Extraction of large-scale structural covariance networks from grey matter volume for Parkinson’s disease classification

Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson's disease

BACKGROUND

The efficacy of levodopa, the most crucial metric for Parkinson's disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response.

METHODS

This retrospective study used two datasets: from the Parkinson's Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001.

RESULTS

For 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized.

CONCLUSION

The T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction.

CRITICAL RELEVANCE STATEMENT

This study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy.

KEY POINTS

This study investigated the predictive value of T1w features for levodopa response. MedicalNet extractor outperformed all other previously published methods with key region visualization. T1w features are more effective than clinical information in levodopa response prediction.

MAPT rs17649553 T allele is associated with better verbal memory and higher small-world properties in Parkinson's disease

Currently, over 90 genetic loci have been found to be associated with Parkinson's disease (PD) in genome-wide association studies, nevertheless, the effects of these genetic variants on the clinical features and brain structure of PD patients are largely unknown. This study investigated the effects of microtubule-associated protein tau (MAPT) rs17649553 (C>T), a genetic variant associated with reduced PD risk, on the clinical manifestations and brain networks of PD patients. We found MAPT rs17649553 T allele was associated with better verbal memory in PD patients. In addition, MAPT rs17649553 significantly shaped the topology of gray matter covariance network and white matter network. Both the network metrics in gray matter covariance network and white matter network were correlated with verbal memory, however, the mediation analysis showed that it was the small-world properties in white matter network that mediated the effects of MAPT rs17649553 on verbal memory. These results suggest that MAPT rs17649553 T allele is associated with higher small-world properties in structural network and better verbal memory in PD.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

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.

Regional rather than global brain age mediates cognitive function in cerebral small vessel disease

The factors and mechanisms underlying the heterogeneous cognitive outcomes of cerebral small vessel disease are largely unknown. Brain biological age can be estimated by machine learning algorithms that use large brain MRI data sets to integrate and compute neuroimaging-derived age-related features. Predicted and chronological ages difference (brain-age gap) reflects advanced or delayed brain aging in an individual. The present study firstly reports the brain aging status of cerebral small vessel disease. In addition, we investigated whether global or certain regional brain age could mediate the cognitive functions in cerebral small vessel disease. Global and regional (400 cortical, 14 subcortical and 28 cerebellum regions of interest) brain-age prediction models were constructed using grey matter features from MRI of 1482 healthy individuals (age: 18–92 years). Predicted and chronological ages differences were obtained and then applied to non-stroke, non-demented individuals, aged ≥50 years, from another community-dwelling population (I-Lan Longitudinal Aging Study cohort). Among the 734 participants from the I-Lan Longitudinal Aging Study cohort, 124 were classified into the cerebral small vessel disease group. The cerebral small vessel disease group demonstrated significantly poorer performances in global cognitive, verbal memory and executive functions than that of non-cerebral small vessel disease group. Global brain-age gap was significantly higher in the cerebral small vessel disease (3.71 ± 7.60 years) than that in non-cerebral small vessel disease (−0.43 ± 9.47 years) group (P = 0.003, η² = 0.012). There were 82 cerebral cortical, 3 subcortical and 4 cerebellar regions showing significantly different brain-age gap between the cerebral small vessel disease and non-cerebral small vessel disease groups. Global brain-age gap failed to mediate the relationship between cerebral small vessel disease and any of the cognitive domains. In 89 regions with increased brain-age gap in the cerebral small vessel disease group, seven regional brain-age gaps were able to show significant mediation effects in cerebral small vessel disease-related cognitive impairment (we set the statistical significance P < 0.05 uncorrected in 89 mediation models). Of these, the left thalamus and left hippocampus brain-age gap explained poorer global cognitive performance in cerebral small vessel disease. We demonstrated the interconnections between cerebral small vessel disease and brain age. Strategic brain aging, i.e. advanced brain aging in critical regions, may be involved in the pathophysiology of cerebral small vessel disease-related cognitive impairment. Regional rather than global brain-age gap could potentially serve as a biomarker for predicting heterogeneous cognitive outcomes in patients with cerebral small vessel disease.

Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach

To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.

