Hi experts,

In my fMRI experiment, two conditions were compared: a high disgust condition and a low disgust condition. The high disgust condition involved presenting participants with disgusting images, while the low disgust condition presented the same images but with the disgusting elements digitally removed. During fMRI scanning, participants passively viewed stimuli from both conditions. After scanning, participants rated the level of disgust for each set of stimuli on a scale of 0 to 10.

Three results were observed:

1. The disgust ratings for the high disgust condition were significantly higher than those for the low disgust condition, with ratings close to 10 for the high disgust condition and close to 0 for the low disgust condition.
2. Beta values in a specific brain region were significantly higher (t-test) for the low disgust condition than for the high disgust condition, consistent with existing references indicating a response to this type of digital image processing.
3. When examining the relationship (Pearson correlation) between the difference in activation (beta values: high disgust condition - low disgust condition) of this region and the difference in ratings (high disgust condition rating - low disgust condition rating) across all participants, a significant positive correlation was found. Almost all activation differences were negative, while rating differences were positive.

On one hand, from the perspective of activation, this brain region appears to respond more strongly to the low disgust condition. On the other hand, from a correlation standpoint, it exhibits the opposite effect.

How can these results be interpreted?

Thank you!

So for context, i am wrapping up my 3rd semester of my comp sci degree, and have 3 more to go. I plan on studying neuroscience and eventually going to grad school for a PhD in computational neuro/ comp psychiatry. I am doing undergrad research here exploring the role of reward anticipation and its affect on processing of novelty is various domains of psychiatric symptomology. Unfortunately, my current research relies on behavioral data alone. I'd like to continue my research as an undergrad when i major in neuroscience. Problem is, I'm dirt poor, and would like to do my undergrad degree in state, then do grad school at a larger university that's more acclaimed and has better opportunities . I feel like going to a smaller university will help eliminate some of the stress associated with larger universities, and offer some benefits such as r smaller class sizes, and having an easier time having my research proposals granted.

The university i am looking at is Mercer university in middle Georgia, its a research institution, but not a very large/ acclaimed one. I did some digging and tried to look at research opportunities for undergrads. It didn't seem like the school had a neuroimaging department. However, it i came across an article where the school recently received access to Fnirs tech, and there seems to be an initiative to give students access to this tech. Its not fMRI, but i am wondering if you can localize patterns of activity accurately enough to study LC- Cerebral- cerebellar dynamics, specifically through the context of measuring different types of prediction errors and looking at novelty through the lense of LC 's role in dynamic encoding of PE's , I'd like my future research to be focused on predictive processing, or at least while I'm doing my undergrad. I tried to find some literature on the topic, but unfortunately couldn't find any solid answers. I don't even think they have EEG equipment ffs

Can i use eye/ pupil tracking software to indicate LC activation?. If not, are there any techniques i can use to look at the LC function indirectly?

Would i be better off biting the bullet and going to a school with fMRI / other modalities, and risk having to navigate the larger classes/ compete for opportunity?

I have a call scheduled with the director of neuroscience at mercer tomorrow, i plan on inquiring about it, but would like to hear your opinions first.

I'd appreciate any insight, thanks in advance.

Has anyone done TFCE from “scratch”, defining their own adjacency matrix? I’m hoping to replicate this analysis: https://www.biorxiv.org/content/10.1101/2022.04.11.485553v1.article-info

Which, basically, uses DTI tracts to make otherwise adjacent voxels more/less connected if they’re in/out of the tract.

Hi all, I am new to Neuroimaging and am preprocessing my first subject (I have practiced before with UCL and ABB but this is my first time with my own data). I am using SPM to preprocess Delay Discounting (task based, event related design) data. I have followed a Frankenstein of advice from the SPM manual and Andy’s Brain Blog and I think I have chosen all my options correctly. I have an annotated document with screenshots of all changes I made the standard preprocessing steps and why. I am wondering if someone would be willing to review this doc and make sure there are no glaringly obvious errors. Please let me know if you’re willing to help! I am excited to move onto first level analysis but I don’t want to start with incorrect data.

Hello all,

I am not sure if this is the correct group to ask for this type of request but would anyone happen to know where I could find someone familiar with segmentation in ITK Snap that would be open to some freelance work ? Thank you!

