It’s one of the truisms of human life that teenagers often do silly, stupid and/or dangerous things. We certainly don’t need science to tell us that. One reason this seems to be the case is that, on average, teens have trouble optimally weighing risk vs. reward. I’m not excluding myself from this characterization. In fact, I sometimes marvel that I survived my teen years intact, if at all.
One stupid thing I and many of my friends did as teenagers was to try smoking.
There are no rational arguments in favor of smoking. Yet, according to one study, most adult smokers start before age 19 and 70% of all adolescents report trying a cigarette at least once. Messages directed to adolescents and teens against smoking often fall on deaf ears. In addition to subpar decision making skills, many teens hold the advice of their elders in low regard and are apt to pick up a cigarette as an act of defiance or as a show of their burgeoning adulthood. Hollywood films certainly contribute to the smoking mythology through their noble portrayals of troubled, rebellious or misunderstood teens reaching for smokes when the chips are down.
Although the health risks associated with smoking are widely known, a recent Nature Neuroscience study has provided even more reasons for teens to avoid smoking. Researchers from the Netherlands found that in adolescent rats, exposure to smoking led to increased and long lasting impulsiveness and impaired measures of attention in adulthood. In contrast, adult animals exposed to similar levels of nicotine did not show these long term effects. So, the impulsiveness that might lead a teen to try smoking could be increased by the very product of that impulsiveness, creating a vicious feedback cycle. Nasty business.
What’s the molecular mechanism? A proteomic analysis of 297 different mPFC proteins in adolescent rats who had exposure to smoking showed that levels of mGLuR2 was downregulated. mGluR2 is a metabotropic glutamatergic receptor that sits on the presynaptic side of synapses in the mPFC, a brain region important for cognitive control and attention. Adolescent nicotine exposure resulted in decreased mGLuR2 signaling and reduced plasticity in the mPFC. This was shown to be directly related to attentional deficits by a rescue experiment in which a glutamate agonist was injected into the mPFC in vivo. The agonist restored attentional performance to normal levels but didn’t affect impulsiveness.
In sum, the adolescent brain is more susceptible to the consequences of smoking via alteration of synaptic mGluR2 protein levels. It’s known that nicotine exerts its initial effects on the brain through nicotinic acetylcholine receptor activation and this study suggests that adolescents are uniquely vulnerable to increased activation of these receptors.
The researchers acknowledge that the sequence of molecular events is unknown, but speculate that “…mGluR2 levels following nicotine exposure at the end of adolescence compensate for nicotine’s actions and inhibit neurotransmitter release.” One caveat to keep in mind is that it’s unknown the extent to which this effect would be replicated in humans. Another important point is that in this experiment, nicotine was dissolved in solution and administered subcutaneously. Cigarettes contain many other carcinogens and toxins to which adolescents could be particularly vulnerable, so the long-term cognitive effects of cigarette smoking could extend far beyond those uncovered in this study, which looked just at nicotine.
The takeaway message is definitely clear: Parents, do everything you can to keep your kids from smoking!*
*I’m an advocate for making the sale of tobacco and tobacco related products illegal. Tobacco offers no clear benefits to society, with the exception of a few thousand jobs and the enrichment of a couple handfuls of business executives, while incurring great economic costs to the nation as a whole. Although nicotine has shown some cognitive benefits to individuals in certain disease states, all in all its mostly detrimental. (I’m a member of that most annoyingly vociferous group of anti-smoking activists: ex-smokers. Maybe we’re so adamant because we know how hard it is to quit!) That long-term cognitive deficits are now being shown to accrue in cigarette smoking teens is hopefully another nail in the coffin of the tobacco industry and smoking culture in general, which will perhaps be looked back upon as a sign of the special ignorance of our times.
Counotte DS, Goriounova NA, Li KW, Loos M, van der Schors RC, Schetters D, Schoffelmeer AN, Smit AB, Mansvelder HD, Pattij T, & Spijker S (2011). Lasting synaptic changes underlie attention deficits caused by nicotine exposure during adolescence. Nature neuroscience, 14 (4), 417-9 PMID: 21336271
As many a former smoker will probably attest, quitting cigarettes ranks high in the hard-to-kick category. I made several unsuccessful attempts before finally kicking the habit after a 10 year pack-a-day run. Ultimately what worked for me was to go cold turkey, but there were perhaps other alternatives which I might have tried. In a paper from Nature Neuroscience, researchers from University of Michigan provided participants with interventions involving individually tailored messages* designed to encourage quitting and found that participants’ brain activity while listening to the messages predicted how likely they would be to successfully quit smoking.
*Tailored messages are statements about an individuals’ issues and thoughts about quitting smoking, derived from pre-screen interviews with them. e.g., “You are worried that when angry or frustrated, you may light up”.
Here’s the premise: Anti-smoking messages custom made for an individual can be more effective than generic ones, but only if said individual processes those messages in a self directed manner. Past research has shown a specific set of neural regions – primarily the mPFC and precuneus/posterior cingulate – to be associated with self referential thinking. Therefore, researchers hypothesized, activity in these brain regions while processing tailored anti-smoking messages might predict the likelihood of quitting.
The experiment was carried out over three days with a follow-up visit four months later.
Day 1: 91 participants completed a health assessment, demographic questionnaire and a psychosocial characteristics scale related to quitting smoking. Responses were then used to create smoking cessation messages tailored to each individual.
Day 2: Participants went into scanner and performed 2 fMRI tasks: The first task had participants listen to anti-smoking messages of three different types: personally tailored anti-smoking, non-tailored anti-smoking and neutral.
Here are some examples of what they heard:
A concern you have is being tempted to smoke when around other smokers.
Something else that you feel will tempt you after you quit is because of a craving.
