Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies... Show morePerforming quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures using FreeSurfer-processed scans. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans acquired in three different waves in a longitudinal study. Different supervised-learning models were then compared to predict manual quality ratings using FreeSurfer segmented output data. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of two novel unseen datasets (total N = 872). Finally, analyses of age effects showed that younger participants were more likely to have lower scan quality, underlining that scan quality might confound findings attributed to age effects. These outcomes indicate that this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies. Show less
Adolescence has been described as a unique period for self-concept development, with an intensified alertness to social comparison as a mechanism for self-knowledge and self-evaluation. However, it... Show moreAdolescence has been described as a unique period for self-concept development, with an intensified alertness to social comparison as a mechanism for self-knowledge and self-evaluation. However, it remains difficult to disentangle the specific influence of these social comparisons on the development of self-descriptions in adolescence. Moreover, it is still unclear how social comparisons impact upon the development of self-views in different domains, such as physical, academic and social self-views. The goal of this study was therefore to examine the development of self-descriptions in different domains across adolescence, and to experimentally test how the development of these self-descriptions is altered by an explicit social comparison context. For this purpose, we developed two tasks which both asked participants (aged 9-25-years, N = 202) for trait self-descriptions but differed in the salience of a social comparison. Results showed consistent age-differences with more positive self-views for children and adolescents in the age-range 9–14 years. The context of explicit social comparison yielded similar as well as additional age-differences that were more dependent upon valence and domain. Moreover, mid-adolescents (15–17 y) were most negatively affected by these social comparisons relative to other ages. Together, this study made a first step in disentangling the specific influence of social comparison outcomes within the development of general self-descriptions, and highlights the importance of social context in studying self-concept in adolescence. Show less
There has been a large spike in longitudinal fMRI studies in recent years, and so it is essential that researchers carefully assess the limitations and challenges afforded by longitudinal designs.... Show moreThere has been a large spike in longitudinal fMRI studies in recent years, and so it is essential that researchers carefully assess the limitations and challenges afforded by longitudinal designs. In this article, we provide an overview of important considerations for longitudinal fMRI research in developmental samples, including task design, sampling strategies, and group-level analyses. We first discuss considerations for task designs, weighing the pros and cons of many commonly used tasks, as well as outlining how the tasks may be impacted by repeated exposure. Secondly, we review the types of group-level analyses that can be conducted on longitudinal fMRI data, analyses which must account for repeated measures. Finally, we review and critique recent longitudinal studies that have emerged in the past few years. Show less
Van der Cruijsen, R.; Peters, S.; Aar, L.P.E. van der; Crone, E.A. 2018
Neuroimaging studies in adults showed that cortical midline regions including medial prefrontal cortex (mPFC) and posterior parietal cortex (PPC) are important in self-evaluations. The goals of... Show moreNeuroimaging studies in adults showed that cortical midline regions including medial prefrontal cortex (mPFC) and posterior parietal cortex (PPC) are important in self-evaluations. The goals of this study were to investigate the contribution of these regions to self-evaluations in late childhood, adolescence, and early adulthood, and to examine whether these differed per domain (academic, physical and prosocial) and valence (positive versus negative). Also, we tested whether this activation changes across adolescence. For this purpose, participants between ages 11–21-years (N = 150) evaluated themselves on trait sentences in an fMRI session. Behaviorally, adolescents rated their academic traits less positively than children and young adults. The neural analyses showed that evaluating self-traits versus a control condition was associated with increased activity in mPFC (domain-general effect), and positive traits were associated with increased activity in ventral mPFC (valence effect). Self-related mPFC activation increased linearly with age, but only for evaluating physical traits. Furthermore, an adolescent-specific decrease in striatumactivation for positive self traits was found. Finally, we found domain-specific neural activity for evaluating traits in physical (dorsolateral PFC, dorsal mPFC) and academic (PPC) domains. Together, these results highlight the importance of domain distinctions when studying self-concept development in late childhood, adolescence, and early adulthood. Show less
