Corpus Callosum and Cingulum Tractography in Parkinson's Disease

White matter connectivity networks predict levodopa-induced dyskinesia in Parkinson's disease

BACKGROUND

Although levodopa-induced dyskinesia-relevant white matter change has been evaluated, it is uncertain whether these changes may reflect the underlying predisposing conditions leading to the development of levodopa-induced dyskinesia.

OBJECTIVE

To elucidate the role of white matter connectivity networks in the development of levodopa-induced dyskinesia in drug-naïve Parkinson's disease.

METHODS

We recruited 30 patients who developed levodopa-induced dyskinesia within 5 years from MRI acquisition (vulnerable-group), 47 patients who had not developed levodopa-induced dyskinesia within 5 years (resistant-group), and 28 controls. We performed comparative analyses of whole-brain white matter integrity and connectivity using tract-based spatial and network- and degree-based statistics. We evaluated the predictability of levodopa-induced dyskinesia development and relationship with its latency, using the average connectivity strength as a predictor in Cox- and linear-regression, respectively.

RESULTS

Mean-diffusivity was lower mainly at the left frontal region in the vulnerable-group compared to the resistant-group. Network-based statistics identified a subnetwork consisting of the bilateral fronto-striato-pallido-thalamic and lateral parietal regions (subnetwork A) and degree-based statistics identified four subnetworks (hub-subnetwork) consisting of edges centered on the left superior frontal gyrus, left putamen, left insular, or left precentral gyrus, where the vulnerable-group had stronger connectivity compared to the resistant-group. Stronger connectivity within the subnetwork A and hub-subnetwork centered on the left superior frontal gyrus was a predictor of levodopa-induced dyskinesia development independent of known risk factors and had an inverse relationship with its latency.

CONCLUSIONS

Our data suggest that white matter connectivity subnetworks within corticostriatal regions play a pivotal role in the development of levodopa-induced dyskinesia.

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.

Local White Matter Fiber Clustering Differentiates Parkinson's Disease Diagnoses

Scans without evidence of dopaminergic deficit (SWEDD) patients are often misdiagnosed with Parkinson's disease (PD) but have normal dopamine transporter scans. We hypothesised that white matter tracts associated with motor and cognition functions may be affected differently by SWEDD and PD. Automatically annotated fibre clustering (AAFC) is a novel clustering method based on diffusion magnetic resonance imaging (dMRI) tractography that enables highly robust reconstruction of white matter tracts that are composed of corresponding clusters. This study aimed to investigate the white matter properties in the subdivisions of white matter tracts among SWEDD and PD groups. We applied AAFC to identify white matter tracts related to motion and cognition functions in the dataset consisting of SWEDD (n = 22), PD (n = 30) and normal control (NC) (n = 30). Then, we resampled 200 nodes along fibres of cluster, and the diffusion metric values corresponding to each node were calculated and used for comparison. Compared with NC, PD showed significant difference (p < 0.05) in two clusters in thalamo-frontal (TF), one cluster in thalamo-parietal (TP) and one cluster in thalamo-occipital (TO), whereas SWEDD presented no significant difference. Three clusters in cingulum bundle (CB) commonly exhibited significant differences in PD versus SWEDD and NC versus SWEDD. The support vector machine classifier achieved high accuracies in PD-NC, PD-SWEDD and NC-SWEDD classifications. This outcome validated these local white matter differences were useful to separate the three groups. These results suggest that PD exerts more significant effects on thalamo tracts than SWEDD, and unique microstructural changes occur in CB tract in SWEDD.

Neurocognitive and psychiatric disorders-related axonal degeneration in Parkinson's disease

Neurocognitive and psychiatric disorders have significant consequences for quality of life in patients with Parkinson's disease (PD). In the current study, we evaluated microstructural white matter (WM) alterations associated with neurocognitive and psychiatric disorders in PD using neurite orientation dispersion and density imaging (NODDI) and linked independent component analysis (LICA). The indices of NODDI were compared between 20 and 19 patients with PD with and without neurocognitive and psychiatric disorders, respectively, and 25 healthy controls using tract‐based spatial statistics and tract‐of‐interest analyses. LICA was applied to model inter‐subject variability across measures. A widespread reduction in axonal density (indexed by intracellular volume fraction [ICVF]) was demonstrated in PD patients with and without neurocognitive and psychiatric disorders, as compared with healthy controls. Compared with patients without neurocognitive and psychiatric disorders, patients with neurocognitive and psychiatric disorders exhibited more extensive (posterior predominant) decreases in axonal density. Using LICA, ICVF demonstrated the highest contribution (59% weight) to the main effects of diagnosis that reflected widespread decreases in axonal density. These findings suggest that axonal loss is a major factor underlying WM pathology related to neurocognitive and psychiatric disorders in PD, whereas patients with neurocognitive and psychiatric disorders had broader axonal pathology, as compared with those without. LICA suggested that the ICVF can be used as a useful biomarker of microstructural changes in the WM related to neurocognitive and psychiatric disorders in PD.