Structural Covariance of the Ipsilesional Primary Motor Cortex in Subcortical Stroke Patients with Motor Deficits

The analysis of structural covariance has emerged as a powerful tool to explore the morphometric correlations among broadly distributed brain regions. However, little is known about the interactions between the damaged primary motor cortex (M1) and other brain regions in stroke patients with motor deficits. This study is aimed at investigating the structural covariance pattern of the ipsilesional M1 in chronic subcortical stroke patients with motor deficits. High-resolution T1-weighted brain images were acquired from 58 chronic subcortical stroke patients with motor deficits (29 with left-sided lesions and 29 with right-sided lesions) and 50 healthy controls. Structural covariance patterns were identified by a seed-based structural covariance method based on gray matter (GM) volume. Group comparisons between stroke patients (left-sided or right-sided groups) and healthy controls were determined by a permutation test. The association between alterations in the regional GM volume and motor recovery after stroke was investigated by a multivariate regression approach. Structural covariance analysis revealed an extensive increase in the structural interactions between the ipsilesional M1 and other brain regions in stroke patients, involving not only motor-related brain regions but also non-motor-related brain regions. We also identified a slightly different pattern of structural covariance between the left-sided stroke group and the right-sided stroke group, thus indicating a lesion-side effect of cortical reorganization after stroke. Moreover, alterations in the GM volume of structural covariance brain regions were significantly correlated to the motor function scores in stroke patients. These findings indicated that the structural covariance patterns of the ipsilesional M1 in chronic subcortical stroke patients were induced by motor-related plasticity. Our findings may help us to better understand the neurobiological mechanisms of motor impairment and recovery in patients with subcortical stroke from different perspectives.

Detraining Effect on Pulmonary and Cardiovascular Autonomic Function and Functional Outcomes in Patients With Parkinson's Disease After Respiratory Muscle Training: An 18-Month Follow-Up Study

Background: The effect of 3-month respiratory muscle training (RMT) on pulmonary and autonomic function and functional outcomes has been demonstrated in patients with Parkinson's disease (PD); however, there is a paucity of information on the durability of the training effect. In this study, we monitored the pulmonary and cardiovascular autonomic function and clinical severity scales until 18 months after the cessation of RMT to elucidate the detraining effect after RMT.

Methods: All patients with PD receiving RMT were assessed with clinical severity scales as well as pulmonary and autonomic function tests at four different stages (baseline on enrollment, immediately after 3 months of RMT, and 6 and 18 months after cessation of RMT). A control group of PD patients who did not receive RMT was also recruited for comparison. Pulmonary function parameters, including forced vital capacity (FVC), forced expiratory volume in one second (FEV1), maximum inspiratory pressure (MIP), and maximum expiratory pressure (MEP), were assessed. Cardiovascular autonomic function was assessed using measures including heart rate response to deep breathing (HRDB), Valsalva ratio, and baroreflex sensitivity. Clinical severity scores were also measured using the Hoehn and Yahr staging and the Unified Parkinson's Disease Rating Scale (UPDRS).

Results: The results showed significant improvements in MIP, MEP, HRDB, and UPDRS immediately after RMT. Despite some decay, the improvements in pulmonary function (MIP and MEP) and functional outcomes (UPDRS) remained significant until 6 months of detraining (9 months after enrollment). However, the improvement in cardiovascular autonomic function (HRDB) was reversed after 6 months of detraining.

Conclusions: Based on these findings, we recommend that RMT may be repeated after at least 6 months after previous session (9 months after enrollment) for patients with PD to maintain optimal therapeutic effects.

Altered gray matter structural covariance networks at both acute and chronic stages of mild traumatic brain injury

Cognitive and emotional impairments observed in mild traumatic brain injury (mTBI) patients may reflect variances of brain connectivity within specific networks. Although previous studies found altered functional connectivity (FC) in mTBI patients, the alterations of brain structural properties remain unclear. In the present study, we analyzed structural covariance (SC) for the acute stages of mTBI (amTBI) patients, the chronic stages of mTBI (cmTBI) patients, and healthy controls. We first extracted the mean gray matter volume (GMV) of seed regions that are located in the default-mode network (DMN), executive control network (ECN), salience network (SN), sensorimotor network (SMN), and the visual network (VN). Then we determined and compared the SC for each seed region among the amTBI, the cmTBI and the healthy controls. Compared with healthy controls, the amTBI patients showed lower SC for the ECN, and the cmTBI patients showed higher SC for the both DMN and SN but lower SC for the SMN. The results revealed disrupted ECN in the amTBI patients and disrupted DMN, SN and SMN in the cmTBI patients. These alterations suggest that early disruptions in SC between bilateral insula and the bilateral prefrontal cortices may appear in amTBI and persist into cmTBI, which might be potentially related to the cognitive and emotional impairments.

Homogeneous grey matter patterns in patients with obsessive-compulsive disorder

BACKGROUND

Changes in grey matter volume have frequently been reported in patients with obsessive-compulsive disorder (OCD). Most studies performed whole brain or region-of-interest based analyses whereas grey matter volume based on structural covariance networks has barely been investigated up to now. Therefore, the present study investigated grey matter volume within structural covariance networks in a sample of 228 participants (n = 117 OCD patients, n = 111 healthy controls).

