Reproducible analysis of disease space via principal components using the novel R package syndRomics

Myeloid Cell Association with Spinal Cord Injury-Induced Neuropathic Pain and Depressive-like Behaviors in LysM-eGFP Mice

Spinal cord injury (SCI) affects ∼500,000 people worldwide annually, with the majority developing chronic neuropathic pain. Following SCI, approximately 60% of these individuals are diagnosed with comorbid mood disorders, while only ∼21% of the general population will experience a mood disorder in their lifetime. We hypothesize that nociceptive and depressive-like dysregulation occurs after SCI and is associated with aberrant macrophage infiltration in segmental pain centers. We completed moderate unilateral C5 spinal cord contusion on LysM-eGFP reporter mice to visualize infiltrating macrophages. At 6-weeks post-SCI, mice exhibit nociceptive and depressive-like dysfunction compared to naïve and sham groups. There were no differences between the sexes, indicating that sex is not a contributing factor driving nociceptive or depressive-like behaviors after SCI. Utilizing hierarchical cluster analysis, we classified mice based on endpoint nociceptive and depressive-like behavior scores. Approximately 59.3% of the SCI mice clustered based on increased paw withdrawal threshold to mechanical stimuli and immobility time in the forced swim test. SCI mice displayed increased myeloid cell presence in the lesion epicenter, ipsilateral C7-8 dorsal horn, and C7-8 DRGs as evidenced by eGFP, CD68, and Iba1 immunostaining when compared to naïve and sham mice. This was further confirmed by SCI-induced alterations in the expression of genes indicative of myeloid cell activation states and their associated secretome in the dorsal horn and dorsal root ganglia. In conclusion, moderate unilateral cervical SCI caused the development of pain-related and depressive-like behaviors in a subset of mice and these behavioral changes are consistent with immune system activation in the segmental pain pathway. PERSPECTIVE: These experiments characterized pain-related and depressive-like behaviors and correlated these changes with the immune response post-SCI. While humanizing the rodent is impossible, the results from this study inform clinical literature to closely examine sex differences reported in humans to better understand the underlying shared etiologies of pain and depressive-like behaviors following central nervous system trauma.

Genetic diversity drives extreme responses to traumatic brain injury and post-traumatic epilepsy

Traumatic brain injury (TBI) is a complex and heterogeneous condition that can cause wide-spectral neurological sequelae such as behavioral deficits, sleep abnormalities, and post-traumatic epilepsy (PTE). However, understanding the interaction of TBI phenome is challenging because few animal models can recapitulate the heterogeneity of TBI outcomes. We leveraged the genetically diverse recombinant inbred Collaborative Cross (CC) mice panel and systematically characterized TBI-related outcomes in males from 12 strains of CC and the reference C57BL/6J mice. We identified unprecedented extreme responses in multiple clinically relevant traits across CC strains, including weight change, mortality, locomotor activity, cognition, and sleep. Notably, we identified CC031 mouse strain as the first rodent model of PTE that exhibit frequent and progressive post-traumatic seizures after moderate TBI induced by lateral fluid percussion. Multivariate analysis pinpointed novel biological interactions and three principal components across TBI-related modalities. Estimate of the proportion of TBI phenotypic variability attributable to strain revealed large range of heritability, including >70% heritability of open arm entry time of elevated plus maze. Our work provides novel resources and models that can facilitate genetic mapping and the understanding of the pathobiology of TBI and PTE.

Fusobacterium nucleatum dysregulates inflammatory cytokines and NLRP3 inflammasomes in oral cells

OBJECTIVE

This study aimed to clarify the difference in Fusobacterium nucleatum (F. nucleatum) induced inflammatory cytokines and nod-like receptor protein 3 (NLRP3) inflammasomes dysregulation among three periodontal cells.

METHODS

Oral epithelial cells (HIOECs), THP-1 macrophages, and human gingival fibroblasts (HGFs) were exposed to F. nucleatum with/without adenosine triphosphate (ATP) and nigericin (Nig). Cell morphology was assessed by scanning electron microscopy. qRT-PCR, protein microarrays, and bioinformatic methods were used to evaluate the cytokines and their complex interplay. NLRP3 inflammasomes activation was detected by western blotting and ELISA.

