Traumatic brain injury (TBI) presents significant challenges in both clinical and research settings due to its complexity and heterogeneity (Bouchard et al., 2022; Dennis et al., 2022; Goh et al., 2014). Many of these challenges stem from the wide variety of injury mechanisms, severities, and resulting neurological effects, most of which lead to unique difficulties during data analysis. Clinically, TBI can range from mild TBIs to severe brain trauma, each manifesting differently across patients in terms of symptoms, cognitive impairments, and recovery trajectories (Lizhnyak & Ottens, 2015; Rostowsky et al., 2021). Research efforts are further complicated by the variability of injury locations, diffuse versus focal damage, and secondary injuries prompted by inflammation and swelling, all of which contribute to highly individualized patient outcomes. Neuroinformatics plays a crucial role in addressing these challenges by enabling the integration and analysis of multimodal data from neuroimaging, electrophysiology, and genomics (Irimia et al., 2013). Through advanced computational methods, neuroinformatics can help to uncover patterns in these complex datasets. This facilitates the development of predictive models and individualized therapeutic strategies which drive more precise diagnoses and interventions for TBI patients (Dennis et al., 2017, 2023).

Advances in neuroinformatics are transforming the ways in which we understand, analyze, and treat TBI. They do so by providing sophisticated tools for managing large datasets, by integrating diverse types of information, and by developing predictive models of injury outcomes. For example, modern neuroimaging techniques such as magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) generate vast amounts of high-resolution data that can capture subtle structural and functional changes in the brain post-injury (Imms et al., 2023). Neuroinformatics enables the organization and analysis of these datasets, thereby allowing researchers to identify injury biomarkers and to track brain changes across time. Additionally, neuroinformatics platforms facilitate the integration of diverse data types, including clinical assessments, imaging, genetics, and molecular data, thereby offering comprehensive views of TBI effects on the brain. Machine learning (ML) and data-driven models, key components of neuroinformatics, can be used to predict patient outcomes, such as the likelihood of recovery or the risk of developing long-term cognitive deficits. These predictive tools help clinicians to personalize treatment plans by tailoring rehabilitation strategies and medical interventions to individual patient profiles, based on both historical data and real-time monitoring (Maas et al., 2022; Zeiler et al., 2021).

This special issue of Neuroinformatics brings together cutting-edge research on the use of computational approaches to study TBI and related neurological conditions. The articles included highlight innovations in data analysis, imaging techniques, and ML applications. For example, Edelstein et al. highlight a critical and underexplored area in TBI research: sports-related concussions in female athletes. These authors emphasize the need for sex-specific approaches in mTBI diagnosis and management, pointing out the limitations of traditional clinical methods in capturing subtle brain changes in female athletes. The authors propose that neuroinformatics, particularly ML, offers a promising solution by enabling the integration of multimodal neuroimaging data and linking it to sex-specific biological mechanisms. By leveraging advanced data analysis and feature identification techniques, Edelstein et al. advocate for bridging the knowledge gap in concussion research between male and female athletes, thereby advancing our understanding of how brain architecture responds to injury, treatment, and recovery in women. This article is particularly relevant to the field of neuroinformatics, as it demonstrates the power of computational tools to enhance precision medicine, ensuring female athletes receive tailored care based on their unique neurobiological responses to head injuries.

Expanding on the use of ML in TBI research, Guo et al. address the “black box” problem of ML, a critical issue pertaining to the lack of interpretability in the application of deep neural networks (DNNs) to neuroimaging. These authors address the need for interpretability using saliency mapping, which facilitates anatomic interpretability of DNN results. By comparing seven popular saliency map approaches, Guo et al. evaluate how well these methods can assign interpretability to DNN models estimating brain age (BA) from MRI scans. This work is particularly relevant to neuroinformatics, as it highlights the importance of explainability when applying ML to complex medical data. The authors’ large dataset of over 13,000 cognitively normal adults allows for a robust assessment of how effectively each saliency method captures known features of brain aging, such as ventricle dilation and hippocampal atrophy. The research not only underscores the importance of model transparency in clinical applications but also points to the integrated gradients method as the most reliable tool for localizing relevant brain features. By evaluating these saliency approaches, the article contributes to the ongoing trend of enhancing the interpretability of deep learning models in neuroimaging, fostering trust in their use for clinical and research purposes.

