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Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics.

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Title: Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics.
Authors: Kirshenbaum, Jaclyn S., Chahal, Rajpreet, Ho, Tiffany C., King, Lucy S., Gifuni, Anthony J., Mastrovito, Dana, Coury, Saché M., Weisenburger, Rachel L., Gotlib, Ian H.
Source: Journal of Child Psychology & Psychiatry. Jun2022, Vol. 63 Issue 6, p701-714. 14p. 5 Charts.
Subjects: Brain physiology, Patient aftercare, Mathematical models, Functional connectivity, Magnetic resonance imaging, Regression analysis, Severity of illness index, Suicidal ideation, Theory, Descriptive statistics, Evaluation, Psychosocial factors, Adolescence
Abstract: Background: Suicidal ideation (SI) typically emerges during adolescence but is challenging to predict. Given the potentially lethal consequences of SI, it is important to identify neurobiological and psychosocial variables explaining the severity of SI in adolescents. Methods: In 106 participants (59 female) recruited from the community, we assessed psychosocial characteristics and obtained resting‐state fMRI data in early adolescence (baseline: aged 9–13 years). Across 250 brain regions, we assessed local graph theory‐based properties of interconnectedness: local efficiency, eigenvector centrality, nodal degree, within‐module z‐score, and participation coefficient. Four years later (follow‐up: ages 13–19 years), participants self‐reported their SI severity. We used least absolute shrinkage and selection operator (LASSO) regressions to identify a linear combination of psychosocial and brain‐based variables that best explain the severity of SI symptoms at follow‐up. Nested‐cross‐validation yielded model performance statistics for all LASSO models. Results: A combination of psychosocial and brain‐based variables explained subsequent severity of SI (R2 =.55); the strongest was internalizing and externalizing symptom severity at follow‐up. Follow‐up LASSO regressions of psychosocial‐only and brain‐based‐only variables indicated that psychosocial‐only variables explained 55% of the variance in SI severity; in contrast, brain‐based‐only variables performed worse than the null model. Conclusions: A linear combination of baseline and follow‐up psychosocial variables best explained the severity of SI. Follow‐up analyses indicated that graph theory resting‐state metrics did not increase the prediction of the severity of SI in adolescents. Attending to internalizing and externalizing symptoms is important in early adolescence; resting‐state connectivity properties other than local graph theory metrics might yield a stronger prediction of the severity of SI. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics.
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  Data: <searchLink fieldCode="AR" term="%22Kirshenbaum%2C+Jaclyn+S%2E%22">Kirshenbaum, Jaclyn S.</searchLink><br /><searchLink fieldCode="AR" term="%22Chahal%2C+Rajpreet%22">Chahal, Rajpreet</searchLink><br /><searchLink fieldCode="AR" term="%22Ho%2C+Tiffany+C%2E%22">Ho, Tiffany C.</searchLink><br /><searchLink fieldCode="AR" term="%22King%2C+Lucy+S%2E%22">King, Lucy S.</searchLink><br /><searchLink fieldCode="AR" term="%22Gifuni%2C+Anthony+J%2E%22">Gifuni, Anthony J.</searchLink><br /><searchLink fieldCode="AR" term="%22Mastrovito%2C+Dana%22">Mastrovito, Dana</searchLink><br /><searchLink fieldCode="AR" term="%22Coury%2C+Saché+M%2E%22">Coury, Saché M.</searchLink><br /><searchLink fieldCode="AR" term="%22Weisenburger%2C+Rachel+L%2E%22">Weisenburger, Rachel L.</searchLink><br /><searchLink fieldCode="AR" term="%22Gotlib%2C+Ian+H%2E%22">Gotlib, Ian H.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Child+Psychology+%26+Psychiatry%22">Journal of Child Psychology & Psychiatry</searchLink>. Jun2022, Vol. 63 Issue 6, p701-714. 14p. 5 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Brain+physiology%22">Brain physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+aftercare%22">Patient aftercare</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+connectivity%22">Functional connectivity</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Severity+of+illness+index%22">Severity of illness index</searchLink><br /><searchLink fieldCode="DE" term="%22Suicidal+ideation%22">Suicidal ideation</searchLink><br /><searchLink fieldCode="DE" term="%22Theory%22">Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation%22">Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Psychosocial+factors%22">Psychosocial factors</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescence%22">Adolescence</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Background: Suicidal ideation (SI) typically emerges during adolescence but is challenging to predict. Given the potentially lethal consequences of SI, it is important to identify neurobiological and psychosocial variables explaining the severity of SI in adolescents. Methods: In 106 participants (59 female) recruited from the community, we assessed psychosocial characteristics and obtained resting‐state fMRI data in early adolescence (baseline: aged 9–13 years). Across 250 brain regions, we assessed local graph theory‐based properties of interconnectedness: local efficiency, eigenvector centrality, nodal degree, within‐module z‐score, and participation coefficient. Four years later (follow‐up: ages 13–19 years), participants self‐reported their SI severity. We used least absolute shrinkage and selection operator (LASSO) regressions to identify a linear combination of psychosocial and brain‐based variables that best explain the severity of SI symptoms at follow‐up. Nested‐cross‐validation yielded model performance statistics for all LASSO models. Results: A combination of psychosocial and brain‐based variables explained subsequent severity of SI (R2 =.55); the strongest was internalizing and externalizing symptom severity at follow‐up. Follow‐up LASSO regressions of psychosocial‐only and brain‐based‐only variables indicated that psychosocial‐only variables explained 55% of the variance in SI severity; in contrast, brain‐based‐only variables performed worse than the null model. Conclusions: A linear combination of baseline and follow‐up psychosocial variables best explained the severity of SI. Follow‐up analyses indicated that graph theory resting‐state metrics did not increase the prediction of the severity of SI in adolescents. Attending to internalizing and externalizing symptoms is important in early adolescence; resting‐state connectivity properties other than local graph theory metrics might yield a stronger prediction of the severity of SI. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/jcpp.13512
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 701
    Subjects:
      – SubjectFull: Brain physiology
        Type: general
      – SubjectFull: Patient aftercare
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Functional connectivity
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      – SubjectFull: Magnetic resonance imaging
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      – SubjectFull: Adolescence
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              Text: Jun2022
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