Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning
Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In this study resting-state functional magnetic resonance imaging was used to measure the differences in connectivity and activation seen in major depres...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2024.1427532/full |
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| author | Morteza Fattahi Milad Esmaeil-Zadeh Hamid Soltanian-Zadeh Hamid Soltanian-Zadeh Hamid Soltanian-Zadeh Reza Rostami Jamil Mansouri Gholam-Ali Hossein-Zadeh Gholam-Ali Hossein-Zadeh |
| author_facet | Morteza Fattahi Milad Esmaeil-Zadeh Hamid Soltanian-Zadeh Hamid Soltanian-Zadeh Hamid Soltanian-Zadeh Reza Rostami Jamil Mansouri Gholam-Ali Hossein-Zadeh Gholam-Ali Hossein-Zadeh |
| author_sort | Morteza Fattahi |
| collection | DOAJ |
| description | Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In this study resting-state functional magnetic resonance imaging was used to measure the differences in connectivity and activation seen in major depressive disorder (MDD) patients with and without suicidal ideation and the control group. For our investigation, a brain atlas containing 116 regions of interest was used. We also used four voxel-based connectivity models, including degree centrality, the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity, and voxel-mirrored Homotopic Connectivity. Feature selection was conducted using a sequential backward floating selection approach along with a Random Forest Classifier and Elastic Net. While all four models yield significant results, fALFF demonstrated higher accuracy rates in classifying the three groups. Further analysis revealed three features that demonstrated statistically significant differences between these three, resulting in a 90.00% accuracy rate. Prominent features identified from our analysis, with suicide ideation as the key variable, included the Superior frontal gyrus (dorsolateral and orbital parts), the median cingulate, and the paracingulate gyri. These areas are associated with the Central Executive Control Network (ECN), the Default Mode Network, and the ECN, respectively. Comparing the results of MDD patients with suicidal ideation to those without suicidal ideations suggests dysfunctions in decision-making ability, in MDD females suffering from suicidal tendencies. This may be related to a lack of inhibition or emotion regulation capability, which contributes to suicidal ideations. |
| format | Article |
| id | doaj-art-0b2e94ad42294b9b9b3323411d45ee43 |
| institution | Kabale University |
| issn | 1662-5161 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj-art-0b2e94ad42294b9b9b3323411d45ee432025-01-08T06:11:58ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612025-01-011810.3389/fnhum.2024.14275321427532Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learningMorteza Fattahi0Milad Esmaeil-Zadeh1Hamid Soltanian-Zadeh2Hamid Soltanian-Zadeh3Hamid Soltanian-Zadeh4Reza Rostami5Jamil Mansouri6Gholam-Ali Hossein-Zadeh7Gholam-Ali Hossein-Zadeh8School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Cognitive Science, Institute for Research in Fundamental Sciences (IPM), Tehran, IranDepartments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United StatesSchool of Psychology and Education, University of Tehran, Tehran, IranSchool of Psychology and Education, Kharazmi University, Karaj, IranSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Cognitive Science, Institute for Research in Fundamental Sciences (IPM), Tehran, IranSpontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In this study resting-state functional magnetic resonance imaging was used to measure the differences in connectivity and activation seen in major depressive disorder (MDD) patients with and without suicidal ideation and the control group. For our investigation, a brain atlas containing 116 regions of interest was used. We also used four voxel-based connectivity models, including degree centrality, the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity, and voxel-mirrored Homotopic Connectivity. Feature selection was conducted using a sequential backward floating selection approach along with a Random Forest Classifier and Elastic Net. While all four models yield significant results, fALFF demonstrated higher accuracy rates in classifying the three groups. Further analysis revealed three features that demonstrated statistically significant differences between these three, resulting in a 90.00% accuracy rate. Prominent features identified from our analysis, with suicide ideation as the key variable, included the Superior frontal gyrus (dorsolateral and orbital parts), the median cingulate, and the paracingulate gyri. These areas are associated with the Central Executive Control Network (ECN), the Default Mode Network, and the ECN, respectively. Comparing the results of MDD patients with suicidal ideation to those without suicidal ideations suggests dysfunctions in decision-making ability, in MDD females suffering from suicidal tendencies. This may be related to a lack of inhibition or emotion regulation capability, which contributes to suicidal ideations.https://www.frontiersin.org/articles/10.3389/fnhum.2024.1427532/fullresting-state fMRImajor depressive disordersuicide ideationfeature selectionRandom Forest Classifierelastic net |
| spellingShingle | Morteza Fattahi Milad Esmaeil-Zadeh Hamid Soltanian-Zadeh Hamid Soltanian-Zadeh Hamid Soltanian-Zadeh Reza Rostami Jamil Mansouri Gholam-Ali Hossein-Zadeh Gholam-Ali Hossein-Zadeh Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning Frontiers in Human Neuroscience resting-state fMRI major depressive disorder suicide ideation feature selection Random Forest Classifier elastic net |
| title | Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning |
| title_full | Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning |
| title_fullStr | Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning |
| title_full_unstemmed | Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning |
| title_short | Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning |
| title_sort | classification of female mdd patients with and without suicidal ideation using resting state functional magnetic resonance imaging and machine learning |
| topic | resting-state fMRI major depressive disorder suicide ideation feature selection Random Forest Classifier elastic net |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2024.1427532/full |
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