Family conflict is associated with longitudinal changes in insular‐striatal functional connectivity during adolescent risk taking under maternal influence

Abstract

Maternal presence has marked effects on adolescent neurocognition during risk taking, influencing teenagers to make safer decisions. However, it is currently unknown whether maternal buffering changes over the course of adolescence itself, and whether its effects are robust to individual differences family relationship quality. In the current longitudinal study, 23 adolescents completed a risk-taking task under maternal presence during an fMRI scan before and after the transition to high school. Behavioral results reveal that adolescent risk taking increased in the presence of mother across a one year period. At the neural level, we found adolescents reporting higher family conflict showed longitudinal increases in functional coupling between the anterior insula (AI) and ventral striatum (VS) when making safe decisions in the presence of their mother, which was associated with increased real-world risk taking. These findings show that individual differences in family relationship quality undermine effective development of AI-VS connectivity resulting in increased risk taking.

Neural correlates of sibling closeness and association with externalizing behavior in adolescence

“…Similar to previous findings examining parents’ impact on adolescent risk-taking (e.g. Guassi Moreira and Telzer, 2017 ), we hypothesized that sibling closeness would be associated with modulated neural responses in the anterior insula, VS and vlPFC during safe decision-making, and this neural activation would, in turn, predict less self-reported externalizing problems. Given that sibling relationships contribute to adolescent externalizing behavior above and beyond the effects of parent and peer relationships (Stormshak et al ., 2004 ; Defoe et al ., 2013 ), we examined the unique role of sibling closeness controlling for parent and peer closeness.…”

“…Of interest, the anterior insula is a critical region in the association between family discord and adolescent decision-making to avoid risks (i.e. safe decision-making; Guassi Moreira and Telzer, 2017 ). In addition, mother presence is associated with heightened activity in the ventral striatum (VS; Guassi Moreira and Telzer, 2018 ) and ventrolateral prefrontal cortex (vlPFC) during safe decision-making (Telzer et al ., 2015 ).…”

A simultaneous [11C]raclopride positron emission tomography and functional magnetic resonance imaging investigation of striatal dopamine binding in autism

“…Uncorrected results with a voxel-wise threshold of z > 2.3 ( p < 0.012), and minimum cluster size of 20 shows that the ASD group demonstrated decreased activation in a 34-voxel cluster in the left putamen, as well as other cortical and cerebellar regions (see Supplemental Materials VI ). The size of this left putamen cluster exceeded our planned small volume correction for the striatum based on approaches used in prior studies examining this region 49 – 51 . For completeness, other clusters showing ASD < control activation differences at this threshold and size are also included in Table 1 , but findings outside of the striatum should be considered exploratory given that these are uncorrected results at a liberal threshold.…”

Differential effects of parent and peer presence on neural correlates of risk taking in adolescence

“…Standard preprocessing was conducted using the FSL FMRIBs Software Library (FSL v6.0, Oxford, UK; https://fsl.fmrib.ox.ac.uk/fsl/ ). We corrected for head motion using MCFLIRT (Jenkinson, 2002 ). Data were skull-stripped with BET (Smith, 2002 ), spatially smoothed with a 6 mm full width half maximum (FWHM) Gaussian kernel, and a high-pass temporal filtering with a 128 s cutoff was applied to remove low-frequency drift across time (Gaussian-weighted least squares straight line fitting; sigma = 64.0 s).…”

“…Data were skull-stripped with BET (Smith, 2002 ), spatially smoothed with a 6 mm full width half maximum (FWHM) Gaussian kernel, and a high-pass temporal filtering with a 128 s cutoff was applied to remove low-frequency drift across time (Gaussian-weighted least squares straight line fitting; sigma = 64.0 s). Image co-registration was done using a three-step registration procedure (EPI to T2 to T1), and each functional image was resampled to 2 × 2 × 2 mm and warped to the standard Montreal Neurological Institute 2 mm brain using FLIRT (Jenkinson, 2001 ; 2002 ). Moreover, to remove artifact signals such as motion and physiological noise, we applied an independent component analysis (ICA) denoising procedure using MELODIC (Beckmann, 2004 ), combined with an automated signal classification toolbox (an average of 4.02 components or 11.33% were removed; classifier NP-threshold = 0.3; for more details see Tohka et al ., 2008 ).…”