Prediction of brain age suggests accelerated atrophy after traumatic brain injury.
Ontology highlight
ABSTRACT: Objective
The long-term effects of traumatic brain injury (TBI) can resemble observed in normal ageing, suggesting that TBI may accelerate the ageing process. We investigate this using a neuroimaging model that predicts brain age in healthy individuals and then apply it to TBI patients. We define individuals' differences in chronological and predicted structural "brain age," and test whether TBI produces progressive atrophy and how this relates to cognitive function.Methods
A predictive model of normal ageing was defined using machine learning in 1,537 healthy individuals, based on magnetic resonance imaging-derived estimates of gray matter (GM) and white matter (WM). This ageing model was then applied to test 99 TBI patients and 113 healthy controls to estimate brain age.Results
The initial model accurately predicted age in healthy individuals (r = 0.92). TBI brains were estimated to be "older," with a mean predicted age difference (PAD) between chronological and estimated brain age of 4.66 years (±10.8) for GM and 5.97 years (±11.22) for WM. This PAD predicted cognitive impairment and correlated strongly with the time since TBI, indicating that brain tissue loss increases throughout the chronic postinjury phase.Interpretation
TBI patients' brains were estimated to be older than their chronological age. This discrepancy increases with time since injury, suggesting that TBI accelerates the rate of brain atrophy. This may be an important factor in the increased susceptibility in TBI patients for dementia and other age-associated conditions, motivating further research into the age-like effects of brain injury and other neurological diseases.
SUBMITTER: Cole JH
PROVIDER: S-EPMC4403966 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:It is commonly observed that patients with bone fracture concomitated with traumatic brain injury (TBI) had significantly increased fracture healing while the underlying mechanisms were not fully revealed. Long non-coding RNAs (lncRNAs) were known for paly complicated roles in bone homeostasis while its role in TBI accelerated fracture were rarely reported. The present study was designed to determine the role of lncRNAs in TBI accelerated fracture via transcriptome sequencing and further bioinformatical analysis. Blood samples from 3 fracture only patients, 3 fracture concomitated with TIB patients and 3 healthy controls were harvested and were subsequently subjected to transcriptome lncRNAs sequencing. Differentially expression genes were identified, and pathway enrichment were performed by Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyze. High high-dimensional data portraying by self-organizing map (SOM) machine learning was applied to further interpret the data. A xCell method were then used to predict cellular behavior in all samples based on gene expression profiles and a lncRNA-cell interaction network were generated. A total of 874 differentially expressed genes were identified, of which about 26% were lncRNAs. Those identified lncRNAs were mainly enriched on traumatic brain injury related- and damage repair related-pathways. SOM analyzes revealed that those differentially expressed lncRNAs could be divided into three major module implications and were mainly enriched on transcriptional regulation and immune related signal pathways, which promote us to further explore cellular behaviors based on differentially expressed lncRNAs. We have predicted that basophils, CD8+ T effector memory cells, B cells, and naïve B cells were significantly down-regulated while microvascular endothelia cells were predicated significantly up-regulated with TBI/Fr group was the lowest or highest, respectively. ENSG00000278905, ENSG00000240980, ENSG00000255670, and ENSG00000196634 were the most differentially expression lcnRNA that related to all changes of cellular behavior. The present study has revealed for the first time that several critical lncRNAs may participate in TBI accelerated fracture potentially via regulating cellular behaviors of basophils, cytotoxic T cells, B cells and endothelia cells.
2021-05-24 | GSE171718 | GEO
Project description:It is commonly observed that patients with bone fracture concomitant with traumatic brain injury (TBI) had significantly increased fracture healing, but the underlying mechanisms were not fully revealed. Long non-coding RNAs (lncRNAs) are known to play complicated roles in bone homeostasis, but their role in TBI accelerated fracture was rarely reported. The present study was designed to determine the role of lncRNAs in TBI accelerated fracture via transcriptome sequencing and further bioinformatics analyses. Blood samples from three fracture-only patients, three fracture concomitant with TBI patients, and three healthy controls were harvested and were subsequently subjected to transcriptome lncRNA sequencing. Differentially expressed genes were identified, and pathway enrichment was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. High-dimensional data visualization by self-organizing map (SOM) machine learning was applied to further interpret the data. An xCell method was then used to predict cellular behavior in all samples based on gene expression profiles, and an lncRNA-cell interaction network was generated. A total of 874 differentially expressed genes were identified, of which about 26% were lncRNAs. Those identified lncRNAs were mainly enriched on TBI-related and damage repair-related pathways. SOM analyses revealed that those differentially expressed lncRNAs could be divided into three major module implications and were mainly enriched on transcriptional regulation and immune-related signal pathways, which promote us to further explore cellular behaviors based on differentially expressed lncRNAs. We have predicted that basophils, CD8+ T effector memory cells, B cells, and naïve B cells were significantly downregulated, while microvascular endothelial cells were predicted to be significantly upregulated in the Fr/TBI group, was the lowest and highest, respectively. ENSG00000278905, ENSG00000240980, ENSG00000255670, and ENSG00000196634 were the most differentially expressed lncRNAs related to all changes of cellular behavior. The present study has revealed for the first time that several critical lncRNAs may participate in TBI accelerated fracture potentially via regulating cellular behaviors of basophils, cytotoxic T cells, B cells, and endothelial cells.
