into our personal directories and running from there so the files outputted during the preprocessing steps aren't placed in the master directory, but that is starting to create a space issue. I've been perusing through the forum and haven't seen any posts related to my question. I've also been trying to look through the conn scripts to see if there was a fix that way, but that avenue hasn't been fruitful thus far. 

Thanks in advance for any help/advice you can provide![/quote]

Use compressed files?

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Is there an option somewhere that will have conn-toolbox use .nii.gz files instead of their uncompressed counterpart (.nii)?
I have a 2T drive, but find that the project size explodes as the data are processed.

RE: Complex contrast definition

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Hi, has anyone got nay thoughts on this?

RE: Sliding Window correlation matrices

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Hi Alfonzo - any info on this posting???

thanks 

G

I can see that there are files with names like resultsROI_Subject001_Condition001.mat, that contain matrices that look promising. In particular, the variable called Z presumably refers to Z-scores from the ROI to ROI correlations. Is that the case? Further, is there a way to get the actual correlation values from conn?

components in acompcor

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Hi,
regarding the aCompCor method, how are the components calculated in CONN? What are the different variables with which we calculate the principal components? Are the variables different voxels, ROIs or something else?
Thanks,
Sam

RE: Error While Trying to Registering Preprocessed Resting State Scans to Preprocessed Structural Scans

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I double checked that I selected 'Functional direct coregistration to structural without reslicing (rigid body transformation)' but the error persists.  I attached a screenshot of the data preprocessing pipeline before I hit 'start'.  In the menus after this I choose the defaults.

Changes in rsFC from pre to post scan: how to determine directionality?

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Dear Alfonso, dear All,

In a ROI-to-ROI analysis, I am interested in the change between two resting-state sessions: rest1 (-1) & rest2 (1). My research question is whether participants who show an increase in amygdala-anterior insula connectivity in rest2 relative to rest1, will show higher posttraumatic stress disorder (ptsd) symptoms. Thus my connectivity scores are a predictor for PTSD symptoms (I do this calculation outside of CONN).

 I already read elsewhere (https://www.nitrc.org/forum/forum.php?thread_id=3558&forum_id=1144) that :

[i]The standard way to present this would be as a conjunction of two contrasts, one contrast (for example) could be defined as [1,1] (for selecting only those areas that show positive correlations on average across both conditions), and the second contrast could be defined as [1,-1] (for selecting those areas that show higher connectivity in the first condition compared to the second condition). The conjunction of the two (those areas that show significant one-sided effects in *both* of these individual contrasts) will inform you which areas show higher positive connectivity in the first condition compared to the second condition (similarly, for example, the conjunction of [-1,-1] and [-1,1] will tell you which areas show higher negative connectivity in the first condition compared to the second)[/i]

Yet, I have troubles finding out how to define this conjunction contrast; I basically want to extract values indicating how much each individual showed higher amygdala-anterior insula connectivity in rest2 vs. rest1. At the conditions tab I thus thought to input following contrast: [1 1; -1 1]; However, when I do this, I get two different correlations for each individual, which I don't entirely understand. What would the first correlation represent - the positive connectivity strength between amygdala and insula across both conditions? And would the second correlation indicate the increase of positive correlation strength from rest1 to rest2?

Sorry for these basic questions, just started working with rsfMRI analyses in CONN, and would be really grateful for your thoughts on this.
Thank you in advance!
Best,
Laila

Ttimeseries extraction: What is the difference betweeen 1st eigenvariate, mean, and 1st PC in conn

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Dear Alfonso,

I am writing you because I would like to understand the difference between the timeseries based on

1) mean
2) 1st PC
3) 1st eigenvariate (hidden option explained here: https://www.nitrc.org/forum/message.php?msg_id=5074)

I noticed that the 1st eigenvariate and the mean correlate, but the 1st PC and the mean do not. This was a bit surprising to me.

Many thanks for your response,

Kind regards,

Basil

Denoising and V2V doubts

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Dear CONN users,

Recently we received the comments of a reviewer and I am having some problems to find some information about CONN processing:

1. Linear detrending and band-pass filtering: we receive the comment that they both remove low-frequencies, but even if it is true, the regression that linear detrend does is not performed by applying a pass filter. Is that the reason why both are included in the CONN's default denoising pipeline? 

