White matter microstructure changes in the thalamus in Parkinson disease with depression: A diffusion tensor MR imaging study

Role of the globus pallidus in motor and non-motor symptoms of Parkinson's disease

The globus pallidus plays a pivotal role in the basal ganglia circuit. Parkinson's disease is characterized by degeneration of dopamine-producing cells in the substantia nigra, which leads to dopamine deficiency in the brain that subsequently manifests as various motor and non-motor symptoms. This review aims to summarize the involvement of the globus pallidus in both motor and non-motor manifestations of Parkinson's disease. The firing activities of parvalbumin neurons in the medial globus pallidus, including both the firing rate and pattern, exhibit strong correlations with the bradykinesia and rigidity associated with Parkinson's disease. Increased beta oscillations, which are highly correlated with bradykinesia and rigidity, are regulated by the lateral globus pallidus. Furthermore, bradykinesia and rigidity are strongly linked to the loss of dopaminergic projections within the cortical-basal ganglia-thalamocortical loop. Resting tremors are attributed to the transmission of pathological signals from the basal ganglia through the motor cortex to the cerebellum-ventral intermediate nucleus circuit. The cortico-striato-pallidal loop is responsible for mediating pallidi-associated sleep disorders. Medication and deep brain stimulation are the primary therapeutic strategies addressing the globus pallidus in Parkinson's disease. Medication is the primary treatment for motor symptoms in the early stages of Parkinson's disease, while deep brain stimulation has been clinically proven to be effective in alleviating symptoms in patients with advanced Parkinson's disease, particularly for the movement disorders caused by levodopa. Deep brain stimulation targeting the globus pallidus internus can improve motor function in patients with tremor-dominant and non-tremor-dominant Parkinson's disease, while deep brain stimulation targeting the globus pallidus externus can alter the temporal pattern of neural activity throughout the basal ganglia-thalamus network. Therefore, the composition of the globus pallidus neurons, the neurotransmitters that act on them, their electrical activity, and the neural circuits they form can guide the search for new multi-target drugs to treat Parkinson's disease in clinical practice. Examining the potential intra-nuclear and neural circuit mechanisms of deep brain stimulation associated with the globus pallidus can facilitate the management of both motor and non-motor symptoms while minimizing the side effects caused by deep brain stimulation.

Mediodorsal thalamus projection to medial prefrontal cortical mediates social defeat stress-induced depression-like behaviors

Clinical studies have shown that the mediodorsal thalamus (MD) may play an important role in the development of depression. However, the molecular and circuit mechanisms by which the mediodorsal thalamus (MD) participates in the pathological processes of depression remain unclear. Here, we show that in male chronic social defeat stress (CSDS) mice, the calcium signaling activity of glutamatergic neurons in MD is reduced. By combining conventional neurotracer and transneuronal virus tracing techniques, we identify a synaptic circuit connecting MD and medial prefrontal cortex (mPFC) in the mouse. Brain slice electrophysiology and fiber optic recordings reveal that the reduced activity of MD glutamatergic neurons leads to an excitatory-inhibitory imbalance of pyramidal neurons in mPFC. Furthermore, activation of MD glutamatergic neurons restores the electrophysiological properties abnormal in mPFC. Optogenetic activation of the MD-mPFC circuit ameliorates anxiety and depression-like behaviors in CSDS mice. Taken together, these data support the critical role of MD-mPFC circuit on CSDS-induced depression-like behavior and provide a potential mechanistic explanation for depression.

Thalamic segmentation based on diffusion tensor imaging in patients with trigeminal neuralgia

Classical trigeminal neuralgia (CTN) refers to episodic pain that is strictly confined to the trigeminal distribution area, and the thalamus is an important component of the trigeminal sensory pathway. Probabilistic tracking imaging algorithm was used to identify specific connections between the thalamus and the cortex, in order to identify structural changes in the thalamus of patients with CTN and perform thalamic segmentation. A total of 32 patients with CTN and 32 healthy controls underwent DTI-MRI scanning (3.0 T). Differences in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) between the groups were studied. Correlation analysis was performed with clinical course and pain level. Compared to the healthy controls, patients in the CTN group had significantly reduced FA, increased AD, RD and MD in somatosensory subregion of the bilateral thalamus, increased RD in frontal subregion, increased RD and MD in motor subregion. Correlation analysis showed that patient history was positively correlated with pain grading, and that medical history was positively correlated with significantly reduced FA in somatosensory subregion, negatively correlated with increased RD and MD in motor subregion. We used DTI-based probabilistic fiber tracking to discover altered structural connectivity between the thalamus and cerebral cortex in patients with CTN and to obtain a thalamic segmentation atlas, which will help to further understand the pathophysiology of CTN and serve as a future reference for thalamic deep brain stimulation electrode implantation for the treatment of intractable pain.

Volume of Enlarged White Matter of Thalamus among Individuals with Depression on Magnetic Resonance Image: A Study of BrainSuite Segmentation

Background

The thalamus, located in the diencephalon, regulates emotions and memories. If there is a problem in this area of the brain, it can cause an amnestic syndrome characterized by difficulties in remembering and recognizing things. The objective of this study was to identify changes in the volume of the thalamus while contrasting them among individuals with depression.

Materials and Methods

The study involved measuring the volumes of the white matter of the thalamus in 79 patients with depression (42 males and 37 females) between 20 and 40 years (24 ± 5.51). This was compared to a control group of 53 individuals (24 ± 4.91) consisting of 29 males and 24 females, who were comparable in terms of sex and age. The measurements were taken employing BrainSuite version 18a. 021 Win 64bit software on a Philips 1.5 Tesla Magnetom Avanto Vision System magnetic resonance imaging (MRI). The Magnetization Prepared Rapid Acquisition (MPRA) was utilized to acquire three-dimensional images with T1 weighting.

Results

The volume of white matter in the respective right and left thalamus was 5.09 cm3 and 4.58 cm3 (±standard deviation (SD) = 6.43 and 4.74) among individuals with depression. In the control group, the volume of white matter in the right and left thalamus was 3.66 cm3 and 4.16 cm3 (±SD = 3.99 and 5.06), respectively. The P-value is more than 0.05. The average volume of white matter in the right and left thalamus of females with depression and controls was 6.47 cm3 and 6.77 cm3 (with SD of 4.17 and 4.3), and 3.25 cm3 and 3.13 cm3 (with SD of 6.55 and 6.77), respectively.

Conclusions

Our data suggest that individuals with depression exhibit an augmentation in the white matter of the thalamus, particularly in female patients where there is an upsurge in white matter volume. Depression appears to be linked to a decrease in volume on the left side of the brain.

