Multimodal neuroimaging and suicidality in a US population-based sample of school-aged children

Vidal‐Ribas, Pablo; Janiri, Delfina; Doucet, Gaëlle E.; Pornpattananangkul, Narun; Nielson, Dylan M.; Frangou, Sophia; Stringaris, Argyris

doi:10.1101/19013193

Cited by 9 publications

(11 citation statements)

References 58 publications

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“…The fMRI-based measures included did not seem to contribute to the classification of children with STB In contrast to these findings, previous studies have found that functional brain alterations in the prefrontal cortex are related to STB [20, 21] and contribute to the classification of suicidal youth [22]. However, our findings are consistent with a neuroimaging-specific evaluation of this same cohort, in which no association was found between suicidal thoughts and behaviors and functional neuroimaging measures [23]. These discrepant findings between ABCD and other studies could potentially be explained by the younger age of participants in the ABCD study, the fact that ABCD is a population study, or methodological issues that have been described elsewhere [24].…”

Section: Discussionsupporting

confidence: 78%

Predictors of suicidal thoughts and behavior in children: results from penalized logistic regression analyses in the ABCD study

Velzen

Toenders

Avila-Parcet

et al. 2021

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Despite numerous efforts to predict suicide risk in children, the ability to reliably identify youth that will engage in suicide thoughts or behaviors (STB) has remained remarkably unsuccessful. To further knowledge in this area, we apply a novel machine learning approach and examine whether children with STB could be differentiated from children without STB based on a combination of sociodemographic, physical health, social environmental, clinical psychiatric, cognitive, biological and genetic characteristics. The study sample included 5,885 unrelated children (50% female, 67% white) between 9 and 11 years old from the Adolescent Brain Cognitive Development (ABCD) study. Both parents and youth reported on children's STB and based on these reports, we divided children into three subgroups: 1. children with current or past STB, 2. children with psychiatric disorder but no STB (clinical controls) and 3. healthy control children. We performed binomial penalized logistic regression analysis to distinguish between groups. The analyses were performed separately for child-reported STB and parent-reported STB. Results showed that we were able to distinguish the STB group from healthy controls and clinical controls (area under the receiver operating characteristics curve (AUROC) range: 0.79-0.81 and 0.70-0.78 respectively). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC range 0.49-0.59). Factors that differentiated the STB group from the clinical control group included family conflict, prodromal psychosis symptoms, impulsivity, depression severity and a history of mental health treatment. Future research is needed to determine if these variables prospectively predict subsequent suicidal behavior.

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Section: Discussionsupporting

confidence: 78%

Predictors of suicidal thoughts and behavior in children: results from penalized logistic regression analyses in the ABCD study

Velzen

Toenders

Avila-Parcet

et al. 2021

Preprint

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“…A recent study that examined the association between STBs and brain structure in over 6,000 younger children aged 9-10 years in the Adolescent Brain Cognitive Development (ABCD) study did not reveal significant structural alterations on the frontal cortex in association with STBs (36).…”

Section: Discussionmentioning

confidence: 96%

“…Thus, in the larger overall sample of this study, more substantial heterogeneity was introduced by including a variety of mental health conditions. A recent study that examined the association between STBs and brain structure in over 6,000 younger children aged 9-10 years in the Adolescent Brain Cognitive Development (ABCD) study did not reveal significant structural alterations on the frontal cortex in association with STBs (36). This may be related to the fact that the ABCD study is a general population sample with only a few children diagnosed with mood disorders, and STBs were less common and severe.…”

Section: Discussionmentioning

confidence: 99%

Structural brain alterations associated with suicidal thoughts and behaviors in young people: results across 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium

Velzen

Dauvermann

Čolić

et al. 2021

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Objective Identifying brain differences associated with suicidal thoughts and behaviors (STBs) in young people is critical to understanding their development and generating effective approaches to early intervention and prevention. The ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium analyzed neuroimaging data harmonized across sites to examine brain morphology associated with STBs in youth. Methods First, we examined associations among regional brain structure and STBs, which were assessed in six samples of youth with mood disorders, using the Columbia Suicide Severity Rating Scale (C-SSRS; N=577). Second, we combined this sample with a larger sample (total 21 sites) in which STBs were assessed using various instruments. MRI metrics were compared among healthy controls without STBs (HC; N=688), clinical controls without STBs (CC; N=648), and young people with psychiatric diagnoses and current suicidal ideation (N=406). In separate analyses, MRI metrics were compared among HCs (N=335), CCs (N=768), and suicide attempters (N=254). Results In the homogeneous C-SSRS sample, surface area of the frontal pole was lower in young people with mood disorders and history of actual suicide attempts (N=163) than those without (N=394; FDR-p<.001; Cohens d=.334). When expanding to more clinically heterogeneous samples, we also found lower surface area of the frontal pole in those with a history of suicide attempts (Cohens d=.22). Conclusions Lower frontal pole surface area may represent a vulnerability for a suicide attempt; however, more research is needed to understand the nature of its relationship to suicide risk.

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“…Important features in this model include white matter connectivity, morphometry, and functional connectivity within the distributed brain network, primarily the neocortex, including the inferior frontal gyrus, insula, and anterior cingulate cortex. The inferior frontal gyrus plays a role in emotional regulation, sustained attention, and language processing 34 , of which abnormalities were associated with suicidality in adolescents 35 . The insula and anterior cingulate cortex, as part of the salience network, are involved in the detection and integration of emotional and sensory stimuli 36 .…”

Section: Discussionmentioning

confidence: 99%

Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging-Derived Phenotypes

Kim

Ahn

Kim

et al. 2021

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Suicide is among the leading causes of death in youth worldwide. Early identification of children with high risk for suicide is key to effective screening and prevention strategies. Brain imaging can show functional or structural abnormalities related to youth suicidality, but literature is scarce. Here we tested the extent to which brain imaging is useful in predicting suicidal risk in children. In the largest to date, multi-site, multi-ethnic, epidemiological developmental samples in the US (N = 6,172; the ABCD study), we trained and validated machine learning models and deep neural networks on the multimodal brain imaging derived phenotypes (morphometry, white matter connectivity, functional activation, and connectivity) along with behavioral and self-reported psychological questionnaire data. The model trained on diffusion white matter connectomes showed the best performance (test AUC-ROC = 74.82) with a one percentage increase compared with the baseline model trained on behavioral and psychological data (test AUC-ROC = 74.16). Models trained on other MRI modalities showed similar but slightly lower performances. Model interpretation showed the important brain features involved in attention, emotion regulation, and motor coordination, such as the anterior cingulate cortex, temporal gyrus, and precentral gyrus. It further showed that the interaction of brain features with depression and impulsivity measures contributed to the optimal prediction of youth suicidality. This study demonstrates the potential utility of a multimodal brain imaging approach to youth suicidality prediction and uncovers the relationships of the psychological and multi-dimensional and multi-modal neural features to youth suicidality.

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Multimodal neuroimaging and suicidality in a US population-based sample of school-aged children

Cited by 9 publications

References 58 publications

Predictors of suicidal thoughts and behavior in children: results from penalized logistic regression analyses in the ABCD study

Predictors of suicidal thoughts and behavior in children: results from penalized logistic regression analyses in the ABCD study

Structural brain alterations associated with suicidal thoughts and behaviors in young people: results across 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium

Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging-Derived Phenotypes

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