An Introduction to latent class growth analysis and growth mixture modeling. First, adults tend to have a fixed sense of self that relies less on feedback from peers. Whereas most studies - including this one- on adolescent well-being have mostly focused on mental well-being, (i.e., the level of depression and anxiety as measured with questionnaires (Orben, 2020)), it is relevant for typically developing adolescents to investigate a broader scope of social well-being, including peer relations; self-concept; school (stress); parental support and resilience (Kross et al., 2020). The cut-off for significant p-values after FDR correction was p<.016. Bold formatting indicates the best fitting model. Note that the developmental trajectories for high and low mental well-being completely overlap in Fig. By doing so, we were able to examine whether individual differences in structural brain maturation might serve as an underlying mechanism driving both individual differences in social media use as well as individual differences in mental well-being. Individual data for both low and high social media users are visualized for time spent on social media (Fig. There were significant sex differences in the class distribution (2 =8.57, p=.003). For cortical thickness, the best fitting model for LPFC, MPFC and pSTS was a quadratic growth trajectory. Estimated intercept and slope growth parameters of the final 2-class solution for social media use. Secondly, as preregistered, we examined whether social media use subgroups differed on baseline and slopes of mental well-being, as measured by levels of depression and anxiety across the three waves. Genetic and environmental influences on structure of the social brain in childhood. Developmental trajectories for mental well-being classes for a) anxiety and depression (input of classification), b) fear of negative evaluation, c) time spent on social media (hours in last two weeks), and d) social media compulsiveness. Before Scans were inspected by a radiologist and no clinically relevant findings were found. The cut-off for significant p-values after FDR correction was p<.013. Meerkerk G.J., van den Eijnden R.J.J.M., Vermulst A.A., Garretsen H.F.L. Third, we tested for differences in the intercepts and slopes of the structural brain regions across the different mental well-being subgroups. Estimated intercept and slope growth parameters for structural brain development for each social media class. However, these measures have their own shortcomings, as these can only record screen time of one device (Kaye et al., 2020), whereas social media is often used on multiple devices. Only the effects with p<.013 survived FDR correction for multiple testing (Table 9). Adolescents are likely to get excited about seeing posts that make them happy, are popular, or are specifically related to their interests. M.A., A.B., and E.C. Careers, Unable to load your collection due to an error. Based on prior research we expected accelerated cortical thinning in the LPFC for adolescent who experience higher levels of depression and anxiety (Bos et al., 2018). GELUK ONDER DRUK? Keep in touch with NewYork-Presbyterian and subscribe to our newsletter. We report weak structural brain differences between high and low social media users: higher social media use across time was related to higher baseline (intercept) cortical thickness in LPFC and MPFC, and individuals with high social media use showed stronger decreases in the LPFC (cortical thickness) and TPJ (surface area), compared to individuals with stable low social media use across time. Table 3 provides an overview of the best fitting (final) latent growth curve model for each of the four ROIs, both for surface area as well as cortical thickness. The first mental well-being class (N=155, 82%) is characterized by stable and low levels of anxiety and depression (Fig. the trails study. The conceptual and methodological mayhem of screen time. Story highlights A study found that teenagers are highly MPlus Web Notes #21. The vast majority of the sample (N=111, 59%) had structural brain data at all three waves (two waves: 21%; one wave: 19%; only behavior data: 1%). Several empirical studies have shown such accelerated brain maturation for behaviors associated with mental well-being and social media use. Social media and depression symptoms: a network perspective. We could not use the TSCORES option to account for the age heterogeneity at each wave, as this was too complex for the data (i.e., too many parameter estimates relative to the number of observations, resulting in non-convergence of the LCGA). Studies have shown, however, that males are more likely to turn to online videogames (Fam, 2018, Greenberg et al., 2010). Dopamine plays a role in how people experience pleasure, so kids could spend their time looking for that next reward from social media instead of experiencing the joys in real life. Keep in touch with NewYork-Presbyterian and subscribe to our newsletter. Paulus Martin P., Squeglia Lindsay M., Bagot Kara, Jacobus Joanna, Kuplicki Rayus, Breslin Florence J., Bodurka Jerzy, Morris Amanda Sheffield, Thompson Wesley K., Bartsch Hauke, Tapert Susan F. Vol. Note that these groups were not compulsively high or low on social media use. How does this study fit into what we know about technology and brain development in general? There is some support for the latter, as a recent study using the same sample showed that better friendship quality was also related to higher baseline levels of MPFC cortical thickness (Becht et al., 2021). Maybe it will make someone more emotionally adept. Whereas the high mental well-being class had equal sex distribution (54% male, 46% female), the low mental well-being class included more females (74%) than males (26%). The site is secure. Despite finding heterogeneity in social media use, this heterogeneity was not associated with general levels of depression and anxiety, nor with specific fear towards negative evaluation. Huttenlocher Peter R. Morphometric study of human cerebral cortex development. 