Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson’s disease

Huang, Xiaofei; He, Qing; Ruan, Xiuhang; Li, Yuting; Kuang, Zhanyu; Wang, Mengfan; Guo, Riyu; Bu, Shuwen; Wang, Zhaoxiu; Yu, Shaode; Chen, Amei; Wei, Xinhua

doi:10.1016/j.nicl.2023.103548

Cited by 4 publications

(10 citation statements)

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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%

“…ML techniques used across all reviewed studies were categorised into 12 categories, some of which overlap: (1) tree based methods (n = 32) [ 22 , 23 , 26 , 27 , 31 , 114 , 116 , 119 , 122 , 125 , 126 , 128 – 130 , 132 , 134 , 137 , 139 , 141 , 145 , 153 , 155 , 157 – 159 , 162 , 164 , 166 , 167 , 170 – 172 ], (2) Support Vector Machines (n = 30) [ 21 , 23 – 25 , 27 , 28 , 30 , 113 , 115 , 117 , 118 , 122 – 124 , 133 , 134 , 138 , 139 , 141 , 142 , 148 – 151 , 153 , 156 – 159 , 161 , …”

Section: Observations and Findingsmentioning

confidence: 99%

“…44 of the 58 supervised learning studies focused on classification activities, 40 of which focused on classification vs NC/HC [ 22 – 26 , 28 , 30 , 31 , 84 , 113 , 114 , 116 , 117 , 119 , 122 , 124 – 126 , 128 , 130 , 133 , 134 , 138 , 142 , 145 , 146 , 148 – 151 , 153 – 155 , 158 , 159 , 161 , 164 , 166 , 171 , 172 ], and four classifying against memory disorders [ 115 , 127 , 139 , 167 ]. 11 studies focused on prediction [ 129 , 131 , 132 , 137 , 140 , 141 , 152 , 156 , 162 , 163 , 170 ], and the remaining three on biomarker identification [ 27 , 123 , 169 ]. Regarding modalities, 23 studies used a combination ...…”

Section: Observations and Findingsmentioning

confidence: 99%

“…11 studies focused on prediction [ 129 , 131 , 132 , 137 , 140 , 141 , 152 , 156 , 162 , 163 , 170 ], and the remaining three on biomarker identification [ 27 , 123 , 169 ]. Regarding modalities, 23 studies used a combination of modalities, with the remaining 35 using only one, with the most commonly used modalities being imaging (n = 29) [ 24 , 27 , 28 , 30 , 31 , 113 , 114 , 117 , 119 , 122 , 129 , 130 , 133 , 137 , 139 – 142 , 148 – 152 , 155 , 156 , 159 , 161 , 163 , 164 ], clinical characteristics (n = 15) [ 84 , 131 , 132 , 137 , 140 , 152 , 153 , 155 , 156 , 159 , 162 – 164 , 169 ,…”

Section: Observations and Findingsmentioning

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: Observations and Findingsmentioning

confidence: 99%

Section: Observations and Findingsmentioning

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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The Role of Machine Learning in Cognitive Impairment in Parkinson’s Disease: A Systematic Review and meta-analysis (Preprint)

Wu,

Cheng,

Xiao

et al. 2024

Preprint

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UNSTRUCTURED Parkinson’s disease (PD) is a common neurodegenerative disease characterized by both motor and non-motor symptoms. Cognitive impairment often occurs early in the disease and can persist throughout its progression, severely impacting patients’ quality of life. The utilization of machine learning (ML) has recently shown promise in identifying cognitive impairment in PD patients. This study aims to summarize different ML models applied to cognitive impairment in PD patients and to identify determinants for improving the diagnosis and predictive power to find cognitive impairment at an early stage. PubMed, Cochrane, Embase, and Web of Science for relevant articles were conducted on March 2, 2024. A total of 43 articles met the criteria, involving 9,139 PD patients and 1,353 healthy controls. A total of 151 models were analyzed, with an accuracy ranging from 60% to 90%. Predictors commonly used in ML models included clinical features, neuroimaging features, and other variables. In the bivariate meta-analysis, including only 12 studies, no significant heterogeneity was observed. Our findings provide a comprehensive summary of various ML models and demonstrate the effectiveness of ML as a tool for diagnosing and predicting cognitive impairment in patients with PD.

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Mild cognitive impairment in Parkinson's disease: current view

Jellinger

2024

Front. Cognit.

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Parkinson's disease (PD), the most common motor movement disorder and second most common neurodegenerative disorder after Alzheimer's disease (AD), is often preceded by a period of mild cognitive impairment (MCI), which is associated with impairment of a variety of cognitive domains including executive function, attention, visuospatial abilities and memory. MCI, a risk factor for developing dementia, affects around 30% of de novo PD patients and can increase to 75% after more than 10 years. While 30–40% remain in the MCI state, up to 60% will convert to dementia. Characteristic findings are slowing of EEG rhythms, frontotemporal hypoperfusion, decreased functional connectivity in the default mode and attentional networks, prefrontal and basal-ganglia-cortical circuits, which often manifests prior to clinical symptoms and overt brain atrophy. The heterogeneity of cognitive phenotypes suggests that a common neurodegenerative process affects multiple functional neuronal networks and neuromodulatory systems that may be superimposed by Lewy body and Alzheimer's-related or other co-pathologies. Sparse neuropathological data for PD-MCI revealed a heterogenous picture with various morphological changes similar to MCI in other diseases. This review highlights the essential epidemiological, clinical, neuroimaging and morphological changes in PD-MCI, available biomarkers, and discusses the heterogenous pathobiological mechanisms involved in its development. In view of its complex pathogenesis, well-designed longitudinal clinico-pathological studies are warranted to clarify the alterations leading to MCI in PD, which may be supported by fluid and neuroimaging biomarkers as a basis for early diagnosis and future adequate treatment modalities of this debilitating disorder.

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Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson’s disease

Cited by 4 publications

References 42 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

The Role of Machine Learning in Cognitive Impairment in Parkinson’s Disease: A Systematic Review and meta-analysis (Preprint)

Mild cognitive impairment in Parkinson's disease: current view

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