Aberrant Intra- and Internetwork Functional Connectivity in Depressed Parkinson's Disease

Decreased resting-state functional connectivity and brain network abnormalities in the prefrontal cortex of elderly patients with Parkinson's disease accompanied by depressive symptoms

This study aimed to explore the brain network characteristics in elderly patients with Parkinson's disease (PD) with depressive symptoms. Thirty elderly PD patients with depressive symptoms (PD-D) and 26 matched PD patients without depressive symptoms (PD-NOD) were recruited based on HAMD-24 with a cut-off of 7. The resting-state functional connectivity (RSFC) was conducted by 53-channel functional near-infrared spectroscopy (fNIRS). There were no statistically significant differences in MMSE scores, disease duration, Hoehn-Yahr stage, daily levodopa equivalent dose, and MDS-UPDRS III between the two groups. However, compared to the PD-NOD group, the PD-D group showed significantly higher MDS-UPDRS II, HAMA-14, and HAMD-24. The interhemispheric FC strength and the FC strength between the left dorsolateral prefrontal cortex (DLPFC-L) and the left frontal polar area (FPA-L) was significantly lower in the PD-D group (FDR p < 0.05). As for graph theoretic metrics, the PD-D group had significantly lower degree centrality (aDc) and node efficiency (aNe) in the DLPFC-L and the FPA-L (FDR, p < 0.05), as well as decreased global efficiency (aEg). Pearson correlation analysis indicated moderate negative correlations between HAMD-24 scores and the interhemispheric FC strength, FC between DLPFC-L and FPA-L, aEg, aDc in FPA-L, aNe in DLPFC-L and FPA-L. In conclusion, PD-D patients show decreased integration and efficiency in their brain networks. Furthermore, RSFC between DLPFC-L and FPA-L regions is negatively correlated with depressive symptoms. These findings propose that targeting DLPFC-L and FPA-L regions via non-invasive brain stimulation may be a potential intervention for alleviating depressive symptoms in elderly PD patients.

Disrupted topologic efficiency of brain functional connectome in de novo Parkinson's disease with depression

Growing evidence supports that depression in Parkinson's disease (PD) depends on disruptions in specific neural networks rather than regional dysfunction. According to the resting-state functional magnetic resonance imaging data, the study attempted to decipher the alterations in the topological properties of brain networks in de novo depression in PD (DPD). The study also explored the neural network basis for depressive symptoms in PD. We recruited 20 DPD, 37 non-depressed PD and 41 healthy controls (HC). The Graph theory and network-based statistical methods helped analyse the topological properties of brain functional networks and anomalous subnetworks across these groups. The relationship between altered properties and depression severity was also investigated. DPD revealed significantly reduced nodal efficiency in the left superior temporal gyrus. Additionally, DPD decreased five hubs, primarily located in the temporal-occipital cortex, and increased seven hubs, mainly distributed in the limbic cortico-basal ganglia circuit. The betweenness centrality of the left Medio Ventral Occipital Cortex was positively associated with depressive scores in DPD. In contrast to HC, DPD had a multi-connected subnetwork with significantly lower connectivity, primarily distributed in the visual, somatomotor, dorsal attention and default networks. Regional topological disruptions in the temporal-occipital region are critical in the DPD neurological mechanism. It might suggest a potential network biomarker among newly diagnosed DPD patients.

Neural functional network of early Parkinson's disease based on independent component analysis

This work explored neural network changes in early Parkinson's disease: Resting-state functional magnetic resonance imaging was used to investigate functional alterations in different stages of Parkinson's disease (PD). Ninety-five PD patients (50 early/mild and 45 early/moderate) and 37 healthy controls (HCs) were included. Independent component analysis revealed significant differences in intra-network connectivity, specifically in the default mode network (DMN) and right frontoparietal network (RFPN), in both PD groups compared to HCs. Inter-network connectivity analysis showed reduced connectivity between the executive control network (ECN) and DMN, as well as ECN-left frontoparietal network (LFPN), in early/mild PD. Early/moderate PD exhibited decreased connectivity in ECN-LFPN, ECN-RFPN, ECN-DMN, and DMN-auditory network, along with increased connectivity in LFPN-cerebellar network. Correlations were found between ECN-DMN and ECN-LFPN connections with UPDRS-III scores in early/mild PD. These findings suggest that PD progression involves dysfunction in multiple intra- and inter-networks, particularly implicating the ECN, and a wider range of abnormal functional networks may mark the progression of the disease.