| S-EPMC8211774 | biostudies-literature
Project description:Brain regions' rates of age-related volumetric change after traumatic brain injury (TBI) are unknown. Here, we quantify these rates cross-sectionally in 113 persons with recent mild TBI (mTBI), whom we compare against 3 418 healthy controls (HCs). Regional gray matter (GM) volumes were extracted from magnetic resonance images. Linear regression yielded regional brain ages and the annualized average rates of regional GM volume loss. These results were compared across groups after accounting for sex and intracranial volume. In HCs, the steepest rates of volume loss were recorded in the nucleus accumbens, amygdala, and lateral orbital sulcus. In mTBI, approximately 80% of GM structures had significantly steeper rates of annual volume loss than in HCs. The largest group differences involved the short gyri of the insula and both the long gyrus and central sulcus of the insula. No significant sex differences were found in the mTBI group, regional brain ages being the oldest in prefrontal and temporal structures. Thus, mTBI involves significantly steeper regional GM loss rates than in HCs, reflecting older-than-expected regional brain ages.
| S-EPMC10395568 | biostudies-literature
Project description:ObjectiveTo determine if posttraumatic nonconvulsive electrographic seizures result in long-term brain atrophy.MethodsProspective continuous EEG (cEEG) monitoring was done in 140 patients with moderate to severe traumatic brain injury (TBI) and in-depth study of 16 selected patients was done using serial volumetric MRI acutely and at 6 months after TBI. Fluorodeoxyglucose PET was done in the acute stage in 14/16 patients. These data were retrospectively analyzed after collection of data for 7 years.ResultscEEG detected seizures in 32/140 (23%) of the entire cohort. In the selected imaging subgroup, 6 patients with seizures were compared with a cohort of 10 age- and GCS-matched patients with TBI without seizures. In this subgroup, the seizures were repetitive and constituted status epilepticus in 4/6 patients. Patients with seizures had greater hippocampal atrophy as compared to those without seizures (21 +/- 9 vs 12 +/- 6%, p = 0.017). Hippocampi ipsilateral to the electrographic seizure focus demonstrated a greater degree of volumetric atrophy as compared with nonseizure hippocampi (28 +/- 5 vs 13 +/- 9%, p = 0.007). A single patient had an ictal PET scan which demonstrated increased hippocampal glucose uptake.ConclusionAcute posttraumatic nonconvulsive seizures occur frequently after TBI and, in a selected subgroup, appear to be associated with disproportionate long-term hippocampal atrophy. These data suggest anatomic damage is potentially elicited by nonconvulsive seizures in the acute postinjury setting.
| S-EPMC2938965 | biostudies-literature
Project description:The emergence of degenerative disease after traumatic brain injury is often described as an acceleration of normal age-related processes. Whether similar molecular processes occur after injury and in age is unclear. Here we identify a functionally dynamic and lasting transcriptional response in glia, mediated by the conserved transcription factor AP1. In the early post-TBI period, glial AP1 is essential for recovery, ensuring brain integrity and animal survival. In sharp contrast, chronic AP1 activation promotes human tau pathology, tissue loss, and mortality. We show a similar process activates in healthy fly brains with age. In humans, AP1 activity is detected after moderate TBI and correlates with microglial activation and tau pathology. Our data provide key molecular insight into glia, highlighting that the same molecular process drives dynamic and contradictory glia behavior in TBI, and possibly age, first acting to protect but chronically promoting disease.
| S-EPMC8553014 | biostudies-literature
Project description:Psychosis is a rare, but particularly serious sequela of traumatic brain injury. However, little is known as to the neurobiological processes that may contribute to its onset. Early evidence suggests that psychotic symptom development after traumatic brain injury may co-occur with hippocampal degeneration, invoking the possibility of a relationship. Particularly regarding the hippocampal head, these degenerative changes may lead to dysregulation in dopaminergic circuits, as is reported in psychoses due to schizophrenia, resulting in the positive symptom profile typically seen in post-injury psychosis. The objective of this study was to examine change in hippocampal volume and psychotic symptoms across time in a sample of moderate-to-severe traumatic brain injury patients. We hypothesized that hippocampal volume loss would be associated with increased psychotic symptom severity. From a database of n = 137 adult patients with prospectively collected, longitudinal imaging and neuropsychiatric outcomes, n = 24 had complete data at time points of interest (5 and 12 months post-traumatic brain injury) and showed increasing psychotic symptom severity on the Personality Assessment Inventory psychotic experiences subscale of the schizophrenia clinical scale across time. Secondary analysis employing stepwise regression with hippocampal volume change (independent variable) and Personality Assessment Inventory psychotic symptom change (dependent variable) from 5 to 12 months post-injury was conducted including age, sex, marijuana use, family history of schizophrenia, years of education and injury severity as control variables. Total right hippocampal volume loss predicted an increase in the Personality Assessment Inventory psychotic experiences subscale (F (1, 22) = 5.396, adjusted R 2 = 0.161, P = 0.030; β = -0.017, 95% confidence interval = -0.018, -0.016) as did volume of the right hippocampal head (F (1, 22) = 5.764, adjusted R 2 = 0.172, P = 0.025; β = -0.019, 95% confidence interval = -0.021, -0.017). Final model goodness-of-fit was confirmed using k-fold (k = 5) cross-validation. Consistent with our hypotheses, the current findings suggest that hippocampal degeneration in the chronic stages of moderate-to-severe traumatic brain injury may play a role in the delayed onset of psychotic symptoms after traumatic brain injury. These findings localized to the right hippocampal head are supportive of a proposed aetiological mechanism whereby atrophy of the hippocampal head may lead to the dysregulation of dopaminergic networks following traumatic brain injury; possibly accounting for observed clinical features of psychotic disorder after traumatic brain injury (including prolonged latency period to symptom onset and predominance of positive symptoms). If further validated, these findings may bear important clinical implications for neurorehabilitative therapies following traumatic brain injury.
