Literature summary with the prescribed articles for The Adolescent Brain (LU) 21/22

Introduction

Adolescence can be seen as a strong contrasting period. In this period people start looking for sensation, but they are also extra sensitive to the development of depression. The question is how brain development is related to this.

Adolescence

Adolescence is the transition from childhood to adulthood. Adolescence starts around the start of puberty and ends when the individual is relatively independent of the parents. This means that the individual can live alone.

Looking at the past, adolescence is seen as a turbulent period. In this period there is a much larger amount of preventable deaths and there is a relatively large amount of psychopathology.

Understanding adolescence

There are two main approaches to understanding adolescents' behaviour and brain development:

• Translation. Behaviour that belongs to adolescence, such as increased time spent with friends, is investigated here. The age-related behaviour entails risks, but also provides important adjustment functions. The changed behavioural pattern associated with adolescence is not specific to adolescents but occurs in many mammals. Because adolescence is an important phase from an evolutionary point of view, animal studies can also contribute to our understanding.

• Transition. As mentioned, adolescence is a transition period between childhood and adulthood. Many different stages of development have been investigated by themselves, with professionals focusing on the young child, for example. However, it is difficult to understand whether a certain process belongs to adolescence when other developmental stages are not considered. The interaction between the various development stages is interesting here.

Various developmental steps are indicated in the article:

• Development non-specific for adolescence

• Development occurring in adolescence

• Development typical of adolescence

Note that the corresponding graphs can also be displayed the other way around: for example, development typically shows a peak during adolescence, but there may also be a trough. Non-specific development can increase linearly, as shown in the figure, but can also decrease linearly.

Neurobiological models

Some neurobiological models are shown in the article:

• Dual system model. The two systems model of willpower forms the basis for this model. According to this model, self-monitoring is the result of the balance between a cold and a warm system. The cool system is emotion-neutral, strategic and flexible, while the warm system is driven by fear, wishes and reflexes. Individual development and stress affect the balance between the two systems.

This model can contribute to our understanding of direct and deferred remuneration. In addition, the dual system model can be used to explain the difference between adolescents and adults in looking for sensation and making risky decisions.

• Triadic model of motivated behaviour. This model explains the differences between adolescents and adults. The limbic (emotional) system is subdivided into two parts:

  • reward (ventral striatum)

  • avoidance (amygdala)

According to this model, motivated behaviour arises from a balance of reward-driven and damage-avoiding behaviour. In adolescence, there is a stronger tendency towards reward-driven behaviour and there is, therefore, an imbalance.

• Imbalance model. Regional neurochemical, structural and functional changes in the brain cause an imbalance in brain circuits. Different brain regions mature earlier and faster than other brain regions. For example, the sensorimotor and subcortical areas are matured faster than the prefrontal cortex. In addition, the sensorimotor cortex reaches its peak in cortical volume in late childhood, while the association cortexes only reach their peak volume in adolescence. The dopamine system, involved in reward, also has a density peak of receptors in early adolescence in the striatum, while this peak in the prefrontal cortex is only reached in adulthood.

The above striking differences are not found in childhood since relatively many brain areas have not yet matured. In adulthood, however, a relatively large number of brain regions have matured, through which even such differences are no longer present.

Self-control

Definition

Self-control is being able to suppress inappropriate emotions, wishes and actions and to display appropriate behaviour instead. A classic example of this is resisting a direct reward to receive a larger reward later (delay of gratification or the Marshmallow task).

The underlying mechanism

The article identifies three areas of the brain that are important for the cognitive and motivational circuit that supports self-control:

• Amygdala. Important for associative learning and determining the value of emotional cues from the environment. This structure can additionally activate the striatum or inhibit it.

• Prefrontal cortex. Here lies the capacity for reasoning and behavioural regulation. This structure modulates the other two structures to suppress actions guided by emotion.

• Ventral striatum. This structure has to do with learning and predicting rewards.

These three structures are modulated by dopamine and the hippocampus.

The brain of the adolescent differs in many ways from that of children and adults. In adolescents, for example, there is reduced top-down regulation of increased emotional responses. In addition, motivated actions are supported by the reduced top-down modulation of the prefrontal cortex.

Incentive for self-monitoring

Self-control seems to increase linearly from childhood to adulthood. Nevertheless, something can be said about this: in the case of incitement, self-control changes. For example, a reward for performance may result in us doing better next time. There are person-specific differences in this area. Individuals can to a greater or lesser extent shift their focus from direct information to a focus on later pay.

Attractive cues

One of the first investigations into reward processing looked at differences in the level of financial remuneration. Both the ventral striatum and the orbitofrontal cortex were found to be sensitive to indications that predicted the greatest reward. This sensitivity was much higher in adolescents than in children and adults.

Adolescents appear to take more risk when they receive direct feedback than adults. Adolescents have a peak in reward sensitivity around the age of fifteen.

