Brain Atrophy and Reorganization of Structural Network in Parkinson's Disease With Hemiparkinsonism

Impaired topological properties of cortical morphological brain networks correlate with motor symptoms in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is characterized by loss of selectively vulnerable neurons within the basal ganglia circuit and progressive atrophy in subcortical and cortical regions. However, the impact of neurodegenerative pathology on the topological organization of cortical morphological networks has not been explored. The aims of this study were to investigate altered network patterns of covariance in cortical thickness and complexity, and to evaluate how morphological network integrity in PD is related to motor impairment.

METHODS

Individual morphological networks were constructed for 50 PD patients and 46 healthy controls (HCs) by estimating interregional similarity distributions in surface-based indices. We performed graph theoretical analysis and network-based statistics to detect PD-related alterations and further examined the correlation of network metrics with clinical scores. Furthermore, support vector regression based on topological characteristics was applied to predict the severity of motor impairment in PD.

RESULTS

Compared with HCs, PD patients showed lower local efficiency (p = 0.004), normalized characteristic path length (p = 0.022), and clustering coefficient (p = 0.005) for gyrification index-based morphological brain networks. Nodal topological abnormalities were mainly in the frontal, parietal and temporal regions, and impaired morphological connectivity was involved in the sensorimotor and default mode networks. The support vector regression model using network-based features allowed prediction of motor symptom severity with a correlation coefficient of 0.606.

CONCLUSIONS

This study identified a disrupted topological organization of cortical morphological networks that could substantially advance our understanding of the network degeneration mechanism of PD and might offer indicators for monitoring disease progression.

Brain connectivity for subtypes of parkinson's disease using structural MRI

Objective. Delineating Parkinson's disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises two major objectives - firstly, subtyping Parkinson's patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step.Approach. Source-based-morphometry decomposition was performed on 131 Parkinson's patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics.Main results. Two distinct Parkinson's subtypes (namely A and B) were detected employing loadings of two components satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p < 0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree.Significance. MRI-based data-driven subtypes show frontal and temporal lobes playing a key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson's datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson's disease may provide subtype-specific treatment.

Unique Brain Network Identification Number for Parkinson's and Healthy Individuals Using Structural MRI

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson's disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject's brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node (i), weighted by a factor of 2-(i-1). The numerical representation (UBNIN) was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. UBNIN ranges observed for PD were 15,360 to 17,768,936,615,460,608, and HC ranges were 12,288 to 17,733,751,438,064,640. This model may be implemented as a neural signature of a person's unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts: A: ≤32 years (n1 = 4, n2 = 5), B: 33-42 years (n1 = 18, n2 = 14), C: 43-52 years (n1 = 42, n2 = 23), D: 53-62 years (n1 = 69, n2 = 22), and E: ≥63 years (n1 = 46, n2 = 6), where n1 and n2 are the number of individuals in PD and HC, respectively, to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox (Version 2019-03-03)-based MATLAB (R2020a) functions. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age-related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study.

Alteration in topological organization characteristics of gray matter covariance networks in patients with prediabetes

OBJECTIVES

Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes.

METHODS

A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters.

RESULTS

The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001).

CONCLUSIONS

The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.

Brain functional network abnormalities in parkinson's disease with mild cognitive impairment

Mild cognitive impairment in Parkinson's disease (PD-M) is related to a high risk of dementia. This study explored the whole-brain functional networks in early-stage PD-M. Forty-one patients with PD classified as cognitively normal (PD-N, n = 17) and PD-M (n = 24) and 24 demographically matched healthy controls (HC) underwent clinical and neuropsychological evaluations and resting-state functional magnetic resonance imaging. The global, regional, and modular topological characteristics were assessed in the brain functional networks, and their relationships to cognitive scores were tested. At the global level, PD-M and PD-N exhibited higher characteristic path length and lower clustering coefficient, local and global efficiency relative to HC. At the regional level, PD-M and PD-N showed lower nodal centrality in sensorimotor regions relative to HC. At the modular level, PD-M showed lower intramodular connectivity in default mode and cerebellum modules, and lower intermodular connectivity between default mode and frontoparietal modules than PD-N, correlated with Montreal Cognitive Assessment scores. Early-stage PD patients showed weaker small-worldization of brain networks. Modular connectivity alterations were mainly observed in patients with PD-M. These findings highlight the shared and distinct brain functional network dysfunctions in PD-M and PD-N, and yield insight into the neurobiology of cognitive decline in PD.