Physical Exercise for Treating the Anxiety and Depression Symptoms of Parkinson's Disease: Systematic Review and Meta-Analysis

BACKGROUND

Depression and anxiety are non-motor symptoms of Parkinson's disease (PD). Physical exercise is a promising approach to reducing neuropsychological burden. We aimed to comprehensively synthesize evidence regarding the use of exercise for treating depression and anxiety symptoms in PD.

METHODS

Systematic review and meta-analysis following PRISMA recommendations. Searches on PubMed, Cochrane Library, Scopus, Web of Science, Embase, and Physiotherapy Evidence Database (PEDro) was conducted. The random-effects model was employed for all analyses with the standardized mean difference as the effect estimate.

RESULTS

Fifty records were retrieved, but only 17 studies met the criteria for the meta-analyses. A moderate to large effect was observed for depression (-.71 [95% CI = -.96 to -.46], 11 studies, 728 individuals), and a small to moderate effect for anxiety (-.39 [95% CI = -.65 to -.14], 6 studies, 241 individuals), when comparing exercise to non-exercise controls. Subgroup analysis revealed significant effects from aerobic (-.95 [95% CI = -1.60, -.31]), mind-body (-1.85 [95% CI = -2.63, -1.07]), and resistance modalities (-1.61 [95% CI = -2.40, -.83]) for depression, and from mind-body (-.67 [95% CI = -1.19 to -.15]) and resistance exercises (-1.00 [95% CI = -1.70 to -.30]) for anxiety.

CONCLUSION

Physical exercise has a relevant clinical impact on depression and anxiety in PD. We discuss the level of the evidence, the methodological limitations of the studies, and give recommendations.

The habenula in Parkinson's disease: Anatomy, function, and implications for mood disorders - A narrative review

Parkinson's disease (PD), a widespread neurodegenerative disorder, often coexists with mood disorders. Degeneration of serotonergic neurons in brainstem raphe nuclei have been linked to depression and anxiety. Additionally, the locus coeruleus and its noradrenergic neurons are among the first areas to degenerate in PD and contribute to stress, emotional memory, motor, sensory, and autonomic symptoms. Another brain region of interest is habenula, which is especially related to anti-reward processing, and its function has recently been linked to PD and to mood-related symptoms. There are several neuroimaging studies that investigated role of the habenula in mood disorders. Differences in habenular size and hemispheric symmetry were found in healthy controls compared to individuals with mood disorders. The lateral habenula, as a link between the dopaminergic and serotonergic systems, is thought to contribute to depressive symptoms in PD. However, there is only one imaging study about role of habenula in mood disorders in PD, although the relationship between PD and mood disorders is known. There is little known about habenula pathology in PD but given these observations, the question arises whether habenular dysfunction could play a role in PD and the development of PD-related mood disorders. In this review, we evaluate neuroimaging techniques and studies that investigated the habenula in the context of PD and mood disorders. Future studies are important to understand habenula's role in PD patients with mood disorders. Thus, new potential diagnostic and treatment opportunities would be found for mood disorders in PD.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Fiber-specific white matter alterations in Parkinson's disease patients with freezing of gait

Freezing of gait (FOG) is a gait disorder that usually occurs in advanced stages of Parkinson's disease (PD). Understanding the underlying mechanism of FOG is important for treatment and prevention. Previous studies have investigated white matter (WM) structure to explore the pathology of FOG. However, the pathology is still unclear, possibly due to the methodological limitation in identifying specific fiber tracts. This study aimed to investigate tract-specific WM structural changes in FOG patients and their relationships with clinical characteristics. We enrolled 19 PD patients with FOG (PD-FOG), 19 without FOG (PD-woFOG) and 21 controls. Fixel-based analysis is a novel framework to avoid the effect of crossing fibers, which provides the metrics to assess WM morphology. By combining a method for segmenting fibers, we identified abnormalities in the specific fiber tracts. Compared to PD-woFOG, PD-FOG showed significant increased fiber-bundle cross-section (FC) in the corpus callosum (CC), fornix (FX), inferior longitudinal fasciculus (ILF), striato-premotor (ST_PREM), superior thalamic radiation (STR), thalamo-premotor (T_PREM), increased fiber density and cross-section (FDC) in the STR, and decreased fiber density (FD) in the CC and ILF. Additionally, the ILF was correlated with motor, cognition and memory, the CC was correlated with anxiety, and the T_PREM was also correlated with cognition. In conclusion, in addition to impairments of WM found in PD-FOG, we found enhancements in WM, which may imply compensatory mechanisms. Furthermore, multiple fiber tracts were correlated with clinical characteristics, especially the ILF, validating the involvement of transmission circuits of multiple distinct information in mechanisms of FOG.

Cingulum and Uncinate Fasciculus Microstructural Abnormalities in Parkinson's Disease: A Systematic Review of Diffusion Tensor Imaging Studies

Diffusion tensor imaging (DTI) is gaining traction in neuroscience research as a tool for evaluating neural fibers. The technique can be used to assess white matter (WM) microstructure in neurodegenerative disorders, including Parkinson disease (PD). There is evidence that the uncinate fasciculus and the cingulum bundle are involved in the pathogenesis of PD. These fasciculus and bundle alterations correlate with the symptoms and stages of PD. PRISMA 2022 was used to search PubMed and Scopus for relevant articles. Our search revealed 759 articles. Following screening of titles and abstracts, a full-text review, and implementing the inclusion criteria, 62 papers were selected for synthesis. According to the review of selected studies, WM integrity in the uncinate fasciculus and cingulum bundles can vary according to symptoms and stages of Parkinson disease. This article provides structural insight into the heterogeneous PD subtypes according to their cingulate bundle and uncinate fasciculus changes. It also examines if there is any correlation between these brain structures' structural changes with cognitive impairment or depression scales like Geriatric Depression Scale-Short (GDS). The results showed significantly lower fractional anisotropy values in the cingulum bundle compared to healthy controls as well as significant correlations between FA and GDS scores for both left and right uncinate fasciculus regions suggesting that structural damage from disease progression may be linked to cognitive impairments seen in advanced PD patients. This review help in developing more targeted treatments for different types of Parkinson's disease, as well as providing a better understanding of how cognitive impairments may be related to these structural changes. Additionally, using DTI scans can provide clinicians with valuable information about white matter tracts which is useful for diagnosing and monitoring disease progression over time.

Brain microstructural alterations of depression in Parkinson's disease: A systematic review of diffusion tensor imaging studies

Depression, a leading cause of disability worldwide, is also the most prevalent psychiatric problem among Parkinson disease patients. Both depression and Parkinson disease are associated with microstructural anomalies in the brain. Diffusion tensor imaging techniques have been developed to characterize the abnormalities in cerebral tissue. We included 11 studies investigating brain microstructural abnormalities in depressed Parkinson's disease patients. The included studies found alterations to essential brain structural networks, including impaired network integrity for specific cortical regions, such as the temporal and frontal cortices. Additionally, findings indicate that microstructural changes in specific limbic structures, such as the prefronto-temporal regions and connecting white matter pathways, are altered in depressed Parkinson's disease compared to non-depressed Parkinson's disease and healthy controls. There remain inconsistencies between studies reporting DTI measures and depression severity in Parkinson disease participants. Additional research evaluating underlying neurobiological relationships between major depression, depressed Parkinson's disease, and non-depressed Parkinson's disease is required to disentangle further mechanisms that underlie depression and related somatic symptoms, in Parkinson disease.