2019. Is it necessarily a bad thing to have more sensitivity to social cues? Verified by Psychology Today Billi Gordon Ph.D. Obesely Speaking Social Media Is Harmful to Your Brain and Relationships Twitter and Facebook: Where cigarettes went to not die. That is, we conducted the same analyses with subgroups based on heterogeneity in social media use and subgroups based on heterogeneity in mental well-being. Fischl Bruce, Sereno Martin I., Tootell Roger B.H., Dale Anders M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Higher scores indicate more compulsive social media use. We found no significant group differences for LPFC, MPFC and pSTS. We computed a mean score for compulsive social media use for each individual at each of the three time-points. There were no significant intercept or linear slope differences between low and high mental well-being on LPFC surface area. Preliminary studies show that these kids, who are now in fourth or fifth grade, have weaker academic skills than other fourth or fifth grade students in the past. Social media provides immediate and often rewarding feedback and easily connects adolescents with their friends (Odgers and Robb, 2020). Bos, Herting Megan M., Mills Kathryn L., Tamnes Christian K. Contextualizing adolescent structural brain development: environmental determinants and mental health outcomes. Much of what happens on screen provides impoverished stimulation of the developing brain compared to reality, he says. Compulsive social media use was measured by self-report of the Compulsive Internet Use Scale (CIUS) (Meerkerk et al., 2009) at three time-points. Estimated intercept and slope growth parameters for time spent on social media, social media compulsiveness, and fear of negative evaluation for each mental well-being class. Social media and well-being: pitfalls, progress, and next steps. Using latent class growth analyses, we report heterogeneity in social media use as well as mental well-being. Https://Www.Statmodel.Com/Examples/Webnotes/Webnote21.Pdf, Https://Ourworldindata.Org/Rise-of-Social-Media. conducted the data collection. Stability of compulsive social media use within individuals over time was moderate (ICC=0.67). Time spent on social media was measured by self-report (see also (Peters et al., 2021). Measuring brain volume by MR imaging: impact of measurement precision and natural variation on sample size requirements. We examined these group differences on cortical thickness and surface area, resulting in eight group comparisons (Fig. Problematic mobile phone use in adolescence: a cross-sectional study. A Brief Version of the Fear of Negative Evaluation Scale. Demographic characteristics per age bin (wave 1). Neuropsychology, Cognitive Behavioral Therapy, Find a Doctor or call We first conducted a univariate LGCA on three waves of mental well-being data to examine the number of subgroups and shape of their developmental trajectories. To this end, we conducted a multivariate latent class growth curve analyses (LCGA (Jung and Wickrama, 2008)) in Mplus (Muthn and Muthn, 1998) on three waves to examine the number of subgroups and shape of their developmental trajectories of time spent on social media and the level of compulsive social media use. National Library of Medicine Next, we examined whether social media use subgroups differed on baseline (intercepts) and development (slopes) of mental well-being (self-reported levels of depression and anxiety) and fear of negative evaluation across the three waves. Klapwijk Eduard T., Kamp Ferdi van de, Meulen Mara van der, Sabine Peters, Wierenga Lara M. Qoala-T: a supervised-learning tool for quality control of freesurfer segmented MRI data. Therefore, we continued our analyses using the two-class solution (Fig. As an additional (non-preregistered) validity check, we also examined whether the social media classes differed on fear of negative evaluation. This is the first study to show that maturation of cortical brain regions is related to both social media use (weakly, not surviving FDR correction), as well as mental well-being (more strongly, surviving FDR correction). The second preregistered aim (see Achterberg et al., 2021) of this study was to examine whether social media subgroups differed in their structural brain development. and transmitted securely. Adolescence can be viewed as a period for social reorienting, as adolescents motivation to engage with peers and to build their own social networks naturally increases (Larson and Richards, 1991; Steinberg and Morris, 2001; Crone and Dahl, 2012). We computed a mean score based on the 12 items for each individual at each of the three time-points. That is, surface area is more influenced by genetic variants than cortical thickness (Grasby et al., 2020). Crone E.A., Elzinga B.M. That lack of face-to-face interaction can lead to depression. Individuals with low mental well-being showed significantly lower baseline surface area in the MPFC and the pSTS, surviving multiple testing correction. The left panel (i) shows cortical thickness and the right panel (ii) shows surface area. Currently, it remains unclear how the social connectedness though social media is associated with brain development throughout adolescence, and vice versa (Crone and Konijn, 2018). Adolescents with high social media use showed higher baseline cortical thickness in LPFC and MPFC and stronger decreases in LPFC and TPJ although these effects did not survive FDR correction for multiple testing. One of the main reasons is how fast the brain grows starting before birth and continuing into early childhood. Moreover, self-reported measures can additionally provide an index of the subjective experience of social media use, for example in terms of compulsiveness. By including both metrics we can examine distinct aspects of structural brain development. Therefore, we continued our analyses with the final two-class solution (Fig. This study was approved by the Medical Ethics Committee of Leiden University Medical Center. In addition, cortical thickness and surface area differ in the degree in which they are influenced by heritability. Solid lines represent significant group differences, dashed lines represent no group differences. the contents by NLM or the National Institutes of Health. Arnarsson. In addition, to confirm the accuracy of the model prediction, a random subset (20%) of the scores >70 at T2 and T3 were selected for additional manual quality checks. Dr. Bender has a special interest in the neuropsychological needs of non-native English speakers and patients born outside of the United States, and is active in professional groups working to reduce disparities in health care. For LPFC surface area development, adolescents with high mental well-being showed a positive quadratic slope, whereas adolescents with low mental well-being showed a negative quadratic slope (2 =5.997, p=.014) (Table 9, Fig. For example, if