Impaired interhemispheric synchrony in Parkinson's disease with depression

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.

Brain Activation Evoked by Motor Imagery in Pediatric Patients with Complete Spinal Cord Injury

BACKGROUND AND PURPOSE

Currently, there is no effective treatment for pediatric patients with complete spinal cord injury. Motor imagery has been proposed as an alternative to physical training for patients who are unable to move voluntarily. Our aim was to reveal the potential mechanism of motor imagery in the rehabilitation of pediatric complete spinal cord injury.

MATERIALS AND METHODS

Twenty-six pediatric patients with complete spinal cord injury and 26 age- and sex-matched healthy children as healthy controls were recruited. All participants underwent the motor imagery task-related fMRI scans, and additional motor execution scans were performed only on healthy controls. First, we compared the brain-activation patterns between motor imagery and motor execution in healthy controls. Then, we compared the brain activation of motor imagery between the 2 groups and compared the brain activation of motor imagery in pediatric patients with complete spinal cord injury and that of motor execution in healthy controls.

RESULTS

In healthy controls, compared with motor execution, motor imagery showed increased activation in the left inferior parietal lobule and decreased activation in the left supplementary motor area, paracentral lobule, middle cingulate cortex, and right insula. In addition, our results revealed that the 2 groups both activated the bilateral supplementary motor area, middle cingulate cortex and left inferior parietal lobule, and supramarginal gyrus during motor imagery. Compared with healthy controls, higher activation in the bilateral paracentral lobule, supplementary motor area, putamen, and cerebellar lobules III-V was detected in pediatric complete spinal cord injury during motor imagery, and the activation of these regions was even higher than that of healthy controls during motor execution.

CONCLUSIONS

Our study demonstrated that part of the motor imagery network was functionally preserved in pediatric complete spinal cord injury and could be activated through motor imagery. In addition, higher-level activation in sensorimotor-related regions was also found in pediatric complete spinal cord injury during motor imagery. Our findings may provide a theoretic basis for the application of motor imagery training in pediatric complete spinal cord injury.

The pathobiological basis of depression in Parkinson disease: challenges and outlooks

Depression, with an estimated prevalence of about 40% is a most common neuropsychiatric disorder in Parkinson disease (PD), with a negative impact on quality of life, cognitive impairment and functional disability, yet the underlying neurobiology is poorly understood. Depression in PD (DPD), one of its most common non-motor symptoms, can precede the onset of motor symptoms but can occur at any stage of the disease. Although its diagnosis is based on standard criteria, due to overlap with other symptoms related to PD or to side effects of treatment, depression is frequently underdiagnosed and undertreated. DPD has been related to a variety of pathogenic mechanisms associated with the underlying neurodegenerative process, in particular dysfunction of neurotransmitter systems (dopaminergic, serotonergic and noradrenergic), as well as to disturbances of cortico-limbic, striato-thalamic-prefrontal, mediotemporal-limbic networks, with disruption in the topological organization of functional mood-related, motor and other essential brain network connections due to alterations in the blood-oxygen-level-dependent (BOLD) fluctuations in multiple brain areas. Other hypothetic mechanisms involve neuroinflammation, neuroimmune dysregulation, stress hormones, neurotrophic, toxic or metabolic factors. The pathophysiology and pathogenesis of DPD are multifactorial and complex, and its interactions with genetic factors, age-related changes, cognitive disposition and other co-morbidities awaits further elucidation.

LPS activates neuroinflammatory pathways to induce depression in Parkinson’s disease-like condition

Aim: This study aimed to observe the effects of lipopolysaccharide (LPS) intraperitoneal (i.p.) injection on rats and investigate how neuroinflammation contributes to the pathogenesis of depression in Parkinson’s disease (dPD).

Methods: Rats were administered LPS (0.5 mg/kg, i.p.) for either 1, 2, or 4 consecutive days to establish a rat model of dPD. The sucrose preference test (SPT), the open field test (OFT), and the rotarod test evaluated depression-like and motor behaviors. Magnetic resonance imaging was used to detect alterations in the intrinsic activity and the integrity of white matter fibers in the brain. The expression of c-Fos, ionized calcium-binding adapter molecule (Iba-1), and tyrosine hydroxylase (TH) was evaluated using immunohistochemistry. The concentration of interleukin-6 (IL-6), tumor necrosis factor (TNF-α), and interleukin-10 (IL-10) was measured using Luminex technology.