| S-EPMC8098106 | biostudies-literature
Project description:BackgroundApolipoprotein E (APOE) ɛ4 is associated with poor outcome following moderate to severe traumatic brain injury (TBI). There is a lack of studies investigating the influence of APOE ɛ4 on intracranial pathology following mild traumatic brain injury (MTBI). This study explores the association between APOE ɛ4 and MRI measures of brain age prediction, brain morphometry, and diffusion tensor imaging (DTI).MethodsPatients aged 16 to 65 with acute MTBI admitted to the trauma center were included. Multimodal MRI was performed 12 months after injury and associated with APOE ɛ4 status. Corrections for multiple comparisons were done using false discovery rate (FDR).ResultsOf included patients, 123 patients had available APOE, volumetric, and DTI data of sufficient quality. There were no differences between APOE ɛ4 carriers (39%) and non-carriers in demographic and clinical data. Age prediction revealed high accuracy both for the DTI-based and the brain morphometry based model. Group comparisons revealed no significant differences in brain-age gap between ɛ4 carriers and non-carriers, and no significant differences in conventional measures of brain morphometry and volumes. Compared to non-carriers, APOE ɛ4 carriers showed lower fractional anisotropy (FA) in the hippocampal part of the cingulum bundle, which did not remain significant after FDR adjustment.ConclusionAPOE ɛ4 carriers might be vulnerable to reduced neuronal integrity in the cingulum. Larger cohort studies are warranted to replicate this finding.
| S-EPMC7865561 | biostudies-literature
Project description:Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24?h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified cross-validation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middle-income countries.
| S-EPMC6881446 | biostudies-literature
Project description:Polytraumatic injuries, specifically long bone fracture and traumatic brain injury (TBI), frequently occur together. Clinical observation has long held that TBI can accelerate fracture healing, yet the complexity and heterogeneity of these injuries has produced conflicting data with limited information on underlying mechanisms. We developed a murine polytrauma model with TBI and fracture to evaluate healing in a controlled system. Fractures were created both contralateral and ipsilateral to the TBI to test whether differential responses of humoral and/or neuronal systems drove altered healing patterns. Our results show increased bone formation after TBI when injuries occur contralateral to each other, rather than ipsilateral, suggesting a role of the nervous system based on the crossed neuroanatomy of motor and sensory systems. Analysis of the humoral system shows that blood cell counts and inflammatory markers are differentially modulated by polytrauma. A data-driven multivariate analysis integrating all outcome measures showed a distinct pathological state of polytrauma and co-variations between fracture, TBI and systemic markers. Taken together, our results suggest that a contralateral bone fracture and TBI alter the local neuroinflammatory state to accelerate early fracture healing. We believe applying a similar data-driven approach to clinical polytrauma may help to better understand the complicated pathophysiological mechanisms of healing.
| S-EPMC6704103 | biostudies-literature
Project description:Outcome after traumatic brain injury (TBI) is typically assessed using the Glasgow outcome scale extended (GOSE) with levels from 1 (death) to 8 (upper good recovery). Outcome prediction has classically been dichotomized into either dead/alive or favorable/unfavorable outcome. Binary outcome prediction models limit the possibility of detecting subtle yet significant improvements. We set out to explore different machine learning methods with the purpose of mapping their predictions to the full 8 grade scale GOSE following TBI. The models were set up using the variables: age, GCS-motor score, pupillary reaction, and Marshall CT score. For model setup and internal validation, a total of 866 patients could be included. For external validation, a cohort of 369 patients were included from Leuven, Belgium, and a cohort of 573 patients from the US multi-center ProTECT III study. Our findings indicate that proportional odds logistic regression (POLR), random forest regression, and a neural network model achieved accuracy values of 0.3-0.35 when applied to internal data, compared to the random baseline which is 0.125 for eight categories. The models demonstrated satisfactory performance during external validation in the data from Leuven, however, their performance were not satisfactory when applied to the ProTECT III dataset.
| S-EPMC10997790 | biostudies-literature