Adolescents are less good at suppressing a response to an unexpected positive cue compared to children and adults. This can be seen neurologically in increased activity in the ventral striatum in adolescents.

Incentives based on performance

In research, participants were told that for some achievements, if they were performed properly, they received a financial reward. The expectation of a reward led to a greater improvement in performance in adolescents than in adults. This is represented in the brain as increased activation in the ventral striatum in adolescents. Unfortunately, baseline measures were not taken into account in these studies. Perhaps adults were already performing so well that it was virtually impossible for them to improve their performance in anticipation of a reward. In addition, the reward for adolescents may be subjectively higher for adolescents than for adults.

Research with a point system instead of a financial reward had to circumvent this last problem. This research showed that adolescents with relatively high rewards are good at not responding impulsively.

Social environment

The social environment influences behaviour throughout life. It seems that the influence of the social environment during adolescence is by far the greatest. For example, a large increase in risky behaviour can be observed in adolescents, and not in adults, when peers are present. This can be indirectly associated with the dopamine-rich part of the ventral striatum. It seems that the presence of peers has a behavioural effect. A study into the extent to which peers ensure behavioural reinforcement has been investigated. Every form of positive reinforcement led to faster response time. In adolescents, there was an increased activation pattern in the premotor circuit with positive social feedback, regardless of the outcome. This was not observed in adults and children.

Self-Control in the event of a threat

Adolescents are not fearless: they overestimate the chance of a negative outcome after risky behaviour. Yet they cannot take this into account sufficiently at the moment, probably due to a combination of peers, the environment and their own emotions.

Indications of danger

The best indication of danger comes from the frightened facial expression of another person. Research shows that we learn fears at an early age. How we can express and suppress this depends on development. The first neuroimaging studies on anxiety focused on the amygdala, the structure that external cues must interpret on emotional value. When a fearful cue is found, the individual is prepared to fight or flee (or even freeze). The lateral nucleus projects onto the central nucleus. This then projects onto the brainstem, the hypothalamus, and the autonomic nervous system. This causes the expression of fear. When the danger cue is no longer present, the fear response is suppressed.

Research into the amygdala among adolescents and adults indicates that adolescents have a greater activation pattern with possible danger expressions. Follow-up research indicates that adolescents also have increased activation compared to children. Adolescents also respond more slowly to anxious cues than to neutral or cheerful cues.

Recent studies have shown that adolescents find it difficult to suppress responses to emotional stimuli. Adolescents, especially men, respond more impulsively to threatening cues than neutral cues compared to children and adults. This can also be seen from increased activity in the limbic cortical areas and the ventral striatum.

The increased level of impulsivity in adolescents could be the result of a not yet sufficiently mature prefrontal top-down regulation of the amygdala.

Conditioned fear

The traditional conditioning process involves the combination of a neutral cue with a negative stimulus. After repeated combining, the neutral cue receives a negative charge. Research into conditioned anxiety among children, adolescents and adults showed that adolescents appeared to be less able to extinguish conditioned fear than children and adults. Similar results have been found in research with mice.

Adolescence can be seen as a period in which anxiety associations are less well regulated, which can lead to reduced self-control when the anxiety is present.

Contextual fear

Contextual anxiety depends on being able to learn from threats in the environment. Research into this has been done among mice. Mice in the adolescence period seemed to suffer little from context-dependent anxiety, compared to younger and adult mice. What is special is that when the adolescent mice grow up, they do show the context-dependent fear (which they had already been taught in adolescence) as adult mice. An explanation may be that the amygdala activity has been dulled by changes in the hippocampus during context-dependent conditioning.

Conclusion

The ability to suppress inappropriate emotions, wishes and actions and replace them with appropriate behaviour diminished when striking environmental cues are present. Both in behaviour and in the brain this leads to an increased reactivity in adolescents compared to children and adults.

Why are the brains programmed that way?

It seems logical that brain mechanisms have evolved around socially relevant cues since social status in evolution is very important for survival.

The adolescence period is by no means unique to humans but is observed in many different mammals. During adolescence, it is important to acquire skills to survive independently of others in adulthood. The search for new things means that new sources can be explored and new (types of) relationships can be entered into. Everything leads to the ultimate goal of adolescence: becoming an adult.

The changes in adolescents' development are helped because adolescents are extra sensitive to socially important cues and are less troubled by potential dangers. Together this causes exploration in the world, beyond the safe environment of a home.

Mental health

One in five adolescents has a mental illness, for example, substance abuse. Early substance abuse is a good predictor of later addiction. In addition, narcotics affect the dopamine system. This reinforces the rewarding effect of the dopamine system, which is very active in adolescence.

