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Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease.

ABSTRACT: Parkinson's disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

SUBMITTER: Mijalkov M

PROVIDER: S-EPMC8805861 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVE:Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI). METHODS:Whole-brain rs-fMRI (EPI, TR = 2.2?s, TE = 30?ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1?mm, acquisition time ? 11?min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance. RESULTS:Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 - 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 - 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 - 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks. CONCLUSION:A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting. ADVANCES IN KNOWLEDGE:Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

Project description:Psychosis disorders share overlapping symptoms and are characterized by a wide-spread breakdown in functional brain integration. Although neuroimaging studies have identified numerous connectivity abnormalities in affective and non-affective psychoses, whether they have specific or unique connectivity abnormalities, especially within the early stage is still poorly understood. The early phase of psychosis is a critical period with fewer chronic confounds and when treatment intervention may be most effective. In this work, we examined whole-brain functional network connectivity (FNC) from both static and dynamic perspectives in patients with affective psychosis (PAP) or with non-affective psychosis (PnAP) and healthy controls (HCs). A fully automated independent component analysis (ICA) pipeline called "Neuromark" was applied to high-quality functional magnetic resonance imaging (fMRI) data with 113 early-phase psychosis patients (32 PAP and 81 PnAP) and 52 HCs. Relative to the HCs, both psychosis groups showed common abnormalities in static FNC (sFNC) between the thalamus and sensorimotor domain, and between subcortical regions and the cerebellum. PAP had specifically decreased sFNC between the superior temporal gyrus and the paracentral lobule, and between the cerebellum and the middle temporal gyrus/inferior parietal lobule. On the other hand, PnAP showed increased sFNC between the fusiform gyrus and the superior medial frontal gyrus. Dynamic FNC (dFNC) was investigated using a combination of a sliding window approach, clustering analysis, and graph analysis. Three reoccurring brain states were identified, among which both psychosis groups had fewer occurrences in one antagonism state (state 2) and showed decreased network efficiency within an intermediate state (state 1). Compared with HCs and PnAP, PAP also showed a significantly increased number of state transitions, indicating more unstable brain connections in affective psychosis. We further found that the identified connectivity features were associated with the overall positive and negative syndrome scale, an assessment instrument for general psychopathology and positive symptoms. Our findings support the view that subcortical-cortical information processing is disrupted within five years of the initial onset of psychosis and provide new evidence that abnormalities in both static and dynamic connectivity consist of shared and unique features for the early affective and non-affective psychoses.

Project description:ObjectiveTo use a multimodal approach to assess brain structural pathways and resting state (RS) functional connectivity abnormalities in patients with Parkinson's disease and freezing of gait (PD-FoG).MethodsT1-weighted, diffusion tensor (DT) MRI and RS functional MRI (fMRI) were obtained from 22 PD-FoG patients and 35 controls on a 3.0 T MR scanner. Patients underwent clinical, motor, and neuropsychological evaluations. Gray matter (GM) volumes and white matter (WM) damage were assessed using voxel based morphometry and tract-based spatial statistics, respectively. The pedunculopontine tract (PPT) was studied using tractography. RS fMRI data were analyzed using a model free approach investigating the main sensorimotor and cognitive brain networks. Multiple regression models were performed to assess the relationships between structural, functional, and clinical/cognitive variables. Analysis of GM and WM structural abnormalities was replicated in an independent sample including 28 PD-FoG patients, 25 PD patients without FoG, and 30 healthy controls who performed MRI scans on a 1.5 T scanner.ResultsCompared with controls, no GM atrophy was found in PD-FoG cases. PD-FoG patients showed WM damage of the PPT, corpus callosum, corticospinal tract, cingulum, superior longitudinal fasciculus, and WM underneath the primary motor, premotor, prefrontal, orbitofrontal, and inferior parietal cortices, bilaterally. In PD-FoG, right PTT damage was associated with a greater disease severity. Analysis on the independent PD sample showed similar findings in PD-FoG patients relative to controls as well as WM damage of the genu and body of the corpus callosum and right parietal WM in PD-FoG relative to PD no-FoG patients. RS fMRI analysis showed that PD-FoG is associated with a decreased functional connectivity of the primary motor cortex and supplementary motor area bilaterally in the sensorimotor network, frontoparietal regions in the default mode network, and occipital cortex in the visual associative network.ConclusionsThis study suggests that FoG in PD can be the result of a poor structural and functional integration between motor and extramotor (cognitive) neural systems.

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Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease.

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