Metacognitive accuracy predicts self-reported quality of life following traumatic brain injury.

OBJECTIVE: Metacognition and quality of life (QoL) are both adversely affected by traumatic brain injury (TBI), but the relation between them is not fully understood. As such, the purpose of this study was to determine the degree to which metacognitive accuracy predicts QoL in individuals with TBI. METHODS: Eighteen participants with moderate-to-severe TBI completed a stimulus-response task requiring the discrimination of emotions depicted in pictures of faces and then provided a retrospective confidence judgment after each response. Metacognitive accuracy was calculated using participants' response accuracy and confidence judgment accuracy. Participants also completed the Quality of Life After Brain Injury (QOLIBRI) questionnaire to assess QoL in various areas of functioning. RESULTS: Performance of a linear regression analysis revealed that higher metacognitive accuracy significantly predicted lower overall QoL. Additionally, higher metacognitive accuracy significantly predicted lower QoL related to cognition and physical limitations. CONCLUSION: The study results provide evidence of an inverse relation between metacognitive performance and QoL following TBI. Metacognitive changes associated with TBI and their relation to QoL have several clinical implications for TBI rehabilitation.

The Role of Neuroinflammation in Neuropsychiatric Disorders Following Traumatic Brain Injury: A Systematic Review.

BACKGROUND: Neuropsychiatric symptoms are common following traumatic brain injury (TBI), but their etiological onset remains unclear. Mental health research implicates neuroinflammation in the development of psychiatric disorders. The presence of neuroinflammatory responses after TBI thus prompts an investigation of their involvement in the emergence of neuropsychiatric disorders postinjury. OBJECTIVE: Review the literature surrounding the role of neuroinflammation and immune response post-TBI in the development of neuropsychiatric disorders. METHODS: A search of scientific databases was conducted for original, empirical studies in human subjects. Key words such as "neuroinflammation," "TBI," and "depression" were used to identify psychopathology as an outcome TBI and the relation to neuroinflammatory response. RESULTS: Study results provide evidence of neuroinflammation mediated post-TBI neuropsychiatric disorders including anxiety, trauma/stress, and depression. Inflammatory processes and stress response dysregulation can lead to secondary cell damage, which promote the development and maintenance of neuropsychiatric disorders postinjury. CONCLUSION: This review identifies both theoretical and empirical support for neuroinflammatory response as feasible mechanisms underlying neuropsychiatric disorders after TBI. Further understanding of these processes in this context has significant clinical implications for guiding the development of novel treatments to reduce psychiatric symptoms postinjury. Future directions to address current limitations in the literature are discussed.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury.

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Establishing ground truth in the traumatic brain injury literature: if replication is the answer, then what are the questions?

The replication crisis poses important challenges to modern science. Central to this challenge is re-establishing ground truths or the most fundamental theories that serve as the bedrock to a scientific community. However, the goal to identify hypotheses with the greatest support is non-trivial given the unprecedented rate of scientific publishing. In this era of high-volume science, the goal of this study is to sample from one research community within clinical neuroscience (traumatic brain injury) and track major trends that have shaped this literature over the past 50 years. To do so, we first conduct a decade-wise (1980-2019) network analysis to examine the scientific communities that shape this literature. To establish the robustness of our findings, we utilized searches from separate search engines (Web of Science; Semantic Scholar). As a second goal, we sought to determine the most highly cited hypotheses influencing the literature in each decade. In a third goal, we then searched for any papers referring to 'replication' or efforts to reproduce findings within our >50 000 paper dataset. From this search, 550 papers were analysed to determine the frequency and nature of formal replication studies over time. Finally, to maximize transparency, we provide a detailed procedure for the creation and analysis of our dataset, including a discussion of each of our major decision points, to facilitate similar efforts in other areas of neuroscience. We found that the unparalleled rate of scientific publishing within the brain injury literature combined with the scarcity of clear hypotheses in individual publications is a challenge to both evaluating accepted findings and determining paths forward to accelerate science. Additionally, while the conversation about reproducibility has increased over the past decade, the rate of published replication studies continues to be a negligible proportion of the research. Meta-science and computational methods offer the critical opportunity to assess the state of the science and illuminate pathways forward, but ultimately there is structural change needed in the brain injury literature and perhaps others.

Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics.

In this critical review, we examine the application of predictive models, for example, classifiers, trained using machine learning (ML) to assist in interpretation of functional neuroimaging data. Our primary goal is to summarize how ML is being applied and critically assess common practices. Our review covers 250 studies published using ML and resting-state functional MRI (fMRI) to infer various dimensions of the human functional connectome. Results for holdout ("lockbox") performance was, on average, ∼13% less accurate than performance measured through cross-validation alone, highlighting the importance of lockbox data, which was included in only 16% of the studies. There was also a concerning lack of transparency across the key steps in training and evaluating predictive models. The summary of this literature underscores the importance of the use of a lockbox and highlights several methodological pitfalls that can be addressed by the imaging community. We argue that, ideally, studies are motivated both by the reproducibility and generalizability of findings as well as the potential clinical significance of the insights. We offer recommendations for principled integration of machine learning into the clinical neurosciences with the goal of advancing imaging biomarkers of brain disorders, understanding causative determinants for health risks, and parsing heterogeneous patient outcomes.