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Shirzadi S, Dadgostar M, Einalou Z, Erdoğan SB, Akin A. Sex based differences in functional connectivity during a working memory task: an fNIRS study. Front Psychol 2024; 15:1207202. [PMID: 38390414 PMCID: PMC10881810 DOI: 10.3389/fpsyg.2024.1207202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 01/17/2024] [Indexed: 02/24/2024] Open
Abstract
Differences in corticocerebral structure and function between males and females and their effects on behavior and the prevalence of various neuropsychiatric disorders have been considered as a fundamental topic in various fields of neuroscience. Recent studies on working memory (WM) reported the impact of sex on brain connectivity patterns, which reflect the important role of functional connectivity in the sex topic. Working memory, one of the most important cognitive tasks performed by regions of the PFC, can provide evidence regarding the presence of a difference between males and females. The present study aimed to assess sex differences in brain functional connectivity during working memory-related tasks by using functional near-infrared spectroscopy (fNIRS). In this regard, nine males and nine females completed a dual n-back working memory task with two target inputs of color and location stimuli in three difficulty levels (n = 0, 1, 2). Functional connectivity matrices were extracted for each subject for each memory load level. Females made less errors than males while spending more time performing the task for all workload levels except in 0-back related to the color stimulus, where the reaction time of females was shorter than males. The results of functional connectivity reveal the inverse behavior of two hemispheres at different memory workload levels between males and females. In the left hemisphere, males exhibited stronger connectivity compared to the females, while stronger connectivity was observed in the females' right hemisphere. Furthermore, an inverse trend was detected in the channel pairs with significant connectivity in the right hemisphere of males (falling) and females (rising) by enhancing working memory load level. Considering both behavioral and functional results for two sexes demonstrated a better performance in females due to the more effective use of the brain. The results indicate that sex affects functional connectivity between different areas in both hemispheres of the brain during cognitive tasks of varying difficulty levels although the general impression is that spatial capabilities are considered as a performance of the brain's right hemisphere. These results reinforce the presence of a sex effect in the functional imaging studies of hemodynamic function and emphasize the importance of evaluating brain network connectivity for achieving a better scientific understanding of sex differences.
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Affiliation(s)
- Sima Shirzadi
- Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mehrdad Dadgostar
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Zahra Einalou
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Sinem Burcu Erdoğan
- Department of Biomedical Engineering, Acibadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
| | - Ata Akin
- Department of Biomedical Engineering, Acibadem Mehmet Ali Aydinlar University, Istanbul, Türkiye
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Khajeh Hosseini MS, Pourmir Firoozabadi M, Badie K, Azad Fallah P. Electroencephalograph Emotion Classification Using a Novel Adaptive Ensemble Classifier Considering Personality Traits. Basic Clin Neurosci 2023; 14:687-700. [PMID: 38628840 PMCID: PMC11016883 DOI: 10.32598/bcn.2022.3830.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 04/12/2022] [Accepted: 06/22/2023] [Indexed: 04/19/2024] Open
Abstract
Introduction The study explores the use of Electroencephalograph (EEG) signals as a means to uncover various states of the human brain, with a specific focus on emotion classification. Despite the potential of EEG signals in this domain, existing methods face challenges. Features extracted from EEG signals may not accurately represent an individual's emotional patterns due to interference from time-varying factors and noise. Additionally, higher-level cognitive factors, such as personality, mood, and past experiences, further complicate emotion recognition. The dynamic nature of EEG data in terms of time series introduces variability in feature distribution and interclass discrimination across different time stages. Methods To address these challenges, the paper proposes a novel adaptive ensemble classification method. The study introduces a new method for providing emotional stimuli, categorizing them into three groups (sadness, neutral, and happiness) based on their valence-arousal (VA) scores. The experiment involved 60 participants aged 19-30 years, and the proposed method aimed to mitigate the limitations associated with conventional classifiers. Results The results demonstrate a significant improvement in the performance of emotion classifiers compared to conventional methods. The classification accuracy achieved by the proposed adaptive ensemble classification method is reported at 87.96%. This suggests a promising advancement in the ability to accurately classify emotions using EEG signals, overcoming the limitations outlined in the introduction. Conclusion In conclusion, the paper introduces an innovative approach to emotion classification based on EEG signals, addressing key challenges associated with existing methods. By employing a new adaptive ensemble classification method and refining the process of providing emotional stimuli, the study achieves a noteworthy improvement in classification accuracy. This advancement is crucial for enhancing our understanding of the complexities of emotion recognition through EEG signals, paving the way for more effective applications in fields such as neuroinformatics and affective computing.
