The neural signature of self-concept development in adolescence: The role of domain and valence distinctions

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 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 striatum activation 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

Dear future me: behavioral and neural mechanisms underlying self-concept development in relation to educational decision-making in adolescence

In this thesis, I investigated the behavioral and neural processes involved in self-concept development in adolescence within the context of future-oriented educational decision-making. The studies presented in this thesis all highlight that self-concept is a multifaceted and complex construct that not only develops in interaction with the social environment, but can also have an impact on someone’s future environment. For example, results from chapter 2 indicated that the social environment, expressed in the outcomes of social comparisons, can affect the positivity of the self to a different extent across multiple domains and different stages of adolescence. Chapter 3 showed how adolescents’ academic self-concept can influence their motivation to stay committed to goals important for their future educational environment, whereas chapter 4 illustrated differences in self-esteem and self-concept clarity in individuals who differed in their experienced problems with choosing this future educational environment. Finally, chapter 5 demonstrated that in late adolescence, sensitivity to outside influences can be used to stimulate self-concept development through training which can ultimately help adolescents in their educational decision-making and adjustment in higher education. Together, these studies provide a comprehensive view on self-concept development in adolescence that takes place within a broader social, and educational context. Pathways through Adolescenc

Development Plan for Kuopion Palloseura

This Bachelor thesis has been commissioned by and written in cooperation with Kuopion Palloseura (KuPS), a professional football club acting on the highest professional level in Finland, the Veikkausliiga. The main objective of this thesis is to provide the commissioning client, KuPS, as well as any other institutions closely related to Finnish football with a widely researched document that will study some of the problems the client is faces in its business. Thorough research and theoretical analysis will lead to clear recommendations on how the addressed issues can be dealt with

The neural correlates of academic self-concept in adolescence and the relation to makeing future-oriented academic choices

This study examined the role of brain regions involved in academic self-evaluation in relation to problems with study orientation. For this purpose, 48 participants between ages 14–20 years evaluated themselves on academic traits sentences in an fMRI session. In addition, participants completed an orientation to study choice questionnaire, evaluated the importance of academic traits, and completed a reading and shortened IQ test as an index of cognitive performance. Behavioral results showed that academic self-evaluations were a more important predictor for problems with study orientation compared to subjective academic importance or academic performance. On a neural level, we found that individual differences in the positivity of academic self-evaluations were reflected in increased precuneus activity. Moreover, precuneus activity mediated the relation between academic self positivity and problems with study orientation. Together, these findings support the importance of studying academic self-concept and its neural correlates in the educational decision-making process

Deep Transfer Learning for Automated Single-Lead EEG Sleep Staging with Channel and Population Mismatches

Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice.However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N=94). Performance of the different training strategies was evaluated using Cohen's Kappa (κ) in eight smaller target datasets consisting of healthy subjects (N=60), patients with obstructive sleep apnea (OSA, N=60), insomnia (N=60), and REM sleep behavioral disorder (RBD, N=22), combined with two EEG channels, F3-M2 and F3-F4. No differences in performance between the training strategies was observed in the agematched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ = .17 on average. We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training

Deep Transfer Learning for Automated Single-Lead EEG Sleep Staging with Channel and Population Mismatches

Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice.However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N=94). Performance of the different training strategies was evaluated using Cohen's Kappa (κ) in eight smaller target datasets consisting of healthy subjects (N=60), patients with obstructive sleep apnea (OSA, N=60), insomnia (N=60), and REM sleep behavioral disorder (RBD, N=22), combined with two EEG channels, F3-M2 and F3-F4. No differences in performance between the training strategies was observed in the agematched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ = .17 on average. We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training

Better self-concept, better future choices?: Behavioral and neural changes after a naturalistic self-concept training program for adolescents

A large number of adolescents experience difficulty when choosing a suitable higher education program that matches their self-views. Stimulating self-concept development could help adolescents to increase their chances of finding a suitable major. We addressed this issue by examining the effects of a naturalistic self-concept training within a gap year context on behavioral and neural correlates of self-evaluations, as well as the long-term effects for future educational decision-making. In total, 38 adolescents/young adults (ages 16-24 years) participated in a 4-wave longitudinal study, with lab visits before, during, and after the training, including behavioral assessments and fMRI. During fMRI-scanning, they rated themselves on positive and negative traits in academic, (pro)social, and physical domains, and additionally filled out questionnaires related to self-esteem and self-concept clarity. Results showed that the positivity of domain-specific self-evaluations, self-esteem, and self-concept clarity increased during the training. Second, participants with lower medial PFC activity during self-evaluation before training showed larger self-esteem increases over the year. Moreover, mPFC activity increased after training for the evaluation of positive but not negative traits. Furthermore, individual differences in the rate of change (slope) in self-concept clarity and social self-evaluations positively predicted social adjustment to college and academic performance 6 months after training. Together, these findings suggest that self-concept can be modulated in late adolescents, with an important role of the medial PFC in relation to enhanced positive self-evaluations, and self-concept clarity as a predictor of future educational outcomes.Pathways through Adolescenc