Axonal injury after TBI can be reliably quantified using plasma NfL, which predicts long-term functional outcomes and progressive neurodegeneration.

Adaptive metabolic response to injury includes the utilization of alternative energy substrates – such as ketone bodies (KB) – to protect the brain against further damage. Here, we examined cerebral ketone metabolism in patients with traumatic brain injury (TBI; n = 34 subjects) monitored with cerebral microdialysis to measure total brain interstitial tissue KB levels (acetoacetate and β-hydroxybutyrate). Nutrition – from fasting vs. stable nutrition state – was associated with a significant decrease of brain KB (34.7 [10th–90th percentiles 10.7–189] µmol/L vs. 13.1 [6.5–64.3] µmol/L, p < 0.001) and blood KB (668 [168.4–3824.9] vs. 129.4 [82.6–1033.8] µmol/L, p < 0.01). Blood KB correlated with brain KB (Spearman’s rho 0.56, p = 0.0013). Continuous feeding with medium-chain triglycerides-enriched enteral nutrition did not increase blood KB, and provided a modest increase in blood and brain free medium chain fatty acids. Higher brain KB at the acute TBI phase correlated with age and brain lactate, pyruvate and glutamate, but not brain glucose. These novel findings suggest that nutritional ketosis was the main determinant of cerebral KB metabolism following TBI. Age and cerebral metabolic distress contributed to brain KB supporting the hypothesis that ketones might act as alternative energy substrates to glucose. Further studies testing KB supplementation after TBI are warranted.

Objective: To evaluate the value of an adjuvant cisternostomy (AC) to decompressive craniectomy (DC) for the management of patients with severe traumatic brain injury (sTBI). Methods: A single-center retrospective quality control analysis of a consecutive series of sTBI patients surgically treated with AC or DC alone between 2013 and 2018. A subgroup analysis, “primary procedure” and “secondary procedure”, was also performed. We examined the impact of AC vs. DC on clinical outcome, including long-term (6 months) extended Glasgow outcome scale (GOS-E), the duration of postoperative ventilation, and intensive care unit (ICU) stay, mortality, Glasgow coma scale at discharge, and time to cranioplasty. We also evaluated and analyzed the impact of AC vs. DC on post-procedural intracranial pressure (ICP) and brain tissue oxygen (PbO₂) values as well as the need for additional osmotherapy and CSF drainage. Results: Forty patients were examined, 22 patients in the DC group, and 18 in the AC group. Compared with DC alone, AC was associated with significant shorter duration of mechanical ventilation and ICU stay, as well as better Glasgow coma scale at discharge. Mortality rate was similar. At 6-month, the proportion of patients with favorable outcome (GOS-E ≥ 5) was higher in patients with AC vs. DC [10/18 patients (61%) vs. 7/20 (35%)]. The outcome difference was particularly relevant when AC was performed as primary procedure (61.5% vs. 18.2%; p = 0.04). Patients in the AC group also had significant lower average post-surgical ICP values, higher PbO₂ values and required less osmotic treatments as compared with those treated with DC alone. Conclusion: Our preliminary single-center retrospective data indicate that AC may be beneficial for the management of severe TBI and is associated with better clinical outcome. These promising results need further confirmation by larger multicenter clinical studies. The potential benefits of cisternostomy should not encourage its universal implementation across trauma care centers by surgeons that do not have the expertise and instrumentation necessary for cisternal microsurgery. Training in skull base and vascular surgery techniques for trauma care surgeons would avoid the potential complications associated with this delicate procedure.

In this study, we examined the early value of automated quantitative pupillary examination, using the Neurological Pupil index (NPi), to predict the long-term outcome of acute brain injured (ABI) patients. We performed a single-centre retrospective study (October 2016–March 2019) in ABI patients who underwent NPi measurement during the first 3 days following brain insult. We examined the performance of NPi—alone or in combination with other baseline demographic (age) and radiologic (CT midline shift) predictors—to prognosticate unfavourable 6-month outcome (Glasgow Outcome Scale 1–3). A total of 145 severely brain-injured subjects (65 traumatic brain injury, TBI; 80 non-TBI) were studied. At each time point tested, NPi <3 was highly predictive of unfavourable outcome, with highest specificity (100% (90–100)) at day 3 (sensitivity 24% (15–35), negative predictive value 36% (34–39)). The addition of NPi, from day 1 following ABI to age and cerebral CT scan, provided the best prognostic performance (AUROC curve 0.85 vs. 0.78 without NPi, p = 0.008; DeLong test) for 6-month neurological outcome prediction. NPi, assessed at the early post-injury phase, has a superior ability to predict unfavourable long-term neurological outcomes in severely brain-injured patients. The added prognostic value of NPi was most significant when complemented with baseline demographic and radiologic information.

This paper introduces BrainFuseNet, a novel lightweight seizure detection network based on the sensor fusion of electroencephalography (EEG) with photoplethysmography (PPG) and accelerometer (ACC) signals, tailored for low-channel count wearable systems. BrainFuseNet utilizes the Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), an innovative loss function incorporating sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The BrainFuseNet-SSWCE approach successfully detects 93.5% seizure events on the CHB-MIT dataset ( 76.34% sample-based sensitivity), for EEG-based classification with only four channels. On the PEDESITE dataset, we demonstrate a sample-based sensitivity and false positive rate of 60.66% and 1.18 FP/h, respectively, when considering EEG data alone. Additionally, we demonstrate that integrating PPG signals increases the sensitivity to 61.22% (successfully detecting 92% seizure events) while decreasing the number of false positives to 1.0 FP/h. Finally, when ACC data are also considered, the sensitivity increases to 64.28% (successfully detecting 95% seizure events) and the number of false positives drops to only 0.21 FP/h for sample-based estimations, with less than one false alarm per day when considering event-based estimations. BrainFuseNet is resource-friendly and well-suited for implementation on low-power embedded platforms, and we evaluate its performance on GAP9, a state-of-the-art parallel ultra-low power (PULP) microcontroller for tiny Machine Learning applications on wearables. The implementation on GAP9 achieves an energy efficiency of 21.43 GMAC/s/W, with an energy consumption per inference of only 0.11 mJ at high performance ( 412.54 MMAC/s). The BrainFuseNet-SSWCE method demonstrates effective and accurate seizure detection on heavily imbalanced datasets while achieving state-of-the-art performance in the false positive rate and being well-suited for deployment on energy-constrained edge devices.