Questions related to fMRI
Hi. I am a graduate student and writing my first first-author scientific journal. It is about fMRI study. I am wondering whether it is fine to copy fMRI Data Acquisition method from our lab's previous articles if I followed exactly the same MRI machine, sequences, TR, TE, and so on and so forth? Or is it still plagiarism and do I have to paraphrase it? If I have to paraphrase it, how should I do? Just change some verbs?
I am working on EEG data and want to find functional connectivity and finally the graph. Would you please help me with which toolboxes are more useful to do this work? GraphVar or Fieldtrip?
I used GraphVar, during the work as it used fMRI datasets in the toolbox it had regions of the brain but my data is EEG and I do not have any details about the regions. To do this I should implement source localization which is really time-consuming. I should do this asap.
Thanks in advance for your help.
I would like to know how you deal with the problem of flocculus’ fMRI signal asymmetry due to the convolution effect. Because the location of the flocculus is relatively deep and close to the base of the skull. Could you please give me some advices about this question and data analysis?
Thank you very much
When using ROIs or Parcellations, they typically come with a resolution (2mm, 1mm, etc). Do the resolutions of a parcellation need to match the functional data I am using? If so, what is this process and how does it work?
I am trying to open fMRI images in my PC but (I think) no appropriate software is present in PC. Hence I am not able to open indidial images in my PC.
In an fMRI study, I will use a categorization test where the competition between prime and target is important. What do you think the prime and target durations should be?
Dear FSL experts,
I used two method to filter my fMRI data.
A. using Feat GUI, click the high-pass filter,
in report.html (autogenerated by feat), the responding code is as follow:
fslmaths prefiltered_func_data_intoform -bptf 33.3333333 -1 -add tempMean prefiltered_func_data_tempfilt
B. using a code:
fslmaths prefiltered_func_data_intoform -bptf 33.3333333 -1 prefiltered_func_data_tempfilt
and then I registered the above two .nii files into MNI template, and had a look on them.
they are so different…… I think the only possible reason is the “-add tempMean” in A method.
why FSL automatically add the tempMean("mean funcdata") in filter stage? method A or B which is better? I will appreciate it if you help me with this question.
In our study, the experiment consists of 4 different sessions. For example, we have 3 independent variables. One of them has 4 levels. Each level of this variable changes within different sessions. The other 3 independent variables change in the event-related. In other words, there are 2 independent variables in each session. So the model of the experiment looks like a MR mixed design. How can we create the model of this design? How can we analyze such a model with SPM? There are 4 different scan files (for 4 sessions). Is it true to merge them into a single folder and make model input and analysis by shifting their onset times? Or how should we proceed? How can we do an inter-sessions event-related design analysis and determine the contrast?
I would be glad if you could help me.
I am trying to programming fMRI (BOLD) data processing via SPM12 using MATLAB. I have four stimuli that each participant should note and respond according to each stimulus. When I want to set conditions for 1st-Level, in data some runs lack some stimuli (some of the stimuli have not been used). for example, there is no second-stimulus in run-2, but in others have been used.
Now, should I define an empty vector/zero value for "Onsets of the Condition" or in general I should not set a second-stimulus condition for run-2?
I am designing my cognitive tasks for fMRI studies these days. I plan to use event-relate design for one of them.
According to the HRF model, the blood oxygen level need some time to reach its peak value and decrease to baseline. My stimulus is around 1 to 1.5 seconds based on participants reaction time. I want to ask whether it is enough to set a 5 seconds intervel between each stimulus.
Thank you very much.
where can I find free mouse brain CT/MRI or fMRI/PET scan images to load into 3d slicer ?
I would like to load some mouse brain images, I would like to eventually use the images to look at connectivity in normal and disease states by making a tractography image. Is there somewhere I can download open source data for this purpose? Also, is there somewhere I can find stereotactic coordinates in a 3D layout ?
I am using fMRI imaging in my study (Flanker Task) where I am lost at how to calculate the Stimulus size. I have the visual angle information for my stimulus but I am unable to convert that information into pixel size. In the fMRI setting, the projector screen is placed at the back of the participant's head behind the scanner and the images are projected to an angular mirror on the head coil which then gets reflected in the participant's eyes.