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Affiliation(s)
- Mohammad Saleh Khajeh Hosseini
- Department of Biomedical Engineering, Faculty of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Kambiz Badie
- Department of Content & E-Services Research, Faculty of IT Research,University of Tehran, Tehran, Iran
- Iran Telecommunication Research Center (ITRC), Tehran, Iran
| | - Parviz Azad Fallah
- Department of Psychology, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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Erdoğan SB, Yükselen G. Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers. SENSORS (BASEL, SWITZERLAND) 2022; 22:5407. [PMID: 35891088 PMCID: PMC9322944 DOI: 10.3390/s22145407] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.
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Tian F, Li H, Tian S, Tian C, Shao J. Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19010509. [PMID: 35010769 PMCID: PMC8744879 DOI: 10.3390/ijerph19010509] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 12/27/2021] [Accepted: 12/27/2021] [Indexed: 12/31/2022]
Abstract
(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.
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Affiliation(s)
- Fangyuan Tian
- Institute of Safety Management & Risk Control, Institute of Safety & Emergency Management, School of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; (F.T.); (S.T.); (C.T.)
| | - Hongxia Li
- Institute of Safety Management & Risk Control, Institute of Safety & Emergency Management, School of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; (F.T.); (S.T.); (C.T.)
- School of Management, Xi’an University of Science and Technology, Xi’an 710054, China
- Correspondence: ; Tel.: +86-152-9159-9962
| | - Shuicheng Tian
- Institute of Safety Management & Risk Control, Institute of Safety & Emergency Management, School of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; (F.T.); (S.T.); (C.T.)
| | - Chenning Tian
- Institute of Safety Management & Risk Control, Institute of Safety & Emergency Management, School of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; (F.T.); (S.T.); (C.T.)
| | - Jiang Shao
- School of Architecture & Design, China University of Mining and Technology, Xuzhou 221116, China;
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Yaqub MA, Hong KS, Zafar A, Kim CS. Control of Transcranial Direct Current Stimulation Duration by Assessing Functional Connectivity of Near-Infrared Spectroscopy Signals. Int J Neural Syst 2021; 32:2150050. [PMID: 34609264 DOI: 10.1142/s0129065721500507] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Transcranial direct current stimulation (tDCS) has been shown to create neuroplasticity in healthy and diseased populations. The control of stimulation duration by providing real-time brain state feedback using neuroimaging is a topic of great interest. This study presents the feasibility of a closed-loop modulation for the targeted functional network in the prefrontal cortex. We hypothesize that we cannot improve the brain state further after reaching a specific state during a stimulation therapy session. A high-definition tDCS of 1[Formula: see text]mA arranged in a ring configuration was applied at the targeted right prefrontal cortex of 15 healthy male subjects for 10[Formula: see text]min. Functional near-infrared spectroscopy was used to monitor hemoglobin chromophores during the stimulation period continuously. The correlation matrices obtained from filtered oxyhemoglobin were binarized to form subnetworks of short- and long-range connections. The connectivity in all subnetworks was analyzed individually using a new quantification measure of connectivity percentage based on the correlation matrix. The short-range network in the stimulated hemisphere showed increased connectivity in the initial stimulation phase. However, the increase in connection density reduced significantly after 6[Formula: see text]min of stimulation. The short-range network of the left hemisphere and the long-range network gradually increased throughout the stimulation period. The connectivity percentage measure showed a similar response with network theory parameters. The connectivity percentage and network theory metrics represent the brain state during the stimulation therapy. The results from the network theory metrics, including degree centrality, efficiency, and connection density, support our hypothesis and provide a guideline for feedback on the brain state. The proposed neuro-feedback scheme is feasible to control the stimulation duration to avoid overdosage.
