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Altınkaynak M, Yeşilbaş D, Batbat T, Güven A, Uğurgöl E, Demirci E, İzzetoğlu M, Dolu N. Multimodal analysis of cortical activation in young male adults with internet gaming disorder: A resting state EEG-fNIRS study. J Psychiatr Res 2024; 177:368-377. [PMID: 39083995 DOI: 10.1016/j.jpsychires.2024.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 07/08/2024] [Accepted: 07/14/2024] [Indexed: 08/02/2024]
Abstract
BACKGROUND Internet gaming disorder (IGD) has become increasingly prevalent worldwide and is recognized as a significant public health concern because of its negative consequences on individuals mental and physical health, social relationships, academic performance and overall well-being. While research on IGD has gained significant momentum in the past decade, the neural substrates underlying this disorder remains unclear. This study aims to investigate resting-state cortical activation in male subjects with IGD using a concurrent functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) hybrid system. METHODS Twenty-two male individuals with IGD (18-23 years old) and twenty-two male healthy, age-matched healthy controls were included in the study. Mean oxygenation of the prefrontal cortex (PFC) and whole head neural activity were measured using fNIRS and EEG respectively, during eyes-open and eyes-closed conditions at the resting state. EEG signals were decomposed into distinct frequency sub-bands with wavelet transform, followed by the analysis of the power spectral density within each band. Mean oxygenation of PFC is measured using a multichannel fNIRS system. RESULTS Results revealed that the individuals with IGD had significantly higher beta power in the frontal region compared to the control group. Individuals with IGD showed significantly increased PFC oxygenation compared to healthy controls. Additionally, both frontal beta power and PFC oxygenation were significantly correlated with IGD severity. However, there were no significant correlations observed between oxygenation and beta powers. CONCLUSION This study is the first to examine resting-state cortical activation using multimodal EEG-fNIRS system in young adults with IGD. Moreover, it provides an important contribution to the understanding of the underlying neural mechanisms of IGD and offer new insights for the diagnosis and intervention of the disorder using multimodal EEG-fNIRS system. Further studies should aim to replicate the findings of this study using a larger and more culturally diverse sample to support the neurophysiological basis of IGD.
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Affiliation(s)
- Miray Altınkaynak
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States.
| | - Demet Yeşilbaş
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Erciyes University, Türkiye.
| | - Turgay Batbat
- Department of Biomedical Engineering, Engineering Faculty, Erciyes University, Türkiye.
| | - Ayşegül Güven
- Department of Biomedical Engineering, Engineering Faculty, Erciyes University, Türkiye; Clinical Engineering Research and Implementation Center, Erciyes University, Türkiye.
| | - Elif Uğurgöl
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Erciyes University, Türkiye.
| | - Esra Demirci
- Department of Child and Adolescent Psychiatry, Medical Faculty, Erciyes University, Türkiye.
| | - Meltem İzzetoğlu
- Electrical and Computer Engineering Department, Villanova University, United States.
| | - Nazan Dolu
- Department of Physiology, Medical Faculty, İstanbul Medipol University, Türkiye.
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Eken A, Nassehi F, Eroğul O. Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review. Rev Neurosci 2024; 35:421-449. [PMID: 38308531 DOI: 10.1515/revneuro-2023-0117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 01/12/2024] [Indexed: 02/04/2024]
Abstract
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.
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Affiliation(s)
- Aykut Eken
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
| | - Farhad Nassehi
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
| | - Osman Eroğul
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
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Chen J, Yu K, Zhuang S, Zhang D. Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system. Front Neurosci 2024; 18:1342744. [PMID: 38779512 PMCID: PMC11110533 DOI: 10.3389/fnins.2024.1342744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper's KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p < 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.
