Motor asymmetry and neuromelanin imaging: Concordance in Parkinson's disease

Interhemispheric reactivity of the subthalamic nucleus sustains progressive dopamine neuron loss in asymmetrical parkinsonism

Parkinson's disease (PD) is characterized by the progressive and asymmetrical degeneration of the nigrostriatal dopamine neurons and the unilateral presentation of the motor symptoms at onset, contralateral to the most impaired hemisphere. We previously developed a rat PD model that mimics these typical features, based on unilateral injection of a substrate inhibitor of excitatory amino acid transporters, L-trans-pyrrolidine-2,4-dicarboxylate (PDC), in the substantia nigra (SN). Here, we used this progressive model in a multilevel study (behavioral testing, in vivo 1H-magnetic resonance spectroscopy, slice electrophysiology, immunocytochemistry and in situ hybridization) to characterize the functional changes occurring in the cortico-basal ganglia-cortical network in an evolving asymmetrical neurodegeneration context and their possible contribution to the cell death progression. We focused on the corticostriatal input and the subthalamic nucleus (STN), two glutamate components with major implications in PD pathophysiology. In the striatum, glutamate and glutamine levels increased from presymptomatic stages in the PDC-injected hemisphere only, which also showed enhanced glutamatergic transmission and loss of plasticity at corticostriatal synapses assessed at symptomatic stage. Surprisingly, the contralateral STN showed earlier and stronger reactivity than the ipsilateral side (increased intraneuronal cytochrome oxidase subunit I mRNA levels; enhanced glutamate and glutamine concentrations). Moreover, its lesion at early presymptomatic stage halted the ongoing neurodegeneration in the PDC-injected SN and prevented the expression of motor asymmetry. These findings reveal the existence of endogenous interhemispheric processes linking the primary injured SN and the contralateral STN that could sustain progressive dopamine neuron loss, opening new perspectives for disease-modifying treatment of PD.

Subregional analysis of striatum iron in Parkinson's disease and rapid eye movement sleep behaviour disorder

The loss of dopamine in the striatum underlies motor symptoms of Parkinson's disease (PD). Rapid eye movement sleep behaviour disorder (RBD) is considered prodromal PD and has shown similar neural changes in the striatum. Alterations in brain iron suggest neurodegeneration; however, the literature on striatal iron has been inconsistent in PD and scant in RBD. Toward clarifying pathophysiological changes in PD and RBD, and uncovering possible biomarkers, we imaged 26 early-stage PD patients, 16 RBD patients, and 39 age-matched healthy controls with 3 T MRI. We compared mean susceptibility using quantitative susceptibility mapping (QSM) in the standard striatum (caudate, putamen, and nucleus accumbens) and tractography-parcellated striatum. Diffusion MRI permitted parcellation of the striatum into seven subregions based on the cortical areas of maximal connectivity from the Tziortzi atlas. No significant differences in mean susceptibility were found in the standard striatum anatomy. For the parcellated striatum, the caudal motor subregion, the most affected region in PD, showed lower iron levels compared to healthy controls. Receiver operating characteristic curves using mean susceptibility in the caudal motor striatum showed a good diagnostic accuracy of 0.80 when classifying early-stage PD from healthy controls. This study highlights that tractography-based parcellation of the striatum could enhance sensitivity to changes in iron levels, which have not been consistent in the PD literature. The decreased caudal motor striatum iron was sufficiently sensitive to PD, but not RBD. QSM in the striatum could contribute to development of a multivariate or multimodal biomarker of early-stage PD, but further work in larger datasets is needed to confirm its utility in prodromal groups.

CEST 2022-three-dimensional amide proton transfer (APT) imaging can identify the changes of cerebral cortex in Parkinson's disease

PURPOSE

Amide proton transfer (APT) imaging has shown its diagnostic and predictive superiority in PD in our previous studies using 2D APT imaging based on deep nuclei. We hypothesized that the pathophysiological abnormality of PD will change the APT-related parameters in the cerebral cortex, and the signal changes can contribute to accurate diagnosis of Parkinson's disease.

