Commentary: Leveraging discovery science to advance child and adolescent psychiatric research--a commentary on Zhao and Castellanos 2016

'Big Data' and 'Population Imaging' are becoming integral parts of inspiring research aimed at delineating the biological underpinnings of psychiatric disorders. The scientific strategies currently associated with big data and population imaging are typically embedded in so-called discovery science, thereby pointing to the hypothesis-generating rather than hypothesis-testing nature of discovery science. In this issue, Yihong Zhao and F. Xavier Castellanos provide a compelling overview of strategies for discovery science aimed at progressing our understanding of neuropsychiatric disorders. In particular, they focus on efforts in genetic and neuroimaging research, which, together with extended behavioural testing, form the main pillars of psychopathology research

Assessing age-dependent multi-task functional co-activation changes using measures of task-potency

Contains fulltext : 197311.pdf (publisher's version ) (Open Access)It is being hypothesised that the developing adolescent brain is increasingly enlisting long-range connectivity, allowing improved communication between spatially distant brain regions. The developmental trajectories of such maturational changes remain elusive. Here, we aim to study how the brain engages in multiple tasks (working memory, reward processing, and inhibition) at the network-level and evaluate how effects of age across these tasks are related to each other. We characterise how the brain departs from its functional baseline architecture towards task-induced functional connectivity modulations using a novel measure called task potency, allowing direct comparison between tasks by defining sensitivity to one or multiple tasks. By applying this method in a sample of healthy participants (N=218) aged 8-30 years, we demonstrate maturational changes in task-dependent functional co-activation over and above baseline connectivity maturation. Our results provide evidence for task-specific maturational windows with different cognitive systems probed by different tasks displaying specific age-range dependencies of strongest developmental change. Our results highlight the use of task potency for modelling developmental trajectories and the impact of differential maturation across tasks. This enables better characterisation of cognitive processes disrupted in neurodevelopmental disorders and may explain the increased level of heterogeneity observed in adolescent population studies.12 p

Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI

Contains fulltext : 155214.pdf (Publisher’s version ) (Closed access)We proposed ICA-AROMA as a strategy for the removal of motion-related artifacts from fMRI data (Pruim et al., submitted for publication). ICA-AROMA automatically identifies and subsequently removes data-driven derived components that represent motion-related artifacts. Here we present an extensive evaluation of ICA-AROMA by comparing our strategy to a range of alternative strategies for motion-related artifact removal: (i) no secondary motion correction, (ii) extensive nuisance regression utilizing 6 or (iii) 24 realignment parameters, (iv) spike regression (Satterthwaite et al., 2013a),(v) motion scrubbing (Power et al., 2012), (vi) aCompCor (Behzadi et al., 2007; Muschelli et al., 2014), (vii) SOCK (Bhaganagarapu et al., 2013), and (viii) ICA-FIX (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014), without re-training the classifier. Using three different functional connectivity analysis approaches and four different multi-subject resting-state fMRI datasets, we assessed all strategies regarding their potential to remove motion artifacts, ability to preserve signal of interest as well as ability to induce loss in temporal degrees of freedom (tDoF). Results demonstrated that ICA-AROMA, spike regression, scrubbing, and ICA-FIX similarly minimized the impact of motion on functional connectivity metrics. However, both ICA-AROMA and ICA-FIX resulted in significantly improved resting-state network reproducibility and decreased loss in tDoF compared to spike regression and scrubbing. In comparison to ICA-FIX, ICA-AROMA yielded improved preservation of signal of interest across all datasets. These results demonstrate that ICA-AROMA is an effective strategy for removing motion-related artifacts from rfMRI data. Our robust and generalizable strategy avoids the need for censoring fMRI data and reduces motion-induced signal variations in fMRI data, while preserving signal of interest and increasing the reproducibility of functional connectivity metrics. In addition, ICA-AROMA preserves the temporal non-artifactual time-series characteristics and limits the loss in tDoF, thereby increasing statistical power at both the subject- and the between-subject analysis level.10 p

L'économie du sport

Sport mondialisé, sport marchandisé. Le poids économique du sport. L'organisation économique du sport professionnel : le modèle fermé et collectiviste des Etats-Unis au service d'une logique de profit; le modèle ouvert et libéral en Europe au service d'une logique sportive

Middle cerebral artery blood velocity during intense static exercise is dominated by a Valsalva maneuver

Functional magnetic resonance imaging can measure distributed and subtle variations in brain responses associated with task performance. However, it is unclear whether the rich variety of responses observed across the brain is functionally meaningful and consistent across individuals. Here, we used a multivariate clustering approach that grouped brain regions into clusters based on the similarity of their task-evoked temporal responses at the individual level, and then established the spatial consistency of these individual clusters at the group level. We observed a stable pseudohierarchy of task-evoked networks in the context of a delayed sequential motor task, where the fractionation of networks was driven by a gradient of involvement in motor sequence preparation versus execution. In line with theories about higher-level cognitive functioning, this gradient evolved in a rostro-caudal manner in the frontal lobe. In addition, parcellations in the cerebellum and basal ganglia matched with known anatomical territories and fiber pathways with the cerebral cortex. These findings demonstrate that subtle variations in brain responses associated with task performance are systematic enough across subjects to define a pseudohierarchy of task-evoked networks. Such networks capture meaningful functional features of brain organization as shaped by a given cognitive context

Massively parallel nonparametric regression, with an application to developmental brain mapping

Contains fulltext : 135961.pdf (publisher's version ) (Closed access)We propose a penalized spline approach to performing large numbers of parallel non-parametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naively performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70000 brain locations. Supplementary materials, including an appendix and an R package, are available online

Revisiting subcortical brain volume correlates of autism in the ABIDE dataset: effects of age and sex

Contains fulltext : 190645.pdf (publisher's version ) (Closed access)BACKGROUND: Autism spectrum disorders (ASD) are characterized by substantial clinical, etiological and neurobiological heterogeneity. Despite this heterogeneity, previous imaging studies have highlighted the role of specific cortical and subcortical structures in ASD and have forwarded the notion of an ASD specific neuroanatomy in which abnormalities in brain structures are present that can be used for diagnostic classification approaches. METHOD: A large (N = 859, 6-27 years, IQ 70-130) multi-center structural magnetic resonance imaging dataset was examined to specifically test ASD diagnostic effects regarding (sub)cortical volumes. RESULTS: Despite the large sample size, we found virtually no main effects of ASD diagnosis. Yet, several significant two- and three-way interaction effects of diagnosis by age by gender were found. CONCLUSION: The neuroanatomy of ASD does not exist, but is highly age and gender dependent. Implications for approaches of stratification of ASD into more homogeneous subtypes are discussed.15 p