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The Pulse of Neurotech
Strange contrast when viewing "Alignment of functional and anatomical MRI data (coregistration)"
Summary of what happened: Hello! I am new to neuroimaging analysis and am preprocessing data using fMRIPrep. When viewing the summary html files, I noticed a strange looking contrast when viewing the epi image under the coregistration section (image attached). It looks almost shiny. This happens with multiple subjects, but not for every run for a given subject. For example, this only happens in 1 of 5 runs for the participant whose image is attached. Does anyone have experience with this or might know what it is/how to proceed? Thank you! Command used (and if a helper script was used, a link t
SDC with opposite PE directions on functional data
Hi all, Here is an example of what our data looks like: sub-{ID}_ses-1_task-rest_dir-PA_run-001_bold.json sub-{ID}_ses-1_task-rest_dir-PA_run-001_events.tsv sub-{ID}_ses-1_task-rest_run-001_bold.nii.gz sub-{ID}_ses-1_task-rest_dir-PA_run-001_bold.nii.gz sub-{ID}_ses-1_task-rest_run-001_bold.json sub-{ID}_ses-1_task-rest_run-001_events.tsv As you can see, we have two resting state runs in opposite PE directions. In our custom preprocessing pipeline, we would take the first 4 volumes of each functional timeseries to do SDC. Is it possible to do something similar in fMRIPrep? A
Should ICA be performed on the entire EEG recording or only on the task period?
Hi everyone, I have an EEG recording that consists of two periods: a task period and a resting-state period. I am only interested in analyzing the EEG data collected during the task. I am planning to use ICA for artifact removal, and I am wondering about the best approach in this situation. Should I first separate the task period from the resting-state period and then run ICA only on the task data? Or is it better to perform ICA on the entire continuous recording, including both the task and rest periods, and then apply the resulting ICA decomposition to the task data? Since my final analysis
EEG/EMG Foundation Challenge 2026 — four generalization tracks, $20,000, registration open
Can your EEG or EMG model still work when the person, recording session, or stimulus changes? That is what we are testing in the EEG/EMG Foundation Challenge 2026. Registration is open now, with four tracks and a $20,000 cash prize pool, plus internship opportunities. ** Pick the problem you want to work on:** Track 1 — EEG-to-Image: identify images from brain activity (guide · Codabench) Track 2 — BCI decoding: decode mental commands across recording sessions (guide · Codabench) Track 3 — Sleep onset: estimate time to sleep onset in unseen participants (guide · Codabench) Track 4 — E
Categorization is ‘baked’ into the brain
I've been meaning to point out this Nature Reviews Neuroscience Perspectives article by Barrett and Miller (the same Miller referenced in the previous post). I pass on just the abstract and initial paragraphs of their model. The article has some very striking graphics, and motivated readers can request a copy of the article from me. (It is important to point out that the choice of the limbic core as the analytic starting point is somewhat arbitrary, rather than being derived from basic data itself.) Abstract Categorization, the grouping of objects, living organisms, actions or events into
vdFISH: Explore two-gene co-expression in an in situ–style viewer on your phone or PC
Hi everyone, I’m developing vdFISH (virtual double fluorescence in situ hybridization), a browser-based app for exploring two-gene co-expression in mouse brain spatial transcriptomic data from the Allen Brain Cell Atlas. I designed its appearance to resemble the double-label fluorescence in situ hybridization images familiar to wet-lab researchers. The idea is to make it easy to explore a simple question: “Where are these two genes expressed, and which cells express both?” You can try it here: Open vdFISH What you can do Choose from a selection of brain regions and coronal sections. Search for
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