We explored the effects of aging on 2 large-scale brain networks, the default mode network (DMN) and the task-positive network (TPN). During functional magnetic resonance imaging scanning, young and older participants carried out 4 visual tasks: detection, perceptual matching, attentional cueing, and working memory. Accuracy of performance was roughly matched at 80% across tasks and groups. Modulations of activity across conditions were assessed, as well as functional connectivity of both networks. Younger adults showed a broader engagement of the DMN and older adults a more extensive engagement of the TPN. Functional connectivity in the DMN was reduced in older adults, whereas the main pattern of TPN connectivity was equivalent in the 2 groups. Age-specific connectivity also was seen in TPN regions. Increased activity in TPN areas predicted worse accuracy on the tasks, but greater expression of a connectivity pattern associated with a right dorsolateral prefrontal TPN region, seen only in older adults, predicted better performance. These results provide further evidence for age-related differences in the DMN and new evidence of age differences in the TPN. Increased use of the TPN may reflect greater demand on cognitive control processes in older individuals that may be partially offset by alterations in prefrontal functional connectivity.

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

There is growing evidence that fluctuations in brain activity may exhibit scale-free (“fractal”) dynamics. Scale-free signals follow a spectral-power curve of the form P(f ) ∝ f−β, where spectral power decreases in a power-law fashion with increasing frequency. In this study, we demonstrated that fractal scaling of BOLD fMRI signal is consistently suppressed for different sources of cognitive effort. Decreases in the Hurst exponent (H), which quantifies scale-free signal, was related to three different sources of cognitive effort/task engagement: 1) task difficulty, 2) task novelty, and 3) aging effects. These results were consistently observed across multiple datasets and task paradigms. We also demonstrated that estimates of H are robust across a range of time-window sizes. H was also compared to alternative metrics of BOLD variability (SDBOLD) and global connectivity (Gconn), with effort-related decreases in H producing similar decreases in SDBOLD and Gconn. These results indicate a potential global brain phenomenon that unites research from different fields and indicates that fractal scaling may be a highly sensitive metric for indexing cognitive effort/task engagement.