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Affiliation(s)
| | | | | | - Dawei Zhang
- Research Center of Optical Instrument and System, Ministry of Education and Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, China
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Chugh N, Aggarwal S, Balyan A. The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG. Clin EEG Neurosci 2024; 55:22-33. [PMID: 37682533 DOI: 10.1177/15500594231193511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder (ADHD). Studies of children's behavior presently face a significant problem in the early and timely diagnosis of this disease. To diagnose this disease, doctors often use the patient's description and questionnaires, psychological tests, and the patient's behavior in which reliability is questionable. Convolutional neural network (CNN) is one deep learning technique that has been used for the diagnosis of ADHD. CNN, however, does not account for how signals change over time, which leads to low classification performances and ambiguous findings. In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. Hence, the proposed hybrid CNN-LSTM model could therefore be utilized to help with the clinical diagnosis of ADHD patients.
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Affiliation(s)
- Nupur Chugh
- Netaji Subhas Institute of Technology, New Delhi, India
| | - Swati Aggarwal
- Netaji Subhas University of Technology, New Delhi, India
| | - Arnav Balyan
- Netaji Subhas Institute of Technology, New Delhi, India
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Ma D, Izzetoglu M, Holtzer R, Jiao X. Deep Learning Based Walking Tasks Classification in Older Adults Using fNIRS. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3437-3447. [PMID: 37594868 PMCID: PMC11044905 DOI: 10.1109/tnsre.2023.3306365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2023]
Abstract
Decline in gait features is common in older adults and an indicator of increased risk of disability, morbidity, and mortality. Under dual task walking (DTW) conditions, further degradation in the performance of both the gait and the secondary cognitive task were found in older adults which were significantly correlated to falls history. Cortical control of gait, specifically in the pre-frontal cortex (PFC) as measured by functional near infrared spectroscopy (fNIRS), during DTW in older adults has recently been studied. However, the automatic classification of differences in cognitive activations under single and dual task gait conditions has not been extensively studied yet. In this paper, by considering single task walking (STW) as a lower attentional walking state and DTW as a higher attentional walking state, we aimed to formulate this as an automatic detection of low and high attentional walking states and leverage deep learning methods to perform their classification. We conduct analysis on the data samples which reveals the characteristics on the difference between HbO2 and Hb values that are subsequently used as additional features. We perform feature engineering to formulate the fNIRS features as a 3-channel image and apply various image processing techniques for data augmentation to enhance the performance of deep learning models. Experimental results show that pre-trained deep learning models that are fine-tuned using the collected fNIRS dataset together with gender and cognitive status information can achieve around 81% classification accuracy which is about 10% higher than the traditional machine learning algorithms. We present additional sensitivity metrics such as confusion matrix, precision and F1 score, as well as accuracy on two-way classification between condition pairings. We further performed an extensive ablation study to evaluate factors such as the voxel locations, channels of input images, zero-paddings and pre-training of deep learning model on their contribution or impact to the classification task. Results showed that using pre-trained model, all the voxel locations, and HbO2 - Hb as the third channel of the input image can achieve the best classification accuracy.
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Demirci GM, DeIngeniis D, Wong WM, Shereen AD, Nomura Y, Tsai CL. Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach. Front Neurosci 2023; 17:1113927. [PMID: 36816117 PMCID: PMC9932505 DOI: 10.3389/fnins.2023.1113927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Prenatal maternal stress (PNMS), including exposure to natural disasters, has been shown to serve as a risk factor for future child psychopathology and suboptimal brain development, particularly among brain regions shown to be sensitive to stress and trauma exposure. However, statistical approaches deployed in most studies are usually constrained by a limited number of variables for the sake of statistical power. Explainable machine learning, on the other hand, enables the study of high data dimension and offers novel insights into the prominent subset of behavioral phenotypes and brain regions most susceptible to PNMS. In the present study, we aimed to identify the most important child neurobehavioral and brain features associated with in utero exposure to Superstorm Sandy (SS). Methods By leveraging an explainable machine learning technique, the Shapley additive explanations method, we tested the marginal feature effect on SS exposures and examined the individual variable effects on disaster exposure. Results Results show that certain brain regions are especially sensitive to in utero exposure to SS. Specifically, in utero SS exposure was associated with larger gray matter volume (GMV) in the right caudate, right hippocampus, and left amygdala and smaller GMV in the right parahippocampal gyrus. Additionally, higher aggression scores at age 5 distinctly correlated with SS exposure. Discussion These findings suggest in utero SS exposure may be associated with greater aggression and suboptimal developmental alterations among various limbic and basal ganglia brain regions.
