Neural substrates underlying effort, time, and risk-based decision making in motivated behavior

Abstract

All mobile organisms rely on adaptive motivated behavior to overcome the challenges of living in an environment in which essential resources may be limited. A variety of influences ranging from an organism’s environment, experiential history, and physiological state all influence a cost-benefit analysis which allows motivation to energize behavior and direct it toward specific goals. Here we review the substantial amount of research aimed at discovering the interconnected neural circuits which allow organisms to carry-out the cost-benefit computations which allow them to behave in adaptive ways. We specifically focus on how the brain deals with different types of costs, including effort requirements, delays to reward and payoff riskiness. An examination of this broad literature highlights the importance of the extended neural circuits which enable organisms to make decisions about these different types of costs. This involves Cortical Structures, including the Anterior Cingulate Cortex (ACC), the Orbital Frontal Cortex (OFC), the Infralimbic Cortex (IL), and prelimbic Cortex (PL), as well as the Baso-Lateral Amygdala (BLA), the Nucleus Accumbens (Nacc), the Ventral Pallidal (VP), the Sub Thalamic Nucleus (STN) among others. Some regions are involved in multiple aspects of cost-benefit computations while the involvement of other regions is restricted to information relating to specific types of costs.

Expression map of 78 brain-expressed mouse orphan GPCRs provides a translational resource for neuropsychiatric research

“…4, top right) and are highly studied in preclinical neuroscience research 45–48 . A selection of sections is shown in Fig.…”

Decline of prefrontal cortical-mediated executive functions but attenuated delay discounting in aged Fischer 344 × brown Norway hybrid rats

“…Aging is accompanied by a range of neural alterations in brain systems that mediate intertemporal choice, executive functions, and reward motivation (Bailey et al, 2016; Bickel et al, 2011; Sasse et al, 2017; Shamosh et al, 2008). For example, biochemical, electrophysiological and pharmacological evidence indicates that the normal balance of excitatory and inhibitory signaling within the mPFC is markedly altered with advanced age (Bañuelos et al, 2014; Beas et al, 2017; Carpenter et al, 2016; McQuail et al, 2016).…”

“…Such alterations have been linked to impaired working memory and cognitive inflexibility, and could directly account for some proportion of the variance in intertemporal choice inasmuch as working memory and/or cognitive flexibility contribute to the increased ability of aged rats to delay gratification. In addition to these cognitive capacities supported directly by the mPFC, this brain region is highly interconnected with other brain regions implicated in intertemporal choice, cognitive flexibility, and incentive motivation, including the orbitofrontal cortex, ventral striatum, and basolateral amygdala (Bailey et al, 2016; Churchwell et al, 2009; Floresco et al, 2008b; Ghods-Sharifi et al, 2009; Hosking et al, 2014; Ishikawa et al, 2008; Jimura et al, 2013; McClure et al, 2004; Samanez-Larkin et al, 2011; Smith et al, 2016; Stalnaker et al, 2009; Tye and Janak, 2007; Wassum and Izquierdo, 2015; Winstanley et al, 2004). Age-associated shifts in excitatory/inhibitory dynamics within PFC may also influence intertemporal choice by altering interactions with these brain regions that integrate cognitive and motivational variables.…”

Increased temporal discounting after chronic stress in CHL1-deficient mice is reversed by 5-HT2C agonist Ro 60-0175

“…Neuronal activation during TD was evaluated in CHL1-KO (n=6) and WT controls (n=6) in PrL, OFC-med, OFC-vlat, Acb-core, Acb-shell, DS-med, and DS-lat, regions with relevant roles in decision making (Bailey et al, 2016; Floresco et al, 2008), and particularly in TD (da Costa Araujo et al, 2010). Fig.3 indicates a reliable decrease in neuronal activation (number of cFos+ cells) in CHL1-KO mice relative to WT controls in PrL ( t (10)=5.65, p <0.01), DS-med ( t (10)=4.96, p <0.01) and DS-lat ( t (10)=4.05, p <0.01), but no reliable changes in the other brain regions (all t s(10)<1.63, p >0.05).…”