Comprehensive morphometry of subcortical grey matter structures in early-stage Parkinson's disease

Gray Matter Atrophy in a 6-OHDA-induced Model of Parkinson's Disease

INTRODUCTION

Magnetic resonance imaging (MRI) based brain morphometric changes in unilateral 6-hydroxydopamine (6-OHDA) induced Parkinson's disease (PD) model can be elucidated using voxel-based morphometry (VBM), study of alterations in gray matter volume and Machine Learning (ML) based analyses.

METHODS

We investigated gray matter atrophy in 6-OHDA induced PD model as compared to sham control using statistical and ML based analysis. VBM and atlas-based volumetric analysis was carried out at regional level. Support vector machine (SVM)-based algorithms wherein features (volume) extracted from (a) each of the 150 brain regions (b) statistically significant features (only) and (c) volumes of each cluster identified after application of VBM (VBM_Vol) were used for training the decision model. The lesion of the 6-OHDA model was validated by estimating the net contralateral rotational behaviour by the injection of apomorphine drug and motor impairment was assessed by rotarod and open field test.

RESULTS AND DISCUSSION

In PD, gray matter volume (GMV) atrophy was noted in bilateral cortical and subcortical brain regions, especially in the internal capsule, substantia nigra, midbrain, primary motor cortex and basal ganglia-thalamocortical circuits in comparison with sham control. Behavioural results revealed an impairment in motor performance. SVM analysis showed 100% classification accuracy, sensitivity and specificity at both 3 and 7 weeks using VBM_Vol.

CONCLUSION

Unilateral 6-OHDA induced GMV changes in both hemispheres at 7th week may be associated with progression of the disease in the PD model. SVM based approaches provide an increased classification accuracy to elucidate GMV atrophy.

The longitudinal volumetric and shape changes of subcortical nuclei in Parkinson's disease

Brain structural changes in Parkinson's disease (PD) are progressive throughout the disease course. Changes in surface morphology with disease progression remain unclear. This study aimed to assess the volumetric and shape changes of the subcortical nuclei during disease progression and explore their association with clinical symptoms. Thirty-four patients and 32 healthy controls were enrolled. The global volume and shape of the subcortical nuclei were compared between patients and controls at baseline. The volume and shape changes of the subcortical nuclei were also explored between baseline and 2 years of follow-up. Association analysis was performed between the volume of subcortical structures and clinical symptoms. In patients with PD, there were significantly atrophied areas in the left pallidum and left putamen, while in healthy controls, the right putamen was dilated compared to baseline. The local morphology of the left pallidum was correlated with Mini Mental State Examination scores. The left putamen shape variation was negatively correlated with changes in Unified Parkinson's Disease Rating Scale PART III scores. Local morphological atrophy of the putamen and pallidum is an important pathophysiological change in the development of PD, and is associated with motor symptoms and cognitive status in patients with PD.

Fine-grained features characterize hippocampal and amygdaloid change pattern in Parkinson's disease and discriminate cognitive-deficit subtype

AIMS

To extract vertex-wise features of the hippocampus and amygdala in Parkinson's disease (PD) with mild cognitive impairment (MCI) and normal cognition (NC) and further evaluate their discriminatory efficacy.

METHODS

High-resolution 3D-T1 data were collected from 68 PD-MCI, 211 PD-NC, and 100 matched healthy controls (HC). Surface geometric features were captured using surface conformal representation, and surfaces were registered to a common template using fluid registration. The statistical tests were performed to detect differences between groups. The disease-discriminatory ability of features was also tested in the ensemble classifiers.

RESULTS

The amygdala, not the hippocampus, showed significant overall differences among the groups. Compared with PD-NC, the right amygdala in MCI patients showed expansion (anterior cortical, anterior amygdaloid, and accessory basal areas) and atrophy (basolateral ventromedial area) subregions. There was notable atrophy in the right CA1 and hippocampal subiculum of PD-MCI. The accuracy of classifiers with multivariate morphometry statistics as features exceeded 85%.

CONCLUSION

PD-MCI is associated with multiscale morphological changes in the amygdala, as well as subtle atrophy in the hippocampus. These novel metrics demonstrated the potential to serve as biomarkers for PD-MCI diagnosis. Overall, these findings from this study help understand the role of subcortical structures in the neuropathological mechanisms of PD cognitive impairment.

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy

OBJECTIVES

This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA).

METHODS

A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process.

RESULTS

The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively.

CONCLUSIONS

Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.

Shared Genetic Architecture between Parkinson's Disease and Brain Structural Phenotypes

BACKGROUND

Patients with Parkinson's disease (PD) have consistently demonstrated brain structure abnormalities, indicating the presence of shared etiological and pathological processes between PD and brain structures; however, the genetic relationship remains poorly understood.

OBJECTIVE

The aim of this study was to investigate the extent of shared genetic architecture between PD and brain structural phenotypes (BSPs) and to identify shared genomic loci.

METHODS

We used the summary statistics from genome-wide association studies to conduct MiXeR and conditional/conjunctional false discovery rate analyses to investigate the shared genetic signatures between PD and BSPs. Subsequent expression quantitative trait loci mapping in the human brain and enrichment analyses were also performed.

RESULTS

MiXeR analysis identified genetic overlap between PD and various BSPs, including total cortical surface area, average cortical thickness, and specific brain volumetric structures. Further analysis using conditional false discovery rate (FDR) identified 21 novel PD risk loci on associations with BSPs at conditional FDR < 0.01, and the conjunctional FDR analysis demonstrated that PD shared several genomic loci with certain BSPs at conjunctional FDR < 0.05. Among the shared loci, 16 credible mapped genes showed high expression in the brain tissues and were primarily associated with immune function-related biological processes.

CONCLUSIONS

We confirmed the polygenic overlap with mixed directions of allelic effects between PD and BSPs and identified multiple shared genomic loci and risk genes, which are likely related to immune-related biological processes. These findings provide insight into the complex genetic architecture associated with PD. © 2023 International Parkinson and Movement Disorder Society.

Association of coffee consumption and striatal volume in patients with Parkinson's disease and healthy controls

BACKGROUND

Mounting studies have demonstrated that coffee consumption significantly reduces the risk of developing Parkinson's disease (PD). However, there have been few investigations about the role of chronic coffee consumption in nigrostriatal structural neurodegeneration in PD. We aimed to investigate whether chronic coffee consumption is associated with the change in striatal volume in PD.

METHODS

In this study, 130 de novo patients with PD and 69 healthy controls were enrolled from the Parkinson's Progression Markers Initiative cohort. Patients with PD and healthy controls were, respectively, divided into three subgroups, including current, ever, and never coffee consumers. Then, striatal volume was compared across the three subgroups. Correlation analyses were performed to assess the relationship between cups consumed per day and striatal volume. Furthermore, we included the factors that may have influenced nigrostriatal dopaminergic neurons in multiple linear regression analyses to identify significant contributing factors to striatal volume in each investigated striatal region.

