Latest / The Biological Psychiatry Podcast / Brain Development and Addictive Screen Use in Adolescents
Transcript
- Elvisha Dhamala: That's all for today's episode. Thank you to Dr. Cardin for joining us to share his work on brain development and addictive screen use in youth. For our listeners, you can find the full paper in Biological Psychiatry, Cognitive Neuroscience, and Neuroimaging. And we'll include the link in our episode description. To learn more about our journals, please visit biologicalpsychiatryjournal.com. You can find The Biological Psychiatry Podcast wherever you get your podcasts. now so you don't miss our next episode and follow us on our social media channels for highlights and updates. Thanks listening. ⁓ This is for informational purposes only and does not constitute medical or professional advice. ⁓ The expressed are those of the participants and do not necessarily reflect those of the Society of Biological Psychiatry, ⁓ So joining me is Dr. Omid Carden from the University of Michigan, senior author of a recent paper in biological psychiatry, cognitive neuroscience and neuroimaging. Welcome to the biological psychiatry podcast. Adolescents today spend more time on screens than ever before, scrolling social media, playing video games, and using their phones. While concerns about screen time are widespread, emerging research suggests that it's not just how much time young people spend on screens, but how they use them that matters for their mental health. Addictive patterns of screen use From the Biological Psychiatry family of journals, is the Biological Psychiatry podcast, ⁓ we discuss the latest research on psychiatric disorders and their treatments. ⁓ you for joining us. I'm your host and Social Media Editor, Elvisha Dhamala. ⁓ A lot of prior work in this area has focused on screen time rather than addictive screen use behaviors. Can you us what the difference is and why that matters? its family of journals, its editors ⁓ rights reserved, including those for text and data mining or use in AI systems. ⁓ Omid Kardan: Sure, screen time is basically how many hours one generally spends using technologies with a screen interface. ⁓ And while related, screen use refers to problematic patterns of screen use that include elements of addiction, such as withdrawal, conflict, relapse, and functional impairment. ⁓ example, you have changes in mood when you stop using the device. Elvisha Dhamala: characterized by mood changes and interference with daily life, have been linked to depression, anxiety, and even suicidal behaviors in youth. But what makes some adolescents more vulnerable to developing these problematic patterns? Today, we're exploring groundbreaking research that examines whether differences in brain development during childhood can predict addictive screen use in early adolescence. Omid Kardan: not being able to stop even if you want to, not being able to focus on your work or your schoolwork about the ⁓ or when use ⁓ kind of a frequent of conflict with your family. Elvisha Dhamala: Your paper mentions that addictive screen use has been linked to serious mental health risks, including suicidal behaviors. Can you tell us a little bit more about what we know about these associations? ⁓ Omid Kardan: distinction between the total screen time addictive screen use is actually ⁓ because some of emerging evidence that suggests that ⁓ addictive screen use ⁓ is more strongly with poor psychological adjustment. ⁓ For example, a work by researchers from ⁓ Columbia and Cornell found that between ⁓ 20 to 30 of American youth that are between to 13 years of age show upward trends in compulsive screen use, well as difficulty disengaging and distress when not using. ⁓ these youth ⁓ elevated risk for suicidal ideation and suicidal behaviors ⁓ about one and a half times ⁓ to the other kids who didn't have these ⁓ trends. Elvisha Dhamala: You also mentioned that there is evidence suggesting prolonged screen time may be an early sign of neurodevelopmental deficits rather than a causal factor. What does that mean and how did that shape your study design? Omid Kardan: Yeah, so this is ⁓ to some extent similar to the chicken or egg question. When you see an association between prolonged screen time and mental health or attention problems, ⁓ would want to consider multiple scenarios. ⁓ ⁓ simplicity, I'll mention three scenarios. ⁓ is the screen use ⁓ first ⁓ causes the increased mental health and attention issues. Two. is that mental health and attention issues are first and they are causing the problematic screen use patterns. ⁓ three is that some other underlying factor is actually causing both of them. So a of studies have tried to look into that ⁓ third where ⁓ polygenic factors and co-tween analysis ⁓ were to understand if genetic factors can explain the association between the child screen time and their mental health or attention problems. they report that a big proportion of the association is genetically confounded, ⁓ giving some evidence in support of that third scenario. Now, in our study, instead of genetic predisposing factors, we were interested in neural factors that may predispose addictive screen use. So we try to predict prospective addictive screen use at a later age looking at neurodevelopmental factors at an earlier age ⁓ a longitudinal manner. Elvisha Dhamala: And this study used data from the ABCD study, which is one of the largest brain imaging studies of adolescents you tell us a little bit more about this data set and why it's so valuable for this type of research? ⁓ Omid Kardan: Yes, the Adolescent Brain Cognitive Development, ABCD study is an ongoing longitudinal study that recruited over 11,000 children across 21 sites in the United States when they were 9 to 10 years of age and has been assessing them with