Back

Neurochemical fingerprinting of the Alzheimers disease continuum using 7 T short-echo-time brain single voxel 1H-MRS.

Dell'Orco, A.; Göschel, L.; Riemann, L. T.; Aydin, S.; Flöel, A.; Tietze, A.; Scheel, M.; Fillmer, A.

2026-10-19 radiology and imaging
10.64898/2026.10.16.26365733 medRxiv
Show abstract

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.