Results: LPS i.p. injections decreased sucrose preference in the SPT, horizontal and center distance in the OFT, and standing time in the rotarod test. The intrinsic activities in the hippocampus (HIP) were significantly reduced in the LPS-4 d group. The integrity of white matter fibers was greatly destroyed within 4 days of LPS treatment. The expression of c-Fos and Iba-1 in the prefrontal cortex, HIP, and substantia nigra increased dramatically, and the number of TH⁺ neurons in the substantia nigra decreased considerably after LPS injection. The levels of IL-6, TNF-α, and IL-10 were higher in the LPS-4 d group than those in the control group.

Conclusion: Injection of LPS (0.5 mg/kg, i.p.) for 4 consecutive days can activate microglia, cause the release of inflammatory cytokines, reduce intrinsic activities in the HIP, destroy the integrity of white matter fibers, induce anhedonia and behavioral despair, and finally lead to dPD. This study proved that LPS injection (0.5 mg/kg, i.p.) for 4 consecutive days could be used to successfully create a rat model of dPD.

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.

The Local Topological Reconfiguration in the Brain Network After Targeted Hub Dysfunction Attacks in Patients With Juvenile Myoclonic Epilepsy

The central brain regions of brain networks have been extensively studied in terms of their roles in various diseases. This study provides a direct measure of the brain's responses to targeted attacks on central regions, revealing the critical role these regions play in patients with juvenile myoclonic epilepsy (JME). The resting-state data of 37 patients with JME and 37 healthy subjects were collected, and brain functional networks were constructed for the two groups of data according to their Pearson correlation coefficients. The left middle cingulate gyrus was defined as the central brain region by the eigenvector centrality algorithm and was attacked by the CLM sequential failure model. The rich-club connection differences between the patients with JME and healthy controls before and after the attacks were compared according to graph theory indices and the number of rich-club connections. We found that the numbers of rich connections in the brain networks of the healthy control group and the group of patients with JME were significantly reduced [p < 0.05, false discovery rate (FDR) correction] before the CLM sequential failure attacks, and no significant differences were observed between the feeder connections and local connections. In the healthy control group, significant rich connection differences were obtained (p < 0.01, FDR correction), and no statistically significant differences were observed regarding the feeder connections and local connections in the brain network before and after CLM failure attacks on the central brain region. No significant differences were obtained between the rich connections, feeder connections, and local connections in patients with JME before and after CLM successive failure attacks on the central brain area. The rich connections, feeder connections, and local connections were not significantly different in the brain networks of the healthy control group and the group of patients with JME after CLM successive failure attacks on the central brain region. We concluded that the damage to the left middle cingulate gyrus is closely linked to various brain disorders, suggesting that this region is of great importance for understanding the pathophysiological basis of myoclonic seizures in patients with JME.

Altered Interhemispheric Functional Connectivity Associated With Early Verbal Fluency Decline After Deep Brain Stimulation in Parkinson’s Disease

Background

Patients with Parkinson’s disease (PD) experience a decline in verbal fluency (VF) immediately after undergoing deep brain stimulation (DBS) of the subthalamic nucleus (STN). This phenomenon is thought to be related to surgical microlesions.

Purpose

We investigated the alterations in interhemispheric functional connectivity after STN-DBS in PD patients. We also evaluated the correlation between these changes and decreased VF scores.

Method

Overall, 30 patients with PD were enrolled in the study. Resting-state functional magnetic resonance imaging scans were performed twice, once before and once after DBS, in PD patients. Voxel-mirrored homotopic connectivity (VMHC) was applied in order to evaluate the synchronicity of functional connectivity between the hemispheres.