Efficacy of non-pharmacological interventions on depressive symptoms in patients with Parkinson's disease: a study protocol for a systematic review and network meta-analysis

INTRODUCTION

Depression is the most dominant non-motor symptom of Parkinson's disease (PD), with a prevalence of up to 50%, and can lead to a range of psychiatric and psychological problems that can affect quality of life and overall functioning. While several randomised controlled trials (RCTs) have tested the effect of certain non-pharmacological interventions on the outcome of PD depression symptoms, the comparative benefits and harms of these remain unclear. We will conduct a systematic review and network meta-analysis to compare the efficacy and safety of different non-pharmacological interventions for patients with PD depression.

METHODS AND ANALYSIS

We will search PubMed, Web of Science, Cochrane, Embase, Google Scholar, the Chinese National Knowledge Infrastructure, the Chinese Biomedical Literature Database, WanFang Data and the Chongqing VIP Database from their inception date to June 2022. The studies will be limited to results published in English or Chinese. The primary outcomes will be the changes in the depressive symptoms, while secondary outcomes will include adverse effects and the quality of life. Two researchers will screen those documents that meet the inclusion criteria, extracting data according to the preset table and evaluating the methodological quality of the included studies using the Cochrane Risk of Bias 2.0 Tool. The STATA and ADDIS statistical software will be used to conduct a systematic review and network meta-analysis. A traditional pairwise meta-analysis and a network meta-analysis will be performed to compare the efficacy and safety of different non-pharmacological interventions, ensuring the robustness of the findings. The Grading of Recommendations Assessment, Development and Evaluation system will be used to assess the overall quality of the body of evidence associated with the main results. The publication bias assessment will be conducted using comparison-adjusted funnel plots.

ETHICS AND DISSEMINATION

All the data for this study will be extracted from published RCTs. As a literature-based systematic review, this study does not require ethical approval. The results will be disseminated through peer-reviewed journals and national/international conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42022347772.

Altered Fractional Amplitude of Low-Frequency Fluctuation in Anxious Parkinson's Disease

OBJECTIVE

Anxiety symptoms are persistent in Parkinson's disease (PD), but the underlying neural substrates are still unclear. In the current study, we aimed to explore the underlying neural mechanisms in PD patients with anxiety symptoms.

METHODS

42 PD-A patients, 41 PD patients without anxiety symptoms (PD-NA), and 40 healthy controls (HCs) were recruited in the present study. All the subjects performed 3.0T fMRI scans. The fractional amplitude of low-frequency fluctuation (fALFF) analysis was used to investigate the alterations in neural activity among the three groups. A Pearson correlation analysis was performed between the altered fALFF value of the PD-A group and anxiety scores.

RESULTS

Compared with HCs, PD-A patients had higher fALFF values in the left cerebellum, cerebellum posterior lobe, bilateral temporal cortex, and brainstem and lower fALFF values in the bilateral inferior gyrus, bilateral basal ganglia areas, and left inferior parietal lobule. Moreover, between the two PD groups, PD-A patients showed higher fALFF values in the right precuneus and lower fALFF values in the bilateral inferior gyrus, bilateral basal ganglia areas, left inferior parietal lobule, and left occipital lobe. Furthermore, Pearson's correlation analysis demonstrated that the right precuneus and left caudate were correlated with the Hamilton Anxiety Rating Scale scores.

CONCLUSION

Our study found that anxiety symptoms in PD patients may be related to alterations of neurological activities in multiple brain regions. Furthermore, these may be critical radiological biomarkers for PD-A patients. Therefore, these findings can improve our understanding of the pathophysiological mechanisms underlying PD-A.

