Brain regions with high connectivity have high metabolic cost and their disruption is associated with neuropsychiatric disorders. Prior neuroimaging studies have identified at the group-level local functional connectivity density (lFCD) hubs, network nodes with high degree of connectivity with neighboring regions, in occipito-parietal cortices. However, the individual patterns and the precision for the location of the hubs were limited by the restricted spatiotemporal resolution of the magnetic resonance imaging (MRI) measures collected at rest. In this work, we show that MRI datasets with higher spatiotemporal resolution (2-mm isotropic; 0.72 s), collected under the Human Connectome Project (HCP), provide a significantly higher precision for hub localization and for the first time reveal lFCD patterns with gray matter (GM) specificity >96% and sensitivity >75%. High temporal resolution allowed effective 0.01–0.08 Hz band-pass filtering, significantly reducing spurious lFCD effects in white matter. These high spatiotemporal resolution lFCD measures had high reliability [intraclass correlation, ICC(3,1) > 0.6] but lower reproducibility (>67%) than the low spatiotemporal resolution equivalents. GM sensitivity and specificity benchmarks showed the robustness of lFCD to changes in model parameter and preprocessing steps. Mapping individual's brain hubs with high sensitivity, specificity, and reproducibility supports the use of lFCD as a biomarker for clinical applications in neuropsychiatric disorders.