Social perspective taking is associated with self-reported prosocial behavior and regional cortical thickness across adolescence

Basic perspective taking and mentalising abilities develop in childhood, but recent studies indicate that the use of social perspective taking to guide decisions and actions has a prolonged development that continues throughout adolescence. Here, we aimed to replicate this research and investigate the hypotheses that individual differences in social perspective taking in adolescence are associated with real-life prosocial and antisocial behavior and differences in brain structure. We employed an experimental approach and a large cross-sectional sample (n=293) of participants aged 7-26 years old to assess age-related improvement in social perspective taking usage during performance of a version of the Director task. In subsamples, we then tested how individual differences in social perspective taking were related to self-reported prosocial behavior and peer relationship problems on the Strengths and Difficulties Questionnaire (SDQ) (n=184) and to magnetic resonance imaging (MRI) measures of regional cortical thickness and surface area (n=226). The pattern of results in the Director task replicated previous findings by demonstrating continued improvement in use of social perspective taking across adolescence. The study also showed that better social perspective taking usage is associated with more self-reported prosocial behavior, as well as to thinner cerebral cortex in regions in the left hemisphere encompassing parts of the caudal middle frontal and precentral gyri and lateral parietal regions. These associations were observed independently of age, and might partly reflect individual developmental variability. The relevance of cortical development was additionally supported by indirect effects of age on social perspective taking usage via cortical thickness

Meta-analysis of generalized additive models in neuroimaging studies

Contains fulltext : 231772.pdf (publisher's version ) (Open Access)Analyzing data from multiple neuroimaging studies has great potential in terms of increasing statistical power, enabling detection of effects of smaller magnitude than would be possible when analyzing each study separately and also allowing to systematically investigate between-study differences. Restrictions due to privacy or proprietary data as well as more practical concerns can make it hard to share neuroimaging datasets, such that analyzing all data in a common location might be impractical or impossible. Meta-analytic methods provide a way to overcome this issue, by combining aggregated quantities like model parameters or risk ratios. Most meta-analytic tools focus on parametric statistical models, and methods for meta-analyzing semi-parametric models like generalized additive models have not been well developed. Parametric models are often not appropriate in neuroimaging, where for instance age-brain relationships may take forms that are difficult to accurately describe using such models. In this paper we introduce meta-GAM, a method for meta-analysis of generalized additive models which does not require individual participant data, and hence is suitable for increasing statistical power while upholding privacy and other regulatory concerns. We extend previous works by enabling the analysis of multiple model terms as well as multivariate smooth functions. In addition, we show how meta-analytic p-values can be computed for smooth terms. The proposed methods are shown to perform well in simulation experiments, and are demonstrated in a real data analysis on hippocampal volume and self-reported sleep quality data from the Lifebrain consortium. We argue that application of meta-GAM is especially beneficial in lifespan neuroscience and imaging genetics. The methods are implemented in an accompanying R package metagam, which is also demonstrated

Relationship between cerebrospinal fluid neurodegeneration biomarkers and temporal brain atrophy in cognitively healthy older adults

It is unclear whether cerebrospinal fluid (CSF) biomarkers of neurodegeneration predict brain atrophy in cognitively healthy older adults, whether these associations can be explained by phosphorylated tau181 (p-tau) and the 42 amino acid form of amyloid-ꞵ (Aꞵ42) biomarkers, and which neural substrates may drive these associations. We addressed these questions in two samples of cognitively healthy older adults who underwent longitudinal structural MRI up to 7 years and had baseline CSF levels of heart-type fatty-acid binding protein [FABP3], total-tau, neurogranin, and neurofilament light [NFL] (n=189, scans=721). The results showed that NFL, total-tau, and FABP3 predicted entorhinal thinning and hippocampal atrophy. Brain atrophy was not moderated by Aꞵ42 and the associations between NFL and FABP3 with brain atrophy were independent of p-tau. The spatial pattern of cortical atrophy associated with the biomarkers overlapped with neurogenetic profiles associated with expression in the axonal (total-tau, NFL) and dendritic (neurogranin) components. CSF biomarkers of neurodegeneration are useful for predicting specific features of brain atrophy in older adults, independently of amyloid and tau pathology biomarkers

Regression-based normative data for the D-KEFS Color-Word Interference Test in Norwegian adults ages 20–85