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.

Research on depression in Parkinson disease: A bibliometric and visual analysis of studies published during 2012-2021

BACKGROUND

The diagnosis and treatment rate of Parkinson disease (PD) with depression has a low diagnostic rate, and there is no consensus on the choice of treatment mode. This study evaluates the global research trends of scientific outputs related to depression in PD from multiple perspectives, using a bibliometric analysis and visualization tool to scientifically analyze the knowledge from the literature.

METHODS

Literature related to depression in PD published from 2012 to 2021 was included and selected from the Web of Science Core Collection database in October 2021. CiteSpace software was used to visualize and analyze co-occurrence analyses for countries, institutions, authors, and keywords.

RESULTS

A total of 4533 articles from the Web of Science database were included. The United States made the largest contribution with the majority of publications (1215; 29.40%). Toronto University was the most productive institution. PD, depression, quality of life, dementia, nonmotor symptom, prevalence, anxiety, Alzheimer disease, symptom, and disorder would be significantly correlated with depression in PD. The current hot spots in this field focus on the following: risk factors for depression in PD, assessment scale of depression in PD, and rehabilitation of depression in PD.

CONCLUSIONS

This analysis not only reveals the current research trends and hotspots but also provides some instructive suggestions on the development of depression in PD.

Identifying Depression in Parkinson's Disease by Using Combined Diffusion Tensor Imaging and Support Vector Machine

Objective

To investigate white matter microstructural alterations in Parkinson's disease (PD) patients with depression using the whole-brain diffusion tensor imaging (DTI) method and to explore the DTI–based machine learning model in identifying depressed PD (dPD).

Methods

The DTI data were collected from 37 patients with dPD and 35 patients with non-depressed PD (ndPD), and 25 healthy control (HC) subjects were collected as the reference. An atlas-based analysis method was used to compare fractional anisotropy (FA) and mean diffusivity (MD) among the three groups. A support vector machine (SVM) was trained to examine the probability of discriminating between dPD and ndPD.

Results

As compared with ndPD, dPD group exhibited significantly decreased FA in the bilateral corticospinal tract, right cingulum (cingulate gyrus), left cingulum hippocampus, bilateral inferior longitudinal fasciculus, and bilateral superior longitudinal fasciculus, and increased MD in the right cingulum (cingulate gyrus) and left superior longitudinal fasciculus-temporal part. For discriminating between dPD and ndPD, the SVM model with DTI features exhibited an accuracy of 0.70 in the training set [area under the receiver operating characteristic curve (ROC) was 0.78] and an accuracy of 0.73 in the test set (area under the ROC was 0.71).

Conclusion

Depression in PD is associated with white matter microstructural alterations. The SVM machine learning model based on DTI parameters could be valuable for the individualized diagnosis of dPD.

White matter damage in patients with mild cognitive impairment in Parkinson's disease

BACKGROUND

This study investigates the correlation between white matter structural damage and cognitive impairment in patients with Parkinson's disease (PD) and mild cognitive impairment (MCI).

METHODS

A total of 40 patients with PD were divided into two groups, i.e., a mild cognitive impairment (PD-MCI) and a normal cognitive (PDN) group, and 20 healthy patients were enrolled as the control group. Changes in the white matter structure of patients with PD were evaluated using diffusion tensor imaging (DTI), and cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) scale and Mini-Mental State Examination (MMSE). Finally, the correlations between the two groups were analyzed.

RESULTS

The Unified Parkinson's Disease Rating Scale score was significantly higher in the PD-MCI than in the PDN group (P=0.008). The total MoCA and MMSE scores in the PD-MCI group were significantly lower than in the PDN and control groups (P<0.01). Patients in PDMCI group were tested by MMSE scale, and the abnormal score rate was 60.0%. Among them, 8 PD patients with normal MMSE total score were found to have mild cognitive impairment by MoCA evaluation. When compared with the PDN and control groups, the MoCA scores for visual space, which is assessed as part of the MoCA scale and generally represents bilateral parietal function; naming; memory; and attention were significantly lower in the PD-MCI group (P<0.001). When compared with the PDN group, the fractional anisotropy (FA) values for the right parietal and left occipital lobes were significantly lower in the PD-MCI group (P=0.005; P=0.018). The relationship between MoCA value and right parietal white matter and left occipital white matter was 0.555, 0.474, respectively. A Pearson's correlation test was conducted to compare the FA values and MoCA scores of the various brain areas in the PD-MCI group and revealed a significant positive correlation between the MoCA score and the white matter of the right parietal and left occipital lobes (P<0.01).

CONCLUSIONS

Patients with PD experience early cognitive impairment, and the MoCA scale can be used for early screening. In addition, the DTI of white matter can highlight early white matter damage. In the current study, the damaged brain areas displayed by DTI were consistent with areas showing decreased MoCA scores and were positively correlated with the severity of 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.

Thalamic white matter macrostructure and subnuclei volumes in Parkinson's disease depression

Depression is a common non-motor feature of Parkinson's disease (PD) which confers significant morbidity and is challenging to treat. The thalamus is a key component in the basal ganglia-thalamocortical network critical to the pathogenesis of PD and depression but the precise thalamic subnuclei involved in PD depression have not been identified. We performed structural and diffusion-weighted imaging (DWI) on 76 participants with PD to evaluate the relationship between PD depression and grey and white matter thalamic subnuclear changes. We used a thalamic segmentation method to divide the thalamus into its 50 constituent subnuclei (25 each hemisphere). Fixel-based analysis was used to calculate mean fibre cross-section (FC) for white matter tracts connected to each subnucleus. We assessed volume and FC at baseline and 14-20 months follow-up. A generalised linear mixed model was used to evaluate the relationship between depression, subnuclei volume and mean FC for each thalamic subnucleus. We found that depression scores in PD were associated with lower right pulvinar anterior (PuA) subnucleus volume. Antidepressant use was associated with higher right PuA volume suggesting a possible protective effect of treatment. After follow-up, depression scores were associated with reduced white matter tract macrostructure across almost all tracts connected to thalamic subnuclei. In conclusion, our work implicates the right PuA as a relevant neural structure in PD depression and future work should evaluate its potential as a therapeutic target for PD depression.