Sparsity Dependent Metrics Depict Alteration of Brain Network Connectivity in Parkinson's Disease

To date, regional brain atrophy unfolded using neuroimaging methods is observed to be the signature of Parkinson's disease (PD). In addition, graph theory-based studies are proving altered structural connectivity in PD. This motivated us to employ regional grey matter volume of PD patients (N=70) for comparative network analysis with an equal number of age- and gender-matched healthy controls (HC). In the current study, normalized grey matter maps obtained from structural magnetic resonance imaging (sMRI) were parcellated into 56 ROI (regions of interest) for construction of symmetric matrix using partial correlation between every pair of regional grey matter volumes. Sparsity thresholding was used to binarize the matrices and MATLAB functions from brain connectivity toolbox were employed to obtain the connectivity metrics. We observed PD with a significantly lower clustering coefficient as well as local efficiency at higher sparsities (above 0.9 and 0.84, respectively) with p<0.05. The right fusiform gyrus was found to be the conserved hub, besides disruption of four hubs and regeneration of five other hubs. Lower clustering coefficient and local efficiency were indicative of reduced local integration and information processing, respectively. Hence, we suggest that global clustering coefficient and local efficiency could have a pivotal role in evaluating network topology. Thereby, our findings confirmed impairment of normal structural brain network topology reflecting disconnectivity mechanisms in PD. Clinical Relevance - Analyzing structural brain connectivity in Parkinson's disease might provide the researchers and clinicians with a signature pattern of the disease to discriminate patients from normal controls.

Deep learning based diagnosis of Parkinson's Disease using diffusion magnetic resonance imaging

The diagnostic performance of a combined architecture on Parkinson's disease using diffusion tensor imaging was evaluated. A convolutional neural network was trained from multiple parcellated brain regions. A greedy algorithm was proposed to combine the models from individual regions into a complex one. Total 305 Parkinson's disease patients (aged 59.9±9.7 years old) and 227 healthy control subjects (aged 61.0±7.4 years old) were enrolled from 3 retrospective studies. The participants were divided into training with ten-fold cross-validation (N = 432) and an independent blind dataset (N = 100). Diffusion-weighted images were acquired from a 3T scanner. Fractional anisotropy and mean diffusivity were calculated and was subsequently parcellated into 90 cerebral regions of interest based on the Automatic Anatomic Labeling template. A convolutional neural network was implemented which contained three convolutional blocks and a fully connected layer. Each convolutional block consisted of a convolutional layer, activation layer, and pooling layer. This model was trained for each individual region. A greedy algorithm was implemented to combine multiple regions as the final prediction. The greedy algorithm predicted the area under curve of 94.1±3.2% from the combination of fractional anisotropy from 22 regions. The model performance analysis showed that the combination of 9 regions is equivalent. The best area under curve was 74.7±5.4% from the right postcentral gyrus. The current study proposed an architecture of convolutional neural network and a greedy algorithm to combine from multiple regions. With diffusion tensor imaging, the algorithm showed the potential to distinguish patients with Parkinson's disease from normal control with satisfactory performance.

Influence of brain atrophy using semiquantitative analysis in [123I]FP-CIT single-photon emission computed tomography by a Monte Carlo simulation study

The specific binding ratio (SBR) is an objective indicator of N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-[123I] iodophenyl) nortropane ([¹²³I]FP-CIT) single-photon emission computed tomography (SPECT) that could be used for the diagnosis of Parkinson’s disease and Lewy body dementia. One of the issues of the SBR analysis is that the setting position of the volume of interest (VOI) may contain cerebral ventricles and cerebral grooves. These areas may become prominent during the brain atrophy analysis; however, this phenomenon has not been evaluated enough. This study thus used Monte Carlo simulations to examine the effect of brain atrophy on the SBR analysis. The brain atrophy model (BAM) used to simulate the three stages of brain atrophy was made using a morphological operation. Brain atrophy levels were defined in the descending order from 1 to 3, with Level 3 indicating to the most severe damage. Projection data were created based on BAM, and the SPECT reconstruction was performed. The ratio of the striatal to background region accumulation was set to a rate of 8:1, 6:1, and 4:1. The striatal and the reference VOI mean value were decreased as brain atrophy progressed. Additionally, the Bolt’s analysis methods revealed that the reference VOI value was more affected by brain atrophy than the striatal VOI value. Finally, the calculated SBR value was overestimated as brain atrophy progressed, and a similar trend was observed when the ratios of the striatal to background region accumulation were changed. This study thus suggests that the SBR can be overestimated in cases of advanced brain atrophy.

