Explore
Projects & resources
The NeuroTech Primer
The essential guide to Brain–Computer Interfaces — start your journey here.
Read the bookMOABB Benchmarks
The community standard for reproducible BCI dataset benchmarking.
View repoIndustry Webinars
Technical workshops from g.tec, Interaxon, Neuroelectrics, ANT Neuro, NIRx & more.
Watch archiveFUS Forward
Advancements in focused ultrasound, with host Charles Caskey.
ListenLocal Chapters
Connect with neurotech enthusiasts in 30+ cities worldwide.
Find a chapterSupport the Mission
Help keep these resources open and free. NeuroTechX is a non-profit.
DonateEvents
What's coming up
NYC Neuromodulation Conference 2026
Marom Bikson's NYC Neuromodulation — the standout meeting on brain stimulation & brain interfaces.
CCN 2026 — Cognitive Computational Neuroscience
9th annual conference uniting cognitive science, AI, and neuroscience.
Live · auto-updating
The Pulse of Neurotech
Nilearn 0.14.0 was released!
Hello y’all !!! We have just released Nilearn 0.14.0! This is a major release with some new features: Nilearn can leverage scikit-learn’s Array API-supported estimators to speed up neuroimaging ML analyses using GPU acceleration. See user guide page. Interactive visualization with view_surf can now be done using niivue as a backend engine. All maskers can now output to pandas or polars dataframe when using transform or accept such dataframes as input to inverse_transform. smooth_img can now work with surfaces. We had a very successful docathon at the OHBM hackathon to include an Examples sect
Contemporary Russian piano composers and Agentic AI
The title of this post makes no sense until I explain that it names the two arenas I have been spending my time in over the past month, taking a vacation from MindBlog posts. (I have missed sharing what I find interesting in such posts with my imaginary audience. My brain needs to imagine being seen, regardless of whether or not that is the case. Even though the analytics show hundreds of clicks or engagements, who knows how many of those are bots or humans? I seldom receive feedback or comment. Anyway, you might take this post as being an 84 year old's 2006 style Blogger version of a
ASLPrep on ADNI-3's GE 3D pCASL
Summary of what happened: dcm2niix converted the asl dicoms into two 3D volumes, one of them is the m0 volume, the other is a deltam volume (described as case 2 over here Arterial Spin Labeling - Brain Imaging Data Structure 1.11.1 ). This is unlike the usual 4D volumes with repeated pairings of label and control images. I tried playing around with the options indcm2niix hoping to obtain the original label control images but to no avail. I ran aslprep on this deltam volume anyway and i encountered the error below. So i’m wondering if deltam volumes are supported at the moment? Command used (an
Top-Down To Bottom-Up: Rhythmic Synchrony Relaxes Social Priors to Enable Change
Abstract (slightly edited) of an article by Connor Wood and accepted for publication in Brain and Behavioral Sciences: Interpersonal synchrony, such as dancing to a shared rhythm, elicits bonding and rewards, leading many to see it as a mechanism for group cohesion. Synchronized activities also reduce ingroup bias, rapidly bond strangers, flatten hierarchies and blur roles, and evoke trance states. Synchrony thus softens many boundaries that “groupishness” requires. Perhaps in consequence, cultural authorities frequently condemn expressive dance music rather than embracing it as a tool
Areas of Hyperintensity after SDC in fMRIPrep 24.0.1
Summary of what happened: Hello. I am preprocessing data using fMRIPrep version 24.0.1. What I noticed is that after applying SDC (Susceptibility distortion correction: FMB (fieldmap-based) - phase-difference map) that many participants have areas of hyperintensity in the OFC which darkens contrast in other regions. I have attached an example below. This is apparent in the sagittal slices. Thanks! Version: 24.0.1 Environment (Docker, Singularity / Apptainer, custom installation): Singularity Data formatted according to a validatable standard? Please provide the output of the validator: BIDS PA
Troubleshooting QSIPrep outcomes for diffusion data
We would love some input on a couple of problems that came up during QC of our diffusion data, preprocessed in QSIPrep. All images here are taken from dmriprepviewer. I posted this in 5 parts, as I was only able to attach 1 image per post. Part 1: We are noticing this red haze appears in many of our images, some more severe than others. Given that it appears in conjunction with motion, we are thinking it may be a result of motion rather than a processing error. Is there anything we can do to fix this, or another potential cause we aren’t aware of? 6 posts - 2 participants Read full topic
Build the future of neurotech, together.
Join thousands of innovators, students, and researchers shaping the field.