Gray matter, white matter and cerebrospinal fluid abnormalities in Parkinson's disease: A voxel-based morphometry study

BACKGROUND

Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by bradykinesia, tremor, and rigidity among other symptoms. With a 70% cumulative prevalence of dementia in PD, cognitive impairment and neuropsychiatric symptoms are frequent.

MATERIALS AND METHODS

In this study, we looked at anatomical brain differences between groups of patients and controls. A total of 138 people with PD were compared to 64 age-matched healthy people using voxel-based morphometry (VBM). VBM is a fully automated technique that allows for the identification of regional differences in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) allowing for an objective comparison of brains of different groups of people. We used statistical parametric mapping for image processing and statistical analysis.

RESULTS

In comparison to controls, PD patients had lower GM volumes in the left middle cingulate, left lingual gyrus, right calcarine and left fusiform gyrus, also PD patients indicated lower WM volumes in the right middle cingulate, left lingual gyrus, right calcarine, and left inferior occipital gyrus. Moreover, PD patients group demonstrated higher CSF in the left caudate compared to the controls.

CONCLUSION

Physical fragility and cognitive impairments in PD may be detected more easily if anatomical abnormalities to the cingulate gyrus, occipital lobe and the level of CSF in the caudate are identified. Thus, our findings shed light on the role of the brain in PD and may aid in a better understanding of the events that occur in PD patients.

Effect of vocal respiratory training on respiratory function and respiratory neural plasticity in patients with cervical spinal cord injury: a randomized controlled trial

In previous studies, researchers have used singing to treat respiratory function in patients with spinal cord injury. However, few studies have examined the way in which vocal training affects respiratory neural plasticity in patients with spinal cord injury. Vocal respiratory training (VRT) is a type of vocal muscle-related treatment that is often a component of music therapy (MT) and focuses on strengthening respiratory muscles and improving lung function. In this randomized controlled study, we analyzed the therapeutic effects of VRT on respiratory dysfunction at 3 months after cervical spinal cord injury. Of an initial group of 37 patients, 26 completed the music therapy intervention, which comprised five 30-minute sessions per week for 12 weeks. The intervention group (n = 13) received VRT training delivered by professional certified music therapists. The control group (n = 13) received respiratory physical therapy delivered by professional physical therapists. Compared with the control group, we observed a substantial increase in respiratory function in the intervention group after the 12-week intervention. Further, the nerve fiber bundles in the respiratory center in the medulla exhibited a trend towards increased diversification, with an increased number, path length, thickness, and density of nerve fiber bundles. These findings provide strong evidence for the effect of music therapeutic VRT on neural plasticity. This study was approved by the Ethics Committee of China Rehabilitation Research Center (approval No. 2020-013-1) on April 1, 2020, and was registered with the Chinese Clinical Trial Registry (registration No. ChiCTR2000037871) on September 2, 2020.

Impaired white matter microstructure associated with severe depressive symptoms in patients with PD

Depression is a common occurrence in patients with Parkinson's disease (PD); however, its pathophysiology is still unclear. This study assessed the association between the integrity of white matter and depressive symptoms in patients with PD. 67 patients with PD were divided into a non-depressed PD group (ndPD, n = 30) and a depressed PD group (dPD, n = 37). The dPD group was further subdivided into a mild-moderately depressed PD (mdPD, n = 22) and a severely depressed PD group (sdPD, n = 15). Tract-Based Spatial Statistics was used to compare fractional anisotropy (FA) between groups. Region-of-interest analysis was used to explore changes in diffusivity indices in the regions showing FA abnormalities. The sdPD patients exhibited significantly reduced FA in the left superior longitudinal fasciculus, uncinate fasciculus, anterior corona radiata, corticospinal tract, and bilateral inferior fronto-occipital fasciculus when compared with the ndPD patients, but the decreased FA was within a smaller area when compared with the mdPD patients. No significant difference in FA was found between the mdPD and ndPD groups. Among the dPD patients, FA values in the left superior longitudinal fasciculus negatively correlated with BDI scores. Impaired white matter integrity in the prefronto-limbic/temporal circuitry, mainly in the left hemisphere, is associated with severe, but not mild-moderate depressive symptoms in patients with 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.

Utility of manual fractional anisotropy measurements in the management of patients with Parkinson disease: a feasibility study with a 1.5-T magnetic resonance imaging system

Background

Diffusion tensor imaging has emerged as a promising tool for quantitative analysis of neuronal damage in Parkinson disease, with potential value for diagnostic and prognostic evaluation.

Purpose

The aim of this study was to examine Parkinson disease-associated alterations in specific brain regions revealed by diffusion tensor imaging and how such alterations correlate with clinical variables.

Material and Methods

Diffusion tensor imaging was performed on 42 Parkinson disease patients and 20 healthy controls with a 1.5-T scanner. Manual fractional anisotropy measurements were performed for the ventral, intermediate, and dorsal portions of the substantia nigra, as well as for the cerebral peduncles, putamen, thalamus, and supplementary motor area. The correlation analysis between these measurements and the clinical variables was performed using χ² variance and multiple linear regression.

Results

Compared to healthy controls, Parkinson disease patients had significantly reduced fractional anisotropy values in the substantia nigra (P < .05). Some fractional anisotropy measurements in the substantia nigra correlated inversely with duration of Parkinson disease and Parkinson disease severity scores. Reduced fractional anisotropy values in the substantia nigra were also correlated inversely with age variable. fractional anisotropy values obtained for the right and left putamen varied significantly between males and females in both groups.

Conclusion

Manual fractional anisotropy measurements in the substantia nigra were confirmed to be feasible with a 1.5-T scanner. Diffusion tensor imaging data can be used as a reliable biomarker of Parkinson disease that can be used to support diagnosis, prognosis, and progression/treatment monitoring.

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.

Investigation of local white matter abnormality in Parkinson's disease by using an automatic fiber tract parcellation

The deficits of white matter (WM) microstructure are involved during Parkinson's disease (PD) progression. Most current methods identify key WM tracts relying on cortical regions of interest (ROIs). However, such ROI methods can be challenged due to low diffusion anisotropy near the gray matter (GM), which could result in a low sensitivity of tract identification. This work proposes an automatic WM parcellation method to improve the accuracy of WM tract identification and locate abnormal tracts by using sensitive features. The proposed method consists of 1) whole brain WM parcellation using an established fiber clustering method, without using any ROIs, 2) features of fasciculus were calculated to quantify diffusion measures at each equal cross-section along the whole cluster. Then, we use the proposed features to investigate the WM difference in PD compared with healthy controls (HC). We also use these features to investigate the relationship of clinical symptoms and specific fiber tracts. The novelty of the proposed method is that it automatically identifies the abnormal WM fibers in cluster degree. Experiment results indicated that the proposed method had advantage in detecting the local WM abnormality by performing between-group statistical analysis in 30 patients with PD and 28 HC. We found 13 hemisphere clusters and 8 commissural clusters had significant group difference (p < 0.05, corrected by FDR method) in local regions, which belonged to multiple fiber tracts including cingulum bundle (CB), inferior occipito-frontal fasciculus (IoFF), corpus callosum (CC), external capsule (EC), uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and thalamo front (TF). We also found clusters that had relevance with clinical indices of cognitive function (2 clusters), athletic function (6 clusters), and depressive state (2 clusters) in these significant clusters. From the experiment results, it confirmed the ability of the proposed method to identify potential WM microstructure abnormality.