METHODS

First, an independent component analysis (ICA) was performed on all subjects' preprocessed T1 images to derive covariance-dependent morphometric networks. Then, grey matter volume from each of the ICA-derived morphometric networks was extracted and compared between the groups. In addition, we performed logistic regressions and receiver operating characteristic (ROC) analyses to investigate whether network-related grey matter volume could serve as a characteristic that allows to differentiate patients from healthy volunteers. Moreover, we assessed grey matter pattern organization by correlating grey matter volume in all networks across all participants. Finally, we explored a potential association between grey matter volume or whole-brain grey matter pattern organization and clinical characteristics in terms of symptom severity and duration of illness.

RESULTS

There were only subtle group differences in network-related grey matter volume. Network-related grey matter volume had moreover a very poor discrimination performance. We found, however, significant group differences with regard to grey matter pattern organization. When correlating grey matter volume in all networks across all participants, patients showed a significantly higher homogeneity across all networks and a significantly lower heterogeneity, as assessed by the coefficient of variation across all networks as well as in several single networks. There was no association with clinical characteristics.

CONCLUSION

The findings of the present study suggest that the pathological mechanisms of OCD reduce interindividual grey matter variability. We assume that common characteristics associated with the disorder may lead to a more uniform, disorder-specific morphometry.

Sex effects on cortical morphological networks in healthy young adults

Understanding sex-related differences across the human cerebral cortex is an important step in elucidating the basis of psychological, behavioural and clinical differences between the sexes. Prior structural neuroimaging studies primarily focused on regional sex differences using univariate analyses. Here we focus on sex differences in cortical morphological networks (CMNs) derived using multivariate modelling of regional cortical measures of volume and surface from high-quality structural MRI scans from healthy participants in the Human Connectome Project (HCP) (n = 1,063) and the Southwest University Longitudinal Imaging Multimodal (SLIM) study (n = 549). The functional relevance of the CMNs was inferred using the NeuroSynth decoding function. Sex differences were widespread but not uniform. In general, females had higher volume, thickness and cortical folding in networks that involve prefrontal (both ventral and dorsal regions including the anterior cingulate) and parietal regions while males had higher volume, thickness and cortical folding in networks that primarily include temporal and posterior cortical regions. CMN loading coefficients were used as input features to linear discriminant analyses that were performed separately in the HCP and SLIM; sex was predicted with a high degree of accuracy (81%-85%) across datasets. The availability of behavioral data in the HCP enabled us to show that male-biased surface-based CMNs were associated with externalizing behaviors. These results extend previous literature on regional sex-differences by identifying CMNs that can reliably predict sex, are relevant to the expression of psychopathology and provide the foundation for the future investigation of their functional significance in clinical populations.

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R ² = 0.88; support vector regression, MAE = 4.42 years, R ² = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R ² = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.

Progressive Gray Matter Atrophy and Abnormal Structural Covariance Network in Ischemic Pontine Stroke

Our aim was to identify the longitudinal changes in gray matter volume (GMV) and secondary alterations of structural covariance after pontine stroke (PS). Structural MRI and behavioral scores were obtained at 1 week, 1 month, 3 months, 6 months in 11 patients with PS. Twenty healthy subjects underwent the same examination only once. We used voxel-based morphometry and seed-based structural covariance to investigate the altered GMV and structural covariance patterns. Furthermore, the associations between the GMV changes and behavioral scores were assessed. With the progression of the disease, GMV decreased significantly in the bilateral cerebellar posterior lobe (ipsilateral Crus II (CBE Crus II_IL) and contralateral Crus I (CBE Crus I_CL)), which were initially detected at the first month and then continued to decrease during the following 6 months. Based on the CBE Crus II_IL and CBE Crus I_CL as seed regions, structural covariance analysis revealed that there were more positively and negatively correlated brain regions in PS group, mainly distributed in the bilateral prefrontal lobe, parietal lobe, temporal lobe, paralimbic system and cerebellum. In addition, PS group showed more additional correlations between these covariant brain regions, and the changes of GMV in these regions were correlated with behavioral scores related to motor and cognitive functions. These findings indicate that PS could lead to significant GMV atrophy in the bilateral cerebellar posterior lobe at the early stage, accompanied by anomalous structural covariance patterns with more covariant brain regions and additional structural connectivity, which may provide useful information for understanding the neurobiological mechanisms of behavioral recovery after PS.

Structural Covariance Network Disruption and Functional Compensation in Parkinson's Disease

Purpose: To investigate the structural covariance network disruption in Parkinson’s disease (PD), and explore the functional alterations of disrupted structural covariance network.