RESULTS

Current coffee consumers had decreased striatal volume compared with ever consumers in controls but not patients with PD. Furthermore, the correlation analyses revealed that cups per day were negatively correlated with striatal volume in current consumers of patients with PD and controls. In addition, multiple linear regression analyses showed that current coffee consumption remained as an independent predictor of a decrease in striatal volume in controls.

CONCLUSIONS

Our study showed that chronic coffee consumption was negatively correlated with striatal volume. In addition, our study showed that chronic coffee consumption was associated with the change in striatal volume in current-rather than ever coffee consumers, which suggests that the chronic effects of caffeine on striatal morphology may fade and even compensate after quitting coffee. Our study provides evidence for the effect of chronic coffee consumption on striatal volume in human brain in vivo.

Midbrain and pons MRI shape analysis and its clinical and CSF correlates in degenerative parkinsonisms: a pilot study

OBJECTIVES

To conduct brainstem MRI shape analysis across neurodegenerative parkinsonisms and control subjects (CS), along with its association with clinical and cerebrospinal fluid (CSF) correlates.

METHODOLOGY

We collected demographic and clinical variables, performed planimetric and shape MRI analyses, and determined CSF neurofilament-light chain (NfL) levels in 84 participants: 11 CS, 12 with Parkinson's disease (PD), 26 with multiple system atrophy (MSA), 21 with progressive supranuclear palsy (PSP), and 14 with corticobasal degeneration (CBD).

RESULTS

MSA featured the most extensive and significant brainstem shape narrowing (that is, atrophy), mostly in the pons. CBD presented local atrophy in several small areas in the pons and midbrain compared to PD and CS. PSP presented local atrophy in small areas in the posterior and upper midbrain as well as the rostral pons compared to MSA. Our findings of planimetric MRI measurements and CSF NfL levels replicated those from previous literature. Brainstem shape atrophy correlated with worse motor state in all parkinsonisms and with higher NfL levels in MSA, PSP, and PD.

CONCLUSION

Atypical parkinsonisms present different brainstem shape patterns which correlate with clinical severity and neuronal degeneration. In MSA, shape analysis could be further explored as a potential diagnostic biomarker. By contrast, shape analysis appears to have a rather limited discriminant value in PSP.

KEY POINTS

• Atypical parkinsonisms present different brainstem shape patterns. • Shape patterns correlate with clinical severity and neuronal degeneration. • In MSA, shape analysis could be further explored as a potential diagnostic biomarker.

Clinical Phenotype Imprints on Brain Atrophy Progression in Parkinson’s Disease

There is much controversy about the link between motor symptom progression and the plethora of reported brain atrophy patterns in idiopathic Parkinson’s disease (PD). The main goal of this study is to provide empirical evidence for unique and common contributions of clinical phenotype characteristics on the dynamic changes of brain structure over time. We analyzed the behavioral and magnetic resonance imaging (MRI) data of PD patients (n = 22) and healthy individuals (n = 21) acquired two years apart through the computational anatomy framework of longitudinal voxel-based morphometry (VBM). This analysis revealed a symmetrical bi-hemispheric pattern of accelerated grey matter decrease in PD extending through the insula, parahippocampal gyrus, medial temporal lobes and the precuneus. We observed a hemisphere-specific correlation between the established scores for motor symptoms severity and the rate of atrophy within motor regions, which was further differentiated by the clinical phenotype characteristics of PD patients. Baseline cerebellum anatomy differences between the tremor-dominant and akineto-rigid PD remained stable over time and can be regarded as trait rather than state-associated features. We interpret the observed pattern of progressive brain anatomy changes as mainly linked to insular areas that determine together with basal ganglia the motor and non-motor phenotype in PD. Our findings provide empirical evidence for the sensitivity of computational anatomy to dynamic changes in PD, offering additional opportunities to establish reliable models of disease progression.

Brain Morphometry Associated With Response to Levodopa and Deep Brain Stimulation in Parkinson Disease

OBJECTIVES

Whether treatment response in patients with Parkinson disease depends on brain atrophy is insufficiently understood. The goal of this study is to identify specific atrophy patterns associated with response to dopaminergic therapy and deep brain stimulation.

MATERIALS AND METHODS

In this study, we analyzed the association of gray matter brain atrophy patterns, as identified by voxel-based morphometry, with acute response to levodopa (N = 118) and subthalamic nucleus deep brain stimulation (N = 39). Motor status was measured as a change in points on the Unified Parkinson's Disease Rating Scale III score. Baseline values were obtained before surgery, after cessation of dopaminergic medication for at least 12 hours; response to medication was assessed after administration of a standardized dose of levodopa. Response to deep brain stimulation was measured three months after surgery in the clinical condition after withdrawal of dopaminergic medication.

RESULTS

Although frontoparietal brain gray matter loss was associated with subpar response to deep brain stimulation, there was no significant link between brain atrophy and response to levodopa.

CONCLUSION

We conclude that response to deep brain stimulation relies on gray matter integrity; hence, gray matter loss may present a risk factor for poor response to deep brain stimulation and may be considered when making decision regarding clinical practice.

Morphological changes in Parkinson's disease based on magnetic resonance imaging: A mini-review of subcortical structures segmentation and shape analysis

Parkinson's disease (PD) is a neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, resulting in clinical symptoms, including bradykinesia, resting tremor, rigidity, and postural instability. The pathophysiological changes in PD are inextricably linked to the subcortical structures. Shape analysis is a method for quantifying the volume or surface morphology of structures using magnetic resonance imaging. In this review, we discuss the recent advances in morphological analysis techniques for studying the subcortical structures in PD in vivo. This approach includes available pipelines for volume and shape analysis, focusing on the morphological features of volume and surface area.

Gray Matter Volume Loss in Proposed Brain-First and Body-First Parkinson's Disease Subtypes

BACKGROUND

α-Synuclein pathology is associated with neuronal degeneration in Parkinson's disease (PD) and considered to sequentially spread across the brain (Braak stages). According to a new hypothesis of distinct α-synuclein spreading directions based on the initial site of pathology, the "brain-first" spreading subtype would be associated with a more asymmetric cerebral and nigrostriatal pathology than the "body-first" subtype.

OBJECTIVE

Here, we tested if proposed markers of brain-first PD (ie, higher dopamine transporter [DaT] asymmetry; absence of rapid eye movement sleep behavior disorder [RBD]) are associated with a greater or more asymmetric reduction in gray matter volume (GMV) in comparison to body-first PD.

METHODS

Data of 255 de novo PD patients and 110 healthy controls (HCs) were retrieved from the Parkinson's Progression Markers Initiative. Structural magnetic resonance images were preprocessed, and GMVs and their hemispherical asymmetry were obtained for each of the neuropathologically defined Braak stages. Group and correlation comparisons were performed to assess differences in GMV and GMV asymmetry between PD subtypes.

