NITRC NITRC Community Forum: open-discussion

NeuroMorpho.Org v8.7 released October 29th, 2026

Giorgio Ascoli — Fri, 30 Oct 2026 12:22:07 GMT

Dear colleagues, We're pleased to announce yesterday’s release of <a href="https://neuromorpho.org/">NeuroMorpho.Org</a> Version 8.7, with 40,417 new tracings from 131 datasets. The database currently tallies 300,495 digital reconstructions of neural morphology from 1081 independent labs worldwide. This major release also includes a new web-based <a href="https://swcstandardization.computational-neuromorpho.org/">data processing tool</a> to help correct common <a href="https://www.imrpress.com/journal/JIN/25/10/10.31083/JIN52273">reconstruction anomalies</a>. Please visit the <a href="https://neuromorpho.org/WIN.jsp">What's new</a> and <a href="https://neuromorpho.org/about.jsp">About</a> pages for details on the growing content and features. We are continuously grateful to <a href="https://neuromorpho.org/acknowl.jsp#DC">all data contributors</a> eager to share their hard-won morphologies with the research community. We apologize if you receive multiple versions of this message through cross-listing, and as always appreciate any and all feedback and comments. Sincerely,
The <a href="https://neuromorpho.org/acknowl.jsp#NMO">NeuroMorpho.Org team</a>
 
 
----------------
<a href="https://krasnow1.gmu.edu/cn3/ascoli">Giorgio Ascoli</a>, PhD
Distinguished University Professor
<a href="https://bioengineering.gmu.edu/">Bioengineering Department</a>, <a href="https://cec.gmu.edu/">College of Engineering & Computing</a>
<a href="https://neuroscience.gmu.edu/">Neuroscience Program</a>, Krasnow Institute for Advanced Study
Director, <a href="https://krasnow1.gmu.edu/cn3">Center for Neural Informatics, Structures, & Plasticity</a>
MS2A1 - <a href="https://www.gmu.edu/">George Mason University</a>, Fairfax, VA 22030-4444 (USA)
Author of “<a href="https://www.amazon.com/Trees-Brain-Roots-Giorgio-Ascoli/dp/0262028980/">Trees of the Brain, Roots of the Mind</a>” (<a href="https://mitpress.mit.edu/9780262028981/trees-of-the-brain-roots-of-the-mind/">MIT Press, 2015</a>)
Founding Editor-in-Chief, <a href="https://link.springer.com/journal/12021">Neuroinformatics</a>
<span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Aptos; mso-ligatures: standardcontextual; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><a href="https://aimbe.org/">AIMBE</a><span style="font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-ligatures: standardcontextual; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> fellow, <span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Aptos; mso-ligatures: standardcontextual; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><a href="https://wi-consortium.org/wiacademy.php">WIA</a><span style="font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-ligatures: standardcontextual; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> fellow

Longitudinal intensity calibration for T1 T2 ratio in MRTool

Hannah Hafner — Fri, 04 Sep 2026 17:28:34 GMT

Dear NITRC community,
Our lab is working with the MRTool toolbox in SPM12 to calculate the T1/T2 ratio for a neurodevelopmental sample. MRTool uses linear scaling and nonlinear scaling with histogram matching to calibrate the T1 and T2 images before taking the ratio. Since we are using longitudinal data, we want to use a common intensity calibration for each subject instead of calibrating each subject's individual timepoint separately. Is there a way we could achieve this? For example, we have used SPM to create an average T1 image for each subject, and were wondering if it would be possible to calibrate each individual session T1 and T2 using the average image in MRTool.
Any help would be greatly appreciated,
Hannah

RE: LLMs in Neuroimaging Workshop - Boston, Oct 16, 2026

Kanwal Shahbaz — Fri, 04 Sep 2026 17:28:34 GMT

Lookinf forward to it.

RE: LLMs in Neuroimaging Workshop - Boston, Oct 16, 2026

Eduardo Gonzalez-Toledo — Fri, 04 Sep 2026 12:29:25 GMT

It would be great if this Course can be attended online!!

LLMs in Neuroimaging Workshop - Boston, Oct 16, 2026

David Kennedy — Thu, 03 Sep 2026 12:55:26 GMT

If you are in Boston in October, please note the following: ReproNim is pleased to announce an upcoming workshop: Neuroimaging, Reproducibility and LLMs: Early promises and future questions Date: October 16th 9:30am - 1:00pm EDT (In person only) Location: MIT - Singleton Auditorium, Building 46 (3rd floor, 3002), 43 Vassar St, Cambridge, MA 02139 Registration: Required but free and open to all! Register at: <a href="https://www.eventbrite.com/e/neuroimaging-reproducibility-and-llms-early-promises-and-future-question-tickets-1999618019324?aff=oddtdtcreator&keep_tld=true" target="_new">https://www.eventbrite.com/e/neuroimagin...</a> The objective of this in-person workshop is to introduce the technology and train neuroscience / neuroimaging researchers on the potential of Large Language Models in brain imaging research. To achieve this, we will show and discuss key directions for which LLMs have - and will have - a fundamental impact, review and contrast research project examples, introduce latest techniques, and discuss potential limitations and risks associated with this transformative technology. Speakers will include: JB Poline (McGill), David Kennedy (UMassMed), Brent McPherson (McGill), Michelle Wang (McGill), Yaroslav Halchenko (Dartmouth), Johanna Bayer (McGill), and Satra Ghosh (MIT).