In addition to substance abuse, there are also many adolescents (around 10 per cent) with an anxiety disorder. This is because being unable to suppress an emotional response when no danger threatens becomes pathological. Adolescents with an anxiety disorder have increased activity in the amygdala and there is a decreased connectivity in the front-amygdala circuit. Desensitization is the most common treatment for an anxiety disorder. This technique works with fear cancellation. Unfortunately, this form of treatment only works in half of the patients. The studies discussed earlier in this article seem to indicate that anxiety killing for adolescents is not the right way to reduce anxiety disorder.

Laws and policies

In recent years, an enormous number of laws and policy documents have been amended in juvenile criminal law. The studies aimed at the development of adolescents and the increased tendency towards risky behaviour and impulsiveness were used. This research indicates that young people should be held responsible for their behaviour, but that there should be a shared responsibility.

Further neuroimaging research into the brain-behavioural relationship in adolescents can make a major contribution to the legal system:

• Immature brain structures during adolescence affect judgment, decision-making, risk-taking, and criminal behaviour.

• Research can contribute to the treatment and rehabilitation of criminal youth.

What is this article about?

Humans are highly social compared to other species. Even 1-year-old toddlers help others. This social behaviour keeps developing and becomes increasingly complex with age. For example, preschoolers mainly use lies for their own benefits, while school-aged children start to lie to protect other’s feelings (white lies).

Developmental changes in social behaviour are related to developmental changes in cognitive functions, such as perspective taking and impulse control. In the current chapter, evidence for the hypothesis that the gradual development of impulse control and perspective-taking skills are related with more strategic thinking and increased incorporation of other’s intentions in social decision-making.

Why use economic games?

Economic games were introduced by behavioural economics. These are used to study psychological and neural mechanisms that underlie social decision-making. Examples are the Ultimatum Game and the Dictator Game. In these games, there are two players. In the Ultimatum Game, one player is given a reward (money, food or anything else). This person is the ‘proposer’, and proposes to split his reward with another player, the ‘responder’. If the responder accepts the proposal, both players receive what was proposed. If the responder rejects, then none of the players receives any reward. In the Dictator Game, the responder is not able to reject the proposal, and thus only receives the amount that the proposer transfers. An assumption of Game theoretical models is that all players should accept all proposals that are above 0, because any free reward would be beneficial. However, this is not true. These games have shown that people do not only care about maximizing their outcomes, they also seem to care about others. For example, in the Dictator Game, the proposer often transfers on average 20-30% of their stakes to the responder, even though they do not have to. In the Ultimatum Game, proposers often choose for an equal split of their stake and responders usually reject offers that are smaller than 20% of the stake. It has also been shown that people do not like to receive less than the proposer, which is called disadvantageous inequity aversion. When people then reject the offer, this might serve as a way to ‘correct’ the inequity.

These paradigms are useful, because they can be used to determine differences between age groups. They also make it easier to quantify complex social behavior which is useful for neuroimaging research. Furthermore, these games can be manipulated so that researchers can gain more understanding of the subcomponents of social decision-making.

How does a preference for fairness develop?

Toddlers are reluctant to engage in prosocial behavior when it is costly to themselves. Studies employing the Dictator Game have shown that although children tend to keep most of the resources to themselves, the size of their donations increase between the ages of 3 and 8. By the age of 9, their donations do not differ from donations made by adults. Thus, there is an increase in costly sharing. This is not due to differences in knowledge about what is fair; infants as young as 15 months already expect resources to be distributed equally. Even though 3-year-olds do not differ from 8-year-olds in beliefs about equal division of rewards, they still keep more than 50% of the rewards to themselves in a Dictator Game. During this age, the willingness to send rewards increases. This means that a developing sense of fairness leads to that children engage in more equal splits.

In addition to fairness considerations, there are also strategic concerns. For instance, adults offer higher shares of the stake when they know that the second player can punish (reject) unfair offers. One can thus look at differences in the Ultimatum Game and Dictator Game as a measure of strategic social behavior. During late childhood (7-10 years), children make higher Ultimatum Game proposals compared to Dictator Game proposals, but their proposals are still smaller than those proposed by adults. During adolescence, the difference between the two games becomes greater, which means that there is a developmental increase in strategic behavior across adolescence. In sum, the prosocial tendency to share with others emerges early, but social behavior becomes increasingly strategic across childhood and adolescence.

What are the cognitive mechanisms underlying developmental change in strategic social behavior?

It has been shown that strategic social behavior is dependent on the capacity to implement behavioral control; a selfish impulse (keeping all resources to oneself) has to be controlled. Proposers also need to be able to take the responder’s perspective into account, to predict what proposals they might accept or reject. This perspective-taking is often called ‘theory of mind’, which young children often are not capable of. In sum, behavioral studies that have employed these game paradigms show that cognitive development which are both related to impulse control and perspective taking play a crucial role in understanding age-related changes in social behavior.

What neural networks are involved in social decision-making?