Result

After undergoing STN-DBS, PD patients demonstrated reduced VMHC value in the posterior cerebellum lobe, angular gyrus, precuneus/posterior cingulate gyrus (PCC), supramarginal gyrus, superior frontal gyrus (SFG) (medial and dorsolateral) and middle frontal gyrus (MFG). In addition, we observed a significant positive correlation between the altered VMHC value in the SFG and MFG and the change of phonemic VF scores.

Conclusion

PD patients demonstrated an interhemispheric coordination disorder in the prefrontal cortex, cerebellum, supramarginal gyrus and DMN after undergoing STN-DBS. The positive correlation between reduced VMHC value in the SFG and MFG and the changes of VF scores provides a novel understanding with regard to the decline of VF after DBS.

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.

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.

Striatal Subdivisions Estimated via Deep Embedded Clustering With Application to Parkinson's Disease

Recent fMRI connectivity-based parcellation (CBP) methods have been developed to obtain homogeneous and functionally coherent brain parcels. However, most of these studies utilize traditional clustering methods that neglect hidden nonlinear features. To enhance parcellation performance, here we propose a deep embedded connectivity-based parcellation (DECBP) framework and apply it to determine functional subdivisions of the striatum in public resting state fMRI data sets. This framework integrates fMRI connectivity features into deep embedded clustering (DEC), a deep neural network based on a stacked autoencoder. Compared to three prevalent clustering methods and their combinations with principal component analysis (PCA), the DECBP exhibited a significantly higher similarity between scans, individuals, and groups, indicating enhanced reproducibility. The generated reliable parcellations were also largely consistent with other public atlases. We further explored the functional subunits in the striatum in a data set from 23 Parkinson's disease (PD) subjects and 27 age-matched healthy controls (HC). All putaminal subregions of PD demonstrated lower interhemispheric connectivity than those of HC, which might reflect imbalance in the pathological progression of PD. Such hypo-connectivity was also observed between putaminal subregions and other brain regions, reflecting neuroimaging manifestations of the altered cortico-striato-thalamo-cortical circuit. These observed weaker couplings were associated with PD severity and duration. Our results support the utilization of the DECBP framework and suggest that abnormal connectivity in putaminal subregions may be a potential indicator of PD.

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.

Functional plasticity abnormalities over the lifespan of first-episode patients with major depressive disorder: a resting state fMRI study

Background

Neurodevelopmental and neurodegenerative theories of depression suggest that patients with major depressive disorder (MDD) may follow abnormal developmental, maturational, and aging processes. However, a lack of lifespan studies has precluded verification of these theories. Herein, we analyzed functional magnetic resonance imaging (fMRI) data to comprehensively characterize age-related functional trajectories, as measured by the fractional amplitude of low frequency fluctuations (fALFF), over the course of MDD.

Methods

In total, 235 MDD patients with age-differentiated onsets and 235 age- and sex-matched healthy controls (HC) were included in this study. We determined the pattern of age-related fALFF changes by cross-sectionally establishing the general linear model (GLM) between fALFF and age over a lifespan. Furthermore, the subjects were divided into four age groups to assess age-related neural changes in detail. Inter-group fALFF comparison (MDD vs. HC) was conducted in each age group and Granger causal analysis (GCA) was applied to investigate effective connectivity between regions.

Results

Compared with the HC, no significant quadratic or linear age effects were found in MDD over the entire lifespan, suggesting that depression affects the normal developmental, maturational, and degenerative process. Inter-group differences in fALFF values varied significantly at different ages of onset. This implies that MDD may impact brain functions in a highly dynamic way, with different patterns of alterations at different stages of life. Moreover, the GCA analysis results indicated that MDD followed a distinct pattern of effective connectivity relative to HC, and this may be the neural basis of MDD with age-differentiated onsets.

Conclusions

Our findings provide evidence that normal developmental, maturational, and ageing processes were affected by MDD. Most strikingly, functional plasticity changes in MDD with different ages of onset involved dynamic interactions between neuropathological processes in a tract-specific manner.

Alterations in intrinsic functional networks in Parkinson's disease patients with depression: A resting-state functional magnetic resonance imaging study

Aims

The aim of this research was to investigate the alterations in functional brain networks and to assess the relationship between depressive impairment and topological network changes in Parkinson's disease (PD) patients with depression (DPD).