In another article published in this issue, Kang et al. explore the role of diffusion tensor imaging (DTI) in detecting subtle white matter abnormalities in TBI patients, particularly those with chronic post-TBI symptoms such as headaches, dizziness, and fatigue. The study focuses on evaluating structural connectivity and measures in TBI patients with or without chronic symptoms. This research reveals that patients with chronic symptoms exhibit altered diffusion parameters compared to controls, suggesting disrupted brain connectivity. This article highlights the potential of advanced neuroimaging techniques, like DTI, to uncover underlying connectivity changes in TBI patients that go unnoticed with conventional imaging. In the context of neuroinformatics, this study exemplifies the growing trend of using quantitative imaging metrics and connectivity analysis to better understand brain network alterations fter injury. By applying these methods, the article provides new insights into the neural mechanisms behind persistent symptoms in TBI patients. This reinforces the importance of advanced data analytics in identifying and managing long-term effects of brain injuries.

DTI tractography is an essential tool for studying brain connectivity and its alterations due to TBI. However, particularly at large scales, tractography has traditionally been a complex and resource-intensive process. Expanding on the importance of DTI analysis in TBI neuroinformatics, Cai et al. present MaPPeRTrac, a significant advancement in TBI tractography neuroinformatics. MaPPeRTrac simplifies and accelerates DTI analsysi by automating the pipeline from MRI data acquisition to the creation of edge density images of structural connectomes. By containerizing software dependencies and using parallel computing, this tool allows researchers to generate massive connectome datasets more efficiently across various high-performance computing environments. The pipeline adheres to FAIR data principles, ensuring reproducibility and accessibility. This is particularly relevant for neuroinformatics research on TBI, where large datasets and detailed structural connectivity analyses are crucial for understanding injury effects and recovery mechanisms. Cai et al. introduce a scalable, user-friendly solution that democratizes tractography, thereby making advanced brain network analysis accessible to a broader research community and accelerating progress in brain injury research.

Neuroimaging challenges similar to those encountered in TBI can be due to related but distinct conditions. One of these is intracerebral hemorrhage (ICH), which is showcased by Zhu et al. in this issue. These authors’ study highlights the growing role of computational models in predicting medical outcomes, specifically focusing on the risk of hydrocephalus following ICH. By retrospectively analyzing clinical data from ICH patients, the authors apply ML techniques, including logistic regression and support vector machine models, to identify key risk factors such as the Glasgow Coma Scale, modified Graeb score, and bleeding volume. This study highlights how neuroinformatics tools can optimize clinical decision-making by offering high-accuracy prediction models. In turn, this can help clinicians to identify at-risk patients and to guide early surgical interventions. The integration of ML in this study exemplifies ongoing trends in neuroinformatics, where large-scale clinical datasets are leveraged to improve personalized treatment approaches and outcomes in neurological conditions. By focusing on predictive modeling, Cai et al. align with the field’s goals of improving prognosis accuracy and tailoring medical interventions based on patient-specific risk profiles.

Current progress in TBI neuroinformatics is not limited to the analysis of human data. For example, in a study published in this issue, Ding et al. focus on an animal model to contribute valuable insights into the pharmacological interventions targeting secondary injury mechanisms after TBI. The study’s relevance to TBI neuroinformatics lies in its potential for integrating molecular data, such as quantitation of nitrate production, nNOS levels, and cGMP pathways, with larger datasets from similar experimental models and clinical studies. By exploring the neuroprotective effects of perampanel, a noncompetitive AMPA receptor antagonist, the study advances our understanding of how targeted pharmacological interventions can modulate key signaling pathways involved in TBI. This work also illustrates ongoing trends in the field, such as the increasing emphasis on combining pharmacological approaches with advanced data analytics to identify therapeutic targets and predict patient outcomes. Furthermore, the detailed time-course analysis of inflammatory and anti-inflammatory factors could be integrated into computational models that aim to simulate and predict the progression of TBI, making it a valuable resource for future neuroinformatics-driven research.

In conclusion, the articles on TBI neuroinformatics published in this issue highlight the powerful intersection of neuroinformatics, ML, and neuroimaging to advance our understanding and treatment of TBI and related conditions. From exploring pharmacological interventions that mitigate neuroinflammation, to enhancing the diagnosis of concussions in female athletes, to developing predictive models for post-hemorrhage complications and uncovering the structural connectivity changes associated with chronic TBI symptoms, these studies represent cutting-edge research that leverages computational tools to address complex clinical challenges. This collection of articles also underscores the importance of interpretability in DNN models applied to neuroimaging, ensuring that these advanced technologies are both effective and trustworthy in clinical settings. Together, insights from these studies are poised to enhance diagnosis, improve treatment strategies, and ultimately guide more personalized approaches to brain injury care, reflecting ongoing trends and innovations within the rapidly evolving field of neuroinformatics.