There are three brain networks which are important in social decision-making: a basic affective network, a cognitive regulatory network, and a ‘mentalizing’ network. The basic affective network consists of the anterior insula, ventral striatum, and the amygdala. These determine whether social stimuli should be approached (positive affect) or avoided (negative affect). Increasing activity in the insula has been related to unreciprocated trust and receiving unfair offers. These brain regions interact with a cognitive regulatory network which consists of the dorsal anterior cingulate cortex (dACC), and regions in the prefrontal cortex (PFC), such as the ventrolateral prefrontal cortex (vIPFC) and the dorsolateral prefrontal cortex (dlPFC). These areas are related to cognitive control over impulses and can help individuals to act in a goal-directed manner when they experience a conflict between self-interest and social norms. These control-related areas are thus crucial for the regulation of strategic social behavior. Both the affective and control regions interact with a third system, the ‘mentalizing’ network. This includes the left and right temporoparietal junction (TPJ), superior temporal sulci, ventral, and dorsal regions of the medial PFC and the temporal poles. These regions are often active in tasks that involve thinking about other people’s mental states, when people have to infer other people’s thoughts, beliefs or desires. Lastly, taking other people’s perspectives in economic games has been associated with activation in regions of the mentalizing network, such as the TPJ and the dorsomedial PFC.

How can we understand social behavior from a developmental neuroscience perspective?

It has been shown that different brain regions mature at different ages. Sensorimotor regions in the occipital and parietal lobes mature first. The dlPFC and the TPJ are the latest brain regions to fully mature, which might explain the protracted developmental pattern in associated functions such as cognitive control and perspective taking. Affective networks mature early, while regions of the cognitive regulatory network mature late. This can explain why affective reactions to unfairness are visible early in development and why strategic considerations during social decision-making develops later. This is in line with studies that have shown that detection of violations of fairness and neural responses in the insula and the dACC mature before adolescence, and that intentionality understanding in fairness decisions develops across adolescence and is related with neural activity that are important for perspective taking (TPJ) and impulse control (dlPFC). It has been suggested that early adolescents tend to make more self-oriented choices, while late adolescents tend to make more other-oriented choices. This is due to differences in the development of the dmPFC and the TPJ.

Introduction

Adolescence is seen as the transition period between childhood and adulthood. During this period there are major changes in cognitive and social-affective reasoning. Adolescents are very sensitive to social events and the influence of peers. The cognitive changes in adolescence seem to originate from the changes in the social cognitive network in the brain during the same period. The medial prefrontal cortex, the temporoparietal junction and the superior temporal sulcus are examples of brain regions that undergo a major change in both functioning and structure during adolescence.

Several studies have shown that adolescents show more risky behaviour when they are in the presence of peers. This seems to be related to brain areas involved in the affective processing of risks and rewards. These areas are more active when peers are present, especially among adolescents.

Few studies have been conducted into the influence of peers in a more positive way. Recent studies seem to indicate that adolescents are becoming more pro-social when peers are around. Current research also builds on this notion and also wants to look at the areas of the brain involved in prosocial behaviour in adolescence. Adult research indicates that the medial prefrontal cortex and the ventral striatum play a role in peer evaluation. The medial prefrontal cortex also seems more active when a social situation is only imagined.

Current study

Research was done in two age groups (12-13 and 15-16 years). They were compared with each other regarding the effects of peer influence. Previous research suggests that young adolescents are more sensitive to this.

The Public Goods Game was used during this investigation. In this social dilemma game, the participant must distribute tokens among themselves and the group. Three conditions were used to assess peers:

• Spectators (actors of similar age) who promoted prosocial behaviour (many tokens for the group: thumbs up).

• Spectators (actors of similar age) who did nothing.

• No spectators.

The spectators and the participants met before the game started. The researchers' prediction was that there should be more activity in the medial prefrontal cortex when spectators were present in young adolescents than in middle adolescents.

Methods

Attendees

The 12-13-year-olds age group included 31 participants, 15 of whom were men. The age group 15-16-year-olds contained 30 participants, including 14 men. More than 90% of the participants were born in the Netherlands. All participants spoke and wrote Dutch fluently. An IQ measurement was done with the WISC-III and the WAIS-III. All IQ scores were within the normal range and there were no significant differences in the age groups.

Experimental research

The participants were told that they were playing the public goods game online with four anonymous other players of the same age. These were also participants in the study. The anonymity would remain guaranteed. Each round the participants received five tokens, with values ​​of 1, 1.50 or 2 euros per token. All tokens given to the group were doubled and then distributed among the group members. This means that the participant has the most money left if he keeps all tokens to himself, while the group benefits the most when each participant donates all tokens to the group. Donating to the group can be seen as prosocial behaviour. The participants could not see what the others were doing and did not get to see the results per round.

72 trials were used, divided into two rounds and three conditions:

For each block of 12 trials, the participant was shown for two seconds in which condition they were. When participants received feedback at the end of the trial, they saw 0 thumbs up at 0 and 1 donated tokens, 2 thumbs up at 2 donated tokens, 4 thumbs up at 3 donated tokens and 5 thumbs up at 4 and 5 tokens. When participants had spectators but received no feedback, the participants were told that the spectators did evaluate, but their feedback would not be visible.

