Abnormal functional activation and maturation of ventromedial prefrontal cortex and cerebellum during temporal discounting in autism spectrum disorder

Abstract

People with autism spectrum disorder (ASD) have poor decision-making and temporal foresight. This may adversely impact on their everyday life, mental health, and productivity. However, the neural substrates underlying poor choice behavior in people with ASD, or its' neurofunctional development from childhood to adulthood, are unknown. Despite evidence of atypical structural brain development in ASD, investigation of functional brain maturation in people with ASD is lacking. This cross-sectional developmental fMRI study investigated the neural substrates underlying performance on a temporal discounting (TD) task in 38 healthy (11-35 years old) male adolescents and adults with ASD and 40 age, sex, and IQ-matched typically developing healthy controls. Most importantly, we assessed group differences in the neurofunctional maturation of TD across childhood and adulthood. Males with ASD had significantly poorer task performance and significantly lower brain activation in typical regions that mediate TD for delayed choices, in predominantly right hemispheric regions of ventrolateral/dorsolateral prefrontal cortices, ventromedial prefrontal cortex, striatolimbic regions, and cerebellum. Importantly, differential activation in ventromedial frontal cortex and cerebellum was associated with abnormal functional brain maturation; controls, in contrast to people with ASD, showed progressively increasing activation with increasing age in these regions; which furthermore was associated with performance measures and clinical ASD measures (stereotyped/restricted interests). Findings provide first cross-sectional evidence that reduced activation of TD mediating brain regions in people with ASD during TD is associated with abnormal functional brain development in these regions between childhood and adulthood, and this is related to poor task performance and clinical measures of ASD. Hum Brain Mapp 38:5343-5355, 2017. © 2017 Wiley Periodicals, Inc.

Machine learning approach to identify resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder

“…Examples of these social interaction impairments among people with ASD include theory of mind, empathy, and facial emotion recognition (Simon Baron-Cohen, Leslie, & Frith, 1985; S. Baron-Cohen & Wheelwright, 2004; Uljarevic & Hamilton, 2013). Consistent with the findings that such social interactions depend on the social brain neural system (Adolphs, 2009), abnormalities within the social brain have been observed among individuals with ASD (Yuta Aoki, Cortese, & Tansella, 2015; Kana, Keller, Cherkassky, Minshew, & Just, 2009; Kleinhans, Richards, Greenson, Dawson, & Aylward, 2016; Murphy et al, 2017; Pelphrey, Shultz, Hudac, & Vander Wyk, 2011). Besides the anatomical extension of abnormalities, individuals with ASD consistently present atypical functional connections (FCs) (Cherkassky, Kana, Keller, & Just, 2006; Di Martino et al, 2014; Uddin, Supekar, & Menon, 2013).…”

“…Since not all individuals with the endophenotype develop ASD clinical diagnosis (Ozonoff et al, 2011), the neural correlates for the endophenotype and the diagnosis may differ. Although we did not investigate the neural correlates for the diagnosis because of the small sample size, neural correlates for the clinical diagnosis of ASD might be located within the social brain (Yuta Aoki et al, 2015; Kana et al, 2009; Kleinhans et al, 2016; Murphy et al, 2017; Pelphrey et al, 2011). Given that there was no significant difference in the WLS of the selected FCs between individuals with ASD and their unaffected siblings, the selected FCs represent the ASD endophenotype, rather than the ASD diagnosis.…”

Social and delay discounting in autism spectrum disorder

“…Moreover, the well‐characterized neural correlates of discounting make such tasks a promising tool for understanding neurobiological mechanisms spanning disorders (Lempert et al, ; MacKillop, ). As the limited existing research on delay discounting in autism spectrum disorders (ASD) is mixed as to whether individuals with ASD show normative (e.g., Antrop et al, ; Demurie, Roeyers, Baeyens, & Sonuga‐Barke, ) or atypical discounting (e.g., Carlisi et al, ; Chantiluke et al, ; Murphy et al, ), and no studies have assessed social discounting in ASD, simultaneously investigating delay and social discounting in a single sample with autism could yield novel insight into cognitive processes underlying ASD.…”

“…In light of these potential insights into ASD, this study is the first to investigate delay and social discounting in a single ASD sample. We recruited adolescents and young adults, age ranges that have successfully completed discounting tasks in prior studies (e.g., Carlisi et al, ; Murphy et al, ), and compared their results to a TD group matched on age, sex, and IQ. Given inconsistencies in prior ASD delay discounting studies and the lack of research on social discounting in ASD, three potential hypotheses could be advanced: first, there might be no group differences on either social or delay discounting; second, there might be equivalent group differences on both tasks; and third, there might be an interaction between group and task type, such that one task might show larger group differences than the other.…”

Sex differences in brain structure: An autism twin study on restricted and repetitive behaviors

“…Furthermore, abnormal intraparietal sulci (Auzias et al 2014) and reduced gyrification of an area near the intraparietal sulcus (Pappaianni et al 2017) were observed in male children with ASD. Functional activation studies have found differential activation in males with ASD in the intraparietal sulcus during motor learning (Travers et al 2015) response shifting (Shafritz et al 2008) tasks, and in the orbital gyri during motor-inhibition 1 5 (Schmitz et al 2006) and temporal delay decision making (Murphy et al 2017), and these differences correlated in most cases with RRBI symptoms (Shafritz et al 2008;Travers et al 2015;Murphy et al 2017). However, differences in functional activation are not necessarily associated with differences in underlying structure.…”