behavioral, cognitive, and brain imaging measurements. It provides a very valuable resource for research because of the large and heterogeneous sample with repeated measurements over years. and it covers the period of development when the onset of most of neurodevelopmental disorders occur. for our study specifically, ⁓ multimodal data covering pre to early adolescence period was particularly important. Elvisha Dhamala: In your study, you examined two potential neurodevelopmental risk factors, cortical maturation and reward processing. So let's start with cortical maturation. What exactly does that mean and how does it relate to cognitive ability or performance? Omid Kardan: ⁓ Cortical maturation in our study was specifically about estimating how functionally mature the cortex looks like in an individual youth. previous work has shown that some of the improvements you see in a child when they are 12 years old compared to when they were 10 as perform cognitive tasks can be by the functional communications in the cortex ⁓ becoming more ⁓ from 10 to 12 within that child. Additionally, some of the individual differences between two kids of the same age in terms of their cognitive performance are also explained by the differences in the degree of the maturity in these cortical communication. Elvisha Dhamala: So is cognitive maturation entirely determined by cortical maturation? Omid Kardan: No, ⁓ ⁓ is definitely associated and ⁓ kind of interesting point that maybe ⁓ we to when we get to details of this Elvisha Dhamala: You used resting state functional MRI to measure something called the anchored functional connectomic maturation score or AFC. Can you tell what that measure specifically ⁓ Omid Kardan: so the AFC score ⁓ ⁓ estimation of the functional maturation of the cortex that I just talked about. ⁓ put it simply, every individual has a unique map of intrinsic coordinated activities across their brain regions, and is sometimes referred to as their connectome. While unique, connectomes on go through some systematic changes over development. from childhood to adulthood. with AFC, we estimate to what extent do these expected developmental changes have progressed in an individual youth's connectome, ⁓ specifically the part of their connectome that only involves cortex. Elvisha Dhamala: And how stable are these measures? So if you scan child today versus tomorrow, will it be relatively consistent or does it vary a lot depending on the state that the child is in? ⁓ Omid Kardan: So there's definitely some with the state and would want to have longer term like over years you definitely see changes and that's one of the things that want to see in a measure ⁓ that you for assessing development. ⁓ day to test-retest of the measure medium to And you have longer ⁓ of data, so more 15 minutes of resting state fMRI, then it becomes more reliable. Elvisha Dhamala: Great. And what does a lower AFC score tell us about a child's brain development? Omid Kardan: Yes, so a lower AFC score, for example, in a 10-year-old compared to their peers, suggests that the cortical functional communications have not matured as far ⁓ their connectome compared to their peers. So a normative growth chart perspective, they have had an overall delayed cortical maturation. Elvisha Dhamala: The second potential risk factor you looked at was reward processing. You used a monetary incentive delay task and focused on the nucleus accumbens. Why that region in particular and using this task? Omid Kardan: Yes, so altered activity as in blunt activity or hyperactivity in the reward system anticipating a reward has been generally implicated in multiple addictive behaviors, including gambling and substance use disorders. And accumbens is a key region in the reward system of the brain and ⁓ specifically regards to anticipation of reward. In terms the task. ⁓ You want to use a task that is simple enough that the participant can do it in the MRI scanner also generates a contrast between trials where a reward is involved and trials where no reward is involved. And monetary incentive delay task is frequently used ⁓ for purpose, ⁓ where each trial, the participant has to press a button within a short window of time after seeing a cue. And the queue also indicates to them whether some money is at stake for that trial or not. So the button presses and instructions are simple enough to be done while lying in the scanner. And you have brain activity recorded when reward is anticipated versus not, which gives that contrast in the reward system and in the nucleus accumbens in particular. Elvisha Dhamala: Can you tell us a little bit more about the sample of participants you were studying? Omid Kardan: ⁓ Yes, so we have, based on the exclusions of the ⁓ data quality, and whether they had the device, for example, who didn't a phone were not included for the ⁓ addictive phone use. If they had ⁓ social media accounts, they were not included for the addictive social media ⁓ use, ⁓ or they having no... ⁓ video gaming devices, they were not included for the addictive video gaming. Based on these different exclusions, we slightly different sample sizes, but generally the minimum was 3,400 to 5,500 participants. Elvisha Dhamala: So let's talk about your main findings. What did you discover about the relationship between cortical maturation at ages 9 to 10, or the baseline time point, and addictive screen use a ⁓ Omid Kardan: We found a small but robust association such that the youth with lower AFC scores at ages 9 to 10 endorsed more addictive screen use at ages 11 to 13 compared to their peers. this analysis was adjusted for the child's overall screen time, ⁓ psychopathology and ⁓ problems, ⁓ well as their family resources and parental history of substance use. Elvisha Dhamala: You also found that cortical maturation was most