The spectators consisted of 44 actors, who changed per trial session. The goal was to introduce all participants to six out of ten actors, which succeeded in 75% of the cases. During the game itself, the participants saw a photo of the actors with neutral facial expression.

Procedure

This research was part of a larger research into relationships with peers. The participants arrived at this study with their best friend, who participated in another part of the study. The participant heard that it was necessary to wait for more participants, after which the (usually 6) actors came in a little later. The participant was introduced to them. The participant would be examined in the game in an MRI scanner. He or she then heard that not all other 'participants' were there yet, but that the MRI procedures would already be explained. The participant already played five practice sessions there. It was also explicitly stated that the participant would not play against the best friend. The scanning session lasted approximately one hour. Afterwards, the participants completed questionnaires and did two sub-tests of the WISC or the WAIS. This was followed by a debriefing and a compensation of 32 euros per participant.

Analytical methods

Two ANOVAs were performed to investigate responses to the stimulus and feedback onset. A 3x3 ANOVA (3 conditions and 3 token values) was used for the stimulus onset and a 3x3 ANOVA for the feedback onset. In addition, a regression analysis was performed on data across the entire brain.

Regions of Interest (ROI) analyses were also carried out to see the differences between the two age groups.

Results

Behavioural analysis

Participants donated more tokens to the group when there were spectators. Most tokens were donated to conditional viewers with feedback. The tokens that were most frequently donated were the tokens with a value of one euro. The tokens with a value of two euros were the least donated. In every condition, more was donated to the group by young adolescents than by middle adolescents.

fMRI analyses at the stimulus onset

The stimulus onset is the moment that the participant decides how he or she will distribute the tokens. The condition had a major effect on various areas of the brain, including the dorsomedial prefrontal cortex, the precuneus superior temporal sulcus and the temporoparietal junction. The value of the token had no effect on the brain and there was also no interaction effect.

When spectators were present, there was more activation in the precuneus, the bilateral temporoparietal junction and the bilateral superior temporal sulcus, compared to the condition without spectators.

When spectators who gave feedback were present, there was also increased activation in the dorsomedial prefrontal cortex, compared to the condition without spectators.

There was no significant interaction effect when looking at the age group. To get a better picture of this, we looked at the ROI analyses. This showed that the young adolescents showed a greater activation difference in the dorsomedial prefrontal cortex and the left superior temporal sulcus in the condition viewer with feedback and no viewer than the middle adolescents.

Regression looked at activation in the brain and height of the donated tokens. In the conditions with feedback-giving spectators versus no spectators, more activation appeared to be in the left temporoparietal junction with a higher donation.

FMRI analyses with the feedback onset

This concerns the analyses of brain activation when the feedback screen is displayed. The repeated ANOVA measurements on the condition and token value show the main effect of condition in many different brain regions, such as the bilateral insula and the right amygdala. There was no main effect for token value and no interaction effect. When the spectator conditions were compared with the no spectator condition, there was additional brain activation in more areas in the important areas that have to do with face recognition at the spectator conditions compared to the no spectator condition. In the condition viewer with feedback, compared to the condition viewer without feedback, increased activity was visible in different brain areas,such as the bilateral insula and the right superior parietal cortex.

There appeared to be no interaction effect between age group and condition. When ROI analyses looked at this extra closely, there appeared to be an interaction effect: the young adolescents had larger activation differences between the viewer with feedback and the viewer without feedback condition than the middle adolescents.

Discussion

The social behaviour of adolescents is characterized by a high sensitivity to the opinion of peers. Current research has shown that peer feedback has a positive effect on the degree of prosocial behaviour during early and middle adolescence. If the feedback from peers is also prosocial in nature (thumbs up), the effects are greatest. Similar results have been found in adults in other studies.

To make a choice to donate in the presence of peers, the dorsomedial prefrontal cortex (involved in social influence), the temporoparietal junction and the superior temporal sulcus become active. The current research showed that prosocial feedback had little influence on the level of activity in these brain regions, only the idea of ​​peer evaluation is sufficient for the increased activity.

Other research into the influence of peers also shows extra activation in the ventral striatum. An explanation for this is that the presence of peers makes behaviour more rewarding. This activation has not been demonstrated in the current study.

Current research has found that prosocial behaviour (donating a higher amount) entails greater brain activity in the temporoparietal junction when the participant was assessed with feedback. This brain structure appears to be involved in altruism.

From a behavioural perspective, younger participants gave a higher amount to the group than older participants. This applied to all conditions, including the condition without peers. This is in line with other studies that indicate that younger adolescents are more prosocial towards strangers. Young adolescents also seem to be the most sensitive to the opinions of peers. This was also found in the brain in the current study: there was greater brain activation among young adolescents when spectators were present.

