A Population-Based Study of Pre-Existing Health Conditions in Traumatic Brain Injury.

Health factors impacting both the occurrence of, and recovery from traumatic brain injury (TBI) vary in complexity, and present genuine challenges to researchers and healthcare professionals seeking to characterize injury consequences and determine prognosis. However, attempts to clarify causal links between injury characteristics and clinical outcomes (including mortality) often compel researchers to exclude pre-existing health conditions (PECs) in their samples, including psychiatric history, medication usage, and other comorbid conditions. In this pre-registered population-based study (total starting n = 939,123 patients), we examined trends in PEC incidence over 22 years in the state of Pennsylvania (1997-2019) in individuals sustaining TBI (n = 169,452) and individuals with orthopedic injury (n = 87,637). The goal was to determine how PECs interact with age and injury severity to influence short-term outcomes. A further goal was to determine whether number of PECs, or specific PEC clusters contributed to worse outcomes within the TBI cohort, compared with orthopedic injury alone. Primary findings indicate that PECs significantly influenced mortality within the TBI cohort; patients having four or more PECs were associated with approximately a two times greater likelihood of dying in acute care (odds ratio [OR] 1.9). Additionally, cluster analyses revealed four distinct PEC clusters that are age and TBI severity dependent. Overall, the likelihood of zero PECs hovers at ∼25%, which is critical to consider in TBI outcomes work and could potentially contribute to the challenges facing intervention science with regard to reproducibility of findings.

Race and ethnicity considerations in traumatic brain injury research: Incidence, reporting, and outcome.

PRIMARY OBJECTIVE: This study has three goals: to determine whether there is a higher rate of traumatic brain injury (TBI) for people of color (POC), whether TBI studies report racial/ethnic demographics, and whether there is a discrepancy in discharge destinations between Whites and POC. We examined whether 1) a higher percentage of POC would sustain head injuries than expected, 2) the majority of TBI studies examined (>50%) would not include racial/ethnic demographics, and 3) Whites would be discharged to further treatment over POC. RESEARCH DESIGN: Retrospective study and literature review. METHODS AND PROCEDURES: Data from the Pennsylvania Trauma System Foundation was used to determine the number of POC with TBI using X2 analysis, as well as where patients with TBI were being discharged using a configural frequency analysis. PubMed was used for the literature search to examine the frequency of reporting race/ethnicity in TBI literature. MAIN OUTCOMES AND RESULTS: Results demonstrated that Blacks sustain more TBIs than would be expected (p < .05), the majority of scientific studies (78%) do not report racial/ethnic demographic information, and Whites are discharged to further care more often than POC. CONCLUSIONS: These findings highlight differences in incidence and treatment of TBI between White individuals and POC, raising important considerations for providers and researchers.

Enhanced default mode connectivity predicts metacognitive accuracy in traumatic brain injury.

OBJECTIVE: To examine the role that intrinsic functional networks, specifically the default mode network, have on metacognitive accuracy for individuals with moderate to severe traumatic brain injury (TBI). METHOD: A sample of 44 individuals (TBI, n = 21; healthy controls [HCs], n = 23) were included in the study. All participants underwent an MRI scan and completed neuropsychological testing. Metacognitive accuracy was defined as participants' ability to correctly judge their item-by-item performance on an abstract reasoning task. Metacognitive values were calculated using the signal detection theory approach of area under the receiver operating characteristic curve. Large-scale subnetworks were created using Power's 264 Functional Atlas. The graph theory metric of network strength was calculated for six subsystem networks to measure functional connectivity. RESULTS: There were significant interactions between head injury status (TBI or HC) and internetwork connectivity between the anterior default mode network (DMN) and salience network on metacognitive accuracy (R2 = 0.13, p = .047) and between the posterior DMN and salience network on metacognitive accuracy (R2 = 0.15, p = .038). There was an interpretable interaction between head injury status and internetwork connectivity between the attention network and salience network on metacognitive accuracy (R2 = 0.13, p = .067). In all interactions, higher connectivity predicted better metacognitive accuracy in the TBI group, but this relationship was reversed for the HC group. CONCLUSION: Enhanced connectivity to both anterior and posterior regions within the DMN facilitates metacognitive accuracy postinjury. These findings are integrated into a larger literature examining network plasticity in TBI. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Diminished neural network dynamics in amnestic mild cognitive impairment.

Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer's dementia (AD) within 30 months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) throughout the course of the disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tijms et al., 2013). In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain "states," or temporal patterns of connectivity across distributed networks, to distinguish individuals with aMCI from healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Our results indicated that individuals with aMCI spent significantly more time in one state in particular, whereas neural network analysis in the HOA sample revealed approximately equivalent representation across four distinct states. Among individuals with aMCI, spending a higher proportion of time in the dominant state relative to a state where participants exhibited high cost (a measure combining connectivity and distance), predicted better language performance and less perseveration. This is the first report to examine neural network dynamics in individuals with aMCI.

Prefrontal gray matter volume predicts metacognitive accuracy following traumatic brain injury.

OBJECTIVE: To examine metacognitive ability (MC) following moderate to severe traumatic brain injury (TBI) using an empirical assessment approach and to determine the relationship between alterations in gray matter volume (GMV) and MC. METHOD: A sample of 62 individuals (TBI n = 34; healthy control [HC] n = 28) were included in the study. Neuroimaging and neuropsychological data were collected for all participants during the same visit. MC was quantified using an approach borrowed from signal detection theory (Type II area under the receiver operating characteristic curve calculation) to evaluate judgments during a modified version of the 3rd edition of the Wechsler Adult Intelligence Scale's Matrix Reasoning subtest where half of the items were presented randomly and half were presented in the order of increasing difficulty. Retrospective confidence judgments were collected on an item-by-item basis. Brain volumetric analyses were conducted using FreeSurfer software. RESULTS: Analyses of the modified Matrix Reasoning task data demonstrated that HCs significantly outperformed TBIs (ordered: d = .63; random: d = .58). There was a significant difference between groups for MC for the randomly presented stimuli (d = .54) but not the ordered stimuli. There was an association between GMV and MC in the TBI group between the right orbital region and MC (R2 = .11). In the HC group, there were associations between the left posterior (R2 = .17), left orbital (R2 = .29), and left dorsolateral (R2 = .21) regions and MC. CONCLUSIONS: These results are consistent with those of previous research on MC in the cognitive neurosciences, but this study demonstrates that injury may moderate the regional contributions to MC. (PsycINFO Database Record

Corrigendum: Dedifferentiation Does Not Account for Hyperconnectivity after Traumatic Brain Injury.

[This corrects the article on p. 297 in vol. 8, PMID: 28769858.].

Dedifferentiation Does Not Account for Hyperconnectivity after Traumatic Brain Injury.

OBJECTIVE: Changes in functional network connectivity following traumatic brain injury (TBI) have received increasing attention in recent neuroimaging literature. This study sought to understand how disrupted systems adapt to injury during resting and goal-directed brain states. Hyperconnectivity has been a common finding, and dedifferentiation (or loss of segregation of networks) is one possible explanation for this finding. We hypothesized that individuals with TBI would show dedifferentiation of networks (as noted in other clinical populations) and these effects would be associated with cognitive dysfunction. METHODS: Graph theory was implemented to examine functional connectivity during periods of task and rest in 19 individuals with moderate/severe TBI and 14 healthy controls (HCs). Using a functional brain atlas derived from 83 functional imaging studies, graph theory was used to examine network dynamics and determine whether dedifferentiation accounts for changes in connectivity. Regions of interest were assigned to one of three groups: task-positive, default mode, or other networks. Relationships between these metrics were then compared with performance on neuropsychological tests. RESULTS: Hyperconnectivity in TBI was most commonly observed as increased within-network connectivity. Network strengths within networks that showed differences between TBI and HCs were correlated with performance on five neuropsychological tests typically sensitive to deficits commonly reported in TBI. Hyperconnectivity within the default mode network (DMN) during task was associated with better performance on Digit Span Backward, a measure of working memory [R2(18) = 0.28, p = 0.02]. In other words, increased differentiation of networks during task was associated with better working memory. Hyperconnectivity within the task-positive network during rest was not associated with behavior. Negative correlation weights were not associated with behavior. CONCLUSION: The primary hypothesis that hyperconnectivity occurs through dedifferentiation was not supported. [corrected]. Instead, enhanced connectivity post injury was observed within network. Results suggest that the relationship between increased connectivity and cognitive functioning may be both state (rest or task) and network dependent. High-cost network hubs were identical for both rest and task, and cost was negatively associated with performance on measures of psychomotor speed and set-shifting.