Optical neuromonitoring provides insight into neurovascular physiology and brain structure and function. These methods rely on spectroscopy to relate light absorption changes to variation of concentrations of physiologic chromophores such as oxy- and deoxyhemoglobin. In clinical or preclinical practice, data quality can vary significantly across wavelengths. In such situations, standard spectroscopic methods may perform poorly, resulting in data loss and limiting field-of-view. To address this issue, and thereby improve the robustness of optical neuromonitoring, we develop, in this manuscript, novel methods to perform spectroscopy even when data quality exhibits wavelength-dependent spatial variation. We sought to understand the impact of spatial, wavelength-based censoring on the physiologic accuracy and utility of hemoglobin spectroscopy. The principles of our analysis are quite general, but to make the methodology tangible we focused on optical intrinsic signal imaging of resting-state functional connectivity in mice. Starting with spectroscopy using four sources, all possible subset spectroscopy matrices were assessed theoretically, using simulated data, and using experimental data. These results were compared against the use of the full spectroscopy matrix to determine which subsets yielded robust results. Our results demonstrated that accurate calculation of changes in hemoglobin concentrations and the resulting functional connectivity network maps was possible even with censoring of some wavelengths. Additionally, we found that the use of changes in total hemoglobin (rather than oxy- or deoxyhemoglobin) yielded results more robust to experimental noise and allowed for the preservation of more data. This new and rigorous image processing method should improve the fidelity of clinical and preclinical functional neuroimaging studies.