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Affiliation(s)
- M Atif Yaqub
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Korea
| | - Amad Zafar
- Department of Electrical Engineering, University of Lahore, Sihala Zone V, Islamabad, Pakistan
| | - Chang-Seok Kim
- Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Korea
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Erdoğan SB, Yükselen G, Yegül MM, Usanmaz R, Kıran E, Derman O, Akın A. Identification of impulsive adolescents with a functional near infrared spectroscopy (fNIRS) based decision support system. J Neural Eng 2021; 18. [PMID: 34479222 DOI: 10.1088/1741-2552/ac23bb] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 09/03/2021] [Indexed: 11/11/2022]
Abstract
Background.The gold standard for diagnosing impulsivity relies on clinical interviews, behavioral questionnaires and rating scales which are highly subjective.Objective.The aim of this study was to develop a functional near infrared spectroscopy (fNIRS) based classification approach for correct identification of impulsive adolescents. Taking into account the multifaceted nature of impulsivity, we propose that combining informative features from clinical, behavioral and neurophysiological domains might better elucidate the neurobiological distinction underlying symptoms of impulsivity.Approach. Hemodynamic and behavioral information was collected from 38 impulsive adolescents and from 33 non-impulsive adolescents during a Stroop task with concurrent fNIRS recordings. Connectivity-based features were computed from the hemodynamic signals and a neural efficiency metric was computed by fusing the behavioral and connectivity-based features. We tested the efficacy of two commonly used supervised machine-learning methods, namely the support vector machines (SVM) and artificial neural networks (ANN) in discriminating impulsive adolescents from their non-impulsive peers when trained with multi-domain features. Wrapper method was adapted to identify the informative biomarkers in each domain. Classification accuracies of each algorithm were computed after 10 runs of a 10-fold cross-validation procedure, conducted for 7 different combinations of the 3-domain feature set.Main results.Both SVM and ANN achieved diagnostic accuracies above 90% when trained with Wrapper-selected clinical, behavioral and fNIRS derived features. SVM performed significantly higher than ANN in terms of the accuracy metric (92.2% and 90.16%, respectively,p= 0.005).Significance.Preliminary findings show the feasibility and applicability of both machine-learning based methods for correct identification of impulsive adolescents when trained with multi-domain data involving clinical interviews, fNIRS based biomarkers and neuropsychiatric test measures. The proposed automated classification approach holds promise for assisting the clinical practice of diagnosing impulsivity and other psychiatric disorders. Our results also pave the path for a computer-aided diagnosis perspective for rating the severity of impulsivity.
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Affiliation(s)
- Sinem Burcu Erdoğan
- Department of Medical Engineering, Acıbadem Mehmet Ali Aydınlar University, İstanbul, Turkey
| | - Gülnaz Yükselen
- Department of Medical Engineering, Acıbadem Mehmet Ali Aydınlar University, İstanbul, Turkey
| | - Mustafa Mert Yegül
- Hemosoft Information Technologies and Training Services Inc., Ankara, Turkey
| | - Ruhi Usanmaz
- Hemosoft Information Technologies and Training Services Inc., Ankara, Turkey
| | - Engin Kıran
- Hemosoft Information Technologies and Training Services Inc., Ankara, Turkey
| | - Orhan Derman
- Department of Pediatrics, Division of Adolescent Medicine, Hacettepe University İhsan Doğramacı Children's Hospital, Ankara, Turkey
| | - Ata Akın
- Department of Medical Engineering, Acıbadem Mehmet Ali Aydınlar University, İstanbul, Turkey
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Akın A. fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases. NEUROPHOTONICS 2021; 8:035008. [PMID: 34604439 PMCID: PMC8482313 DOI: 10.1117/1.nph.8.3.035008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/16/2021] [Indexed: 05/03/2023]
Abstract
Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compulsive disorder, and schizophrenia. Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: cognitive mode network (CM) and default mode network (DM). Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR ( N C R ¯ ) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. Conclusions: N C R ¯ can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.
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Affiliation(s)
- Ata Akın
- Acibadem University, Department of Medical Engineering, Ataşehir, Istanbul, Turkey
- Address all correspondence to Ata Akn,
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Fan S, Blanco‐Davis E, Zhang J, Bury A, Warren J, Yang Z, Yan X, Wang J, Fairclough S. The Role of the Prefrontal Cortex and Functional Connectivity during Maritime Operations: An fNIRS study. Brain Behav 2021; 11:e01910. [PMID: 33151030 PMCID: PMC7821565 DOI: 10.1002/brb3.1910] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 09/08/2020] [Accepted: 10/04/2020] [Indexed: 01/09/2023] Open
Abstract
INTRODUCTION Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision-making can increase the likelihood of a collision. METHODS A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20-min period of sustained attention to locate a target vessel and (2) a 10-min period of decision-making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15-channel functional near-infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph-theoretic measures. RESULTS Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision-making. The graph-theoretic analysis revealed that density declined during decision-making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver. CONCLUSIONS The right lateral area of the PFC is sensitive to watchkeeping and decision-making during operational performance. Graph-theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety-critical performance.
