Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children
Background: Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the use of an assessment method for diagnosis of anxiety during emotional face in preschool children. Method: 45 preschool children participated in this s...
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | Brain Research Bulletin |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0361923025000176 |
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| author | Samira Jafari Hamid Sharini Aliakbar Foroughi Afshin Almasi |
| author_facet | Samira Jafari Hamid Sharini Aliakbar Foroughi Afshin Almasi |
| author_sort | Samira Jafari |
| collection | DOAJ |
| description | Background: Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the use of an assessment method for diagnosis of anxiety during emotional face in preschool children. Method: 45 preschool children participated in this study. Functional Magnetic Resonance Imaging (fMRI) data were taken in fearful and angry conditions. The functional connectivity (FC) for the limbic system were extracted by ROI-to-ROI method. The fMRI biomarkers (FC) were given to machine learning models as input features to diagnose anxiety in children for angry and fearful conditions. Result: The results of the brain mapping comparisons between anxiety and the non-anxiety showed that there was an increased FC between medial prefrontal cortex (MPFC) and right lateral amygdala (RLA) and a decreased FC between left anterior hippocampus (LAH) and left posterior hippocampus (LPH) in the angry condition. There was an increased FC between the pairs of regions, RLA- right anterior hippocampus (RAH), MPFC-LPH, and RAH-LPH in fearful condition. It is possible to use the FC between LAH- right medial amygdala (RMA) and the FC between left medial amygdala (LMA)-RMA, LMA-RLA, LMA-RAH, and left lateral amygdala (LLA)-RLA instead of IQ in angry and fearful conditions, respectively. Based on metrics such as accuracy, recall, precision, and area under the receiver operating characteristic curve, the Logistic Lasso Regression model outperformed the other model in diagnosing anxiety. Conclusion: With these findings, psychiatrists and psychologists can have a better understanding of the brain connectivity in children. |
| format | Article |
| id | doaj-art-5c7c13d2eea842ec8905959c545ac3c1 |
| institution | Kabale University |
| issn | 1873-2747 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain Research Bulletin |
| spelling | doaj-art-5c7c13d2eea842ec8905959c545ac3c12025-02-07T04:46:42ZengElsevierBrain Research Bulletin1873-27472025-02-01221111205Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool childrenSamira Jafari0Hamid Sharini1Aliakbar Foroughi2Afshin Almasi3Modeling in Health Research Center Institute for Futures Studies in Health Kerman University of Medical Sciences, Kerman, IranDepartment of Biomedical Engineering, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, IranDepartment of Psychology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, IranClinical Research Development Center, Imam Khomeini and Mohammad Kermanshahi and Farabi Hospitals, Kermanshah University of Medical Sciences, Kermanshah, Iran; Corresponding author.Background: Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the use of an assessment method for diagnosis of anxiety during emotional face in preschool children. Method: 45 preschool children participated in this study. Functional Magnetic Resonance Imaging (fMRI) data were taken in fearful and angry conditions. The functional connectivity (FC) for the limbic system were extracted by ROI-to-ROI method. The fMRI biomarkers (FC) were given to machine learning models as input features to diagnose anxiety in children for angry and fearful conditions. Result: The results of the brain mapping comparisons between anxiety and the non-anxiety showed that there was an increased FC between medial prefrontal cortex (MPFC) and right lateral amygdala (RLA) and a decreased FC between left anterior hippocampus (LAH) and left posterior hippocampus (LPH) in the angry condition. There was an increased FC between the pairs of regions, RLA- right anterior hippocampus (RAH), MPFC-LPH, and RAH-LPH in fearful condition. It is possible to use the FC between LAH- right medial amygdala (RMA) and the FC between left medial amygdala (LMA)-RMA, LMA-RLA, LMA-RAH, and left lateral amygdala (LLA)-RLA instead of IQ in angry and fearful conditions, respectively. Based on metrics such as accuracy, recall, precision, and area under the receiver operating characteristic curve, the Logistic Lasso Regression model outperformed the other model in diagnosing anxiety. Conclusion: With these findings, psychiatrists and psychologists can have a better understanding of the brain connectivity in children.http://www.sciencedirect.com/science/article/pii/S0361923025000176Functional connectivityFMRI biomarkersEmotional taskDetectionData science |
| spellingShingle | Samira Jafari Hamid Sharini Aliakbar Foroughi Afshin Almasi Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children Brain Research Bulletin Functional connectivity FMRI biomarkers Emotional task Detection Data science |
| title | Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children |
| title_full | Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children |
| title_fullStr | Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children |
| title_full_unstemmed | Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children |
| title_short | Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children |
| title_sort | brain mapping biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children |
| topic | Functional connectivity FMRI biomarkers Emotional task Detection Data science |
| url | http://www.sciencedirect.com/science/article/pii/S0361923025000176 |
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