Personality and local brain structure: Their shared genetic basis and reproducibility

Local cortical architecture is highly heritable and distinct genes are associated with specific cortical regions. Total surface area has been shown to be genetically correlated with complex cognitive capacities, suggesting cortical brain structure is a viable endophenotype linking genes to behavior. However, to what extend local brain structure has a genetic association with cognitive and emotional functioning is incompletely understood. Here, we study the genetic correlation between personality traits and local cortical structure in a large-scale twin sample (Human Connectome Project, n ​= ​1102, 22-37y) and we evaluated whether observed associations reflect generalizable relationships between personality and local brain structure two independent age-matched samples (Brain Genomics Superstructure Project: n ​= ​925, age ​= ​19-35y, enhanced Nathan Kline Institute dataset: n ​= ​209, age: 19-39y). We found a genetic overlap between personality traits and local cortical structure in 10 of 18 observed phenotypic associations in predominantly frontal cortices. However, we only observed evidence in favor of replication for the negative association between surface area in medial prefrontal cortex and Neuroticism in both replication samples. Quantitative functional decoding indicated this region is implicated in emotional and socio-cognitive functional processes. In sum, our observations suggest that associations between local brain structure and personality are, in part, under genetic control. However, associations are weak and only the relation between frontal surface area and Neuroticism was consistently observed across three independent samples of young adults

Transdiagnostic commonalities and differences in resting state functional connectivity of the default mode network in schizophrenia and major depression

Schizophrenia and depression are prevalent psychiatric disorders, but their underlying neural bases remains poorly understood. Neuroimaging evidence has pointed towards the relevance of functional connectivity aberrations in defaultmode network (DMN) hubs, dorso-medial prefrontal cortex and precuneus, in both disorders, but commonalities and differences in resting state functional connectivity of those two regions across disorders has not been formally assessed. Here, we took a transdiagnostic approach to investigate resting state functional connectivity of those two regions in 75 patients with schizophrenia and 82 controls from 4 scanning sites and 102 patients with depression and 106 controls from 3 sites. Our results demonstrate common dysconnectivity patterns as indexed by a significant reduction of functional connectivity between precuneus and bilateral superior parietal lobe in schizophrenia and depression. Furthermore, our findings highlight diagnosis-specific connectivity reductions of the parietal operculum in schizophrenia relative to depression. In light of evidence that points towards the importance of the DMN for social cognitive abilities and well documented impairments of social interaction in both patient groups, it is conceivable that the observed transdiagnostic connectivity alterations may contribute to interpersonal difficulties, but this could not be assessed directly in our study as measures of social behavior were not available. Given the operculum's role in somatosensory integration, diagnosis-specific connectivity reductions may indicate a pathophysiological mechanism for basic self-disturbances that is characteristic of schizophrenia, but not depression. (C) 2015 The Authors. Published by Elsevier Inc

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Internally vs. externally triggered movements in patients with major depression

Psychomotor retardation is a prominent clinical feature of major depression. While several studies investigated these deficits, differences between internally and externally triggered response selection and initiation are less well understood. In the current study, we delineate internally vs. externally driven response selection and initiation in depression and their relation to basic psychomotor functioning.20 inpatients diagnosed with a (unipolar) major depression and 20 closely matched healthy controls performed a computerized motor paradigm assessing differences between internally and externally cued movements. Psychomotor performance and basic memory functions were assessed using a neuropsychological test-battery. To examine within group homogeneity a multivariate clustering approach was applied.Patients featured a global slowing of internally and externally cued response selection compared to controls, as well as impairments in basic psychomotor functioning. Yet, basic motor speed was preserved. Furthermore, patients were more severely impaired when movements involved internal response selection. The data-driven clustering revealed two patient subgroups, which both showed psychomotor disturbances, while only one featured slowing of response selection.The results suggest a differential rather than a global psychomotor slowing in major depression with specific impairments of visuospatial and attentional processing as cognitive aspects of psychomotor functioning. As found for depression, in Parkinson's disease internally cued movements are more severely affected than externally cued reactions. Both may therefore be caused by dopaminergic deregulation due to frontostriatal deficits. Finally, multivariate clustering of behavioral data may be a promising future approach to identify subtypes of psychomotor or cognitive disturbances in different patient populations

EP 34. Functional hierarchy within the neural network for optokinetic 'look' nystagmus

Key nodes of neural networks for ocular motor control and visual motion processing have been localized using saccades, smooth pursuit, and optokinetic nystagmus (OKN). Within the context of an independent fMRI study using OKN, 9 bilateral network nodes were localized comprising cortical eye fields in frontal (FEF), supplementary motor (SEF), cingulate (CEF) and parietal cortex (PEF), visual motion centers MT+ and V6, the superior colliculus (SC), the lateral geniculate nucleus (LGN) and the globus pallidus (GP). Here, we examined the network's functional hierarchy as present in the structural co-variation (SCoV) and resting-state (RS) fMRI, and the effect of RS condition (eyes open/closed) on its' functional connectivity (FC). Two publicly available samples were analyzed consisting of the enhanced NKI sample with RS (TR 1.4s) and structural MR data (n = 124; age 46.7 ± 17.6; 40 male) and the "Beijing: eyes open eyes closed sample" measuring RS (TR 2s; n = 48; age 22.5 ± 2.2; 24 male). For the FC analysis, ICA-based denoising (FSL) was applied before spatial preprocessing (SPM) and band-pass filtering. Each bilateral ROI was represented by the first eigenvariate of the respective voxels' time-series and partial correlation were computed using FSLNets. One group t-tests were computed over Fisher's z transformed correlation coefficients. Each ROIs volume was approximated with voxel-based morphometry (VBM8) using non-linearly modulated gray matter density and partial correlations were computed for SCoV. Hierarchical cluster analysis was applied to determine sub-clustering within the OKN network. Edge-wise comparisons between RS conditions were performed using permutation testing and Bonferroni correction. Both FC and SCoV revealed two major subcluster. MT+ and V6 were similar to LGN and SC. The cortical eye fields clustered together with the GP. As effect of RS condition, with eyes closed the CEF switched to the visual subcluster. The edge-wise comparison revealed generally higher FC with eyes open and in particular a decrease of FC between MT+ and PEF, FEF and SEF as well as between V6 and SEF. Hierarchical clustering based on RS and structural data revealed a task-independent sub-division of the network for ocular-motor control and visual motion processing into two streams either involved in top-down (efferent voluntary) ocular-motor control (FEF, PEF, SEF, GP) and in more bottom-up visual target tracking (MT+, V6, LGN, SC) streams. This general network hierarchy was equally present in the RS with eyes open and eyes closed, with the CEF fulfilling a condition specific role in the network. The edge-wise comparison between RS conditions strengthens the evidence for a specific influence of MT+ on the ocular-motor control subcluster. These findings indicate a systematic influence of the resting condition not only on FC of the visual system, but on the state of the whole OKN network, while a general system hierarchy is omnipresent independent of RS condition