Objective: The Delis-Kaplan Executive Function System (D-KEFS) Color-Word-Interference Test (CWIT; AKA Stroop test) is a widely used measure of processing speed and executive function. While test materials and instructions have been translated to Norwegian, only American age-adjusted norms from D-KEFS are available in Norway. We here develop norms in a sample of 1011 Norwegians between 20 and 85 years. We provide indexes for stability over time and assess demographic adjustments applying the D-KEFS norms. Method: Participants were healthy Norwegian adults from Center for Lifespan Changes in Brain and Cognition (LCBC) (n = 899), the Dementia Disease Initiation (n = 77), and Oslo MCI (n = 35). Using regression-based norming, we estimated linear and non-linear effects of age, education, and sex on the CWIT 1-4 subtests. Stability over time was assessed with intraclass correlation coefficients (ICC). The normative adjustment of the D-KEFS norms was assessed with linear regression models. Results: Increasing age was associated with slower completion on all CWIT subtests in a non-linear fashion (accelerated lowering of performance with older age). Women performed better on CWIT-1&3. Higher education predicted faster completion time on CWIT-3&4. The original age-adjusted norms from D-KEFS did not adjust for sex or education. Furthermore, we observed significant, albeit small effects of age on all CWIT subtests. ICC analyses indicated moderate to good stability over time. Conclusion: We present demographically adjusted regression-based norms and stability indexes for the D-KEFS CWIT subtests. US D-KEFS norms may be inaccurate for Norwegians with high or low educational attainment, especially women

Neuroanatomical Variability of Religiosity

We hypothesized that religiosity, a set of traits variably expressed in the population, is modulated by neuroanatomical variability. We tested this idea by determining whether aspects of religiosity were predicted by variability in regional cortical volume. We performed structural magnetic resonance imaging of the brain in 40 healthy adult participants who reported different degrees and patterns of religiosity on a survey. We identified four Principal Components of religiosity by Factor Analysis of the survey items and associated them with regional cortical volumes measured by voxel-based morphometry. Experiencing an intimate relationship with God and engaging in religious behavior was associated with increased volume of R middle temporal cortex, BA 21. Experiencing fear of God was associated with decreased volume of L precuneus and L orbitofrontal cortex BA 11. A cluster of traits related with pragmatism and doubting God's existence was associated with increased volume of the R precuneus. Variability in religiosity of upbringing was not associated with variability in cortical volume of any region. Therefore, key aspects of religiosity are associated with cortical volume differences. This conclusion complements our prior functional neuroimaging findings in elucidating the proximate causes of religion in the brain

The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan.

Development and aging of the cerebral cortex show similar topographic organization and are governed by the same genes. It is unclear whether the same is true for subcortical regions, which follow fundamentally different ontogenetic and phylogenetic principles. We tested the hypothesis that genetically governed neurodevelopmental processes can be traced throughout life by assessing to which degree brain regions that develop together continue to change together through life. Analyzing over 6000 longitudinal MRIs of the brain, we used graph theory to identify five clusters of coordinated development, indexed as patterns of correlated volumetric change in brain structures. The clusters tended to follow placement along the cranial axis in embryonic brain development, suggesting continuity from prenatal stages, and correlated with cognition. Across independent longitudinal datasets, we demonstrated that developmental clusters were conserved through life. Twin-based genetic correlations revealed distinct sets of genes governing change in each cluster. Single-nucleotide polymorphisms-based analyses of 38,127 cross-sectional MRIs showed a similar pattern of genetic volume-volume correlations. In conclusion, coordination of subcortical change adheres to fundamental principles of lifespan continuity and genetic organization

Inflammation, Amyloid, and Atrophy in The Aging Brain: Relationships with Longitudinal Changes in Cognition

Amyloid deposition occurs in aging, even in individuals free from cognitive symptoms, and is often interpreted as preclinical Alzheimer’s disease (AD) pathophysiology. YKL-40 is a marker of neuroinflammation, being increased in AD, and hypothesized to interact with amyloid-β (Aβ) in causing cognitive decline early in the cascade of AD pathophysiology. Whether and how Aβ and YKL-40 affect brain and cognitive changes in cognitively healthy older adults is still unknown. We studied 89 participants (mean age: 73.1 years) with cerebrospinal fluid samples at baseline, and both MRI and cognitive assessments from two time-points separated by two years. We tested how baseline levels of Aβ42 and YKL-40 correlated with changes in cortical thickness and cognition. Thickness change correlated with Aβ42 only in Aβ42+ participants (<600 pg/mL, n = 27) in the left motor and premotor cortices. Aβ42 was unrelated to cognitive change. Increased YKL-40 was associated with less preservation of scores on the animal naming test in the total sample (r = –0.28, p = 0.012) and less preservation of a score reflecting global cognitive function for Aβ42+ participants (r = –0.58, p = 0.004). Our results suggest a role for inflammation in brain atrophy and cognitive changes in cognitively normal older adults, which partly depended on Aβ accumulation