METHODS

34 patients with sporadic Parkinson's disease (IPD) and 29 age- and sex-matched normal controls (NC) were enrolled in this prospective study. 3D-APT imaging and 3D-T1WI was performed in our participants. A volume-based morphometry algorithm was used and get automated cortical segmentations. Quantitative parameter maps of APT-related metrics were calculated by using SPM and MATLAB. The unpaired Student's t-test or Mann-Whitney U test was used for comparison of these values between IPD and NC groups. The associations between APT-related metrics and clinical assessments were investigated by Spearman correlation analysis. The receiver-operating characteristic (ROC) analysis was used to assess the diagnostic performances. The binary logistic regression model was used to combine the imaging parameters.

RESULTS

There wasn't any correlations between cortical APT-related signals and clinical assessment, including the H&Y scale, the disease duration, the UPDRS III scores and the MMSE scores. The MTRasym, CESTRnr and MTRRex had significantly higher values (p <0.001, corrected by Bonferroni methods) in the IPD group than NC groups in the region of bilateral and total temporal grey matter. The single parameters achieved the best diagnostic performance among all APT-related metrics was MTRRex on the right temporal grey matter, with an area under the ROC curve (AUC) of 0.865. The combined parameters achieved the highest diagnostic performance (AUC: 0.932).

CONCLUSIONS

3D-APT imaging could identify the changes of the cerebral cortex in Parkinson's disease. The cortical changes of APT-related parameters could potentially serve as imaging biomarkers to aid in the non-invasive diagnosis of PD.

Degeneration of nigrostriatal dopaminergic neurons in the early to intermediate stage of dementia with Lewy bodies and Parkinson's disease

OBJECTIVE

To investigate differences in nigrostriatal dopaminergic neuron degeneration between dementia with Lewy bodies (DLB) and Parkinson's disease (PD) in the early to intermediate stage of these diseases.

METHODS

An integrative neuroimaging analysis was developed using 3-Tesla neuromelanin-sensitive MRI and ¹²³I-FP-CIT dopamine transporter SPECT, and the relationship and laterality of three variables, including neuromelanin-related contrast in the substantia nigra (NRC_SN) and locus coeruleus (NRC_LC) and the specific binding ratio (SBR) in the striatum, were examined in detail. Patients with DLB and PD and control subjects (n = 29, 52, and 18, respectively) were enrolled.

RESULTS

A significantly greater decrease in the SBR in the bilateral hemispheres was observed in DLB than in PD. After adjusting for the interhemispheric asymmetry in neuromelanin-related MRI contrast by using the Z-score, linear regression between the NRC_SN and SBR was performed for the most-affected/least-affected sides of the hemispheres as defined by the interhemispheric differences in each variable (SBR, NRC_SN, standardized [SBR + NRC_SN]). In DLB, the highest, albeit statistically non-significant, correlation was observed in the SBR-based, most-affected side. In PD, the highest correlation was observed in the (SBR + NRC_SN)-based, most-affected side, which approximated the value of the clinically-defined, most-affected side. A non-significant correlation was observed only in the (SBR + NRC_SN)-based or clinically-defined, least-affected side.

CONCLUSION

Loss of the soma and presynaptic terminals may occur independently in DLB with a large decrease in the presynaptic terminals. The close relationship observed between the degeneration of the soma and presynaptic terminals suggested that axon degeneration may dominate in PD.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

Increased Effective Connectivity of the Left Parietal Lobe During Walking Tasks in Parkinson's Disease

BACKGROUND

In Parkinson's disease (PD), walking may depend on the activation of the cerebral cortex. Understanding the patterns of interaction between cortical regions during walking tasks is of great importance.

OBJECTIVE

This study investigated differences in the effective connectivity (EC) of the cerebral cortex during walking tasks in individuals with PD and healthy controls.

METHODS

We evaluated 30 individuals with PD (62.4±7.2 years) and 22 age-matched healthy controls (61.0±6.4 years). A mobile functional near-infrared spectroscopy (fNIRS) was used to record cerebral oxygenation signals in the left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left parietal lobe (LPL), and right parietal lobe (RPL) and analyze the EC of the cerebral cortex. A wireless movement monitor was used to measure the gait parameters.

RESULTS

Individuals with PD demonstrated a primary coupling direction from LPL to LPFC during walking tasks, whereas healthy controls did not demonstrate any main coupling direction. Compared with healthy controls, individuals with PD showed statistically significantly increased EC coupling strength from LPL to LPFC, from LPL to RPFC, and from LPL to RPL. Individuals with PD showed decreased gait speed and stride length and increased variability in speed and stride length. The EC coupling strength from LPL to RPFC negatively correlated with speed and positively correlated with speed variability in individuals with PD.