For conversion of visual angle to pixel size, we generally need the visual distance which in this case cant be just the distance between the screen to the eye. I am not sure how to take into account the angular arrangement of the head coil mirror into this calculation or if we need to account for mirror dimension as well.
Following is the information we have,
Given Stimulus Visual angle= 3.28 on x-axis, 0.41 on y-axis
Screen to mirror distance = 160 cm
The mirror is at a 45-degree angle to the head
Mirror to eye distance = 14 cm
Screen dimention: 1920x1080(resolution)
87.5 cm (Width), 48.3 (Height)
Mirror dimentions = 15.5 cm (Width), 12 cm (Height)
Question: What would be the pixel size or dimensions(height/width) for the Stimulus?
Any help would be greatly appreciated. Thank you.
I'm working on an imaging genetics projects with an aim to exploring whether certain allelic variations of a gene modulate BOLD responses and behaviors measured in a social-cognitive task.
When I solely looked into the behavioral data, I found no evidence that the genetic variable (e.g., polygenic risk score) significantly predicted individual participants' task performance (e.g., no zero-order correlation).
However, I found that the genetic variable is linearly associated with the activations in one brain region, and the activation values extracted from this area tha shows significant genetic modulation in turn correlated with the same task performance analyzed above. Let's suppose that: A=polygenic risk score, B = Brain activation, and C= behavioral task performance. All genetic, brain activation, and behavioral data are obtained from the same group of individuals.
What I'm seeing here is as follows:
1. Significant association between A->B
2. Significant association between B->C
3. Non-significant association between A->C.
My (potentially faulty) intuition was that maybe there is a path between these variables, where A is linked with C only via the action of B. Indeed, a mediation analysis based on bootstrapping revealed a significant indirect path linking: A->B->C. No direct effect was significant with or without the mediator. (I understand that this is problematic in Baron-Kenny approach, but I also learned that the A->C relationship is not required as it's equivalent to the total effects, which essentially is the combination between all possible indirect and direct effects.)
In this situation, is it permissible to conclude that the brain activation (B) is mediating the genetic (A) and behavioral (C) variable? I could see someone argue that A->B->C is a more accurate model as you may miss the significant indirect path if you only test the direct path. However, such a postulation just seems counterintuitive. It just doesn't seem to make sense that the genetic modulation on behaviors that was initially absent "suddenly" becomes significant when the brain data are combined.
Or is this just a misguided feeling due to the fact that I happened to perform the behavioral analysis first (mostly due to the format of the paper where you typically introduce the behavioral results prior to the neuroimaging data), and now I feel like I'm making things up with the neuroimaging data that weren't initially consodered...
Any inputs will be greatly appreciated!
I want to consult about a subject that I can't get out of. I am working on an event related design using the backward masking paradigm in fMRI. However, paradigm code and TRs are not synchronized. The flow of the code actually depends on TRs. So the flow of the code is locked to 6. As each TR arrives, it advances the code as if 6 was pressed. As a result, there should be no slippage between code and fMRI. However, the shift duration between computer and MR changes in all sessions. For example, there is a difference of 2 TR in the one, 5 TR in the other, and 8 TR in the other. If anyone has encountered such a problem or has a suggestion, it would be greatly appreciated. How can I solve this problem?
Note: I have written the code in Psychtoolbox.
Basically having this exact same problem
I've got 4 BOLD runs, each 190 volumes, but I guess SPM interprets them as just one volume?
I've used dcm2nii to convert 190 DICOMs to just one 4D nifti file and now, after warping, co-registering, etc. tryina run the GLM model, inserted onset times, but it says
21-Aug-2019 22:08:49 - Running job #2
21-Aug-2019 22:08:49 - Running 'fMRI model specification'
21-Aug-2019 22:08:49 - Failed 'fMRI model specification'
Error using spm_run_fmri_spec (line 131)
Not enough scans in session 1.
In file "C:\Program Files\Polyspace\R2019a\spm\spm12\config\spm_run_fmri_spec.m" (v6562), function "spm_run_fmri_spec" at line 131.
The following modules did not run:
Failed: fMRI model specification
not sure how to specify that there's 190 volumes in each .nii file.
Would anyone like to write a project with me? As part of the Polish LEADER or SONATINA programs.
Leader consists in creating a team and scientific work.
I have a research idea using fMRI, so I am looking for someone who works with fMRI/rsfMRI and would like to join in writing and executing the project. Deadline March/April.
