Within- and across-network alterations of the sensorimotor network in Parkinson’s disease

Caspers, Julian; Rubbert, Christian; Eickhoff, Simon B.; Hoffstaedter, Felix; Südmeyer, Martin; Hartmann, Christian; Sigl, Benjamin; Teichert, Nikolas A; Aissa, Joel; Turowski, Bernd; Schnitzler, Alfons; Mathys, Christian

doi:10.1007/s00234-021-02731-w

Cited by 49 publications

(48 citation statements)

References 73 publications

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“…For the PPMI dataset, the connectivity in the patient group decreases in the SMN (colored purple) system, which integrates primary sensorimotor, premotor, and supplementary motor areas to facilitate voluntary movements. This observation confirms existing neuroimaging studies that have repeatedly shown disorder-related alteration in sensorimotor areas of Parkinson's patients [2]. Furthermore, individuals with PD have lower connectivity within the DMN (colored blue) system compared with healthy controls, which is consistent with the cognition recession study on Parkinson's patients [8,33].…”

Section: Interpretation Analysissupporting

confidence: 89%

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Cui¹,

Dai²,

Zhu³

et al. 2022

Preprint

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Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged as a potential method for modeling complex network data. Deep models, on the other hand, have low interpretability, which prevents their usage in decision-critical contexts like healthcare. To bridge this gap, we propose an interpretable framework to analyze disorder-specific Regions of Interest (ROIs) and prominent connections. The proposed framework consists of two modules: a brain-network-oriented backbone model for disease prediction and a globally shared explanation generator that highlights disorder-specific biomarkers including salient ROIs and important connections. We conduct experiments on three real-world datasets of brain disorders. The results verify that our framework can obtain outstanding performance and also identify meaningful biomarkers. All code for this work is available at https://github.com/HennyJie/IBGNN.git.

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Section: Interpretation Analysissupporting

confidence: 89%

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Cui¹,

Dai²,

Zhu³

et al. 2022

Preprint

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“…The SN and MN play a central role in preparing and executing the motor function ( Tessitore et al, 2019 ). Their connectivity in PD patients is frequently reported as disrupted ( Caspers et al, 2021 ; Chen et al, 2021 ), and these functional changes are correlated with the motor symptoms’ severity progression ( Kojovic et al, 2012 ). In DLB, metabolic connectivity alterations showed a severe involvement of PDMN, LN, and ATTN, consistently with the pathological and clinical heterogeneity of this condition ( Iaccarino et al, 2018 ; Sala et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…In PD patients, we found a loss of connectivity mainly affecting the SN and MN networks showing a poor overlap with HC ( Figures 1 , 2 ). These networks play a central role in motor function ( Tessitore et al, 2019 ) and are usually disrupted in PD patients ( Caspers et al, 2021 ; Chen et al, 2021 ), leading to the motor symptoms’ progression ( Kojovic et al, 2012 ). A recent multimodal imaging study demonstrated the close relationship between dopaminergic depletion, degeneration of nigrostriatal projection, and the destruction of sensory-motor networks in PD, mainly driven by the putamen’s functional impairment ( Ruppert et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Metabolic connectivity of resting-state networks in alpha synucleinopathies, from prodromal to dementia phase

et al. 2022

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Previous evidence suggests that the derangement of large-scale brain networks reflects structural, molecular, and functional mechanisms underlying neurodegenerative diseases. Although the alterations of multiple large-scale brain networks in Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are reported, a comprehensive study on connectivity reconfiguration starting from the preclinical phase is still lacking. We aimed to investigate shared and disease-specific changes in the large-scale networks across the Lewy Bodies (LB) disorders spectrum using a brain metabolic connectivity approach. We included 30 patients with isolated REM sleep behavior disorder (iRBD), 28 with stable PD, 30 with DLB, and 30 healthy controls for comparison. We applied seed-based interregional correlation analyses (IRCA) to evaluate the metabolic connectivity in the large-scale resting-state networks, as assessed by [18F]FDG-PET, in each clinical group compared to controls. We assessed metabolic connectivity changes by applying the IRCA and specific connectivity metrics, such as the weighted and unweighted Dice similarity coefficients (DC), for the topographical similarities. All the investigated large-scale brain resting-state networks showed metabolic connectivity alterations, supporting the widespread involvement of brain connectivity within the alpha-synuclein spectrum. Connectivity alterations were already evident in iRBD, severely affecting the posterior default mode, attentive and limbic networks. Strong similarities emerged in iRBD and DLB that showed comparable connectivity alterations in most large-scale networks, particularly in the posterior default mode and attentive networks. Contrarily, PD showed the main connectivity alterations limited to motor and somatosensory networks. The present findings reveal that metabolic connectivity alterations in the large-scale networks are already present in the early iRBD phase, resembling the DLB metabolic connectivity changes. This suggests and confirms iRBD as a risk condition for progression to the severe LB disease phenotype. Of note, the neurobiology of stable PD supports its more benign phenotype.

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“…Rs-fMRI technology has been very mature in the application of mental diseases, such as PD, Alzheimer's disease and depression (Yan et al, , 2019Yang et al, 2018Yang et al, , 2020Liu et al, 2022). Now, resting-state-based brain functional network studies have identified some intrinsic networks in the brain, such as sensorimotor network (SMN) (Lagana et al, 2020;Caspers et al, 2021;Palmer et al, 2021), fronto-parietal network (FPN) (Engels et al, 2018;Boon et al, 2020), default network (DMN) (Rektorova, 2014;Putcha et al, 2015), etc. For PD, the main research point is to focus on changes in resting brain networks in patients with Parkinsonian movement disorders (Liu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Treatment Enhances Betweenness Centrality of Fronto-Parietal Network in Parkinson’s Patients

Liu

Shi

Wang

et al. 2022

Front. Comput. Neurosci.

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Previous studies have demonstrated a close relationship between early Parkinson’s disease and functional network abnormalities. However, the pattern of brain changes in the early stages of Parkinson’s disease has not been confirmed, which has important implications for the study of clinical indicators of Parkinson’s disease. Therefore, we investigated the functional connectivity before and after treatment in patients with early Parkinson’s disease, and further investigated the relationship between some topological properties and clinicopathological indicators. We included resting state-fMRI (rs-fMRI) data from 27 patients with early Parkinson’s disease aged 50–75 years from the Parkinson’s Disease Progression Markers Initiative (PPMI). The results showed that the functional connectivity of 6 networks, cerebellum network (CBN), cingulo_opercular network (CON), default network (DMN), fronto-parietal network (FPN), occipital network (OCC), and sensorimotor network (SMN), was significantly changed. Compared to before treatment, the main functional connections were concentrated in the CBN after treatment. In addition, the coefficients of these nodes have also changed. For betweenness centrality (BC), the FPN showed a significant improvement in treatment (p < 0.001). In conclusion, the alteration of functional networks in early Parkinson’s patients is critical for clarifying the mechanisms of early diagnosis of the disease.

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Within- and across-network alterations of the sensorimotor network in Parkinson’s disease

Cited by 49 publications

References 73 publications

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Metabolic connectivity of resting-state networks in alpha synucleinopathies, from prodromal to dementia phase

Treatment Enhances Betweenness Centrality of Fronto-Parietal Network in Parkinson’s Patients

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