A new paradigm for stratified cognitive assessment: fusing serum Raman spectroscopy and ensemble learning.

BACKGROUND: In China, the growing need for stratified cognitive assessment demands portable solutions. However, in community-based screening, neuropsychological assessments suffer from low acceptance and low efficiency. Thus, we aimed to develop a serum Raman spectroscopy-based ensemble learning approach for graded cognitive screening, and explore the Raman spectral features of cognitive impairment and their mapping relationship to brain function. METHODS: We recruited 220 subjects for modeling and 40 subjects for validation. Ensemble learning model was built using serum Raman spectra. High-weight features were analyzed for inter-group differences, graph theory properties, and associations with brain networks. RESULTS: We developed a serum Raman spectroscopy-based ensemble learning approach for graded cognitive screening. The model distinguishing normal cognition, mild cognitive impairment, and dementia achieved area under curve of 0.92 (testing) and 0.89 (validation). Raman shifts at 1602, 1002, and 1666 cm⁻¹ showed intensity and nodal changes, linking their modulation to both cognitive decline and altered posterior default mode network (pDMN) interactions. CONCLUSIONS: The Raman spectroscopy-based ensemble learning model was powerful for cognitive screening. The alteration at the 1602, 1002, and 1666 cm⁻¹ represented key Raman signatures of cognitive impairment, reflecting impaired pDMN-related inter-network interactions.