7/ Mapping disorder involvement ⚕️

We derive regional risk maps for 30+ brain disorders 🧠🩹. Their clustering aligns with official ICD-11 clinical classification 🩺 better than clustering maps of risk-gene expression 🧬, & recover symptom co-morbidities

6/ From shared function to shared co-activation

Regions’ LLM-derived cognitive similarity predicts functional co-activation from fMRI, and effects of direct stimulation ⚡️ better than anatomical connectivity, molecular profile, or spatial proximity - also in 🐭&🐒

5/ Conserved molecular circuits for cognition 🧬🧠

We can also extend to macaque🐒 & mouse🐭 previous human studies based on NeuroSynth (Hansen 2021 Nat Hum Behav; Luppi 2024 Nat Biomed Eng). Integration w/ species-specific gene expression 🧬 reveals a cross-species molecular circuit for cognition

4/ A functional cortical hierarchy is latent in the literature

Asking the LLMs about regions and functions one-by-one spontaneously reveals a unimodal-transmodal functional hierarchy 🧠 matching species-specific anatomical hierarchy

3/ Unlocking Cross-Species Discovery 🐒🐭

We can use the LLM-generated maps to decode brain maps from other species. We show this with 🐒 macaque working memory & the recent Neuropixel maps of 🐭 mouse neural activity during cognitive tasks, from International Brain Laboratory (doi.org/10.1038/s415...)

2/ Beyond traditional meta-analysis

LLM maps go beyond traditional meta-analysis tools: they correlate better with 🧠 circuits derived from direct causal interventions from intracranial electrical stimulation ⚡️ and Lesion Network Mapping

1/ AI-driven cognitive mapping

We use Large Language Models (GPT-4, Gemini, Llama, Mistral, DeepSeek) as neuroscience experts, to synthesise the evidence that region X is involved in function/disorder Y. We can do this for humans but also macaque 🐒 & mouse 🐭