To examine the association of posttraumatic headache (PTH) type with postconcussive symptoms (PCS), pain intensity, and fluid cognitive function across recovery after pediatric concussion.

This prospective, longitudinal study recruited children (aged 8–16.99 years) within 24 hours of sustaining a concussion or mild orthopedic injury (OI) from two pediatric hospital emergency departments. Based on parent-proxy ratings of pre- and postinjury headache, children were classified as concussion with no PTH (n = 18), new PTH (n = 43), worse PTH (n = 58), or non-worsening chronic PTH (n = 19), and children with OI with no PTH (n = 58). Children and parents rated PCS and children rated pain intensity weekly up to 6 months. Children completed computerized testing of fluid cognition 10 days, 3 months, and 6- months postinjury. Mixed effects models compared groups across time on PCS, pain intensity, and cognition, controlling for preinjury scores and covariates.

Group differences in PCS decreased over time. Cognitive and somatic PCS were higher in new, chronic, and worse PTH relative to no PTH (up to 8 weeks postinjury; d = 0.34 to 0.87 when significant) and OI (up to 5 weeks postinjury; d = 0.30 to 1.28 when significant). Pain intensity did not differ by group but declined with time postinjury. Fluid cognition was lower across time in chronic PTH versus no PTH (d = −0.76) and OI (d = −0.61) and in new PTH versus no PTH (d = −0.51).

Onset of PTH was associated with worse PCS up to 8 weeks after pediatric concussion. Chronic PTH and new PTH were associated with moderately poorer fluid cognitive functioning up to 6 months postinjury. Pain declined over time regardless of PTH type.

The New Jersey Kids Study (NJKS) is a transdisciplinary statewide initiative to understand influences on child health, development, and disease. We conducted a mixed-methods study of project planning teams to investigate team effectiveness and relationships between team dynamics and quality of deliverables.

Ten theme-based working groups (WGs) (e.g., Neurodevelopment, Nutrition) informed protocol development and submitted final reports. WG members (n = 79, 75%) completed questionnaires including de-identified demographic and professional information and a modified TeamSTEPPS Team Assessment Questionnaire (TAQ). Reviewers independently evaluated final reports using a standardized tool. We analyzed questionnaire results and final report assessments using linear regression and performed constant comparative qualitative analysis to identify central themes.

WG-level factors associated with greater team effectiveness included proportion of full professors (β = 31.24, 95% CI 27.65–34.82), team size (β = 0.81, 95% CI 0.70–0.92), and percent dedicated research effort (β = 0.11, 95% CI 0.09–0.13); age distribution (β = −2.67, 95% CI –3.00 to –2.38) and diversity of school affiliations (β = –33.32, 95% CI –36.84 to –29.80) were inversely associated with team effectiveness. No factors were associated with final report assessments. Perceptions of overall initiative leadership were associated with expressed enthusiasm for future NJKS participation. Qualitative analyses of final reports yielded four themes related to team science practices: organization and process, collaboration, task delegation, and decision-making patterns.

We identified several correlates of team effectiveness in a team science initiative's early planning phase. Extra effort may be needed to bridge differences in team members' backgrounds to enhance the effectiveness of diverse teams. This work also highlights leadership as an important component in future investigator engagement.

Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.

Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.

Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.

We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).

This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.

The association between sleep quality and cognition is widely established, but the role of aging in this relationship is largely unknown.

To examine how age impacts the sleep–cognition relationship and determine whether there are sensitive ranges when the relationship between sleep and cognition is modified. This investigation could help identify individuals at risk for sleep-related cognitive impairment.

Sample included 711 individuals (ages 36.00–89.83, 59.66 ± 14.91, 55.7 % female) from the Human Connectome Project-Aging (HCP-A).

The association between sleep quality (Pittsburgh Sleep Quality Index, PSQI) and cognition (Crystallized Cognition Composite and Fluid Cognition Composite from the NIH Toolbox, the Trail Making Test, TMT, and the Rey Auditory Verbal Learning Test, RAVLT) was measured using linear regression models, with sex, race, use of sleep medication, hypertension, and years of education as covariates. The interaction between sleep and age on cognition was tested using the moderation analysis, with age as both continuous linear and nonlinear (quadratic) terms.

There was a significant interaction term between the PSQI and nonlinear age term (age2) on TMT-B (p = 0.02) and NIH Toolbox crystallized cognition (p = 0.02), indicating that poor sleep quality was associated with worse performance on these measures (sensitive age ranges 50–75 years for TMT-B and 66–70 years for crystallized cognition).

The sleep–cognition relationship may be modified by age. Individuals in the middle age to early older adulthood age band may be most vulnerable to sleep-related cognitive impairment.