Learning and cognitive control through the presence of peers with feedback could play a role in regulating behaviour and adaptation to others. More research is needed here.

In the current study, no distinction was made between the amount of the donation and the feedback that was given (the feedback was purely geared to the number of tokens, not to the value thereof). This has consequences for the interpretation of the results found.

What is this article about?

Humans need to be able to decipher and learn from social signals. Especially during adolescence, the impact of social signals is magnified. When pre-adolescents enter adolescence, they tend to spend more time with their peers, and less time with their parents. They also rely more on their peers than on their parents for guidance and approval. They also show increased attention and higher neural activation in response to peer acceptance. When adolescents feel related to others and when they feel accepted, this is associated with higher self-esteem, and better adjustment in school. In contrast, when adolescents feel rejected, this can lead to school withdrawal, aggression and mental health problems.

Peers impact an adolescent’s behaviour in different ways. For instance, when there are peers in the car, accidents increase. This is only true for adolescents. When there are peers around during a decision-making task, this also leads to higher risky decision-making in adolescents compared to children and adults. Furthermore, when adolescents feel rejected by their peers, they are more likely to engage in risky behaviors, to fit in with the group. One explanation / theory is that feedback from peers serves as a reinforcer, a reward. Research has shown that the neural circuitry that evaluates social (praise, gain, positive affect) rewards overlaps with a neural circuitry that evaluates nonsocial (money, food) rewards. In adults, it has been shown that the ventral striatum (the reward area in the brain) supports learning from social feedback from peers.

In the current article, the goal is to evaluate differences across different age groups in social reinforcement learning from peers, and 8 to 25 year olds will participate. The goal is to determine whether adolescents, compared to children and adults, learn to associate different peers with distinct probabilities of receiving positive feedback. This will be measured with fMRI.

Which methods were used?

Experiment cover story

The experiment consisted of two sessions. In the first session, the participants were lead to believe that they would receive social feedback from peers during a task that they would complete on the second visit. They were shown 5 photographs of gender-, age-, and ethnicity-matched peers. They selected three peers that they would like to interact with. They rated the peers on a scale from 0 to 10 for likeability and attractiveness. Furthermore, participants completed a personal survey where they reported information about themselves. Then, the participants were told that each of the three selected peers would see their survey and the surveys of two others. These peers would write notes on their surveys. Each note that the peers would write would indicate something positive, because the peers had a limited number of notes to write. Notes from peers thus became ‘rewards’.

In the next session, the participants were told that the notes were collected and they would be shown how often the peers decided to write notes to them. However, the delivery of notes was manipulated. Each of the three peers was associated with a distinct probability of social reinforcement, namely ‘rare interaction’ which was defined by 33% of notes and no positive social reinforcement on 66% of the trials. No positive social reinforcement means that the peers decided to write notes for others, but not for the participants. Further, there could be ‘frequent interaction’, which refers to that there was positive social reinforcement on 66% of the trials and no positive social reinforcement on 33% of the trials, and lastly ‘continuous interaction’, which was defined as 100% positive social reinforcement.

What can be concluded?

This experiment showed that there were adolescent-specific age differences in reinforcement learning behavior and neural response patterns. In children and adults, different amounts of positive social reinforcement enhanced learning. In adolescents, all positive social reinforcement lead to lower positive learning rates and higher activity in their response planning circuitry. This means that adolescents do show a unique sensitivity to peers, but adolescent behavior in social contexts is not only explained by simple reinforcement learning theory.

Positive learning rates showed a quadratic patterns, which can mean two things:

1. Adolescents do not learn to discriminate between cues that are associated with different amounts of positive social feedback, or;
2. Adolescents’ behavior is not captured by simple reinforcement learning predictions.

Adolescents demonstrate lower positive learning rates, but this does not mean that they do not learn. At the end of the experiment, adolescents as well as children and adults all rate peers who gave them positive feedback as more likeable. There was also no difference in observed negative learning rates across age groups. The adolescent positive learning profile could be explained by an overall vigilance to the receipt of peer approval. This means that a close friend, but also an anonymous or unknown peer can enhance adolescents’ risk-taking behavior. Lower positive learning rates could also be explained by increased motivation toward which is the socially least reinforcing, which is in line with work that suggests that adolescents engage in risky behaviors when they perceive themselves to be less socially accepted. It was also shown that the anterior to mid insula response is correlated with positive prediction error fluctuations and that this is higher than in children and adults. The insula is responsible for processing subjective feelings and awareness about one’s body, feelings of distress, and overall processing of affective states that are the result of interacting with other people. This non-linear finding in the insula supports the hypothesis that peer approval is emotionally salient to adolescents. Furthermore, the data showed that adolescents, more than children and adults, activated regions with response planning circuitry when they received positive social approval, regardless of who the peer was. The data also showed that the ventral striatum and medial prefrontal cortex were equally engaged across age during social reinforcement learning. This means that fundamental reinforcement learning mechanisms support social reinforcement learning from late childhood to adulthood. It seems that the heightened activity in the insular cortex and regions within motor circuitry of adolescents indicates an affective-motivational sensitivity toward any peer approval. There also seem to be differences between different adolescent’s ages; the naturally occurring peak falls in late adolescence. In sum, there is thus an adolescent-specific effect of positive social feedback from peers on learning and neural activation patterns.