Alterations of Regional Homogeneity in the Mild and Moderate Stages of Parkinson's Disease

Objectives: This study aimed to investigate alterations in regional homogeneity (ReHo) in early Parkinson's disease (PD) at different Hoehn and Yahr (HY) stages and to demonstrate the relationships between altered brain regions and clinical scale scores. Methods: We recruited 75 PD patients, including 43 with mild PD (PD-mild; HY stage: 1.0-1.5) and 32 with moderate PD (PD-moderate; HY stage: 2.0-2.5). We also recruited 37 age- and sex-matched healthy subjects as healthy controls (HC). All subjects underwent neuropsychological assessments and a 3.0 Tesla magnetic resonance scanning. Regional homogeneity of blood oxygen level-dependent (BOLD) signals was used to characterize regional cerebral function. Correlative relationships between mean ReHo values and clinical data were then explored. Results: Compared to the HC group, the PD-mild group exhibited increased ReHo values in the right cerebellum, while the PD-moderate group exhibited increased ReHo values in the bilateral cerebellum, and decreased ReHo values in the right superior temporal gyrus, the right Rolandic operculum, the right postcentral gyrus, and the right precentral gyrus. Reho value of right Pre/Postcentral was negatively correlated with HY stage. Compared to the PD-moderate group, the PD-mild group showed reduced ReHo values in the right superior orbital gyrus and the right rectus, in which the ReHo value was negatively correlated with cognition. Conclusion: The right superior orbital gyrus and right rectus may serve as a differential indicator for mild and moderate PD. Subjects with moderate PD had a greater scope for ReHo alterations in the cortex and compensation in the cerebellum than those with mild PD. PD at HY stages of 2.0-2.5 may already be classified as Braak stages 5 and 6 in terms of pathology. Our study revealed the different patterns of brain function in a resting state in PD at different HY stages and may help to elucidate the neural function and early diagnosis of patients with PD.

Serum Ceruloplasmin Depletion is Associated With Magnetic Resonance Evidence of Widespread Accumulation of Brain Iron in Parkinson's Disease

BACKGROUND

Excessive iron accumulation is one of the main pathogeneses of Parkinson's disease (PD). Ceruloplasmin plays an important role in keeping the iron homoeostasis.

PURPOSE

To explore the association between serum ceruloplasmin depletion and subcortical iron distribution in PD.

STUDY TYPE

Prospective.

POPULATION

One hundred and twenty-one normal controls, 34 PD patients with low serum ceruloplasmin (PD-LC), and 28 patients with normal serum ceruloplasmin (PD-NC).

SEQUENCE

Enhanced susceptibility-weighted angiography (ESWAN) on a 3 T scanner.

ASSESSMENT

Quantitative susceptibility mapping was employed to quantify the regional iron content by using a semi-automatic method. Serum ceruloplasmin concentration was measured from peripheral blood sample. Clinical assessments were conducted by a neurologist.

STATISTICAL TESTS

General linear model was used to compare the intergroup difference of region iron distribution among groups, and the statistics was adjusted by Bonferroni method (P < 0.01). Partial correlation analysis was used to detect the association between regional iron distribution and serum ceruloplasmin concentration (P < 0.05).

RESULTS

Compared with normal controls, significant iron accumulation in substantia nigra, putamen, and red nucleus was observed in PD-LC, while the only region showing significant iron accumulation was SN in PD-NC. Between PD-NC and PD-LC, the iron accumulation in putamen remained significantly different, which had a negative correlation with serum ceruloplasmin in whole PD patients (r = -0.338, P = 0.008).

DATA CONCLUSION

Nigral iron accumulation characterizes PD patients without significant association with serum ceruloplasmin. Differentially, when PD patients appear with reduced serum ceruloplasmin, more widespread iron accumulation would be expected with additionally involving putamen and red nucleus. All these findings provide insightful evidence for the abnormal iron metabolism behind the ceruloplasmin depletion in PD. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: 2.