Methods

Twenty‐two DPD patients, 23 PD patients without depression (NDPD), and 25 matched healthy controls (HCs) were enrolled. All participants were examined by resting‐state functional magnetic resonance imaging scans. Graph theoretical analysis and network‐based statistic methods were used to analyze brain network topological properties and abnormal subnetworks, respectively.

Results

The DPD group showed significantly decreased local efficiency compared with the HC group (P = .008, FDR corrected). In nodal metrics analyses, the degree of the right inferior occipital gyrus (P = .0001, FDR corrected) was positively correlated with the Hamilton Depression Rating Scale scores in the DPD group. Meanwhile, the temporal visual cortex, including the bilateral middle temporal gyri and right inferior temporal gyrus in the HC and NDPD groups and the left posterior cingulate gyrus in the NDPD group, was defined as hub region, but not in the DPD group. Compared with the HC group, the DPD group had extensive weakening of connections between the temporal‐occipital visual cortex and the prefrontal‐limbic network.

Conclusions

These results suggest that PD depression is associated with disruptions in the topological organization of functional brain networks, mainly involved the temporal‐occipital visual cortex and the posterior cingulate gyrus and may advance our current understanding of the pathophysiological mechanisms underlying DPD.

Alterations of Interhemispheric Functional Connectivity in Parkinson's Disease With Depression: A Resting-State Functional MRI Study

BACKGROUND

Depression is the most common non-motor symptom in patients with Parkinson's disease (PD) with unknown mechanisms, but the diagnostic criteria of PD with depression (PDD) are not uniform.

PURPOSE

The aim of the study was to investigate interhemispheric interactions between PDD patients and patients with PD without depression (PDND).

METHODS

The voxel-mirrored homotopic connectivity (VMHC) combined with the seed-based method was used to investigate intrinsic resting-state functional connectivity (RSFC) in 33 PDD patients, 60 PDND, and 47 healthy controls (HCs).

RESULTS

PDD patients exhibited a decreased VMHC in the bilateral medial frontal gyrus and paracentral lobule (MFG/PCL) than did PDND patients. Parkinson's disease with depression had a decreased VMHC in the bilateral precentral gyrus than had PDND and HC (p < 0.05). Parkinson's disease with depression had a decreased homotopic RSFC from the medial frontal gyrus (MFG)/PCL to the contralateral supplementary motor area (SMA) than had PDND (p < 0.05). The decreased homotopic RSFC from the right MFG/PCL to the left SMA was negatively correlated with Hamilton Depression Rating Scale scores (p < 0.05), but not with illness duration, Beck's Depression Inventory, and Unified Parkinson's Disease Rating Scale in PD patients.

CONCLUSIONS

Our findings indicated that the occurrence of depression in Parkinson's disease is associated with the dysfunctional connectivity from the MFG/PCL to the contralateral SMA, which could be used as potential neuroimaging markers for the diagnosis of depression in PD patients.

Bayesian group selection in logistic regression with application to MRI data analysis

We consider Bayesian logistic regression models with group-structured covariates. In high-dimensional settings, it is often assumed that only a small portion of groups are significant, and thus, consistent group selection is of significant importance. While consistent frequentist group selection methods have been proposed, theoretical properties of Bayesian group selection methods for logistic regression models have not been investigated yet. In this paper, we consider a hierarchical group spike and slab prior for logistic regression models in high-dimensional settings. Under mild conditions, we establish strong group selection consistency of the induced posterior, which is the first theoretical result in the Bayesian literature. Through simulation studies, we demonstrate that the proposed method outperforms existing state-of-the-art methods in various settings. We further apply our method to a magnetic resonance imaging data set for predicting Parkinson's disease and show its benefits over other contenders.

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.