RESULTS

PD patients demonstrated significantly smaller bilateral GMVs compared to HCs, in a pattern denoting stage-dependent disease-related brain atrophy. However, the degree of putaminal DaT asymmetry was not associated with reduced GMV or higher GMV asymmetry. Furthermore, RBD-negative and RBD-positive patients did not demonstrate a significant difference in GMV or GMV asymmetry.

CONCLUSIONS

Our findings suggest that putative brain-first and body-first patients do not present diverging brain atrophy patterns. Although certainly not disproving the brain-first/body-first spreading hypothesis, this study fails to provide evidence in support of it. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Contracted thalamic shape is associated with early development of levodopa-induced dyskinesia in Parkinson's disease

Levodopa-induced dyskinesia (LID), a long-term motor complication in Parkinson’s disease (PD), is attributable to both presynaptic and postsynaptic mechanisms. However, no studies have evaluated the baseline structural changes associated with LID at a subcortical level in PD. A total of 116 right-handed PD patients were recruited and based on the LID latency of 5 years, we classified patients into those vulnerable to LID (PD-vLID, n = 49) and those resistant to LID (PD-rLID, n = 67). After adjusting for covariates including dopamine transporter (DAT) availability of the posterior putamen, we compared the subcortical shape between the groups and investigated its association with the onset of LID. The PD-vLID group had lower DAT availability in the posterior putamen, higher parkinsonian motor deficits, and faster increment in levodopa equivalent dose than the PD-rLID group. The PD-vLID group had significant inward deformation in the right thalamus compared to the PD-rLID group. Inward deformation in the thalamus was associated with an earlier onset of LID at baseline. This study suggests that independent of presynaptic dopamine depletion, the thalamus is a major neural substrate for LID and that a contracted thalamic shape at baseline is closely associated with an early development of LID.

Pallidal Structural Changes Related to Levodopa-induced Dyskinesia in Parkinson's Disease

Background

Despite the clinical impact of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD), the mechanism, especially the role of basal ganglia (BG), is not fully elucidated yet. We investigated the BG structural changes related to LID in PD using a surface-based shape analysis technique.

Methods

We recruited patients with PD who developed LID within 3 years (LID group, 28 patients) and who did not develop it after 7 years (non-LID group, 35 patients) from levodopa treatment for the extreme case-control study. BG structure volumes were measured using volumetry analysis and the surface-based morphometry feature (i.e., Jacobian) from the subcortical surface vertices. We compared the volume and Jacobian of meshes in the regions between the two groups. We also performed a correlation analysis between local atrophy and the severity of LID. Additionally, we evaluated structural connectivity profiles from globus pallidus interna and externa (GPi and GPe) to other brain structures based on the group comparison.

Results

The demographic and clinical data showed no significant difference except for disease duration, treatment duration, parkinsonism severity, and levodopa equivalent dose. The LID group had more local atrophies of vertices in the right GPi than the non-LID group, despite no difference in volumes. Furthermore, the LID group demonstrated significantly reduced structural connectivity between left GPi and thalamus.

Conclusion

This is the first demonstration of distinct shape alterations of basal ganglia structures, especially GPi, related to LID in PD. Considering both direct and indirect BG pathways share the connection between GPi and thalamus, the BG pathway plays a crucial role in the development of LID.

Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease

Parkinson’s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts’ visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.

Statistical shape analysis of putamen in early-onset Parkinson's disease

OBJECTIVE

To investigate the shape differences in the putamen of early-onset Parkinson's patients compared with healthy controls and to assess and to assess sub-regional brain abnormalities.

METHODS

This study was conducted using the 3-T MRI scans of 23 early-onset Parkinson's patients and age and gender matched control subjects. Landmark coordinate data obtained and Procrustes analysis was used to compare mean shapes. The relationships between the centroid sizes of the left and right putamen, and the durations of disease examined using growth curve models.

RESULTS

While there was a significant difference between the right putamen shape of control and patient groups, there was not found a significant difference in terms of left putamen. Sub-regional analyses showed that for the right putamen, the most prominent deformations were localized in the middle-posterior putamen and minimal deformations were seen in the anterior putamen.

CONCLUSION

Although they were not as pronounced as those in the right putamen, the deformations in the left putamen mimic the deformations in the right putamen which are found mainly in the middle-posterior putamen and at a lesser extend in the anterior putamen.

Longitudinal Changes in Neuromelanin MRI Signal in Parkinson's Disease: A Progression Marker

BACKGROUND

Development of reliable and accurate imaging biomarkers of dopaminergic cell neurodegeneration is necessary to facilitate therapeutic drug trials in Parkinson's disease (PD). Neuromelanin-sensitive MRI techniques have been effective in detecting neurodegeneration in the substantia nigra pars compacta (SNpc). The objective of the current study was to investigate longitudinal neuromelanin signal changes in the SNpc in PD patients.

METHODS

In this prospective, longitudinal, observational case-control study, we included 140 PD patients and 64 healthy volunteers divided into 2 cohorts. Cohort I included 99 early PD patients (disease duration, 1.5 ± 1.0 years) and 41 healthy volunteers analyzed at baseline (V1), where 79 PD patients and 32 healthy volunteers were rescanned after 2.0 ± 0.2 years of follow-up (V2). Cohort II included 41 progressing PD patients (disease duration, 9.3 ± 3.7 years) and 23 healthy volunteers at V1, where 30 PD patients were rescanned after 2.4 ± 0.5 years of follow-up. Subjects were scanned at 3 T MRI using 3-dimensional T1-weighted and neuromelanin-sensitive imaging. Regions of interest were delineated manually to calculate SN volumes, volumes corrected by total intracranial volume, signal-to-noise ratio, and contrast-to-noise ratio.

RESULTS

Results showed (1) significant reduction in volume and volume corrected by total intracranial volume between visits, greater in progressing PD than nonsignificant changes in healthy volunteers; (2) no significant effects of visit for signal intensity (signal-to-noise ratio); (3) significant interaction in volume between group and visit; (4) greater volume corrected by total intracranial volume at baseline in female patients and greater decrease in volume and increase in the contrast-to-noise ratio in progressing female PD patients compared with male patients; and (5) correlations between neuromelanin SN changes and disease severity and duration.

