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Social cognitive deficits in autism spectrum disorder

May 6, 2011 3 comments

ResearchBlogging.org One of the hallmarks of Autism Spectrum Disorder (ASD) is an impairment in social cognitive skills. This manifests in individuals with ADS having trouble orienting their attention towards people. Accordingly, they also show deficits orienting their attention in response to social cues from others, such as eye gaze, head turns and pointing gestures.

Understanding the social cognitive impairments associated with ASD has been challenging in that studies set in naturalistic settings often reveal the deficit but lab experiments performed on computers don’t.

For example, some naturalistic studies have looked at home movies of infants and found that those later diagnosed with ASD showed less social orienting and were less responsive to cues from others to orient to objects. For example, if their mom was in the room, they would look at her a lot less and they’d also be less likely to respond when their mothers tried to direct their attention to a toy in the room by looking or pointing at it.

However, people with ASD have been shown to respond to non-naturalistic social cues in the lab. Social orienting has been frequently been tested by use of a variation on Michael Posner’s spatial cueing paradigm. This works as follows:

1. Participants are seated in front of a computer
2. A stimulus – a pair of eyes gazing to either side (or straight ahead) or arrows pointing to either side or neither – appears on the screen
3. Shortly after, a stimulus (the target object) appears to one side or the other, either on the side which the eyes or arrows were pointing towards or the opposite side.
4. Participants have to indicate which side the target object appeared on by pressing either a right or left button.
5. Performance on the task is assessed by measuring the amount of time it takes to participants to press the button indicating on which side the target appeared. Most participants, including ASD patients, are as quick with the gaze cue (the eyes) as with the arrow cue.

Posner cue paradigm

(The left side of the above figure shows a single trial (with “directional eyes”), in which participants first see a fixation cross, then one of four directional/non-directional stimuli, after which the target appears either on the same side indicated by the cue or the opposite side. Participants need to indicate which side a target stimulus appeared on by pushing a button. The right side shows the three other trial types (from top to bottom): neutral arrow, directional arrow, neutral eyes)

Past studies have shown that people orient faster to cued (like in the left side of the above figure) versus noncued locations, known as the facilitation effect. Previous studies using this task have produced inconsistent results, but most of them have shown ASD populations performing comparably to non-ASD populations.

In this study, researchers used the above-described cue task to examine the neural mechanisms underlying social orienting in ASD, with the hope that if there were no behavioral differences, neural activity might reveal that ASD individuals are performing the task differently. Other studies have shown that non-ASD populations treat social and non-social cue stimuli differently. It was hoped that neural activity revealed in this study would shed light on the discrepancies in behavioral results for ASD populations in lab versus computer settings.

Results
In terms of behavior, both the control and the ASD group showed quicker responses for gaze and arrow cues with no between group difference, which is consistent with previous lab studies.

However, neural activation patterns showed significant group differences. The control group showed greater activation for social vs. nonsocial cues in many different brain regions, with gaze (eyeball) cues eliciting increased activity in many frontoparietal areas, supporting the idea that neurotypical brains treat social stimuli different from non-social stimuli. The ASD group, on the other hand, showed much less difference in neural activation between social vs. non-social cues. Although these differences in neural activation are too numerous to cover here, one region of interest, superior temporal sulcus (STS), stood out. The STS has been shown to be associated with the perception of eye gaze and other work has suggested the region may be involved in understanding the intentions and mental states of others. In this study, ASD individuals showed decreased STS in the gaze cue condition (versus controls). This data suggests that the STS may not be sensitive toward the social significance of eye gaze in ASD individuals.

Implications
The authors point out that although ASD individuals don’t seem to rely on the same neural circuitry to perceive social cues such as eye gaze, they have found a way to use the low-level perceptual information available in social cues to adapt a strategy that allows them to discern that gaze direction conveys meaning about the environment. That being said, ASD individuals mostly don’t do this very well in more naturalistic environments. So, although this strategy might work in a scanner with “cartoon” eyes and where there are no environmental distractions, it’s unlikely that ASD individuals could adapt this strategy in a naturalistic environment. On the contrary, one could also frame these results from the perspective of the ASD individual; that is, given the non-naturalistic environment of the scanner, and the fact that the task demands were very simple and not dependent on social cognitive processing, why should non-ASD individuals treat the gaze vs. arrow stimuli differently? Why not just rely on low-level information and thus expend less cognitive energy? It’s a good example of the automaticity of social cognitive processes. Give humans a set of cartoon eyeballs to look at and they can’t help but process these as distinct from something non-social.

An additional take away from this paper is that even when one finds no behavioral differences between groups, there might be some interesting differences in neural activity worth exploring via fMRI or EEG.

References

Greene DJ, Colich N, Iacoboni M, Zaidel E, Bookheimer SY, & Dapretto M (2011). Atypical neural networks for social orienting in autism spectrum disorders. NeuroImage, 56 (1), 354-62 PMID: 21334443

Protected: The case of the man who couldn’t find the beat

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Regard thyself and put down the smoke stick

April 15, 2011 Leave a comment

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 Study
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:

Tailored messages
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.
Untailored messages
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.
Neutral messages
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)

Results
Behavioral
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.

fMRI
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.