RESULTS

F. nucleatum adhered to and invaded cells. In HIOECs, F. nucleatum enhanced interleukin (IL)-1α/1β/6/10/13, TNF-α, and interferon (IFN)-γ expression. In THP-1 macrophages, F. nucleatum up-regulated IL-1α/1β/6/10 and TNF-α levels. In HGFs, F. nucleatum increased IL-6 levels. F. nucleatum and ATP synergistically boosted IFN-γ level in THP-1 macrophages and IL-13 level in HGFs. IL-1α/1β/6, and TNF-α served as epicenters of the inflammatory response. Additionally, F. nucleatum activated NLRP3 inflammasomes in HIOECs, and ATP/Nig boosted the activation. F. nucleatum also triggered NLRP3 inflammasomes in THP-1 macrophages, but in HGFs, only NLRP3 and caspase-1 levels were elevated.

CONCLUSION

F. nucleatum infiltrated periodontal supporting cells and dysregulated inflammatory cytokines and NLRP3 inflammasomes.

Preclinical long-term safety of intraspinal transplantation of human dorsal spinal GABA neural progenitor cells

Human pluripotent stem cell (hPSC)-derived neurons have shown promise in treating spinal cord injury (SCI). We previously showed that hPSC-derived dorsal spinal γ-aminobutyric acid (GABA) neurons can alleviate spasticity and promote locomotion in rats with SCI, but their long-term safety remains elusive. Here, we characterized the long-term fate and safety of human dorsal spinal GABA neural progenitor cells (NPCs) in naive rats over one year. All grafted NPCs had undergone differentiation, yielding mainly neurons and astrocytes. Fully mature human neurons grew many axons and formed numerous synapses with rat neural circuits, together with mature human astrocytes that structurally integrated into the rat spinal cord. The sensorimotor function of rats was not impaired by intraspinal transplantation, even when human neurons were activated or inhibited by designer receptors exclusively activated by designer drugs (DREADDs). These findings represent a significant step toward the clinical translation of human spinal neuron transplantation for treating SCI.

Interpretable machine-learning model for real-time, clustered risk factor analysis of sepsis and septic death in critical care

BACKGROUND AND OBJECTIVE

Interpretable and real-time prediction of sepsis and risk factor analysis could enable timely treatment by clinicians and improve patient outcomes. To develop an interpretable machine-learning model for the prediction and risk factor analysis of sepsis and septic death.

METHODS

This is a retrospective observational cohort study based on the Medical Information Mart for Intensive Care (MIMIC-IV) dataset; 69,619 patients from the database were screened. The two outcomes include patients diagnosed with sepsis and the death of septic patients. Clinical variables from ICU admission to outcomes were analyzed: demographic data, vital signs, Glasgow Coma Scale scores, laboratory test results, and results for arterial blood gasses (ABGs). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Model interpretations were based on the Shapley additive explanations (SHAP), and the clustered analysis was based on the combination of K-means and dimensionality reduction algorithms of t-SNE and PCA.

RESULTS

For the analysis of sepsis and septic death, 47,185 and 2480 patients were enrolled, respectively. The XGBoost model achieved a predictive value of area under the curve (AUC): 0.745 [0.731-0.759] for sepsis prediction and 0.8 [0.77, 0.828] for septic death prediction. The real-time prediction model was trained to predict by day and visualize the individual or combined risk factor effects on the outcomes based on SHAP values. Clustered analysis separated the two phenotypes with distinct risk factors among patients with septic death.

CONCLUSION

The proposed real-time, clustered prediction model for sepsis and septic death exhibited superior performance in predicting the outcomes and visualizing the risk factors in a real-time and interpretable manner to distinguish and mitigate patient risks, thus promising immense potential in effective clinical decision making and comprehensive understanding of complex diseases such as sepsis.

Associations of Microvascular Injury-Related Biomarkers With Traumatic Brain Injury Severity and Outcomes: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot Study

