Evolution of brain network dynamics in early Parkinson’s disease with mild cognitive impairment

Chu, Chunguang; Zhang, Zhen; Wang, Jiang; Wang, Liufang; Shen, Xiao; Bai, Lipeng; Li, Zhuo; Dong, Mengmeng; Liu, Chen; Yi, Guosheng; Zhu, Xiaodong

doi:10.1007/s11571-022-09868-1

Cited by 5 publications

(8 citation statements)

References 65 publications

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“…A number of study activities were utilised including diagnosis, (PD vs Healthy Controls (HC), PDD vs HC, PD-NC vs PD-MCI, PD-MCI vs PDD), differential diagnosis (PD-CI/PD-MCI/PDD vs AD vs DLB), identification of biomarkers for PD detection, and the prediction of future CI states. Most studies focused on diagnostic activities (n = 48) [ 21 – 26 , 28 , 30 , 31 , 84 , 113 , 114 , 116 – 119 , 121 , 122 , 124 – 126 , 128 , 130 , 133 – 136 , 138 , 142 , 144 – 146 , 148 – 151 , 153 – 155 , 157 – 161 , 164 , 166 , 171 , 172 ], followed by prediction (n = 12) [ 29 , 129 , 131 , 132 , 137 , 140 , 141 , 152 , 156 , 162 , 163 , 170 ], biomarker identification (n = 6) [ 27 , 120 , 123 , 143 , 147 , 169 ], and differential diagnosis (n = 4) [...…”

Section: Observations and Findingsmentioning

confidence: 99%

“…The most commonly used data modalities were imaging (n = 33) [ 24 , 27 – 31 , 113 , 114 , 117 , 119 , 122 , 129 , 130 , 133 , 137 , 139 – 143 , 147 – 152 , 155 , 156 , 159 – 161 , 163 , 164 ], clinical characteristics (n = 17) [ 84 , 118 , 131 , 132 , 137 , 140 , 152 , 153 , 155 , 156 , 159 , 160 , 162 – 164 , 169 , 170 ], EEG (n = 11) [ 25 , 26 , 120 , 125 – 128 , 135 , 136 , 146 , 151 ], and neuropsychological profile (n = 10) [ 115 , 118 , 132 , 134 , 142 , 157 , 158 …”

Section: Observations and Findingsmentioning

confidence: 99%

“…This high performance indicates that classification of CI in PD may be suited in some cases to the use of clustering alongside other ML techniques, however this will require further validation across other modalities. Chu et al [ 120 ] used DR based feature identification to analyse EEG data from 33 PD patients (13 PD-MCI, 20 PD-NC) to discover biomarkers for MCI development. This EEG trained an NMF model based on sliding window techniques to track sub-networks in functional brain networks.…”

Section: Observations and Findingsmentioning

confidence: 99%

“…Unsupervised learning. Four studies used unsupervised learning techniques to analyse CI in PD, two used unsupervised alone [120,144] and two used unsupervised in combination with supervised techniques [21,118]. Of these studies, three focused on classification (PD-MCI vs PD-NC) [21,118,144] and one focused on biomarker identification [120].…”

Section: Plos Onementioning

confidence: 99%

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Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Altham,

Zhang,

Pereira

2024

PLoS ONE

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Background Parkinson’s Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. Methods To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson’s Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. Results Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson’s Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson’s Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. Conclusions Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.

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Section: Observations and Findingsmentioning

confidence: 99%

Section: Observations and Findingsmentioning

confidence: 99%

Section: Observations and Findingsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Altham,

Zhang,

Pereira

2024

PLoS ONE

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“…Disruption of the network between the bilateral superior medial frontal gyrus and anterior/middle cingulate cortex appeared relevant for self-awareness of cognition and error processes [ 201 ]. In early PD with MCI, a subnetwork predominantly linking temporo-parietal-occipital lobes decreased in both expression and flexibility, reflecting the degree of CI [ 271 ].…”

Section: Brain Network Studiesmentioning

confidence: 99%

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Jellinger

2023

IJMS

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Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

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Brain Network Alterations in Chronic Spinal Cord Injury: Multilayer Community Detection Approach

Farahani,

Ismaila,

Sadowsky

et al. 2024

Neurotrauma Reports

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Evolution of brain network dynamics in early Parkinson’s disease with mild cognitive impairment

Cited by 5 publications

References 65 publications

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Brain Network Alterations in Chronic Spinal Cord Injury: Multilayer Community Detection Approach

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