Recognizing Dementia from Neuropsychological Tests with State Space Models

Abstract

Early detection of dementia is critical for timely medical intervention and improved patient outcomes. Neuropsychological tests are widely used for cognitive assessment but have traditionally relied on manual scoring. Automatic dementia classification (ADC) systems aim to infer cognitive decline directly from speech recordings of such tests. We propose Demenba, a novel ADC framework based on state space models, which scale linearly in memory and computation with sequence length. Trained on over 1,000 hours of cognitive assessments administered to Framingham Heart Study participants, some of whom were diagnosed with dementia through adjudicated review, our method outperforms prior approaches in fine-grained dementia classification by 21%, while using fewer parameters. We further analyze its scaling behavior and demonstrate that our model gains additional improvement when fused with large language models, paving the way for more transparent and scalable dementia assessment tools12.1Code: https://github.com/lwang114/Demenba2This work was supported by the Framingham Heart Study’s National Heart, Lung, and Blood Institute contract N01-HC-25195; National Institutes of Health grants U19-AG068753, R01- AG016495, R01-AG008122, R01AG033040. The authors would also like to thank the staff and participants of the Framingham Heart Study.