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Article ; Online: Prediction of East Asian Brain Age using Machine Learning Algorithms Trained With Community-based Healthy Brain MRI.

Simfukwe, Chanda / Youn, Young Chul

2022 Volume 21, Issue 4, Page(s) 138–146

Abstract: Background and purpose: Magnetic resonance imaging (MRI) helps with brain development analysis and disease diagnosis. Brain volumes measured from different ages using MRI provides useful information in clinical evaluation and research. Therefore, we ... ...

Abstract	Background and purpose: Magnetic resonance imaging (MRI) helps with brain development analysis and disease diagnosis. Brain volumes measured from different ages using MRI provides useful information in clinical evaluation and research. Therefore, we trained machine learning models that predict the brain age gap of healthy subjects in the East Asian population using T1 brain MRI volume images. Methods: In total, 154 T1-weighted MRIs of healthy subjects (55-83 years of age) were collected from an East Asian community. The information of age, gender, and education level was collected for each participant. The MRIs of the participants were preprocessed using FreeSurfer(https://surfer.nmr.mgh.harvard.edu/) to collect the brain volume data. We trained the models using different supervised machine learning regression algorithms from the scikit-learn (https://scikit-learn.org/) library. Results: The trained models comprised 19 features that had been reduced from 55 brain volume labels. The algorithm BayesianRidge (BR) achieved a mean absolute error (MAE) and r squared (R Conclusions: The MAE and R
Language	English
Publishing date	2022-10-31
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	3015713-4
ISSN	2384-0757 ; 1738-1495
ISSN (online)	2384-0757
ISSN	1738-1495
DOI	10.12779/dnd.2022.21.4.138
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Article ; Online: Classification of Aβ State From Brain Amyloid PET Images Using Machine Learning Algorithm.

Simfukwe, Chanda / Lee, Reeree / Youn, Young Chul

Dementia and neurocognitive disorders

2023 Volume 22, Issue 2, Page(s) 61–68

Abstract: Background and purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each ... ...

Abstract	Background and purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.
Language	English
Publishing date	2023-04-30
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	3015713-4
ISSN	2384-0757 ; 1738-1495
ISSN (online)	2384-0757
ISSN	1738-1495
DOI	10.12779/dnd.2023.22.2.61
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Article ; Online: Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset.

Kim, Min-Jae / Youn, Young Chul / Paik, Joonki

NeuroImage

2023 Volume 272, Page(s) 120054

Abstract: For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical annotations called Chung-Ang University Hospital EEG (CAUEEG), which has event history, patient's age, and corresponding diagnosis labels. We also designed ... ...

Abstract	For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical annotations called Chung-Ang University Hospital EEG (CAUEEG), which has event history, patient's age, and corresponding diagnosis labels. We also designed two reliable evaluation tasks for the low-cost, non-invasive diagnosis to detect brain disorders: i) CAUEEG-Dementia with normal, mci, and dementia diagnostic labels and ii) CAUEEG-Abnormal with normal and abnormal. Based on the CAUEEG dataset, this paper proposes a new fully end-to-end deep learning model, called the CAUEEG End-to-end Deep neural Network (CEEDNet). CEEDNet pursues to bring all the functional elements for the EEG analysis in a seamless learnable fashion while restraining non-essential human intervention. Extensive experiments showed that our CEEDNet significantly improves the accuracy compared with existing methods, such as machine learning methods and Ieracitano-CNN (Ieracitano et al., 2019), due to taking full advantage of end-to-end learning. The high ROC-AUC scores of 0.9 on CAUEEG-Dementia and 0.86 on CAUEEG-Abnormal recorded by our CEEDNet models demonstrate that our method can lead potential patients to early diagnosis through automatic screening.
MeSH term(s)	Humans ; Deep Learning ; Electroencephalography/methods ; Algorithms ; Cognitive Dysfunction/diagnosis ; Dementia/diagnosis
Language	English
Publishing date	2023-03-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2023.120054
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Zs.A 3664: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: A machine-learning algorithm for predicting brain age using Rey-Osterrieth complex figure tests of healthy participants.

Simfukwe, Chanda / Youn, Young Chul / Jeong, Ho Tae

Applied neuropsychology. Adult

2023 , Page(s) 1–6

Abstract: Objective: Neuropsychologists widely use the Rey-Osterrieth complex figure test (RCFT) as part of neuropsychological test batteries to evaluate cognitive function and assess constructional ability, with age being the most significant factor. Our study ... ...

