Characterization of attentional event-related potential from REM sleep behavior disorder patients based on explainable machine learning

Kim, Hyun; Seo, Pukyeong; Kim, Min Ju; Huh, Jun Il; Sunwoo, Jae Gun; Su, Kwang; Jeong, El; Kim, Han Joon; Jung, Ki Young; Kim, Do-Won

doi:10.1016/j.cmpb.2023.107496

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…A number of RBD topics that were not covered in this review due to space limitations have recently been reported and reviewed [9,75,84]. Some of these topics include RBD in children, adolescents, and adults under 50 years old; acute RBD; stress disorders and RBD; gut microbiome dysbiosis in RBD [85]; abnormal activation of motor cortical network during phasic REM sleep in iRBD [86]; NREM sleep EEG oscillations in iRBD [87]; altered resting-state thalamo-occipital functional connectivity in iRBD [88]; quantitative actigraphic assessment of treatment response in RBD [89]; digital biomarkers in RBD [90][91][92]; machine learning research in RBD [93,94]; and animal models of RBD [95][96][97][98]. In conclusion, RBD is situated at an important and ever-expanding crossroads of clinical (sleep) medicine, neurology, and neuroscience.…”

Section: Discussionmentioning

confidence: 99%

Update on Rapid-Eye-Movement Sleep Behavior Disorder (RBD): Focus on Its Strong Association with α-Synucleinopathies

Schenck

2023

CTN

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REM sleep behavior disorder (RBD) is a parasomnia in which the customary generalized skeletal muscle atonia of REM sleep, “REM-atonia”, is compromised, allowing for the injurious acting-out of dreams. RBD can be idiopathic/isolated (iRBD) or symptomatic of neurological disorders, and can be triggered by most antidepressants. RBD mainly affects middle-aged and older adults, and is strongly linked with alpha-synucleinopathies, mainly Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). iRBD is now known to be the earliest and strongest predictor of future PD/DLB, which has stimulated a major international clinical and basic science research effort to enroll iRBD patients for upcoming neuroprotective/disease-modifying trials and to identify the most promising interventions to test in these cohorts. This review will provide the latest pertinent information on the rapidly expanding field of RBD. The methods included a PubMed literature search that included PubCrawlers, which utilizes the NCBI (National Center for Biotechnology Information) E-utils tools for publication retrieval, using the keywords “REM sleep behavior disorder” and “RBD”. The results yielded the latest updates on iRBD as prodromal PD/DLB, with the most promising biomarkers for phenoconversion provided, along with a presentation of three clinical research consortiums that are systematically gathering patients in preparation for enrollment in upcoming clinical trials: (i) The International RBD Study Group; (ii) The North American Prodromal Synucleinopathy (NAPS and NAPS2) Consortium; and (iii) The FARPRESTO Italian multicenter RBD research consortium. In addition, updates on the Parasomnia Overlap Disorder (RBD + NREM parasomnia) and on narcolepsy-RBD are provided, along with new epidemiologic data, the latest RBD management guidelines, and updates on animal models of RBD. Emerging areas of critical RBD research are also highlighted. In conclusion, RBD is a notable example of clinical and translational neuroscience research.

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Section: Discussionmentioning

confidence: 99%

Update on Rapid-Eye-Movement Sleep Behavior Disorder (RBD): Focus on Its Strong Association with α-Synucleinopathies

Schenck

2023

CTN

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“…In a recent study, we identified the spatial characteristics of dysfunctional cortical activities of patients with neurological disorders 20 , 21 based on a 2dCNN trained by 2d data representing current densities on the cortical surface within a critical temporal period, which is supposed to be crucial for working memory 22 . The temporal period was determined based on prior knowledge of the cognitive function under consideration, which may be misleading and has resulted in limitations in the objective identification of crucial characteristics solely based on a data-driven approach.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal characteristics of cortical activities of REM sleep behavior disorder revealed by explainable machine learning using 3D convolutional neural network

Kim

Seo

Byun

et al. 2023

Sci Rep

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Isolated rapid eye movement sleep behavior disorder (iRBD) is a sleep disorder characterized by dream enactment behavior without any neurological disease and is frequently accompanied by cognitive dysfunction. The purpose of this study was to reveal the spatiotemporal characteristics of abnormal cortical activities underlying cognitive dysfunction in patients with iRBD based on an explainable machine learning approach. A convolutional neural network (CNN) was trained to discriminate the cortical activities of patients with iRBD and normal controls based on three-dimensional input data representing spatiotemporal cortical activities during an attention task. The input nodes critical for classification were determined to reveal the spatiotemporal characteristics of the cortical activities that were most relevant to cognitive impairment in iRBD. The trained classifiers showed high classification accuracy, while the identified critical input nodes were in line with preliminary knowledge of cortical dysfunction associated with iRBD in terms of both spatial location and temporal epoch for relevant cortical information processing for visuospatial attention tasks.

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Automatic and Machine Learning Methods for Detection and Characterization of REM Sleep Behavior Disorder

Cesari,

Rechichi

2024

Springer Optimization and Its Applications

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Characterization of attentional event-related potential from REM sleep behavior disorder patients based on explainable machine learning

Cited by 4 publications

References 52 publications

Update on Rapid-Eye-Movement Sleep Behavior Disorder (RBD): Focus on Its Strong Association with α-Synucleinopathies

Update on Rapid-Eye-Movement Sleep Behavior Disorder (RBD): Focus on Its Strong Association with α-Synucleinopathies

Spatiotemporal characteristics of cortical activities of REM sleep behavior disorder revealed by explainable machine learning using 3D convolutional neural network

Automatic and Machine Learning Methods for Detection and Characterization of REM Sleep Behavior Disorder

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