someone has a stroke and the area of the brain involved in speech is permanently damaged, intensive speech therapy can support new neural connections and help the brain rewire itself to give a person their speech back. Very little is known about the association between longitudinal brain development and social media use in adolescence (Crone and Konijn, 2018). That is, all three evaluation criteria were lower for the two-class solution (AIC: 193.25, BIC: 225.67, and ssBIC: 193.97) compared to the one-class solution (AIC: 338.46, BIC: 357.91, and ssBIC: 338.91). Onderzoek Naar Het Mentaal Welbevinden van Jongeren in Nederland. Data of three participants were excluded at T1, two at T2 and one at T3. In general, our sample scored relatively high on mental well-being, which is in line with reports showing that Dutch youth in general report high mental well-being (Currie et al., 2012, Inchley et al., 2020). Through the chat option and multi-video player, online video gaming can also be seen as a form of social media (Pea and Hancock, 2006). Watson D., Friend R. Measurement of social-evaluative anxiety. The social brain in adolescence. Developmental changes in the structure of the social brain in late childhood and adolescence. Higher scores indicate lower mental well-being. E.K. Kaye Linda K., Orben Amy, Ellis David A., Hunter Simon C., Houghton Stephen. A time of change: behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues. However, the associations are clearly differential: whereas social media use was related to intercept and slope differences in mostly cortical thickness, mental well-being was related to differences in surface area only. Dashed lines represent no group differences. Ksters Mia P., Chinapaw Mai J.M., Zwaanswijk Marieke, van der Wal Marcel F., Koot Hans M. Structure, reliability, and validity of the revised child anxiety and depression scale (RCADS) in a multi-ethnic urban sample of Dutch children. wrote the manuscript with assistance from A.B. Changes in Memory Processes. van der Meulen M., Steinbeis N., Achterberg M., van IJzendoorn M.H., Crone E.A. The majority of results for well-being remained significant after FDR correction, indicating stronger associations. Appendix ASupplementary data associated with this article can be found in the online version at doi:10.1016/j.dcn.2022.101088. Our second aim was to test whether the two social media classes differed in the structural brain development of LPFC, MPFC, TPJ and pSTS. Mental well-being was also related to differences in structural brain development, but this was reflected in surface area rather than cortical thickness. We dont have enough information to know the long-term ramifications to brain health as yet, since this is the first generation where social media has been prevalent since birth. The compulsive internet use scale (CIUS): some psychometric properties. Our first preregistered aim (see Achterberg et al., 2021) was to examine heterogeneity in developmental trajectories of social media use across adolescence. First, adolescents who used social media more than their peers showed higher baseline cortical thickness in lateral prefrontal cortex (PFC) and medial PFC; and stronger decreases in the lateral PFC and temporal parietal junction. There were no significant differences between the high and low social media use class on (a) mental well-being (measured with anxiety and depression) and (b) Fear of negative evaluation. The MPFC, TPJ and pSTS regions are based on the social brain regions as defined in (Mills et al., 2014a, Mills et al., 2014b). 4c-d). We expected to find at least two subgroups: one that shows a developmental increase in social media use (both time spent and compulsiveness) and one that shows a developmental increase in time spent on social media, but stable low compulsiveness. Orben Amy, Przybylski Andrew K. The association between adolescent well-being and digital technology use. These findings demonstrate that although social media use and mental well-being were both associated with differential trajectories of brain development, the associations we report are distinct. All T1-weighted scans of the first wave (T1) were manually checked, using the protocol described in (Klapwijk et al., 2019). Within our sample, Cronbachs alpha based on the 12 items was excellent for all three waves (T1: =0.96, T2: =0.97; T3: =0.97). To empirically determine the number of latent classes that best fit the data, used the AIC (Akaike, 1974); BIC (Schwarz, 1978), and the ssaBIC (Sclove, 1987), with lower values indicating a better model fit. Ortiz-Ospina, E. 2019. Schwarz Gideon. We hypothesized that individuals with high social media use would show stronger decrease of surface area and cortical thickness in these regions compared to individuals with stable low social media use across time. There are a lot of ways that using technology and specifically social media affects your brain. Kleinjan M., Pieper I., Stevens G., van de Klundert N., Rombouts M., Boer M., Lammers J. Den Haag; 2020. We will specifically focus on two structural brain metrics: surface area and cortical thickness. As compulsive social media use often is interpreted as negative contextual factor, we hypothesized that individuals with high social media use would show a stronger decrease of surface area and cortical thickness (in the LPFC, MPFC, TPJ and pSTS) compared to individuals with stable low social media use across time. Pretorius Claudette, Chambers Derek, Coyle David. We excluded one participant at the second wave, who reported spending 336h on social media in the last two weeks (implicating that this participant spent 24/7 online, which we deemed unrealistic). Sclove Stanley L. Application of model-selection criteria to some problems in multivariate analysis. For comparison and overview purposes, we will refer to the first group as Low Social Media Use and the second group as High Social Media Use. In contrast to the questions regarding time spent on social media, the questions concerning compulsive social media use were specifically aimed at the use of Facebook. van der Meulen M., Wierenga L.M., Achterberg M., Drenth N., van IJzendoorn M.H., Crone E.A. Secondly, we examined whether mental well-being subgroups differed on baseline and slopes of social media use (both time spent and compulsiveness), and fear of negative evaluation across the three waves. None of the significant differences in brain development between high and low social media users survived