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.

A longitudinal fixel-based analysis of white matter alterations in patients with Parkinson's disease

INTRODUCTION

Disruption to white matter pathways is an important contributor to the pathogenesis of Parkinson's disease. Fixel-based analysis has recently emerged as a useful fiber-specific tool for examining white matter structure. In this longitudinal study, we used Fixel-based analysis to investigate white matter changes occurring over time in patients with Parkinson's disease.

METHODS

Fifty patients with idiopathic Parkinson's disease (27 men and 23 women; mean age: 61.8 ± 6.1 years), were enrolled. Diffusion-weighted imaging and clinical examinations were performed at three different time points (baseline, first follow-up [after a mean of 24±2 months], and second follow-up [after a mean of 40 ± 3 months]). Additional 76 healthy control subjects (38 men and 38 women; mean age: 62.3 ± 5.5 years) were examined at baseline. The following fixel-based metrics were obtained: fiber density (FD), fiber bundle cross-section (FC), and a combined measure of both (FDC). Paired comparisons of metrics between three different time points were performed in patients. Linear regression was implemented between longitudinal changes of fixel-based metrics and the corresponding modifications in clinical parameters. A family-wise error corrected p < 0.05 was considered statistically significant.

RESULTS AND DISCUSSIONS

Early degeneration in the splenium of corpus callosum was identified as a typical alteration of Parkinson's disease over time. At follow-up, we observed significant FDC reductions compared with baseline in white matter, noticeably in corpus callosum; tapetum; cingulum, posterior thalamic radiation, corona radiata, and sagittal stratum. We also identified significant FC decreases that reflected damage to white matter structures involved in Parkinson's disease -related pathways. Fixel-based metrics were found to relate with a deterioration of 39-item Parkinson's Disease Questionnaire, Unified Parkinson's Disease Rating Scale and activity of daily living. A Parkinson's disease -facilitated aging effect was observed in terms of white matter disruption.

CONCLUSION

This study provides a thorough fixel-based profile of longitudinal white matter alterations occurring in patients with Parkinson's disease and new evidence of FC as an important role in white matter degeneration in this setting.

Altered Global Synchronizations in Patients With Parkinson's Disease: A Resting-State fMRI Study

Background: Abnormalities of cognitive and movement functions are widely reported in Parkinson’s disease (PD). The mechanisms therein are complicated and assumed to a coordination of various brain regions. This study explored the alterations of global synchronizations of brain activities and investigated the neural correlations of cognitive and movement function in PD patients.

Methods: Thirty-five age-matched patients with PD and 35 normal controls (NC) were enrolled in resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Degree centrality (DC) was calculated to measure the global synchronizations of brain activity for two groups. Neural correlations between DC and cognitive function Frontal Assessment Battery (FAB), as well as movement function Unified Parkinson’s Disease Rating Scale (UPDRS-III), were examined across the whole brain within Anatomical Automatic Labeling (AAL) templates.

Results: In the PD group, increased DC was observed in left fusiform gyrus extending to inferior temporal gyrus, left middle temporal gyrus (MTG) and angular gyrus, while it was decreased in right inferior opercular-frontal gyrus extending to superior temporal gyrus (STG). The DC in a significant region of the fusiform gyrus was positively correlated with UPDRS-III scores in PD (r = 0.41, p = 0.0145). Higher FAB scores were shown in NC than PD (p < 0.0001). Correlative analysis of PD between DC and FAB showed negative results (p < 0.05) in frontal cortex, whereas positive in insula and cerebellum. As for the correlations between DC and UPDRS-III, negative correlation (p < 0.05) was observed in bilateral inferior parietal lobule (IPL) and right cerebellum, whereas positive correlation (p < 0.05) in bilateral hippocampus and para-hippocampus gyrus (p < 0.01).

Conclusion: The altered global synchronizations revealed altered cognitive and movement functions in PD. The findings suggested that the global functional connectivity in fusiform gyrus, cerebellum and hippocampus gyrus are critical regions in the identification of cognitive and movement functions in PD. This study provides new insights on the interactions among global coordination of brain activity, cognitive and movement functions in PD.

