Really excited to share our new preprint led by @ahmadmsamara with @zaidzada_, Tammy Vanderwal, and @HassonLab titled "Cortical language areas are coupled via a soft hierarchy of model-based linguistic features"

RT @IrisVanRooij: Uncritical adoption of AI “undermines our basic pedagogical values and principles of scientific integrity. It prevents us….

RT @ralph_ep: New preprint!. tl;dr — We ran around late at night to record wild rats in NYC and figured out how to quantify their behavior….

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RT @ykamit: Our new study in @NatComputSci, led by Haibao Wang, presents a neural code converter aligning brain activity across individuals….

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RT @MarianneReddan: Check out our latest work in @NatureComms - we show that others’ feelings and our inferences can both be predicted from….

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RT @HassonLab: Finally, we developed a set of interactive tutorials for preprocessing and running encoding models to get you started. Happy….

RT @HassonLab: We’re excited to share our unique ECoG dataset for natural language comprehension. The paper is now on Scientific Data (http….

RT @TheHongLab: Thrilled to share our latest work in @Nature studying inter-brain neural dynamics in both biological and artificial intelli….

RT @HassonLab: How do different languages converge on a shared neural substrate for conceptual meaning? We’re excited to share our latest p….

RT @isaacrchristian: Ayoo new publication out with the man @samnastase and infamous Michael Graziano!. When we medi….

We're very excited to share this work and happy to hear your feedback! If you're attending @OHBM 2025, @ahmadmsamara will be presenting a poster on this project (poster #804, June 25 and 26)—be sure to stop by and chat with him about it! #OHBM2025.

Our findings suggest that different language areas are coupled via a mixture of linguistic features—this yields what we refer to as a "soft hierarchy" from lower-order to higher-order language areas, and may facilitate efficient, context-sensitive language processing.

Taking a slightly different approach, we assess how well specific model features capture larger-scale patterns of connectivity. We find that feature-specific model connectivity partly recapitulates stimulus-driven cortical network configuration.

We observe a clear progression of feature-specific connectivity from early auditory to lateral temporal areas, advancing from acoustic-driven connectivity to speech- and finally language-driven connectivity.

We show that early auditory areas are coupled to intermediate language areas via lower-level acoustic and speech features. In contrast, higher-order language and default-mode regions are predominantly coupled through more abstract language features.

We developed a model-based framework for quantifying stimulus-driven, feature-specific connectivity between regions. We used parcel-wise encoding models to align feature-specific embeddings to brain activity and then evaluated how well these models generalize to other parcels.

Put differently, ISFC can tell us *where* and *how much* connectivity is driven by the stimulus, but not *what* stimulus features are driving the connectivity. How can we begin to unravel what linguistic features are shared across different language regions?.

Following the logic of intersubject correlation (ISC) analysis, intersubject functional connectivity (ISFC) isolates stimulus-driven connectivity between regions (e.g., in response to naturalistic stimuli)—but is agnostic to the content of the stimulus shared between regions.