Nervousness is something we all experience at various points in our lives. Whether it’s before a big presentation, a job interview, or a social event,

I remember one time I had to give a speech in front of my whole class. I was so nervous, I couldn’t even say my name. And That’s how powerful nervousness can be.

You might already know some common ways to deal with nervousness, like taking deep breaths, chewing gum, or thinking positively.

But while finding a better solution on how I can overcome nervousness, I found a great research study on the neuroscience of Visualization.

Now, you might be wondering, how can visualization help with nervousness?

You see, Visualization is the process of creating mental images or pictures in one’s mind.

It involves using sensory information and the imagination to simulate experiences and situations that feel real despite not being physically present. And research has shown that the brain often can’t tell the difference between a visualized image and actual reality. This means that when you visualize a specific action or outcome, the same areas of your brain are activated as when you actually perform that action.

If you want to have a better understanding on how visualization helps to overcome nervousness, I have created an animated video to share what I learned.

how to overcome nervousness

If you prefer reading, I have included important reference links below.

I hope you find this informative. I'd love to hear your thoughts on it!

Cheers!

https://neuroscience.stanford.edu/news/reality-constructed-your-brain-here-s-what-means-and-why-it-matters

https://visiting-subconscious.com/sci-visualize-brain/

https://psychologydictionary.org/nervousness/

https://psycnet.apa.org/doiLanding?doi=10.1037%2Fint0000108

https://dictionary.apa.org/visualization

https://www.psychologytoday.com/intl/blog/click-here-for-happiness/202308/how-visualization-can-benefit-your-well-being

https://www.bbc.com/future/article/20160928-how-anxiety-warps-your-perception

Hello,

I have plenty of experience in EEG analysis but I have never worked with fMRI. I want to parcellate the fMRI data (https://openneuro.org/datasets/ds005003/versions/1.0.2) using the Yeo 7 resting state networks parcellation. I found a NIFTI file about this specific atlas on Fieldtrip Toolbox but I do not know how to go from the 4-D matrix of the functional NIFTI to a 2-D matrix of network activity. While I can do some coding in Python, I would prefer a MATLAB solution. I have seen several toolboxes but I cannot find a straightforward answer/tutorial. Can anyone point me to the right direction?

Hey!

Attempted to downloaded FSL on my macbook and it said I needed Python 3. After downloading python, in the terminal where I have to give the command to be able to open FSL it says "Python 3 command not found". Any advice? Is there anything extra I need to do?

Hello lovely people of r/neuroimaging, I hope you are all well! I am currently a clinical RA, planning on pursuing a PhD in clinical psychology. I minored in neuroscience, and recently I have realized I'd love to utilize neuroimaging in translational research in the future. I'd love to join a lab that uses neuroimaging for my PhD, and figured it would be good to build a larger foundation of knowledge regarding neuroimaging. I am planning on applying to programs in the upcoming cycle.

It seems to me that I could:

1. try to find a part time volunteer RA position that gets me some hands on neuroimaging experience, or
2. take courses.

While I know research experience would look great, I also feel like courses could better prepare to to engage in (semi) independent research. Additionally, while I have foundational knowledge in neuroscience from my minor, I am unsure if I have the the experience to get a volunteer, part time research position where I would be contributing in a meaningful way.

I wanted to ask everyone here if they had thoughts - whether it'd be better to prioritize (in my limited time) trying to get a research position, or taking courses.

A follow up question regarding courses - would it be worth taking a for-credit course if I can find one? There are a plethora of free online courses (e.g. MIT opencoursewear, MOOCs, etc). I realize that these don't communicate a level of competence the way a for-credit course would, but if I can get to a solid level of understanding and can communicate that in my SOP and interviews, it seems as though it could still be helpful, but I don't know.

If anyone has any suggestions I would be quite appreciative!! Also if anyone knows of any good online neuroimaging courses (either for-credit or not) I would be very grateful!

PS - I know this post makes it seem all about getting into a PhD program, but to be clear I care a lot about understanding neuroimaging on a deep conceptual level - just with the competitiveness of clinical psych programs I want to make sure I am using my time well.