CONCLUSIONS

We observed a progressive and measurable decrease in neuromelanin-based SN signal and volume in PD, which might allow a direct noninvasive assessment of progression of SN loss and could represent a target biomarker for disease-modifying treatments. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Structural and functional neuroimaging changes associated with cognitive impairment and dementia in Parkinson's disease

This study seeks a better understanding of possible pathophysiological mechanisms associated with cognitive impairment and dementia in Parkinson's disease using structural and functional MRI. We investigated resting-state functional connectivity of important subdivisions of the caudate nucleus, putamen and thalamus, and also how the morphology of these structures are impacted in the disorder. We found cognitively unimpaired Parkinson's disease subjects (n = 33), compared to controls (n = 26), display increased functional connectivity of the dorsal caudate, anterior putamen and mediodorsal thalamic subdivisions with areas across the frontal lobe, as well as reduced functional connectivity of the dorsal caudate with posterior cortical and cerebellar regions. Compared to cognitively unimpaired subjects, those with mild cognitive impairment (n = 22) demonstrated reduced functional connectivity of the mediodorsal thalamus with the paracingulate cortex, while also demonstrating increased functional connectivity of the mediodorsal thalamus with the posterior cingulate cortex, compared to subjects with dementia (n = 17). Extensive volumetric and surface-based deflation was found in subjects with dementia compared to cognitively unimpaired Parkinson's disease participants and controls. Our research suggests that structures within basal ganglia-thalamocortical circuits are implicated in cognitive impairment and dementia in Parkinson's disease, with cognitive impairment and dementia associated with a breakdown in functional connectivity of the mediodorsal thalamus with para- and posterior cingulate regions of the brain respectively.

GraphNet-based imaging biomarker model to explain levodopa-induced dyskinesia in Parkinson's disease

BACKGROUND AND OBJECTIVE

Levodopa-induced dyskinesia (LID) is a disabling complication of Parkinson's disease (PD). Imaging-based measurements, especially those related to the surface shape of the basal ganglia, have shown potential for explaining the severity of LID in PD. Here, we aimed to explore a novel application of the methodology to find biomarkers of LID severity in PD using regularization.

METHODS

We proposed an application of graph-constrained elastic net (GraphNet) regularization to detect surface-based shape biomarkers explaining the severity of LID and compared the approach with other conventional regularization methods. To examine the methods, we used two independent datasets, one as a training dataset to build the model, and the other dataset was used to validate the constructed model.

RESULTS

We found that the left striatum (putamen was the greatest and the caudate was second) was the most significant surface-based biomarker related to the severity of LID. Our results improved the interpretability of identified surface-based biomarkers compared to competing methods. We also found that GraphNet regularization improved prediction of the severity of LID better than the conventional regularization methods. Our model performed better in terms of root-mean-squared error and correlation coefficient between predicted and actual clinical scores.

CONCLUSION

The proposed algorithm offers an advantage of interpretable anatomical variations related to the deformation of the cortical surface. The experimental results showed that GraphNet regularization was robust to identify surface-based shape biomarkers related to both hypokinetic and hyperkinetic movement disorders.

Brain Morphological Alterations Are Detected in Early-Stage Parkinson's Disease with MRI Morphometry

BACKGROUND AND PURPOSE

To detect brain morphological alterations in patients with early Parkinson's disease (PD) by using magnetic resonance imaging (MRI) morphometry under radiological diagnostic conditions.

METHODS

T1-weighted brain images of 18 early PD patients and 18 age-sex-matched healthy controls (HCs) were analyzed with free software Computational Anatomy Toolbox (CAT12). Regional cortical thickness (rCTh) in 68 atlas-defined regions-of-interest (ROIs) and subcortical gray matter volume (SGMV) in 14 atlas-defined ROIs were determined and compared between patients and HCs by paired comparison using both ROI-wise and voxel-wise analyses. False-discovery rate (FDR) was used multiple comparison correction. Possible correlations between brain morphological changes in patients and clinical observations were also analyzed.

RESULTS

Comparing to the HCs, the ROI-wise analysis revealed rCTh thinning significantly in left medial orbitofrontal (P = .001), by trend (P < .05 but not significant after FDR correction) in four other ROIs located in frontal and temporal lobes, and a volume decreasing trend in left pallidum of the PD patients, while the voxel-wise analysis revealed one cluster with rCTh thinning trend located between left insula and superior temporal region of the patients. In addition, the patients showed more distinct rCTh thinning in ipsilateral hemisphere and SGMV deceasing trends in contralateral hemisphere in respect of the symptom-onset body side.

CONCLUSION

Brain morphological alterations in early PD patients are evident despite of their inconspicuous findings in standard MRI. Quantitative morphological measurements with CAT12 may be an applicable add-on tool for clinical diagnosis of early PD. These results have to be verified in future studies with larger patient samples.

Cohort profile: the Oxford Parkinson's Disease Centre Discovery Cohort MRI substudy (OPDC-MRI)

PURPOSE

The Oxford Parkinson's Disease Centre (OPDC) Discovery Cohort MRI substudy (OPDC-MRI) collects high-quality multimodal brain MRI together with deep longitudinal clinical phenotyping in patients with Parkinson's, at-risk individuals and healthy elderly participants. The primary aim is to detect pathological changes in brain structure and function, and develop, together with the clinical data, biomarkers to stratify, predict and chart progression in early-stage Parkinson's and at-risk individuals.

PARTICIPANTS

Participants are recruited from the OPDC Discovery Cohort, a prospective, longitudinal study. Baseline MRI data are currently available for 290 participants: 119 patients with early idiopathic Parkinson's, 15 Parkinson's patients with pathogenic mutations of the leucine-rich repeat kinase 2 or glucocerebrosidase (GBA) genes, 68 healthy controls and 87 individuals at risk of Parkinson's (asymptomatic carriers of GBA mutation and patients with idiopathic rapid eye movement sleep behaviour disorder-RBD).

FINDINGS TO DATE

Differences in brain structure in early Parkinson's were found to be subtle, with small changes in the shape of the globus pallidus and evidence of alterations in microstructural integrity in the prefrontal cortex that correlated with performance on executive function tests. Brain function, as assayed with resting fMRI yielded more substantial differences, with basal ganglia connectivity reduced in early Parkinson'sand RBD. Imaging of the substantia nigra with the more recent adoption of sequences sensitive to iron and neuromelanin content shows promising results in identifying early signs of Parkinsonian disease.

FUTURE PLANS

Ongoing studies include the integration of multimodal MRI measures to improve discrimination power. Follow-up clinical data are now accumulating and will allow us to correlate baseline imaging measures to clinical disease progression. Follow-up MRI scanning started in 2015 and is currently ongoing, providing the opportunity for future longitudinal imaging analyses with parallel clinical phenotyping.

Striatal shape alteration as a staging biomarker for Parkinson's Disease

Parkinson's Disease provokes alterations of subcortical deep gray matter, leading to subtle changes in the shape of several subcortical structures even before the manifestation of motor and non-motor clinical symptoms. We used an automated registration and segmentation pipeline to measure this structural alteration in one early and one advanced Parkinson's Disease (PD) cohorts, one prodromal stage cohort and one healthy control cohort. These structural alterations are then passed to a machine learning pipeline to classify these populations. Our workflow is able to distinguish different stages of PD based solely on shape analysis of the bilateral caudate nucleus and putamen, with balanced accuracies in the range of 59% to 85%. Furthermore, we compared the significance of each of these subcortical structure, compared the performances of different classifiers on this task, thus quantifying the informativeness of striatal shape alteration as a staging bio-marker for PD.

MRI biomarkers of motor and non-motor symptoms in Parkinson's disease

Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.