Functional and structural alterations as diagnostic imaging markers for depression in de novo Parkinson's disease

Background

Depression in Parkinson's disease (PD) is identified and diagnosed with behavioral observations and neuropsychological measurements. Due to the large overlaps of depression and PD symptoms in clinical manifestations, it is challenging for neurologists to distinguish and diagnose depression in PD (DPD) in the early clinical stage of PD. The advancement in magnetic resonance imaging (MRI) technology provides potential clinical utility in the diagnosis of DPD. This study aimed to explore the alterations of functional and structural MRI in DPD to produce neuroimaging markers in discriminating DPD from non-depressed PD (NDPD) and healthy controls (HC).

Methods

We recruited 20 DPD, 37 NDPD, and 41 HC matched in age, gender, and education years. The patients' diagnosis with PD was de novo. The differences in regional homogeneity (ReHo), voxel-wise degree centrality (DC), cortical thickness, cortical gray matter (GM) volumes, and subcortical GM volumes among these groups were detected, and the relationship between altered indicators and depression was analyzed. Moreover, the receiver operating characteristic (ROC) analysis was performed to assess the diagnostic efficacy of altered indicators for DPD.

Results

Compared to NDPD and HC, DPD showed significantly increased ReHo in left dorsolateral superior frontal gyrus (DSFG) and DC in left inferior temporal gyrus (ITG), and decreased GM volumes in left temporal lobe and right Amygdala. Among these altered indicators, ReHo value in left DSFG and DC values in left ITG and left DSFG were significantly correlated with the severity of depression in PD patients. Comparing DPD and NDPD, the ROC analysis revealed a better area under the curve value for the combination of ReHo value in left DSFG and DC value in left ITG, followed by each independent indicator. However, the difference is not statistically significant.

Conclusion

This study demonstrates that both functional and structural impairments are present in DPD. Among them, ReHo value of left DSFG and DC value of left ITG are equally well suited for the diagnosis and differential diagnosis of DPD, with a combination of them being slightly preferable. The multimodal MRI technique represents a promising approach for the classification of subjects with PD.

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 &lt; 0.05, Voxel size &gt; 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 &lt; 0.05, voxel size &gt; 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.

Abnormal cortical atrophy and functional connectivity are associated with depression in Parkinson’s disease

Objective

This study aimed to investigate the association of altered cortical thickness and functional connectivity (FC) with depression in Parkinson’s disease (PD).

Materials and methods

A total of 26 non-depressed PD patients (PD-ND), 30 PD patients with minor depression (PD-MnD), 32 PD patients with major depression (PD-MDD), and 30 healthy controls (HC) were enrolled. Differences in cortical thickness among the four groups were assessed, and the results were used to analyze FC differences in regions of cortical atrophy. Binary logistic regression and receiver operating characteristic (ROC) curve analyses were also performed to identify clinical features and neuroimaging biomarkers that might help in the prediction of PD-MDD.

Results

Patients with PD-MDD showed decreased cortical thickness compared to patients with PD-ND in the left superior temporal and right rostral middle frontal gyri (RMFG), as well as weak FC between the left superior temporal gyrus and right cerebellum posterior lobe and between right RMFG and right inferior frontal gyrus and insula. The combination of cortical thickness, FC, and basic clinical features showed strong potential for predicting PD-MDD based on the area under the ROC curve (0.927, 95% CI 0.854–0.999, p < 0.001).

Conclusion

Patients with PD-MDD show extensive cortical atrophy and FC alterations, suggesting that cortical thickness and FC may be neuroimaging-based diagnostic biomarkers for PD-MDD.

Altered voxel-level whole-brain functional connectivity in multiple system atrophy patients with depression symptoms

BACKGROUND

It is yet unknown if the whole-brain resting-state network is altered in multiple system atrophy with symptoms of depression. This study aimed to investigate if and how depression symptoms in multiple system atrophy are associated with resting-state network dysfunction.

METHODS

We assessed the resting-state functional network matric using Degree centrality (DC) coupling with a second ROI-wise functional connectivity (FC) algorithm in a multimodal imaging case-control study that enrolled 32 multiple system atrophy patients with depression symptoms (MSA-D), 30 multiple system atrophy patients without depression symptoms (MSA-ND), and 34 healthy controls (HC).