Brain structural correlates of depressive symptoms in Parkinson's disease patients at different disease stage

Parkinson's disease (PD) pathology may damage emotion circuit and cause depression. We investigated whether the neural basis of depressive symptoms varies at different PD stages. Seventy-six healthy controls (HC) and 98 PD patients (divided into early and middle stage groups) underwent brain magnetic resonance imaging (MRI) and general neuropsychological tests. Voxel-based morphometry and tract-based analysis were used to study the association between brain structural alterations and the Hamilton Depression Scale 17 Item (HAMD-17) scores in different groups. Comparing with HC group, PD patients showed widespread brain alterations in both gray and white matter. The HAMD-17 scores were positively correlated with GM volume in the right pre-central gyrus of early PD patients. In the middle stage group, HAMD-17 scores were positively correlated with GM volume in midbrain and right superior temporal gyrus, and negatively associated with GM volume in left anterior cingulate and superior frontal gyrus. In white matter analysis, The HAMD-17 scores were positively correlated with fractional anisotropy value of the bilateral inferior fronto-occipital fasciculus in the early stage group, but not the middle stage group. We concluded that the neural basis of depressive symptoms might be distinct in different stages of PD, implying the need for differential treatments.

Homotopic region connectivity during concussion recovery: A longitudinal fMRI study

OBJECTIVES

To (i) investigate alterations in homotopic functional connectivity (hfc) in concussed patients relative to healthy controls (HC) and to (ii) interrogate whether hfc in concussed patients normalized during the recovery process. The relationship between symptom recovery and change in hfc was assessed using post-hoc analyses.

METHODS

This study included 15 concussed patients (mean age = 39.1, SD = 10.1; sex: 13 females, 2 males) and 15 HC (mean age = 39.1, SD = 11.7; sex: 13 females, 2 males). Hfc patterns were interrogated using resting-state magnetic resonance imaging (rs-MRI) for 29 a priori selected pain-processing regions. Concussed patients underwent imaging at two time-points; at 1-month post-concussion (mean time following concussion: 28 days, SD = 9.5) and again at 5-months post-concussion (mean time following concussion: 121 days, SD = 13). At both time-points, symptoms associated with concussion were assessed using the Sports Concussion Assessment Tool (SCAT-3).

RESULTS

Concussed patients had significantly weaker hfc in the following six regions 1-month post-concussion compared to HC: middle cingulate, posterior insula, middle occipital, spinal trigeminal nucleus, precentral and the pulvinar. There were no regions of significantly stronger hfc in concussed patients relative to HC. Longitudinally, patients showed significant symptom recovery 5-months post-concussion and had significant strengthening of hfc patterns in seven homotopic ROIs: middle cingulate, posterior insula, middle occipital, secondary somatosensory area, spinal trigeminal nucleus, precentral, and the pulvinar. Post-hoc analyses indicated a significant negative correlation between somatosensory functional connectivity strengthening and symptom severity.

CONCLUSION

At 1-month post-concussion, patients had significantly weaker hfc in a number of pain-processing regions relative to HC. However, over a period of 5-months, region-pair connectivity showed significant recovery and normalization. Those patients with more successful symptom recovery at 5-months post-concussion had more functional somatosensory strengthening, suggesting an association between functional strengthening and post-concussion symptom recovery.

Imaging behavioural complications of Parkinson's disease

In addition to motor symptoms, behavioural complications are commonly found in patients with Parkinson's disease (PD). Behavioural complications, including depression, anxiety, apathy, impulse control disorder and psychosis, together have a large impact on PD patient's quality of life. Many neuroimaging studies using PET, SPECT and MRI techniques have been conducted to study the underlying neural mechanisms of PD pathogenesis and pathophysiology in relation to its behavioural complications. This review will survey these PET, SPECT and MRI studies to describe the current understanding of the neuro-chemical, functional and structural changes associated with behavioural complications in PD patients.

