Current neuroimaging studies face methodological limitations in neural-phenotypic integration for biomarker validation. We present a multiscale computational framework integrating graph-theoretic connectivity analysis with dual-threshold statistical validation and unsupervised phenotypic stratification. Our approach combines Welch's t-test, FDR correction, and effect size thresholds to prioritize biological relevance over statistical artifacts. Applied to task-based fMRI from 86 participants across the schizophrenia spectrum, we extracted nodal and network-level metrics from sparse weighted functional graphs. Familial validation through a four-group design dissociated state-dependent from trait-related markers, establishing cerebellar-cortical hypoconnectivity as illness-specific rather than genetic risk. Consensus clustering of cognitive and symptomatic features identified three stable phenotypic subtypes with distinct neuropsychological profiles, highlighting the potential utility of phenotype stratification. This framework demonstrates that integrating multiscale graph analysis with dual-threshold validation and unsupervised stratification establishes a reproducible computational standard for biomarker discovery, significantly enhancing biological interpretability beyond traditional neurological analysis.