Exploring white matter microstructure and olfaction dysfunction in early parkinson disease: diffusion MRI reveals new insight

Olfaction dysfunction is considered as a robust marker of prodromal Parkinson disease (PD). Measurement of olfaction function as a screening test is unsatisfactory due to long lead time interval and low specificity for detection of PD. Use of imaging markers might yield more accurate predictive values and provide bases for combined use of imaging and clinical markers for early PD. Diffusion MRI connectometry was conducted on 85 de novo PD patients in and 36 healthy controls to find: first, white matter tracts with significant difference in quantitative anisotropy between PD groups with various degrees of olfaction dysfunction and second, second fibers with correlation with University of Pennsylvania Smell Identification Test (UPSIT) score in each group using a multiple regression analysis considering age, sex, GDS and MoCA score. Local connectomes were determined in seven of all the possible comparisons, correcting for false discovery rate (FDR). PD patients with anosmia and normal olfaction had the highest number of fibers with decreased connectivity in left inferior longitudinal fasciculus, bilateral fornix, bilateral middle cerebellar peduncle (MCP), bilateral cingulum, bilateral corticospinal tract (CST) and body, genu and splenium of corpus callosum (CC) (FDR = 0.0013). In multiple regression analysis, connectivity in the body, genu and splenium of CC and bilateral fornix had significant negative correlation (FDR between 0.019 and 0.083), and bilateral cingulum and MCP had significant positive correlation (FDR between 0.022 and 0.092) with UPSIT score. White matter connectivity in healthy controls could not be predicted by UPSIT score using the same model. The results of this study provide compelling evidence that microstructural degenerative changes in these areas underlie the clinical phenotype of prodromal olfaction dysfunction in PD and that diffusion parameters of these areas might be able to serve as signature markers for early detection of PD. This is the first report that confirms a discriminative role for UPSIT score in identifying PD specific changes in white matter microstructure. Our results open a window to identify microstructural signatures of prodromal PD in white matter.

Resting functional connectivity and mild cognitive impairment in Parkinson’s disease. An electroencephalogram study

Objective: Parkinson’s disease (PD) is characterized by cognitive deficits. There is not clarity about electroencephalogram (EEG) connectivity related to the cognitive profile of patients. Our objective was to evaluate connectivity over resting EEG in nondemented PD. Methods: PD subjects with and without mild cognitive impairment (MCI) were assessed using coherence from resting EEG for local, intra and interhemispheric connectivity. Results: PD subjects without MCI (PD-nMCI) had lower intra and interhemispheric coherence in alpha2 compared with controls. PD with MCI (PD-MCI) showed higher intra and posterior interhemispheric coherence in alpha2 and beta1, respectively, in comparison to PD-nMCI. PD-MCI presented lower frontal coherence in beta frequencies compared with PD-nMCI. Conclusion: EEG coherence measures indicate distinct cortical activity in PD with and without MCI.

White matter abnormalities in the corpus callosum with cognitive impairment in Parkinson disease

OBJECTIVE

To evaluate microstructural characteristics of the corpus callosum using diffusion tensor imaging (DTI) and their relationships to cognitive impairment in Parkinson disease (PD).

METHODS

Seventy-five participants with PD and 24 healthy control (HC) participants underwent structural MRI brain scans including DTI sequences and clinical and neuropsychological evaluations. Using Movement Disorder Society criteria, PD participants were classified as having normal cognition (PD-NC, n = 23), mild cognitive impairment (PD-MCI, n = 35), or dementia (PDD, n = 17). Cognitive domain (attention/working memory, executive function, language, memory, visuospatial function) z scores were calculated. DTI scalar values, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), were established for 5 callosal segments on a midsagittal plane, single slice using a topographically derived parcellation method. Scalar values were compared among participant groups. Regression analyses were performed on cognitive domain z scores and DTI metrics.

RESULTS

Participants with PD showed increased AD values in the anterior 3 callosal segments compared to healthy controls. Participants with PDD had significantly increased AD, MD, and RD in the anterior 2 segments compared to participants with PD-NC and most anterior segment compared to participants with PD-MCI. FA values did not differ significantly between participants with PD and participants with HC or among PD cognitive groups. The strongest associations for the DTI metrics and cognitive performance occurred in the most anterior and most posterior callosal segments, and also reflected fronto-striatal and posterior cortical type cognitive deficits, respectively.

CONCLUSIONS

Microstructural white matter abnormalities of the corpus callosum, as measured by DTI, may contribute to PD cognitive impairment by disrupting information transfer across interhemispheric and callosal-cortical projections.

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.