Neuropathology and pathogenesis of extrapyramidal movement disorders: a critical update-I. Hypokinetic-rigid movement disorders

Extrapyramidal movement disorders include hypokinetic rigid and hyperkinetic or mixed forms, most of them originating from dysfunction of the basal ganglia (BG) and their information circuits. The functional anatomy of the BG, the cortico-BG-thalamocortical, and BG-cerebellar circuit connections are briefly reviewed. Pathophysiologic classification of extrapyramidal movement disorder mechanisms distinguish (1) parkinsonian syndromes, (2) chorea and related syndromes, (3) dystonias, (4) myoclonic syndromes, (5) ballism, (6) tics, and (7) tremor syndromes. Recent genetic and molecular-biologic classifications distinguish (1) synucleinopathies (Parkinson's disease, dementia with Lewy bodies, Parkinson's disease-dementia, and multiple system atrophy); (2) tauopathies (progressive supranuclear palsy, corticobasal degeneration, FTLD-17; Guamian Parkinson-dementia; Pick's disease, and others); (3) polyglutamine disorders (Huntington's disease and related disorders); (4) pantothenate kinase-associated neurodegeneration; (5) Wilson's disease; and (6) other hereditary neurodegenerations without hitherto detected genetic or specific markers. The diversity of phenotypes is related to the deposition of pathologic proteins in distinct cell populations, causing neurodegeneration due to genetic and environmental factors, but there is frequent overlap between various disorders. Their etiopathogenesis is still poorly understood, but is suggested to result from an interaction between genetic and environmental factors. Multiple etiologies and noxious factors (protein mishandling, mitochondrial dysfunction, oxidative stress, excitotoxicity, energy failure, and chronic neuroinflammation) are more likely than a single factor. Current clinical consensus criteria have increased the diagnostic accuracy of most neurodegenerative movement disorders, but for their definite diagnosis, histopathological confirmation is required. We present a timely overview of the neuropathology and pathogenesis of the major extrapyramidal movement disorders in two parts, the first one dedicated to hypokinetic-rigid forms and the second to hyperkinetic disorders.

Anxiety in Parkinson's disease is associated with reduced structural covariance of the striatum

BACKGROUND

Anxiety is highly prevalent in Parkinson's disease (PD) and has great negative impact on quality of life. Functional and structural neuroimaging studies have contributed to our understanding of the symptomatology of PD but still little is known about the pathophysiology of PD-related anxiety.

METHODS

We used seed-based structural covariance analysis to study the anatomical network correlates of anxiety in PD. Structural covariance analysis is based on the statistical correlation between regional brain volumes measured on T1-weighted magnetic resonance images. We investigated the association between anxiety symptoms, as measured by the Beck Anxiety Inventory (BAI), and seed-to-whole-brain structural covariance networks in 115 patients with idiopathic PD using five bilateral seeds: basolateral amygdala, centromedial-superficial amygdala, dorsal caudate nucleus, dorsal-caudal putamen, and nucleus accumbens.

RESULTS

Severity of anxiety correlated negatively with structural covariance between the left striatal sub-regions and the contralateral caudate nucleus. Moreover, severity of anxiety was associated with reduced structural covariance between the right dorsal caudate nucleus and ipsilateral ventrolateral prefrontal cortex and between the left nucleus accumbens and ipsilateral dorsolateral prefrontal cortex. Structural covariance of the amygdalar seeds did not correlate with anxiety.

CONCLUSIONS

We interpret these findings as a reduced interhemispheric cooperation between the left and right striatum and reduced prefrontal-striatal connectivity, possibly related to impaired 'top-down' regulation of emotions. These findings shed more light on the pathophysiology of PD-related anxiety LIMITATIONS: This study did not include PD patients with an anxiety disorder.

Structural Brain Network Alteration and its Correlation With Structural Impairments in Patients With Depression in de novo and Drug-Naïve Parkinson's Disease

Purpose: Depression is common in Parkinson's disease (PD) and is correlated with the severity of motor deficits and quality of life. The present study aimed to investigate alterations in the structural brain network related to depression in Parkinson's disease (d-PD) and their correlations with structural impairments of white matter (WM).

Materials and Methods: Data were acquired from the Parkinson Progression Markers Initiative (PPMI) database. A total of 84 de novo and drug-naïve PD patients were screened and classified into two groups according to the 15-item Geriatric Depression Scale (GDS-15): d-PD (n = 28) and nondepression in PD (nd-PD, n = 56). Additionally, 37 healthy controls (HC) were screened. All subjects underwent DTI and 3D-T₁WI on a 3.0 T MR scanner. Individual structural brain networks were constructed and analyses were performed using graph theory and network-based statistics (NBS) at both global and local levels. Differences in global topological properties were explored among the three groups. The association models between node and edge changes and the GDS-15 were constructed to detect regions that were specifically correlated with d-PD. Tract-based spatial statistics (TBSS) was used to detect structural impairments of WM between the d-PD and nd-PD groups. The correlations between altered global topological properties and structural impairments were analyzed in the d-PD group.