Abstract	Objective: Neuropsychologists widely use the Rey-Osterrieth complex figure test (RCFT) as part of neuropsychological test batteries to evaluate cognitive function and assess constructional ability, with age being the most significant factor. Our study investigated a supervised machine learning (ML) algorithm to predict brain age gap using RCFT drawings from the healthy elderly community for early dementia detection. Participants and methods: RCFT drawings from 1,970 healthy subjects (ages 45-90 years) were collected from the Korean elderly community. We recorded subject demographic information including: age, gender, and education level. We trained the ML model with RCFT copies, immediate recall, delayed recall, and education level of the healthy subjects using CNN regression algorithm from Keras (https://keras.io/) with the Tensorflow library. Results: The performance was evaluated by the mean absolute error (MAE) and root mean squared error (RMSE) between the predicted age and the chronological age based on a test dataset of 300 healthy subjects. The CNN regression model achieved an MAE of 7.2 years in predicting the brain age gap of the subjects, with an RMSE of 8.9 years. Conclusion: The MAE and RMSE accuracies of the CNN regression model predicting the brain age gap showed the model could be a potential biomarker for individual brain aging and a cost-effective method for early dementia detection.
Language	English
Publishing date	2023-01-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	2673736-X
ISSN	2327-9109 ; 2327-9095
ISSN (online)	2327-9109
ISSN	2327-9095
DOI	10.1080/23279095.2022.2164198
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bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article: Integration of amyloid-β oligomerization tendency as a plasma biomarker in Alzheimer's disease diagnosis.

Pyun, Jung-Min / Youn, Young Chul / Park, Young Ho / Kim, SangYun

Frontiers in neurology

2023 Volume 13, Page(s) 1028448

Abstract: Introduction: There has been significant development in blood-based biomarkers targeting amyloidopathy of Alzheimer's disease (AD). However, the guidelines for integrating such biomarkers into AD diagnosis are still inadequate. Multimer Detection System- ...

Abstract	Introduction: There has been significant development in blood-based biomarkers targeting amyloidopathy of Alzheimer's disease (AD). However, the guidelines for integrating such biomarkers into AD diagnosis are still inadequate. Multimer Detection System-Oligomeric Amyloid-β (MDS-OAβ) as a plasma biomarker detecting oligomerization tendency is available in the clinical practice. Main text: We suggest how to interpret the results of plasma biomarker for amyloidopathy using MDS-OAβ with neuropsychological test, brain magnetic resonance imaging (MRI), and amyloid PET for AD diagnosis. Combination of each test result differentiates various stages of AD, other neurodegenerative diseases, or cognitive impairment due to the causes other than neurodegeneration. Discussion: A systematic interpretation strategy could support accurate diagnosis and staging of AD. Moreover, comprehensive use of biomarkers that target amyloidopathy such as amyloid PET on brain amyloid plaque and MDS-OAβ on amyloid-β oligomerization tendency can complement to gain advanced insights on amyloid-β dynamics in AD.
Language	English
Publishing date	2023-01-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2022.1028448
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Article: qEEG as Biomarker for Alzheimer's Disease: Investigating Relative PSD Difference and Coherence Analysis.

Simfukwe, Chanda / Han, Su-Hyun / Jeong, Ho Tae / Youn, Young Chul

Neuropsychiatric disease and treatment

2023 Volume 19, Page(s) 2423–2437

Abstract: Purpose: Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain's cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to ... ...

Abstract	Purpose: Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain's cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to detect different stages of Alzheimer's disease (AD) through quantitative EEG (qEEG) analysis. This paper investigates the difference in the abnormalities of resting state EEG (rEEG) signals between eyes-open (EOR) and eyes-closed (ECR) in AD by analyzing 19-scalp electrode EEG signals and making a comparison with healthy controls (HC). Participants and methods: The rEEG data from 534 subjects (ages 40-90) consisting of 269 HC and 265 AD subjects in South Korea were used in this study. The qEEG for EOR and ECR states were performed separately for HC and AD subjects to measure the relative power spectrum density (PSD) and coherence with functional connectivity to evaluate abnormalities. The rEEG data were preprocessed and analyzed using EEGlab and Brainstorm toolboxes in MATLAB R2021a software, and statistical analyses were carried out using ANOVA. Results: Based on the Welch method, the relative PSD of the EEG EOR and ECR states difference in the AD group showed a significant increase in the delta frequency band of 19 EEG channels, particularly in the frontal, parietal, and temporal, than the HC groups. The delta power band on the source level was increased for the AD group and decreased for the HC group. In contrast, the source activities of alpha, beta, and gamma frequency bands were significantly reduced in the AD group, with a high decrease in the beta frequency band in all brain areas. Furthermore, the coherence of rEEG among different EEG electrodes was analyzed in the beta frequency band. It showed that pair-wise coherence between different brain areas in the AD group is remarkably increased in the ECR state and decreased after subtracting out the EOR state. Conclusion: The findings suggest that examining PSD and functional connectivity through coherence analysis could serve as a promising and comprehensive approach to differentiate individuals with AD from normal, which may benefit our understanding of the disease.
Language	English
Publishing date	2023-11-09
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2186503-6
ISSN	1178-2021 ; 1176-6328
ISSN (online)	1178-2021
ISSN	1176-6328
DOI	10.2147/NDT.S433207
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Zs.A 6068: Show issues

