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

Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis

Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.

Evaluation of putamen area and cerebral peduncle with surrounding cistern in patients with Parkinson's disease: is there a difference from controls in cranial MRI?

OBJECTIVES

Nigrostriatal dopaminergic neuron loss is essential in pathogenesis of Parkinson's disease (PD). The purpose of this study was to evaluate nigrostriatal structures including the putamen, cerebral peduncle, widths of interpeduncular cistern, and ambient cistern around the midbrain with conventional cranial magnetic resonance images (MRI) in patients with PD.

METHODS

The MRI of 56 subjects was included, which was selected from the radiological data system for this retrospective study. The 29 patients with idiopathic PD were included and their disease duration, Hoehn&Yahr stage, and Levodopa equivalent dose (LED) were recorded. The 27 controls had a normal neurologic examination and cranial MRI. All subjects in the patient and control groups had right-hand dominance. Putamen and cerebral peduncle areas and widths of interpeduncular and ambient cisterns were measured in T2 sequences of MRI. Further statistical analysis was applied to exclude gender and age effect on areas.

RESULTS

The areas of putamen and cerebral peduncles were significantly reduced in patients with PD compared to the control bilaterally (p < 0.001). Enlargement of interpeduncular and ambient cisterns in patients was higher than in controls, and it was significant (p < 0.001). A correlation was not observed between measurement results and clinical characteristics of patients with PD. Only the cerebral peduncle area/ambient cistern width ratio was significantly correlated with disease duration positively (right r = 0.46 p = 0.012, left r = 0.389 p = 0.037).

CONCLUSION

Clinicians should be careful with conventional MRIs of patients with idiopathic PD in practice. It may be different from controls without any neurological disorder, particularly putamen, cerebral peduncles, interpeduncular, and ambient cisterns.

Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI

Background

Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).

Methods

155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.

Results

Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.

Conclusion

The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

Investigation of Altered Spontaneous Brain Activity Patterns in Herpes Zoster Keratitis Using the Percent Amplitude of Fluctuation Method: A Resting-State Functional Magnetic Resonance Imaging Study

Purpose

The purpose of this study was to use the percent amplitude of fluctuation (PerAF) to study the changes in brain activity and nerve function of herpes zoster keratitis (HZK) patients.

Methods

We recruited 20 HZK patients and 20 healthy controls (HCs). Each of these groups included ten males and ten females and were matched in weight and age. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI). The percent amplitude of fluctuation (PerAF) method was used for analysis and detected differences between the two groups in the neurological function of brain areas. We also applied the receiver operating characteristic (ROC) curve to analyze the two groups and did a correlation analysis between the PerAF value, anxiety and depression score, and visual acuity.

Results

The PerAF signal at the right putamen and right precentral gyrus was significantly higher in patients than in HCs. However, the PerAF value of the left inferior temporal was lower in patients than in HCs. In addition, the HZK patients' anxiety and depression score (HADS) and visual acuity (V.A.) Log MAR negatively correlated with the PerAF value at the left inferior temporal gyrus.

Conclusion

HZK patients had some changes in brain regions, and the changes were also related to their mood and visual acuity. These findings might contribute to other studies on the potential pathological mechanism, disease development, prognosis, and brain function in HZK patients.

Proteomic Profiling Reveals Specific Molecular Hallmarks of the Pig Claustrum

The present study, employing a comparative proteomic approach, analyzes the protein profile of pig claustrum (CLA), putamen (PU), and insula (IN). Pig brain is an interesting model whose key translational features are its similarities with cortical and subcortical structures of human brain. A greater difference in protein spot expression was observed in CLA vs PU as compared to CLA vs IN. The deregulated proteins identified in CLA resulted to be deeply implicated in neurodegenerative (i.e., sirtuin 2, protein disulfide-isomerase 3, transketolase) and psychiatric (i.e., copine 3 and myelin basic protein) disorders in humans. Metascape analysis of differentially expressed proteins in CLA vs PU comparison suggested activation of the α-synuclein pathway and L1 recycling pathway corroborating the involvement of these anatomical structures in neurodegenerative diseases. The expression of calcium/calmodulin-dependent protein kinase and dihydropyrimidinase like 2, which are linked to these pathways, was validated using western blot analysis. Moreover, the protein data set of CLA vs PU comparison was analyzed by Ingenuity Pathways Analysis to obtain a prediction of most significant canonical pathways, upstream regulators, human diseases, and biological functions. Interestingly, inhibition of presenilin 1 (PSEN1) upstream regulator and activation of endocannabinoid neuronal synapse pathway were observed. In conclusion, this is the first study presenting an extensive proteomic analysis of pig CLA in comparison with adjacent areas, IN and PUT. These results reinforce the common origin of CLA and IN and suggest an interesting involvement of CLA in endocannabinoid circuitry, neurodegenerative, and psychiatric disorders in humans.

