Abnormal Topological Network in Parkinson's Disease With Impulse Control Disorders: A Resting-State Functional Magnetic Resonance Imaging Study.
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ABSTRACT:
SUBMITTER: Zhu X
PROVIDER: S-EPMC8419312 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Impulse control disorders (ICD) in Parkinson's disease (PD) might be attributed to misestimate of rewards or the failure to curb inappropriate choices. The mechanisms underlying ICD were reported to involve the lateralization of monoamine network. Our objective was to probe the significant role of lateralization in the pathogenesis of ICD. Twenty-one PD patients with ICD (PD-ICD), thirty-three without ICD (PD-no ICD), and thirty-seven healthy controls (HCs) were recruited and performed T1-weighted, diffusion tensor imaging (DTI) scans and resting state functional magnetic resonance imaging (rs-fMRI). By applying the Voxel-mirrored Homotopic Connectivity (VMHC) and Freesurfer, we evaluated participants' synchronicity of functional connectivity and structural changes between hemispheres. Also, tract-based spatial statistics (TBSS) was applied to compare fiber tracts differences. Relative to PD-no ICD group, PD-ICD group demonstrated reduced VMHC values in middle frontal gyrus (MFG). Compared to HCs, PD-ICD group mainly showed decreased VMHC values in MFG, middle and superior orbital frontal gyrus (OFG), inferior frontal gyrus (IFG) and caudate, which were related to reward processing and inhibitory control. The severity of impulsivity was negatively correlated with the mean VMHC values of MFG in PD-ICD group. Receiver operating characteristic (ROC) curves analyses uncovered that the mean VMHC values of MFG might be a potential marker identifying PD-ICD patients. However, we found no corresponding asymmetrical alteration in cortical thickness and no significant differences in fractional anisotropy (FA) and mean diffusivity (MD). Our results provided further evidence for asymmetry of functional connectivity in mesolimbic reward and response inhibition network in ICD.
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Project description:Introduction: Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier using a support vector machine to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting-state functional magnetic resonance imaging. Materials and Methods: First, we selected 66 seed regions based on prior related studies, then we calculated the full correlation matrix between them before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. Results: We identified 29 features of interest that were highly predictive of the FMD condition, classifying patients and controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, left dorsolateral prefrontal cortex, left cerebellum, and left posterior insula. Conclusions: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward, and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients. Clinical-Trials.gov ID: NCT00500994 Impact statement Our research presents novel results that further elucidate the pathophysiology of functional movement disorder (FMD) with a machine learning model that classifies FMD and healthy controls correctly 80% of the time. Herein, we demonstrate how known differences in resting-state functional magnetic resonance imaging connectivity in FMD patients can be leveraged to better understand the complex pattern of neural changes in these patients. Knowing that there are measurable predictable differences in brain activity in patients with FMD may help both clinicians and patients conceptualize and better understand the illness at the point of diagnosis and during treatment. Our methods demonstrate how an effective combination of machine learning and qualitative approaches to analyzing functional brain connectivity can enhance our understanding of abnormal patterns of brain activity in FMD patients.
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Project description:In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.
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Project description:Postpartum depression (PPD) is a major public health concern with significant consequences for mothers, their children, and their families. However, less is known about its underlying neuropathological mechanisms. The voxel-based degree centrality (DC) analysis approach provides a new perspective for exploring the intrinsic dysconnectivity pattern of whole-brain functional networks of PPD. Twenty-nine patients with PPD and thirty healthy postpartum women were enrolled and received resting-state functional magnetic resonance imaging (fMRI) scans in the fourth week after delivery. DC image, clinical symptom correlation, and seed-based functional connectivity (FC) analyses were performed to reveal the abnormalities of the whole-brain functional network in PPD. Compared with healthy controls (HCs), patients with PPD exhibited significantly increased DC in the right hippocampus (HIP.R) and left inferior frontal orbital gyrus (ORBinf.L). The receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of the above two brain regions is all over 0.7. In the seed-based FC analyses, the PPD showed significantly decreased FC between the HIP.R and right middle frontal gyrus (MFG.R), between the HIP.R and left median cingulate and paracingulate gyri (DCG.L), and between the ORBinf.L and the left fusiform (FFG.L) compared with HCs. The PPD showed significantly increased FC between the ORBinf.L and the right superior frontal gyrus, medial (SFGmed.R) compared with HCs. Mean FC between the HIP.R and DCG.L positively correlated with EDPS scores in the PPD group. This study provided evidence of aberrant DC and FC within brain regions in patients with PPD, which was associated with the default mode network (DMN) and limbic system (LIN). Identification of these above-altered brain areas may help physicians to better understand neural circuitry dysfunction in PPD.