Striatum Shape Hypertrophy in Early Stage Parkinson's Disease With Excessive Daytime Sleepiness

INTRODUCTION

Excessive daytime sleepiness (EDS) is one of the common and burdensome non-motor symptoms of Parkinson's disease (PD). However, the underlying neuropathology mechanism in PD patients with EDS (PD-EDS) remains unclear. The present study aims to delineate potential locations of structural alteration of subcortical regions in early stage and drug-naïve PD-EDS.

METHODS

The study had 252 patients with PD and 92 matched healthy controls (HC). EDS was estimated with the Epworth Sleepiness Scale, with a cutoff of 10. Ultimately, 59 patients were considered as PD-EDS. The remaining 193 were PD patients without EDS (PD-nEDS). FMRIB's Integrated Registration and Segmentation Tool (FIRST) was employed to assess the volumetric and surface alterations of subcortical nuclei in PD and PD-EDS.

RESULTS

Volumetric analyses found no difference in the subcortical nucleus volume between PD and HC, or PD-EDS and PD-nEDS groups. The shape analyses revealed the local atrophic changes in bilateral caudate and right putamen in patients with PD. In addition, the hypertrophic changes were located in the right putamen and left pallidum in PD-EDS than in PD-nEDS.

CONCLUSION

Our findings revealed the regional hypertrophy of the striatum in PD-EDS. Our results indicate that local hypertrophic striatum would be a valuable early biomarker for detecting the alteration in PD-EDS. The shape analysis contributes valuable information when investigating PD-EDS.

Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity

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White matter disruption occurs in Parkinson's disease across several brain regions.

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DTI properties could identify clinically distinct subtypes of Parkinson's disease.

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Structural neural disruption can predict clinical outcomes in Parkinson's disease.

Objectives: Investigating biomarkers to demonstrate progression of Parkinson's disease (PD) is of high priority. We investigated the association of brain structural properties with progression of clinical outcomes and their ability to differentiate clinical subtypes of PD.

Methods: A comprehensive set of clinical features was evaluated at baseline and 4.5-year follow-up for 144 de-novo PD patients from the Parkinson's Progression Markers Initiative. We created a global composite outcome (GCO) by combining z-scores of non-motor and motor symptoms, motor signs, overall activities of daily living and global cognition, as a single numeric indicator of prognosis. We classified patients into three subtypes based on multi-domain clinical criteria: ‘mild motor-predominant’, ‘intermediate’ and ‘diffuse-malignant’. We analyzed diffusion-weighted scans at the early drug-naïve stage and extracted fractional anisotropy and mean diffusivity (MD) of basal ganglia and cortical sub-regions. Then, we employed graph theory to calculate network properties and used network-based statistic to investigate our primary hypothesis.

Results: Baseline MD of globus pallidus was associated with worsening of motor severity, cognition, and GCO after 4.5 years of follow-up. Connectivity disruption at baseline was correlated with decline in cognition, and increase in GCO. Baseline MD of nucleus accumbens, globus pallidus and basal-ganglia were linked to clinical subtypes at 4.5-year of follow-up. Disruption in sub-cortical networks associated with being subtyped as ‘diffuse-malignant’ versus ‘mild motor-predominant’ after 4.5 years.

Conclusions: Diffusion imaging analysis at the early de-novo stage of PD was able to differentiate clinical sub-types of PD after 4.5 years and was highly associated with future clinical outcomes of PD.

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography

Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson’s disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3 T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.

Increased functional connectivity of thalamic subdivisions in patients with Parkinson's disease

Parkinson’s disease (PD) affects 2–3% of the population over the age of 65 with loss of dopaminergic neurons in the substantia nigra impacting the functioning of basal ganglia-thalamocortical circuits. The precise role played by the thalamus is unknown, despite its critical role in the functioning of the cerebral cortex, and the abnormal neuronal activity of the structure in PD. Our objective was to more clearly elucidate how functional connectivity and morphology of the thalamus are impacted in PD (n = 32) compared to Controls (n = 20). To investigate functional connectivity of the thalamus we subdivided the structure into two important regions-of-interest, the first with putative connections to the motor cortices and the second with putative connections to prefrontal cortices. We then investigated potential differences in the size and shape of the thalamus in PD, and how morphology and functional connectivity relate to clinical variables. Our data demonstrate that PD is associated with increases in functional connectivity between motor subdivisions of the thalamus and the supplementary motor area, and between prefrontal thalamic subdivisions and nuclei of the basal ganglia, anterior and dorsolateral prefrontal cortices, as well as the anterior and paracingulate gyri. These results suggest that PD is associated with increased functional connectivity of subdivisions of the thalamus which may be indicative alterations to basal ganglia-thalamocortical circuitry.

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.

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

Central modulation of parasympathetic outflow is impaired in de novo Parkinson's disease patients

Task- and stimulus-based neuroimaging studies have begun to unveil the central autonomic network which modulates autonomic nervous system activity. In the present study, we aimed to evaluate the central autonomic network without the bias constituted by the use of a task. Additionally, we assessed whether this circuitry presents signs of dysregulation in the early stages of Parkinson’s disease (PD), a condition which may be associated with dysautonomia. We combined heart-rate-variability based methods for time-varying assessments of the autonomic nervous system outflow with resting-state fMRI in 14 healthy controls and 14 de novo PD patients, evaluating the correlations between fMRI time-series and the instantaneous high-frequency component of the heart-rate-variability power spectrum, a marker of parasympathetic outflow. In control subjects, the high-frequency component of the heart-rate-variability power spectrum was significantly anti-correlated with fMRI time-series in several cortical, subcortical and brainstem regions. This complex central network was not detectable in PD patients. In between-group analysis, we found that in healthy controls the brain activation related to the high-frequency component of the heart-rate-variability power spectrum was significantly less than in PD patients in the mid and anterior cingulum, sensorimotor cortex and supplementary motor area, insula and temporal lobe, prefrontal cortex, hippocampus and in a region encompassing posterior cingulum, precuneus and parieto-occipital cortex. Our results indicate that the complex central network which modulates parasympathetic outflow in the resting state is impaired in the early clinical stages of PD.

Thalamic shape and volume abnormalities in female patients with panic disorder

The thalamus is believed to play crucial role in processing viscero-sensory information, and regulating the activity of amygdala in patients with panic disorder (PD). Previous functional neuroimaging studies have detected abnormal activation in the thalamus in patients with PD compared with healthy control subjects (HC). Very few studies, however, have investigated for volumetric abnormalities in the thalamus in patients with PD. Furthermore, to the best of our knowledge, no previous study has investigated for shape abnormalities in the thalamus in patients with PD. Twenty-five patients with PD and 25 HC participants (all female) were recruited for the study. A voxel-wise volume comparison analysis and a vertex-wise shape analysis were conducted to evaluate structural abnormalities in the PD patients compared to HC. The patients with PD demonstrated significant gray matter volume reductions in the thalamus bilaterally, relative to the HC. The shape analysis detected significant inward deformation in some thalamic regions in the PD patients, including the anterior nucleus, mediodorsal nucleus, and pulvinar nucleus. PD patients showed shape deformations in key thalamic regions that are believed to play a role in regulating emotional and cognitive functions.