I would like them to be people >35 years old, fMRI specialists. (Age is one of the assumptions of the project leader).
I'm full of hope
I have set up a full factorial model on the second level in which I compare two groups (factor 1: experimental group vs. control) and two time points: pre vs post (factor 2).
I was wondering whether the contrast images for factor 2 can stem from the same first-level GLM? Specifically, there, I modelled the pre- and post-session within one GLM and defined various contrasts already on the first level, for instance, differential contrast pre vs. post (1 -1 and other regressors 0), mean activity pre (1 0), mean activity post (0 1). For the second-level full factorial model , I guess I would use then the mean activity pre (1 0) and mean activity post (0 1) contrast images from the first level as inputs (for the two levels of factor 2).
Alternatively, one could run two first-level GLMs - one for only the pre session, and one for only the post-session, and then use those mean activity contrast images for the second-level model.
However, I would prefer the first option, because then one could already compare the pre vs. post activity on the first-level. This way one could also use this difference contrast on the second level and run for instance a simple two-sample t-test, comparing the two groups.
I hope that my prefered variant (modeling both pre- and post-session in one first-level model) is also fine for the second-level full factorial model. Or is the alternative variant (separate first-level models for pre and post) preferable? And if so, why? I am not completely sure if these two variants should produce the same or similar results anyway. My intuition is yes, at least similar. On the other hand, I think that when modeling both pre- and post-session in one model the mean baseline activity to which, for instance, pre-activity-only is compared, is quite different (because it also includes post-activity) then when keeping them separate.
Maybe someone can help. Thanks a lot!
I am new to fMRI and I am working with rat fMRI data. I am performing the preprocessing steps in fsl (FMRIB Software Library) and since it designed for human brain, I must scale up the voxel size of rat brain image by 10.
I know that I probably must work with scl_slope and scl_inter in nifti header and I use fsledithd command to change the scl_slope to 10 (Now it is set to 1). but when I change the scl_slope in nifti header, the image doesn't change ( the voxel size remains unchanged.) and I think I must use fslcreatehd command to make these change to my image but I don't know how to do that. if this procedure is right please tell me how I must do that.
Also, I edit the voxel size directly with fsledithd(edit pixdim1, pixdim2 and pixdim3 instead of scl_slope and scl_inter) and it seems that it works, but, I don't know that it is correct to edit those parameter instead of scl_slope or not.
Hi. It is well known that we lose consciousness when we fell asleep, as an individual, we stop being conscious of external and internal stimuli, at least, in most cases. In no-dream sleep, brain activity should be one that can unconsciously manage internal and external stimuli, and should not experiment significant changes in blood flow to the brain, as if we were to observe with fMRI, nearly no BOLD signal would appear. Am I right about this last thing? If not, please reply. Thanks to all.
1. Science folks unanimously agree about a fixed definition for “Theory of Mind” or "Mentalizing"! That I think is a philosophical paradox as theory of mind has this very pivotal cannon of being open in empathizing and understanding different beliefs through attribution of mental state as in beliefs, intentions, knowledge and emotion. Thus, how could we all agree to establish one definition for “Theory of Mind” which conveys elimination of all other definitions or disregard openness to other definitions of “Theory of Mind” itself that might be quite different from the typical definition of theory of mind. As if one might staunchly emphasize that they believe in animal advocacy and rights but still keeps a songbird in a cage! I believe that some definitions cannot be strictly defined as a single omnipresent definition and depending on the subject and application would vary in definition. Theory of Mind can have one definition in mentally healthy human and yet another but not incorrect definition in mentally disordered people. Not to mention that theory of mind has been reported to be a personality trait in non-human primates as well!
Do you agree... or disagree?
2. There are neuropsychological assessments through sessions or interviews... But what about an efficient or statistically reliable "experimental" method of assessment for theory of mind in humans and non-human primates?! Theory of mind is a rather complex behavior which not only includes one's own beliefs or intents but also deciphers those of the others.
We have used "eyes task", etc. during neuroimaging to evaluate theory of mind. Scientists using such simple tasks and fMRI with all its limitations in terms of noise, resolution and processing to test mentalizing. Results are amazing but couldn't we develop better tasks and less limited modalities to assess a behavior with such complexity that involves numerous brain regions at once?