You are worried that when angry or frustrated, you may light up.
Some people are tempted to smoke to control their weight or hunger.
Smokers also light up when they need to concentrate.
Certain moods or feelings, places, and things you do can make you want to smoke.
Oil was formed from the remains of animals and plants that lived millions of years ago.
Sighted in the Pacific Ocean, the world’s tallest sea wave was 112 feet.
Wind is simple air in motion. It is caused by the uneven heating of the earth’s surface by the sun.
Then, participants completed a self appraisal task to identify brain regions active during self relevant thought processes. In this task, participants saw adjectives appear on the screen and had to either rate how much the adjective described them or whether the adjective was positive or negative.
Day 3: Participants completed a web-based smoking cessation program and were instructed to quit smoking. (They were given a supply of nicotine patches to get themselves started)
Experimenters checked in with subjects four months later to see if they were abstaining from smoking. Out of 87 who participated in the smoking cessation program, 45 were not smoking, while 42 were still (or had quit briefly and restarted) smoking.
Subjects were given a surprise memory test for the anti-smoking messages they’d received four months prior and remembered self relevant, tailored messages most well. However, their memory performance was not related to whether they successfully quit smoking.
As for the fMRI data, experimenters used a mask of tailored vs. untailored message conditions AND self-appraisal to identify the region common to both processes. This seems like a mild case of double dipping, no? That is, finding a brain region that responds to the condition of interest (in this case, voxels more active in tailored vs. untailored conditions) and then using the same data to test the hypothesis. Ideally, the ROI would be obtained independently of the main task.
A blow by blow on the different contrasts of interest:
1. Researchers looked at brain regions more active during tailored vs. untailored messages and found differential activation in the regions below.
There are, I think, some problems here; mainly, that the task differences for processing tailored vs non-tailored statements may extend beyond self relevant thinking to (1) memory processes employed in processing either category of stimuli; that is, episodic (tailored) vs. semantic (non-tailored), (2) cognitive effort, (3) elicitation of visual vs. non-visual memory, (4) processing fluency and (5) affect or reward responses. Thus, the difference in brain activation found in this task might reflect something other than just self referential processing.
2. The localizer task (used to isolate neural areas involved in self appraisal) had participants process adjectives either by relating them to self or by judging their affective value. This suggests an alternative explanation for the categorical contrast in that it isn’t specific to self per se, but really more specific to people vs. non-people. A more widely used version of this task has participants process adjectives with regards to self or an other. As a further control, a third condition is often included in which participants identify whether words are in upper case or lower case. The contrast applied is (self – control) – (other – control). It’s not clear why the researchers chose the task they did, which seems significantly noisier.
Here’s the contrast from the present study:
And here’s a contrast from another study (Jenkins 2010) that looked at three different types of self-referential processing.
Although roughly similar, the current study shows cortical midline activation seems to be much more dorsal than that found in Jenkins (2010). Using an ROI derived from this localizer task to correlate neural activity in tailored vs. untailored statements with quitting led to a non-significant result (from supplementary materials). This could explain why the researchers used the composite mask to define the ROI.
3. Again, the primary ROI was defined as a composite of overlapping regions between the self reference task AND the tailored vs. untailored statements task, which was used to compare neural activity with quitting behavior. They found that activity in these regions – which included dmPFC, precuneus and angular gyrus – during tailored smoking cessation messages predicted the likelihood of successfully abstaining from smoking. dmPFC and precuneus activation also individually predicted smoking cessation success, although angular gyrus did not.
This study provides clear evidence that participants processed tailored vs. non-tailored messages about smoking differently, and that this difference corresponded to their ability to stop smoking. However,
(1) neither task effectively isolates self referential processing,
(2) the region of activation was much more dorsal than that usually found in this literature (Northoff & Bermpohl, 2004; Schneider et al.,2008; Uddin, Iacoboni, Lange, & Keenan, 2007; Gillihan & Farah, 2005),
(3) an independently obtained ROI yielded insignificant results and
(4) mPFC and precuneus subserve an untold number of cognitive processes beyond self reflection.
Therefore, it seems a bit of a stretch to claim the neural activation found in this study is indicative of self referential processing.
Chua HF, Ho SS, Jasinska AJ, Polk TA, Welsh RC, Liberzon I, & Strecher VJ (2011). Self-related neural response to tailored smoking-cessation messages predicts quitting. Nature neuroscience, 14 (4), 426-7 PMID: 21358641
Jenkins AC, & Mitchell JP (2010). Medial prefrontal cortex subserves diverse forms of self-reflection. Social neuroscience, 1-8 PMID: 20711940
Northoff, G. (2005). Emotional-cognitive integration, the self, and cortical midline structures Behavioral and Brain Sciences, 28 (02) DOI: 10.1017/S0140525X05400047
Gillihan, S., & Farah, M. (2005). Is Self Special? A Critical Review of Evidence From Experimental Psychology and Cognitive Neuroscience. Psychological Bulletin, 131 (1), 76-97 DOI: 10.1037/0033-2909.131.1.76
SCHNEIDER, F., BERMPOHL, F., HEINZEL, A., ROTTE, M., WALTER, M., TEMPELMANN, C., WIEBKING, C., DOBROWOLNY, H., HEINZE, H., & NORTHOFF, G. (2008). The resting brain and our self: Self-relatedness modulates resting state neural activity in cortical midline structures Neuroscience, 157 (1), 120-131 DOI: 10.1016/j.neuroscience.2008.08.014
UDDIN, L., IACOBONI, M., LANGE, C., & KEENAN, J. (2007). The self and social cognition: the role of cortical midline structures and mirror neurons Trends in Cognitive Sciences, 11 (4), 153-157 DOI: 10.1016/j.tics.2007.01.001