FDR correction for multiple testing. Social media However, we deviated from the preregistration by not including the cuneus as this control region was not of primary interest for the goals of this study. The procedure and technical details are described elsewhere (Fischl et al., 1999, Fischl et al., 1999, Reuter et al., 2012). Future studies should aim to replicate these findings using larger samples, for example using the U.S. Adolescent Brain Cognitive Development (ABCD) study (Paulus et al., 2019; That is, the effects of social media on well-being varies substantially between adolescents (Beyens et al., 2020). Although both social media use and mental well-being were associated with LPFC and MPFC development, social media use was additionally associated with the TPJ development, whereas mental well-being was associated with differences in pSTS development. Desikan Rahul S., Florent S.gonne, Bruce Fischl, Quinn Brian T., Dickerson Bradford C., Blacker Deborah, Buckner Randy L., Dale Anders M., Paul Maguire R., Hyman Bradley T., Albert Marilyn S., Killiany Ronald J. Indeed, we found significant group differences, such that individuals with low mental well-being had significantly higher intercepts on fear of negative evaluation than individuals with high mental well-being (2 =45.89, p<.001), see Table 8 and Fig. That is, adolescents with high social media use showed a stronger linear decease and a stronger quadratic increase than individuals with low social media use (linear: 2 =4.85, p=.028; quadratic: 2 =3.88, p=.049), (Table 6). Leary M.R. Specifically, we tested whether heterogeneity in social media use and mental well-being were related to (similar) individual differences in structural brain development. To get a balanced number of participants in terms of age across waves, additional participants were included in the younger age range at T2, and in the older age range at T3. 2014. The bottom line shows individual data for both low and high social media users for (c) time spent on social media and (d) compulsive social media use. van der Cruijsen R., Peters S., van der Aar L.P.E., Crone E.A. This is called neuroplacticity, which means that the brain has the ability to evolve and change over time based on whatever has happened to it in the past. Parcellation of the cortex into gyral regions was based on the Desikan-Killiany atlas (Desikan et al., 2006). Version 3, July 16. Possibly, the differences are associated with sample characteristics. Statistical Analysis With Latent Variables Users Guide. Also, it is important to use technology purposefully and avoid multitasking and distraction. Asparouhov, T., B. Muthn. We used a false discovery rate (FDR) correction to control for the proportion off type I errors. Thus, although social media use and mental well-being are both associated with differences in brain development, the associations are distinct. That's because, as children enter puberty, the areas of the brain "associated with our craving for 'social rewards,' such as visibility, attention and positive feedback from The brief FNE consists of 12 items (e.g., I rarely worry about what kind of impression I am making on someone and I am afraid others will not approve of me) which could be rated on a 5-point Likert scale (never, sometimes, often, always. Moreover, adolescents with low mental well-being showed a weaker quadratic slope, relative to adolescents with high mental well-being (2 =11.23, p=.001), see Fig. The second mental well-being class (N=34, 18%) is characterized by higher and stable levels of anxiety and depression (Fig. There were no significant differences in IQ between high and low social media users (t(158)=1.51, p=.132). Does the revised child anxiety and depression scale (RCADS) measure anxiety symptoms consistently across adolescence? Similar to our analyses on social media classes, we tested whether the two mental well-being classes differed in the structural brain development of LPFC, MPFC, TPJ and pSTS. We expected that the subgroup with high social media use (both time spent and compulsiveness) across time will show a decrease in mental well-being over time (Twenge et al., 2018), whereas the group with increased time spent on social media and stable low compulsiveness will display higher and stable levels of mental well-being (Beyens et al., 2020, Odgers and Jensen, 2020, Orben, 2020). Beyens Ine, Loes Pouwels J., van Driel Irene I., Keijsers Loes, Valkenburg Patti M. The effect of social media on well-being differs from adolescent to adolescent. Last, we examined whether similar associations between longitudinal structural brain development could be found based on heterogeneity in mental well-being (compared to heterogeneity in social media use). Studies show that the brain scans of heavy social media users look very similar to those addicted to drugs or gambling. Could this sensitivity to rewards turn into an addiction? To limit the number of statistical tests, we computed bilateral estimates by combining structural measures for each hemisphere. One high-resolution 3D T1-weighted scan was acquired for each participant (Field of view (FOV, in mm)=224 (ap) x 177 (rl) x 168 (fh); TR=9.8ms; TE=4.6ms; flip angle=8; 140 slices; voxel size 0.8750.8750.875mm). Decreases in brain grey matter volume, which can be seen as a proxy for brain maturation (Huttenlocher, 1990), continue until the mid-twenties (Gogtay et al., 2004, Mills et al., 2016, Tamnes et al., 2017). Therefore, our measure of might have underestimated the compulsiveness of social media use. For comparison and overview purposes, we will refer to the first group as High Mental Well-being and the second group as Low Mental Well-being. official website and that any information you provide is encrypted FOIA Estimating the dimension of a model. Gogtay N., Giedd J.N., Lusk L., Hayashi K.M., Greenstein D., Vaituzis A.C., Nugent T.F., Herman D.H., Clasen L.S., Toga A.W., Rapoport J.L., Thompson P.M. Youth of today grow up in a digital social world but the effects on well-being and brain development remain debated. Mehta Paras D., West Stephen G. Putting the individual back into individual growth curves. As preregistered, we included all participants with and without missing values in our analyses and handled missing data using full information maximum likelihood (FIML) in Mplus (Muthn and Muthn, 1998). Wilmer H.H., Chein J.M. On the other hand, these results might indicate that some adolescent are more sensitive to social media use than others, and this pattern already emerges early in adolescence. Vol. Daily companionship in late childhood and early adolescence: Changing developmental contexts. What brain regions were affected? Blakemore S.J., Mills K.L. Changing brains: how longitudinal functional magnetic resonance imaging studies can inform us about cognitive and social-affective growth trajectories. 