CONCLUSION

In individuals with PD, the left prefrontal cortex may be regulated by the left parietal lobe during walking. This may be the result of functional compensation in the left parietal lobe.

Effective R2 relaxation rate, derived from dual-contrast fast-spin-echo MRI, enables detection of hemisphere differences in iron level and dopamine function in Parkinson's disease and healthy individuals

BACKGROUND

Clinical estimates of brain iron concentration are achievable with quantitative transverse relaxation rate R₂, via time-consuming multiple spin-echo (SE) sequences. The objective of this study was to investigate whether quantitative iron-sensitive information may be derived from 3.0 T dual-contrast fast-spin-echo (FSE) sequences (typically employed in anatomical non-quantitative evaluations), as a routinely-collected alternative to evaluate iron levels in healthy (HC) and Parkinson's disease (PD) brains.

NEW METHOD

MRI 3.0 T FSE data from the Parkinson's Progression Markers Initiative (PPMI) (12 PD, 12 age- and gender-matched HC subjects) were cross-sectionally and longitudinally evaluated. A new measure, 'effective R₂', was calculated for bilateral subcortical grey matter (caudate nucleus, putamen, globus pallidus, red nucleus, substantia nigra). Linear regression analysis was performed to correlate 'effective R₂' with models of age-dependent brain iron concentration and striatal dopamine transporter (DaT) receptor binding ratio.

RESULTS

Effective R₂ was strongly correlated with estimated brain iron concentration. In PD, putaminal effective R₂ difference was observed between the hemispheres contra-/ipsi-lateral to the predominantly symptomatic side at onset. This hemispheric difference was correlated with the putaminal DaT binding ratios in PD.

COMPARISON WITH EXISTING METHOD(S)

Effective R₂, derived from rapid dual-contrast FSE sequences, showed viability as an alternative to R₂ from SE sequences. Linear correlation of effective R₂ with estimated iron concentration was comparable to documented iron-dependent R₂. The effective R₂ correlation coefficient was consistent with theoretical R₂ iron-dependence at 3.0 T.

CONCLUSIONS

Effective R₂ has clinical potential as a fast quantitative method, as an alternative to R₂, to aid evaluation of brain iron levels and DaT function.

NigraNet: An automatic framework to assess nigral neuromelanin content in early Parkinson's disease using convolutional neural network

BACKGROUND

Parkinson's disease (PD) demonstrates neurodegenerative changes in the substantia nigra pars compacta (SNc) using neuromelanin-sensitive (NM)-MRI. As SNc manual segmentation is prone to substantial inter-individual variability across raters, development of a robust automatic segmentation framework is necessary to facilitate nigral neuromelanin quantification. Artificial intelligence (AI) is gaining traction in the neuroimaging community for automated brain region segmentation tasks using MRI.

OBJECTIVE

Developing and validating AI-based NigraNet, a fully automatic SNc segmentation framework allowing nigral neuromelanin quantification in patients with PD using NM-MRI.

METHODS

We prospectively included 199 participants comprising 144 early-stage idiopathic PD patients (disease duration = 1.5 ± 1.0 years) and 55 healthy volunteers (HV) scanned using a 3 Tesla MRI including whole brain T1-weighted anatomical imaging and NM-MRI. The regions of interest (ROI) were delineated in all participants automatically using NigraNet, a modified U-net, and compared to manual segmentations performed by two experienced raters. The SNc volumes (Vol), volumes corrected by total intracranial volume (C_vol), normalized signal intensity (NSI) and contrast-to-noise ratio (CNR) were computed. One-way GLM-ANCOVA was performed while adjusting for age and sex as covariates. Diagnostic performance measurement was assessed using the receiver operating characteristic (ROC) analysis. Inter and intra-observer variability were estimated using Dice similarity coefficient (DSC). The agreements between methods were tested using intraclass correlation coefficient (ICC) based on a mean-rating, two-way, mixed-effects model estimates for absolute agreement. Cronbach's alpha and Bland-Altman plots were estimated to assess inter-method consistency.