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Affiliation(s)
- Gozde M. Demirci
- The Graduate Center, City University of New York, New York, NY, United States
| | - Donato DeIngeniis
- Queens College, City University of New York, New York, NY, United States
| | - Wai Man Wong
- The Graduate Center, City University of New York, New York, NY, United States
- Queens College, City University of New York, New York, NY, United States
| | - A. Duke Shereen
- The Graduate Center, City University of New York, New York, NY, United States
| | - Yoko Nomura
- The Graduate Center, City University of New York, New York, NY, United States
- Queens College, City University of New York, New York, NY, United States
| | - Chia-Ling Tsai
- The Graduate Center, City University of New York, New York, NY, United States
- Queens College, City University of New York, New York, NY, United States
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Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1034983. [PMID: 36387766 PMCID: PMC9663223 DOI: 10.1155/2022/1034983] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/04/2022] [Accepted: 10/08/2022] [Indexed: 11/09/2022]
Abstract
It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.
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Bourguignon NJ, Bue SL, Guerrero-Mosquera C, Borragán G. Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research. FRONTIERS IN NEUROERGONOMICS 2022; 3:934234. [PMID: 38235461 PMCID: PMC10790898 DOI: 10.3389/fnrgo.2022.934234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/20/2022] [Indexed: 01/19/2024]
Abstract
Neuroergonomics focuses on the brain signatures and associated mental states underlying behavior to design human-machine interfaces enhancing performance in the cognitive and physical domains. Brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have been considered key methods for achieving this goal. Recent research stresses the value of combining EEG and fNIRS in improving these interface systems' mental state decoding abilities, but little is known about whether these improvements generalize over different paradigms and methodologies, nor about the potentialities for using these systems in the real world. We review 33 studies comparing mental state decoding accuracy between bimodal EEG-fNIRS and unimodal EEG and fNIRS in several subdomains of neuroergonomics. In light of these studies, we also consider the challenges of exploiting wearable versions of these systems in real-world contexts. Overall the studies reviewed suggest that bimodal EEG-fNIRS outperforms unimodal EEG or fNIRS despite major differences in their conceptual and methodological aspects. Much work however remains to be done to reach practical applications of bimodal EEG-fNIRS in naturalistic conditions. We consider these points to identify aspects of bimodal EEG-fNIRS research in which progress is expected or desired.
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Affiliation(s)
| | - Salvatore Lo Bue
- Department of Life Sciences, Royal Military Academy of Belgium, Brussels, Belgium
| | | | - Guillermo Borragán
- Center for Research in Cognition and Neuroscience, Université Libre de Bruxelles, Brussels, Belgium
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Arpaia P, Covino A, Cristaldi L, Frosolone M, Gargiulo L, Mancino F, Mantile F, Moccaldi N. A Systematic Review on Feature Extraction in Electroencephalography-Based Diagnostics and Therapy in Attention Deficit Hyperactivity Disorder. SENSORS 2022; 22:s22134934. [PMID: 35808424 PMCID: PMC9269717 DOI: 10.3390/s22134934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 02/01/2023]
Abstract
A systematic review on electroencephalographic (EEG)-based feature extraction strategies to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is presented. The analysis is realized at an executive function level to improve the research of neurocorrelates of heterogeneous disorders such as ADHD. The Quality Assessment Tool for Quantitative Studies (QATQS) and field-weighted citation impact metric (Scopus) were used to assess the methodological rigor of the studies and their impact on the scientific community, respectively. One hundred and one articles, concerning the diagnostics and therapy of ADHD children aged from 8 to 14, were collected. Event-related potential components were mainly exploited for executive functions related to the cluster inhibition, whereas band power spectral density is the most considered EEG feature for executive functions related to the cluster working memory. This review identifies the most used (also by rigorous and relevant articles) EEG signal processing strategies for executive function assessment in ADHD.