Biomarkers of Parkinson's disease: Striatal sub-regional structural morphometry and diffusion MRI

Parkinson's disease (PD) is a progressive neurological disorder that has no reliable biomarkers. The aim of this study was to explore the potential of semi-automated sub-regional analysis of the striatum with magnetic resonance imaging (MRI) to distinguish PD patients from controls (i.e., as a diagnostic biomarker) and to compare PD patients at different stages of disease.

With 3 Tesla MRI, diffusion- and T1-weighted scans were obtained on two occasions in 24 PD patients and 18 age-matched, healthy controls. PD patients completed one session on and the other session off dopaminergic medication. The striatum was parcellated into seven functionally disparate sub-regions. The segmentation was guided by reciprocal connections to distinct cortical regions. Volume, surface-based morphometry, and integrity of white matter connections were calculated for each striatal sub-region.

Test-retest reliability of our volume, morphometry, and white matter integrity measures across scans was high, with correlations ranging from r = 0.452, p < 0.05 and r = 0.985, p < 0.001. Global measures of striatum such as total striatum, nucleus accumbens, caudate nuclei, and putamen were not significantly different between PD patients and controls, indicating poor sensitivity of these measures, which average across sub-regions that are functionally heterogeneous and differentially affected by PD, to act as diagnostic biomarkers. Further, these measures did not correlate significantly with disease severity, challenging their potential to serve as progression biomarkers. In contrast, a) decreased volume and b) inward surface displacement of caudal-motor striatum—the region first and most dopamine depleted in PD—distinguished PD patients from controls. Integrity of white matter cortico-striatal connections in caudal-motor and adjacent striatal sub-regions (i.e., executive and temporal striatum) was reduced for PD patients relative to controls. Finally, volume of limbic striatum, the only striatal sub-region innervated by the later-degenerating ventral tegmental area in PD, was reduced in later-stage compared to early stage PD patients a potential progression biomarker.

Segmenting striatum based on distinct cortical connectivity provided highly sensitive MRI measures for diagnosing and staging PD.

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Using 3T structural and diffusion tensor MRI, we explored potential biomarkers in PD.

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Striatum was parcellated into 7 functional sub-regions based on cortical connectivity.

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Volume of caudal-motor region was significantly smaller in PDs compared to controls.

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Volume of limbic region was sensitive to PD disease progression.

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Striatal sub-regions provided sensitive measures of the presence and progression of PD.

Structural Imaging in Atypical Parkinsonism

Qualitative and quantitative structural magnetic resonance imaging offer objective measures of the underlying neurodegeneration in atypical parkinsonism. Regional changes in tissue volume, signal changes and increased deposition of iron as assessed with different structural MRI techniques are surrogate markers of underlying neurodegeneration and may reflect cell loss, microglial proliferation and astroglial activation. Structural MRI has been explored as a tool to enhance diagnostic accuracy in differentiating atypical parkinsonian disorders (APDs). Moreover, the longitudinal assessment of serial structural MRI-derived parameters offers the opportunity for robust inferences regarding the progression of APDs. This review summarizes recent research findings as (1) a diagnostic tool for APDs as well as (2) as a tool to assess longitudinal changes of serial MRI-derived parameters in the different APDs.

Longitudinal relationships among depressive symptoms, cortisol, and brain atrophy in the neocortex and the hippocampus

OBJECTIVE

Depression is associated with accelerated aging and age-related diseases. However, mechanisms underlying this relationship remain unclear. The aim of this study was to longitudinally assess the link between depressive symptoms, brain atrophy, and cortisol levels.

METHOD

Participants from the Betula prospective cohort study (mean age = 59 years, SD = 13.4 years) underwent clinical, neuropsychological and brain 3T MRI assessments at baseline and a 4-year follow-up. Cortisol levels were measured at baseline in four saliva samples. Cortical and hippocampal atrophy rates were estimated and compared between participants with and without depressive symptoms (n = 81) and correlated with cortisol levels (n = 49).

RESULTS

Atrophy in the left superior frontal gyrus and right lingual gyrus developed in parallel with depressive symptoms, and in the left temporal pole, superior temporal cortex, and supramarginal cortex after the onset of depressive symptom. Depression-related atrophy was significantly associated with elevated cortisol levels. Elevated cortisol levels were also associated with widespread prefrontal, parietal, lateral, and medial temporal atrophy.

CONCLUSION

Depressive symptoms and elevated cortisol levels are associated with atrophy of the prefrontal and limbic areas of the brain.

Cerebral Imaging Markers of GBA and LRRK2 Related Parkinson's Disease and Their First-Degree Unaffected Relatives

Cerebral atrophy has been detected in patients with Parkinson's disease (PD) both with and without dementia, however differentiation based on genetic status has thus far not yielded robust findings. We assessed cortical thickness and subcortical volumes in a cohort of PD patients and healthy controls carriers of the G2019S mutation in the LRRK2 gene and the common GBA mutations, in an attempt to determine whether genetic status influences structural indexes. Cortical thickness and subcortical volumes were computed and compared between six groups of participants; idiopathic PD, GBA-PD, LRRK2-PD, non-manifesting non-carriers (NMNC), GBA-non-manifesting carriers (NMC) and LRRK2-NMC utilizing the FreeSurfer software program. All participants were cognitively intact based on a computerized cognitive assessment battery. Fifty-seven idiopathic PD patients, 9 LRRK2-PD, 12 GBA-PD, 49 NMNC, 41 LRRK2-NMC and 14 GBA-NMC participated in this study. Lower volumes among patients with PD compared to unaffected participants were detected in bilateral hippocampus, nucleus accumbens, caudate, thalamus, putamen and amygdala and the right pallidum (p = 0.016). PD patients demonstrated lower cortical thickness indexes in a majority of regions assessed compared with non-manifesting participants. No differences in cortical thickness and subcortical volumes were detected within each of the groups of participants based on genetic status. Mutations in the GBA and LRRK2 genes are not important determinants of cortical thickness and subcortical volumes in both patients with PD and non-manifesting participants. PD is associated with a general reduction in cortical thickness and sub-cortical atrophy even in cognitively intact patients.

Striatal changes in Parkinson disease: An investigation of morphology, functional connectivity and their relationship to clinical symptoms

We sought to investigate morphological and resting state functional connectivity changes to the striatal nuclei in Parkinson disease (PD) and examine whether changes were associated with measures of clinical function. Striatal nuclei were manually segmented on 3T-T1 weighted MRI scans of 74 PD participants and 27 control subjects, quantitatively analysed for volume, shape and also functional connectivity using functional MRI data. Bilateral caudate nuclei and putamen volumes were significantly reduced in the PD cohort compared to controls. When looking at left and right hemispheres, the PD cohort had significantly smaller left caudate nucleus and right putamen volumes compared to controls. A significant correlation was found between greater atrophy of the caudate nucleus and poorer cognitive function, and between greater atrophy of the putamen and more severe motor symptoms. Resting-state functional MRI analysis revealed altered functional connectivity of the striatal structures in the PD group. This research demonstrates that PD involves atrophic changes to the caudate nucleus and putamen that are linked to clinical dysfunction. Our work reveals important information about a key structure-function relationship in the brain and provides support for caudate nucleus and putamen atrophy as neuroimaging biomeasures in PD.

