Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.

Recent studies have found a deleterious effect of age on a wide variety of measures of functional connectivity, and some hints at a relationship between connectivity at rest and cognitive functioning. However, few studies have combined multiple functional connectivity methods, or examined them over a wide range of adult ages, to try to uncover which metrics and networks seem to be particularly sensitive to age-related decline across the adult lifespan. The present study utilized multiple resting state functional connectivity methods in a sample of adults from 20–80 years old to gain a more complete understanding of the effect of aging on network function and integrity. Whole-brain results showed that aging results in weakening average within-network connectivity, lower system segregation and local efficiency, and higher participation coefficient. Network-level results suggested that nearly every primary sensory and cognitive network faces some degree of age-related decline, including reduced within-network connectivity, higher network-based participation coefficient, and reduced network-level local efficiency. Further, some of these connectivity metrics showed relationships with cognitive performance. Thus, these results suggest that a multi-method analysis of functional connectivity data may be critical to capture the full effect of aging on the health of brain networks.

Purpose To better understand the relationship between exposure to concussive and subconcussive head impacts, white matter integrity, and functional task-related neural activity in former U.S. football athletes. Materials and Methods Between 2011 and 2013, 61 cognitively unimpaired former collegiate and professional football players (age range, 52-65 years) provided informed consent to participate in this cross-sectional study. Participants were stratified across three crossed factors: career duration, concussion history, and primary playing position. Fractional anisotropy (FA) and blood oxygen level-dependent (BOLD) percent signal change (PSC) were measured with diffusion-weighted and task-related functional magnetic resonance imaging, respectively. Analyses of variance of FA and BOLD PSC were used to determine main or interaction effects of the three factors. Results A significant interaction between career duration and concussion history was observed; former college players with more than three concussions had lower FA in a broadly distributed area of white matter compared with those with zero to one concussion (t29 = 2.774; adjusted P = .037), and the opposite was observed for former professional players (t29 = 3.883; adjusted P = .001). A separate interaction between concussion history and position was observed: Nonspeed players with more than three concussions had lower FA in frontal white matter compared with those with zero to one concussion (t25 = 3.861; adjusted P = .002). Analysis of working memory-task BOLD PSC revealed a similar interaction between concussion history and position (all adjusted P < .004). Overall, former players with lower FA tended to have lower BOLD PSC across three levels of a working memory task. Conclusion Career duration and primary playing position seem to modify the effects of concussion history on white matter structure and neural recruitment. The differences in brain structure and function were observed in the absence of clinical impairment, which suggested that multimodal imaging may provide early markers of onset of traumatic neurodegenerative disease. RSNA, 2017 Online supplemental material is available for this article.

Research on the cognitive neuroscience of aging has identified myriad neurocognitive processes that are affected by the aging process, with a focus on identifying neural correlates of cognitive function in aging. The present study aimed to test whether inter-network connectivity among 6 cognitive networks is sensitive to age-related changes in neural efficiency and cognitive functioning. A factor analytic connectivity approach was used to model network interactions during 11 cognitive tasks grouped into 4 primary cognitive domains: vocabulary, perceptual speed, fluid reasoning, and episodic memory. Results showed that both age and task domain were related to inter-network connectivity, and that some of the connections among the networks were associated with performance on the in-scanner tasks. These findings demonstrate that inter-network connectivity among several cognitive networks is not only affected by aging and task demands, but also shows a relationship with task performance. As such, future studies examining inter-network connectivity in aging should consider multiple networks, and multiple task conditions, in order to better measure dynamic patterns of network flexibility over the course of cognitive aging.

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