RESULTS

Compared to HC, MSA-D showed more extensive DC hub dysfunction in the left precentral and right middle frontal cortex than MSA-ND. A direct comparison between MSA-D and MSA-ND detected increased DC in the right anterior cingulum cortex, but decreased DC in the left cerebellum lobule IV and lobule V, left middle pole temporal cortex, and right superior frontal cortex. Only right anterior cingulum cortex mean DC values showed a positive correlation with depression severity, and used ACC as seed, a second ROI-wise functional connectivity further revealed MSA-D patients showed decreased connectivity between the ACC and right thalamus and right middle temporal gyrus (MTG).

CONCLUSIONS

These findings revealed that dysfunction of rACC, right middle temporal lobe and right thalamus involved in depressed MSA. Our study might help to the understanding of the neuropathological mechanism of depression in MSA.

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson’s Disease

Background

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. 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.

Methods

We 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.

Results

We 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.

Conclusion

Our 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.

Altered Dynamic Functional Connectivity in de novo Parkinson’s Disease Patients With Depression

Background

Depression is one of the most prevalent and disturbing non-motor symptoms in Parkinson’s disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics in de novo patients with PD with depression (dPD).

Methods

We performed dFC analysis on the data of resting-state functional MRI from 21 de novo dPD, 34 de novo patients with PD without depression (ndPD), and 43 healthy controls (HCs). Group independent component analysis, a sliding window approach, followed by k-means clustering were conducted to assess functional connectivity states (which represented highly structured connectivity patterns reoccurring over time) and temporal properties for comparison between groups. We further performed dynamic graph-theoretical analysis to examine the variability of topological metrics.

Results

Four distinct functional connectivity states were clustered via dFC analysis. Compared to patients with ndPD and HCs, patients with dPD showed increased fractional time and mean dwell time in state 2, characterized by default mode network (DMN)-dominated and cognitive executive network (CEN)-disconnected patterns. Besides, compared to HCs, patients with dPD and patients with ndPD both showed weaker dynamic connectivity within the sensorimotor network (SMN) in state 4, a regionally densely connected state. We additionally observed that patients with dPD presented less variability in the local efficiency of the network.

Conclusions

Our study demonstrated that altered network connection over time, mainly involving the DMN and CEN, with abnormal dynamic graph properties, may contribute to the presence of depression in patients with PD.

Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

The altered functional modular organization in systemic lupus erythematosus: an independent component analysis study

The aim of this study was to investigate the abnormities in functional connectivity (FC) within each modular network and between modular networks in patients with systemic lupus erythematosus (SLE). Twelve meaningful modular networks were identified via independent component analysis from 41 patients and 40 volunteers. Parametric tests were used to compare the intra- and intermodular FC between the groups. Partial correlation analysis was used to seek the relationships between abnormal FCs and the clinical data. Compared to the controls, SLE patients showed decreased intramodular FC in the anterior default mode network (aDMN), posterior default mode network (pDMN), ventral attention network (VAN), and sensorimotor network (SMN) and increased intramodular FC in the medial visual network (mVN) and left frontoparietal network. In addition, SLE patients showed decreased intermodular FC between the SMN and the lateral visual network (lVN), between the SMN and the VAN, and between the pDMN and the lVN and exhibited increased intermodular FC between the SMN and the salience network (SAN), between the pDMN and the SAN, and between the aDMN and the VAN. Moreover, we found several correlations among the abnormal FCs and the Mini-Mental State Examination in SLE patients. Mild cognitive impairment is compensated by the hyperconnectivity between the aDMN and the VAN, while severe cognitive impairment tends to be compensated by the hyperconnectivity between the SMN and the SAN. The FC value between the SMN and the SAN and between the aDMN and the VAN may serve as neuroimaging markers for monitoring cognitive progression in SLE patients.

Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis

INTRODUCTION

The current diagnosis of Parkinson's disease (PD) comorbidity with depression (DPD) largely depends on clinical evaluation. However, the modality may tend to lack precision in detecting PD with depression. A radiomic approach that combines functional connectivity and activity with clinical scores has the potential to achieve accurate and differential diagnosis between PD and DPD.

