Topological Abnormalities of Functional Brain Network in Early-Stage Parkinson’s Disease Patients With Mild Cognitive Impairment

Recent studies have demonstrated structural and functional alterations in Parkinson’s disease (PD) with mild cognitive impairment (MCI). However, the topological patterns of functional brain networks in newly diagnosed PD patients with MCI are unclear so far. In this study, we used functional magnetic resonance imaging (fMRI) and graph theory approaches to explore the functional brain network in 45 PD patients with MCI (PD-MCI), 22 PD patients without MCI (PD-nMCI), and 18 healthy controls (HC). We found that the PD-MCI, PD-nMCI, and HC groups exhibited a small-world architecture in the functional brain network. However, early-stage PD-MCI patients had decreased clustering coefficient, increased characteristic path length, and changed nodal centrality in the default mode network (DMN), control network (CN), somatomotor network (SMN), and visual network (VN), which might contribute to factors for MCI symptoms in PD patients. Our results demonstrated that PD-MCI patients were associated with disrupted topological organization in the functional network, thus providing a topological network insight into the role of information exchange in the underlying development of MCI symptoms in PD patients.

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Gaussian embedding-based functional brain connectomic analysis for amnestic mild cognitive impairment patients with cognitive training

10.1101/779744 ◽

2019 ◽

Author(s):

Mengjia Xu ◽

Zhijiang Wang ◽

Haifeng Zhang ◽

Dimitrios Pantazis ◽

Huali Wang ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Stage ◽

Brain Networks ◽

Disease Diagnosis ◽

Brain Regions ◽

Brain Network ◽

Amnestic Mild Cognitive Impairment ◽

Functional Brain ◽

Functional Brain Network

AbstractIdentifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimer’s disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a new quantitative functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration.

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Olfactory Function and Diffusion Tensor Imaging as Markers of Mild Cognitive Impairment in Early Stages of Parkinson's Disease

Clinical EEG and Neuroscience ◽

10.1177/15500594211058263 ◽

2021 ◽

pp. 155005942110582

Author(s):

Sophie A. Stewart ◽

Laura Pimer ◽

John D. Fisk ◽

Benjamin Rusak ◽

Ron A. Leslie ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Diffusion Tensor Imaging ◽

Mild Cognitive Impairment ◽

White Matter ◽

Neurodegenerative Disorder ◽

Diffusion Tensor ◽

Early Stage ◽

White Matter Integrity

Parkinson's disease (PD) is a neurodegenerative disorder that is typified by motor signs and symptoms but can also lead to significant cognitive impairment and dementia Parkinson's Disease Dementia (PDD). While dementia is considered a nonmotor feature of PD that typically occurs later, individuals with PD may experience mild cognitive impairment (PD-MCI) earlier in the disease course. Olfactory deficit (OD) is considered another nonmotor symptom of PD and often presents even before the motor signs and diagnosis of PD. We examined potential links among cognitive impairment, olfactory functioning, and white matter integrity of olfactory brain regions in persons with early-stage PD. Cognitive tests were used to established groups with PD-MCI and with normal cognition (PD-NC). Olfactory functioning was examined using the University of Pennsylvania Smell Identification Test (UPSIT) while the white matter integrity of the anterior olfactory structures (AOS) was examined using magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) analysis. Those with PD-MCI demonstrated poorer olfactory functioning and abnormalities based on all DTI parameters in the AOS, relative to PD-NC individuals. OD and microstructural changes in the AOS of individuals with PD may serve as additional biological markers of PD-MCI.

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Neuroimaging in Parkinson’s disease dementia: connecting the dots

Brain Communications ◽

10.1093/braincomms/fcz006 ◽

2019 ◽

Vol 1 (1) ◽

Cited By ~ 13

Author(s):

