Brain connectivity markers in advanced Parkinson’s disease for predicting mild cognitive impairment

Lin, Hai; Liu, Zesi; Yan, Wei; Zhang, Doudou; Liu, Jiali; Xu, Bin; Li, Weiping; Zhang, Qiusheng; Cai, Xiaodong

doi:10.1007/s00330-021-08086-3

Cited by 16 publications

(18 citation statements)

References 45 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%

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%

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 support vector machine analysis based on inter group dynamic amplitude of low-frequency fluctuations was reported with significantly increased classification accuracy and abnormal brain activity [10]. The random forest algorithm has been successfully implemented for brain connectivity markers and cognitive impairment [11], [12]. A brain network graph analysis was also proposed on PD diagnosis using rs-fMRI, which achieved an average accuracy of 95% and identified disease associated brain network alterations [13].…”

Section: Introductionmentioning

confidence: 99%

Early Disease Stage Characterization in Parkinson’s Disease from Resting-state fMRI Data Using a Long Short-term Memory Network

Guo¹,

Tinaz²,

Dvornek³

2022

Preprint

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Parkinson’s disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to classify early stages 1 and 2 and detect brain function alterations. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. Some machine learning approaches like support vector machine and logistic regression have been successfully applied in the early diagnosis of PD using fMRI data, which outperform classifiers based on manually selected morphological features. However, the early-stage characterization in FC changes has not been fully investigated. Given the complexity and non-linearity of fMRI data, we propose the use of a long short-term memory (LSTM) network to characterize the early stages of PD. The study included 84 subjects (56 in stage 2 and 28 in stage 1) from the Parkinson’s Progression Markers Initiative (PPMI), the largest-available public PD dataset. Under a repeated 10-fold stratified cross-validation, the LSTM model reached an accuracy of 71.63%, 13.52% higher than the best traditional machine learning method, indicating significantly better robustness and accuracy compared with other machine learning classifiers. We used the learned LSTM model weights to select the top brain regions that contributed to model prediction and performed FC analyses to characterize functional changes with disease stage and motor impairment to gain better insight into the brain mechanisms of PD.

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“…EEG coherence analysis has been used in evaluating functional connectivity (FC) in the cortex as well as other established methods ( esupp. Table 1 ) [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] . Therefore, is an indicator of PD severity, since abnormal FC is considered a compensatory mechanism of the brain.…”

Section: Introductionmentioning

confidence: 99%

“… Highlights We studied brain functional connectivity in 20 patients with PD-MCI and 10 MCI patients without Parkinsonism. Cognitive impairment was related to decreased coherence in the alpha range [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] . Regional coherence in left FP had a higher correlation with cognitive function.…”

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confidence: 99%

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Neural synchronization analysis of electroencephalography coherence in patients with Parkinson’s disease-related mild cognitive impairment

Mano

Kinugawa

Ozaki

et al. 2022

Clinical Parkinsonism & Related Disorders

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Brain connectivity markers in advanced Parkinson’s disease for predicting mild cognitive impairment

Cited by 16 publications

References 45 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

Early Disease Stage Characterization in Parkinson’s Disease from Resting-state fMRI Data Using a Long Short-term Memory Network

Neural synchronization analysis of electroencephalography coherence in patients with Parkinson’s disease-related mild cognitive impairment

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