Mild traumatic brain injury (mTBI) initiating long-term effects on white matter integrity resembles brain-aging changes, implying an aging process accelerated by mTBI. This longitudinal study aims to investigate the mTBI-induced acceleration of the brain-aging process by developing a neuroimaging model to predict brain age. The brain-age prediction model was defined using relevance vector regression based on fractional anisotropy from diffusion tensor imaging of 523 healthy individuals. The model was used to estimate the brain-predicted age difference (brain-PAD) between the chronological and estimated brain age in 116 acute mTBI patients and 63 healthy controls. Fifty patients were followed for 6 ∼ 12 months to evaluate the longitudinal changes in brain-PAD. We investigated whether brain-PAD was greater in patients of older age, post-concussion complaints, and apolipoprotein E (APOE) ɛ4 genotype, and whether it had the potential to predict neuropsychological outcomes. The brain-age prediction model predicted brain age accurately ( r = 0.96). The brains of mTBI patients in the acute phase were estimated to be “older,” with greater brain-PAD (2.59 ± 5.97 years) than the healthy controls (0.12 ± 3.19 years) ( p < 0.05), and remained stable 6–12 month post-injury (2.50 ± 4.54 years). Patients who were older or who had post-concussion complaints, rather than APOE ɛ4 genotype, had greater brain-PADs ( p < 0.001, p = 0.024). Additionally, brain-PAD in the acute phase predicted information processing speed at the 6 ∼ 12 month follow-up ( r = -0.36, p = 0.01). In conclusion, mTBI accelerates the brain-aging process, and brain-PAD may be capable of evaluating aging-associated issues post-injury, such as increased risks of neurodegeneration.

Mild traumatic brain injury (mTBI) is the most prevalent neurological insult and leads to long-lasting cognitive impairment. Neuroimaging studies have discovered abnormalities in brain network connectivity following mTBI as the underlying neural basis of cognitive deficits. However, the pathophysiologic mechanisms involved in imaging alterations remain elusive. Proteins neuron-specific enolase (NSE) and ubiquitin C terminal hydrolase 1 are reliable markers for neuronal cell-body damage, both of which have been demonstrated to be increased in serum following mTBI. Therefore, we conducted a longitudinal study to examine relationships between abnormal brain network connectivity and serum neuronal biomarkers and their associations with cognitive recovery following mTBI. Sixty patients were followed-up at 1 week and 3 months post-injury and 41 controls were recruited. Resting-state functional magnetic resonance imaging was used to build a functional connectivity matrix within large-scale intrinsic networks, and their topological properties were analyzed using graph theory measures. We found that, compared with controls, mTBI patients showed significant decreases in a number of nodal characteristics in default mode network (DMN), salience network, and executive network ( p < 0.05, false discovery rate corrected) at 3 months post-injury. Linear regression analysis found elevated serum NSE in acute phase could predict lower efficiency and degree centrality of anterior DMN at 3 months post-injury. In addition, efficiency and degree centrality of anterior DMN were negatively associated with working memory. Our study showed neuronal injury was associated with alterations in brain network connectivity after mTBI. These findings can facilitate capability to predict the brain functional outcomes and cognitive recovery in mTBI.

Mild traumatic brain injury (TBI) is considered to induce abnormal intrinsic functional connectivity within resting-state networks (RSNs). The objective of this study was to estimate the role of sex in intrinsic functional connectivity after acute mild TBI. We recruited a cohort of 54 patients (27 males and 27 females with mild TBI within 7 days post-injury) from the emergency department (ED) and 34 age-, education-matched healthy controls (HCs; 17 males and 17 females). On the clinical scales, there were no statistically significant differences between males and females in either control group or mild TBI group. To detect whether there was abnormal sex difference on functional connectivity in RSNs, we performed independent component analysis (ICA) and a dual regression approach to investigate the between-subject voxel-wise comparisons of functional connectivity within seven selected RSNs. Compared to female patients, male patients showed increased intrinsic functional connectivity in motor network, ventral stream network, executive function network, cerebellum network and decreased connectivity in visual network. Further analysis demonstrated a positive correlation between the functional connectivity in executive function network and insomnia severity index (ISI) scores in male patients (r = 0.515, P = 0.006). The abnormality of the functional connectivity of RSNs in acute mild TBI showed the possibility of brain recombination after trauma, mainly concerning male-specific.

Background: Population‐based studies on grade III gliomas are still lacking. The purpose of our study was to investigate epidemiological characteristics, survival, and risk factors of these tumors.Patients and methods: All data of patients with grade III gliomas were extracted from the Surveillance, Epidemiology, and End Results database. This database provides analysis to evaluate age‐adjusted incidence, incidence‐based mortality, and limited‐duration prevalence. The trends of incidence and mortality were modeled using Joinpoint program. Relative survival was also available in this database. Univariate and multivariate analyses were used to access the prognostic significance of risk factors on cancer‐specific survival. Nomogram was constructed to predict 3‐, 5‐, and 10‐year survival.Results: Our study showed that during 2000‐2013, the incidence was stable and the mortality rate dropped significantly with APC as −1.95% (95% CI: −3.35% to −0.54%). Patients aged 40‐59 had the highest prevalent cases. The 1‐, 3‐, 5‐, and 10‐year relative survival rates for all patients were 74.7%, 52.8%, 44.4%, and 32.4%. And it varied by risk factors. Cox regression analysis showed older age, male, black race, divorced status, histology of AA, tumor size <3.5 cm and no surgery were associated with worse survival.Conclusion: Our study provides reasonable estimates of the incidence, mortality, and prevalence for patients with grade III gliomas during 2000‐2013. The results of relative survival and Cox regression analysis revealed that age, race, sex, year of diagnosis, tumor site, histologic type, tumor size, and surgery were the identifiable prognostic indicators. The effects of radiotherapy still need further study. We integrated these risk factors to construct an effective clinical prediction model.

Background: Crystallized intelligence (Gc) and fluid intelligence (Gf) are regarded as distinct intelligence components that statistically correlate with each other. However, the distinct neuroanatomical signatures of Gc and Gf in adults remain contentious.Methods: Machine learning cross-validated elastic net regression models were performed on the Human Connectome Project Young Adult dataset (N = 1089) to characterize the neuroanatomical patterns of structural magnetic resonance imaging variables that are associated with Gc and Gf. The observed relationships were further examined by linear mixed-effects models. Finally, intraclass correlations were computed to examine the similarity of the neuroanatomical correlates between Gc and Gf.Results: The results revealed distinct multi-region neuroanatomical patterns predicted Gc and Gf, respectively, which were robust in a held-out test set (R² = 2.40, 1.97%, respectively). The relationship of these regions with Gc and Gf was further supported by the univariate linear mixed effects models. Besides that, Gc and Gf displayed poor neuroanatomical similarity.Conclusion: These findings provided evidence that distinct machine learning-derived neuroanatomical patterns could predict Gc and Gf in healthy adults, highlighting differential neuroanatomical signatures of different aspects of intelligence.