NOA in hand, we are ready to launch research in the NeuroPRISM lab!🚀 Appreciate @noahghola @ResearchAtNU @NUGlobalNews taking time to profile this @NIMHgov R00-funded project Now hiring a fully-funded postdoc—reach out if interested & spread the word! https://t.co/CEiqiZqCQC

A bit late in posting this … Now out (and on the cover!) of the June issue of AJP: “Transdiagnostic Connectome-Based Prediction of Craving“ Using task-based functional connectomes, we predicted self-reported cravings in 274 individuals with CPM. https://t.co/h1wovxAOl9

In new work out in Medical Image Analysis, we present  CAROT: Cross Atlas Remapping via Optimal Transport. CAROT touches on some of the vibrant areas of functional MRI, predictive modeling, neuroimaging, & connectomics including privacy & open science 1/16 https://t.co/mVhmCZarfB

Thrilled to announce @psychonetrics & I are joining the @Northeastern Center for Cognitive & Brain Health as TT Assistant Professors THIS SUMMER!🎉🥳🍾I'll be in Psych+BioEng! We were struck by the sense of community in the fast-growing Center—a perfect home for our new labs 1/3

#MontréalFire Day 5 after An's missing Her parents' Canada Visa was approved yesterday. High living costs in Canada & US + travel will be a big burden on the family’s financial situation. We are raising funds to help cover their travel/living expenses:

Do you do: 👶 fetal/infant/toddler (FIT) neuroimaging? 💻 machine learning? Want to do both? If so, we’re excited to announce a new primer and review on FIT-specific machine learning —a reflection on 100+ studies from the past decade https://t.co/uVAMP1v3vL 1/5

1/ So excited and proud to share our latest work, out now in @Nature: https://t.co/VVpKGfplcV. Despite all we’ve learned from them, models relating brain activity to phenotype fail consistently in a subset of people—specifically, people who defy sample stereotypes. A brief 🧵:

For our 5⃣ #OSR panel, Dustin Scheinost @DScheinost & Roza Gunes Bayrak @redgreenblues will lead a discussion on Social Bias in Machine Learning. June 23 (10:30 GMT+1) - Featuring four terrific panelists:

Can’t be there in person, but I’m there in spirit! #OHBM2022 We previously showed how broader-scale/multivariate inference improves power. Our poster builds on that by showing how much we then sacrifice specificity +@DScheinost @mandyfmejia @AndrewZalesky Feel free to DM any qs

Grateful to have worked with such a fantastic team including @MaxRolison @CarrRickyyy @liang_qinghao Hannah Peterson @JDadashkarimi @M_Farruggia Alice Hahn @sNeuroble @lichsar @AngelPollatou @ajdneuro @DScheinost 2/

We're hiring postdocs @HDSIUCSD and jointly in collaboration with the Simons Institute at Berkeley https://t.co/UCtnU0hOPp https://t.co/8TFzYaDtxu Please RT! #postdoc #postdocjobs #datascience #AcademicJobs

Great work led by @LinkTejavibulya Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity @abigails_greene @CarrRickyyy @sNeuroble @DScheinost

Thrilled to announce our latest work! Here, we use simultaneous resting state fMRI and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. 1/n

1/3 Excited about this new work by @WendyLuo_ demonstrating loads of functional connectivity information contained within-nodes of a typical atlas. https://t.co/zmfc5ggeq5

I am excited to share my latest work about optimal transport with @DScheinost and @aminkarbasi : Title: Data-driven mapping between functional connectomes using optimal transport preprint: https://t.co/0XCXi1FKpp code: https://t.co/ntPjLXCJlK #MICCAI2021 @MICCAI_Society

6) Link to code https://t.co/FiEdVy8Ak0

5) There are more interesting results in the paper: comparison to PCA, embeddings relationship with participation coefficient, etc. Check out the paper if you are interested!

4) We didn't forget about resting-state! By extending the resting-state scans on the manifold built with task scans, we found overall widespread resting-state embeddings. But during resting-state, brain states are shifted more frequently than during task.

3) We also created task-based temporal trajectories based on the embedding. Each task shows different temporal patterns, which can be related to its cognitive load dynamically.

2) For example, all the tasks can be organized by proportion in high/low cog states. And this simple ordering can reveal the task's cognitive load and is aligned with previous literature.