METHODS

In this study, we aimed to employ the radiomic approach to extract large-scale features of functional connectivity and activity for differentiating among DPD, PD with no depression (NDPD), and healthy controls (HC). We extracted 6,557 features of five types from all subjects including clinical characteristics, resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and voxel-mirrored homotopic connectivity (VMHC). Lasso, random forest, and support vector machine (SVM) were implemented for feature selection and dimension reduction based on the training sets, and the prediction performance for different methods in the testing sets was compared.

RESULTS

The results showed that nineteen features were selected for the group of DPD versus HC, 34 features were selected for the group of NDPD versus HC, and 17 features were retained for the group of DPD versus NDPD. In the testing sets, Lasso prediction achieved the accuracies of 0.95, 0.96, and 0.85 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. Random forest achieved the accuracies of 0.90, 0.82, and 0.90 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively, while SVM yielded the accuracies of 1, 0.86 and 0.65 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively.

CONCLUSIONS

By identifying aberrant functional connectivity and activity as potential biomarkers, the radiomic approach facilitates a deeper understanding and provides new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.

Dynamic functional connectivity changes in Parkinson's disease patients with REM sleep behavior disorder

BACKGROUND

Rapid eye movement (REM) sleep behavior disorder (RBD) is one of the common nonmotor symptoms of Parkinson's disease (PD), characterized by frequently occurring REM sleep without muscle atonia. Our aim was to explore dynamic network connection changes in PD patients with RBD.

METHOD

On the basis of RBD screening questionnaire (RBDSQ), 126 PD patients were classified into those with probable RBD symptoms (PD-pRBD) and without probable RBD (PD-npRBD). We applied independent component analysis, sliding window approach and k-means clustering methods to clarify dynamic functional connectivity alterations.

RESULTS

In contrast to PD-npRBD, PD-pRBD patients were liable to engage in a brain pattern mainly marked by weaker positive couplings between visual network (VIS) and default mode network (DMN), DMN and basal ganglia network (BG), and within DMN (State IV). In addition, we discovered that both PD patients with or without pRBD had shorter dwell time and fewer occurrences in State III, characterized by positive correlations between VIS and DMN, BG and DMN, and positive within-network coupling of sensorimotor network (SMN), relative to healthy controls.

CONCLUSIONS

Our study suggested that the weaker positive couplings between VIS and DMN, DMN and BG, and within DMN in State IV could be involved in the pathogenesis of PD patients with probable RBD on an overall level.

Functional Connectome in Parkinson's Disease and Parkinsonism

PURPOSE OF REVIEW

There has been an exponential growth in functional connectomics research in neurodegenerative disorders. This review summarizes the recent findings and limitations of the field in Parkinson's disease (PD) and atypical parkinsonian syndromes.

RECENT FINDINGS

Increasingly more sophisticated methods ranging from seed-based to network and whole-brain dynamic functional connectivity have been used. Results regarding the disruption in the functional connectome vary considerably based on disease severity and phenotypes, and treatment status in PD. Non-motor symptoms of PD also link to the dysfunction in heterogeneous networks. Studies in atypical parkinsonian syndromes are relatively scarce. An important clinical goal of functional connectomics in neurodegenerative disorders is to establish the presence of pathology, track disease progression, predict outcomes, and monitor treatment response. The obstacles of reliability and reproducibility in the field need to be addressed to improve the potential of the functional connectome as a biomarker for these purposes in PD and atypical parkinsonian syndromes.

Distinct neural circuits are associated with subclinical neuropsychiatric symptoms in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) can present with neuropsychiatric symptoms (here, anxiety, depression, and apathy) at any stage of the disease. We investigated the neural correlates of subclinical neuropsychiatric symptoms in relation to motor and cognitive symptoms in a high-functioning PD cohort.

METHODS

Brain morphometry of the cognitively intact, early-stage (Hoehn & Yahr 2) PD group (n = 48) was compared to matched controls (n = 37). Whole-brain, pairwise, resting-state functional connectivity measures were correlated with neuropsychiatric symptom, motor exam, and global cognitive scores of the PD group.

