Abstract
BackgroundDepression, one of the most frequent non-motor symptoms in Parkinson’s disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. However, the underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks in de novo PD patients with depression.MethodsWe performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21 de novo PD patients with depression (dPD), 34 de novo PD patients without depression (ndPD), and 43 healthy controls (HCs) to extract functional networks. Intranetwork and internetwork connectivity was calculated for comparison between groups, correlation analysis, and predicting the occurrence of depression in PD.ResultsWe observed an ordered decrease of connectivity among groups within the ventral attention network (VAN) (dPD < ndPD < HCs), mainly located in the left middle temporal cortex. Besides, dPD patients exhibited hypoconnectivity between the auditory network (AUD) and default mode network (DMN) or VAN compared to ndPD patients or healthy controls. Correlation analysis revealed that depression severity was negatively correlated with connectivity value within VAN and positively correlated with the connectivity value of AUD-VAN in dPD patients, respectively. Further analysis showed that the area under the curve (AUC) for dPD prediction was 0.863 when combining the intranetwork connectivity in VAN and internetwork connectivity in AUD-DMN and AUD-VAN.ConclusionOur results demonstrated that early dPD may be associated with abnormality of attention bias and especially auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker to predict depression in PD.
Highlights
Parkinson’s disease (PD) affects 2–3% of the population over 65 years of age, which is the second most common neurodegenerative disorder next to Alzheimer’s disease (Poewe et al, 2017)
A total of 34 ndPD patients, 21 depression in PD (dPD) patients, and 43 healthy controls were eventually included in the further analysis
Analysis of variance results showed that the ventral attention network (VAN) exhibited a significant difference in intranetwork connectivity among dPD, ndPD, and Healthy controls (HCs) groups (P < 0.05, false discovery rate (FDR)-corrected) (Figure 2A)
Summary
Parkinson’s disease (PD) affects 2–3% of the population over 65 years of age, which is the second most common neurodegenerative disorder next to Alzheimer’s disease (Poewe et al, 2017). As one of the most frequent non-motor symptoms, the overall prevalence of depression in PD (dPD) patients can reach up to 17–35% (Reijnders et al, 2008; Aarsland et al, 2011). How neurotransmitters or other risk factors contribute to the onset and progression of dPD remains largely ambiguous. A better understanding of the underlying pathogenetic mechanisms is crucial for the early diagnosis and treatment considering the serious impact on patients’ quality of life. Depression, one of the most frequent non-motor symptoms in Parkinson’s disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. The underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks in de novo PD patients with depression
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