Autonomic dysfunction and white matter microstructural changes in drug-naïve patients with Parkinson's disease

BACKGROUND

Autonomic dysfunction (AD) is one of the non-motor features of Parkinson's disease (PD). Some symptoms tend to occur in the early stages of PD. AD also has a great impact on patient's quality of life. In this study, we aimed to discover the association between AD (Scales for Outcomes in Parkinson's disease-Autonomic, SCOPA-AUT) and microstructural changes in white matter tracts in drug-naïve early PD patients to elucidate the central effects of autonomic nervous system impairments.

METHOD

In total, this study included 85 subjects with PD recruited from the Parkinson's Progression Markers Initiative (PPMI) database. Among the 85 PD patients, 38 were in Hoehn & Yahr stage 1 (HY1PD) and 47 were in stage 2 (HY2PD). Diffusion magnetic resonance imaging (DMRI) data were reconstructed in the MNI space using q-space diffeomorphic reconstruction to obtain the spin distribution function. The spin distribution function (SDF) values were used in DMRI connectometry analysis. We investigated through diffusion MRI connectometry the structural correlates of white matter tracts with SCOPA-AUT subscores and total score.

RESULTS

Connectometry analysis also revealed positive association with white matter density in bilateral corticospinal tract in HY1PD patients and negative association in genu of corpus callosum (CC) and, bilateral cingulum in both groups. In addition, there were associations between gastrointestinal, sexual, thermoregulatory and urinary items and structural brain connectivity in PD.

CONCLUSION

Our study reveals positive correlation, suggesting neural compensations in early PD. Cingulum and CC tracts have well-known roles in PD pathology, compatible with our findings that bring new insights to specific areas of AD and its role in central nervous system (CNS) neurodegeneration, paving the way for using prodromal makers in the diagnosis and treatment of PD.

[Voxel-based analysis of cerebral blood flow changes in Parkinson disease using arterial spin labeling technique]

OBJECTIVE

To explore the imaging biomarker for early diagnosis and disease course monitoring of Parkinson disease (PD) in arterial spin labeling (ASL) technique.

METHODS

Between July, 2014 and May, 2017, 23 patients with PD underwent magnetic resonance imaging (MRI) and ASL examinations in our hospital, including 13 in the early stage and 10 in advanced stages. Voxel-based analysis (VBA) was used to observe the regional cerebral blood flow (rCBF) characteristics in PD patients in different stages and three-dimensional continuous arterial spin labeling (3D CASL) was used to analyze the mean cerebral blood flow (mCBF).

RESULTS

No significant difference was found in mCBF among PD patients in the early stage, patients in advanced stages and normal control subjects (P=0.30). Compared with the normal control group, the patients with early-stage PD had decreased rCBF in resting state mainly in the right superior occipital gyrus and the right superior frontal gyrus as revealed by VBA (P < 0.001); the patients with advanced PD showed decreased rCBF mainly in the left precentral gyrus and the postcentral gyrus (P < 0.001). The patients with advanced PD exhibited lowered rCBF in the right substantia nigra and the bilateral corpus callosum as compared with the early-stage patients (P < 0.001).

CONCLUSIONS

VBA of ASL reveals rCBF alterations in association with the disease progression in PD patients, suggesting that this technique might provide assistance in identification of potential markers for early PD diagnosis and for monitoring the disease course.

Update on Molecular Imaging in Parkinson's Disease

Advances in radionuclide tracers have allowed for more accurate imaging that reflects the actions of numerous neurotransmitters, energy metabolism utilization, inflammation, and pathological protein accumulation. All of these achievements in molecular brain imaging have broadened our understanding of brain function in Parkinson's disease (PD). The implementation of molecular imaging has supported more accurate PD diagnosis as well as assessment of therapeutic outcome and disease progression. Moreover, molecular imaging is well suited for the detection of preclinical or prodromal PD cases. Despite these advances, future frontiers of research in this area will focus on using multi-modalities combining positron emission tomography and magnetic resonance imaging along with causal modeling with complex algorithms.

White Matter Changes Correlates of Peripheral Neuroinflammation in Patients with Parkinson's Disease

Neuroinflammatory pathology has long been identified to contribute to the pathology of Parkinson disease. Early microstructural changes in white matter tracts might give a clue for earlier detection of PD. We investigated through diffusion MRI connectometry the structural correlates of white matter tracts of 81 patients with PD with whole blood neutrophil-to-lymphocyte ratio (NLR), controlling for age and sex. Diffusion data were reconstructed in the MNI space using q-space diffeomorphic reconstruction to obtain the spin distribution function. The spin distribution function (SDF) values were used in DMRI connectometry analysis. The connectometry analyses identified white matter QA of the following fibers to be correlated with NLR score after adjustment for age and sex: bilateral cingulum, body and left crus of fornix, bilateral corticospinal tract (CST), and body and splenium of corpus callosum (CC) and superior cerebellar peduncle with decreased connectivity related to NLR (FDR = 0.04542). Keeping with emerging evidence on the role of neuroinflammation in PD pathology, these results with functional relevance to prodromal Parkinson disease, bring new insights to pivotal role of peripheral inflammation in CNS neurodegeneration.