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Affiliation(s)
- Pasquale Arpaia
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
- Interdepartmental Research Center on Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, 80121 Naples, Italy
- Correspondence:
| | - Attilio Covino
- Villa delle Ginestre, Rehabilitation Center, 80040 Naples, Italy; (A.C.); (F.M.)
| | - Loredana Cristaldi
- Department of Electronics, Information e Bioengineering, Milan Polytechnic, 20133 Milan, Italy;
| | - Mirco Frosolone
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
| | - Ludovica Gargiulo
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
| | - Francesca Mancino
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
| | - Federico Mantile
- Villa delle Ginestre, Rehabilitation Center, 80040 Naples, Italy; (A.C.); (F.M.)
| | - Nicola Moccaldi
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
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Functional near-infrared spectroscopy in developmental psychiatry: a review of attention deficit hyperactivity disorder. Eur Arch Psychiatry Clin Neurosci 2022; 272:273-290. [PMID: 34185132 PMCID: PMC9911305 DOI: 10.1007/s00406-021-01288-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 06/21/2021] [Indexed: 01/26/2023]
Abstract
Research has linked executive function (EF) deficits to many of the behavioral symptoms of attention deficit hyperactivity disorder (ADHD). Evidence of the involvement of EF impairment in ADHD is corroborated by accumulating neuroimaging studies, specifically functional magnetic resonance imaging (fMRI) studies. However, in recent years, functional near-infrared spectroscopy (fNIRS) has become increasingly popular in ADHD research due to its portability, high ecological validity, resistance to motion artifacts, and cost-effectiveness. While numerous studies throughout the past decade have used fNIRS to examine alterations in neural correlates of EF in ADHD, a qualitative review of the reliability of these findings compared with those reported using gold-standard fMRI measurements does not yet exist. The current review aims to fill this gap in the literature by comparing the results generated from a qualitative review of fNIRS studies (children and adolescents ages 6-16 years old) to a meta-analysis of comparable fMRI studies and examining the extent to which the results of these studies align in the context of EF impairment in ADHD. The qualitative analysis of fNIRS studies of ADHD shows a consistent hypoactivity in the right prefrontal cortex in multiple EF tasks. The meta-analysis of fMRI data corroborates altered activity in this region and surrounding areas during EF tasks in ADHD compared with typically developing controls. These findings indicate that fNIRS is a promising functional brain imaging technology for examining alterations in cortical activity in ADHD. We also address the disadvantages of fNIRS, including limited spatial resolution compared with fMRI.
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Park S, Lee J, Khan S, Wahab A, Kim M. Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:596. [PMID: 35062556 PMCID: PMC8778908 DOI: 10.3390/s22020596] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/27/2021] [Accepted: 01/10/2022] [Indexed: 02/06/2023]
Abstract
Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO3)2) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO3)2 molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an 84.6% balanced accuracy for the cross-batch testing task.
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Affiliation(s)
- Seongyong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; (S.P.); (S.K.)
| | - Jaeseok Lee
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea;
- Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
| | - Shujaat Khan
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; (S.P.); (S.K.)
| | - Abdul Wahab
- Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;
| | - Minseok Kim
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea;
- Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
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PLS-DA Model for the Evaluation of Attention Deficit and Hyperactivity Disorder in Children and Adolescents through Blood Serum FTIR Spectra. Molecules 2021; 26:molecules26113400. [PMID: 34205185 PMCID: PMC8199998 DOI: 10.3390/molecules26113400] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/22/2021] [Accepted: 06/01/2021] [Indexed: 11/17/2022] Open
Abstract
Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects ~10% of the world's population of children, and about 30-50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2-5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700-900 cm-1 range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.
