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Dear AFNI users-
We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:
https://discuss.afni.nimh.nih.gov
Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.
The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.
Sincerely,
AFNI HQ
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Dear AFNI experts,
I have been asked to report despiking statistics (number of volumes / voxels despiked in each subject). For what I can see, this information was not "saved" anywhere with the standard 3dDespiking procedure - implemented using afni_proc.py.
So there are my questions:
- how can I keep track how how many spikes are corrected in voxels?
- how much do you think th
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smndpln
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AFNI Message Board
Dear Gang,
Thanks for the answer.
This is useful, but my question is slightly different.
I have already extracted and visualized the estimated HDR shape in many regions of interest (I have around 30 ROIs if I consider significant peaks).
The problem is: these results are hard to interpret, globally.
However, I noticed that some regions (e.g. left inferior frontal gyrus and left superior
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smndpln
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AFNI Message Board
Dear AFNI experts,
I performed a group analysis and the effect of the factor A was significant in many voxels (30 peaks, using automated search algorithms).
The group analysis was performed with 3dMVM after a individual-level GLM on 40 subjects; activity was estimated using TENTzero function (10 non-zero betas, over 12 seconds).
Given many results, the regions of interest which are modulat
by
smndpln
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AFNI Message Board
Dear AFNI experts,
my aim is to evaluate if a zero-centered variable (X) predicts the topography of functional connectivity (FC) maps across subjects.
More specifically: I obtained FC maps from 40 subjects from a seed region; each subject is associated with a value in X. FC maps are unthresholded z-Fisher transformed maps from r values (no zeros).
I want:
(i) to statistically test if X pred
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smndpln
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AFNI Message Board
Dear Gang,
Thanks!
> You captured the HDR with only four time points?
The model was a TENTzero with 10 non-zero estimates, but I focused on specific timepoints given the group analysis results. Do you think this is uncorrect / introducing bias?
> (ME) I have two cluster-corrected maps of interest: the interaction of Axfactor1 and Bxfactor1.
> (Gang Chen) You may throw away
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smndpln
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AFNI Message Board
Dear AFNI experts,
I ran the following group analysis after applying a TENT model to estimate brain activity:
3dMVM \
-prefix groupAna_factor1 \
-wsVars 'A*B*Time' \
-wsMVT \
-bsVars 'factor1' \
-qVars 'factor1' \
-qVarCenters '0' \
-dataTable \
Subj A B Time factor1 InputFile \
S1 A1 B1 t1 0.1968 ../
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smndpln
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AFNI Message Board
The same scale is used for both images ([-0.4 1.2]), and the brightness represents the z-Fisher transformed value of the Pearson's r.
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smndpln
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AFNI Message Board
Dear Paul,
I realize the problem is quite deep, here.
Anyway, as they may be useful, I attach the two functional connectivity matrices obtained using the two methods (one subject, two concatenated 7.5 minutes resting-state runs).
You may want to know that the two approaches produces similar - but not identical - outputs (the 2D correlation coefficient between the two matrices is 0.83).
Ther
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smndpln
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AFNI Message Board
Hi Paul,
regarding your question #2.
Well, I will tell you the difference as I get the results. Anyway, I'd like to have your specific opinion here.
It is important to note that I am using the Glasser's parcellation (360 parcels, from the Nature paper). Each parcel is a RoI.
By default, one can expect a decrease in correlation values, especially regarding intra-parcel connecti
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smndpln
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AFNI Message Board
Thanks for the answer.
1) You are right. I was looking at the output of 3dmaskdump, which returns values with a precision of 6 decimal places (mine was just an example, sorry for being imprecise).
Is there a way to output only the first - for example - 3 decimal places? That would be useful...
2) Got it. So I suspect that the procedure which I implemented - cycling through voxels - is the
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smndpln
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AFNI Message Board
Dear AFNI experts,
regarding 3dNetCorr
1) Is there a way to approximate output values? (e.g., 0.65286124125 -> 0.653)
2) There seem to be two ways to calculate ROI-based functional connectivity:
Estimating the average time series across voxels in each ROI, and then calculating their correlation; Estimating the correlation among each voxel in each ROI, and then calculating their ave
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smndpln
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AFNI Message Board
Dear AFNI experts,
I am using 3dNetCorr to extract 360-by-360 matrices of functional connectivity among parcels.
A colleague advised me that the standard procedure used by 3dNetCorr (i.e., averaging time series and than correlating) may not be the most accurate choice. In fact, it can be argued that correlating each voxel-pair (among each couple of RoI) and then averaging the correlations is
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smndpln
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AFNI Message Board
Thanks for your answer,
> Is the reason you have a 2 x 2 structure because each of the two stimulus presentations is associated with one motor response?
