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

Effects of cognitive-motor dual task training on cognitive and physical performance in healthy children and adolescents: A scoping review

Simultaneous dual- or multitasking training has been used in manifold ways to improve cognitive-motor performance in different age groups. Dual task (DT) training is assumed to improve both, single task (ST) motor and cognitive performance, but particularly, performance under dual tasking conditions. Further, DT interventions have been shown to be beneficial for motor skill learning and cognitive performance as well as academic achievements in children and adolescents. The aim of this scoping review was to summarize current evidence on different cognitive-motor interventions that practice motor and cognitive performance simultaneously in children and adolescents and to identify training regimes that are most effective to improve cognitive or motor performance in this target group.

METHODS

Four electronic databases were searched (Pubmed, MEDLINE, Web of Science and APA Psycinfo) until May 2021. Following the PRISMA guidelines, title, abstract, and full-text screening as well as quality assessment was done by two independent reviewers. Studies were eligible if they (1) were published in English or German language, (2) accessible as a full-text version, (3) included at least one group of children or adolescents with a mean age of 4 to 21 years, (4) used dual-tasks as part of the intervention, (5) conducted one or more training sessions, and (6) reported at least one cognitive or motor outcome. The main outcome measures were cognitive and motor as well as cognitive-motor DT performance. Due to the heterogeneity in the characteristics of the included studies, we designed this review as a scoping review.

RESULTS

Seven studies met the inclusion criteria (n = 543, age four to 14 years, 47.1% female). One study reported two intervention experiments. Studies differed in sample size (20-189) as well as in type of training (specific or general DT training) and dose (frequency: one session/week to 110 sessions within 22 weeks). Overall, task-specific improvements in physical and cognitive functions were found, but not consistently across all interventions. Two interventions out of five interventions that measured motor performance demonstrated improvement in that domain, especially in balance. Three out of five interventions that measured cognitive functions found improved cognition. Only one study examined DT performance post training but failed to gain significant improvements in comparison to a control group. Studies only occasionally integrated training principles like individualization or progression in the design of their intervention.

DISCUSSION

The results indicate that DT training interventions may improve physical and/or cognitive functions in children and adolescents. Best practice recommendations for training regimes cannot be derived as outcomes differed a lot and were not systematically assessed across studies. Future studies should integrate more principles of training monitoring and aspects like individualization and progression to provide ideal training control and achieve better DT training results. Further, more high-quality trials are needed that adhere to the previous concepts.

PSYCINFO CLASSIFICATION

2340 Cognitive Processes 2820 Cognitive & Perceptual Development. 3720 Sports.

Task-generic and task-specific connectivity modulations in the ADHD brain: an integrated analysis across multiple tasks

Attention-deficit/hyperactivity disorder (ADHD) is associated with altered functioning in multiple cognitive domains and neural networks. This paper offers an overarching biological perspective across these. We applied a novel strategy that extracts functional connectivity modulations in the brain across one (P_single), two (P_mix) or three (P_all) cognitive tasks and compared the pattern of modulations between participants with ADHD (n-89), unaffected siblings (n = 93) and controls (n = 84; total N = 266; age range = 8-27 years). Participants with ADHD had significantly fewer P_all connections (modulated regardless of task), but significantly more task-specific (P_single) connectivity modulations than the other groups. The amplitude of these P_single modulations was significantly higher in ADHD. Unaffected siblings showed a similar degree of P_all connectivity modulation as controls but a similar degree of P_single connectivity modulation as ADHD probands. P_all connections were strongly reproducible at the individual level in controls, but showed marked heterogeneity in both participants with ADHD and unaffected siblings. The pattern of reduced task-generic and increased task-specific connectivity modulations in ADHD may be interpreted as reflecting a less efficient functional brain architecture due to a reduction in the ability to generalise processing pathways across multiple cognitive domains. The higher amplitude of unique task-specific connectivity modulations in ADHD may index a more "effortful" coping strategy. Unaffected siblings displayed a task connectivity profile in between that of controls and ADHD probands, supporting an endophenotype view. Our approach provides a new perspective on the core neural underpinnings of ADHD.

Identifying a task-invariant cognitive reserve network using task potency

Cognitive reserve (CR) is thought to protect against the consequence of age- or disease-related structural brain changes across multiple cognitive domains. The neural basis of CR may therefore comprise a functional network that is actively involved in many different cognitive processes. To investigate the existence of such a "task-invariant" CR network, we measured functional connectivity in a cognitively normal sample between 20 and 80 years old (N ​= ​265), both at rest and during the performance of 11 separate tasks that aim to capture four latent cognitive abilities (i.e. vocabulary, episodic memory, processing speed, and fluid reasoning). For each individual, we determined the change in functional connectivity from the resting state to each task state, which is referred to as "task potency" (Chauvin et al., 2018, 2019). Task potency was calculated for each pair among 264 nodes (Power et al., 2012) and then summarized across tasks reflecting the same cognitive ability. Subsequently, we established the correlation between task potency and IQ or education (i.e. CR factors). We identified a set of 57 pairs in which task potency showed significant correlations with IQ, but not education, across all four cognitive abilities. These pairs were included in a principal component analysis, from which we extracted the first component to obtain a latent variable reflecting task potency in this task-invariant CR network. This task potency variable was associated with better episodic memory (β ​= ​0.19, p ​< ​.01) and fluid reasoning performance (β ​= ​0.17, p ​< ​.01) above and beyond the effects of cortical thickness (range [absolute] β ​= ​0.28-0.32, p ​< ​.001). Our identification of this task-invariant network contributes to a better understanding of the mechanism underlying CR, which may facilitate the development of CR-enhancing treatments. Our work also offers a useful alternative operational measure of CR for future studies.

Thresholding functional connectomes by means of mixture modeling

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.