To Stop or Not to Stop?

Science 3 February 2012:  Vol. 335 no. 6068 pp. 546-548  DOI: 10.1126/science.1218170
Perspective –  Neuroscience
To Stop or Not to Stop?

Nora D. Volkow, Ruben D. Baler

E-mail: nvolkow@nida.nih.gov

The traffic light turns from green to yellow. With just one car between you and the intersection, your brain sprints into action, calculating distances and relative velocities; checking for hurried pedestrians; simulating the mental processes of the driver in front of you; and readying every muscle in preparation for a split-second decision that could literally change your life: Which pedal? So many of our mental processes, at any given moment, rely upon this constant, “high stakes” computation involving powerful impulses on one hand and inhibitory control on the other. This balancing act of neuronal signals is at the core of complex human behaviors and thus, its malfunction could have a range of adverse medical and social consequences. On page 601 of this issue, Ersche et al. (1) identify common abnormalities in fronto-striatal brain systems associated with poorer self-control in both drug-dependent individuals and their nonaddicted siblings.

The balance between activating and inhibitory signals in the brain is so central to a successful life that the level of childhood self-control is predictive of a wide range of long-term outcomes, including physical and mental health, substance abuse, financial independence, criminal behavior (2), and even mortality. The ability to control this balance is strongly influenced by genetic background, environmental conditions, and their interactions. Therefore, uncovering individual differences in the function and organization of the brain circuits underlying the ability to exert self-control should help us better understand disorders characterized by “impaired control,” not only substance use disorders (including addiction) but also conditions such as obesity, pathological gambling, attention deficit hyperactivity disorder, and obsessive-compulsive disorders.

Risk and protective factors in balance.
As a result of genetic, developmental, and environmental interactions, each of the brain regions shown contributes differentially toward the net balance between adaptive or maladaptive factors. Affected (addicted) and unaffected (nonaddicted) siblings shared several vulnerability (purple) features. The presence of discordant (red) regions renders the unaffected siblings phenotypically closer to unrelated controls (nonaddicted unrelated) with regard to the overall balance between risk and protective factors. The balance between these factors determines whether or not an individual will be able to stop an inappropriate impulse to act. Personality traits that either increase risk include high impulsivity, stress reactivity, novelty seeking, conditioning/habits, negative emotionality, and poor interoceptive awareness; protective factors include positive emotionality, robust inhibitory control and executive functions, balanced reward sensitivity, strong coping skills, and frustration management. Right inferior frontal cortex (IFC), orbitofrontal cortex (OFC), striatum (STR), amygdala (Am), precuneus (PC).

CREDIT: B. STRAUCH/SCIENCE
Ersche et al. identify abnormalities in the connectivity between drive and control circuits in the brain that are associated with poorer behavioral control of prepotent responses not only in addicted individuals but also in their nonaddicted siblings as compared to a control group of unrelated healthy individuals. The authors assessed the density (by fractional anisotropy) of white matter fiber tracts, which are the axonal fibers that transmit neural signals between brain regions. They found a correlation between the density of tracts adjacent to the right inferior frontal cortex, which is a key region involved with inhibitory control (3), and performance in a classical test of inhibitory control called the “stop signal reaction time,” which provides a measure of self-control. Notably, the fiber tract density and self-control deficits—compared to a control group—were as pronounced in stimulant-dependent individuals as in their non-dependent siblings. This finding is important for it suggests not only that dysfunctions in the fronto-striatal circuits that handicap self-control [and are a hallmark of addiction] are influenced by genetics, but also that the increased vulnerability for substance use disorders engendered by these dysfunctions can be overcome. But why did one sibling become addicted while the other did not? Possibly, different environmental exposures contributed to their different trajectories, even though most sibling pairs had a shared childhood environment, at least until adolescence. Alternatively, the nonaddicted sibling may have benefited from resilience factors not present in the addicted one that buffered his or her risk for a substance abuse disorder.