Aging and Positive Mood: Longitudinal Neurobiological and Cognitive Correlates

OBJECTIVE

Despite the losses commonly associated with aging, older adults seem to possess particularly preserved emotional regulation. To further understand this phenomenon, the authors examined longitudinal trajectories between age, depressive symptoms, brain structure, and cognition.

METHODS

Seven hundred and sixteen functionally intact older adults (age M = 67.9, 56.8% female), followed longitudinally (visit range: 1-13, M = 2.5), completed cognitive testing and the Geriatric Depression Scale (GDS). A subset (N = 327) underwent 3T brain MRI. Mixed-effects linear regression models were conducted controlling for sex, education, and total intracranial volume.

RESULTS

There was a significant interaction between age and time on GDS, such that GDS improved with increasing age over time, but attenuated around age 71 (age*time b = 0.10, p <0.001). Fractional anisotropy (FA) and mean diffusivity interacted with age to predict longitudinal changes in GDS (FA: b = -0.02, p = 0.01; MD: b = 0.03, p = 0.007), such that age-related benefits on GDS were attenuated in those with declining FA. Executive function (EF) and processing speed also interacted with age to predict longitudinal changes in GDS (EF: b = -0.04, p = 0.03; speed: b = 0.04, p = 0.04). Again, the positive effect of age on GDS attenuated in those with worsening EF and speed. There were no associations with memory, semantic fluency, or gray matter (p values >0.05).

CONCLUSION

EF, processing speed, and white matter integrity moderated the longitudinal relationship between age and mood. Previous studies demonstrate the link between positivity and better cognitive control, leading to improved mood in older adults. Our results are not only consistent, but establish a potential neurobiological correlate. Future research further exploring biological mechanisms driving psychological processes may have important therapeutic implications.

Fixel-based analysis reveals fiber-specific alterations during the progression of Parkinson's disease

Disruption of brain circuits is one of the core mechanisms of Parkinson's disease (PD). Understanding structural connection alterations in PD is important for effective treatment. However, due to methodological limitations, most studies were unable to account for confounding factors such as crossing fibers and were unable to identify damages to specific fiber tracts. In the present study, we aimed to demonstrate tract-specific white matter structural changes in PD patients and their relationship with clinical symptoms. Ninety-eight PD patients, divided into early (ES) and middle stage (MS) groups, and 76 healthy controls (HCs) underwent brain magnetic resonance imaging scans and clinical assessments. Fixel-based analysis was used to investigate fiber tract alterations in PD patients. Compared to HCs, the PD patients showed decreased fiber density (FD) in the corpus callosum (CC), increased FD in the cortical spinal tract (CST), and increased fiber-bundle cross-section (FC, log-transformed: log-FC) in the superior cerebellar peduncle (SCP). Analysis of variance (ANOVA) revealed significant differences in FD in the CST and log-FC in the SCP among the three groups. Post-hoc analysis revealed that the mean FD values of the CST were higher in ES and MS patient groups compared to HCs, and the mean log-FC values of the SCP were higher in ES and MS patient groups compared to HCs. Additionally, the FD values of the CC in PD patients were negatively correlated with the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) scores (r = -0.257, p = 0.032), Hamilton Depression Rating Scale 17 Items (HAMD-17) scores (r = -0.230, p = 0.033), and Hamilton Anxiety Scale (HAMA) scores (r = -0.248, p = 0.032). Moreover, log-FC values of the SCP (r = 0.274, p = 0.028) and FD values of the CST (r = 0.384, p < 0.001) were positively correlated with the UPDRS-III scores. We concluded that PD patients had both decreased and increased white matter integrity within specific fiber bundles. Additionally, these white matter alterations were different across disease stages, suggesting the occurrence of complex pathological and compensatory changes during the development of PD.

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.

Drug Choices and Advancements for Managing Depression in Parkinson's Disease

Depression is a frequent non-motor symptom of Parkinson’s disease (PD), and may even precede the onset of motor symptoms of parkinsonism. Beyond its negative influence on mood, depression in PD is frequently associated with other neuropsychiatric symptoms and with late-stage complications such as dementia. Despite its profound impact on the quality of life and cognitive functioning in PD, depression in PD is often under-recognized and poorly treated.

Pathophysiological studies demonstrated that depression in PD is associated with global dysfunction of interactions between discrete brain areas rather than focal structural or functional abnormalities, and that it is sustained by pathological changes of several neurotransmitter/receptor complexes.

In general, all traditional antidepressants and some dopamine agonists have been found to be safe and well-tolerated to treat depressive symptoms in PD, despite initial warning on worsening of parkinsonism. Available data suggest that the time-course of response differs among antidepressants. Efficacy results from clinical trials with antidepressant in PD are, however, rather uncertain, although pooled analysis suggests a moderate benefit. Several issues may critically impact the results of clinical trials with antidepressants in PD, including the correct psychiatric diagnosis, the overlap of symptoms between depression and PD, and the selection of appropriate end-points and rating scales.

Early limbic microstructural alterations in apathy and depression in de novo Parkinson's disease

BACKGROUND

Whether structural alterations underpin apathy and depression in de novo parkinsonian patients is unknown. The objectives of this study were to investigate whether apathy and depression in de novo parkinsonian patients are related to structural alterations and how structural abnormalities relate to serotonergic or dopaminergic dysfunction.

METHODS

We compared the morphological and microstructural architecture in gray matter using voxel-based morphometry and diffusion tensor imaging coupled with white matter tract-based spatial statistics in a multimodal imaging case-control study enrolling 14 apathetic and 13 nonapathetic patients with de novo Parkinson's disease and 15 age-matched healthy controls, paired with PET imaging of the presynaptic dopaminergic and serotonergic systems.

RESULTS

De novo parkinsonian patients with apathy had bilateral microstructural alterations in the medial corticostriatal limbic system, exhibiting decreased fractional anisotropy and increased mean diffusivity in the anterior striatum and pregenual anterior cingulate cortex in conjunction with serotonergic dysfunction. Furthermore, microstructural alterations extended to the medial frontal cortex, the subgenual anterior cingulate cortex and subcallosal gyrus, the medial thalamus, and the caudal midbrain, suggesting disruption of long-range nondopaminergic projections originating in the brainstem, in addition to microstructural alterations in callosal interhemispheric connections and frontostriatal association tracts early in the disease course. In addition, microstructural abnormalities related to depressive symptoms in apathetic and nonapathetic patients revealed a distinct, mainly right-sided limbic subnetwork involving limbic and frontal association tracts.

