iiinova — where your digital self keeps evolving

jerrytang.bsky.social @jerrytang.bsky.social · 08/09/2026

Check out our GitHub repo if you want to integrate this framework into your research! We're really excited to see the scientific and clinical applications of cross-participant mapping github.com/HuthLab/rapi... 7/7

github.com

GitHub - HuthLab/rapid-cortical-mapping

Contribute to HuthLab/rapid-cortical-mapping development by creating an account on GitHub.

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jerrytang.bsky.social @jerrytang.bsky.social · 08/09/2026

This approach can also generalize across modalities: here we trained models on brain responses to stories and transferred them by aligning brain responses to silent movies. This could be effective for mapping concepts in children and patients with language impairments 6/7

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jerrytang.bsky.social @jerrytang.bsky.social · 08/09/2026

We found that cross-participant models trained on 24 *minutes* of calibration data produce similar maps as within-participant models trained on 5 *hours* of data! 5/7

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jerrytang.bsky.social @jerrytang.bsky.social · 08/09/2026

First we train a model on brain responses from existing reference participants. Then we show a much smaller set of calibration stimuli to each new goal participant. By aligning brain responses to the calibration stimuli, we can transfer the model to the goal participant 4/7

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jerrytang.bsky.social @jerrytang.bsky.social · 08/09/2026

To address this bottleneck, we developed a framework for using functional alignment to transfer encoding models across participants 3/7

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jerrytang.bsky.social @jerrytang.bsky.social · 08/09/2026

Encoding models and cortical maps could be very helpful for clinical applications such as localizing implant and stimulation sites. But these models require many hours of brain responses from each participant, which can be infeasible for clinical populations 2/7

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jerrytang.bsky.social @jerrytang.bsky.social · 08/09/2026

Our new study on cross-participant cortical mapping (with @alexanderhuth.bsky.social) is out in @imagingneurosci.bsky.social! direct.mit.edu/imag/article... 1/7

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jerrytang.bsky.social @jerrytang.bsky.social · 09/04/2026

This was an incredibly rewarding project to work on. Thanks to the amazing team that made this possible! Carly Millanski, Allison Chen, Lisa Wauters, Jordyn Anders, Shilpa Shamapant, @smwilson.bsky.social, @alexanderhuth.bsky.social, @mayalhenry.bsky.social 8/8

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jerrytang.bsky.social @jerrytang.bsky.social · 09/04/2026

We also found good decoding performance from individual brain regions. This suggests that we could move our decoder from fMRI into more portable systems. The best regions differed across participants so we think fMRI will remain very important for localizing recording sites 7/8

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jerrytang.bsky.social @jerrytang.bsky.social · 09/04/2026

Moving forward we hope to develop practical systems that can support communication. We found that decoding performance reliably improved with the amount of training data. So we’re optimistic that there's plenty of room for improvement 6/8

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jerrytang.bsky.social @jerrytang.bsky.social · 09/04/2026

Next we explored why this works. We found that conceptual processing was largely spared outside of damaged brain regions. This suggests that our approach could generalize across patients with a wide range of lesion profiles and speech / language impairments 5/8

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jerrytang.bsky.social @jerrytang.bsky.social · 09/04/2026

We found that the decoder predictions could describe what the participants with aphasia were hearing about / seeing / imagining! This shows how brain-computer interfaces could predict the concepts that patients are thinking about but struggling to express 4/8

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jerrytang.bsky.social @jerrytang.bsky.social · 09/04/2026

We previously showed that concepts can be decoded from neurologically healthy participants using functional MRI. Here we used a transfer learning approach where decoders are trained on neurologically healthy participants and then transferred to participants with aphasia using stories and movies 3/8

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jerrytang.bsky.social @jerrytang.bsky.social · 09/04/2026

Aphasia is one of the most common and debilitating effects of stroke. Patients with aphasia struggle with different aspects of language (e.g. word finding, grammatical construction, phonological encoding). But many patients have relatively spared conceptual knowledge 2/8

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jerrytang.bsky.social @jerrytang.bsky.social · 09/04/2026

We're excited to share our new study on decoding brain activity in participants with post-stroke aphasia! We think this is an important step towards cognitive brain-computer interfaces for patients with language disorders www.biorxiv.org/content/10.6... 1/8

biorxiv.org

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Reposted by @jerrytang.bsky.social

Catherine Chen @cathychen23.bsky.social · 06/03/2026

Our work on bilingual language processing is now out in @pnas.org! Our fMRI study compares cortical representations btwn native and non-native languages. We find that representations are largely similar, but systematically modulated btwn languages www.pnas.org/doi/10.1073/...

Schematic of semantic tuning shifts from L2-English to L1-Chinese

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Liberty Hamilton @libertysays.bsky.social · 03/03/2026

My group is hiring a full time research coordinator to work with our collaborators in Houston on understanding speech and language development in children with epilepsy. Great for folks looking to get direct experience with clinical/translational research. Please repost! jobs.bcm.edu/job/Research...

jobs.bcm.edu

Research Coordinator I - TCH Neurosurgery

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Reposted by @jerrytang.bsky.social

Sam Nastase @samnastase.bsky.social · 01/10/2025

I'm recruiting PhD students to join my new lab in Fall 2026! The Shared Minds Lab at @usc.edu will combine deep learning and ecological human neuroscience to better understand how we communicate our thoughts from one brain to another.

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jerrytang.bsky.social @jerrytang.bsky.social · 06/02/2025

Practically, our approach may enable us to adapt our semantic language decoders for people with impaired language comprehension. I’ve been working with @mayalhenry.bsky.social and @alexanderhuth.bsky.social to test our approach in people with aphasia, stay tuned! 5/5

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jerrytang.bsky.social @jerrytang.bsky.social · 06/02/2025

We tested our approach on neurologically healthy participants, and found that silent movies are nearly as effective as narrative stories for transferring semantic decoders. Scientifically, this adds to the growing evidence that semantic representations are shared between language and vision 4/5

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jerrytang.bsky.social @jerrytang.bsky.social · 06/02/2025

Our new approach can decode language from a participant without language data! First we train decoders on reference participants with language data. Then we use *silent movies* to align brain responses across participants. Finally we decode new participant responses using the reference decoders 3/5

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jerrytang.bsky.social @jerrytang.bsky.social · 06/02/2025

Language decoders that target semantic representations have the potential to help people with aphasia, who struggle to map concepts to lexical-phonological output. But many people with aphasia have impaired language comprehension, and current decoders are trained on brain responses to language 2/5

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jerrytang.bsky.social @jerrytang.bsky.social · 06/02/2025

I'm excited to share our new paper (with @alexanderhuth.bsky.social) on transferring language decoders across participants and modalities! authors.elsevier.com/a/1kZRD3QW8S... 1/5

authors.elsevier.com

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