End-to-end deep learning for identifying Alzheimer’s disease signatures in spectral fluorescence imaging of normal-appearing brain regions in 5xFAD mice

PurposeAlzheimer’s disease (AD) is the leading cause of dementia, characterized by the accumulation of amyloid-β and tau aggregates in the brain. Current diagnostic approaches primarily detect mature plaques, but growing evidence suggests that pre-fibrillar aggregates are present earlier in normal-appearing tissue and may provide a valuable window for intervention. We propose the first proof-of-concept end-to-end deep learning (DL) model to identify AD signatures directly from normal-appearing tissue (i.e., background parenchyma) of raw spectral fluorescence data.ApproachTo develop and evaluate our DL model, we used one million signals extracted from spectral fluorescence images of brain cortex sections collected from wild-type and 5xFAD mice (a transgenic strain carrying five familial human AD mutations).ResultsOur results demonstrate that AD-related signatures can indeed be detected in normal-appearing brain cortex regions. Experiments revealed that model performance depends on input dimensions, training set size, and the preprocessing kernel size. The optimal configuration used 1000×32 input, 9000 training samples, and a 3×3 kernel, achieving ≥95% accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve. Importantly, no mice were misclassified, and saliency analysis indicated that a broad range of spectral fluorescence wavelengths contributed to the model’s decisions.ConclusionsThese findings highlight the potential of DL-based analysis of spectral fluorescence data to enable earlier detection of AD-related changes, supporting a shift toward a more proactive strategy for biomarker discovery.