<![CDATA[Introduction: Early-stage neurodegeneration — clinically expressed as mild cognitive impairment (MCI) — lacks accessible imaging biomarkers that link microstructural white-matter injury to functional network disruption. We evaluated whether integrating diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) at 3.0-Tesla improves detection of MCI and substantiates a structural-to-functional disconnection mechanism. Methods: In this STARD-2015-compliant, prospective cross-sectional diagnostic accuracy study at a tertiary hospital in Palembang, Indonesia, 85 participants (45 MCI, 40 age-, sex- and education-matched controls) underwent single-scanner 3.0-T MRI. DTI metrics (fractional anisotropy [FA], mean diffusivity [MD]) were derived by tract-based spatial statistics and posterior-cingulate-seeded Default Mode Network (DMN) connectivity by CONN. Consensus clinical diagnosis (Petersen criteria) was the blinded reference standard. Diagnostic accuracy used ROC/DeLong AUC, Wilson 95% confidence intervals (CIs), Cohen κ, and multivariable logistic regression. Results: Posterior-cingulum FA was lower (0.399 ± 0.058 vs 0.489 ± 0.052, p<0.001) and MD higher in MCI; PCC–mPFC connectivity was reduced (0.392 ± 0.124 vs 0.586 ± 0.136, p<0.001). FA discriminated MCI with AUC 0.903 (95% CI 0.831–0.974; sensitivity 82.2%, specificity 90.0%); a combined FA+FC model reached AUC 0.901 with sensitivity 95.6% and NPV 93.8% but did not exceed FA alone (DeLong p=0.90). FA and connectivity were strongly correlated (r=0.79, 95% CI 0.69–0.86, p<0.001). Inter-reader agreement was substantial (κ=0.74 and 0.67). Conclusion: Multimodal 3.0-T DTI and rs-fMRI provides an accurate, radiation-free signature of early-stage neurodegeneration; the coupling between cingulum microstructure and DMN connectivity is consistent with structural disconnection being associated with functional decoupling and offers a deployable tool for tertiary referral centres.]]>
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