RESULTS

Factor analysis of highly collinear anxiety, depression, and apathy scores revealed a single principal component (i.e., composite neuropsychiatric symptom score) explaining 71.6% of variance. There was no collinearity between the neuropsychiatric, motor, and cognitive scores. Compared to controls, PD group showed only subcortical changes including amygdala and nucleus accumbens atrophy, and greater pallidal volume. Reduced functional connectivity in the limbic cortical-striatal circuits and increased functional connectivity between the cerebellum and occipito-temporal regions were associated with a more impaired neuropsychiatric profile. This functional connectivity pattern was distinct from those associated with motor deficits and global cognitive functioning. The individual components of the neuropsychiatric symptoms also exhibited unique connectivity patterns.

LIMITATIONS

Patients were scanned in "on-medication" state only and a control group with similar neuropsychiatric symptoms was not included.

CONCLUSION

Abnormal functional connectivity of distinct neural circuits is present even at the subclinical stage of neuropsychiatric symptoms in PD. Neuropsychiatric phenotyping is important and may facilitate early interventions to "reorganize" these circuits and delay/prevent clinical symptom onset.

Networks Are Associated With Depression in Patients With Parkinson's Disease: A Resting-State Imaging Study

Background

Disturbance of networks was recently proposed to be associated with the occurrence of depression in Parkinson's disease (PD). However, the neurobiological mechanism of depression underlying PD remains unclear.

Objective

This study was conducted to investigate whether intra-network and inter-network brain connectivity is differently changed in PD patients with and without depression (PDD and PDND patients, respectively).

Methods

Forty-one PDD patients, 64 PDND patients, and 55 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI). The default mode network (DMN), executive control network (ECN), salience network (SN), precuneus network (PCUN), and sensorimotor network (SMN) were extracted using independent component analysis (ICA), and then the functional connectivity (FC) values within and between these networks were measured.

Results

PDD patients exhibited abnormal FC values within the DMN, ECN, SN, PCUN, and SMN. In addition, PDD patients demonstrated decreased connectivity between anterior SN (aSN) and bilateral ECN, between posterior SN (pSN) and dorsal DMN (dDMN), and between PCUN and dDMN/SMN/bilateral ECN. Connectivity within the left hippocampus of dDMN and the right medial superior frontal gyrus of aSN was a significant predictor of depression level in PD patients.

Conclusions

Aberrant intra- and inter-network FC is involved in several important hubs in the large-scale networks, which can be a biomarker for distinguishing PDD from PDND.

Variable Selection Using Nonlocal Priors in High-Dimensional Generalized Linear Models With Application to fMRI Data Analysis

High-dimensional variable selection is an important research topic in modern statistics. While methods using nonlocal priors have been thoroughly studied for variable selection in linear regression, the crucial high-dimensional model selection properties for nonlocal priors in generalized linear models have not been investigated. In this paper, we consider a hierarchical generalized linear regression model with the product moment nonlocal prior over coefficients and examine its properties. Under standard regularity assumptions, we establish strong model selection consistency in a high-dimensional setting, where the number of covariates is allowed to increase at a sub-exponential rate with the sample size. The Laplace approximation is implemented for computing the posterior probabilities and the shotgun stochastic search procedure is suggested for exploring the posterior space. The proposed method is validated through simulation studies and illustrated by a real data example on functional activity analysis in fMRI study for predicting Parkinson’s disease.

Neuropsychiatric aspects of Parkinson disease psychopharmacology: Insights from circuit dynamics

Parkinson disease (PD) is a neurodegenerative disorder with a complex pathophysiology characterized by the progressive loss of dopaminergic neurons within the substantia nigra. Persons with PD experience several motoric and neuropsychiatric symptoms. Neuropsychiatric features of PD include depression, anxiety, psychosis, impulse control disorders, and apathy. In this chapter, we will utilize the National Institutes of Mental Health Research Domain Criteria (RDoC) to frame and integrate observations from two prevailing disease constructions: neurotransmitter anomalies and circuit physiology. When there is available evidence, we posit how unified translational observations may have clinical relevance and postulate importance outside of PD. Finally, we review the limited evidence available for pharmacologic management of these symptoms.