Traumatic brain injury (TBI) is characterized by heterogeneity in terms of injury severity, mechanism, outcome, and pathophysiology. A single biomarker alone is unlikely to capture the heterogeneity of even one injury subtype, necessitating the use of panels of biomarkers. Herein, we focus on traumatic cerebrovascular injury and investigate associations of a panel of 16 vascular injury-related biomarkers with indices of TBI severity and outcomes using data from 159 participants in the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) Pilot Study. Associations of individual biomarkers and clusters of biomarkers identified using non-linear principal components analysis with TBI severity and outcomes were assessed using logistic regression models and Spearman's correlations. As individual biomarkers, higher levels of thrombomodulin, angiopoietin (Ang)-2, von Willebrand factor, and P-selectin were associated with more severe injury; higher levels of Ang-1, Tie2, vascular endothelial growth factor (VEGF)-C, and basic fibroblast growth factor (bFGF) were associated with less severe injury (all p < 0.05 in age-adjusted models). After false discovery rate correction for multiple comparisons, higher levels of Ang-2 remained associated with more severe injury and higher levels of Ang-1, Tie2, and bFGF remained associated with less severe injury at a p < 0.05 level. In principal components analysis, principal component (PC)1, comprised of Ang1, bFGF, P-selectin, VEGF-C, VEGF-A, and Tie2, was associated with less severe injury (age-adjusted odds ratio [OR]: 0.63, 95% confidence interval [CI]: 0.44-0.88 for head computer tomography [CT] positive vs. negative) and PC2 (Ang-2, E-selectin, Flt-1, placental growth factor, thrombomodulin, and vascular cell adhesion protein 1) was associated with greater injury severity (age-adjusted OR: 2.29, 95% CI: 1.49-3.69 for Glasgow Coma Scale [GCS] 3-12 vs. 13-15 and age-adjusted OR 1.59, 95% CI: 1.11-2.32 for head CT positive vs. negative). Neither individual biomarkers nor PCs were associated with outcomes in adjusted models (all p > 0.05). In conclusion, in this trauma-center based population of acute TBI patients, biomarkers of microvascular injury were associated with TBI severity.

Examining litter specific variability in mice and its impact on neurodevelopmental studies

Our current understanding of litter variability in neurodevelopmental studies using mice may limit translation of neuroscientific findings. Higher variance of measures across litters than within, often termed intra-litter likeness, may be attributable to both pre- and postnatal environment. This study aimed to assess the litter-effect within behavioral assessments (2 timepoints) and anatomy using T1-weighted magnetic resonance images across 72 brain region volumes (4 timepoints) (36 C57bl/6J inbred mice; 7 litters: 19F/17M). Between-litter comparisons of brain and behavioral measures and their associations were evaluated using univariate and multivariate techniques. A power analysis using simulation methods was then performed on modeled neurodevelopment and to evaluate trade-offs between number-of-litters, number-of-mice-per-litter, and sample size. Our results show litter-specific developmental effects, from the adolescent period to adulthood for brain structure volumes and behaviors, and for their associations in adulthood. Our power simulation analysis suggests increasing the number-of-litters in experimental designs to achieve the smallest total sample size necessary for detecting different rates of change in specific brain regions. Our results demonstrate how litter-specific effects may influence development and that increasing the litters to the total sample size ratio should be strongly considered when designing neurodevelopmental studies.

Parieto-Occipital Injury on Diffusion MRI Correlates with Poor Neurologic Outcome following Cardiac Arrest

BACKGROUND AND PURPOSE

MR imaging of the brain provides unbiased neuroanatomic evaluation of brain injury and is useful for neurologic prognostication following cardiac arrest. Regional analysis of diffusion imaging may provide additional prognostic value and help reveal the neuroanatomic underpinnings of coma recovery. The purpose of this study was to evaluate global, regional, and voxelwise differences in diffusion-weighted MR imaging signal in patients in a coma after cardiac arrest.

MATERIALS AND METHODS

We retrospectively analyzed diffusion MR imaging data from 81 subjects who were comatose for >48 hours following cardiac arrest. Poor outcome was defined as the inability to follow simple commands at any point during hospitalization. ADC differences between groups were evaluated across the whole brain, locally by using voxelwise analysis and regionally by using ROI-based principal component analysis.

RESULTS

Subjects with poor outcome had more severe brain injury as measured by lower average whole-brain ADC (740 [SD, 102] × 10-6 mm2/s versus 833 [SD, 23] × 10-6 mm2/s, P < .001) and larger average volumes of tissue with ADC below 650 × 10-6 mms/s (464 [SD, 469] mL versus 62 [SD, 51] mL, P < .001). Voxelwise analysis showed lower ADC in the bilateral parieto-occipital areas and perirolandic cortices for the poor outcome group. ROI-based principal component analysis showed an association between lower ADC in parieto-occipital regions and poor outcome.