Introduction

There is a dramatic increase in publications about the adolescent brain, but there has been less attention given to the role of puberty in the process of brain development in adolescents. Puberty is about the neuroendocrinological development of the adrenal glands, gonads and the growth that leads to reproductive competence. There are also a lot of physical, psychological and social changes. Earlier there was the hypothesis that pubertal development lead to the changes in the brain that occur during adolescence. There was a lack of empirical data to support this hypothesis. Since then there have been studies conducted. This article evaluates the evidence that is found about changes in brain structure, function and connectivity that coincide with pubertal development.

Measuring pubertal development

Puberty comes with adrenarche (activation of the hypothalamic-pituitary-adrenal axis) and gonadarche (reactivation of hypothalamic-pituitary-gonadal axis which leads to gonadal activation) which leads to an increase in sex hormones. Age is often used as a determination for pubertal development in animals, but humans show variability in onset (range of 5 years) and tempo of puberty. Therefore it is important to have specific measures. In humans there are two ways of measuring the pubertal development: assessing levels of sex steroid hormones or by assessing objective physical development (like body hair, gonadal development, breast development, menarche).

With the help of phenotypic pubertal assessment scales, the length of exposure to hormones, the levels of exposure and the sensitivity to hormones can be determined. Examples of questionnaires are the Pubertal Development Scale (PDS) and the Tanner Scale. Participants (or parents, teachers, clinicians) answer questions about (their own) pubertal development. When using these questionnaires, the influence of the physical changes during puberty on how adolescents or others perceive themselves, can also be measured. There are also limitations to this kind of assessments, because when there are multiple assessors, there is variability in the answers. Also, the perception people have about their pubertal development is highly influenced by their (social) culture.     

There can also be hormonal measures using serum, saliva or urine to measure the extent of pubertal development. These are useful, because of their objectivity it can help to compare between individuals. There are again, limitations, such as that hormonal concentrations vary from day to day (diurnal variation) and that there are cyclical patterns. They are also influenced by environmental and internal stressors. There is also a lack of understanding of these variations.

Animal evidence for role of puberty and pubertal hormones in the development of the brain

It seems as if puberty is a second sensitive period for sex steroid hormone effects on the brain (the first period being the perinatal period). It could also be that the first sensitive period is until the end of puberty. Research shows that males who are deprived of testosterone during puberty exhibit less developed social and sexual behaviors. Estradiol during adolescence leads to female reproductive behavior in mice and it seems essential for female play behavior in adulthood. These results provide evidence for sex steroid-dependent organizational development and shows that puberty and pubertal hormones play a role in this.

Sex steroid hormone exposure leads to new cell formation and proliferation. Also it seems that these hormones have an influence on cell death: for example, female rats experience greater cell death than male rats during early puberty. So it seems that ovarian hormones may promote cell death in the medial prefrontal cortex (mPFC). A third way in which hormones during puberty influence brain structure and organization is that they influence the complexity and organization of neural dendrites in the brain.

Sex hormones also influence functional brain development. For instance, there have been androgen receptors (AR) and estrogen receptors (ER) found in the brain. These ARs and ERs are found in multiple regions in the brain where they vary in their concentration.

Human studies on the role of puberty and pubertal hormones in the development of the brain

There have been no sex differences found in timing of gray matter development during puberty between men and women, even though this was thought to be true for a long time. Improvements in research methods (specific measures of puberty and longitudinal designs from different cohorts) lead to more reliable findings.

Cortical development

The only published longitudinal study about cortical development indices that pubertal development was related to decreases in gray matter volume. Cortical gray matter contains cortical thickness and surface area.

Subcortical development

It seems that the relationship between puberty and structural brain development is complex and non-linear. It also interacts with the effects of age.

Sex differences in brain structure

The biggest sex difference regarding the brain is in overall volume. This specifically means that boys and men have on average larger brains than girls and women. This could be partially explained by differences in body size. This finding can have implications for research! Also, it seems that boys show greater variability in brain volumes than girls do.

Puberty and Functional Changes in Humans

It seems that during mid-adolescence there is heightened neural activity in the subcortical brain regions that are associated with processing emotions such as rewards, happiness and fear. Some researchers have suggested that this increase is because of heightened sex hormones. These sex hormones may increase the sensitivity in the brain regions. Researchers have also suggested that pubertal development advance social-cognitive processes which rely on social brain network areas. There is some evidence for these hypotheses, but the results are mixed.