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Article: Clinical Aspects of the Differential Diagnosis of Parkinson's Disease and Parkinsonism.

Shin, Hae-Won / Hong, Sang-Wook / Youn, Young Chul

Journal of clinical neurology (Seoul, Korea)

2022 Volume 18, Issue 3, Page(s) 259–270

Abstract: Parkinsonism is a clinical syndrome presenting with bradykinesia, tremor, rigidity, and postural instability. Nonmotor symptoms have recently been included in the parkinsonian syndrome, which was traditionally associated with motor symptoms only. Various ...

Abstract	Parkinsonism is a clinical syndrome presenting with bradykinesia, tremor, rigidity, and postural instability. Nonmotor symptoms have recently been included in the parkinsonian syndrome, which was traditionally associated with motor symptoms only. Various pathologically distinct and unrelated diseases have the same clinical manifestations as parkinsonism or parkinsonian syndrome. The etiologies of parkinsonism are classified as neurodegenerative diseases related to the accumulation of toxic protein molecules or diseases that are not neurodegenerative. The former class includes Parkinson's disease (PD), multiple-system atrophy, progressive supranuclear palsy, and corticobasal degeneration. Over the past decade, clinical diagnostic criteria have been validated and updated to improve the accuracy of diagnosing these diseases. The latter class of disorders unrelated to neurodegenerative diseases are classified as secondary parkinsonism, and include drug-induced parkinsonism (DIP), vascular parkinsonism, and idiopathic normal-pressure hydrocephalus (iNPH). DIP and iNPH are regarded as reversible and treatable forms of parkinsonism. However, studies have suggested that the absence of protein accumulation in the nervous system as well as managing the underlying causes do not guarantee recovery. Here we review the differential diagnosis of PD and parkinsonism, mainly focusing on the clinical aspects. In addition, we describe recent updates to the clinical criteria of various disorders sharing clinical symptoms with parkinsonism.
Language	English
Publishing date	2022-03-23
Publishing country	Korea (South)
Document type	Journal Article ; Review
ZDB-ID	2500489-X
ISSN	2005-5013 ; 1738-6586
ISSN (online)	2005-5013
ISSN	1738-6586
DOI	10.3988/jcn.2022.18.3.259
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Article: Is Functional Connectivity after a First Unprovoked Seizure Different Based on Subsequent Seizures and Future Diagnosis of Epilepsy?

Koo, Ga Eun / Jeong, Ho Tae / Youn, Young Chul / Han, Su-Hyun

Journal of epilepsy research

2022 Volume 12, Issue 2, Page(s) 62–67

Abstract: Background and purpose: There are no highly sensitive biomarkers for epilepsy to date. Recently, promising results regarding functional connectivity analysis have been obtained, which may improve epilepsy diagnosis even in the absence of visible ... ...

Abstract	Background and purpose: There are no highly sensitive biomarkers for epilepsy to date. Recently, promising results regarding functional connectivity analysis have been obtained, which may improve epilepsy diagnosis even in the absence of visible abnormality in electroencephalography. We aimed to investigate the differences in functional connectivity after a first unprovoked seizure between patients diagnosed with epilepsy within 1 year due to subsequent seizures and those who were not. Methods: We compared quantitative electroencephalography power spectra and functional connectivity between 12 patients who were diagnosed with epilepsy (two or more unprovoked seizures) within 1 year and 17 controls (those not diagnosed within 1 year) using iSyncBrain Results: In the epilepsy group, quantitative electroencephalography showed lower alpha2 band power in left frontal, central, superior temporal, and parietal regions and higher beta2 power in both frontal, central, temporal, occipital, and left parietal regions compared with the control group. Additionally, epilepsy patients had significantly lower connectivity in alpha2 and beta2 bands than the controls. Conclusions: Patients experiencing their first unprovoked seizure presented different brain function according to whether they have subsequent seizures and future epilepsy. Our results propose the potential clinical ability to diagnose epilepsy after the first unprovoked seizure in the absence of interictal epileptiform discharges.
Language	English
Publishing date	2022-12-30
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2771719-7
ISSN	2233-6257 ; 2233-6249
ISSN (online)	2233-6257
ISSN	2233-6249
DOI	10.14581/jer.22011
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Article ; Online: Neuropsychological test using machine learning for cognitive impairment screening.

