Reduced dorsal striatal gray matter volume predicts implicit suicidal ideation in adolescents

Abstract

Suicidal ideation (SI), a potent risk factor for suicide attempts, increases in adolescence. While alterations in dopaminergic functioning have been implicated in suicidal acts-particularly in adults-we do not know whether morphological alterations in dopamine-rich regions of the brain, such as the striatum, are vulnerability factors for the emergence of SI in adolescents. At baseline, a community sample of 152 adolescents (89 female; mean age: 11.41 ± 1.01 years) completed a magnetic resonance imaging (MRI) scan that was used to estimate gray matter volumes (GMVs) of three striatal structures: caudate, nucleus accumbens and putamen. At a 24 month follow-up session, participants completed a self-report measure of SI frequency [Suicidal Ideation Questionnaire (SIQ)] and the death version of the Implicit Association Test (IAT). Robust linear regression models were conducted to predict SIQ and IAT scores from striatal GMV. Bilateral putamen and left caudate GMV significantly predicted IAT scores (all Ps < 0.03). No other associations were significant (all Ps > 0.05). Our finding of reduced dorsal striatal GMV predicting implicit SI may indicate that downstream dopaminergic dysfunction is implicated in the development of overt suicidal behaviors. Self-reported SI was not associated with striatal GMV, suggesting that biological correlates of suicide risk may correlate specifically with objective measurements of SI in adolescents.

Fig. 1

Gray matter segmentations of the striatum. All GMV and total ICV estimates were extracted in individual brain space. Structures visualized here are from a representative subject, displayed from an inferior perspective. A = anterior; P = posterior; R = right; L = left.

Fig. 2

Reduced GMV of the dorsal striatum predict IAT d-scores. Higher IAT d-scores at follow-up were significantly associated with reductions in GMV of the (A) right putamen, (B) left putamen and (C) left caudate. For visualization, all data points are raw values and all intercept and slopes are estimated from robust linear regression models that include age at baseline, age at follow-up, Tanner stage at baseline, CDI scores at baseline, CDI scores at follow-up and total ICV as covariates.