Reward processing

When participants won during a gambling task, they showed heightened activity in their ventral striatum and ventral medial prefrontal cortex (VMPFC). These regions are often implicated to be the core of the reward network in the brain. Also, higher levels of testosterone (for both boys and girls) lead to more activity in the ventral striatum. For girls, higher levels of estradiol was correlated with stronger activity in the VMPFC. The latter finding could not be replicated, though the effect of testosterone has been established well over multiple studies. Testosterone also seems to lead to more risk-taking behavior. Estradiol does not seem to significantly relate to risk taking. It even seems as if estradiol leads to less risk-taking!

Processing faces that express emotion

It seems that the amygdala is active when processing emotion on faces, especially when these faces express fear. This neural response in the amygdala when viewing fearful faces peaks during mid-adolescence. But, there has also been found more amygdala activity when viewing happy faces and the results depend on sex. Results have shown that the higher the testosterone levels, the larger the increases in activity in the amygdala and in the ventral striatum when observing fearful faces. This was found for boys as well as for girls. Also, the individuals who showed an increase in ventral striatum activity also showed an increase in activity in the amygdala. This suggests that feelings of fear and of reward are both involved during processing facial expressions.

One study examined how males and females solved incongruent information. This would tell the researchers something about how emotional faces have an impact on thoughts and actions. The study was like this: participants were instructed to approach happy and avoid angry faces, this was the congruent condition. The incongruent condition was to avoid the happy faces and approach the angry faces. It seems that adolescents who had higher testosterone levels, regardless of their sex, showed more activity in the anterior prefrontal cortex for the incongruent condition. Adolescents who had lower levels of testosterone showed stronger activity in the amygdala for incongruent trials. These results show that during pubertal maturation activity from the limbic system (amygdala) shift to more prefrontal control activity (anterior prefrontal cortex).

Processing social cognitive emotions

It seems that pubertal development leads to neural-cognitive development in regions in the brain that are important for the processing of social-cognitive emotions. It is important to note that the literature on neural activity and pubertal changes is mixed: some studies do show that there are neural changes during puberty and others show that this is the other way around. There have not been many studies conducted that link neural development with behavior or that statistically compare sexes. This leads to less directions for future research. But, some directions for further research are to control for menstrual cycle, to include reports from the individual self as from others and to include hormone levels. Also, it is recommended to include boys as well as girls in the studies.

Sex-differences in puberty related neural activity

Reward processing develops in adolescence and there have been sex differences reported. For example, men showed more activation in brain regions for rewards when they could get monetary rewards and women showed greater activation in social-related rewards. During adulthood there were not always sex differences found. This could be because of the nature of the reward or sample sizes. It seems that emotional processing develops in adolescence. There also seems to be evidence for sex differences in adulthood: there seems to be greater activity in the amygdala and gray matter for women than for men and there seems to be greater activity in the insula and prefrontal cortex for men than for women.

In spatial tasks, there is a significant parietal activation for men and women, but women show more frontal lobe activation than men. This could be a compensation for their, on average poorer performance compared to men on spatial tasks. Also, when processing language, men and women use regions in their left hemisphere, but women show some activation in the right hemisphere too.

The role of puberty in brain connectivity

Connectivity in the brain is very important, because deficits lead to neuropsychiatric illnesses (depression, schizophrenia) which often start during puberty. Since sex hormones influence the connectivity, it is important to investigate the relation between the increase of sex hormones during puberty and brain connectivity.

Structural connectivity

The communication between brain regions occurs through axonal pathways that are the base of the structural white matter in the brain. The process of myelination of these pathways continue up to adolescence. The studies that investigate the white matter connections and the pubertal hormones are limited. There is, however, evidence for that estradiol and testosterone are related to the microstructure of white matter. Also, it seems that pubertal development may be related to the maturation of the white matter connectivity.

Functional connectivity

Adults show a more focal pattern in functional connectivity and long-distance connections than children and adolescents do. Children and adolescents show a more diffuse pattern of their functional connections. They also seem to have short-range connectivity. It also seems that during adolescence there is a fine-tuning of the connections between the subcortical and cortical prefrontal and limbic circuits.

It seems that for adults, injecting testosterone leads to disruption in the functional connectivity in their subcortical-cortical functions.

There also seems to be more connectivity in the default mode network (DMN) in women than in men. The DMN involves the posterior cingulate cortex, the medial prefrontal cortex, the angular cortex hippocampus, and precunes. These are the areas that are involved in mentalizing and memory. Men seem to have more connectivity in their visual and dorsal attention networks than women do.

Summary

So, there have not been many studies conducted that study the relation between pubertal hormones and brain connectivity. Recommendations for future research are to focus on the whole brain so that the effects of hormones on the brain can be better understood. This is because all the connections in the brain contribute to a network. Even small changes in parts of this network can have a large effect on the network as a whole. Connectomics is the study of all the connections in the brain.