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.

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.

Diffusion alterations associated with Parkinson's disease symptomatology: A review of the literature

Parkinson's disease (PD) is a heterogeneous neurological disorder with a variety of motor and non-motor symptoms. The underlying mechanisms of these symptoms are not fully understood. An increased interest in structural connectivity analyses using diffusion tensor imaging (DTI) in PD has led to an expansion of our understanding of the impact of abnormalities in diffusivity on phenotype. This review outlines the contribution of these abnormalities to symptoms of PD including bradykinesia, tremor and non-tremor phenotypes, freezing of gait, cognitive impairment, mood, sleep disturbances, visual hallucinations and olfactory dysfunction. Studies have shown that impairments in cognitive functioning are related to diffusion abnormalities in frontal and parietal regions, as well as in the corpus callosum and major fibres connecting midbrain and subcortical structures with the neocortex. However, the impact of diffusion alterations on motor, mood and other symptoms of PD are less well understood. The findings presented here highlight the challenges faced and the potential areas of future research avenues where DTI may be beneficial. Larger cohort studies and standardized imaging protocols are required to investigate current promising preliminary findings.

Conventional 3T brain MRI and diffusion tensor imaging in the diagnostic workup of early stage parkinsonism

Introduction

The aim of this study is to evaluate whether the diagnostic accuracy of 3 T brain MRI is improved by region of interest (ROI) measures of diffusion tensor imaging (DTI), to differentiate between neurodegenerative atypical parkinsonism (AP) and Parkinson’s disease (PD) in early stage parkinsonism.

Methods

We performed a prospective observational cohort study of 60 patients presenting with early stage parkinsonism and initial uncertain diagnosis. At baseline, patients underwent a 3 T brain MRI including DTI. After clinical follow-up (mean 28.3 months), diagnoses could be made in 49 patients (30 PD and 19 AP). Conventional brain MRI was evaluated for regions of atrophy and signal intensity changes. Tract-based spatial statistics and ROI analyses of DTI were performed to analyze group differences in mean diffusivity (MD) and fractional anisotropy (FA), and diagnostic thresholds were determined. Diagnostic accuracy of conventional brain MRI and DTI was assessed with the receiver operating characteristic (ROC).

Results

Significantly higher MD of the centrum semiovale, body corpus callosum, putamen, external capsule, midbrain, superior cerebellum, and superior cerebellar peduncles was found in AP. Significantly increased MD of the putamen was found in multiple system atrophy–parkinsonian form (MSA-P) and increased MD in the midbrain and superior cerebellar peduncles in progressive supranuclear palsy (PSP). The diagnostic accuracy of brain MRI to identify AP as a group was not improved by ROI measures of MD, though the diagnostic accuracy to identify MSA-P was slightly increased (AUC 0.82 to 0.85).

Conclusion

The diagnostic accuracy of brain MRI to identify AP as a group was not improved by the current analysis approach to DTI, though DTI measures could be of added value to identify AP subgroups.

Genetics of microstructure of the corpus callosum in older adults

The current study sought to examine the relative influence of genetic and environmental factors on corpus callosum (CC) microstructure in a community sample of older adult twins. Analyses were undertaken in 284 healthy older twins (66% female; 79 MZ and 63 DZ pairs) from the Older Australian Twins Study. The average age of the sample was 69.82 (SD = 4.76) years. Brain imaging scans were collected and DTI measures were estimated for the whole CC as well as its five subregions. Parcellation of the CC was performed using Analyze. In addition, white matter lesion (WMLs) burden was estimated. Heritability and genetic correlation analyses were undertaken using the SOLAR software package. Age, sex, scanner, handedness and blood pressure were considered as covariates. Heritability (h²) analysis for the DTI metrics of whole CC, indicated significant h² for fractional anisotropy (FA) (h² = 0.56; p = 2.89×10⁻¹⁰), mean diffusivity (MD) (h² = 0.52; p = 0.30×10⁻⁶), radial diffusivity (RD) (h² = 0.49; p = 0.2×10⁻⁶) and axial diffusivity (AD) (h² = 0.37; p = 8.15×10⁻⁵). We also performed bivariate genetic correlation analyses between (i) whole CC DTI measures and (ii) whole CC DTI measures with total brain WML burden. Across the DTI measures for the whole CC, MD and RD shared 84% of the common genetic variance, followed by MD- AD (77%), FA - RD (52%), RD - AD (37%) and FA – MD (11%). For total WMLs, significant genetic correlations indicated that there was 19% shared common genetic variance with whole CC MD, followed by CC RD (17%), CC AD (16%) and CC FA (5%). Our findings suggest that the CC microstructure is under moderate genetic control. There was also evidence of shared genetic factors between the CC DTI measures. In contrast, there was less shared genetic variance between WMLs and the CC DTI metrics, suggesting fewer common genetic variants.