CONCLUSIONS

Early limbic microstructural alterations specifically related to apathy and depression emphasize the role of early disruption of ascending nondopaminergic projections and related corticocortical and corticosubcortical networks which underpin the variable expression of nonmotor and neuropsychiatric symptoms in Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

The relationship of anxious and depressive symptoms in Parkinson's disease with voxel-based neuroanatomical and functional connectivity measures

BACKGROUND

Anxiety and depression are two frequent comorbidities of Parkinson's disease (PD). However, the underlying neural mechanism is still unclear and the studies on their neural correlates were insufficient.

METHODS

Using voxel-based neuroanatomical and functional connectivity (FC) measures, i.e. grey matter volume, fractional anisotropy, and weighted degree centrality (WD), we examined their correlations with the severity levels of anxious and depressive symptoms in 36 PD patients.

RESULTS

Positive correlations were shown between anxiety and the WDs in the left amygdala, and between depression and short-ranged WDs in the left parahippocampal gyrus. Using these two regions as the seeds, we found that the severity levels of anxiety and depression were positively correlated with the FCs between the two seeds and the areas in the default mode network (DMN), while negatively correlated with the FCs between the two seeds and the prefrontal and superior temporal cortices. Anxiety was also positively correlated with the FC between the amygdala and the superior parietal lobule.

LIMITATIONS

The severity levels of anxious and depressive symptoms of our participants is relatively mild than some previous studies. The cross-sectional design of this study cannot clarify the etiological relationship between PD and two comorbidities.

CONCLUSIONS

Our results were in line with the key roles of the amygdala and parahippocampal gyrus in anxiety and depression, and reflected the distinct effects of the DMN, prefrontal and superior temporal cortices, and sensory-motor regions on emotional regulation. The identification of these neural substrates might assist clinical monitoring mood disturbances in PD.

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.

Lesions of mediodorsal thalamic nucleus reverse abnormal firing of the medial prefrontal cortex neurons in parkinsonian rats

The dysfunction of the medial prefrontal cortex is associated with affective disorders and non-motor features in Parkinson’s disease. However, the exact role of the mediodorsal thalamic nucleus in the function of the prefrontal cortex remains unclear. To study the possible effects of the mediodorsal thalamic nucleus on the neurological function of the medial prefrontal cortex, a model of Parkinson’s disease was established by injecting 8 µg 6-hydroxydopamine into the substantia nigra compacta of rats. After 1 or 3 weeks, 0.3 µg ibotenic acid was injected into the mediodorsal thalamic nucleus of the midbrain. At 3 or 5 weeks after the initial injury, neuronal discharge in medial prefrontal cortex of rat brain was determined electrophysiologically. The numbers of dopamine-positive neurons and tyrosine hydroxylase immunoreactivity in substantia nigra compacta and ventral tegmental area were detected by immunohistochemical staining. Results demonstrated that after injury, the immunoreactivity of dopamine neurons and tyrosine hydroxylase decreased in the substantia nigra compacta and ventral tegmental areas of rats. Compared with normal medial prefrontal cortical neurons, at 3 and 5 weeks after substantia nigra compacta injury, the discharge frequency of pyramidal neurons increased and the discharge pattern of these neurons tended to be a burst-discharge, with an increased discharge interval. The discharge frequency of interneurons decreased and the discharge pattern also tended to be a burst-discharge, but the discharge interval was only higher at 3 weeks. At 3 weeks after the combined lesions, the discharge frequency, discharge pattern and discharge interval were restored to a normal level in pyramidal neurons and interneurons in medial prefrontal cortex. These findings have confirmed that mediodorsal thalamic nucleus is involved in regulating neuronal activities of the medial prefrontal cortex. The changes in the function of the mediodorsal thalamic nucleus may be associated with the abnormal discharge activity of the medial prefrontal cortex neurons after substantia nigra compacta injury. All experimental procedures were approved by the Institutional Animal Care and Use Committee of Xi’an Jiaotong University, China (approval No. XJTULAC2017-067) on August 26, 2017.

Comparison of structural connectivity in Parkinson's disease with depressive symptoms versus non-depressed: a diffusion MRI connectometry study

ABSTRACTObjective:Research on psychobiological markers of Parkinson's disease (PD) remains a hot topic. Non-motor symptoms such as depression and REM sleep behavior disorder (RBD) each attribute to a particular neurodegenerative cluster in PD, and might enlighten the way for early prediction/detection of PD. The neuropathology of mood disturbances remains unclear. In fact, a few studies have investigated depression using diffusion magnetic resonance imaging (diffusion MRI).

METHOD

Diffusion MRI of PD patients without comorbid RBD was used to assess whether microstructural abnormalities are detectable in the brain of 40 PD patients with depression compared to 19 patients without depression. Diffusion MRI connectometry was used to carry out group analysis between age- and gender-matched PD patients with and without depressive symptoms. Diffusion MRI connectometry is based on spin distribution function, which quantifies the density of diffusing water and is a sensitive and specific analytical method to psychological differences between groups.

RESULTS

A significant difference (FDR = 0.016129) was observed in the left and right uncinate fasciculi, left and right inferior longitudinal fasciculi, left and right fornices, left inferior fronto-occipital fasciculus, right corticospinal tract, genu of corpus callosum, and middle cerebellar peduncle.

CONCLUSION

These results suggest the prominent circuits involved in emotion recognition, particularly negative emotions, might be impaired in comorbid depressive symptoms in PD.

High angular resolution diffusion imaging correlates of depression in Parkinson's disease: a connectometry study

Depression is a significant disabling feature in Parkinson's disease (PD). However, the neuropathology of this comorbidity is still unclear. In fact, few studies have tried to elucidate the neural correlates of depression in PD and have mostly examined specific regions of interest. In this study, we applied diffusion MRI connectometry, a powerful complementary approach to investigate alterations in whole white matter pathways regarding the severity of depressive symptoms. Using a multiple regression model, the correlation of severity of depressive symptoms assessed by the Hospital Anxiety and Depression Scale (HADS) with white matter connectivity was surveyed in 27 non-demented PD patients related to 26 age, sex, and educational level-matched healthy subjects. Results revealed areas, where white matter quantitative anisotropy (QA) was correlated with depression score in PD patients, without any significant association in healthy controls. The analysis showed a significant negative association (false discovery rate < 0.05) between scores on depression subscale of HADS in PD patients and QA of left Cingulum, Genu, and Splenium of the Corpus Callosum, and anterior and posterior limbs of the right internal capsule. This finding might improve our understanding of the neural basis of depression and its severity in PD.

