Preoperative Electroencephalography‐Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation

Geraedts, Victor J.; Koch, Milan; Kuiper, Roy; Kefalas, Marios; Bäck, Thomas; Hilten, Jacobus J. van; Wang, Hao; Middelkoop, Huub A. M.; Gaag, Niels A. van der; Contarino, Maria Fiorella; Tannemaat, Martijn R.

doi:10.1002/mds.28661

Cited by 16 publications

(13 citation statements)

References 56 publications

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“…Such specific markers from scalp recordings are still largely unknown. Advanced analytical methods such as network connectivity measures, automated classifiers, and machine learning approaches offer significant promise for increasing this knowledge 39 , 40 . EEG microstate analysis is a methodological approach that reflects alterations in the spatiotemporal dynamics of large-scale brain networks 15 , 41 .…”

Section: Discussionmentioning

confidence: 99%

The effect of deep brain stimulation in Parkinson’s disease reflected in EEG microstates

Lamoš

Bočková

Goldemundová

et al. 2023

npj Parkinsons Dis.

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Mechanisms of deep brain stimulation (DBS) on cortical networks were explored mainly by fMRI. Advanced analysis of high-density EEG is a source of additional information and may provide clinically useful biomarkers. The presented study evaluates EEG microstates in Parkinson’s disease and the effect of DBS of the subthalamic nucleus (STN). The association between revealed spatiotemporal dynamics of brain networks and changes in oscillatory activity and clinical examination were assessed. Thirty-seven patients with Parkinson’s disease treated by STN-DBS underwent two sessions (OFF and ON stimulation conditions) of resting-state EEG. EEG microstates were analyzed in patient recordings and in a matched healthy control dataset. Microstate parameters were then compared across groups and were correlated with clinical and neuropsychological scores. Of the five revealed microstates, two differed between Parkinson’s disease patients and healthy controls. Another microstate differed between ON and OFF stimulation conditions in the patient group and restored parameters in the ON stimulation state toward to healthy values. The mean beta power of that microstate was the highest in patients during the OFF stimulation condition and the lowest in healthy controls; sources were localized mainly in the supplementary motor area. Changes in microstate parameters correlated with UPDRS and neuropsychological scores. Disease specific alterations in the spatiotemporal dynamics of large-scale brain networks can be described by EEG microstates. The approach can reveal changes reflecting the effect of DBS on PD motor symptoms as well as changes probably related to non-motor symptoms not influenced by DBS.

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Section: Discussionmentioning

confidence: 99%

The effect of deep brain stimulation in Parkinson’s disease reflected in EEG microstates

Lamoš

Bočková

Goldemundová

et al. 2023

npj Parkinsons Dis.

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“…For PD patients (co-cited references of cluster 4 “machine learning” indicated that QEEG could provide reliable biomarkers for nonmotor symptom severity and progression ( 28 , 57 ). Besides, the citing articles in this cluster pointed out that preoperative QEEG biomarkers could predict cognitive deterioration of PD after subthalamic deep brain stimulation with high accuracy by using a machine learning pipeline ( 58 , 59 ).…”

Section: Discussionmentioning

confidence: 99%

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

Yao

Zhu

et al. 2022

Front. Psychiatry

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BackgroundWith the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.MethodsQEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.ResultsA total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.ConclusionThe present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

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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%

“…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%

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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Preoperative Electroencephalography‐Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation

Cited by 16 publications

References 56 publications

The effect of deep brain stimulation in Parkinson’s disease reflected in EEG microstates

The effect of deep brain stimulation in Parkinson’s disease reflected in EEG microstates

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

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

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