At risk of being risky: The relationship between “brain age” under emotional states and risk preference

Abstract

Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10–25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the “brain age” of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that “brain age” across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception – a pattern exemplified greatest in young-adults (ages 18–21). The results are suggestive of a specified functional brain phenotype that relates to being at “risk to be risky.”

Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising

“…Additionally, measurements of structural connectivity, such as fractional anisotropy and diffusivity obtained with diffusion tensor imaging ( (Erus et al 2015), R = 0.89, R 2 = 79%), have also been used to successfully predict an individual's age with multivariate machine learning. Recently, task-related FC, a measurement of the transient changes in regional coherence during task performance, has been used to predict age with moderate accuracy, explaining 42% of variance related to age in a validation set (Rudolph et al 2017). Approaches that combine information from multiple imaging modalities (T1, T2, and diffusion weighted imaging (Brown et al 2012), R = 0.96, R 2 = 92%) have been shown to achieve the highest prediction performance.…”

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

“…These studies have examined both structural and functional network organization in a wide variety of samples, including healthy young adults (Power et al, 2013;Zanto and Gazzaley, 2013), developmental cohorts (Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), older adults (Baniqued et al, 2018;Gallen et al, 2016), and a plethora of neurological and psychiatric populations (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). We have gained a better understanding of typical and atypical human brain organization from these efforts.…”

“…These two ROI sets sample the cortex well, representing a diverse set of brain areas that can be organized into functional networks. Many investigators have used them to describe functional brain organization in a variety of healthy samples (Power et al, 2013;Zanto and Gazzaley, 2013), lifespan cohorts (Baniqued et al, 2018;Gallen et al, 2016;Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), as well as populations with neurologic and psychiatric diseases (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). However, the first set (264 volumetric ROIs) under-samples subcortical and cerebellar structures, as only 17 ROIs are non-cortical, and the second set (333 parcels) is restricted to the cortex only, similar to other popular ROI sets, e.g.…”

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

“…The former fits neatly with extant literature that child and adolescent development is marked by dramatic changes in affective responding (Casey et al, 2010;Ernst et al, 2006;Larson & Lampman-Petraitis, 1989). Because the SaN is theorized to be implicated in the bottom-up generation and maintenance of emotional responses (Feldman Barrett & Satpute, 2013), it is somewhat unsurprising that we observed it to be the best network for discriminating between the two age groups in our studiesespecially given that data were collected during an affective task and emerging evidence suggests that information about development are uniquely encoded in network activity elicited by affective states (Rudolph et al, 2017). Future work might wish to examine whether similar age effects are observed in different networks during performance of social or cognitive tasks for which ability also changes across development (Blakemore & Mills, 2014;Crone & Dahl, 2012).…”