Abstract
Much is known concerning the underlying mechanisms of Parkinson’s disease (PD) with depression, but our understanding of this disease at the neural-system level remains incomplete. This study used resting-state functional MRI (rs-fMRI) and independent component analysis (ICA) to investigate intrinsic functional connectivity (FC) within and between large-scale neural networks in 20 depressed PD (dPD) patients, 35 non-depressed PD (ndPD) patients, and 34 healthy controls (HC). To alleviate the influence caused by ICA model order selection, this work reported results from analyses at 2 levels (low and high model order). Within these two analyses, similar results were obtained: 1) dPD and ndPD patients relative to HC had reduced FC in basal ganglia network (BGN); 2) dPD compared with ndPD patients exhibited increased FC in left frontoparietal network (LFPN) and salience network (SN), and decreased FC in default-mode network (DMN); 3) dPD patients compared to HC showed increased FC between DMN and LFPN. Additionally, connectivity anomalies in the DMN, LFPN and SN correlated with the depression severity in patients with PD. Our findings confirm the involvement of BGN, DMN, LFPN and SN in depression in PD, facilitating the development of more detailed and integrative neural models of PD with depression.
Highlights
Much is known concerning the underlying mechanisms of Parkinson’s disease (PD) with depression, but our understanding of this disease at the neural-system level remains incomplete
Resting-state functional magnetic resonance imaging (fMRI), as a novel non-invasive approach to measuring baseline brain activity and connectivity, has been increasingly utilized to uncover the neural underpinnings of dPD10–14. Those resting-state fMRI (rs-fMRI) studies using the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) methods highlighted that dPD patients had abnormal resting brain activity in the prefrontal and limbic regions, such as amygdala, orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), anterior cingulate cortex (ACC), compared with non-depressed Parkinson disease (PD) patients[10, 12,13,14]
Given that several prefrontal, cingulated, basal ganglia (BG), and limbic regions are well documented to be implicated in dPD6, 9, 12, 13, this study sought to determine (1) whether the corresponding neural networks composed of these regions displayed aberrant interactions within each network and between them in dPD patients by comparing with non-depressed PD (ndPD) and healthy subjects and (2) if so, whether the detected aberrant interactions between dPD and ndPD patients were related to the severity of depression in PD
Summary
Much is known concerning the underlying mechanisms of Parkinson’s disease (PD) with depression, but our understanding of this disease at the neural-system level remains incomplete. With positron emission tomography (PET), single-photon emission computed tomography (SPECT), and task-based functional magnetic resonance imaging (fMRI), functional anomalies in several brain regions are related to depressed PD patients (dPD), including the dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), insula, thalamus, amygdala, ventral striatum, and caudate[5,6,7,8,9] Those findings lend support to the viewpoint that prefrontal cortex, basal ganglia (BG), and limbic system are involved in dPD. Resting-state fMRI (rs-fMRI), as a novel non-invasive approach to measuring baseline brain activity and connectivity, has been increasingly utilized to uncover the neural underpinnings of dPD10–14 Those rs-fMRI studies using the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) methods highlighted that dPD patients had abnormal resting brain activity in the prefrontal and limbic regions, such as amygdala, OFC, DLPFC, MPFC, ACC, compared with non-depressed PD (ndPD) patients[10, 12,13,14]. The results of this study will contribute to our knowledge of the neural network disruption in PD with depression
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