I'm sorry I wasn't clear. No, there are 4 runs, and each run represents a condition:
- First run (first condition): factor I level A, factor II level A;
- Second run (second condition): factor I level A, factor II level B;
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smndpln
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AFNI Message Board
Thanks Gang,
In the GAM approach, for each subject I estimated:
1) the activity associated with the stimulus presentation;
2) the activity associated with the motor response.
Important infos:
- The two are both estimated using the function GAM in 3DDeconvolve;
- There is no collinearity between regressors. And the two activation maps (at the single subject level) are different, and cohe
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smndpln
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AFNI Message Board
Thanks Gang for your answer.
However, I still have this doubt: can the two betas associated with the two regressors (Stimulus and Response, modeled at the individual level using GAM functions) be considered multiple response functions (better analyzed in a single model with 3dMVM)?
EDIT: ok, I think you mean using 3dANOVA3 and treating the two regressors (Stimulus, Response) as a random fac
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smndpln
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AFNI Message Board
Dear AFNI experts,
We have data from a event-related experiment.
- The average duration of trials is 14 seconds (11.5 - 16.5 s);
- There are two events: Stimulus and Response. Stimulus starts at trial onset; Response starts 4.5, 5 or 5.5 seconds after trial onset.
- Individual level analysis: we are considering both a TENT (11 basis functions) and a GAM (Stimulus and Response regressors) ap
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smndpln
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AFNI Message Board
Thank you for the answer,
anyway, to me it is unclear what you are suggesting. Can you please specify?
Simone
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smndpln
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AFNI Message Board
Loading required package: lme4
Loading required package: Matrix
Loading required package: reshape2
Loading required package: lsmeans
Loading required package: estimability
************
Welcome to afex. Important changes in the current version:
- Functions for ANOVAs have been renamed to: aov_car(), aov_ez(), and aov_4().
- ANOVA functions return an object of class 'afex_aov' as
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smndpln
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AFNI Message Board
Dear AFNI experts,
I'm trying to analyze data from 17 subject using 3dMVM. Brain activity was estimated using TENT basis functions (8 basis functions). I have 2 additional within subjects factors: Condition (Neutral, Negative), and Delay (Short, Medium, Long). This means 17 * 8 * 2 * 3 = 816 response values.
When I run the analysis, 3dMVM correctly reports the contingency tables and su
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smndpln
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AFNI Message Board
Dear AFNI experts,
Suppose an experimental design with 4 different conditions of a fast event-related paradigm.
Each condition is presented in a single separate run. So there are 4 runs and each run is paired with a unique condition.
Suppose to analyze data with a single GLM.
My question is: given the different baseline for each run, the result of general linear contrasts between conditio
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smndpln
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AFNI Message Board
Dear Gang,
I was actually referring to a mask which combines all of the regions of interests.
Simone
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smndpln
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AFNI Message Board
Dear AFNI experts,
I understand that the assumption of Gaussian shape for spatial autocorrelation function (ACF) of the noise is essentially wrong.
Anyway, I was wondering how spatial restrictions affect the estimation of acf/fwhm values. To give an example, the usage of a mask with separate brain regions (say PCC, dlPFC and mPFC) in a GLM (and in the consequent 3dClustSim) will lead to less
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smndpln
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AFNI Message Board
Thanks.
Supposing that I'm not allowed to reduce dimensionality, because I need one coefficient for each predictor, is there a way to conduct a penalized/regularized regression?
Or, alternatively, what may be the best strategy in this situation?
Simone
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smndpln
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AFNI Message Board
Dear AFNI experts,
my aim is to run a multiple linear regression on resting state functional connectivity maps from 17 subjects.
Predictors are represented by a 10-dimensional questionnaire.
I'm particularly interested in obtaining R2 coefficient, for each predictor, for each voxel. It is clear that I need to take in account dependencies between predictors, in this analysis.
I
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smndpln
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AFNI Message Board
Hi Gang,
In my study, I used 3dLME to run linear mixed-effect models on functional connectivity data. In the model, the factor condition is the fixed effect, and the factor react is the random effect. Is a repeated measures design.
I was wondering if
Bij = a0j + a1j*Xij + g0i + g1i*Xij + eij
is the correct expression to cite.
Here's my 3dLME code:
3dLME \
-prefix LME_$see
by
smndpln
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AFNI Message Board