Modeling Individual Differences in Brain Development

Becht, Andrik; Mills, Kathryn L.

doi:10.1016/j.biopsych.2020.01.027

Cited by 46 publications

(30 citation statements)

References 56 publications

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“…The SEM framework also affords flexibility in specifying error structures, including errors that are correlated between measurement occasions ( Tisak and Tisak, 1996 ), making it especially well-suited to asking questions of change that require longitudinal data. Further, the use of latent modeling addresses questions of individuals differences in brain development as it can accommodate heterogeneity often observed and related outcomes ( Becht and Mills, 2020 ; King et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Modeling longitudinal changes in hippocampal subfields and relations with memory from early- to mid-childhood

Canada

Hancock

Riggins

2021

Developmental Cognitive Neuroscience

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Highlights During early- to mid-childhood, subfield development differs along the longitudinal axis of the hippocampus. CA1 subfield volume increases between 4−5 years in hippocampal head. CA2-4/DG and subiculum volume increases between 5−6 years in hippocampal body. Changes in CA1 and subiculum between 4−5 years relate to memory between 4−5 years.

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Section: Introductionmentioning

confidence: 99%

Modeling longitudinal changes in hippocampal subfields and relations with memory from early- to mid-childhood

Canada

Hancock

Riggins

2021

Developmental Cognitive Neuroscience

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“…, 2014 ). Yet, no studies have tested whether adolescents show individual differences in the rate of change in social brain regions ( Foulkes and Blakemore, 2018 ; Becht and Mills, 2020 ), despite an increasing interest to use information on individual differences in brain development to predict mental health outcomes ( Rosenberg et al. , 2018 ).…”

mentioning

confidence: 99%

“…, 2018 ). An important direction for research on structural brain development is to use this approach and test for variability in slope patterns over time and link these to behavioural variability ( Foulkes and Blakemore, 2018 ; Becht and Mills, 2020 ). Therefore, the first aim of this study was to statistically test whether there were significant individual differences in within-subject change in social brain development from late childhood into young adulthood across three time points, which could then be used to predict individual differences in social functioning.…”

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confidence: 99%

Beyond the average brain: individual differences in social brain development are associated with friendship quality

Becht

Wierenga

Mills

et al. 2020

Social Cognitive and Affective Neuroscience

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We tested whether adolescents differ from each other in the structural development of the social brain and whether individual differences in social brain development predicted variability in friendship quality development. Adolescents (N = 299, Mage T1 = 13.98 years) were followed across three biannual waves. We analysed self-reported friendship quality with the best friend at T1 and T3, and bilateral measures of surface area and cortical thickness of the medial prefrontal cortex (mPFC), posterior superior temporal sulcus (pSTS), temporoparietal junction (TPJ) and precuneus across all waves. At the group level, growth curve models confirmed non-linear decreases of surface area and cortical thickness in social brain regions. We identified substantial individual differences in levels and change rates of social brain regions, especially for surface area of the mPFC, pSTS and TPJ. Change rates of cortical thickness varied less between persons. Higher levels of mPFC surface area and cortical thickness predicted stronger increases in friendship quality over time. Moreover, faster cortical thinning of mPFC surface area predicted a stronger increase in friendship quality. Higher levels of TPJ cortical thickness predicted lower friendship quality. Together, our results indicate heterogeneity in social brain development and how this variability uniquely predicts friendship quality development.

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“…The measures of impulse control and sensation seeking are proxies of neurobiological processes of cognitive control and socioemotional reactivity. This calls for a replication and extension of the present study including both behavioral and neurobiological measures (see also Becht & Mills, 2020 ).…”

Section: Discussionmentioning

confidence: 79%

On Imbalance of Impulse Control and Sensation Seeking and Adolescent Risk: An Intra-individual Developmental Test of the Dual Systems and Maturational Imbalance Models

Meeus

Vollebergh

Branje

et al. 2021

J Youth Adolescence

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Heterogeneity in development of imbalance between impulse control and sensation seeking has not been studied until now. The present study scrutinized this heterogeneity and the link between imbalance and adolescent risk. Seven-wave data of 7,558 youth (50.71% males; age range from 12/13 until 24/25) were used. Three developmental trajectories were identified. The first trajectory, “sensation seeking to balanced sensation seeking”, included participants with a higher level of sensation seeking than impulse control across all ages. The second trajectory, “moderate dominant control”, included participants showing moderate and increasing impulse control relative to sensation seeking across all ages. The third trajectory, “strong late dominant control”, included participants showing the highest level of impulse control which was about as strong as sensation seeking from early to middle adolescence and became substantially stronger from late adolescence to early adulthood. Although the systematic increase of impulse control in all subgroups is in line with both models, neither of these combined trajectories of control and sensation seeking was predicted by the Dual Systems Model or the Maturational Imbalance Model. Consistent with both models the “sensation seeking to balanced sensation seeking” trajectory showed the highest level of substance use. It can be concluded that, even though both theories adequately predict the link between imbalance and risk, neither the Dual Systems Model nor the Maturational Imbalance Model correctly predict the heterogeneity in development of imbalance between impulse control and sensation seeking.

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Modeling Individual Differences in Brain Development

Cited by 46 publications

References 56 publications

Modeling longitudinal changes in hippocampal subfields and relations with memory from early- to mid-childhood

Modeling longitudinal changes in hippocampal subfields and relations with memory from early- to mid-childhood

Beyond the average brain: individual differences in social brain development are associated with friendship quality

On Imbalance of Impulse Control and Sensation Seeking and Adolescent Risk: An Intra-individual Developmental Test of the Dual Systems and Maturational Imbalance Models

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