Adaptive distance metric learning for diffusion tensor image segmentation

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Quantitative volumetric analysis of the optic radiation in the normal human brain using diffusion tensor magnetic resonance imaging-based tractography

To attain the volumetric information of the optic radiation in normal human brains, we performed diffusion tensor imaging examination in 13 healthy volunteers. Simultaneously, we used a brain normalization method to reduce individual brain variation and increase the accuracy of volumetric information analysis. In addition, tractography-based group mapping method was also used to investigate the probability and distribution of the optic radiation pathways. Our results showed that the measured optic radiation fiber tract volume was a range of about 0.16% and that the fractional anisotropy value was about 0.53. Moreover, the optic radiation probability fiber pathway that was determined with diffusion tensor tractography-based group mapping was able to detect the location relatively accurately. We believe that our methods and results are helpful in the study of optic radiation fiber tract information.

A Comparative White Matter Study with Parkinson's disease, Parkinson's Disease with Dementia and Alzheimer's Disease

Alzheimer's disease (AD) and Parkinson's disease (PD) are among the most common neurodegenerative disorders affecting older populations. AD is characterized by impaired memory and cognitive decline while the primary symptoms of PD include resting tremor, bradykinesia and rigidity. In PD, mild cognitive changes are frequently present, which could progress to dementia (PD dementia (PDD)). PDD and AD dementias are different in pathology although the difference in microstructural changes remains unknown. To further understand these diseases, it is essential to understand the distinct mechanism of their microstructural changes. We used diffusion tensor imaging (DTI) to investigate white matter tract differences between early stage individuals with AD (n=14), PD (n=12), PDD (n=9), and healthy non-demented controls (CON) (n=13). We used whole brain tract based spatial statistics (TBSS) and a region of interest (ROI) analysis focused on the substantia nigra (SN). We found that individuals with PDD had more widespread white matter degeneration compared to PD, AD, and CON. Individuals with AD had few regional abnormalities in the anterior and posterior projections of the corpus callosum while PD and CON did not appear to have significant white matter degeneration when compared to other groups. ROI analyses showed that PDD had the highest diffusivity in the SN and were significantly different from CON. There were no significant ROI differences between CON, PD, or AD. In conclusion, global white matter microstructural deterioration is evident in individuals with PDD, and DTI may provide a means with which to tease out pathological differences between AD and PD dementias.

Diffusional kurtosis imaging of cingulate fibers in Parkinson disease: comparison with conventional diffusion tensor imaging

OBJECTIVE

The pathological changes in Parkinson disease begin in the brainstem; reach the limbic system and ultimately spread to the cerebral cortex. In Parkinson disease (PD) patients, we evaluated the alteration of cingulate fibers, which comprise part of the limbic system, by using diffusional kurtosis imaging (DKI).

METHODS

Seventeen patients with PD and 15 age-matched healthy controls underwent DKI with a 3-T MR imager. Diffusion tensor tractography images of the anterior and posterior cingulum were generated. The mean kurtosis (MK) and conventional diffusion tensor parameters measured along the images in the anterior and posterior cingulum were compared between the groups. Receiver operating characteristic (ROC) analysis was also performed to compare the diagnostic abilities of the MK and conventional diffusion tensor parameters.

RESULTS

The MK and fractional anisotropy (FA) in the anterior cingulum were significantly lower in PD patients than in healthy controls. The area under the ROC curve was 0.912 for MK and 0.747 for FA in the anterior cingulum. MK in the anterior cingulum had the best diagnostic performance (mean cutoff, 0.967; sensitivity, 0.87; specificity, 0.94).

CONCLUSIONS

DKI can detect alterations of the anterior cingulum in PD patients more sensitively than can conventional diffusion tensor imaging. Use of DKI can be expected to improve the ability to diagnose PD.