4a). aErasmus University Rotterdam, The Netherlands, dAmsterdam University Medical Center, The Netherlands. We preregistered to not delete outliers on the brain and behavior variables in our analyses (except for the MRI scans that were of insufficient quality, see section 4.3.3.). Our results show the importance of examining individual difference in brain maturation and provide a starting point to further examine neural mechanisms that could explain which adolescents thrive by social media and which might be harmed. Inchley, J., D. Currie, S. Budisavljevic, T. Torsheim, A. Jastad, A. Cosma, C. Kelly, J.M. p-values indicate whether the intercept and slopes were significantly different from zero (two-tailed t-tests). Volkow Nora D., Koob George F., Croyle Robert T., Bianchi Diana W., Gordon Joshua A., Koroshetz Walter J., Prez-Stable Eliseo J., Riley William T., Bloch Michele H., Conway Kevin, Deeds Bethany G., Dowling Gayathri J., Grant Steven, Howlett Katia D., Matochik John A., Morgan Glen D., Murray Margaret M., Noronha Antonio, Spong Catherine Y., Wargo Eric M., Warren Kenneth R., Weiss Susan R.B. Here we review the neural development in Although the smaller classes (low social media use (N=52) and low mental well-(N=34) covered more than 15% of the total sample, which we preregistered as the minimum threshold to have meaningful interpretation of the results. Estimated intercept and slope growth parameters for mental well-being (anxiety and depression measured by RCADS) and fear of negative evaluation for each social media class. This project is part of the larger Leiden Self-Concept study (see Open Science Framework: https://osf.io/8gc6x), in which 187 individuals were followed across three annual MRI assessments. Moreover, they found that stronger structural connectivity between ventral striatum and MPFC was related to more engagement with smartphones, whereas connectivity between ventral striatum and dorsal LPFC was related to less engagement (Wilmer et al., 2019). Kross Ethan, Verduyn Philippe, Sheppes Gal, Costello Cory K., Jonides John, Ybarra Oscar. Kids dont have frontal lobes that are fully functional. Participants and parents of minors provided written informed consent. Currie, C., C. Zanotti, A. Morgan, D. Currie, M. de Looze, C. Roberts, O. Samdal, O.R. Social media can let you interact and engage with people you never would have before, but it can also be a space for cyberbullying or questionable content. Moreover, Black and Hispanic youth reported more screen time than White and Asian youth (Paulus et al., 2019). Whereas the low social media class had equal sex distribution (55% male, 45% female), the high social media class included more females (68%) than males (32%). 5d). Ferschmann Lia, Marieke G.N. Evidence for this hypothesis comes from accelerated cortical thinning across adolescence for high versus slow social media users. WebThe early years of a childs life are very important for later health and development. One potential explanation for lower estimated social media use in males might originate from the way we targeted social media, which was solely aimed at profile platforms (i.e., Twitter, Facebook, Instagram). The same way you can re-teach someone to speak after theyve had a stroke, I think as we better understand the implications of virtual learning, we have the opportunity to support our youth who didnt get the traditional kind of learning or in-person experiences due to COVID. has been supported through the Gravitation grant of the Dutch Ministry of Education, Culture, and Science and Netherlands Organization for Scientific Research granted to the Consortium Individual Development (CID) (024.001.003). 32. Steen R.G., Hamer R.M., Lieberman J.A. There were significant sex differences in the class distribution (2 =7.83, p=.006). M.A., A.B., R.C., I.G., J.S., E.K., E.A.C. Blakemore S.J. In addition to prior literature that examined direct associations between social media use and mental well-being, the current study was novel in that we also investigated the relation between both processes and associated brain development. To this end, we first saved the individual intercept and slopes of the final latent growth curve models (see section 4.3.5.). From dopamine spikes to phantom vibrations, social media The part of the brain responsible for judgement, reasoning and rewards, called the dorsolateral prefrontal cortex, was also impacted. Crone E.A., Konijn E.A. As depicted in Table 5, we found no significant differences in social media use for high and low mental well-being classes (see Fig. The estimated intercept and slope growth parameters of the final 2-class solution can be found in Table 4. That is, we examined whether the same associations between heterogeneity in social media use and structural brain development could be found based on heterogeneity in mental well-being. Structural brain development between childhood and adulthood: convergence across four longitudinal samples. Stability of mental well-being within individuals over time was high (ICC=0.85). Controlling the false discovery rate - a practical and powerful approach to multiple testing. Solid lines represent significant group differences, dashed lines represent no group differences. Alternatively, social media use early in development may influence brain development. 4a). We found no evidence for a direct relation between social media use and mental well-being in the current community sample. Researchers found that kids who habitually check social media had changes in parts of the brain that control social rewards and punishment. These results highlight the need to examine individual differences in the association between social media and well-being, using longitudinal data (Crone and Elzinga, 2015). Teenagers, screens and social media: a narrative review of reviews and key studies. 