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.
Mesial-temporal lobe epilepsy (mTLE), a neurological disorder characterized by abnormal synchronous discharges in a
large cell population, affects the hemodynamic activities of functional networks remote from the epileptogenic zone and causes widespread deficits in brain functions. Although a number of resting-state fMRI studies have found altered spatial patterns in the canonical resting-state networks (RSNs) in patients with mTLE, including the default mode network (DMN), dorsal lateral attention network (DAN), auditory network (AUN), somatosensory network (SMN) and visual network (VIN), none of these studies has addressed the question whether the frequencies of hemodynamic oscillations in these RSNs were altered. In the present study, we have proposed a network-based temporal clustering analysis (TCA) method to characterize the resting hemodynamic activity of a large-scale functional network. First, the RSNs were identified in healthy controls as well in the left mTLE patients using independent component analysis (ICA). Then, a time course representing the hemodynamic activity of each RSN was extracted by counting the number of the voxels that were activated simultaneously at each time point within the network. Finally, the power spectral density (PSD) of the time course was estimated. Our results have demonstrated significant differences in the frequency profiles of the SMN, VIN and left DAN between the patients and controls: the peaks of these spectra shifted toward a lower frequency in the patients, while more power was distributed over higher frequency bands in the healthy controls. However, no significant difference has been found in the AUN, DMN and right DAN. These features might serve as biomarkers to differentiate the patients from controls.