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

An Update on Parkinson's Disease and its Neurodegenerative Counterparts

INTRODUCTION

Neurodegenerative disorders are a group of diseases that cause nerve cell degeneration in the brain, resulting in a variety of symptoms and are not treatable with drugs. Parkinson's disease (PD), prion disease, motor neuron disease (MND), Huntington's disease (HD), spinal cerebral dyskinesia (SCA), spinal muscle atrophy (SMA), multiple system atrophy, Alzheimer's disease (AD), spinocerebellar ataxia (SCA) (ALS), pantothenate kinase-related neurodegeneration, and TDP-43 protein disorder are examples of neurodegenerative diseases. Dementia is caused by the loss of brain and spinal cord nerve cells in neurodegenerative diseases.

BACKGROUND

Even though environmental and genetic predispositions have also been involved in the process, redox metal abuse plays a crucial role in neurodegeneration since the preponderance of symptoms originates from abnormal metal metabolism.

METHOD

Hence, this review investigates several neurodegenerative diseases that may occur symptoms similar to Parkinson's disease to understand the differences and similarities between Parkinson's disease and other neurodegenerative disorders based on reviewing previously published papers.

RESULTS

Based on the findings, the aggregation of alpha-synuclein occurs in Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies. Other neurodegenerative diseases occur with different protein aggregation or mutations.

CONCLUSION

We can conclude that Parkinson's disease, Multiple system atrophy, and Dementia with Lewy bodies are closely related. Therefore, researchers must distinguish among the three diseases to avoid misdiagnosis of Multiple System Atrophy and Dementia with Lewy bodies with Parkinson's disease symptoms.

Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design

Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.

Noradrenergic neuromodulation in ageing and disease

The locus coeruleus (LC) is a small brainstem structure located in the lower pons and is the main source of noradrenaline (NA) in the brain. Via its phasic and tonic firing, it modulates cognition and autonomic functions and is involved in the brain's immune response. The extent of degeneration to the LC in healthy ageing remains unclear, however, noradrenergic dysfunction may contribute to the pathogenesis of Alzheimer's (AD) and Parkinson's disease (PD). Despite their differences in progression at later disease stages, the early involvement of the LC may lead to comparable behavioural symptoms such as preclinical sleep problems and neuropsychiatric symptoms as a result of AD and PD pathology. In this review, we draw attention to the mechanisms that underlie LC degeneration in ageing, AD and PD. We aim to motivate future research to investigate how early degeneration of the noradrenergic system may play a pivotal role in the pathogenesis of AD and PD which may also be relevant to other neurodegenerative diseases.

Anesthetic modulation of water diffusion: Insights from a diffusion tensor imaging study

Diffusion tensor imaging (DTI) in animal models are essential for translational neuroscience studies. A critical step in animal studies is the use of anesthetics. Understanding the influence of specific anesthesia regimes on DTI-derived parameters, such as fractional anisotropy (FA) and mean diffusivity (MD), is imperative when comparing results between animal studies using different anesthetics. Here, the quantification of FA and MD under different anesthetic regimes, alpha-chloralose and isoflurane, is discussed. We also used a range of b-values to determine whether the anesthetic effect was b-value dependent. The first group of rats (n = 6) was anesthetized with alpha-chloralose (80 mg/kg), whereas the second group of rats (n = 7) was anesthetized with isoflurane (1.5%). DTI was performed with b-values of 500, 1500, and 1500s/mm2, and the MD and FA were assessed individually. Anesthesia-specific differences in MD were apparent, as manifested by the higher estimated MD under isoflurane anesthesia than that under alpha-chloralose anesthesia (P < 0.001). MD values increased with decreasing b-value in all regions studied, and the degree of increase when rats were anesthetized with isoflurane was more pronounced than that associated with alpha-chloralose (P < 0.05). FA quantitation was also influenced by anesthesia regimens to varying extents, depending on the brain regions and b-values. In conclusion, both scanning parameters and the anesthesia regimens significantly impacted the quantification of DTI indices.