185. You can see delays in language acquisition and problems with sustained attention and multitasking. government site. A critical question that remains largely unanswered is how adolescents abundant media use may impact them developmentally in terms of structural brain Tooley Ursula A., Bassett Danielle S., Mackey Allyson P. Environmental influences on the pace of brain development. We chose this measure as it is more closely related to social media use and therefore might be more sensitive for class differences. Moreover, adolescents with low mental well-being showed a positive quadratic slope, whereas adolescents with low mental well-being showed a negative quadratic slope (2 =8.30, p=.004), see Fig. For each individual we saved the individual intercept and slopes of the final latent growth curve model. These results show a nuanced perspective on the presumed relations between social media use and well-being and provide a starting point to further examine neural mechanisms that could explain which adolescents thrive by social media and which might be harmed. Moreover, our latent class growth analyses resulted in unequal groups in terms of sample size. For example, accelerated cortical thinning of the prefrontal cortex has been associated with higher levels of depression within a non-clinical adolescent sample (Bos et al., 2018). Social media use classes. WebThis issue of Dialogues in Clinical Neuroscience explores in a multifaceted manner how, by what means, and with what possible effects digital media use affects brain functionfor the good, the bad, and the ugly sides of human existence. Whereas previous studies examined social media use across a small age range using cross-sectional designs (Paulus et al., 2019) or longitudinal experience sample methods (ESM) across a narrow time span (Beyens et al., 2020) we examined the developmental trajectory of social media use using a cohort-sequential design across the whole span of adolescence (1025 years), including three annual measures per individual. Beyond the average brain: individual differences in social brain development are associated with friendship quality. On a behavioral level, we first examined whether the mental well-being subgroups differed on social media use, both the amount of time spent on social media as social media compulsiveness. were involved in data interpretation. designed the research. Within our sample, Cronbachs alpha based on the 14 items was excellent for all three waves (T1: =0.93, T2: =0.92; T3: =0.94). The current study made a start for this by defining social media use based on multivariate classification including time spent on social media as well as a measure of subjective compulsiveness. That is, all three model fit evaluation criteria were lower for the two-class solution (AIC: 3825.48, BIC: 3916.25, and ssaBIC: 3827.56) compared to the one-class solution (AIC: 4063.81, BIC: 4128.65, and ssaBIC: 4065.29). So, despite the increasing worry about the effects of social media use on developing adolescents, our longitudinal approach shows that in general, social media use is stable across adolescence. Crone E., Dahl R. Understanding adolescence as a period of socialaffective engagement and goal flexibility. Crone E.A., Achterberg M., Dobbelaar S., Euser S., van den Bulk B.G., van der Meulen M., van Drunen L., Wierenga L.M., Bakermans-Kranenburg M.J., van IJzendoorn M.H. Besides differences in structural brain metrics, we also found associations in distinct brain regions. The first aim was to examine heterogeneity in developmental trajectories of social media use (time spent on social media and the level of compulsive social media use) across adolescence. Structural brain development differences between low and high social media users for (a) lateral prefrontal cortex (LPFC), (b) medial prefrontal cortex (MPFC), (c) temporal parietal junction (TPJ), and (d) posterior superior temporal sulcus (pSTS). Estimated intercept and slope growth parameters for structural brain development for each mental well-being class. 2018. Estimated intercept and slope growth parameters of the final 2-class solution for mental well-being. Attention. Fit indices of the latent growth models for surface area (SA) and cortical thickness (CT) of lateral prefrontal cortex (LPFC), medial prefrontal cortex (MPFC), temporal parietal junction (TPJ), and posterior superior temporal sulcus (pSTS). The current study examined whether the same neural mechanisms are associated with individual differences in longitudinal trajectories of both social media use as well as mental well-being. The internal consistency of the CIUS has shown to be high (=0.90, (Meerkerk et al., 2009)). She specializes in the evaluation and treatment of patients of all ages with a wide range of neurological and cognitive disorders. It should be noted that the effects we report are small and, for social media subgroup analyses, do not survive correction for multiple testing. That is, we expected that the time spent on social media would increase in both groups, but our analyses show stable time spent on social media across adolescence (i.e., no significant slope effects, see Table 4). The https:// ensures that you are connecting to the 1998. This study was preregistered as Longitudinal associations between social media use and structural brain development across adolescence: https://doi.org/10.17605/OSF.IO/DGMBX. The tween group, ages 8-t0-12, spend four to six hours on phones or watching screens. The LCGA revealed that a two-class solution, without controlling the intercept and slopes for age, provided the best fit to the data. Its an incredibly important time for them to be spending so much time with two-dimensional relationships as opposed to engaging with people in real life. Are these changes to the brain reversible? Kids have rapidly changing brains that are most malleable in childhood, so in this discussion of social media changing brain development, its possible they will develop new neural connections in response to their experiences online. Patel It Like It Is: Effects of social media on brain development Moreover, such traits might also be related to mental well-being, which could partly explain why social media and mental well-being are sometimes (but not always) associated (Odgers and Jensen, 2020, Orben, 2020, Orben and Przybylski, 2019). Mental well-being was measured at all three time-points by self-report of the Dutch version of the Revised Child Anxiety and Depression Scale (RCADS (Ksters et al., 2015; Mathyssek et al., 2013)). Accessibility We expected that individuals with relatively high social media use (both time spent and compulsiveness) will show accelerated cortical thinning (stronger decrease of surface area and cortical thickness) in