Results: The global efficiency and characteristic path length of the structural brain network were impaired in the d-PD group compared with those in the nd-PD and HC groups. Thirteen nodes and 1 subnetwork with 10 nodes and 12 edges specifically correlated with d-PD were detected. The left hippocampus, left parahippocampal, left lingual, left middle occipital, left inferior occipital, left fusiform, left middle temporal, and left inferior temporal regions were all involved in the results of node and edge analysis. No WM microstructural impairments were identified in the d-PD group.

Conclusion: Our study suggests that the integration of the structural brain network is impaired with disrupted connectivity of limbic system and visual system in the de novo and drug-naïve d-PD patients.The topological properties assessing integration of the structural brain network can serve as a potential objective neuroimaging marker for early diagnosis of d-PD.

Impaired long contact white matter fibers integrity is related to depression in Parkinson's disease

AIMS

Depression is one of the most common nonmotor symptoms in Parkinson's disease (PD). But the pathogenesis is still unclear. Studies have shown that depression in PD is closely related to the white matter abnormalities, but the number of studies is still very small and lack of whole brain white matter lesions study.

METHODS

In this study, we investigated whole brain white matter integrity in 31 depressed PD patients and 37 nondepressed PD patients by diffusion tensor imaging.

RESULTS

There was no difference in age, gender, age of onset, disease duration, Hoehn-Yahr scale, Unified Parkinson's Disease Rating Scale scores-III, and Mini-Mental State Examination scores between the two groups. The only difference was the Hamilton Depression Rating Scale. Depressed PD patients showed reduced fractional anisotropy values in the left anterior corona radiata, left posterior thalamic radiation, left cingulum, left superior longitudinal fasciculus, left sagittal stratum (including inferior longitudinal fasciculus and inferior fronto-occipital fasciculus), and left uncinate fasciculus. In patients with depression, the Hamilton Depression Rating Scale (HDRS) was negatively correlated with the FA value in the left cingulum (r = -0.712, P = .032) and left superior longitudinal fasciculus (r = -0.699, P = .025).

CONCLUSIONS

This study suggested depression in PD was related to impaired white matter integrity especially the long contact fibers in the left hemisphere. These findings may be helpful for further understanding the potential mechanisms underlying depression in PD.

Altered Functional and Causal Connectivity of Cerebello-Cortical Circuits between Multiple System Atrophy (Parkinsonian Type) and Parkinson's Disease

Lesions of the cerebellum lead to motor and non-motor deficits by influencing cerebral cortex activity via cerebello-cortical circuits. It remains unknown whether the cerebello-cortical “disconnection” underlies motor and non-motor impairments both in the parkinsonian variant of multiple system atrophy (MSA-P) and Parkinson’s disease (PD). In this study, we investigated both the functional and effective connectivity of the cerebello-cortical circuits from resting-state functional magnetic resonance imaging (rs-fMRI) data of three groups (26 MSA-P patients, 31 PD patients, and 30 controls). Correlation analysis was performed between the causal connectivity and clinical scores. PD patients showed a weakened cerebellar dentate nucleus (DN) functional coupling in the posterior cingulate cortex (PCC) and inferior parietal lobe compared with MSA-P or controls. MSA-P patients exhibited significantly enhanced effective connectivity from the DN to PCC compared with PD patients or controls, as well as declined causal connectivity from the left precentral gyrus to right DN compared with the controls, and this value is significantly correlated with the motor symptom scores. Our findings demonstrated a crucial role for the cerebello-cortical networks in both MSA-P and PD patients in addition to striatal-thalamo-cortical (STC) networks and indicated that different patterns of cerebello-cortical loop degeneration are involved in the development of the diseases.