Objective: A sensorimotor network structural phenotype predicted motor task performance in a previous study in Huntington's disease (HD) gene carriers. We investigated in the visual network whether... Show moreObjective: A sensorimotor network structural phenotype predicted motor task performance in a previous study in Huntington's disease (HD) gene carriers. We investigated in the visual network whether structure - function - behaviour relationship patterns, and the effects of the HD mutation, extended beyond the sensorimotor network.Methods: We used multimodal visual network MRI structural measures (cortical thickness and white matter connectivity), plus visual evoked potentials and task performance (Map Search; Symbol Digit Modalities Test) in healthy controls and HD gene carriers.Results: Using principal component (PC) analysis, we identified a structure - function relationship common to both groups. PC scores differed between groups indicating white matter disorganization (higher RD, lower FA) and slower, and more disperse, VEP signal transmission (higher VEP P100 latency and lower VEP P100 amplitude) in HD than controls while task performance was similar.Conclusions: HD may be associated with reduced white matter organization and efficient visual network function but normal task performance.Significance: These findings indicate that structure - function relationships in the visual network, and the effects of the HD mutation, share some commonalities with those in the sensorimotor network. However, implications for task performance differ between the two networks suggesting the influence of network specific factors. (C) 2019 Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology. Show less
Karch, J.D.; Fivelich, E.; Wenger, E.; Lisofski, N.; Becker, M.; Butler, O.; ... ; Kühn, S. 2019
Adequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and... Show moreAdequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and control for variables that are predictive of within-person variance. To this end, we applied both classical statistical methods and machine-learning-inspired approaches to structural magnetic resonance imaging (sMRI) data of six participants aged 24–31 years gathered at 40–50 occasions distributed over 6–8 months from the Day2day study. We explored the within-person associations between 21 variables covering physiological, affective, social, and environmental factors and global measures of brain volume estimated by VBM8 and FreeSurfer. Time since the first scan was reliably associated with Freesurfer estimates of grey matter volume and total cortex volume, in line with a rate of annual brain volume shrinkage of about 1 percent. For the same two structural measures, time of day also emerged as a reliable predictor with an estimated diurnal volume decrease of, again, about 1 percent. Furthermore, we found weak predictive evidence for the number of steps taken on the previous day and testosterone levels. The results suggest a need to control for time-of-day effects in sMRI research. In particular, we recommend that researchers interested in assessing longitudinal change in the context of intervention studies or longitudinal panels make sure that, at each measurement occasion, (a) a given participant is measured at the same time of day; (b) all participants are measured at about the same time of day. Furthermore, the potential effects of physical activity, including moderate amounts of aerobic exercise, and testosterone levels on MRI-based measures of brain structure deserve further investigation. Show less
Although the outcomes of our daily-life risky decisions are often unknown (e.g., receiving or not receiving a fine after running a red light), the probabilities of these outcomes may also vary in... Show moreAlthough the outcomes of our daily-life risky decisions are often unknown (e.g., receiving or not receiving a fine after running a red light), the probabilities of these outcomes may also vary in uncertainty. That is, the probabilities may be known (risk) or unknown (ambiguity), which influences risk taking behavior to a great extent. A developmental phase associated with heightened risk taking is adolescence, yet how adolescents process risk and ambiguity, and the relation with real-life risk taking, remain elusive. Moreover, individual differences in observed risk taking behavior remain largely overlooked. In this PhD thesis risk and ambiguity processing in adolescents were decomposed using behavioral economics and fMRI, and related to real-life risk taking. The results indicated that risk and ambiguity differentially impact risk-taking behavior, and are processed by different neural mechanisms. In addition, individual variation in task-related and real-life risk taking highlighted that adolescence is not a phase of heightened risk taking for everyone. Moreover, it was found that real-life risk-taking and prosocial tendencies were both predicted by fun seeking, suggesting this trait may make individuals differentially susceptible to positive or negative outcomes. Together, this thesis points towards a more nuanced perspective on adolescent risk taking and its underlying components. Show less
