Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach
IntroductionPrenatal 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...
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1113927/full |
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| author | Gozde M. Demirci Donato DeIngeniis Wai Man Wong Wai Man Wong A. Duke Shereen Yoko Nomura Yoko Nomura Chia-Ling Tsai Chia-Ling Tsai |
| author_facet | Gozde M. Demirci Donato DeIngeniis Wai Man Wong Wai Man Wong A. Duke Shereen Yoko Nomura Yoko Nomura Chia-Ling Tsai Chia-Ling Tsai |
| author_sort | Gozde M. Demirci |
| collection | DOAJ |
| description | IntroductionPrenatal 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).MethodsBy 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.ResultsResults 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.DiscussionThese findings suggest in utero SS exposure may be associated with greater aggression and suboptimal developmental alterations among various limbic and basal ganglia brain regions. |
| first_indexed | 2024-03-08T00:40:37Z |
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| id | doaj.art-c1ed7e51356949a3b5ea341e5873e343 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-08T00:40:37Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-c1ed7e51356949a3b5ea341e5873e3432024-02-15T12:50:36ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-02-011710.3389/fnins.2023.11139271113927Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approachGozde M. Demirci0Donato DeIngeniis1Wai Man Wong2Wai Man Wong3A. Duke Shereen4Yoko Nomura5Yoko Nomura6Chia-Ling Tsai7Chia-Ling Tsai8The Graduate Center, City University of New York, New York, NY, United StatesQueens College, City University of New York, New York, NY, United StatesThe Graduate Center, City University of New York, New York, NY, United StatesQueens College, City University of New York, New York, NY, United StatesThe Graduate Center, City University of New York, New York, NY, United StatesThe Graduate Center, City University of New York, New York, NY, United StatesQueens College, City University of New York, New York, NY, United StatesThe Graduate Center, City University of New York, New York, NY, United StatesQueens College, City University of New York, New York, NY, United StatesIntroductionPrenatal 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).MethodsBy 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.ResultsResults 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.DiscussionThese findings suggest in utero SS exposure may be associated with greater aggression and suboptimal developmental alterations among various limbic and basal ganglia brain regions.https://www.frontiersin.org/articles/10.3389/fnins.2023.1113927/fullmachine learningexplainable AIprenatal maternal stressSuperstorm Sandybrain volumechild behavior |
| spellingShingle | Gozde M. Demirci Donato DeIngeniis Wai Man Wong Wai Man Wong A. Duke Shereen Yoko Nomura Yoko Nomura Chia-Ling Tsai Chia-Ling Tsai Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach Frontiers in Neuroscience machine learning explainable AI prenatal maternal stress Superstorm Sandy brain volume child behavior |
| title | Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach |
| title_full | Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach |
| title_fullStr | Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach |
| title_full_unstemmed | Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach |
| title_short | Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach |
| title_sort | superstorm sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood a machine learning approach |
| topic | machine learning explainable AI prenatal maternal stress Superstorm Sandy brain volume child behavior |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1113927/full |
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