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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23946 |
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| _version_ | 1866912673779679232 |
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| author | Songdechakraiwut, Tananun |
| author_facet | Songdechakraiwut, Tananun |
| contents | We present a connectome-informed LLM framework that encodes dynamic fMRI connectivity as temporal sequences, applies robust normalization, and maps these data into a representation suitable for a frozen pre-trained LLM for clinical prediction. Applied to early Alzheimer's detection, our method achieves sensitive prediction with error rates well below clinically recognized margins, with implications for timely Alzheimer's intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging LLMs for Early Alzheimer's Prediction Songdechakraiwut, Tananun Computation and Language We present a connectome-informed LLM framework that encodes dynamic fMRI connectivity as temporal sequences, applies robust normalization, and maps these data into a representation suitable for a frozen pre-trained LLM for clinical prediction. Applied to early Alzheimer's detection, our method achieves sensitive prediction with error rates well below clinically recognized margins, with implications for timely Alzheimer's intervention. |
| title | Leveraging LLMs for Early Alzheimer's Prediction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.23946 |