Indeed, comparison of the brain structural differences between the addicted and the non-addicted siblings with the non-related controls showed that there were shared abnormalities in gray matter volumes between siblings (increases in putamen and amygdala and decreases in posterior insula), but also some differences. Most striking were differences in the volume of the medial orbitofrontal cortex (corresponding to medial Brodmann Area 11) and the precuneus (medial Brodmann area 7), which were smaller in the afflicted siblings relative to the nonafflicted siblings. The orbitofrontal cortex is implicated in salience attribution and enables behavioral flexibility in response to changing contexts. Smaller volumes and reduced activity in the orbitofrontal cortex of drug-addicted individuals may confer vulnerability to compulsive and inflexible behaviors (4, 5). In addicted subjects, reduced activity in the orbitofrontal cortex (as well as in anterior cingulate gyrus) has been shown to be associated with reduced amounts of dopamine D2 receptors in the striatum (6-8), which are linked to a greater risk for impulsivity and compulsive drug administration (9, 10). By contrast, subjects who had a high familial risk for alcoholism but were not alcoholics showed an increased number of dopamine D2 receptors in the striatum, which was associated with higher activity in the orbitofrontal cortex (and also in the anterior cingulate gyrus) (11). Increased activity in the orbitofrontal cortex (and also in the anterior cingulate gyrus) has been associated with higher scores in positive emotionality, a personality trait that is considered to protect against substance use disorders (12) both in individuals at high genetic risk for alcoholism (11) and in non-drug-abusing healthy volunteers (13). Unlike their non-addicted siblings, addicted siblings also had smaller volumes in the precuneus, which is involved with self-referential processing and like the middle orbitofrontal cortex also plays an important role in the evaluation of social stimuli. Both metabolic activity and concentration of cannabinoid type 1 receptors in the precuneus are negatively correlated with high-sensation or novelty-seeking behavior (14, 15), a personality trait that increases the risk for substance use disorder. Thus, differences in the balance between the number and severity of vulnerability factors (e.g., poor self-control and traits such as novelty-seeking and reward dependency) versus that of resilience factors (e.g., positive emotionality) may have contributed to the divergent trajectories between siblings, regarding substance use disorders, despite the shared poor self-control (see the figure).

The similarity of the structural changes in striatal and prefrontal brain regions of drug-dependent and nonaddicted siblings is hardly surprising considering the high heritability of global brain volumes particularly in the frontal lobe areas (16). The findings of Ersche et al. are also consistent with genetic studies showing that polymorphisms that influence behavioral measures of inhibitory control are mediated by their impact on prefrontal-striatal circuitry. Indeed, preclinical studies have started to clarify the molecular mechanisms that modulate prefrontal-striatal communication during risky decision-making (17). For example, in the prefrontal cortex of rodents, dopamine D1 receptor signaling mediates working memory in a nonlinear fashion such that performance is degraded not only during insufficient (aging) but also during excessive (acute stress) transmission. Similarly, in the striatum, dopamine D2 receptor signaling relates to performance through an inverted U-shape function, because both suboptimal as well as supraoptimal dopamine signaling can lead to maladaptive excessive or deficient risk-taking, respectively.

The finding by Ersche et al. that decreased connectivity in the right inferior frontal lobe is linked to poor self-control has clinical implications, for it provides a potential biomarker that can be targeted for interventions to strengthen it. Indeed, several childhood and adolescent interventions can improve executive function and self-control, although studies have not yet assessed whether they strengthen fronto-striatal connectivity. In more general terms, a deeper, endophenotype-based understanding of personality traits that can promote resiliency and their degree of malleability may help prevent adverse trajectories such as those leading to substance use disorder or other conditions with underlying deficits in self-control.

References

K. D. Ersche et al., Science 335, 601 (2012).

T. E. Moffitt et al., Proc. Natl. Acad. Sci. U.S.A. 108, 2693 (2011).

A. R. Aron, T. W. Robbins,R. A. Poldrack, Trends Cogn. Sci. 8, 170 (2004).

N. D. Volkow, J. S. Fowler, Cereb. Cortex 10, 318 (2000).

T. R. Franklin et al., Biol. Psychiatry 51, 134 (2002).

N. D. Volkow et al., Synapse 14, 169 (1993).

N. D. Volkow et al., Am. J. Psychiatry 158, 2015 (2001).

N. D. Volkow et al., J. Neurosci. 27, 12700 (2007).

B. J. Everitt et al., Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3125 (2008).

B. Lee et al., J. Neurosci. 29, 14734 (2009).

N. D. Volkow et al., Arch. Gen. Psychiatry 63, 999 (2006).

T. A. Wills et al., Dev. Psychol. 37, 283 (2001).

N. D. Volkow et al., Mol. Psychiatry 16, 818 (2011).

Y. Hakamata et al., Neurosci. Lett. 396, 33 (2006).

K. Van Laere et al ., Arch. Gen. Psychiatry 66, 196 (2009).

J. S. Peper et al., Hum. Brain Mapp. 28, 464 (2007).

N. W. Simon et al ., J. Neurosci. 31, 17660 (2011).