strongly associated with addictive video gaming in particular. Why do you think video gaming might show the strongest relationship compared to social media or phone use in general? Omid Kardan: Yeah, so we can speculate multiple possibilities, but I think one contributing factor, especially related to the addictive social media use, might be that the age range our study was not old enough for media use to be as commonly permitted by the compared, for example, to video For phone use versus video gaming, We do see more endorsement of addictive phonies within the female participants. And that you do get the association between lower AFC and addictive phonies specifically. Elvisha Dhamala: you that the phone use was more common in females. Was video gaming more common in males or was it equally common across both males and females? Omid Kardan: Yes, it was much more common in males for video gaming. Elvisha Dhamala: So this association between lower cortical maturation and video gaming, was that also stronger in males or was it similar across everyone that played these games? Omid Kardan: So we didn't find a statistically significant difference in the strengths of the association AFC predicting addictive video gaming between male and female participants. ⁓ We saw that for addictive phone use. Elvisha Dhamala: You also measured cognitive task performance through things like working memory, inhibitory control, and processing speed. was the relationship that you found between cortical maturation, cognitive performance, and addictive screen use? Omid Kardan: Yes, We did find some unexpected results there. when focusing on addictive screen use over and above total screen time and general attention problems, in cognitive tasks were not significantly associated with addictive screen use. ⁓ the participants' AFC was independently related to both their cognitive performance and addictive use. Elvisha Dhamala: So delayed cortical maturation predicted addictive screen use, but actual cognitive task performance did not. What does that tell us about the pathway from brain development to addictive behaviors? Omid Kardan: That's a great question that may involve multiple scenarios needs further investigation, but keep it simple, I'll pick one of kind of one that asserts multifinality in brain development pace. So I think the of our model, delay like cortical maturation ⁓ is part of an neurodevelopmental factor that in some kids is with poorer cognitive performance. and in others is related to compulsive screen use. Elvisha Dhamala: For the reward processing pathway that you looked at, you only found weak evidence linking the lower nucleus accumbens activation to addictive screen use. This is somewhat surprising given that there's a lot of research linking addiction and reward circuitry. Why do you think the reward findings in this study were less robust than the cortical maturation findings? Omid Kardan: Yeah, so it could be that the reward sensitivity peaks a bit later in development, maybe around or after 14 years of age. we didn't have as much power in this younger sample to detect hypo or hypersensitivity in reward anticipation. Another is that the task not particularly engaging for the kids to evoke the brain responses robustly. It is also possible that altered reward sensitivity is more of a consequence of addictive behaviors rather than a predisposing factor. research would need to be done to kind of find evidence for against ⁓ these different Elvisha Dhamala: One key limitation that you mentioned in the paper is that addictive screen use was not measured at baseline when kids were nine to 10. So you couldn't account for how they were using screens in the past. How does this affect how we should interpret your findings, especially when it comes to causality or directionality of these relationships? Omid Kardan: Yes, we could only account for total screen time at baseline. We didn't have this particular addictive ⁓ screen use questionnaires at that time. means that ⁓ have no way to be sure that the reported addictive screen use at ages 12 to 13 were not already present at, example, eight years of age. So our should be interpreted as correlational bidirectional associations. until we include further waves of the ABCD study and account for time points the model. Elvisha Dhamala: on topic of later time points in the data, how do you expect these kids' overall patterns in terms of screen use and mental health and brain development will change over time? And how might that shape these relationships that you're interested in? Omid Kardan: Yes. So I think that, there's going to be an upward trend. Usually 13, actually, adolescents become more independent at least want to be more independent and, their parental ⁓ on ⁓ of phones and video games and all that less, deterministic for them. So I expect that to increase. Hopefully, future research ⁓ waves of the ABCD can confirm that ⁓ our ⁓ factors we identified here will continue to be predictive for ages. And ⁓ that way, we will have more. longitudinal and ⁓ ⁓ assess if there are ⁓ who becomes or less. predictable our ⁓ neurodevelopmental model and whether means something. Elvisha Dhamala: If there's one key message you want parents, clinicians, and policymakers to take away from your work, what would it be? Omid Kardan: Yeah, so I think for parents, would say that even if your children have good cognitive control, you still consider monitoring their screen use activities. if you addictive patterns of use, such as mood changes after stopping ⁓ or failing to meet definitely develop a plan with child. For clinicians, would say at this stage, I can't make recommendations. need more research and better effect sizes. For policymakers, I'm inclined to say, don't defund the endeavor. It only harm the nation. thank you so much.