CONCLUSIONS

Brain injury affecting the parieto-occipital region measured with quantitative ADC analysis was associated with poor outcomes after cardiac arrest. These results suggest that injury to specific brain regions may influence coma recovery.

Cortical profiles of numerous psychiatric disorders and normal development share a common pattern

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.

Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research

Understanding recovery from TBI is complex, involving multiple systems and modalities. The current study applied modern data science tools to manage this complexity and harmonize large-scale data to understand relationships between gene expression and behavioral outcomes in a preclinical model of chronic TBI (cTBI). Data collected by the Moody Project for Translational TBI Research included rats with no injury (naïve animals with similar amounts of anesthetic exposure to TBI and sham-injured animals), sham injury, or lateral fluid percussion TBI, followed by recovery periods up to 12 months. Behavioral measures included locomotor coordination (beam balance neuroscore) and memory and cognition assessments (Morris water maze: MWM) at multiple timepoints. Gene arrays were performed using hippocampal and cortical samples to probe 45,610 genes. To reduce the high dimensionality of molecular and behavioral domains and uncover gene-behavior associations, we performed non-linear principal components analyses (NL-PCA), which de-noised the data. Genomic NL-PCA unveiled three interpretable eigengene components (PC2, PC3, and PC4). Ingenuity pathway analysis (IPA) identified the PCs as an integrated stress response (PC2; EIF2-mTOR, corticotropin signaling, etc.), inflammatory factor translation (PC3; PI3K-p70S6K signaling), and neurite growth inhibition (PC4; Rho pathways). Behavioral PCA revealed three principal components reflecting the contribution of MWM overall speed and distance, neuroscore/beam walk, and MWM platform measures. Integrating the genomic and behavioral domains, we then performed a 'meta-PCA' on individual PC scores for each rat from genomic and behavioral PCAs. This meta-PCA uncovered three unique multimodal PCs, characterized by robust associations between inflammatory/stress response and neuroscore/beam walk performance (meta-PC1), stress response and MWM performance (meta-PC2), and stress response and neuroscore/beam walk performance (meta-PC3). Multivariate analysis of variance (MANOVA) on genomic-behavioral meta-PC scores tested separately on cortex and hippocampal samples revealed the main effects of TBI and recovery time. These findings are a proof of concept for the integration of disparate data domains for translational knowledge discovery, harnessing the full syndromic space of TBI.

Toward automated prediction of sudden unexpected death in epilepsy

Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to study. No prediction or prevention of SUDEP is currently available in a clinical setting. In the past decade, significant advances have been made in our knowledge of the pathophysiologic cascades that lead to SUDEP. In particular, studies of brain, heart, and respiratory functions in both human patients at the epilepsy monitoring unit and animal models during fatal seizures provide critical information to integrate computational tools for SUDEP prediction. The rapid advances in automated seizure detection and prediction algorithms provide a fundamental framework for their adaption in predicting SUDEP. If a SUDEP can be predicted, then there will be a potential for medical intervention to be administered, either by their caregivers or via an implanted device automatically delivering electrical stimulation or medication, and finally save lives from fatal seizures. This article presents recent developments of SUDEP studies focusing on the pathophysiologic basis of SUDEP and computational implications of machine learning techniques that can be adapted and extended for SUDEP prediction. This article also discusses some novel ideas for SUDEP prediction and rescue including principal component analysis and closed-loop intervention.

Prehospital Factors Predict Outcomes in Pediatric Trauma: A Principal Component Analysis

BACKGROUND

Trauma team activation leveling decisions are complex and based on many variables. Accurate triage decisions improve patient safety and resource utilization. Our purpose was to establish proof-of-concept for using principal component analysis (PCA) to identify multivariate predictors of injury severity and to assess their ability to predict outcomes in pediatric trauma patients. We hypothesized that we could identify significant principal components (PCs) among variables used for decisions regarding trauma team activation and that PC scores would be predictive of outcomes in pediatric trauma.

METHODS

We conducted a retrospective review of the trauma registry (1/2014-12/2020) at our pediatric trauma center, including all pediatric patients (age < 18 y) who triggered a trauma team activation. Data included patient demographics, prehospital report, Injury Severity Score, and outcomes. Four significant principal components were identified using PCA. Differences in outcome variables between the highest and lowest quartile for PC score were examined.