Quantitative Susceptibility Mapping and Resting State Network Analyses in Parkinsonian Phenotypes-A Systematic Review of the Literature

An imbalance of iron metabolism with consecutive aggregation of α-synuclein and axonal degeneration of neurons has been postulated as the main pathological feature in the development of Parkinson’s disease (PD). Quantitative susceptibility mapping (QSM) is a new imaging technique, which enables to measure structural changes caused by defective iron deposition in parkinsonian brains. Due to its novelty, its potential as a new imaging technique remains elusive for disease-specific characterization of motor and non-motor symptoms (characterizing the individual parkinsonian phenotype). Functional network changes associated with these symptoms are however frequently described for both magnetoencephalography (MEG) and resting state functional magnetic imaging (rs-fMRI). Here, we performed a systematic review of the current literature about QSM imaging, MEG and rs-fMRI in order to collect existing data about structural and functional changes caused by motor and non-motor symptoms in PD. Whereas all three techniques provide an effect in the motor domain, the understanding of network changes caused by non-motor symptoms is much more lacking for MEG and rs-fMRI, and does not yet really exist for QSM imaging. In order to better understand the influence of pathological iron distribution onto the functional outcome, whole-brain QSM analyses should be integrated in functional analyses (especially for the non-motor domain), to enable a proper pathophysiological interpretation of MEG and rs-fMRI network changes in PD. Herewith, a better understanding of the relationship between neuropathological changes, functional network changes and clinical phenotype might become possible.

Altered resting-state voxel-level whole-brain functional connectivity in depressed Parkinson's disease

BACKGROUND

Depression is one of the most common non-motor symptoms in Parkinson's disease (PD), but its pathogenesis is still not very clear. Recently, degree centrality, a voxel-level whole-brain functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging, has provided the most promising way to explore the neural network mechanisms underlying depressed PD.

METHODS

Degree centrality, voxel-wise image and clinical symptoms correlation and secondary seed-based FC analyses were performed in twenty-seven drug-naïve, early stage depressed PD patients, 27 non-depressed PD patients and 27 healthy controls (HCs) to reveal voxel-level whole-brain FC changes in depressed PD.

RESULTS

Compared with the HCs, depressed PD and non-depressed PD patients shared similar brain degree centrality abnormalities mainly in the basal ganglia, insular cortex, motor cortices, default mode network, prefrontal gyrus and the cerebellum. However, compared with non-depressed PD, depressed PD showed degree centrality abnormalities in the right middle prefrontal gyrus, anterior cingulate cortices, supplementary motor cortices and cerebellum lobule VI. The right middle prefrontal gyrus degree centrality abnormalities were correlated with the clinical depression severity, and using it as a seed, a secondary seed-based FC analysis further revealed the FC changes in the anterior cingulate cortices and the cerebellum lobule VI.

CONCLUSIONS

Our findings revealed that dysfunction in extensive brain areas were involved in depressed PD, and among these regions, the right middle prefrontal gyrus, anterior cingulate cortices and the cerebellum may pose as pathogenesis hubs underlying depressed PD.

Separate neural representations of depression, anxiety and apathy in Parkinson's disease

Depression, anxiety and apathy are distinct neuropsychiatric symptoms that highly overlap in Parkinson’s disease (PD). It remains unknown whether each symptom is uniquely associated with a functional network dysfunction. Here, we examined whether individual differences in each neuropsychiatric symptom predict functional connectivity patterns in PD patients while controlling for all other symptoms and motor function. Resting-state functional connectivity MRI were acquired from 27 PD patients and 29 healthy controls. Widespread reduced functional connectivity was identified in PD patients and explained by either the neuropsychiatric or motor symptoms. Depression in PD predicted increased functional connectivity between the orbitofrontal, hippocampal complex, cingulate, caudate and thalamus. Apathy in PD predicted decreased caudate-thalamus and orbitofrontal-parahippocampal connectivity. Anxiety in PD predicted three distinct types of functional connectivity not described before: (i) increased limbic-orbitofrontal cortex; (ii) decreased limbic-dorsolateral prefrontal cortex and orbitofrontal-dorsolateral prefrontal cortices and (iii) decreased sensorimotor-orbitofrontal cortices. The first two types of functional connectivity suggest less voluntary and more automatic emotion regulation. The last type is argued to be specific to PD and reflect an impaired ability of the orbitofrontal cortex to guide goal-directed motor actions in anxious PD patients.