3. This is a social trait! When someone fails to develop mentalizing (either due to neurological disorders such as schizophrenia, autism... or due to still unknown mechanisms of acquired behaviors and social environment) which can be represented as a wide spectrum of complex behaviors from misunderstanding/incomprehension of beliefs or intentions which are different, unempathetic adamancy to disrespecting or denigrating and disparaging others' beliefs; wouldn't it be quite tricky to merely observe and assess underlying brain functions in a singled out subject via current imaging or experimental modalities?
We have three groups in our study ( a block-design fMRI study); unfortunately, sample sizes are small (12-9-10). We want to compare the activations in some conditions between three groups, so we extracted beta values for four different conditions. In other studies, one-way ANOVA is usually preferred, but we are not sure because our small sample size. Is it appropriate to use one-way ANOVA, or should we choose a non-parametric test?
In our study, 3 different conditions and control tasks of each conditions will be evaluated. Therefore, the duration of the experiment may slightly exceed the time required for a suitable fMRI acquisition. We want to reduce the duration of the experiment by determining the minimum ITI times we can use, which are suitable for measuring the hemodynamic response. In our previous studies, we used ITIs at ranging from 2000-4000-6000 ms, and it is generally recommended in the literature to have a minimum of 2000-5000 ms. However, we also came across some recent event-related fMRI studies using ITI under these times (for example; 600-800-1000 ms or 1500-6000 ms). So I would be very happy if you could help with this.
I have two questions regarding DCM.
My experiment: I have a 2x2 factorial design with the factors movement (active/passive) and feedback (visual/audiovisual). Subjects move their hand (active condition) or their hand is moved by a machine (passive conditon). Feedback about the movement is given either by visual feedback (visual condition) or by both auditive and visual feedback (audiovisual condition). Data was acquired in 5 sessions separated by a short break. Data acquisition was stopped between sessions. I therefore have to concatenate the sessions before extracting the eigenvariates.
My questions are:
1. I concatenated the sessions before extracting eigenvariates using two different approaches. First, I used spm_fmri_concatenate. Second, I modelled the session regressors in first level (similar to the PPI exercise in SPM manual). The results are similar, but they are quite different from the original results (without concatenation). Is this possible? If so, what would be the way to proceed?
2. In my first DCM analysis I am interested in connectivity differences during active and passive movements leading to visual feedback. I do not want to analyze the influence of audiovisual feedback. How to proceed best? In my models, I can not simply neglect the audiovisual condition because they might be influencing activity in the visual cortex. A first option would be to model the audiovisual feedback in the first level design matrix and use, during the extraction of the eigenvariates, an effect-of-interest contrast that excludes the time series variability related to the audiovisual conditions. In my DCM analysis, I then do not have to account for the effect of audiovisual feedback anymore. A second option would be to extract the time series without trying to remove the audiovisual effects, but instead model these effects for instance as direct inputs (C-matrix) in the visual and the auditory cortex (and perhaps also in the B-matrix), but considerung this parameter as parameter-of-no-interest. What would be your suggestions on this?
Currently we are working on a review that surveys the cognitive/neural mechanisms of tactile working memory. We propose a sensory recruitment model, which suggests that prefrontal regions interact with somatosensory cortex to encode, maintain and retrieve tactile working memory. Please leave your email address if of interests.
I would like to add respiration data as a nuisance regressor to a resting state fMRI analysis. As the fMRI data is recorded at a frequency of 0.5 Hz (TR = 2000ms) the respiration data has to be down-sampled from its 1000 hz.
I am currently struggling to decide on a down-sampling method. My current preferred choice is MATLAB's decimate function which simply decreases the sampling rate and automatically applies appropriate low-pass filters to avoid aliasing.
I was, however, hoping to find a more nuanced filter which averages values in certain ranges rather than cut them out entirely.
My friend is preparing for a systmatic review in the neural basis of instruction-based learning. Instruction-based learning (IBL) refers to learning to perform tasks based on instruction rapidy. You will be responsible for part of the writting and editing (specifically the role of ACC and IFS). Leave your email address if you are interested.
Intruction-based learning refers to the ability to learn from the instruction rapidly. Many recent studies have investigated the neural mechanisms of this fundemental processes. Currently, we are interested in doing a systmatic review on this topic. Please leave your email address if you are interested.