these regions compared to individuals with stable low social media compulsiveness use across time (Prinstein et al., 2020). As a result of the accelerated longitudinal design of the Self-concept study (van der Cruijsen et al., 2018, Becht et al., 2020), the age range is relatively wide at each time point (e.g., at T1 the age ranges from 11 to 21 years). These platforms give adolescents the opportunity for increased social interaction at a time when their brains are especially sensitive to social feedback, particularly reward feedback. Prinstein Mitchell J., Nesi Jacqueline, Telzer Eva H. Commentary: an updated agenda for the study of digital media use and adolescent development future directions following odgers & Jensen (2020). The study demonstrated two pathways of heterogeneity in brain development. Thus, in line with our hypothesis, we found accelerated cortical thinning for individuals with high social media use in the LPFC. Adolescents are likely to get excited about seeing posts Exclusion criteria before participation were MRI-contraindications, left-handedness and a current or previous diagnosis of a neurological or psychiatric disorder. Third, we tested for differences in the intercepts and slopes of the structural brain regions across the different social media subgroups. Nevertheless, our results provide a starting point in generating more specific hypothesis to further unravel the underlying brain mechanisms that are associated with individual differences in both social media use and mental well-being. Aalbers George, Richard J.Mc.Nally, Alexandre Heeren, Sanne de Wit, Eiko I.Fried. These findings are in line with systematic reviews that often report twice as many nonsignificant associations than positive associations (for example, see (Seabrook et al., 2016)). Seabrook Elizabeth M., Kern Margaret L., Rickard Nikki S. Social networking sites, depression, and anxiety: a systematic review. What do these changes in brain development mean? In case the model fit of the final model (linear vs quadratic) did not significantly improve (based on lower AIC, BIC, ssaBIC) with the inclusion of sex as a covariate, we dropped sex as a covariate from the final growth curve models. We report no associations between social media use and mental well-being, whereas prior studies did report small, but statistically significant, associations between social media usage and depressive symptoms (for a systematic review, see (McCrae et al., 2017)). (NeuroImage). For the TPJ surface area, there was no significant differences in intercept (p=.716), but in line with our hypothesis, we did find accelerated cortical thinning. Screen media activity and brain structure in youth: evidence for diverse structural correlation networks from the ABCD Study. Although the two-class solution is consistent with our pre-registered hypothesis, the trajectories of the subgroups did not exactly match our expectations. Littles missing completely at random (MCAR) test showed that missing data patterns of questionnaire measures and brain variables were MCAR (2(16)=25.52, p=.061). Wired to be connected? Wierenga Lara M., Langen Marieke, Oranje Bob, Durston Sarah. Even though there was no direct association between social media use and mental well-being, heterogeneity in social media use and mental well-being were both associated with individual differences in structural brain development. Interestingly, the associations between social media and structural brain development were in general observed in cortical thickness development. 2b, Table 5). 1c) and social media compulsiveness (Fig. As a library, NLM provides access to scientific literature. Orben Amy. Specifically, we examined whether the same neural mechanisms are associated with individual differences in longitudinal trajectories of both social media use as well as mental well-being. We know from previous studies that more than 50 percent of kids 4 years and younger are engaging with smartphones or tablets on a regular basis. 1d). We preregistered to examine whether the social media subgroups differed on mental well-being (measured by levels of depression and anxiety across the three waves) as external validity check for the subgroups. Muthn, Linda K., Bengt O. Muthn. Duncan Terry E., Duncan Susan C. The ABCs of LGM: an introductory guide to latent variable growth curve modeling. An official website of the United States government. This is one of the reasons why social scrolling can be so addictive. Other brain areas involved in the processing of positive or negative emotions showed lower sensitivity to social anticipation in those who checked more frequently than those who did not. Exploratively, we conducted the same analyses with subgroups based on heterogeneity in mental well-being. Similarly, for compulsive social media use, while we reported significant differences in intercepts of compulsiveness between the classes, our results indicated stable compulsiveness across development. We found no significant group differences in cortical thickness in any of the four ROIs (Table 9). Specifically, regions important for social processing such as the medial prefrontal cortex (MPFC), posterior superior temporal sulcus (pSTS) and temporal parietal junction (TPJ), as well as regions involved with cognitive control, such as the lateral prefrontal cortex (LPFC), show extensive decreases in grey matter volume (i.e., cortical thinning) across adolescence (Becht et al., 2020, Mills et al., 2014a, Mills et al., 2014b). As many as 96% of the adolescents use social media on a daily basis (Odgers and Robb, 2020), yet little is known about the impact on this intense social connectedness on adolescent development. We made use of data from the accelerated longitudinal Self-Concept Study (van der Cruijsen et al., 2018), which included 189 typically developing Dutch adolescents at three time points spanning approximately 5 years. The internal consistency of the Dutch RCADS has been shown to be good (s=0.700.96) in 813-year-olds (Ksters et al., 2015). To prevent head motion, foam inserts surrounded the participants head. The Fear of Negative Evaluation (FNE) questionnaire measures discomfort and distress in interpersonal interactions (Watson & Friend, 1969). 