Disrupted morphological grey matter networks in early-stage Parkinson's disease

While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

The neural correlates of gait improvement by rhythmic sound stimulation in adults with Parkinson's disease - A functional magnetic resonance imaging study

INTRODUCTION

Adults with Parkinson's disease (PD) experience gait disturbances that can sometimes be improved with rhythmic auditory stimulation (RAS); however, the underlying physiological mechanism for this improvement is not well understood. We investigated brain activation patterns in adults with PD and healthy controls (HC) using functional magnetic resonance imaging (fMRI) while participants imagined gait with or without RAS.

METHODS

Twenty-seven adults with PD who could walk independently and walked more smoothly with rhythmic auditory cueing than without it, and 25 age-matched HC participated in this study. Participants imagined gait in the presence of RAS or white noise (WN) during fMRI.

RESULTS

In the PD group, gait imagery with RAS activated cortical motor areas, including supplementary motor areas and the cerebellum, while gait imagery with WN additionally recruited the left parietal operculum. In HC, the induced activation was limited to cortical motor areas and the cerebellum for both the RAS and WN conditions. Within- and between-group analyses demonstrated that RAS reduced the activity of the left parietal operculum in the PD group but not in the HC group (condition-by-group interaction by repeated measures analysis of variance, p < 0.05).

CONCLUSION

During gait imagery in adults with PD, the left parietal operculum was less activated by RAS than by WN, while no change was observed in HC, suggesting that rhythmic auditory stimulation may support the sensory-motor networks involved in gait, thus alleviating the overload of the parietal operculum and compensating for its dysfunction in these patients.

Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease

Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.

Asymmetrical nigral iron accumulation in Parkinson's disease with motor asymmetry: an explorative, longitudinal and test-retest study

Parkinson's disease (PD) is commonly characterized by asymmetrical motor impairment. This study aimed to clarify the iron distributions in PD patients with significant motor asymmetry and their longitudinal alterations. This study included 123 PD patients and 121 normal controls. Thirty-eight PD patients were revisited. PD patients with significant motor asymmetry were identified by using an objective criterion. Inter-group, inter-hemisphere and inter-visit differences of regional tissue susceptibility were analyzed. Iron accumulation in dominantly and non-dominantly affected substantia nigra (SN) were observed in PD patients with motor asymmetry compared with normal controls (p < 0.005, Bonferroni corrected). Iron accumulation in the dominantly affected SN was significantly higher than that in the non-dominantly affected SN (p < 0.01, Bonferroni corrected). After follow-up, time effect on the iron content in SN was observed, directing to decrease in PD patients with motor asymmetry without hemispherical difference (p < 0.05). In conclusion, asymmetrical iron accumulation in SN was associated with the motor asymmetry in PD at baseline, while along the disease evolution iron content in SN became longitudinally decreased. All these findings provide new evidence for PD pathogenesis that the abnormal iron metabolism in SN is complicated and not always unidirectional.

Disrupted interhemispheric coordination with unaffected lateralization of global eigenvector centrality characterizes hemiparkinsonism

OBJECTIVE

The motor dysfunctions always affect hemi-body first in Parkinson's disease (PD). However, the interhemispheric relationships in patients with only unilateral motor impairment were barely known to date. We aimed to investigate the interhemispheric functions using resting-state functional Magnetic resonance imaging (RS-fMRI) for further understanding the pathogenesis of PD.

METHODS

Forty-three unilateral-symptomatic PD patients (UPD, Hoehn-Yahr staging scale, H-Y: 1-1.5), and 54 age-, gender-, education-matched normal controls (NC) were recruited. All subjects underwent MRI scanning and clinical evaluations. The interhemispheric coordination (Voxel-Mirrored Homotopic Connectivity, VMHC) and hemispheric dominance pattern (laterality index of eigenvector centrality mapping, LI-ECM) were calculated. Afterwards, correlation analyses and receiver operating characteristic (ROC) curve analysis were employed.

RESULTS

Compared with NC, UPD group showed significantly decreased VMHC in bilateral sensorimotor regions which was negatively correlated with the motor score. Furthermore, at the cut-off homotopic connectivity of 0.604, statistically significant ability of VMHC to discriminate UPD from NC with area under ROC curve (AUC) = 0.759, p < 0.001; specificity = 74.4%; sensitivity = 68.5% was observed. No difference was detected in UPD patients as for ECM and LI-ECM.