Update on diffusion MRI in Parkinson's disease and atypical parkinsonism

Differentiating Parkinson's disease (PD) from other types of neurodegenerative atypical parkinsonism (AP) can be challenging, especially in early disease stages. Routine brain magnetic resonance imaging (MRI) can show atrophy or signal changes in several parts of the brain with fairly high specificity for particular forms of AP, but the overall diagnostic value of routine brain MRI is limited. In recent years, various advanced MRI sequences have become available, including diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Here, we review available literature on the value of diffusion MRI for identifying and quantifying different patterns of neurodegeneration in PD and AP, in relation to what is known of underlying histopathologic changes and clinical presentation of these diseases. Next, we evaluate the value of diffusion MRI to differentiate between PD and AP and the potential value of serial diffusion MRI to monitor disease progression. We conclude that diffusion MRI may quantify patterns of neurodegeneration which could be of additional value in clinical use. Future prospective clinical cohort studies are warranted to assess the added diagnostic value of diffusion MRI.

Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis

OBJECTIVES

We performed a systematic review to assess alterations in measures of diffusion tensor imaging (DTI) in parkinsonian syndromes, exploring the potential role of DTI in diagnosis and as a candidate biomarker.

METHODS

We searched EMBASE and Medline databases for DTI studies comparing parkinsonian syndromes or related dementias with controls or another defined parkinsonian syndrome. Key details for each study regarding participants, imaging methods, and results were extracted. Estimates were pooled, where appropriate, by random-effects meta-analysis.

RESULTS

Of 333 results, we identified 43 studies suitable for inclusion (958 patients, 764 controls). DTI measures detected alterations in all parkinsonian syndromes, with distribution varying differentially with disease type. Nine studies were included in a meta-analysis of the substantia nigra in Parkinson disease. A notable effect size was found for lowered fractional anisotropy in the substantia nigra for patients with Parkinson disease vs controls (-0.639, 95% confidence interval -0.860 to -0.417, p < 0.0001).

CONCLUSION

DTI may be a promising biomarker in parkinsonian syndromes and have a future role in differential diagnosis. Larger cohort studies are required to investigate some encouraging preliminary findings. Given the complexity of the parkinsonian syndromes, it is likely that any potential DTI biomarker would be used in combination with other relevant biomarkers.

DTI correlates of distinct cognitive impairments in Parkinson's disease

The spectrum of cognitive symptoms in Parkinson's disease (PD) can span various domains, including executive function, language, attention, memory, and visuospatial skills. These symptoms may be attributable to the degradation of projection fibers associated with the underlying neurodegenerative process. The primary purpose of this study is to find microstructural correlates of impairments across these cognitive domains in PD using diffusion tensor imaging (DTI). Sixteen patients with PD with comprehensive neuropsychological evaluation and DTI data were retrospectively studied. Fractional anisotropy (FA) and mean diffusivity (MD) were assessed using regions-of-interest (ROI) analysis and confirmed with a voxel-based approach. Executive function directly correlated with FA and inversely correlated with MD in mostly frontal white matter tracts, especially the anterior limb of the internal capsule and genu of the corpus callosum. Likewise, language and attentional performance demonstrated correlations with DTI parameters in the frontal regions, but the attention domain additionally recruited regions widespread throughout the brain, with the most significant correlation identified in cingulate gyrus (cingulum). Lastly, memory impairment mainly involved MD alterations within the fornix. No significant correlations were found between visuospatial skills and DTI measures. Despite some overlap, unique patterns of white matter diffusivity underlie impairments in distinct cognitive domains in patients with PD. DTI combined with neurocognitive tests may be a valuable biomarker for identifying cognitive impairments in PD.

Relationship between cognitive impairment and white-matter alteration in Parkinson's disease with dementia: tract-based spatial statistics and tract-specific analysis

Objectives

We investigated the relationship between white-matter alteration and cognitive status in Parkinson’s disease (PD) with and without dementia by using diffusion tensor imaging.

Methods

Twenty PD patients, 20 PDD (Parkinson’s disease with dementia) patients and 20 age-matched healthy controls underwent diffusion tensor imaging. The mean diffusivity and fractional anisotropy (FA) map of each patient group were compared with those of the control group by using tract-based spatial statistics. Tractography images of the genu of the corpus callosum fibre tracts were generated, and mean diffusivity and FA were measured.

Results

FA values in many major tracts were significantly lower in PDD patients than in control subjects; in the prefrontal white matter and the genu of the corpus callosum they were significantly lower in PDD patients than in PD patients. There was a significant correlation between the Mini-Mental State Examination (MMSE) scores and the FA values of the prefrontal white matter and the genu of the corpus callosum in patients with PD.

Conclusions

Our study shows a relationship between cognitive impairment and alteration of the prefrontal white matter and genu of the corpus callosum. These changes may be useful in assessing the onset of dementia in PD patients.

Key Points

• Dementia is a common and important non-motor sign of Parkinson’s disease (PD).

• The neuropathological basis of dementia in PD is not clear.