Is there anybody who is interested in collaborating with a project regarding neuropsychology of language mechanisms. This work would be a review including papers using fMRI and EEG. We hypothrize the the mutipled demand cortex may play an role in language, but its role is more executive rather than linguistic. Leave your email address if you are interested.
We will conduct a big study for finding different biomarkers for depression. We will combine different modalities (fMRI, MRI, microbiome, genomics etc) in order to explore possible biomarkers (and combination of information between different modalities)
Is it possible to compute a power sample since the variables and combination of them are multiple (actually thousands) and there is not a specific biomarker that we want to test
I am trying to analyze some fMRI data and neuRosim (https://cran.r-project.org/web/packages/neuRosim/index.html) package in R, was recommended to me. However, I could not find a comprehensive tutorial and detail examples. I would be very grateful for suggestions and recommendations. Thank you in advance!
Typically, BOLD signals are regarded as how much effort that people process a cognitive task.
But, is that possible that 'the efficacy of how people use this activation' could determine behavioural performance?
For example, in order to conduct a memory task, people might recruit more brain activation (can be intepreted as pomping more petrol). But different groups of people might vary in the efficacy of using this extra BOLD activation (different cars use the same amount of petrol but with different efficacy of petrol consumption).
I know that there is a term, called neural efficacy, but different from the concept above.
neural efficacy is like the ability of filling more petrol, but not the efficacy of using it.
So, I'm wondering if there is a relevant theory about this concept?
I'm performing 1st-level fMRI analyses in SPM12 for a within-subject study. In my design, each participant performed 10 runs per condition, with 3 different conditions globally. Then, I come out with 30 sessions per participants.
At the moment of the contrasts, I face the problem of the replication over sessions: is it possible to replicate & scale the contrast X, but only over N sessions? I would to perform the same contrast over the 10 sessions of the first condition, scaling them, but I can only choose the option "don't replicate" or "replicate and scale", and I cannot specify any session interval. This means that I only can replicate the same contrast either over the 30 sessions or nothing? In this second case, does the absence of scaling be a problem in my analysis?
Thank you in advance
My friend is looking for coauthors in Psychology & Cognitive Neuroscience field. Basically you will be responsible for paraphrasing, creating figures, and collecting references for a variety of publications. Please leave your email address if you are interested. 10 hours a week is required as there is a lot of projects to be done!
We are working on a review regarding the relationship between language and the mutiple-demand network. You will be responsible for addressing the reviewer's criticisms. Please leave your email address if you are interested.
is there anyone doing simultaneous LFP/EEG-fMRI?
How do you detect and remove the FMRI Artifacts?
Are there Python toolboxes that already do that that I missed?
I finished analyzing task-based fMRI activations for our data by using SPM12. However, I need to do quality control for the collected data and also for the analysis part. However, I do not know how can I do this?
I would be glad, if you could guide me through.
Thank you in advance.
I need the images loaded into MATLAB for the analysis. I have the .nii segmentation file and in the instructions for practice I was asked to
manipulate the files as .hdr and .img, which, it says, MATLAB can read.
My question is, Is there an easy way to convert a file saved as .nii to .hdr and .img files? I need to discard the first scans of an fMRI time series to avoid T1 effects. I am totally new in this topic. So I am, for now, just learning the basics to complete a data analysis and I am stucked here.
I'm a new entry in the fMRI field, my main expertise is EEG-related.
I have a problem with my BOLD signal analysis (using SPM). I have run an event-related fMRI design (2 by 2) when the participants were simply asked to rate a series of videos. When computing the contrasts between the two levels of each factor, only in one (out of the four) contrast I can see brain activity partially overlapping with the ventricle system (for uncorrected p < 0.001). For FDR correction (p < 0.05), just a few small clusters of activity survive. Since no brain activation was assumed for that specific contrast, I think to be stuck with some kind of artifact (i.e., breathing-related). I've already checked at the single-subject level and such an unexpected activity is visible in almost 50% of the participants only in that contrast (checked using uncorrected p = 0.1 or lower). Considering that the other three contrasts are artifact-free, I don't think it is a general pre-processing issue.
Any suggestions or possible solutions?