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Project description:Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way.
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Project description:It is widely known that blood oxygenation level dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) is an indirect measure for neuronal activations through neurovascular coupling. The BOLD signal is also influenced by many non-neuronal physiological fluctuations. In previous resting state (RS) fMRI studies, we have identified a moving systemic low frequency oscillation (sLFO) in BOLD signal and were able to track its passage through the brain. We hypothesized that this seemingly intrinsic signal moves with the blood, and therefore, its dynamic patterns represent cerebral blood flow. In this study, we tested this hypothesis by performing Dynamic Susceptibility Contrast (DSC) MRI scans (i.e. bolus tracking) following the RS scans on eight healthy subjects. The dynamic patterns of sLFO derived from RS data were compared with the bolus flow visually and quantitatively. We found that the flow of sLFO derived from RS fMRI does to a large extent represent the blood flow measured with DSC. The small differences, we hypothesize, are largely due to the difference between the methods in their sensitivity to different vessel types. We conclude that the flow of sLFO in RS visualized by our time delay method represents the blood flow in the capillaries and veins in the brain.
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Project description:Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.
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Project description:ObjectivesLate-life depression (LLD) has negative impacts on somatic, emotional and cognitive domains of the lives of patients. Elucidating the abnormality in the brain networks of LLD patients could help to strengthen the understanding of LLD pathophysiology, however, the studies exploring the spontaneous brain activity in LLD during the resting state remain limited. This study aimed at identifying the voxel-level whole-brain functional connectivity changes in LLD patients.MethodsFifty patients with late-life depression (LLD) and 33 healthy controls were recruited. All participants underwent a resting-state functional magnetic resonance imaging scan to assess the voxel-wise degree centrality (DC) changes in the patients. Furthermore, DC was compared between two patient subgroups, the late-onset depression (LOD) and the early-onset depression (EOD).ResultsCompared with the healthy controls, LLD patients showed increased DC in the inferior parietal lobule, parahippocampal gyrus, brainstem and cerebellum (p < 0.05, AlphaSim-corrected). LLD patients also showed decreased DC in the somatosensory and motor cortices and cerebellum (p < 0.05, AlphaSim-corrected). Compared with EOD patients, LOD patients showed increased centrality in the superior and middle temporal gyrus and decreased centrality in the occipital region (p < 0.05, AlphaSim-corrected). No significant correlation was found between the DC value and the symptom severity or disease duration in the patients after the correction for multiple comparisons.ConclusionsThese findings indicate that the intrinsic abnormality of network centrality exists in a wide range of brain areas in LLD patients. LOD patients differ with EOD patients in cortical network centrality. Our study might help to strengthen the understanding of the pathophysiology of LLD and the potential neural substrates underlie related emotional and cognitive impairments observed in the patients.
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Project description:Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has been used to study the effects of anesthetic agents on correlated intrinsic neural activity. Previous studies have focused primarily on intravenous agents. The authors studied the effects of sevoflurane, an inhaled anesthetic.Resting-state BOLD fMRI was acquired from 10 subjects before sedation and from 9 subjects rendered unresponsive by 1.2% sevoflurane. The fMRI data were analyzed taking particular care to minimize the impact of artifact generated by head motion.BOLD correlations were specifically weaker within the default mode network and ventral attention network during sevoflurane-induced unconsciousness, especially between anterior and posterior midline regions. Reduced functional connectivity between these same networks and the thalamus was also spatially localized to the midline frontal regions. The amplitude of BOLD signal fluctuations was substantially reduced across all brain regions. The importance of censoring epochs contaminated by head motion was demonstrated by comparative analyses.Sevoflurane-induced unconsciousness is associated with both globally reduced BOLD signal amplitudes and selectively reduced functional connectivity within cortical networks associated with consciousness (default mode network) and orienting to salient external stimuli (ventral attention network). Scrupulous attention to minimizing the impact of head motion artifact is critical in fMRI studies using anesthetic agents.
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Project description:Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (?92%) and the occipital network, which includes the medial visual cortical areas (?94%), and consistent for the attention network (?80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (?80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.
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