Rimona S Weil ◽

Joey K Hsu ◽

Ryan R Darby ◽

Louis Soussand ◽

Michael D Fox

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Meta Analysis ◽

Likelihood Estimation ◽

Brain Network ◽

Activation Likelihood Estimation ◽

Stage Specificity ◽

Network Mapping

Abstract Dementia is a common and devastating symptom of Parkinson’s disease but the anatomical substrate remains unclear. Some evidence points towards hippocampal involvement but neuroimaging abnormalities have been reported throughout the brain and are largely inconsistent across studies. Here, we test whether these disparate neuroimaging findings for Parkinson’s disease dementia localize to a common brain network. We used a literature search to identify studies reporting neuroimaging correlates of Parkinson’s dementia (11 studies, 385 patients). We restricted our search to studies of brain atrophy and hypometabolism that compared Parkinson’s patients with dementia to those without cognitive involvement. We used a standard coordinate-based activation likelihood estimation meta-analysis to assess for consistency in the neuroimaging findings. We then used a new approach, coordinate-based network mapping, to test whether neuroimaging findings localized to a common brain network. This approach uses resting-state functional connectivity from a large cohort of normative subjects (n = 1000) to identify the network of regions connected to a reported neuroimaging coordinate. Activation likelihood estimation meta-analysis failed to identify any brain regions consistently associated with Parkinson’s dementia, showing major heterogeneity across studies. In contrast, coordinate-based network mapping found that these heterogeneous neuroimaging findings localized to a specific brain network centred on the hippocampus. Next, we tested whether this network showed symptom specificity and stage specificity by performing two further analyses. We tested symptom specificity by examining studies of Parkinson’s hallucinations (9 studies, 402 patients) that are frequently co-morbid with Parkinson’s dementia. We tested for stage specificity by using studies of mild cognitive impairment in Parkinson’s disease (15 studies, 844 patients). Coordinate-based network mapping revealed that correlates of visual hallucinations fell within a network centred on bilateral lateral geniculate nucleus and correlates of mild cognitive impairment in Parkinson’s disease fell within a network centred on posterior default mode network. In both cases, the identified networks were distinct from the hippocampal network of Parkinson’s dementia. Our results link heterogeneous neuroimaging findings in Parkinson’s dementia to a common network centred on the hippocampus. This finding was symptom and stage-specific, with implications for understanding Parkinson’s dementia and heterogeneity of neuroimaging findings in general.

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Predictors of Mild Cognitive Impairment in Early-Stage Parkinson's Disease

Dementia and Geriatric Cognitive Disorders Extra ◽

10.1159/000351421 ◽

2013 ◽

Vol 3 (1) ◽

pp. 168-178 ◽

Cited By ~ 15

Author(s):

Brenda Hanna-Pladdy ◽

Katherine Jones ◽

Romeo Cabanban ◽

Rajesh Pahwa ◽

Kelly E. Lyons

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Stage

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IC-P-174: Changes of functional brain network in mild cognitive impairment and Alzheimer's disease: Graph theoretical analysis of brain glucose metabolism

Alzheimer s & Dementia ◽

10.1016/j.jalz.2011.05.188 ◽

2011 ◽

Vol 7 ◽

pp. S79-S79

Author(s):

Eun Hyun Seo ◽

Dong Young Lee ◽

Jun-Sung Park ◽

Uicheul Yoon ◽

Jong-Min Lee ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Glucose Metabolism ◽

Theoretical Analysis ◽

Brain Network ◽

Brain Glucose ◽

Brain Glucose Metabolism ◽

Functional Brain ◽

Graph Theoretical Analysis ◽

Functional Brain Network

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Functional Hubs in Mild Cognitive Impairment

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127415500340 ◽

2015 ◽

Vol 25 (03) ◽

pp. 1550034 ◽

Cited By ~ 9

Author(s):

Adrián Navas ◽

David Papo ◽

Stefano Boccaletti ◽

F. Del-Pozo ◽

Ricardo Bajo ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Short Term Memory ◽

Brain Networks ◽

Memory Task ◽

Brain Network ◽

Control Group ◽

Functional Brain ◽

Functional Brain Network ◽

Functional Brain Networks

We investigate how hubs of functional brain networks are modified as a result of mild cognitive impairment (MCI), a condition causing a slight but noticeable decline in cognitive abilities, which sometimes precedes the onset of Alzheimer's disease. We used magnetoencephalography (MEG) to investigate the functional brain networks of a group of patients suffering from MCI and a control group of healthy subjects, during the execution of a short-term memory task. Couplings between brain sites were evaluated using synchronization likelihood, from which a network of functional interdependencies was constructed and the centrality, i.e. importance, of their nodes was quantified. The results showed that, with respect to healthy controls, MCI patients were associated with decreases and increases in hub centrality respectively in occipital and central scalp regions, supporting the hypothesis that MCI modifies functional brain network topology, leading to more random structures.

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Disrupted Topological Organization of Resting-State Functional Brain Network in Subcortical Vascular Mild Cognitive Impairment

CNS Neuroscience & Therapeutics ◽

10.1111/cns.12424 ◽

2015 ◽

Vol 21 (10) ◽

pp. 846-854 ◽

Cited By ~ 20

Author(s):

Li-Ye Yi ◽

Xia Liang ◽

Da-Ming Liu ◽

Bo Sun ◽

Sun Ying ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Resting State ◽

Brain Network ◽

Functional Brain ◽

Functional Brain Network ◽

Topological Organization

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Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17072594 ◽

2020 ◽

Vol 17 (7) ◽

pp. 2594 ◽

Cited By ~ 4

Author(s):

Haewon Byeon

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Random Forest ◽

Decision Tree ◽

Prediction Model ◽

Prediction Accuracy ◽

Early Stage ◽

Normal Cognition

Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson’s disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson’s Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson’s disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson’s Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.

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