Levodopa responsiveness and white matter alterations in Parkinson's disease: A DTI-based study and brain network analysis: A cross-sectional study

BACKGROUND

Patients with Parkinson's disease (PD) present various responsiveness to levodopa, but the cause of such differences in levodopa responsiveness is unclear. Previous studies related the damage of brain white matter (WM) to levodopa responsiveness in PD patients, but no study investigated the relationship between the structural brain network change in PD patients and their levodopa responsiveness.

METHODS

PD patients were recruited and evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS). Each patient received a diffusion tensor imaging (DTI) scan and an acute levodopa challenge test. The improvement rate of UPDRS-III was calculated. PD patients were grouped into irresponsive group (improvement rate < 30%) and responsive group (improvement rate ≥ 30%). Tract-based spatial statistics (TBSS), deterministic tracing (DT), region of interest (ROI) analysis, and automatic fiber identification (AFQ) analyses were performed. The structural brain network was also constructed and the topological parameters were calculated.

RESULTS

Fifty-four PD patients were included. TBSS identified significant differences in fractional anisotropy (FA) values in the corpus callosum and other regions of the brain. DT and ROI analysis of the corpus callosum found a significant difference in FA between the two groups. Graph theory analysis showed statistical differences in global efficiency, local efficiency, and characteristic path length.

CONCLUSION

PD patients with poor responsiveness to levodopa had WM damage in multiple brain areas, especially the corpus callosum, which might cause disruption of information integration of the structural brain network.

The interplay between structural and functional connectivity in early stage Parkinson's disease patients

The mechanisms underlying cognitive disturbances in Parkinson's disease (PD) are poorly understood but likely to depend on the ongoing degenerative processes affecting structural and functional connectivity (FC). This pilot study examined patterns of FC alterations during a cognitive task using EEG and structural characteristics of white matter (WM) pathways connecting these activated regions in early-stage PD. Eleven PD patients and nine healthy controls (HCs) underwent EEG recording during an auditory oddball task and MRI scans. Source localization was performed and Gaussian mixture model was fitted to identify brain regions with high power during task performance. These areas served as seed regions for connectivity analysis. FC among these regions was assessed by measures of magnitude squared coherence (MSC), and phase-locking value (PLV), while structural connectivity was evaluated using fiber tracking based on diffusion tensor imaging (DTI). The paracentral lobule (PL), superior parietal lobule (SPL), superior and middle frontal gyrus (SMFG), parahippocampal gyrus, superior and middle temporal gyri (STG, MTG) demonstrated increased activation during task performance. Compared to HCs, PD showed lower FC between SMFG and PL and between SMFG and SPL in MSC (p = 0.012 and p = 0.036 respectively). No significant differences between the groups were observed in PLV and the measured DTI metrics along WM tracts. These findings demonstrate that in early PD, cognitive performance changes might be attributed to FC alterations, suggesting that FC is affected early on in the degenerative process, whereas structural damage is more prominent in advanced stages as a result of the disease burden accumulation.

Disrupted white matter integrity in the brain of type 1 diabetes is associated with peripheral neuropathy and abnormal brain metabolites

AIMS

We aimed to quantify microstructural white matter abnormalities using magnetic resonance imaging and examine their associations with 1) brain metabolite and volumes and 2) clinical diabetes-specific characteristics and complications in adults with type 1 diabetes mellitus (T1DM) and distal symmetric peripheral neuropathy (DSPN).

METHODS

Diffusion tensor images (DTI) obtained from 46 adults with T1DM and DSPN and 28 healthy controls were analyzed using tract-based spatial statistics and were then associated with 1) brain metabolites and volumes and 2) diabetes-specific clinical characteristics (incl. HbA_1c, diabetes duration, level of retinopathy, nerve conduction assessment).

RESULTS

Adults with T1DM and DSPN had reduced whole-brain FA skeleton (P = 0.018), most prominently in the inferior longitudinal fasciculus and retrolenticular internal capsule (P < 0.001). Reduced fractional anisotropy (FA) was associated with lower parietal N-acetylaspartate/creatine metabolite ratio (r = 0.399, P = 0.006), brain volumes (P ≤ 0.002), diabetes duration (r = -0.495, P < 0.001) and sural nerve amplitude (r = 0.296, P = 0.046). Additionally, FA was reduced in the subgroup with concomitant proliferative retinopathy compared to non-proliferative retinopathy (P = 0.03). No association was observed between FA and HbA_1c.