Thanks in advance,
In my study I would like to do a joint analysis of two task-fMRI studies. In these two studies the same task, but different scanners were used with two different study groups. I compared neural activity of patient subgroups in both cases with SPM12, and now I would like to run the joint analysis with additional correction for the site/scanner effects.
What would be the appropriate method/analysis to use to measure site effect? I'm interested in any options, but would prefer methods which I can run using SPM or MatLab.
Thanks in advance!
My doubt was regarding calculating LI value using resting-state data.
The task-based fMRI sequence for a pre-surgery patient was processed and calculated using the LI toolbox in Matlab-SPM but the Lateralisation was bilateral. So, to try a different technique, we took the resting-state data of the patient and I processed this sequence using the CONN toolbox and used the spmT contrast file created in CONN to calculate the LI by using the LI toolbox in SPM.
I wasn't sure if this technique was accurate because we normally use the contrast file created in the SPM fMRI tool to calculate the LI value and I wanted to know if this particular method is applicable.
I am looking for a fMRI data viewer without MATLAB, as my manager asks me to share the spm.m file with him so he want to move the curser around and adjust the p value to explore what regions are being activated. (He does not have a background in brain science and have zero experience with MATLAB so I cannot just share a table with him)
Unfortunately, most commonly used tools requires MATLAB such as xjview. I am also looking into BrainVoyager but it requires a license and it seems that you will have to re-preprocess the data. So I am curious if any know any fMRI data viewer that does not depend on Matlab
I am an senior undergraduate student and majored in psychology. I really want to learn somthing about fMRI, however, there is little rescource in our university. I have no idea of what kind of book or website could be helpful for me to start learning it, and waiting for your advice.
I have fMRI scans from 2 different acquisitions for the same subject,
All parameters are the same (TR, sequence etc.).
There is slight shift in the slice-prescription between the two.
What would be the best way (FLIRT, SPM?) to motion correct, and co-register both the functional scans and take an average?
What are temporal limits in determining brain metastability? In other words, how long does the minimum length of the scan segment using fMRI have to be for the result to be of any value?
I'm particularly interested in measuring metastability during decision making. If I have two different decision conditions, which are shown alternately in the fMRI paradigm (so let's say condition A and B, and paradigm look like it: ABABABAB....), will "slicing" the data and combining them later produce reliable results in the context of metastability estimation (so I could calculate metastability for A and B condition separately)?
I came across a publication where a similar operation was performed (Alderson, TH, Bokde, AL, Kelso, JS, Maguire, L. and Coyle, D., 2020. Metastable neural dynamics underlies cognitive performance across multiple behavioral paradigms. Human brain mapping, 41 ( 12), pp. 3212-3234.). However, it concerned cutting out fixation elements between trials and joining whole blocks. I, on the other hand, want to cut and join the trials, then compare the level of metastability between the two conditions. Is it possible, or not really good idea?
I am trying to extract peak coordinates from given clusters in an 3D fMRI group image, e.g. a thresholded ICA z-score map.
Using MATLAB/SPM I am able to extract a single peak coordinate for each cluster by numbering the clusters with spm_bwlabel(), listing the voxel values per cluster and searching for the maximum value. However, I'd like to extract more than only one peak that might exist within each cluster (e.g. the SPM results viewer displays up to 3 peaks by default).
Did I miss an obvious solution or an SPM function, respectively? I could imagine to, within a main cluster, search for "subclusters" and again compute their peak values - but there might be an easier solution?
Any help is appreciated! :)
Best regards, Leon
The question arose during analysis of two of our datasets, both under 3T
One study data I have has p50 intensity around 20000
Another one has an intensity in 3 digits (500 ish)
When looking for public datasets, the intensity are roughly ranging from XX to XXXX, but can't quite find one where intensity are in 10k+ range.
My question is:
Some of the previous studies (eg.
Or was the data collected is simply wrong
I wanna to do the registration of a 4D file of fMRI modality ( I used feat from fsl brain but it doesn't transform data to standard space) so if someone know a method that can generate a 4D registred image.
I'm looking into ways how to do an a-priori power analysis for an fMRI experiment where the main analysis will be a representational similarity analysis (RSA).
The experiment will present the same stimuli in two successive fMRI-sessions (with a behavioral training in between). For each fMRI session, I plan to do a model-based RSA on brain responses elicited by the stimuli. Voxel restriction will be done with a searchlight procedure. The most interesting outcome will be the difference in these results between the two training sessions, as estimated with a GLM contrast.