OASIS3 - individual scan acquisition year and country

Daniel Franco — Thu, 09 Jul 2026 13:21:00 GMT

Hi all,
Is there a way to obtain the acquisition year for each scan (or at least the year of baseline per subject, so that I can derive a year of image acquisition)? Similarly, is country of residence available? I'm assuming all participants reside within the United States.
I'm currently needing this data to assign contextual features in my analysis.
Thanks in advance, Daniel

Subject: Fully HIPAA compliant Deployment for OASIS-3 Longitudinal Data Mapping

MohammedYusuf Modan — Mon, 06 Jul 2026 17:09:50 GMT

<div class="paragraph normal ng-star-inserted" data-start-index="702">Hi Saif,</div>
<div class="paragraph normal ng-star-inserted" data-start-index="708">We have the exact technical infrastructure required to instantly accelerate your OASIS-3 neuroimaging research and ADRC Clinical Data workflows.</div>
<div class="paragraph normal ng-star-inserted" data-start-index="852">To ensure your study can efficiently map subject IDs and calculate "days-from-entry" alignments to the corresponding MRI/PET sessions, we can deploy a dedicated, locked-down research "Spoke" tailored specifically for your team.</div>
<div class="paragraph normal ng-star-inserted" data-start-index="1079">Our Research Setup Engine will bypass the burden of building a local data environment so you can focus entirely on selecting the clinical assessments closest to each imaging session. Your deployment will include:</div>
<ul class="ng-star-inserted">
<li class="paragraph list-item normal ng-star-inserted" data-start-index="1291">Secure Python FastAPI Backend: A dedicated, containerized environment to safely and rapidly execute your longitudinal data parsing and mapping scripts.</li>
<li class="paragraph list-item normal ng-star-inserted" data-start-index="1442">The BigQuery "Evidence Lake": A highly secure, centralized data storage pool designed for sub-second data retrieval and structuring complex, multi-year datasets.</li>
<li class="paragraph list-item normal ng-star-inserted" data-start-index="1603">Zero-Trust Processing & Ephemeral Memory: Our architecture ensures that data processing sessions are instantly wiped from local memory once your data is securely routed to the Evidence Lake, maintaining absolute data privacy.</li>
</ul>
<div class="paragraph normal ng-star-inserted" data-start-index="1828">Additionally, should your neuroimaging research ever scale into active clinical application, please note that this same infrastructure natively supports our advanced AI clinical documentation and autonomous compliance monitoring modules, which can be activated on demand.</div>
<div class="paragraph normal ng-star-inserted" data-start-index="2099">Let us know if you would like us to provision this secure environment for your study, and we can immediately initiate the setup.</div>
<div class="paragraph normal ng-star-inserted" data-start-index="2099"> </div>
<div class="paragraph normal ng-star-inserted" data-start-index="2099">Best,</div>
<div class="paragraph normal ng-star-inserted" data-start-index="2099">— Manager, Quantum Security Syndicate-m Ascent Enterprise, LLC Secure Posture Portal: www.quantumaudit.health Direct Support: support@quantumaudit.health</div>

spmT on 3d render visibility

Arshad Zaman — Mon, 06 Jul 2026 17:09:50 GMT

Hi, 
Does anyone know if it is possible to improve spm activations to better be  visible on the 3d render view? while in A+C+S activations apprear fine, on the render brain image the surface activations are bearly visible. I know one can play around with the depth/Azimuth functions for repective cross sections, however those are only really useul if you can also see the surface activations clearly too. is there anyway to bring the sruface activations actually onto the sruface of the 3d rendered brain? I have played around with options, darkest, brightest, etc but no joy. I have seen a few online tutorials, however there too the surface activations are not clearly visible at all.
Any suggesitons will be much appreciated.
Bw,
Arshad

Clinical Toolbox for SPM - Troubleshooting

Kilian Frohlich — Thu, 02 Jul 2026 13:18:53 GMT

Dear Community,
I have been using the clinical toolbox for years. Now, it produced following error the first time:
clinical_mrnormseg ERROR: Dimension mismatch 
 
Anyone familiar with this? 
 
Yours!

RE: Question about clinical labels in OASIS-3

saif_khan — Mon, 29 Jun 2026 15:20:01 GMT

I will download the ADRC Clinical Data spreadsheet and use the subject ID together with the days-from-entry information to match the clinical assessments to the corresponding MRI/PET sessions. Since OASIS-3 is longitudinal, I will select the clinical assessment closest to each imaging session when assigning labels.
Thank you also for pointing me to the Imaging Data Dictionary and the ADRC Clinical Data documentation. This information is very useful for my study.