2. ICC analysis: The reviewer ask us for the correlation threshold of ICC. In Martuzzi et al., 2011 there are described two different methods (ICC-degree and ICC-power). Given that the ICC-power does not required for a previous threshold, I am guessing that is the one that CONN applies. Am I right?

Thanks in advance,

Inés

ROI-to-ROI analysis comparing patients and controls

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To whom it may concern,

I´m at the second level analysis in my project. I want to investigate if there´s a difference in connectivity between mpfc and PCC in the default mode network in patients compared to controls (ROI-to-ROI analysis). (Please see attached files for print screens).

As setup I choose, between subject contrast [1 0; 0 1] - Any effect among patients and controls. 

In the Results explorer I think an f-test has been done. Could someone please help me interpret these results and guide on how to ha a connectivity value for 1) the patients, 2) the controls can be obtained?

Best Regards, 
Hanna riksson

MATLAB and JAVA

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How does this affect Conn Toolbox?

>> conn
Warning: JAVACOMPONENT will be removed in a future release. For more information, see Recommendations for Java and ActiveX Users on mathworks.com.
> In javacomponent (line 82)
In conn (line 122)

Which fix do you use here:https://www.mathworks.com/products/matlab/app-designer/java-swing-alternatives.html?s_tid=pi_app_designer_R2019b_javacomponent

Best regards,
Paul

RE: Contrast for Condition * Second Level Covariate Contrast

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[color=#000000]Hi Aaron,[/color]

If I am understanding correctly you have selected 'AllSubjects' and 'pers' in the 'subject effects' list, and entered a [0 1] between-subjects contrast, and have selected 'treatment' and 'placebo' in the 'conditions' list, and entered there a [1 -1] contrast. Is this correct? If so, then yes, that is the correct specification to evaluate potential differences in the association between connectivity and personality variables when comparing the 'treatment' vs. 'placebo' conditions (or equivalently, evaluate whether the size of the connectivity differences when comparing treatment vs. placebo conditions within each subject is associated with the personality variable).

Best
Alfonso 

[i]Originally posted by Aaron Smith:[/i][quote]Hello all,

I was hoping someone could verify my thinking on this setup for seed to voxel analysis. I currently have two (first-level, within-subject) conditions (treatment and placebo) and have a personality (second-level) covariate. I'm currently trying to model an interaction where the association between my personality variable (pers) and connectivity is different in treatment relative to placebo. That is, the connectivity varying as a function of pers is different between treatment and placebo.

I have run the contrast [0 1 1 -1] for all subjects, pers, treatment, and placebo, respectively. Is this a correct contrast? I have tried to verify this by extracting the results of this contrast (which returned a significantly positive beta) as an ROI and doing an additional ROI to ROI analysis between the seed and result. The ROI to ROI analysis showed that pers was positively associated with connectivity in these two regions during treatment, and negative during placebo. My current interpretation is therefore a change in slope between connectivity and personality as a function of my conditions.

Any guidance is greatly appreciated.[/quote]

RE: GLM for clinical variables as 2nd level covariates

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[color=#000000]Hi Apoorva,[/color]

[color=#000000]Yes, that second-level model is defined perfectly (that analysis will look at associations -in the patient group only- between FC and clinical scores, while correcting for those associations that may be mediated by age). If you want to double-check, click on the "n=###" button to take a look at the 2nd-level design matrix, you should see there that the analysis only includes patients and the degrees of freedom is equal to the number of patients minus 3[/color]

[color=#000000]Best[/color]
[color=#000000]Alfonso[/color]
[i]Originally posted by apoorva safai:[/i][quote]Hi Alfonso,

I have a question for applying GLM for checking the association between functional connectivity (FC) in patient group and their clinical score.
I have 2 groups HC and patients (PAT), with clinical scores avaliable only for PAT group. So my GLM has - PAT (PAT=1, HC=0), clinical scores (PAT=clinical scores, HC=0), AGE_PAT (PAT=PAT age, HC=0) and contrast [0,1,0]. I would like to know is this GLM model is correct? Are the clinical scores variable correctly defined?