Longitudinal Morphometric Changes in the Corticospinal Tract Shape After Hemorrhagic Stroke

Deep intracerebral hemorrhage (ICH) exerts a direct force on corticospinal tracts (CST) causing shape deformation. Using serial MRI, Generalized Procrustes Analysis (GPA), and Principal Components Analysis (PCA), we temporally evaluated the change in CST shape. Thirty-five deep ICH patients with ipsilesional-CST deformation were serially imaged on a 3T-MRI with a median imaging time of day-2 and 84 of onset. Anatomical and diffusion tensor images (DTI) were acquired. Using DTI color-coded maps, 15 landmarks were drawn on each CST and the centroids were computed in 3 dimensions. The contralesional-CST landmarks were used as a reference. The GPA outlined the shape coordinates and we superimposed the ipsilesional-CST shape at the two-time points. A multivariate PCA was applied to identify eigenvectors associated with the highest percentile of change. The first three principal components representing CST deformation along the left-right (PC1), anterior-posterior (PC2), and superior-inferior (PC3) respectively were responsible for 57.9% of shape variance. The PC1 (36.1%, p < 0.0001) and PC3 (9.58%, p < 0.01) showed a significant deformation between the two-time points. Compared to the contralesional-CST, the ipsilesional PC scores were significantly (p < 0.0001) different only at the first-timepoint. A significant positive association between the ipsilesional-CST deformation and hematoma volume was observed. We present a novel method to quantify CST deformation caused by ICH. Deformation most often occurs in left-right axis (PC1) and superior-inferior (PC3) directions. As compared to the reference, the significant temporal difference at the first time point suggests CST restoration over time.

Abnormal dynamic functional connectivity changes correlated with non-motor symptoms of Parkinson’s disease

Background

Non-motor symptoms are common in Parkinson’s disease (PD) patients, decreasing quality of life and having no specific treatments. This research investigates dynamic functional connectivity (FC) changes during PD duration and its correlations with non-motor symptoms.

Methods

Twenty PD patients and 19 healthy controls (HC) from PPMI dataset were collected and used in this study. Independent component analysis (ICA) was performed to select significant components from the entire brain. Components were grouped into seven resting-state intrinsic networks. Static and dynamic FC changes during resting-state functional magnetic resonance imaging (fMRI) were calculated based on selected components and resting state networks (RSN).

Results

Static FC analysis results showed that there was no difference between PD-baseline (PD-BL) and HC group. Network averaged connection between frontoparietal network and sensorimotor network (SMN) of PD-follow up (PD-FU) was lower than PD-BL. Dynamic FC analysis results suggested four distinct states, and each state’s temporal characteristics, such as fractional windows and mean dwell time, were calculated. The state 2 of our study showed positive coupling within and between SMN and visual network, while the state 3 showed hypo-coupling through all RSN. The fractional windows and mean dwell time of PD-FU state 2 (positive coupling state) were statistically lower than PD-BL. Fractional windows and mean dwell time of PD-FU state 3 (hypo-coupling state) were statistically higher than PD-BL. Outcome scales in Parkinson’s disease–autonomic dysfunction scores of PD-FU positively correlated with mean dwell time of state 3 of PD-FU.

Conclusion

Overall, our finding indicated that PD-FU patients spent more time in hypo-coupling state than PD-BL. The increase of hypo-coupling state and decrease of positive coupling state might correlate with the worsening of non-motor symptoms in PD patients. Dynamic FC analysis of resting-state fMRI can be used as monitoring tool for PD progression.

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

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