Depression is prevalent among patients with Parkinson's disease (PD); however the pathophysiology of depression in PD is not well understood. In order to investigate how depression and motor impairments differentially and interactively affect specific brain regions in Parkinson's disease, we introduced a new data driven approach, namely Frequency Component Analysis (FCA), to decompose the resting-state functional magnetic resonance imaging data of 59 subjects with Parkinson's disease into different frequency bands. We then evaluated the main effects of motor severity and depression, and their interactive effects on the BOLD-fMRI signal oscillation energy in these specific frequency components. Our results show that the severity of motor symptoms is more negatively correlated with energy in the frequency band of 0.10-0.25Hz in the bilateral thalamus (THA), but more positively correlated with energy in the frequency band of 0.01-0.027Hz in the bilateral postcentral gyrus (PoCG). In contrast, the severity of depressive symptoms is more associated with the higher energy of the high frequency oscillations (>0.1Hz) but lower energy of 0.01-0.027Hz in the bilateral subgenual gyrus (SGC). Importantly, the interaction between motor and depressive symptoms is negatively correlated with the energy of high frequency oscillations (>0.1Hz) in the substantia nigra/ventral tegmental area (SN/VTA), left hippocampus (HIPP), left inferior orbital frontal cortex (OFC), and left temporoparietal junction (TPJ), but positively correlated with the energy of 0.02-0.05Hz in the left inferior OFC, left TPJ, left inferior temporal gyrus (ITG), and bilateral cerebellum. These results demonstrated that FCA was a promising method in interrogating the neurophysiological implications of different brain rhythms. Our findings further revealed the neural bases underlying the interactions as well the dissociations between motor and depressive symptoms in Parkinson's disease.