Simfukwe, Chanda / Kim, SangYun / An, Seong Soo / Youn, Young Chul

Applied neuropsychology. Adult

2022 , Page(s) 1–6

Abstract: Objectives: Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-consuming and requires a specialized clinician. For this reason, we trained machine learning models that ... ...

Abstract	Objectives: Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-consuming and requires a specialized clinician. For this reason, we trained machine learning models that detect normal controls (NC), cognitive impairment (CI), and dementia among subjects. Patients and methods: A total number of 14,927 subject datasets were collected from the formal neuropsychological assessments Seoul Neuropsychological Screening Battery (SNSB) by well-qualified neuropsychologists. The dataset included 44 NPTs of SNSB, age, education level, and diagnosis of each participant. The dataset was preprocessed and classified according to three different classes NC, CI, and dementia. We trained machine-learning with a supervised machine learning classifier algorithm support vector machine (SVM) 30 times with classification from scikit-learn (https://scikit-learn.org/stable/) to distinguish the prediction accuracy, sensitivity, and specificity of the models; NC Results: The trained model's 30 times mean accuracies for predicting cognitive states were as follows; NC Conclusion: Based on the results, the SVM algorithm is more appropriate in training models on an imbalanced dataset for a good prediction accuracy compared to natural network and logistic regression algorithms. The NC
Language	English
Publishing date	2022-06-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	2673736-X
ISSN	2327-9109 ; 2327-9095
ISSN (online)	2327-9109
ISSN	2327-9095
DOI	10.1080/23279095.2022.2078210
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 4952: Show issues

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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Comparison of RCF Scoring System to Clinical Decision for the Rey Complex Figure Using Machine-Learning Algorithm.

Simfukwe, Chanda / An, Seong Soo / Youn, Young Chul

Dementia and neurocognitive disorders

2021 Volume 20, Issue 4, Page(s) 70–79

Abstract: Background and purpose: Interpreting the Rey complex figure (RCF) requires a standard RCF scoring system and clinical decision by clinicians. The interpretation of RCF using clinical decision by clinicians might not be accurate in the diagnosing of mild ...

Abstract	Background and purpose: Interpreting the Rey complex figure (RCF) requires a standard RCF scoring system and clinical decision by clinicians. The interpretation of RCF using clinical decision by clinicians might not be accurate in the diagnosing of mild cognitive impairment (MCI) or dementia patients in comparison with the RCF scoring system. For this reason, a machine-learning algorithm was used to demonstrate that scoring RCF using clinical decision is not as accurate as of the RCF scoring system in predicting MCI or mild dementia patients from normal subjects. Methods: The RCF dataset consisted of 2,232 subjects with formal neuropsychological assessments. The RCF dataset was classified into 2 datasets. The first dataset was to compare normal vs. abnormal and the second dataset was to compare normal vs. MCI vs. mild dementia. Models were trained using a convolutional neural network for machine learning. Receiver operating characteristic curves were used to compare the sensitivity, specificity, and area under the curve (AUC) of models. Results: The trained model's accuracy for predicting cognitive states was 96% with the first dataset (normal vs. abnormal) and 88% with the second dataset (normal vs. MCI vs. mild dementia). The model had a sensitivity of 85% for detecting abnormal with an AUC of 0.847 with the first dataset. It had a sensitivity of 78% for detecting MCI or mild dementia with an AUC of 0.778 with the second dataset. Conclusions: Based on this study, the RCF scoring system has the potential to present more accurate criteria than the clinical decision for distinguishing cognitive impairment among patients.
Language	English
Publishing date	2021-10-31
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	3015713-4
ISSN	2384-0757 ; 1738-1495
ISSN (online)	2384-0757
ISSN	1738-1495
DOI	10.12779/dnd.2021.20.4.70
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