Dustin Scheinost (@DScheinost) header
Dustin Scheinost (@DScheinost) profile picture

Associate Prof in @YaleRadiology (he/him/his) functional connectivity | predictive modeling | fetal-infant fMRI co-founder @FIT_NGIn | developer @SuiteYale

New Haven, CT
Joined May 2012
Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)Photo by Dustin Scheinost (@DScheinost)
Excited to share @JeanSYe_ work now out in @NeuroCellPress! We found that during development, people become more variable in how much they engage recurring brain states.
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External validation is a great way to improve rigor in neuroimaging. Although a simple idea is simple—test a predictive model on another dataset—the practice is not. In our Perspective in @naturemethods, we discuss these limitations and offer solutions: nature.com/articles/s41592-0…
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1/ In neuroimaging, “another dataset” can mean many things. Is it the same population? Same scanner/protocol? Same phenotype? Same preprocessing? These dataset shifts can be viewed as a bug (reducing external success) or a feature (highlighting the model’s robustness).
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We found reduced brain state flexibility in OUD, with inflexible dynamics linked to lower cognitive control—especially after viewing opioid-related images. Curious about our brain state flexibility methods? See @JeanSYe_’s previous work for details: biologicalpsychiatryjournal.…
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Can neuroimaging predictive models survive across diverse real-world data?🧠🌎We put models to the test across 3 large-scale unharmonized datasets with over 2,800 participants. See our new work in this special issue of Developmental Cognitive Neuroscience: sciencedirect.com/science/ar…
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We asked if predictive models can pass the "dataset shift” challenge—where training and test datasets differ in race, age, sex, recruitment geography, clinical symptoms, behavioral paradigms, and imaging acquisition.
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Awesome work @JDadashkarimi !! Also, check out his earlier conference works at #MICCAI and #CISS to see how CAROT evolved. link.springer.com/chapter/10… link.springer.com/chapter/10… ieeexplore.ieee.org/abstract… @MICCAI_Society @MiccaiStudents
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 sciencedirect.com/science/ar…
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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. ajp.psychiatryonline.org/doi…
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A "craving network" across individuals was found. These individuals came from various backgrounds, including alcohol-use disorder, cocaine-use disorder, obesity, and controls. Together these suggest a common neural signature for craving-motivated behaviors.
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Hey, fetal, infant, & toddler (FIT) neuroimagers Come to our symposium if you’re at or around Yale on May 17. We will showcase exciting current FIT neuroimaging work at @Yale.
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Our rockstar lineup includes: Kasia Chawarska talking about searching for brain biomarkers in neonates Laura Ment presenting on the development of social brain networks.
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Going to #SOBP, check out our exciting work. @salonimehta1997 has two posters on structural (poster #F16 Fri 5 PM - 7 PM) and functional (poster #S16 Sat 5 PM - 7 PM) differences in opioid use disorder.
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Replying to @FIT_NGIn
@FIT_NGIn folks, check out our new review/primer on machine learning in fetal, infant, toddler (FIT) neuroimaging in @BiologicalPsyc1 We review 134 works(!) using MRI, EEG, fNIRS, MEG, and other modalities and detail extra considerations for FIT samples authors.elsevier.com/a/1gZ0j…
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We find that 14% of citations are self-citations (i.e., an author of the paper is also an author of the cited work). Though this varies based on field (neurology: 17%, psychiatry: 16%, general neuroscience: 11%). 2/9
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For International Lefthanders Day #lefthandersday, check out @LinkTejavibulya’s work on connectivity differences between right and left handers in @NeuroImage_EiC sciencedirect.com/science/ar…
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While many works have focused on canonical networks, like the language or motor networks, we show that connectivity differences extend to all networks and regions with a large amount of differences in the cerebellum!
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Happy to share that our symposium on “Connectome based neurofeedback” has been accepted at #rtFIN2022 @rtFIN2022. @monicarosenb, @lichsar, @benlthompson and I will be presenting initial results, where we provide neurofeedback from connectome-based machine learning models.
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If interested, check out the overall method sciencedirect.com/science/ar… as well as some of the models that we are targeting, including opioid abstinence: rdcu.be/cRVR9, attention: rdcu.be/cRVRO, craving: medrxiv.org/content/10.1101/…
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Abstracts submitted! Paris here we come! #fitng2022 @FIT_NGIn
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Getting reviewer reminder emails on Christmas is kind of annoying. I know these are automatically generated and sent, but I think adding a bit of code to avoid sending them on holidays would be worthwhile.
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Functional Connectivity for the Language Network in the Developing Brain: 30 Weeks of Gestation to 30 Months of Age doi.org/10.1093/cercor/bhab4… TLDR: We used fetal, infant, and toddler fMRI to look at longitudinal changes in functional connectivity for Broca’s and Wernicke’s areas.
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Using longitudinal fetal to neonatal fMRI, we show that interhemispheric connectivity for these regions rapidly develops during the perinatal transition. In contrast, intrahemispheric connectivity did not increase during this period.
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New work by Karim Ibrahim with @sNeuroble and @ycscsukhodolsk at @molpsychiatry. We use connectome-based predictive models to investigate brain-behavior associations of aggression in children from a range of mental health backgrounds. rdcu.be/cAVn1
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If you like it, also checkout our similar work predicting irritability in a different transdiagnostic sample of children at @npp_journal rdcu.be/cAVnP with @JDadashkarimi, @esfinn, @MelissaBrotman, @WL_tseng.
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In time for the @FIT_NGIn event on 9/17, two new longitudinal papers from us. 1)ID rate and test-retest are low in infant connectivity tinyurl.com/sn5v7f66 @ajdneuro 2)Altered connectivity in older children with ASD is present by 9-months tinyurl.com/wdanbcv2 @MaxRolison
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