Methods: A cohort of 100 PD patients and 70 healthy participants underwent structural and functional magnetic resonance scanning. Independent component analysis (ICA) was applied separately to both deformation-based morphometry (DBM) maps and functional maps with the same calculating parameters (both decomposed into 20 independent components (ICs) and computed 20 times the Infomax algorithm in ICASSO). Disrupted structural covariance network in PD patients was identified, and then, we performed goodness of fit analysis to obtain the functional network that showed the highest spatial overlap with it. We investigated the relationship between structural covariance network and functional network alterations. Finally, to further understand the structural and functional alterations over time, we performed a longitudinal subgroup analysis (51 patients were followed up for 2 years) with the same procedures.

Results: In a cross-sectional analysis, PD patients showed decreased structural covariance between anterior and posterior cingulate subnetworks. The functional components showed best overlap with anterior and posterior cingulate structural subnetworks were selected as anterior and posterior cingulate functional subnetworks. The functional connectivity between them was significantly increased [assessed by Functional Network Connectivity (FNC) toolbox]; and the increased functional connectivity was negatively correlated with cingulate structural covariance network integrity. Longitudinal subgroup analysis showed cingulate structural covariance network disruption was worse at follow-up, while the functional connectivity between anterior and posterior cingulate network was increased at baseline and decreased at follow-up.

Conclusion: This study indicated that the cingulate structural covariance network displayed a high susceptibility in PD patients. This study indicated that the cingulate structural covariance network displayed a high susceptibility in PD patients. Considering that disrupted structural covariance network coexisted with enhanced/remained functional activity during disease development, enhanced functional activity underlying the disrupted cingulate structural covariance network might represent a temporal compensation for maintaining clinical performance.

Simultaneously Improved Pulmonary and Cardiovascular Autonomic Function and Short-Term Functional Outcomes in Patients with Parkinson's Disease after Respiratory Muscle Training

Both pulmonary function and autonomic function are impaired in patients with Parkinson’s diseases (PD). This study tested the hypothesis that respiratory muscle training (RMT) can not only improve pulmonary function, but also simultaneously improve cardiovascular autonomic function and short-term functional outcomes in patients with PD. Pulmonary function was measured by the forced vital capacity (FVC), forced expiratory volume in one second (FEV1), maximum inspiratory pressures (MIP), and maximum expiratory pressures (MEP). Cardiovascular autonomic function was measured by the heart rate response to deep breathing (HRDB), Valsalva ratio, baroreflex sensitivity, and spectral analysis. The functional and severity scores were measured by the Hoehn and Yahr stage and Unified Parkinson’s Disease Rating Scale (UPDRS). These measures were evaluated in patients with PD before and after 3 months of RMT, compared with a control group of PD patients without RMT. The results showed significant improvement of clinical scores (total UPDRS and UPDRS I, II and III) after RMT (p < 0.0001). Concerning pulmonary function, the parameters of MIP and MEP improved significantly. The parameters of cardiovascular function also improved after RMT, although only HRDB reached statistical significance. Based on the results of our study, RMT can not only improve both pulmonary and cardiovascular autonomic function, but can also improve short-term functional outcomes in patients with PD.

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

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

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

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

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

Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry

A network-level investigation of the volumetric changes of subcortical stroke patients is still lacking. Here, we explored the alterations of structural covariance caused by subcortical stroke with automated brain volumetry. T1-weighed brain MRI scans were obtained from 63 normal controls (NC), 46 stroke patients with infarct in left internal capsule (CI_L), 33 stroke patients with infarct in right internal capsule (CI_R). We performed automatic anatomical segmentation of the T1-weighted brain images with AccuBrain. Volumetric structural covariance analyses were first performed within the basal ganglia structures that were both identified by voxel-based morphometry with AAL atlas and AccuBrain. Subsequently, we additionally included the infratentorial regions that were particularly quantified by AccuBrain for the structural covariance analyses and investigated the alterations of anatomical connections within these subcortical regions in CI_L and CI_R compared with NC. The association between the regional brain volumetry and motor function was also evaluated in stroke groups. There were significant and extensive volumetric differences in stroke patients. These significant regions were generally symmetric for CI_L and CI_R group depending on the side of stroke, involving both regions close to lesions and remote regions. The structural covariance analyses revealed the synergy volume alteration in subcortical regions both in CI_L and CI_R group. In addition, the alterations of volumetric structural covariance were more extensive in CI_L group than CI_R group. Moreover, we found that the subcortical regions with atrophy contributed to the deficits of motor function in CI_R group but not CI_L group, indicating a lesion-side effect of brain volumetric changes after stroke. These findings indicated that the chronic subcortical stroke patients have extensive disordered anatomical connections involving the whole-brain level network, and the connections patterns depend on the lesion-side.

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Chronic subcortical stroke patients show extensive brain volumetric atrophy.

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Subcortical stroke patients show disordered structural covariance network pattern.

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Brain volumetric and connections patterns change depend on the lesion-side.