Structural MRI in Idiopathic Parkinson's Disease

Among modern neuroimaging modalities, magnetic resonance imaging (MRI) is a widely available, non-invasive, and cost-effective method to detect structural and functional abnormalities related to neurodegenerative disorders. In the last decades, MRI have been widely implemented to support PD diagnosis as well as to provide further insights into motor and non-motor symptoms pathophysiology, complications and treatment-related effects. Different aspects of the brain morphology and function may be derived from a single scan, by applying different analytic approaches. Biomarkers of neurodegeneration as well as tissue microstructural changes may be extracted from structural MRI techniques. In this chapter, we analyze the role of structural imaging to differentiate PD patients from controls and to define neural substrates of motor and non-motor PD symptoms. Evidence collected in the premotor PD phase will be also critically discussed. White matter as well as gray matter integrity imaging studies has been reviewed, aiming to highlight points of strength and limits to their potential application in clinical settings.

The use of diffusional kurtosis imaging and neurite orientation dispersion and density imaging of the brain in major depressive disorder

Diffusional kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) are new diffusional magnetic resonance imaging (dMRI) techniques for the characterization of neural tissues in human brain. In this study, we used these dMRI techniques to evaluate the whole-brain microstructural changes in patients with major depressive disorder (MDD). Twenty-three patients with MDD and 26 healthy subjects underwent dMRI. We compared the dMRI metrics between the 2 groups and examined the relationships between the metrics and the clinical symptoms of MDD. The MDD patients showed significant fractional anisotropy reduction in the bilateral parietal, right parieto-occipital, and right superior temporal corti, compared with the controls. Mean kurtosis values were significantly reduced in MDD patients in the right superior temporal cortex and bilateral posterior thalamic radiation. Neurite density index reductions were found in the right superior temporal cortex, bilateral insulae, right inferior frontal cortex, left parahippocampal region, left middle cerebellar peduncle, and right cerebellum. Regarding the orientation dispersion index (ODI), we detected significant decreases in the left thalamus and left occipital cortex, and significant increases in the bilateral superior longitudinal fasciculi and left posterior thalamic radiation tract. Further, there were significant positive correlations between the total Hamilton Depression Rating scale-21 scores and the ODI values in the right frontal gyri. These results suggest that the DKI and NODDI methods may provide more information about microstructural abnormalities in patients with MDD than the DTI method. It is thus expected that these techniques will be adopted as the informative methods for neuroimaging study.

Depression, anxiety, and apathy in Parkinson's disease: insights from neuroimaging studies

Depression, anxiety and apathy are common mood disturbances in Parkinson's disease (PD) but their pathophysiology is unclear. Advanced neuroimaging has been increasingly used to unravel neural substrates linked to these disturbances. A systematic review is provided of neuroimaging findings in depression, anxiety and apathy in PD. A PubMed, MEDLINE and EMBASE search of peer‐reviewed original research articles on these mood disturbances in PD identified 38 studies on depression, eight on anxiety and 14 on apathy in PD. Most of the imaging studies used either position emission tomography or single‐photon emission computed tomography techniques. These studies generally suggest increased neural activity in the prefrontal regions and decreased functional connectivity between the prefrontal−limbic networks in depressed patients. Functional imaging studies revealed an inverse correlation between dopaminergic density in the caudate and putamen with the severity of anxiety in PD. There was no consistent correlation between dopaminergic density of thalamus and anxiety. Studies demonstrated both positive and inverse correlations between apathy and metabolism or activity in the striatum, amygdalar, prefrontal, temporal and parietal regions. The clinical variability of study subjects and differences in image pre‐processing and analytical strategies may contribute to discrepant findings in these studies. Both nigrostriatal and extra‐nigrostriatal pathways (in particular the frontal region and its connecting areas) are affected in mood disorders in PD. Identifying the relative contributions of these neural pathways in PD patients with overlapping motor and mood symptoms could provide new pathophysiological clues for the development of better therapeutic targets for affected patients.

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.

Biomarkers for Parkinson's Disease: Recent Advancement

As a multi-factorial degenerative disease, Parkinson's disease (PD) leads to tremor, gait rigidity, and hypokinesia, thus hampering normal living. As this disease is usually detected in the later stages when neurons have degenerated completely, cure is on hold, ultimately leading to death due to the lack of early diagnostic techniques. Thus, biomarkers are required to detect the disease in the early stages when prevention is possible. Various biomarkers providing early diagnosis of the disease include those of imaging, cerebrospinal fluid, oxidative stress, neuroprotection, and inflammation. Also, biomarkers, alone or in combination, are used in the diagnosis and evolution of PD. This review encompasses various biomarkers available for PD and discusses recent advances in their development.

Imaging the Etiology of Apathy, Anxiety, and Depression in Parkinson's Disease: Implication for Treatment

Apathy, depression, and anxiety are among the most important non-motor signs of Parkinson's disease (PD). This may be encountered at early stages of illness and represent a major source of burden. Understanding their pathophysiology is a major prerequisite for efficient therapeutic strategies. Anatomical and metabolic imaging studies have enabled a breakthrough by demonstrating that widespread abnormalities within the limbic circuits notably the orbitofrontal and anterior cingulate cortices, amygdala, thalamus, and ventral striatum are involved in the pathophysiology of depression, anxiety, and apathy in PD. Functional imaging has further shown that mesolimbic dopaminergic but also serotonergic lesions play a major role in the mechanisms of these three neuropsychiatric manifestations, which has direct therapeutic implications.

Microstructural analyses of the posterior cerebellar lobules in relapsing-onset multiple sclerosis and their implication in cognitive impairment

BACKGROUND

The posterior cerebellar lobules seem to be the anatomical substrate of cognitive cerebellar processes, but their microstructural alterations in multiple sclerosis (MS) remain unclear.

OBJECTIVES

To correlate diffusion metrics in lobules VI to VIIIb in persons with clinically isolated syndrome (PwCIS) and in cognitively impaired persons with MS (CIPwMS) with their cognitive performances.

METHODS

Sixty-nine patients (37 PwCIS, 32 CIPwMS) and 36 matched healthy subjects (HS) underwent 3T magnetic resonance imaging, including 3D T1-weighted and diffusion tensor imaging (DTI). Fractional anisotropy (FA) and mean diffusivity (MD) were calculated within each lobule and in the cerebellar peduncles. We investigated the correlations between cognitive outcomes and the diffusion parameters of cerebellar sub-structures and performed multiple linear regression analysis to predict cognitive disability.

RESULTS

FA was generally lower and MD was higher in the cerebellum and specifically in the vermis Crus II, lobules VIIb and VIIIb in CIPwMS compared with PwCIS and HS. In hierarchical regression analyses, 31% of the working memory z score variance was explained by FA in the left lobule VI and in the left superior peduncle. Working memory was also associated with MD in the vermis Crus II. FA in the left lobule VI and right VIIIa predicted part of the information processing speed (IPS) z scores.

