Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912524804292608 |
|---|---|
| author | Wang, Yifan Sun, Jingyuan Zheng, Jichen Zhang, Yunhao Ye, Chunyu Li, Jixing Zong, Chengqing Wang, Shaonan |
| author_facet | Wang, Yifan Sun, Jingyuan Zheng, Jichen Zhang, Yunhao Ye, Chunyu Li, Jixing Zong, Chengqing Wang, Shaonan |
| contents | The striking alignment between large language models (LLMs) and human brain activity positions them as powerful models of healthy cognition. This parallel raises a fundamental question: if LLMs can model the intact brain, can we lesion them to simulate the linguistic deficits of the injured brain? In this work, we introduce a methodology to model aphasia - a complex language disorder caused by neural injury - by selectively disabling components in a modular Mixture-of-Experts (MoE) language model. We simulate distinct aphasia subtypes, validate their linguistic outputs against real patient speech, and then investigate functional recovery by retraining the model's remaining healthy experts. Our results demonstrate that lesioning functionally-specialized experts for syntax or semantics induces distinct impairments that closely resemble Broca's and Wernicke's aphasia, respectively. Crucially, we show that freezing the damaged experts and retraining the intact ones on conversational data restores significant linguistic function, demonstrating a computational analogue for rehabilitation. These findings establish modular LLMs as a powerful and clinically-relevant potential framework for modeling the mechanisms of language disorders and for computationally exploring novel pathways for therapy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia Wang, Yifan Sun, Jingyuan Zheng, Jichen Zhang, Yunhao Ye, Chunyu Li, Jixing Zong, Chengqing Wang, Shaonan Neurons and Cognition The striking alignment between large language models (LLMs) and human brain activity positions them as powerful models of healthy cognition. This parallel raises a fundamental question: if LLMs can model the intact brain, can we lesion them to simulate the linguistic deficits of the injured brain? In this work, we introduce a methodology to model aphasia - a complex language disorder caused by neural injury - by selectively disabling components in a modular Mixture-of-Experts (MoE) language model. We simulate distinct aphasia subtypes, validate their linguistic outputs against real patient speech, and then investigate functional recovery by retraining the model's remaining healthy experts. Our results demonstrate that lesioning functionally-specialized experts for syntax or semantics induces distinct impairments that closely resemble Broca's and Wernicke's aphasia, respectively. Crucially, we show that freezing the damaged experts and retraining the intact ones on conversational data restores significant linguistic function, demonstrating a computational analogue for rehabilitation. These findings establish modular LLMs as a powerful and clinically-relevant potential framework for modeling the mechanisms of language disorders and for computationally exploring novel pathways for therapy. |
| title | Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2508.04749 |