The Network Model of Depression as a Basis for New Therapeutic Strategies for Treating Major Depressive Disorder in Parkinson's Disease

Resting-state brain network in Parkinson’s disease with different degrees of depression

Objective

The aim of this study is to explore the neural network mechanism of Parkinson’s disease (PD) with different degrees of depression using independent component analysis (ICA) of the functional connectivity changes in the forehead, limbic system, and basal ganglia regions.

Methods

A total of 106 patients with PD were divided into three groups: PD with moderate-severe depression (PDMSD, n = 42), PD with mild depression (PDMD, n = 29), and PD without depression (PDND, n = 35). Fifty gender- and age-matched healthy subjects were recruited as a control group (HC). Three-dimensional T1-weighted image and resting-state functional magnetic resonance imaging (RS-fMRI) data were collected.

Results

Different functional connectivity was observed in the left precentral gyrus, right precuneus, right inferior frontal gyrus, right medial and paracingulate gyrus, left supplementary motor area, right brain insula, and the inferior frontal gyrus of the left orbit among the four groups (ANOVA, P < 0.05, Voxel size > 5). Both PDMD and PDMSD exhibited increased functional connectivity in the superior-posterior default-mode network (spDMN) and left frontoparietal network (LFPN); they also exhibited a decreased functional connectivity in the interior Salience Network (inSN) when compared with the PDND group. The functional connectivity within the inSN network was decreased in the PDMSD group when compared with the PDMD group (Alphasim correction, P < 0.05, voxel size > 5).

Conclusion

PD with different degrees of depression has abnormal functional connectivity in multiple networks, which is an important neurobiological basis for the occurrence and development of depression in PD. The degree of decreased functional connectivity in the inSN network is related to the degree of depression in patients with PD-D, which can be an imaging marker for PD to judge the severity of depression.

Neural Networks to Recognize Patterns in Topographic Images of Cortical Electrical Activity of Patients with Neurological Diseases

Software such as EEGLab has enabled the treatment and visualization of the tracing and cortical topography of the electroencephalography (EEG) signals. In particular, the topography of the cortical electrical activity is represented by colors, which make it possible to identify functional differences between cortical areas and to associate them with various diseases. The use of cortical topography with EEG origin in the investigation of diseases is often not used due to the representation of colors making it difficult to classify the disease. Thus, the analyses have been carried out, mainly, based on the EEG tracings. Therefore, a computer system that recognizes disease patterns through cortical topography can be a solution to the diagnostic aid. In view of this, this study compared five models of Convolutional Neural Networks (CNNs), namely: Inception v3, SqueezeNet, LeNet, VGG-16 and VGG-19, in order to know the patterns in cortical topography images obtained with EEG, in Parkinson's disease, Depression and Bipolar Disorder. SqueezeNet performed better in the 3 diseases analyzed, with Parkinson's disease being better evaluated for Accuracy (88.89%), Precison (86.36%), Recall (91.94%) and F1 Score (89.06%), the other CNNs had less performance. In the analysis of the values of the Area under ROC Curve (AUC), SqueezeNet reached (93.90%) for Parkinson's disease, (75.70%) for Depression and (72.10%) for Bipolar Disorder. We understand that there is the possibility of classifying neurological diseases from cortical topographies with the use of CNNs and, thus, creating a computational basis for the implementation of software for screening and possible diagnostic assistance.

Direct Medial Entorhinal Cortex Input to Hippocampal CA3 Is Crucial for eEF2K Inhibitor-Induced Neuronal Oscillations in the Mouse Hippocampus

The hippocampal formation plays a vital role in memory formation and takes part in the control of the default neuronal network activity of the brain. It also represents an important structure to analyze drug-induced effects on subregion-specific synchronization of neuronal activity. However, the consequences of an altered functional state of synapses for subregion-specific synchronization of neuronal microcircuits remain to be fully understood. Therefore, we analyzed the direct interaction of neuronal microcircuits utilizing a genetically encoded calcium sensor (GCaMP6s) and local field potential (LFP) recording in acute hippocampal-entorhinal brain slices in response to a modulator of synaptic transmission. We observed that application of the eukaryotic elongation factor-2 kinase (eEF2K) inhibitor A484954, induced a large-scale synchronization of neuronal activity within different regions of the hippocampal formation. This effect was confirmed by the recording of extracellular LFPs. Further, in order to understand if the synchronized activity depended on interconnected hippocampal areas, we lesioned adjacent regions from each other. These experiments identified the origin of A484954-induced synchronized activity in the hippocampal CA3 subfield localized near the hilus of the dentate gyrus. Remarkably, the synchronization of neuronal activity in the hippocampus required an intact connection with the medial entorhinal cortex (MEC). In line with this observation, we detected an increase in neuronal activity in the MEC area after application of A484954. In summary, inhibition of eEF2K alters the intrinsic activity of interconnected neuronal microcircuits dominated by the MEC-CA3 afferents.

Effectiveness of imaging genetics analysis to explain degree of depression in Parkinson's disease

Depression is one of the most common and important neuropsychiatric symptoms in Parkinson's disease and often becomes worse as Parkinson's disease progresses. However, the underlying mechanisms of depression in Parkinson's disease are not clear. The aim of our study was to find genetic features related to depression in Parkinson's disease using an imaging genetics approach and to construct an analytical model for predicting the degree of depression in Parkinson's disease. The neuroimaging and genotyping data were obtained from an openly accessible database. We computed imaging features through connectivity analysis derived from tractography of diffusion tensor imaging. The imaging features were used as intermediate phenotypes to identify genetic variants according to the imaging genetics approach. We then constructed a linear regression model using the genetic features from imaging genetics approach to describe clinical scores indicating the degree of depression. As a comparison, we constructed other models using imaging features and genetic features based on references to demonstrate the effectiveness of our imaging genetics model. The models were trained and tested in a five-fold cross-validation. The imaging genetics approach identified several brain regions and genes known to be involved in depression, with the potential to be used as meaningful biomarkers. Our proposed model using imaging genetic features predicted and explained the degree of depression in Parkinson's disease appropriately (adjusted R2 larger than 0.6 over five training folds) and with a lower error and higher correlation than with other models over five test folds.

Depression in Parkinson's Disease: The Contribution from Animal Studies

Besides being better known for causing motor impairments, Parkinson's disease (PD) can also cause many nonmotor symptoms, like depression and anxiety, which can cause significant loss of life quality and may not respond to regular drugs treatment. In this review, we discuss the depression in PD, based on data from studies in humans and rodents. Depression frequency seems higher in PD patients than in general population, despite high variation in data due to diagnosis disparities. Development of depression in PD seems more likely to be caused by the nigrostriatal pathway degeneration than as a consequence of the awareness of disease prognostic, and it seems to be related to dopaminergic, noradrenergic, and serotoninergic synapses deficits. The dopaminergic role could be more significant, since it can modulate the release of the others, and its depletion is progressive, due to the degenerative feature of PD. Highly regarded in major depression, serotonin can be depleted in rats after nigrostriatal damage, but data from human patients are more conflicting. Animal studies can help in understanding the neurobiological mechanisms of depression in PD and the pursuit for more effective drugs for its treatment, but they lack the complexity of the disease progression, especially the nondopaminergic degeneration.