RESULTS

Using both methods, Vol, C_vol, NSI and CNR measurements differed between PD and HV with an effect of sex for C_vol and CNR. ICC values between the methods demonstrated optimal agreement for C_vol and CNR (ICC > 0.9) and high reproducibility (DSC: 0.80) was also obtained. The SNc measurements also showed good to excellent consistency values (Cronbach's alpha > 0.87). Bland-Altman plots of agreement demonstrated no association of SNc ROI measurement differences between the methods and ROI average measurements while confirming that 95 % of the data points were ranging between the limits of mean difference (d ± 1.96xSD). Percentage changes between PD and HV were -27.4 % and -17.7 % for Vol, -30.0 % and -22.2 % for C_vol, -15.8 % and -14.4 % for NSI, -17.1 % and -16.0 % for CNR for automatic and manual measurements respectively. Using automatic method, in the entire dataset, we obtained the areas under the ROC curve (AUC) of 0.83 for Vol, 0.85 for C_vol, 0.79 for NSI and 0.77 for CNR whereas in the training dataset of 0.96 for Vol, 0.95 for C_vol, 0.85 for NSI and 0.85 for CNR. Disease duration correlated negatively with NSI of the patients for both the automatic and manual measurements.

CONCLUSIONS

We presented an AI-based NigraNet framework that utilizes a small MRI training dataset to fully automatize the SNc segmentation procedure with an increased precision and more reproducible results. Considering the consistency, accuracy and speed of our approach, this study could be a crucial step towards the implementation of a time-saving non-rater dependent fully automatic method for studying neuromelanin changes in clinical settings and large-scale neuroimaging studies.

Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease

AIMS

To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction.

METHODS

77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen.

CONCLUSION

The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.

Aberrant Changes in Cortical Complexity in Right-Onset Versus Left-Onset Parkinson's Disease in Early-Stage

There is increasing evidence to show that motor symptom lateralization in Parkinson’s disease (PD) is linked to non-motor features, progression, and prognosis of the disease. However, few studies have reported the difference in cortical complexity between patients with left-onset of PD (LPD) and right-onset of PD (RPD). This study aimed to investigate the differences in the cortical complexity between early-stage LPD and RPD. High-resolution T1-weighted magnetic resonance images of the brain were acquired in 24 patients with LPD, 34 patients with RPD, and 37 age- and sex-matched healthy controls (HCs). Cortical complexity including gyrification index, fractal dimension (FD), and sulcal depth was analyzed using surface-based morphometry via CAT12/SPM12. Familywise error (FWE) peak-level correction at p < 0.05 was performed for significance testing. In patients with RPD, we found decreased mean FD and mean sulcal depth in the banks of the left superior temporal sulcus (STS) compared with LPD and HCs. The mean FD in the left superior temporal gyrus (STG) was decreased in RPD compared with HCs. However, in patients with LPD, we did not identify significantly abnormal cortical complex change compared with HCs. Moreover, we observed that the mean FD in STG was negatively correlated with the 17-item Hamilton Depression Scale (HAMD) among the three groups. Our findings support the specific influence of asymmetrical motor symptoms in cortical complexity in early-stage PD and reveal that the banks of left STS and left STG might play a crucial role in RPD.

Hybrid PET-MRI for early detection of dopaminergic dysfunction and microstructural degradation involved in Parkinson's disease

Dopamine depletion and microstructural degradation underlie the neurodegenerative processes in Parkinson’s disease (PD). To explore early alterations and underlying associations of dopamine and microstructure in PD patients utilizing the hybrid positron emission tomography (PET)-magnetic resonance imaging (MRI). Twenty-five PD patients in early stages and twenty-four matched healthy controls underwent hybrid ¹⁸F-fluorodopa (DOPA) PET-diffusion tensor imaging (DTI) scanning. The striatal standardized uptake value ratio (SUVR), DTI maps (fractional anisotropy, FA; mean diffusivity, MD) in subcortical grey matter, and deterministic tractography of the nigrostriatal pathway were processed. Values in more affected (MA) side, less affected (LA) side and mean were analysed. Correlations and mediations among PET, DTI and clinical characteristics were further analysed. PD groups exhibited asymmetric pattern of dopaminergic dysfunction in putamen, impaired integrity in the microstructures (nigral FA, putaminal MD, and FA of nigrostriatal projection). On MA side, significant associations between DTI metrics (nigral FA, putaminal MD, and FA of nigrostriatal projection) and motor performance were significantly mediated by putaminal SUVR, respectively. Early asymmetric disruptions in putaminal dopamine concentrations and nigrostriatal pathway microstructure were detected using hybrid PET-MRI. The findings further implied that molecular degeneration mediates the modulation of microstructural disorganization on motor dysfunction in the early stages of PD.