Noradrenergic Dysfunction in Alzheimer's and Parkinson's Diseases-An Overview of Imaging Studies

Noradrenergic dysfunction contributes to cognitive impairment in Alzheimer's Disease (AD) and Parkinson's Disease (PD). Conventional therapeutic strategies seek to enhance cholinergic and dopaminergic neurotransmission in AD and PD, respectively, and few studies have examined noradrenergic dysfunction as a target for medication development. We review the literature of noradrenergic dysfunction in AD and PD with a focus on human imaging studies that implicate the locus coeruleus (LC) circuit. The LC sends noradrenergic projections diffusely throughout the cerebral cortex and plays a critical role in attention, learning, working memory, and cognitive control. The LC undergoes considerable degeneration in both AD and PD. Advances in magnetic resonance imaging have facilitated greater understanding of how structural and functional alteration of the LC may contribute to cognitive decline in AD and PD. We discuss the potential roles of the noradrenergic system in the pathogenesis of AD and PD with an emphasis on postmortem anatomical studies, structural MRI studies, and functional MRI studies, where we highlight changes in LC connectivity with the default mode network (DMN). LC degeneration may accompany deficient capacity in suppressing DMN activity and increasing saliency and task control network activities to meet behavioral challenges. We finish by proposing potential and new directions of research to address noradrenergic dysfunction in AD and PD.

Cortical and subcortical gray matter bases of cognitive deficits in REM sleep behavior disorder

OBJECTIVE

To investigate cortical and subcortical gray matter abnormalities underlying cognitive impairment in patients with REM sleep behavior disorder (RBD) with or without mild cognitive impairment (MCI).

METHODS

Fifty-two patients with RBD, including 17 patients with MCI, were recruited and compared to 41 controls. All participants underwent extensive clinical assessments, neuropsychological examination, and 3-tesla MRI acquisition of T1 anatomical images. Vertex-based cortical analyses of volume, thickness, and surface area were performed to investigate cortical abnormalities between groups, whereas vertex-based shape analysis was performed to investigate subcortical structure surfaces. Correlations were performed to investigate associations between cortical and subcortical metrics, cognitive domains, and other markers of neurodegeneration (color discrimination, olfaction, and autonomic measures).

RESULTS

Patients with MCI had cortical thinning in the frontal, cingulate, temporal, and occipital cortices, and abnormal surface contraction in the lenticular nucleus and thalamus. Patients without MCI had cortical thinning restricted to the frontal cortex. Lower patient performance in cognitive domains was associated with cortical and subcortical abnormalities. Moreover, impaired performance on olfaction, color discrimination, and autonomic measures was associated with thinning in the occipital lobe.

CONCLUSIONS

Cortical and subcortical gray matter abnormalities are associated with cognitive status in patients with RBD, with more extensive patterns in patients with MCI. Our results highlight the importance of distinguishing between subgroups of patients with RBD according to cognitive status in order to better understand the neurodegenerative process in this population.

Network connectivity determines cortical thinning in early Parkinson's disease progression

Here we test the hypothesis that the neurodegenerative process in Parkinson’s disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (n = 105) and matched controls (n = 57) underwent T1-MRI at entry and 1 year later as part of the Parkinson’s Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a “disease reservoir” evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer’s disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment.

In Parkinson’s disease (PD), neurodegeneration spreads from the brainstem to the cerebral cortex. Here, in a longitudinal study of PD patients, the authors found that cortical thinning followed neural connectivity from a “disease reservoir”.

Numerical and length densities of microvessels in the human brain: Correlation with preferential orientation of microvessels in the cerebral cortex, subcortical grey matter and white matter, pons and cerebellum

To provide basic data on the local differences in density of microvessels between various parts of the human brain, including representative grey and white matter structures of the cerebral hemispheres, the brain stem and the cerebellum, we quantified the numerical density N_V and the length density L_V of microvessels in two human brains. We aimed to correlate the density of microvessels with previously published data on their preferential orientation (anisotropy). Microvessels were identified using immunohistochemistry for laminin in 32 samples harvested from the following brain regions of two adult individuals: the cortex of the telencephalon supplied by the anterior, middle, and posterior cerebral artery; the basal ganglia (putamen and globus pallidus); the thalamus; the subcortical white matter of the telencephalon; the internal capsule; the pons; the cerebellar cortex; and the cerebellar white matter. N_V was calculated from the number of vascular branching points and their valence, which were assessed using the optical disector in 20-μm-thick sections. L_V was estimated using counting frames applied to routine sections with randomized cutting planes. After correction for shrinkage, N_V in the cerebral cortex was 1311±326mm^-3 (mean±SD) and L_V was 255±119mm^-2. Similarly, in subcortical grey matter (which included the basal ganglia and thalamus), N_V was 1350±445mm^-3 and L_V was 328±117mm^-2. The vascular networks of cortical and subcortical grey matter were comparable. Their densities were greater than in the white matter, with N_V=222±147mm^-3 and L_V=160±96mm^-2. N_V was moderately correlated with L_V. In parts of brain with greater N_V, blood vessels lacked a preferential orientation. Our data were in agreement with other studies on microvessel density focused on specific brain regions, but showed a greater variability, thus mapping the basic differences among various parts of brain. To facilitate the planning of other studies on brain vascularity and to support the development of computational models of human brain circulation based on real microvascular morphology; stereological data in form of continuous variables are made available as supplements.

Predicting auditory feedback control of speech production from subregional shape of subcortical structures

Although a growing body of research has focused on the cortical sensorimotor mechanisms that support auditory feedback control of speech production, much less is known about the subcortical contributions to this control process. This study examined whether subregional anatomy of subcortical structures assessed by statistical shape analysis is associated with vocal compensations and cortical event-related potentials in response to pitch feedback errors. The results revealed significant negative correlations between the magnitudes of vocal compensations and subregional shape of the right thalamus, between the latencies of vocal compensations and subregional shape of the left caudate and pallidum, and between the latencies of cortical N1 responses and subregional shape of the left putamen. These associations indicate that smaller local volumes of the basal ganglia and thalamus are predictive of slower and larger neurobehavioral responses to vocal pitch errors. Furthermore, increased local volumes of the left hippocampus and right amygdala were predictive of larger vocal compensations, suggesting that there is an interplay between the memory-related subcortical structures and auditory-vocal integration. These results, for the first time, provide evidence for differential associations of subregional morphology of the basal ganglia, thalamus, hippocampus, and amygdala with neurobehavioral processing of vocal pitch errors, suggesting that subregional shape measures of subcortical structures can predict behavioral outcome of auditory-vocal integration and associated neural features. Hum Brain Mapp 39:459-471, 2018. © 2017 Wiley Periodicals, Inc.