RESULTS

1090 pediatric patients were included. The 4 significant PCs accounted for >96% of the overall date variance. The first PC was a composite of prehospital Glasgow Coma Scale and Revised Trauma Score (RTS) and was predictive of outcomes, including injury severity, length of stay, and mortality. The second PC was characterized primarily by prehospital systolic blood pressure (SBP) and high PC scores were associated with increased length of stay. The third and fourth PCs were characterized by patient age and by prehospital RTS and SBP, respectively.

CONCLUSIONS

We demonstrate that, using information available at the time of trauma team activation, PCA can be used to identify key predictors of patient outcome. While the ultimate goal is to create a machine learning-based predictive tool to support and improve clinical decision making, this study serves as a crucial step toward developing a deep understanding of the features of the model and their behavior with actual clinical data.

LEVEL OF EVIDENCE

III; Diagnostic test.

Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain

Chronic low back pain (LBP) is a leading cause of disability and opioid prescriptions worldwide, representing a significant medical and socioeconomic problem. Clinical heterogeneity of LBP limits accurate diagnosis and precise treatment planning, culminating in poor patient outcomes. A current priority of LBP research is the development of objective, multidimensional assessment tools that subgroup LBP patients based on neurobiological pain mechanisms, to facilitate matching patients with the optimal therapies. Using unsupervised machine learning on full body biomechanics, including kinematics, dynamics, and muscle forces, captured with a marker-less depth camera, this study identified a forward-leaning sit-to-stand strategy (STS) as a discriminating movement biomarker for LBP subjects. A forward-leaning STS strategy, as opposed to a vertical rise strategy seen in the control participants, is less efficient and results in increased spinal loads. Inefficient STS with the subsequent higher spinal loading may be a biomarker of poor motor control in LBP patients as well as a potential source of the ongoing symptomology.

Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research

Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

FAIR Data Reuse in Traumatic Brain Injury: Exploring Inflammation and Age as Moderators of Recovery in the TRACK-TBI Pilot

The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients.

The impact of deep space radiation on cognitive performance: From biological sex to biomarkers to countermeasures

In the coming decade, astronauts will travel back to the moon in preparation for future Mars missions. Exposure to galactic cosmic radiation (GCR) is a major obstacle for deep space travel. Using multivariate principal components analysis, we found sex-dimorphic responses in mice exposed to accelerated charged particles to simulate GCR (GCRsim); males displayed impaired spatial learning, whereas females did not. Mechanistically, these GCRsim-induced learning impairments corresponded with chronic microglia activation and synaptic alterations in the hippocampus. Temporary microglia depletion shortly after GCRsim exposure mitigated GCRsim-induced deficits measured months after the radiation exposure. Furthermore, blood monocyte levels measured early after GCRsim exposure were predictive of the late learning deficits and microglia activation measured in the male mice. Our findings (i) advance our understanding of charged particle–induced cognitive challenges, (ii) provide evidence for early peripheral biomarkers for identifying late cognitive deficits, and (iii) offer potential therapeutic strategies for mitigating GCR-induced cognitive loss.

Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury

Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a broad range of biological and functional outcome measures. Thus, multivariate analytic approaches are needed to capture the linkage between biological and neurobehavioral outcomes. Injury-induced neuroinflammation (NI) presents a particularly challenging therapeutic target, since NI is involved in both degeneration and repair. Here, we used big-data integration and large-scale analytics to examine a large dataset of preclinical efficacy tests combining five different blinded, fully counter-balanced treatment trials for different acute anti-inflammatory treatments for cervical spinal cord injury in rats. Multi-dimensional discovery, using topological data analysis (TDA) and principal components analysis (PCA) revealed that only one showed consistent multidimensional syndromic benefit: intrathecal application of recombinant soluble TNFα receptor 1 (sTNFR1), which showed an inverse-U dose response efficacy. Using the optimal acute dose, we showed that clinically-relevant 90 min delayed treatment profoundly affected multiple biological indices of NI in the first 48 h after injury, including reduction in pro-inflammatory cytokines and gene expression of a coherent complex of acute inflammatory mediators and receptors. Further, a 90 min delayed bolus dose of sTNFR1 reduced the expression of NI markers in the chronic perilesional spinal cord, and consistently improved neurological function over 6 weeks post SCI. These results provide validation of a novel strategy for precision preclinical drug discovery that is likely to improve translation in the difficult landscape of CNS trauma, and confirm the importance of TNFα signaling as a therapeutic target.