References
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

ResearchBlogging.org

Mirror Neurons and Mentalizing

April 6, 2011 Leave a comment

Perhaps few findings in the cognitive sciences have received more press in recent years than the discovery by Rizolatti and colleagues in macque monkeys of mirror neurons; that is, neurons that preferentially activate both when a monkey performs some action and when observing someone else perform the same action. There is evidence that these neurons exist in humans, although it’s indirect (however, see Keysers 2010). They’ve quite captivated the publics’ attention, these crafty little neurons.

The mirror neuron system is thought to help primates, non-human and human, understand what others are doing by simulating the motor plan of an observed action and also allowing for prediction of the most likely outcome of an observed action. In other words, mirror neurons are sensitive both to actions and outcomes, and to some extent, inferring the why behind the what. Many have suggested that they play a significant role in comprehending mental states and empathic processes. But it’s in regards to these latter claims where the evidence is not as clear.

So, how does the brain intuit others’ inherently unobservable mental states in the absence of biological action? Much of the research evidence points to the mentalizing system, also known as the theory-of-mind network, as the neural network tasked to the job (see meta-analysis by Van Overwalle and Baetens, 2009). Anatomically speaking, these networks are distinct, with the mirror neurons located primarily in the ifraparietal sulcus, superior temporal sulcus and the prefrontal cortex, while the mentalizing system constitutes a distinct set of brain regions that lie along the cortical midline and in the temporal lobes, including the mPFC, TPJ, temporal poles, PCC and posterior STS.

One of the big challenges in this area of research is in designing tasks that are able to effectively disentangle processing of motor action from mentalizing. This is quite a challenge because it’s difficult to know what kind of mental process participants are applying to any given set of social stimuli. Do participants engage in higher-order abstract mentalizing automatically, and even when the stimuli might not necessarily demand it? How can we know what mental process subjects are engaging in? In other words, how might one capture the distinction between perceiving what others are doing vs. obtaining a more abstract representation of why they might be doing it?

UCLA’s Bob Spunt and colleagues (2011) designed a study that would attempt to do just that. They had participants observe short video clips of a human performing an action and directed the participants, in the scanner, to covertly describe each video clip in terms of (1) what an actor was doing, (2) why he was doing it, (3) how we was doing it or (4) to just passively view the video. They were to start the process of covert description once the video started playing, begin their description with the word “he” (e.g. he is reading) and to press a button once they were done.

(Thanks to the researchers for providing the video)

For example, in the above example, participants might have covertly described that the man is reading (WHAT), that he wants to learn or is bored (WHY), or that he is flipping pages or gripping the book (HOW).

This had the effect of creating three levels of mentalizing “depth” while holding the action component constant. If the mirror neuron network was involved in the mentalizing process, then one would expect to see neural activation increases in the mirror neuron network covarying with the increase in participants presumed mentalizing about the actor. And if the mirror neuron network was involved in mentalizing, then one would expect to see increased activations in neural regions which have been previously suggested to contain mirror neurons.

Results
In support of the theory that mirror neurons don’t play a significant role in mentalizing, the researchers found no increase in the mirror neuron network in response to increases in mentalizing. But they did find increased activation in brain regions associated with mentalizing, including dorsal and ventral medial pFC, posterior cingulate cortex, and the temporal poles.

Conclusion
The study does provide another piece of support to the position that although the mirror neuron system might be necessary in understanding actions of the body, it’s not sufficient to explain the cognitive processes required to infer unobservable mental states.

References
Spunt, R., Satpute, A., & Lieberman, M. (2011). Identifying the What, Why, and How of an Observed Action: An fMRI Study of Mentalizing and Mechanizing during Action Observation Journal of Cognitive Neuroscience, 23 (1), 63-74 DOI: 10.1162/jocn.2010.21446

Keysers, C., & Gazzola, V. (2010). Social Neuroscience: Mirror Neurons Recorded in Humans Current Biology, 20 (8) DOI: 10.1016/j.cub.2010.03.013

Van Overwalle F, & Baetens K (2009). Understanding others’ actions and goals by mirror and mentalizing systems: a meta-analysis. NeuroImage, 48 (3), 564-84 PMID: 19524046

ResearchBlogging.org

worthy links

March 21, 2011 Leave a comment

Nice piece from the Boston Globe on the positive benefits of solitude. (My friend Adam Waytz, from the Harvard psychology department, gets a mention.)

Congenitally blind people use visual cortex to do language processing

An essay by June Carbone regards the role of neuroscience in determining punishment for adolescents who commit crimes such as murder. Focused on a recent US Supreme Court decision on the juvenile death penalty, the piece points out some of the limitations of applying neuroscientific findings to issues of jurisprudence.

A new study from researchers at Northeastern University says day traders make more money when they stay with the herd.

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