The development of reliable computational tools for diagnosing and stratifying cognitive and neurodegenerative conditions remains a major challenge in translational neuroscience. Variability in clinical presentation, overlapping symptomatology, and heterogeneity in intermediate diagnostic categories complicate automated classification. This study proposes a methodology for constructing diagnostic tools based on spiking neural networks (SNNs) that uses relevant features calculated from virtual reality tasks to assess cognitive domains commonly affected in early dementia. These domains include episodic memory, executive function, and spatial navigation. Three diagnostic groups were considered: Normal Cognition (ED1), Subtle Cognitive Impairment (ED2), and Mild Cognitive Impairment (ED3). Feature selection was performed by ranking predictors according to the magnitude of their regression coefficients, and the most informative variables were used as inputs to the SNN classifier. To assess the influence of clinical labeling on model behavior, four stratification schemes (U0.5a, U0.5b, U0.5c, U1a) were defined and evaluated. Classification accuracy was computed on the training and independent test sets, and uncertainty was quantified using 95% confidence intervals. Results indicate that model performance is strongly influenced by the definition of intermediate clinical categories, highlighting the importance of stratification strategy in computational diagnostics. The findings support the feasibility of SNN-based diagnostic frameworks while emphasizing the need for careful feature optimization and clinically grounded labeling procedures. Future work will extend validation to larger cohorts and longitudinal data to assess robustness and generalizability.