I think this is not uncommon as i found other experiments adopting a similar analysis procedure. I found no clue however on how to estimate the necessary sample size to achieve a certain statistical power (say 80%).
Since this is a bit of a frankenstein made from other common statistical approaches, I'm not sure if the general logic of fMRI-power analysis applies here.
Has anybody experience in this area or can point me to literature that contemplates this issue?
I'm doing an fMRI analysis using SPM and I have a question regarding the first level analysis . Due to some technical issue, one of my participant's scan got divided into two sessions (each session with scan number starting from 1). I understand I can do the pre-processing and 1st level design specification seperately, and then in the generate a contrast for 2nd level analysis (with other participant who only have one scan and one 1st level specification) - but would that be a valid thing to do? Since the design matrix for this specific participant would be different from that of others, is it OK to compare the contrasts - does it depend on how i define the contrast for the specific participant?
Any help is appreciated. I have read this post:
Recently, I have got a bunch of fMRI data collected from an experiment using a block design. However, it has a very long task block (lasting for 120 seconds) and containing five different task conditions with only one block for each condition! I have got some information from Friston's book (statistical parametric mapping), which suggested to avoid block longer than 50 seconds and not to compare two conditions that separated far apart over time.
So, here is the question, is this dataset still useful? If so, which method is suitable to analyze it?
Attachment is the diagram of that experimental design.
Any help would be appreciated.
I would like to correct for drop-outs during my longitudinal fMRI study. With drop-out I mean not the signal drop-out (incomplete brain coverage), but the participants who not take part in either the first or the second time point of the study.
I know multi level models for SPSS, but for SPM12 I am not currently aware of any models which can correct for these drop-outs.
Thank you in advance for replying / trying to find a solution for my issue,
I'm performing an fMRI analysis for my master thesis using SPM and I've never worked with fMRI data before. I've succesuffy perform first level analysis but came to an obstacle. Study design consists of 2 sessions, one right after another. In the first session, subjects performed control MIST task, while in the second session the stressor was presented during the MIST task. So the first session was control session and the second session was stress session. There were two conditions in both sessions; feedback (when pariticpants received feedback) VS. rest (everything else/baseline). I was first interested in contrast between feedback and rest which shows us activation that was purely result of the feedback in both sessions. I've succesfully defined T-contrast and already observed the effect of feedback. Then i came to a problem; I want to compare the effect of feedback between control session and stress session, so I could see if there was a larger activation after feedback in stress session in comparison to control session. I've searched a lot but didn't find anything helpful. So my question is, at which point i can compare first session with the second session, and how can i do that? Should that just be another contrast like feedback vs. rest, so session1 vs. session2? Or this is the matter of second level analysis? Thanks a lot for any kind of suggestions and help!
We created an experiment with stimuli generated and presented by the Psychophysics Toolbox.
What's the best way for synchronizing and collecting triggers from the tomograph? Does anyone have an example?
For equipment testing purposes my research group wants to acquire an fMRI data set with frontal cortex activation, as anterior as possible.
What would be the most reliable (yet simple) task to achieve this in a single participant? Basically, I'm looking for the prefrontal equivalent of a rotating checkerboard.
A study performed at the University of Michigan examined whether brain activity is associated with treatment response to exposure-based CBT.
Eighty-seven patients with OCD were randomly assigned to receive 12 weeks of CBT or a control intervention called stress management therapy. Before treatment, researchers conducted functional MRI (fMRI) brain scans while patients performed a series of tasks. They completed the symptom severity scale Yale-Brown Obsessive Compulsive Scale (Y-BOCS) throughout treatment.
The patients with the most significant response to CBT showed more activation in several brain areas before starting treatment. The active regions are associated with cognitive control and reward processing. These data suggest that brain scans could identify biomarkers to personalize treatment in OCD.
I apply a detrended fluctuation analysis (DFA) to rs-fMRI signal before and after some task. Certain areas show an increase in Hurst exponent in after-task activity in comparison to the initial state. I have found a few articles which report about a decrease of DFA index in a task (like here https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3197021/) and a decline have been explained as the growth of cognitive efforts.
But how can be explained an increase from a functional and cognitive point of view? Maybe exist an article related to this case on EEG or MEG data, that I did not find?