Hi everyone,

I'm hoping for recs on how to keep my data organized. I work with EEG and fMRI and as you all know, these analyses can generate tons of files. I have a hard time going back to old directories and remembering what batch code goes with what files and I end up redoing analyses much of the time just to ensure I know what was done. I'm familiar with BIDS but even in keeping with that format, I still get hung up on what code was used to generate which file. This is especially a problem for me with EEG data, which is the bulk of my work. Does anyone have any recs for learning data management best practices? Any coursera courses or systems I can look up online that really work for you all? One thing I've started doing is just saving a copy of my script in the same folder as the files it generates, BIDS be damned. That seems to have helped a little, but I am still having difficulty.

Is there anything else I could/should be doing?

Edit: I mistyped. My eeg files are not in BIDS, just the fMRI. We are attempting to make that change but the guidelines for EEG are not as straightforward as for fMRI.

Hello everyone, I’m new with neuroimaging and have been trying to download some data from NITRC. Only problem is the Download speeds are so slow that I’m looking at a Download time of 50 days for the preprocessed ABIDE data set. If any of you have some suggestions I would love to hear it.

Dear all researchers

I have downloaded PET scans from the ADNI database for my research. Formerly, ADNI would provide the data in different formats. However, they have omitted this feature recently. I need to convert the Ecat7 images ('image.v') to the DICOM format, but ccannot find any working solutions. May I kindly ask for your expert guide if you know of any solution?

Thanks in advance

Hi everyone, I'm a masters student and I'm working with fmri data obtained from an adaptation protocol, in which there were presented 9 objects. The data is already preprocessed. I'm going to explain as I'm working with only one subject because the analysis is within subject. So, for this subject I have 9 beta values files, each one represents the brain activity during each object presentation. However, I noticed that the data has some signal from a frequency that doesn't seem explained physiologically and I want to remove that noise using the regressors "1 0 1 0 1 0 1 0 1" and "0 1 0 1 0 1 0 1 0" which may explain that signal and therefore remove it from the data. I tried looking for ways to do a glm to regress out this on spm or on fsl, but I'm having trouble to find something like my case, where I want to remove that signal from beta files and not from the raw time series data. In short, I want the results to be the same 9 beta files but without those signal variations. Sorry for the long question and if it's something simple and I'm just complicating stuff.

I've been diving into some neuroimaging analyses lately, specifically with Bash scripting, FreeSurfer, and FSL. I am wondering if anyone in the community has experience using ChatGPT for guidance or assistance in these areas. I am wondering if it has helped in streamlining or even improving bash scripts for analysis pipelines.

If you've tried it out, I'd love to hear about your experiences. Did ChatGPT provide helpful insights, tips, or solutions to your questions related to neuroimaging analyses? Any specific challenges you tackled with its assistance?

I have a question on registration techniques with neuroimaging data. I have some dti and flair sequences that I would like to register in flair space. Are there good techniques for this besides ANTsApplyTransforms and flirt?

as science twitter descends into madness, and bluesky is starting up, but has some issues of requiring invitations to join (and therefore less buy in?), as well as an algorithm that seems to make it hard to find the best posts, I wonder why we don't use reddit for science discussion in the way that science twitter does/did. That is, to have interesting, far reaching discussions. Any thoughts on that?

Is fNIRS ever going to be considered as an alternative way to neuroimaging compare to fMRI?

https://link.springer.com/chapter/10.1007/978-3-031-36021-3_58

So i am master in neuroscience student and my master thesis is on effect of tms , tes on mortor cortex ecitibility . I would love to do a phd . But dont knw which field to go into is my thesis topic mostly Align to neurophysiology / plasticity anybdy in same field can you please shade some light into it. Thank you . I have a module in neuroimaging too in my master progrm

Our lives are filled with evidence of how easy it is to get stuck in a spiral of negativity because negative thoughts are capable of dragging down even the most resilient people.

It’s easy to say “think positive,” but how can you think positively when something happens and the first thought that comes to mind is always negative?

So Why do negative thoughts always seem to have more power over us than positive ones?

According to psychologists, our Negative thoughts often carry more weight than positive ones, and this phenomenon is called the negativity bias*.*

It helped our ancestors survive in a dangerous world. They had to pay attention to anything that could hurt them. But today, we don’t face the same threats, yet our brains still act as if we do. That’s why we often ignore the good and dwell on the bad. This is why we’re more likely to believe someone who criticizes us and doubt those who compliment us.

Negativity bias gives negative thoughts an edge over positive ones, where our brain is just trying to do its job to keep us safe.