To explore early alterations and underlying associations of dopamine levels and microstructure in Parkinson’s Disease (PD), Shang et al use a hybrid positron emission tomography (PET)-magnetic resonance imaging (MRI) approach in early stage patients and age-matched controls. Their data implies that molecular degeneration mediates the effects of microstructural disorganization on motor dysfunction in the early stages of PD.

The enigma and implications of brain hemispheric asymmetry in neurodegenerative diseases

The lateralization of the human brain may provide clues into the pathogenesis and progression of neurodegenerative diseases. Though differing in their presentation and underlying pathologies, neurodegenerative diseases are all devastating and share an intriguing theme of asymmetrical pathology and clinical symptoms. Parkinson’s disease, with its distinctive onset of motor symptoms on one side of the body, stands out in this regard, but a review of the literature reveals asymmetries in several other neurodegenerative diseases. Here, we review the lateralization of the structure and function of the healthy human brain and the common genetic and epigenetic patterns contributing to the development of asymmetry in health and disease. We specifically examine the role of asymmetry in Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis, and multiple sclerosis, and interrogate whether these imbalances may reveal meaningful clues about the origins of these diseases. We also propose several hypotheses for how lateralization may contribute to the distinctive and enigmatic features of asymmetry in neurodegenerative diseases, suggesting a role for asymmetry in the choroid plexus, neurochemistry, protein distribution, brain connectivity and the vagus nerve. Finally, we suggest how future studies may reveal novel insights into these diseases through the lens of asymmetry.

Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item-score models

This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross-over formulation comparison trial in 161 patients with early-stage Parkinson's disease, and a 24-week, parallel-group, placebo-controlled efficacy trial in 393 patients with advanced-stage Parkinson's disease. We applied item response theory to estimate the patients' symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of -3.7 (-5.4 to -2.0) and -9.3 (-11 to -7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item-score modeling as -2.0 (-4.0 to -1.0) and -9.0 (-11 to -8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials.

Comparing the Clinical and Neuropsychological Characteristics of Parkinson's Disease With and Without Freezing of Gait

Introduction

Freezing of gait (FOG) is one of the most common walking problems in Parkinson’s disease (PD). Impaired cognitive function is believed to play an important role in developing and aggravating FOG in PD. But some evidence suggests that motor function discrepancy may affect testing results. Therefore, we think it is necessary for PD-FOG(+) and PD-FOG(−) patients to complete neuropsychological tests under similar motor conditions.

Methods

This study recruited 44 idiopathic PD patients [PD-FOG(+) n = 22, PD-FOG(−) n = 22] and 20 age-matched healthy controls (HC). PD-FOG(+) and PD-FOG(−) patients were matched for age, year of education, and Hoehn and Yahr score (H&Y). All participants underwent a comprehensive battery of neuropsychological assessment, and demographical and clinical information was also collected.

Results

PD patients showed poorer cognitive function, higher risks of depression and anxiety, and more neuropsychiatric symptoms compared with HC. When controlling for age, years of education, and H&Y, there were no statistical differences in cognitive function between PD-FOG(+) and PD-FOG(−) patients. But PD-FOG(+) patients had worse motor and non-motor symptoms than PD-FOG(−) patients. PD patients whose motor symptoms initiated with rigidity and initiated unilaterally were more likely to experience FOG.

Conclusion

Traditional neuropsychological testing may not be sensitive enough to detect cognitive impairment in PD. Motor symptoms initiated with rigidity and initiated unilaterally might be an important predictor of FOG.

Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson's disease using machine learning

Cognitive impairments are prevalent in Parkinson's disease (PD), but the underlying mechanisms of their development are unknown. In this study, we aimed to predict global cognition (GC) in PD with machine learning (ML) using structural neuroimaging, genetics and clinical and demographic characteristics. As a post-hoc analysis, we aimed to explore the connection between novel selected features and GC more precisely and to investigate whether this relationship is specific to GC or is driven by specific cognitive domains. 101 idiopathic PD patients had a cognitive assessment, structural MRI and blood draw. ML was performed on 102 input features including demographics, cortical thickness and subcortical measures, and several genetic variants (APOE, MAPT, SNCA, etc.). Using the combination of RRELIEFF and Support Vector Regression, 11 features were found to be predictive of GC including sex, rs894280, Edinburgh Handedness Inventory, UPDRS-III, education, five cortical thickness measures (R-parahippocampal, L-entorhinal, R-rostral anterior cingulate, L-middle temporal, and R-transverse temporal), and R-caudate volume. The rs894280 of SNCA gene was selected as the most novel finding of ML. Post-hoc analysis revealed a robust association between rs894280 and GC, attention, and visuospatial abilities. This variant indicates a potential role for the SNCA gene in cognitive impairments of idiopathic PD.