CONCLUSIONS

This hypothesis-generating study provided that altered white matter microstructural abnormalities in T1DM with DSPN were associated with reduced metabolites central for neuronal communications and diabetes complications, indicating that peripheral neuropathic complications are often accompanied by central neuropathy.

Impact of chronic exposure to legacy environmental contaminants on the corpus callosum microstructure: A diffusion MRI study of Inuit adolescents

Exposure to environmental contaminants is an important public health concern for the Inuit population of northern Québec, who have been exposed to mercury (Hg), polychlorinated biphenyls (PCBs) and lead (Pb). During the last 25 years, the Nunavik Child Development Study (NCDS) birth cohort has reported adverse associations between these exposures and brain function outcomes. In the current study, we aimed to determine whether contaminant exposure is associated with alterations of the corpus callosum (CC), which plays an important role in various cognitive, motor and sensory function processes. Magnetic resonance imaging (MRI) was administered to 89 NCDS participants (mean age ± SD = 18.4 ± 1.2). Diffusion-weighted imaging was assessed to characterize the microstructure of the CC white matter in 7 structurally and functionally distinct regions of interest (ROIs) using a tractography-based segmentation approach. The following metrics were computed: fiber tract density, fractional anisotropy (FA), axial diffusivity (AD) and radial diffusivity (RD). Multiple linear regression models adjusted for sex, age, current alcohol/drug use and fish nutrients (omega-3 fatty acids and selenium) were conducted to assess the association between diffusion-weighted imaging metrics and Hg, PCB 153 and Pb concentrations obtained at birth in the cord blood and postnatally (mean values from blood samples at 11 and 18 years of age). Exposures were not associated with fiber tract density. Nor were significant associations found with cord and postnatal blood Pb concentrations for FA. However, pre- and postnatal Hg and PCB concentrations were significantly associated with higher FA of several regions of the CC, namely anterior midbody, posterior midbody, isthmus, and splenium, with the most pronounced effects observed in the splenium. FA results were mainly associated with lower RD. This study shows that exposure to Hg and PCB 153 alters the posterior microstructure of the CC, providing neuroimaging evidence of how developmental exposure to environmental chemicals can impair brain function and behavior in late adolescence.

Tremor associated with similar structural networks in Parkinson's disease and essential tremor

INTRODUCTION

Despite substantial clinical and pathophysiological differences, the characteristics of tremor in Parkinson's disease (PD) and essential tremor (ET) patients bear certain similarities. The presented study delineates tremor-related structural networks in these two disorders.

METHODS

42 non-advanced PD patients (18 tremor-dominant, 24 without substantial tremor), 17 ET, and 45 healthy controls underwent high-angular resolution diffusion-weighted imaging acquisition to reconstruct their structural motor connectomes as a proxy of the anatomical interconnections between motor network regions, implementing state-of-the-art globally optimised probabilistic tractography.

RESULTS

When compared to healthy controls, ET patients exhibited higher structural connectivity in the cerebello-thalamo-cortical network. Interestingly, the comparison of tremor-dominant PD patients and PD patients without tremor yielded very similar results - higher structural connectivity in tremor-dominant PD sharing multiple nodes with the tremor network detected in ET, despite the generally lower structural connectivity between basal ganglia and frontal cortex in the whole PD group when compared to healthy controls.

CONCLUSION

The higher structural connectivity of the cerebello-thalamo-cortical network seems to be the dominant tremor driver in both PD and ET. While it appears to be the only tremor-related network in ET, its combination with large scale hypoconnectivity in the frontal cortico-subcortical network in PD may explain different clinical features of tremor in these two disorders.

Utility of Multi-Modal MRI for Differentiating of Parkinson's Disease and Progressive Supranuclear Palsy Using Machine Learning

Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects. Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods. Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone. Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.