CONCLUSIONS

The disrupted interhemispheric coordination in bilateral sensorimotor regions may have significant implications for elucidating the mechanisms underlying the hemiparkinsonism and enabling the uncovering of complex mechanisms of PD.

Global and regional connectivity analysis of resting-state function MRI brain images using graph theory in Parkinson's disease

OBJECTIVES

Parkinson's disease (PD) is the second most common neurodegenerative disorder which resists around 10 million people worldwide. It develops when nerve cells in a region of the brain that regulates movement become damaged; the symptoms usually begin gradually and become critical over time. In this study, we proposed to investigate the topological properties of functional brain networks within healthy controls (HCs) and PD patients. Also, we evaluated the gender difference among PD patients through graph theoretical approach.

MATERIALS AND METHODS

The rs-fMRI (resting-state functional magnetic resonance imaging) data of fifty-one PD patients and healthy controls was applied to generate the brain functional connectome. The functional whole-brain connectome was constructed by thresholding partial correlation matrices of 160 regions from Dosenbach brain atlas. From the graph theory approach, global and nodal metrics were analysed, and we observed considerable changes in PD patients in comparison with healthy controls.

RESULTS

Findings suggest that there is a significant difference in the topological characteristics of PD patients, and this was found to be evident in the default mode network (DMN) and occipital regions.

CONCLUSION

This study provides essential insights from network changes to the clinically relevant information for the PD progression.

Oscillation-specific nodal alterations in early to middle stages Parkinson's disease

BACKGROUND

Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson's disease (PD) across early stage to middle stage by using graph theory-based analysis.

METHODS

Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated.

RESULTS

Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity.

CONCLUSIONS

Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.

Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI

Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analysis through new methods is the aim of any recent research. Functional magnetic resonance imaging (fMRI) is a prominent modality for investigating the human brain's neural substrate, especially when cognitive impairment occurs. The present study was an attempt to investigate the brain network's differences between MS patients and HCs using graph-theoretic measures constructed by an effective connectivity measure through statistical tests. The results of the significant measures were then evaluated through machine learning methods. To this end, we gathered blood-oxygen level dependent (BOLD) fMRI data of the participants during the execution of paced auditory serial addition test (PASAT). Granger causality analysis (GCA) was then employed between brain regions' time series on each subject in order to construct a brain network. Afterward, the Wilcoxon rank-sum test was implemented to find the alteration of brain networks between the mentioned groups. According to the results, Global flow coefficient was significantly different between HCs and patients. Moreover, MS disease impacted several areas of the brain including Hippocampus, Para Hippocampal, Thalamus, Cuneus, Superior temporal gyrus, Heschl, Caudate, Medial Frontal Superior Gyrus, Fusiform, Pallidum, and several parts of Cerebellum in centrality measures and local flow coefficient. Most of the obtained regions were related to the cognitive impacts of the disease. We also found the best subset of graph features by means of Fisher score, and classified them to evaluate the features strength for the discrimination of MS patients from HCs via several machine learning methods. Having used the combination of Wilcoxon rank-sum test and Fisher score, we were able to classify MS patients from HCs using linear support vector machine (SVM) with an accuracy of 95%. With regard to the few existing studies on brain network of MS patients, especially during a cognitive task execution, our findings showed that the selected graph measures by Wilcoxon rank-sum test and Fisher score from the GCA-based brain networks resulted in a promising classification accuracy.

Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder. It has a delitescent onset and a slow progress. The clinical manifestations of PD in patients are highly heterogeneous. Thus, PD diagnosis process is complex and mainly depends on the professional knowledge and experience of the physician. Magnetic resonance imaging (MRI) could detect the small changes in the brain of PD patients, and quantitative analysis of brain MRI may improve the clinical diagnosis efficiency. However, due to the complexity of clinical courses in PD and the high dimensionality in multimodal MRI data, traditional mathematical analysis could not effectively extract the huge information in them. Up to now, the accuracy of PD diagnosis in large sample size is still unsatisfying. As artificial intelligence (AI) is becoming more mature, varieties of statistical models and machine learning (ML) algorithms have been used for quantitative imaging data analysis to explore a diagnostic result. This review aims to state an overview of existing research recently that used statistical ML/AI methods to perform quantitative analysis of MR image data for the study of PD diagnosis. First we review the recent research in three subareas: diagnosis, differential diagnosis, and subtyping of PD. Then we described the overall workflow from MR image to classification result. Finally, we summarized a critical assessment of the current research and provide some recommendations for likely future research developments and trends.