Despite all of this, the real reason is that our brains can’t comprehend negatives.

After reading research studies and articles, I made an animated video to illustrate the topic. If you prefer reading, I have included important reference links below

Citing :

The negativity bias: Conceptualization, quantification, and individual differences https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/negativity-bias-conceptualization-quantification-and-individual-differences/3EB6EF536DB5B7CF34508F8979F3210E

Good Things Don’t Come Easy (to Mind) https://econtent.hogrefe.com/doi/10.1027/1618-3169/a000124

True or false? How Our Brain Processes Negative Statements, Association for Psychological Science (APS) https://www.psychologicalscience.org/news/releases/true-or-false-how-our-brain-processes-negative-statements.html

Why Our Negative Thoughts Are So Powerful

https://www.psychologytoday.com/us/blog/a-deeper-wellness/202309/why-our-negative-thoughts-are-so-powerful

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Does anybody have experience running a basic SSVEP (the most basic black/white flickering square paradigm possible) EEG analysis? I have a few basic/fundamental questions that are proving hard to find online. I just need somebody who has run a study and knows what the normal data looks like to ask a few questions. Feel free to comment or DM me

Hi All,

I have a very small dataset that I have done VBM on using the CAT12 toolbox. I have 12 scans from 6 individuals for a pre-post paired T test using the whole group. However, what I would like to do is extract single subject data for each individual looking at the differences between pre and post to get a sense of where my average is coming from.

If this were an analysis using fMRI data, I would just extract my thresholded SPM map at the first level for each individual and overlay those. However, because this is structural data there is no first level. So far, I have tried performing a singe paired T-test on each individual, however this throws an error when I attempt to create contrasts, probably because the model is overfit. I've also tried just plotting my beta files generated for each individual using a visualizer like fsleyes/mricron, but I am having trouble understanding how to threshold to get my desired p value, especially since I would like to constrain my results with an (a-priori) inclusive mask.

Is there any way to visualize individual results for VMB analysis with the CAT12 toolbox? (similar to first level results in a functional SPM analysis?)

Hi All,

Absolute newbie and slowly starting to use SPM

I have run into an issue with my data set, I have MRI images (not fMRI) that have been sagitally acquired.

When I go to use the GUI for slice time correction, I am unsure how to account for the slices being in the x-axis and how to code for the slice order.

Advice would be much appreciated!

Many thanks!

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Hi all,

I was wondering whether anybody has run different ICA denoising procedures (which may capture slightly different sources of noise) twice at different times within the same preprocessing pipeline? e.g. for fMRI or EEG.

e.g.

raw data > some alignment > ICA x > ICA y > processed data

There seems little on whether this should or should not be done.

In my mind, adding the second ICA denoising strategy would remove any noise leftover by the first. As such, you would be left with data minimally affected by noise. I have heard that this may lead to unintended consequences, but this has not been explicated to me - and I cannot find sources on this issue.

Any links or suggestions or considerations would be greatly appreciated.

Hi All,

I am using SPM to model a task in the scanner that has 4 levels. Each instance of the task is listed in a .mat file with durations, names, and onsets so it can be added as a multiple conditions file when specifying the first level.

I also have physiological data that was recorded simultaneously with the fmri. I want to add this as a regressor of interest. What I want to do is assess the effect of the average of this physiological activity throughout each of the instances of the task. (ie if there are ten tasks, I have 10 values of my physiological variable that were calculated by averaging the values for that variable across the duration of each instance.)

I want to know if I am able to enter the physiological variable as a vector of length 10 so that one value is associated with each instance. An alternative would be creating a timeseries whose length is the same as the number of TRs, and add it to my "multiple regressors" file, however this will take a lot more time than just entering the 10 value vectors I already have.

Can I achieve what I want the first way or will I have to buckle down and create the timeseries? Apologies if this is a simple question; its been a while since I've used SPM.

For my project, i have two sets of dMRI (two shells of 1000 and 2000mm) with opposite phase encoding directions. These should be fitted into the tensor model and I get the value of MD and so on. I just followed the instructions from the FSL site, but is it necessary to separate the shell to 1000mm and 2000mm to calculate?

Hi all ladies, gentlemen, and all in between! This is my first post in Neuroimaging subreddit. I would like to ask you to name this phenomenon on the picture (only wrong answers)