2012. A.B. Blakemore S.J. Neural and behavioral signatures of social evaluation and adaptation in childhood and adolescence: The Leiden Consortium on Individual Development (L-CID). The JAMA study said the kids who habitually checked social media showed a distinct neurodevelopment trajectory within various regions of the brain. This provides some evidence that mental well-being is related to social evaluation, but our data did not indicate a direct link between mental well-being and social media use in general. Surface area development has been shown to be sensitive to genetic variants (Grasby et al., 2020). 2a, Table 5). Prevalence of Internet gaming disorder in adolescents: a meta-analysis across three decades. For MPFC surface area development, we found significant differences between low and high mental well-being on intercept, linear- and quadratic slope (Table 9). Jung Tony, Wickrama K.A.S. Post-processing of the scan quality was conducted using a hybrid manual-automatic quality assessment tool, Qoala-T (Klapwijk et al., 2019). 1). However, these findings all stem from cross-sectional studies and although these provide interesting insights in associations age-effects, actual individual development can only be captured using longitudinal designs (Crone and Elzinga, 2015, Steen et al., 2007). Future studies, using longitudinal twin designs and bivariate behavioral genetic modelling should elaborate on these findings (Crone et al., 2020; van der Meulen et al., 2020). Indeed, a recent report of Unicef showed that, despite the overall high mental well-being in Dutch youth, most adolescents experience high levels of school stress (Kleinjan et al., 2020). 47. Social Determinants of Health and Well-Being among Young People. Next, the Qoala-T algorithm was run on all scans from T1, T2, and T3. Having your posts liked, and liking other people's posts, on social media activates your brain's reward center. Its too early to say if social media was the only cause of these changes, but it does suggest that more studies are needed to better understand brain development in the age of social media. The second social media class (N=52, 28%) was characterized by i) relatively high and stable time spent on social media, and ii) relatively high and stable compulsive social media use (Fig. Next, we tested intercept and slope differences on brain development across the identified subgroups in LCGA. Individual differences in cortical thinning have been related to behavioral outcomes. Additional descriptive analyses on data that we did not include in the preregistration or the longitudinal analyses showed that in our sample females were indeed more likely to report compulsiveness on Facebook, whereas males were more likely to report compulsiveness with Online Gaming (see Supplementary materials). The best fitting model for surface area for all ROIs was a quadratic growth trajectory including sex as covariate. By doing so, we were able to test whether cortical thinning of social and cognitive control regions in the brain might serve as similar neural correlates associated with both individual differences in social media use as well as individual differences in mental well-being. These intercepts and slopes were then used to test intercept and slope differences on brain development across subgroups, using Wald Chi-Squared tests. By doing so, we were able to examine whether cortical thinning of social and cognitive control regions in the brain might serve as an underlying mechanism driving both individual differences in social media use as well as individual differences in mental well-being. Data, study materials and analysis scripts are available at DataverseNL through https://doi.org/xx. Moreover, as preregistered, every subgroup needed to cover at least 15% of the sample (N=25) for meaningful interpretation and subsequent analyses. In contrast, adolescents with lower mental well-being showed lower baseline levels of surface area in the medial PFC and posterior superior temporal sulcus relative to their peers. Achterberg M., Becht A., van der Cruijsen R., van de Groep I.H., Spaans J., Klapwijk E., Crone E. Longitudinal associations between social media use and structural brain development across adolescence. In adulthood, social media use is also linked to activation in the brains reward centers, but two key differences may lessen harm, Prinstein said. Moreover, these regions have been related to social media use specifically (MPFC, LPFC, Wilmer et al., 2019) or social relations in general (TPJ, pSTS, (Becht et al., 2021, Blakemore, 2008). Although we preregistered most of our analyses and hypotheses, we included many analyses and tests. Health Behaviour in School-Aged Children (HBSC) Study: International Report from the 2009/2010 Survey. Spotlight on Adolescent Health and Well-Being. The intraclass correlation coefficient (ICC) demonstrated moderate stability of time spent on social media across waves (ICC=0.74). By doing so, we were able to test whether cortical thinning of social and cognitive control regions in the brain might serve as an underlying mechanism driving both individual differences in social media use as well as individual differences in mental well-being. But a recent study in JAMA Pediatrics suggests social media is changing something else too: the brain development of adolescents. Its the part of the brain that makes us fear, that makes us react. Heightened sensitivity to peer feedback during adolescence has left caregivers worried about social media platforms and social media use among adolescents (Martinotti et al., 2011). Grasby Katrina L., Jahanshad Neda, Painter Jodie N., Colodro-Conde Luca, Bralten Janita, Hibar Derrek P., Lind Penelope A., Pizzagalli Fabrizio, Ching Christopher R.K., McMahon Mary Agnes B., Shatokhina Natalia, Zsembik Leo C.P., Thomopoulos Sophia I., Zhu Alyssa H., Strike Lachlan T., Agartz Ingrid, Alhusaini Saud, Almeida Marcio A.A., Alns Dag, Amlien Inge K., Andersson Micael, Ard Tyler, Armstrong Nicola J., Ashley-Koch Allison, Atkins Joshua R., Bernard Manon, Brouwer Rachel M., Buimer Elizabeth E.L., Blow Robin, Brger Christian, Cannon Dara M., Chakravarty Mallar, Chen Qiang, Cheung Joshua W., Couvy-Duchesne Baptiste, Dale Anders M., Dalvie Shareefa, de Araujo T.nia K., de Zubicaray Greig I., de Zwarte Sonja M.C., den Braber Anouk, Doan Nhat Trung, Dohm Katharina, Ehrlich Stefan, Engelbrecht Hannah-Ruth, Erk Susanne, Fan Chun Chieh, Fedko Iryna O., Foley Sonya F., Ford Judith M., Fukunaga Masaki, Garrett Melanie E., Ge Tian, Giddaluru Sudheer, Goldman Aaron L., Green Melissa J., 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The genetic architecture of the human cerebral cortex.

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