Neural correlates of distinct cognitive phenotypes in early Parkinson's disease

OBJECTIVE

Cognitive decline is common in Parkinson's disease (PD), but changes can occur in a variety of cognitive domains. The lack of a single cognitive phenotype complicates diagnosis and tracking. In an earlier study we used a data-driven approach to identify distinct cognitive phenotypes of early PD. Here we identify the morphometric brain differences between those different phenotypes compared with cognitively normal PD participants.

METHODS

Six different cognitive classes were included (Weak, Typical, Weak-Visuospatial/Strong-Memory, Weak-Visuospatial, Amnestic, Strong). Structural differences between each class and the Typical class were assessed by deformation-based morphometry.

RESULTS

The different groups evidenced different patterns of atrophy. Weak class had frontotemporal and insular atrophy; Weak-Visuospatial/Strong-Memory class had frontotemporal, insular, parietal, and putamen atrophy; Weak-Visuospatial class had Rolandic operculum; Amnestic class had left frontotemporal, occipital, parietal and insular atrophy when compared to the Typical class. The Strong class did not have any atrophy but had significant differences in left temporal cortex in comparison to the Typical class.

CONCLUSIONS

Structural neuroimaging differences are evident in PD patients with distinct cognitive phenotypes even very early in the disease process prior to the emergence of frank cognitive impairment. Future studies will elucidate whether these have prognostic value in identifying trajectories toward dementia, or if they represent groups sensitive to different treatments.

Iron-related nigral degeneration influences functional topology mediated by striatal dysfunction in Parkinson's disease

In Parkinson's disease (PD), iron accumulation in the substantia nigra (SN) exacerbates oxidative stress and α-synuclein aggregation, leading to neuronal death. However, the influence of iron-related nigral degeneration on the subcortical function and global network configuration in PD remains unknown. Ninety PD patients and 38 normal controls underwent clinical assessments and multimodality magnetic resonance imaging scans. Iron accumulation in the inferior SN and disrupted functional connectivity between the bilateral striatums were observed in PD, and negative correlation between them was found in the whole population. The binarized functional network exhibited enhanced global efficiency and reduced local efficiency while the weighted functional network exhibited reduction in both, and both changes were correlated with nigral iron accumulation in PD. Mediation analysis demonstrated that the functional connectivity between bilateral striatums was a mediator between the nigral iron accumulation and weighted functional network alterations. In conclusion, our findings reveal that iron-related nigral degeneration possibly influences the functional topology mediated by striatal dysfunction, which extends the scientific understanding of PD pathogenesis.

Alterations of Brain Structural Network in Parkinson's Disease With and Without Rapid Eye Movement Sleep Behavior Disorder

Background and objective

Rapid eye movement sleep behavior disorder (RBD) has a strong association with alpha synucleinpathies such as Parkinson’s disease (PD) and PD patients with RBD tend to have a poorer prognosis. However, we still know little about the pathogenesis of RBD in PD. Therefore, we aim to detect the alterations of structural correlation network (SCN) in PD patients with and without RBD.

Materials and methods

A total of 191 PD patients, including 51 patients with possible RBD (pRBD) and 140 patients with non-possible RBD, and 76 normal controls were included in the present study. Structural brain networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches.

Results

There was no difference in global properties among the three groups. Significant enhanced regional nodal measures in limbic system, frontal-temporal regions, and occipital regions and decreased nodal measures in cerebellum were found in PD patients with pRBD (PD-pRBD) compared with PD patients without pRBD. Besides, nodes in frontal lobe, temporal lobe, and limbic system were served as hubs in both two PD groups, and PD-pRBD exhibited additionally recruited hubs in limbic regions.

Conclusion

Based on the SCN analysis, we found PD-pRBD exhibited a reorganization of nodal properties as well as the remapping of the hub distribution in whole brain especially in limbic system, which may shed light to the pathophysiology of PD with RBD.