• DTI shows abnormalities in the prefrontal white matter in PD with dementia.

• Prefrontal white matter alteration may be useful biomarker of dementia in PD.

Corpus callosum in aging and neurodegenerative diseases

SUMMARY The corpus callosum (CC) is a major white matter bundle that connects primarily homologous areas of the cortex. The structure may be involved in interhemispheric communication and enable the lateralization of certain cerebral functions. Despite its possible role as the main conduit for interhemispheric communication, interest from researchers has, at times, been sparse. Renewed interest has led to research that has shown that the CC may play a role in both cognitive aging and neurodegenerative diseases including Alzheimer´s disease and frontotemporal dementia. Studies employing structural MRI and diffusion-weighted MRI have found distinct subregional patterns of callosal atrophy in aging, Alzheimer´s disease and frontotemporal dementia. Furthermore, imaging studies may help to elucidate the underlying pathological mechanisms of callosal atrophy. The present review aims to provide an overview of the current knowledge of the structure and function of the CC and its role in aging and neurodegenerative disease.

Intact limbic-prefrontal connections and reduced amygdala volumes in Parkinson's disease with mild depressive symptoms

BACKGROUND

Depression is very common in Parkinson's disease (PD). The neuropathological basis for this remains unclear; however, dysfunction in prefrontal and limbic regions may play a role.

METHODS

We examined non-demented PD patients with and without depression and healthy controls (n = 6 per group) for differences in limbic structures and connections between these structures and the prefrontal cortex. Depressed individuals were selected from a representative sample of 33 PD patients using scores from the 15 question geriatric depression scale (GDS). Magnetic resonance diffusion tensor imaging (DTI) tractography was used to examine the structural integrity of the uncinate fasciculus (UF), a white matter tract that projects from the hippocampus, amygdala and temporal pole to the orbitofrontal cortex, and the corpus callosum. Integrity of the UF and corpus callosum was established through measures of mean diffusivity (MD), fractional anisotropy (FA) and tract length. A volumetric analysis of the hippocampal head, body and tail, as well as the amygdala was performed to determine whether volume differences in these structures in PD relate to depression.

RESULTS

The depressed PD group showed smaller amygdala volumes compared to healthy controls, but the groups did not differ on any other measure.

CONCLUSIONS

The present study found intact limbic connectivity but suggests that amygdala atrophy may be present in Parkinson's disease with depression. Further work is needed to replicate these findings.

An update on brain imaging in parkinsonian dementia

Disturbances of cognition are frequent in Parkinson's disease (PD). Unlike severe loss of dopamine early in PD, extensive cholinergic losses have been consistently reported in PD with dementia. Cholinergic imaging suggests that basal forebrain cholinergic system degeneration appears early in PD and worsens with dementia development. Cortical cholinergic denervation is similar in PD with dementia and dementia with Lewy bodies, supporting a common disease spectrum, at least with respect to cholinergic pathology. Presence of cerebral amyloidopathy in the setting of parkinsonism may accelerate cognitive decline. Novel MRI techniques illustrate the widespread presence of neurodegeneration in PD with dementia, affecting white matter tracts and connectivity functions. This review will outline current concepts regarding dementia development in PD and discuss their correlation with functional and structural neuroimaging including PET and MRI.

Cognitive impairment and whole brain diffusion in patients with neuromyelitis optica after acute relapse

The objective of this study investigated cognitive impairments and their correlations with fractional anisotropy (FA) and mean diffusivity (MD) in patients with neuromyelitis optica (NMO) without visible lesions on conventional brain MRI during acute relapse. Twenty one patients with NMO and 21 normal control subjects received several cognitive tests to assess cognitive function. Head diffusion tensor imaging (DTI) of all patients with NMO were collected with a 3-T MR system. Correlations of cognitive test scores and whole brain FA and MD were examined by voxel-based analysis. Region-of-interest analysis was applied to the significantly correlated regions which the most frequently appeared. We found that NMO patients without visible brain lesions had significantly impaired learning and memory, decreased information processing speed, and damaged attention compared with normal control subjects. These impaired cognitive domains were significantly correlated with FA and MD in local regions of corpus callosum, anterior cingulate and medial frontal cortex. In corpus callosum of NMO patients, mean FA was significantly lower and mean MD higher than normal control subjects. Our findings suggest that cognitive impairments in learning and memory, information processing speed and attention occur in NMO patients without visible brain lesions during acute relapse. The impairments in immediate and short-term memory in NMO patients may be due to information encoding deficits in the process of information acquisition. The corpus callosum of such patients may have local microscopic damages that play a role in cognitive impairments during acute relapse.