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Affiliation(s)
- Shiqi Fan
- Intelligent Transport Systems Research CentreWuhan University of TechnologyWuhanChina
- National Engineering Research Centre for Water Transport Safety (WTSC)MOSTWuhanChina
- Liverpool LogisticsOffshore and Marine (LOOM) Research InstituteLiverpool John Moores UniversityLiverpoolUK
| | - Eduardo Blanco‐Davis
- Liverpool LogisticsOffshore and Marine (LOOM) Research InstituteLiverpool John Moores UniversityLiverpoolUK
| | - Jinfen Zhang
- Intelligent Transport Systems Research CentreWuhan University of TechnologyWuhanChina
- National Engineering Research Centre for Water Transport Safety (WTSC)MOSTWuhanChina
| | - Alan Bury
- Liverpool LogisticsOffshore and Marine (LOOM) Research InstituteLiverpool John Moores UniversityLiverpoolUK
| | - Jonathan Warren
- Liverpool LogisticsOffshore and Marine (LOOM) Research InstituteLiverpool John Moores UniversityLiverpoolUK
| | - Zaili Yang
- Liverpool LogisticsOffshore and Marine (LOOM) Research InstituteLiverpool John Moores UniversityLiverpoolUK
| | - Xinping Yan
- Intelligent Transport Systems Research CentreWuhan University of TechnologyWuhanChina
- National Engineering Research Centre for Water Transport Safety (WTSC)MOSTWuhanChina
| | - Jin Wang
- Liverpool LogisticsOffshore and Marine (LOOM) Research InstituteLiverpool John Moores UniversityLiverpoolUK
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fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses. Sci Rep 2020; 10:22041. [PMID: 33328535 PMCID: PMC7745044 DOI: 10.1038/s41598-020-79053-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 12/01/2020] [Indexed: 02/04/2023] Open
Abstract
This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students’ cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.
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Functional Network Alterations in Patients With Amnestic Mild Cognitive Impairment Characterized Using Functional Near-Infrared Spectroscopy. IEEE Trans Neural Syst Rehabil Eng 2020; 28:123-132. [DOI: 10.1109/tnsre.2019.2956464] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Faskhodi MM, Einalou Z, Dadgostar M. Diagnosis of Alzheimer’s disease using resting-state fMRI and graph theory. Technol Health Care 2018; 26:921-931. [DOI: 10.3233/thc-181312] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | - Zahra Einalou
- Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mehrdad Dadgostar
- Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
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Barahimi S, Einalou Z, Dadgostar M. STUDIES ON SCHIZOPHRENIA AND DEPRESSIVE DISEASES BASED ON FUNCTIONAL NEAR-INFRARED SPECTROSCOPY. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2018. [DOI: 10.4015/s101623721830002x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Many investigations have been carried out on functional near infrared spectroscopy (fNIRS) applications in depression and schizophrenia patients that are most mysterious and costliest mental disorders in current society. fNIRS is a new optical method which assesses brain cortex hemodynamic and nervous activities non-invasively and it has been used in medicine as a study tool. Most of the researches of this approach have assessed the homodynamic response of frontal and temporal regions by means of various cognitive tasks. In this research, first, the cognitive task execution techniques have been explained concisely, and then some findings of fNIRS-based researches about depression and schizophrenia have been summarized and assessed. In fNIRS studies that have used various devices with different number of channels, the brain cortex functionality in schizophrenia and depressive patients has been investigated. The results demonstrate that a decrease in prefrontal regions activities can be observed in schizophrenia and depressive patients. Also more detailed studies illustrate ventrolateral, prefrontal and frontopolar region disorders. In severe depressive patients, a decrease in activities of prefrontal and temporal regions has been detected. Therefore, by paying attention to the deficiencies in these regions’ functions, it is possible to treat these diseases.
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Affiliation(s)
- Shekoufe Barahimi
- Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Zahra Einalou
- Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mehrdad Dadgostar
- Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
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Dadgostar M, Setarehdan SK, Shahzadi S, Akin A. CLASSIFICATION OF SCHIZOPHRENIA USING SVM VIA fNIRS. BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2018. [DOI: 10.4015/s1016237218500084] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In the present study, a classification of functional near-infrared spectroscopy (fNIRS) based on support vector machine (SVM) is presented. It is a non-invasive method monitoring human brain function by evaluating the concentration variation of oxy-hemoglobin and deoxy-hemoglobin. fNIRS is a functional optical imaging technology that measures the neural activities and hemodynamic responses in brain. The data were gathered from 11 healthy volunteers and 16 schizophrenia of the same average age by a 16-channel fNIRS (NIROXCOPE 301 system developed at the Neuro-Optical Imaging Laboratory, continuous-wave dual wavelength). Schizophrenia is a mental disorder that is characterized by mental processing collapse and weak emotional responses. This mental disorder is usually accompanied by a serious disturbance in social and occupational activities. The signals were initially preprocessed by DWT to remove any systemic physiological impediment. A preliminary examination by the genetic algorithm (GA) suggested that some channels of the recreated fNIRS signals required further investigation. The energy of these recreated channel signals was computed and utilized for signal arrangement. We used SVM-based classifier to determine the cases of schizophrenia. The result of six channels is higher than 16 channels. The results demonstrated a classification precision of about 87% in the discovery of schizophrenia in the healthy subjects.
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Affiliation(s)
- Mehrdad Dadgostar
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Sohrab Shahzadi
- Functional Neurosurgery Research Center, Shohada Tajrish Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ata Akin
- Department of Medical Engineering, Acibadem University, Istanbul, Turkey
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