Thank you

Apoorva[/quote]

Sliding windows across two sessions

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Hello forum,

I have two resting state sessions that I defined in the set-up. Each contains 20, 35-second windows. I was hoping to have 40 total separate windows, but it appears that window1 from session1 and session2 is combined into a single condition (averages across them?). In the roi-2-roi output I have 20 conditions and was hoping to have 40.

Is there a way in the set-up to define the windows such that across two sessions I maintain individual windows? I tried forcing the first onset value for session2 to be 367s (not 0) but that did not work.

Best,
Sarah Kark

First-level covaraites of interest, functional connectivity

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Dear all,

I did a first level analysis of our voice stimuli for which we have 4 first-level covaraites of interest (mean and variation of voice energy and voice fundamental frequency), in SPM.

Our question regards the modulations of some voice areas dependent and independent of the covariates. So for wholebrain analyses it's all done, but when importing (SPM import) to CONN the trial-level covariates of the SPM.mat disappear.

Is there a way to compute connectivity (gPPI, ROItoROI analyses) dependently/independently of these covariates?
Should I enter them in CONN directly? (will take time but if I have to...)

I know CONN takes the covariates in SPM.Sess.C, but it seems not those in SPM.Sess.U.P.

Thanks in advance for your input!

All the best,
Leo

Minimum T-values in ROI-to-ROI

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Dear CONN experts,

I am exporting the results of positive and negative T-tests from the ROI-to-ROI results explorer, involving multiple ROIs, in order to plot connectivity matrices with T-values in MATLAB. I use "export mask" and save as a .mat.

I have two questions regarding this function:


1. In order to threshold the colorbar correctly and report the results accurately, it would be useful to know the minimum T-value when I threshold at, for example, analysis-level p<0.05 FDR.
I have not been able to find the precise T-thresholds in the CONN ROI-to-ROI results.
Is this possible?

2. Preferably, I would export the results of a two-tailed test. However, the matrix R that is exported only contains absolute values.
Is it possible to export the matrix and keep the negative connectivity values?


Best wishes,
Andreas

3 Research Felloships in Neuroimaging at the University of Bari

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The Group of Psychiatric Neuroscience at the University of Bari Aldo Moro (Italy) aims to discover
the biological sources of differences between individuals that make them at risk of - or resilient to -
psychiatric disorders.
We will publish 3 calls for research fellowships in neuroimaging in different projects with
multiple PIs. One of the positions is reserved for magnetoencephalography (1 year renewable up to
4 years, funding already secured; official call here), the other two for fMRI (1 for 1 year renewable,
1 for 2 years renewable; official call pending). The positions are targeted at postdoctoral fellows.
Outstanding predoctoral candidates will be evaluated. The expected starting dates are between
March and July 2020. Expressions of interest are welcome before December 31st.

The group is headed by Prof. Alessandro Bertolino and investigates the association of genetic
variability with brain phenotypes, with a special focus on subjects at risk for psychiatric
disorders (for recent articles from the lab, see [1], [2], [3]). Successful candidates will work in the
lab of Brain Imaging, Networks, and Data mining (BIND) directed by Dr. Giulio Pergola. The
department has an in-house Elekta Triux magnetoencephalography with a dedicated technician.
Two 3T MRI scanners are available in collaborating hospitals. Prof. Giuseppe Blasi and his team
have access to and routinely tests patients, relatives of patients, and individuals at risk for
psychosis. The environment is interdisciplinary, including psychologists, biotechnologists, and
MDs, and internationally oriented, e.g., including Marie Curie awardees Dr. Pergola and Dr.
Linda A. Antonucci, along with several other members trained for > 1 year in UK, USA, and
Finland.

The ideal applicant is a neuroimager with international experience and a PhD in Neuroscience,
Cognitive Sciences, Neuropsychology, Computational Science, or any quantitative research field.
Coding experience (Matlab, Python, or R) is required, whereas machine learning and graph
theory expertise are a plus. Successful candidates are expected to have at least one
published/submitted lead author paper. We expect the successful candidate to be motivated
and research-oriented. It is equally important to have a team-working attitude and a motivation to fit
in the group. Proficiency in Italian is not required.