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Özbeyaz A. EEG-Based classification of branded and unbranded stimuli associating with smartphone products: comparison of several machine learning algorithms. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05779-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Hilger K, Sassenhagen J, Kühnhausen J, Reuter M, Schwarz U, Gawrilow C, Fiebach CJ. Neurophysiological markers of ADHD symptoms in typically-developing children. Sci Rep 2020; 10:22460. [PMID: 33384437 PMCID: PMC7775445 DOI: 10.1038/s41598-020-80562-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 12/23/2020] [Indexed: 12/24/2022] Open
Abstract
Children with attention-deficit/hyperactivity disorder (ADHD) are characterized by symptoms of inattention, impulsivity, and hyperactivity. Neurophysiological correlates of ADHD include changes in the P3 component of event-related brain potentials (ERPs). Motivated by recent advances towards a more dimensional understanding of ADHD, we investigate whether ADHD-related ERP markers relate to continuous variations in attention and executive functioning also in typically-developing children. ERPs were measured while 31 school children (9-11 years) completed an adapted version of the Continuous Performance Task that additionally to inhibitory processes also isolates effects of physical stimulus salience. Children with higher levels of parent-reported ADHD symptoms did not differ in task performance, but exhibited smaller P3 amplitudes related to stimulus salience. Furthermore, ADHD symptoms were associated with the variability of neural responses over time: Children with higher levels of ADHD symptoms demonstrated lower variability in inhibition- and salience-related P3 amplitudes. No effects were observed for ERP latencies and the salience-related N2. By demonstrating that ADHD-associated neurophysiological mechanisms of inhibition and salience processing covary with attention and executive functioning in a children community sample, our study provides neurophysiological support for dimensional models of ADHD. Also, temporal variability in event-related potentials is highlighted as additional indicator of ADHD requiring further investigation.
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Affiliation(s)
- Kirsten Hilger
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany. .,Department of Psychology I, University Würzburg, Marcusstr. 9-11, 97070, Würzburg, Germany. .,IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.
| | - Jona Sassenhagen
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Jan Kühnhausen
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.,LEAD Graduate School and Research Network, Eberhard Karls University Tübingen, Tübingen, Germany.,Department of Psychiatry, Psychosomatics and Psychotherapy in Childhood and Adolescence, University Hospital Tübingen, Tübingen, Germany
| | - Merle Reuter
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.,Department of Psychology, University Tübingen, Tübingen, Germany
| | - Ulrike Schwarz
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.,Department of Psychology, University Tübingen, Tübingen, Germany
| | - Caterina Gawrilow
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.,Department of Psychology, University Tübingen, Tübingen, Germany
| | - Christian J Fiebach
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany.,IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.,Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
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Izzetoglu M, Holtzer R. Effects of Processing Methods on fNIRS Signals Assessed During Active Walking Tasks in Older Adults. IEEE Trans Neural Syst Rehabil Eng 2020; 28:699-709. [PMID: 32070987 DOI: 10.1109/tnsre.2020.2970407] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Functional near infrared spectroscopy (fNIRS) is a noninvasive optics-based neuroimaging modality successfully applied to real-life settings. The technology uses light in the near infrared range (650-950nm) to track changes in oxygenated (HbO2) and deoxygenated hemoglobin (Hb) obtained from measured light intensity using light-tissue interaction principles. fNIRS data processing involves artifact removal and hemodynamic signal conversion using modified Beer-Lambert law (MBLL) to obtain Hb and HbO2, reliably. fNIRS signals can get contaminated by various noise sources of physiological and non-physiological origins. Various algorithms have been proposed for the elimination of artifacts from frequency selective filters to blind source separation methods. Hemodynamic signal extraction using raw intensity measurements at multiple wavelengths based on MBLL usually involves apriori knowledge of certain conversion parameters such as molar extinction coefficients ( ε ) and differential path length factor (DPF). Different sets of conversion parameters dependent upon wavelength, chromophores, and age have been reported. Variation in processing algorithms and parameters can cause differences in Hb and HbO2 extraction which can in turn change study outcomes. Using fNIRS, we have previously shown significant increases in oxygenation in the prefrontal cortex from Single-Task-Walking (STW) to Dual-task-Walking (DTW) conditions in older adults due to greater cognitive demands inherent in the latter condition. In the current study, we re-analyzed our data and determined that although using different conversion parameters i.e. ε and age dependent DPF and filter cut-off frequencies at 0.14 and 0.08Hz including those designed to remove confounding effects of Mayer waves had caused some linear increases or decreases on the extracted Hb and HbO2 signals, those effects were minimal in task related comparisons and hence, the overall study outcomes.
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