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.