Modelling item scores of Unified Parkinson's Disease Rating Scale Part III for greater trial efficiency

AIMS

The multipart Unified Parkinson's Disease Rating Scale is the standard instrument in clinical trials. A sum of scores for all items in 1 or more parts of the instrument is usually analysed. Without accounting for relative importance of individual items, this sum of scores conceivably does not optimize the power of the instrument. The aim was to compare the ability to detect drug effect in slowing down motor function deterioration, as measured by Part III of the Scale-motor examinations-between the item scores and the sum of scores.

METHODS

We used data from 423 patients in a Parkinson's disease progression trial to estimate the symptom severity by item response modelling; modelled symptom progression using the severity and the sum of scores; and conducted simulations to compare the sensitivity of detecting a broad range of hypothetical drug effects on progression using the severity and the sum of scores.

RESULTS

The severity endpoint was far more sensitive than the sum of scores for detecting treatment effects, e.g. requiring 275 vs. 625 patients per arm to achieve 60% probability of trial success for detecting a range of potential effects in a 2-year trial. Nontremor items related to the left side of the body seemed most informative. The domain relevance of tremor items appeared questionable.

CONCLUSION

This analysis generated clear evidence that longitudinal modelling of item scores can enhance trial efficiency and success. It also called for reassessing the placement of the tremor items in the instrument.

Motor Asymmetry and Interocular Retinal Thickness in Parkinson's Disease

BACKGROUND

To analyze the relationship between interocular difference of retinal thickness and motor asymmetry in Parkinson's disease (PD).

METHODS

Prospective case-control series analyzed 62 eyes of 31 patients with PD and 62 eyes of 31 age- and sex-matched control. Ophthalmologic examinations including optical coherence tomography (OCT) scans were performed in both groups, and in the patients with PD, motor function was evaluated on the Unified Parkinson's Disease Rating Scale part III (UPDRS-III) to determine the clinically more affected side. Peripapillary retinal nerve fiber layer thickness (pRNFLT) and macular retinal thickness (mRT) were measured in both eyes, after which the interocular asymmetry of the OCT parameters was determined. Additionally, the more and less affected sides of the UPDRS-III were evaluated using Symmetric index.

RESULTS

The average and quadrant pRNFLT and mRT values between the two groups were not different, but the interocular asymmetry of the average mRT and asymmetry index of retinal thickness (AIRT) of temporal mRT were significantly higher in the PD patients than in the controls (P = 0.026 and 0.044). The sum of UPDRS-III showed a discrepancy between the more and less affected sides (P = 0.002); the calculated Symmetric index was 0.21 ± 0.19, which suggested asymmetric motor symptoms. The Symmetric index of UPDRS-III showed significant relations for interocular asymmetry of superior mRT and AIRT of average mRT (P = 0.001 and 0.008).

CONCLUSION

In the PD patients, the interocular asymmetry of mRT was larger than in the controls, and the motor symptoms were asymmetric. Additionally, the interocular asymmetry of mRT showed a significant correlation with motor-symptom laterality.

Motor asymmetry related cerebral perfusion patterns in Parkinson's disease: An arterial spin labeling study

Persisting asymmetry of motor symptoms are characteristic of Parkinson's disease (PD). We investigated the possible lateralized effects on regional cerebral blood flow (CBF), CBF‐connectivity, and laterality index (LI) among PD subtypes using arterial spin labeling (ASL). Forty‐four left‐sided symptom dominance patients (PDL), forty‐eight right‐sided symptom dominance patients (PDR), and forty‐five matched HCs were included. Group comparisons were performed for the regional normalized CBF, CBF‐connectivity and LI of basal ganglia (BA) subregions. The PDL patients had lower CBF in right calcarine sulcus and right supramarginal gyrus compared to the PDR and the HC subjects. Regional perfusion alterations seemed more extensive in the PDL than in the PDR group. In the PDL, correlations were identified between right thalamus and motor severity, between right fusiform gyrus and global cognitive performance. None of correlations survived after multiple comparisons correction. The significantly altered CBF‐connectivity among the three groups included: unilateral putamen, unilateral globus pallidus, and right thalamus. LI score in the putamen was significantly different among groups. Motor‐symptom laterality in PD may exhibit asymmetric regional and interregional abnormalities of CBF properties, particularly in PDL patients. This preliminary study underlines the necessity of classifying PD subgroups based on asymmetric motor symptoms and the potential application of CBF properties underlying neuropathology in PD.