CONCLUSION

DTI indicators of cerebellar microstructural damage were associated with cognitive deficits in MS. Our results suggested that cerebellar lobular alterations have an impact on attention, working memory and IPS.

The role of diffusion magnetic resonance imaging in Parkinson's disease and in the differential diagnosis with atypical parkinsonism

Parkinson's disease is one of the most common neurodegenerative diseases. Clinically, it is characterized by motor symptoms. Parkinson's disease should be differentiated from atypical parkinsonism conditions. Conventional magnetic resonance imaging is the primary imaging method employed in order to facilitate the differential diagnosis, and its role has grown after the development of advanced techniques such as diffusion-weighted imaging. The purpose of this article was to review the role of magnetic resonance imaging in Parkinson's disease and in the differential diagnosis with atypical parkinsonism, emphasizing the diffusion technique.

Study on Lesion Assessment of Cerebello-Thalamo-Cortical Network in Wilson's Disease with Diffusion Tensor Imaging

Wilson's disease (WD) is a genetic disorder of copper metabolism with pathological copper accumulation in the brain and any other tissues. This article aimed to assess lesions in cerebello-thalamo-cortical network with an advanced technique of diffusion tensor imaging (DTI) in WD. 35 WD patients and 30 age- and sex-matched healthy volunteers were recruited to accept diffusion-weighted images with 15 gradient vectors and conventional magnetic resonance imaging (MRI). The DTI parameters, including fractional anisotropy (FA) and mean diffusion (MD), were calculated by diffusion kurtosis estimator software. After registration, patient groups with FA mappings and MD mappings and normal groups were compared with 3dttest and receiver-operating characteristic (ROC) curve analysis, corrected with FDR simulations (p = 0.001, α = 0.05, cluster size = 326). We found that the degree of FA increased in the bilateral head of the caudate nucleus (HCN), lenticular nucleus (LN), ventral thalamus, substantia nigra (SN), red nucleus (RN), right dentate nucleus (DN), and decreased in the mediodorsal thalamus and extensive white matter. The value of MD increased in HCN, LN, SN, RN, and extensive white matter. The technique of DTI provides higher sensitivity and specificity than conventional MRI to detect Wilson's disease. Besides, lesions in the basal ganglia, thalamus, and cerebellum might disconnect the basal ganglia-thalamo-cortical circuits or dentato-rubro-thalamic (DRT) track and disrupt cerebello-thalamo-cortical network finally, which may cause clinical extrapyramidal symptoms.

Imaging the Nonmotor Symptoms in Parkinson's Disease

Parkinson's disease is acknowledged to be a multisystem syndrome, manifesting as a result of multineuropeptide dysfunction, including dopaminergic, cholinergic, serotonergic, and noradrenergic deficits. This multisystem disorder ultimately leads to the presentation of a range of nonmotor symptoms, now appreciated to be an integral part of the disease-specific spectrum of symptoms, often preceding the diagnosis of motor Parkinson's disease. In this chapter, we review the dopaminergic and nondopaminergic basis of these symptoms by exploring the neuroimaging evidence based on several techniques including positron emission tomography, single-photon emission computed tomography molecular imaging, magnetic resonance imaging, functional magnetic resonance imaging, and diffusion tensor imaging. We discuss the role of these neuroimaging techniques in elucidating the underlying pathophysiology of NMS in Parkinson's disease.

Application of high-field magnetic resonance imaging in Parkinson's disease

The present study aimed to observe the structural changes of the extracorticospinal tract in Parkinson's disease (PD) using susceptibility-weighted imaging (SWI) and diffusion tensor imaging (DTI) magnetic resonance (MR) scans. The association of DTI parameters and brain-iron accumulation with PD was examined and imaging signs useful in the diagnosis of PD were explored. The study included 30 patients with PD and 30 age- and gender-matched healthy controls who underwent routine MR, SWI and DTI scans. The corrected phase (CP) values of the substantia nigra (SN), red nucleus (RN), globus pallidus (GP) and putamen (PUT) were measured, and fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were obtained. Significant differences were found in the CP values between the PD and control groups in the SN, RN and PUT, but there were no differences in other regions of interest (ROIs). The FA values of the SN and PUT in the PD group were significantly decreased compared with those of the control group, but there was no significant difference in the FA values of the GP. Furthermore, there was no significant inter-group difference in the ADC values of any ROIs. In conclusion, SWI is a method useful for evaluating brain-iron deposition in PD. Increasing iron storage levels have previously been shown to be associated with PD pathogenesis but not with the degree of PD severity. FA values may be useful for diagnosing PD, and DTI may offer some insight into PD pathomechanisms and clinical diagnosis.

A voxel-based diffusion tensor imaging study in unipolar and bipolar depression

OBJECTIVE

The absence of neurobiological diagnostic markers of bipolar disorder (BD) leads to its frequent misdiagnosis as unipolar depression (UD). We investigated if changes in fractional anisotropy (FA) could help to differentiate BD from UD in the state of depression.

METHODS

Using diffusion tensor imaging (DTI) we employed a voxel-based analysis approach to examine fractional anisotropy (FA) in 86 patients experiencing an acute major depressive episode according to DSM-IV (N=39 BD, mean age 39.2 years; N=43 UD, mean age 39.0 years), and 42 healthy controls (HC, mean age 36.1 years). The groups did not differ in sex, age or total education time. FA was investigated in white matter (FA >.2) and hypothesis-driven anatomically defined tracts (region-of-interest [ROI] analysis). Additionally, an exploratory gray matter FA analysis was performed.

RESULTS

White matter analysis showed decreased FA in the right corticospinal tract in UD vs HC and in the right corticospinal tract/superior longitudinal fascicle in BD vs HC and also in BD vs UD. ROI analysis revealed decreased FA in BD vs UD in the corpus callosum and in the cingulum. Gray matter exploratory analysis revealed decreased FA in the left middle frontal gyrus and in the right inferior frontal gyrus in UD vs HC, and in the left superior medial gyrus in BD vs HC.

CONCLUSION

This is one of very few studies directly showing differences in FA between BD and UD. Gray matter FA changes in prefrontal areas might be precursors for future prefrontal gray matter abnormalities in these disorders.

Pathology of behavior in PD: What is known and what is not?

Abnormal behavior in Parkinson's disease (PD) stems from a complex orchestration of impaired neural networks that result from PD-related neurodegeneration across multiple levels. Typically, cellular and tissue abnormalities generate neurochemical changes and disrupt specific regions of the brain, in turn creating impaired neural circuits and dysfunctional global networks. The objective of this chapter is to provide an overview of the array of pathological changes that have been linked to different behavioral symptoms of PD such as depression, anxiety, apathy, fatigue, impulse control disorders, psychosis, sleep disorders and dementia.