Consider applying even if you only have strong coding and analytic skills – definitely apply if you
think you have smart ideas on how to analyze multimodal neuroimaging data from hundreds
of subjects.
The University of Bari Aldo Moro is one the largest Italian universities (50.000 students, 1500
permanent professors and researchers, 1500 units of technicians and administrative staff). Bari is a
sunny city located by the sea, and the third largest Italian city south of Rome. The cost of living is
low relative to Italian standards.
Please contact Dr. Giulio Pergola sending a CV and the contact details of at least two references
if you would like to have more information on the positions and on the ongoing projects
(giulio.pergola@uniba.it).

RE: Differences in SPM vs CONN 2nd level results

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Dear All,

I have the same problem, that I get different results in CONN, SPM12 and xjView.
In the Appendix is one example of the seed-to-voxel analysis in the Default Mode Network (mPFC) at a
voxel threshold of p< 0.01 (p-uncorrected)
Cluster threshold p< 0.05 (cluster size p-FDR-corrected)
T(16)_min = 2.92  ---   k_(min)= 369

In xjView:
Type: T df: 16
Threshold
-- p value = 0.01
-- intensity = 2.5835
-- cluster size = 369
Number of clusters found: 4

Why do I get a different T-intensity value in xjView as compared to CONN? If I manually Change the T-value to 2.92, which is the same value I get in CONN, my p-value changes to p=0.0050081 and then I get the same clusters as in Conn.

I'd be very grateful if anyone could help and explain this to me. Thanks!
Nadja

Correlating behavioral measures with functional networks

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Dear CONN users,

I have analyzed 24 subject data for executive functioning task in CONN. The task contains three conditions GNG (go nogo block), Go (Go block) and rest block. I want to understand:

(1) Executive function network for the contrast GNG>GO. 
To understand the executive function network, I use the contrast for subject effects, ALL subjects, IQ, as 1 0, and the condition contrast for conditions GNG and GO as [1 -1].

(2) How the connectivity of the executive network (for the contrast GNG>Go) varies with the score of participants for some reading measures while controlling for IQ.
So, in order do so, I am setting the between subjects contrast for subject effects, ALL subjects, IQ, Reading score as  0 0 1, and the condition contrast for conditions GNG and GO as [1 -1].

[color=#ff0000]When I do so, I do observe connections at FDR corrected threshold 0.05 that vary with reading score (for eg., R STG - R SPL), that [b]actually does not appear[/b] in the Executive function network for the contrast GNG>GO at FDR corrected threshold 0.05.[/color]

My questions are:
1. Am I setting the contrasts right?
How do I explain the results am I am observing? If a connection is not surviving in the network for executive function for the contrast GNG>GO, how can the connection strength of the same vary with the reading score?

2. I only want to analyze the modulation of the connections that survive the FDR corrected threshold of 0.05 (for Executive function network for the contrast GNG>GO) with reading ability. How do I do that?



Thanks in advance,
Avantika

Slice Timing Correction in SPM for Multiband Acquisition

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Hi All,

I'm looking to perform slice timing correction on my fMRI data using SPM. We used a multiband of 3 with a TR = 2, odd-even ascending interleave fashion. The SPM wiki tells us:

[i]If you use multi-band acquisition, you cannot use the slice order as an input to slice timing correction, since a slice order cannot represent multiple slices acquired at the same time (if it was a matrix it would be possible, but SPM only accepts a vector). However, you can use the slice timing instead of slice order when using a multi-band EPI acquisition[30] ·[31]. If you do know your slice order but not your slice timing, you can artificially create a slice timing manually, by generating artificial values from the slice order with equal temporal spacing, and then scale the numbers on the TR, so that the last temporal slices timings = TR - TR/(nslices/multiband_channels).[/i]

However I was wondering about the spatial order of our inputs. Is the program expecting the order of slices in my vector to be ascending, represented spatially from foot to head? i.e. an interleaved (odd-even) 15 slice multiband 3 acquisition with a 0.1 second inter-slice interval would be: 0, 0.3, 0.1, 0.4, 0.2; 0, 0.3, 0.1, 0.4, 0.2;0, 0.3, 0.1, 0.4, 0.2.

Thanks in advance,
Rhys Yewbrey
PhD Candidate Supervised by Katja Kornysheva
Bangor University
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