Lack of Asymmetry of Nigrostriatal Dopaminergic Function in Healthy Subjects

OBJECTIVE

In right-handed patients with Parkinson's disease (PD) or isolated rapid eye movement sleep behavior disorder, dopamine transporter (DAT) [(123)I]β-carboxymethyoxy-3-β-(4-iodophenyl) tropane single photon emission computed tomography (SPECT) shows predominant nigrostriatal deficit in the left striatum. This suggests that in PD patients, the nigrostriatal system of the dominant hemisphere is more susceptible to disease-related dysfunction. To confirm this hypothesis, we investigated whether the nigrostriatal function is symmetric in healthy controls and in patients with PD.

METHODS

In 113 right-handed healthy controls and 279 right-handed early-PD patients, we examined the striatal dopaminergic terminals function in each hemisphere using DAT-SPECT.

RESULTS

In the controls, DAT-SPECT showed symmetric specific binding ratios in the putamen and caudate nucleus of each hemisphere. In patients with PD, the specific binding ratio was lower in the left than in the right putamen.

CONCLUSIONS

Right-handed healthy controls have symmetric nigrostriatal dopaminergic function. The left hemispheric predominance of nigrostriatal deficit seen in right-handed premotor and manifest PD represents an early pathological feature of the disease. © 2020 International Parkinson and Movement Disorder Society.

Microstructural abnormalities of substantia nigra in Parkinson's disease: A neuromelanin sensitive MRI atlas based study

Microstructural changes associated with degeneration of dopaminergic neurons of the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) have been studied using Diffusion Tensor Imaging (DTI). However, these studies show inconsistent results, mainly due to methodological variations in delineation of SNc. To mitigate this, our work aims to construct a probabilistic atlas of SNc based on a 3D Neuromelanin Sensitive MRI (NMS‐MRI) sequence and demonstrate its applicability to investigate microstructural changes on a large dataset of PD. Using manual segmentation and deformable registration we created a novel SNc atlas in the MNI space using NMS‐MRI sequences of 27 healthy controls (HC). We first quantitatively evaluated this atlas and then employed it to investigate the micro‐structural abnormalities in SNc using diffusion MRI from 133 patients with PD and 99 HCs. Our results demonstrated significant increase in diffusivity with no changes in anisotropy. In addition, we also observed an asymmetry of the diffusion metrics with a higher diffusivity and lower anisotropy in the left SNc than the right. Finally, a multivariate classifier based on SNc diffusion features could delineate patients with PD with an average accuracy of 71.7%. Overall, from this work we establish a normative baseline for the SNc region of interest using NMS‐MRI while the application on PD data emphasizes on the contribution of diffusivity measures rather than anisotropy of white matter in PD.

Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI

Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Current techniques employ estimation of contrast ratios of the SNc, visualized on NMS-MRI, to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc. To mitigate this, our work establishes a computer-based analysis technique that uses convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. Our technique not only performs with a superior testing accuracy (80%) as compared to contrast ratio-based classification (56.5% testing accuracy) and radiomics classifier (60.3% testing accuracy), but also supports discriminating PD from atypical parkinsonian syndromes (85.7% test accuracy). Moreover, it has the capability to locate the most discriminative regions on the neuromelanin contrast images. These discriminative activations demonstrate that the left SNc plays a key role in the classification in comparison to the right SNc, and are in agreement with the concept of asymmetry in PD. Overall, the proposed technique has the potential to support radiological diagnosis of PD while facilitating deeper understanding into the abnormalities in SNc.

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A novel convolutional neural net (CNN) based marker for Parkinson’s disease (PD) based on neuromelanin sensitive images

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The classifier demonstrates high accuracy in delineating PD from healthy controls when compared to other techniques

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The class activation maps demonstrated significant asymmetry reflecting the clinical asymmetry observed in PD