The Association of PTSD Symptom Severity with Localized Hippocampus and Amygdala Abnormalities

Background

The hippocampus and amygdala have been repeatedly implicated in the psychopathology of posttraumatic stress disorder (PTSD). While numerous structural neuroimaging studies examined these two structures in PTSD, these analyses have largely been limited to volumetric measures. Recent advances in vertex-based neuroimaging methods have made it possible to identify specific locations of subtle morphometric changes within a structure of interest.

Methods

In this cross-sectional study, we used high-resolution magnetic resonance imaging to examine the relationship between PTSD symptomatology, as measured using the Clinician Administered PTSD Scale for the DSM-IV, and structural shape of the hippocampus and amygdala using vertex-wise shape analyses in a group of combat-exposed U.S. Veterans (N = 69).

Results

Following correction for multiple comparisons and controlling for age and cranial volume, we found that participants with more severe PTSD symptoms showed an indentation in the anterior half of the right hippocampus and an indentation in the dorsal region of the right amygdala (corresponding to the centromedial amygdala). Post hoc analysis using stepwise regression suggest that among PTSD symptom clusters, arousal symptoms explain most of the variance in the hippocampal abnormality, whereas reexperiencing symptoms explain most of the variance in the amygdala abnormality.

Conclusion

The results provide evidence of localized abnormalities in the anterior hippocampus and centromedial amygdala in combat-exposed U.S. Veterans suffering from PTSD symptoms. This novel finding provides a more fine-grained analysis of structural abnormalities in PTSD and may be informative for understanding the neurobiology of the disorder.

Magnetic resonance imaging for the diagnosis of Parkinson's disease

The differential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology and error rates in the clinical diagnosis can be high even at specialized centres. Despite several limitations, magnetic resonance imaging (MRI) has undoubtedly enhanced the diagnostic accuracy in the differential diagnosis of neurodegenerative parkinsonism over the last three decades. This review aims to summarize research findings regarding the value of the different MRI techniques, including advanced sequences at high- and ultra-high-field MRI and modern image analysis algorithms, in the diagnostic work-up of Parkinson's disease. This includes not only the exclusion of alternative diagnoses for Parkinson's disease such as symptomatic parkinsonism and atypical parkinsonism, but also the diagnosis of early, new onset, and even prodromal Parkinson's disease.

Stereological Evaluation of the Brains in Patients with Parkinson’s disease Compared to Controls

Parkinson’s disease (PD) is a chronic and progressive neurological disorder. A tetrad of bradykinesia, rigidity, tremor and postural instability are the core features of the disease. The aim of this study was to evaluate stereological changes in the brain of patients with PD and compare them with that of healthy controls. This case-control study was conducted on 29 patients with PD and 12 controls (C) in Zahedan, Iran. All subjects enrolled into the study through the convenience sampling method. MRI images of the brains of two groups in frontal and sagittal axis with consecutive 5mm distance slices were captured. Parameters including total volume (V) and volume density (Vv) of different parts of the brain were estimated based on Cavalries’ point counting stereological method. To analyze the data, descriptive statistics, Mann-Whitney U-Test applied for comparing the PD and C groups were used. Significance level was set at p<0.05. Our study showed that the volume of the brain and total volume and volume density (Vv) of cerebral hemispheres, cerebellum, ventricles, hippocampus, pons, mid brain and superior cerebellar peduncles in the PD group did not indicate significant difference from the control group. Total volume of brain stem in PD group wasn’t significantly different from the control group. The volume density of brain stem (p= 0.012) and total volume and volume density of middle cerebellar peduncle (p< 0.0001) in PD group were significantly larger than the control group. This study shows that PD stereological parameters related to volume and volume density of middle cerebellar peduncle and volume density of brain stem were significantly larger in patients compared to the controls. Therefore, stereological parameters can be used for early diagnosis and probably for follow-up in patients with PD.

Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis

BACKGROUND

Neuroimaging studies help us better understand the pathophysiology and symptoms of Parkinson's disease (PD). In several of these studies, diffusion tensor imaging (DTI) was used to investigate structural changes in cerebral tissue. Although data have been provided as regards to specific brain areas, a whole brain meta-analysis is still missing.

METHODS

We compiled 39 studies in this meta-analysis: 14 used fractional anisotropy (FA), 1 used mean diffusivity (MD), and 24 used both indicators. These studies comprised 1855 individuals, 1087 with PD and 768 healthy controls. Regions of interest were classified anatomically (subcortical structures; white matter; cortical areas; cerebellum). Our statistical analysis considered the disease effect size (DES) as the main variable; the heterogeneity index (I2) and Pearson's correlations between the DES and co-variables (demographic, clinical and MRI parameters) were also calculated.

RESULTS

Our results showed that FA-DES and MD-DES were able to distinguish between patients and healthy controls. Significant differences, indicating degenerations, were observed within the substantia nigra, the corpus callosum, and the cingulate and temporal cortices. Moreover, some findings (particularly in the corticospinal tract) suggested opposite brain changes associated with PD. In addition, our results demonstrated that MD-DES was particularly sensitive to clinical and MRI parameters, such as the number of DTI directions and the echo time within white matter.

CONCLUSIONS

Despite some limitations, DTI appears as a sensitive method to study PD pathophysiology and severity. The association of DTI with other MRI methods should also be considered and could benefit the study of brain degenerations in PD.

MRI volumetric morphometry in vascular parkinsonism

Vascular parkinsonism is a difficult clinical differential diagnosis in elderly subjects. We aimed at identifying morphometric markers in the brain of elderly patients with vascular parkinsonism (VP) compared with age-matched patients with Parkinson's disease (PD) and healthy controls. In this multicenter prospective study, 46 patients (80 ± 5 years old; male 32) with parkinsonism (32 PD and 14 VP) and 29 controls (mean age 78 ± 3 years; male 21) underwent brain MRI on a 3-T scanner including T1 MPRAGE and FLAIR sequences. Volumetric morphometry was obtained using Morphobox software and compared between patients and controls. Receiver operating characteristics curve analysis with computation of area under the curve (AUC) was used to compare diagnostic values. Caudate nucleus and white matter hyperintense lesions (WMHL) volumes appeared significantly higher in patients with VP. Normalized caudate volume of at least 0.67% and normalized WMHL of at least 1.11% identified patients with VP from patients with PD and controls with similar performances (p > 0.25). Caudate nucleus and WMHL volumes were positively correlated (ρ = 0.74, p < 0.0001), suggesting vascular disease related remodelling in elderly subjects. Caudate nucleus and WMHL MRI volumes might be used as additional markers to help identify patients with VP in the initial workup of elderly subjects with parkinsonian symptoms.