Epilepsy Due to Mild TBI in Children: An Experience at a Tertiary Referral Center

Rationale: Posttraumatic epilepsy (PTE) is a common cause of morbidity in children after a traumatic brain injury (TBI), occurring in 10–20% of children following severe TBI. PTE is diagnosed after two or more unprovoked seizures occurring 1-week post TBI. More often, studies have focused on children with epilepsy due to severe TBI. We aim to understand the utility of head computed tomography (HCT), EEG, and the risk of developing drug-resistant epilepsy in children after mild TBI. Method: We retrospectively studied 321 children with TBI at a tertiary pediatric referral center during a 10-year period. Mild TBI was defined as loss of consciousness (LOC) or amnesia < 30 min, moderate TBI as LOC or amnesia between 30 min and 1 day, and severe TBI as LOC or amnesia > 1 day, subdural hemorrhage, or contusion. Multiple clinical variables were reviewed, including past and present antiepileptic drug(s), seizure control, and mode of injury. First and subsequent post-TBI EEGs/prolonged video-EEGs were obtained acutely, subacutely, and/or chronically (range, day 1–3 years, median 1 month). Descriptive analyses were conducted using medians and ranges for continuous data. Categorical data were reported using frequencies and percentages, while comparisons between groups were made using Fisher’s exact test for small sample sizes. Results: Forty-seven children were diagnosed with posttraumatic epilepsy: eight children (17%) due to mild TBI, 39 children (83%) due to severe TBI. For the eight children with mild TBI whom all had an accidental trauma (non-inflicted), the median follow-up time was 25 months (range 1.5 months–84 months). The median age was 10 years (range 4–18 years), and the median age at the time of injury was seven years (range: 23 months–13 years). No relevant previous medical history was present for six patients (80%), and two patients’ (20%) relevant previous medical histories were unknown. Seven patients (88%) had no history of seizures, and patient #6 (12%) had unknown seizure history. Six patients (75%) had normal routine EEG(s). Patient #6 (13%) had an abnormal VEEG 3 months after the initial normal routine EEG, while patient #1 (13%) had an initial prolonged EEG 8 months after TBI. Compared to the 39 patients with severe TBI, 31 (79%) of whom had abnormal EEGs (routine and/or prolonged with video), mild TBI patients were more likely to have normal EEGs, p = 0.005. Head CT scans were obtained acutely for seven patients (90%), all of which were normal. One patient only had brain magnetic resonance imaging (MRI) 8 months after the injury. Compared to the 39 patients with severe TBI, all of whom had abnormal HCTs, mild TBI patients were less likely to have abnormal HCTs, p < 0.0001. In patients with mild TBI, no patient had both abnormal EEG/VEEG and HCT, and no one was on more than one Antiepileptic drug (AED), p < 0.005. Six patients (75%) had MRIs, of which five (63%) were normal. Two patients (#1, 7) did not have MRIs, while one patient’s (#4) MRI was unavailable. Five patients (63%) had a seizure <24 h post TBI, while the rest had seizures after the first week of injury. Conclusion: Children with epilepsy due to mild TBI, loss of consciousness, or amnesia < 30 min are more likely to have normal HCT and EEG and to be on 0–1 AED. Limitations of our study include the small sample size and retrospective design. The current findings add to the paucity of data in children who suffer from epilepsy due to mild TBI.

Experimental Imaging Study of Encephalomalacia Fluid-Attenuated Inversion Recovery (FLAIR) Hyperintense Lesions in Posttraumatic Epilepsy

This study introduced new MRI techniques such as neurite orientation dispersion and density imaging (NODDI); NODDI applies a three-compartment tissue model to multishell DWI data that allows the examination of both the intra- and extracellular properties of white matter tissue. This, in turn, enables us to distinguish the two key aspects of axonal pathology—the packing density of axons in the white matter and the spatial organization of axons (orientation dispersion (OD)). NODDI is used to detect possible abnormalities of posttraumatic encephalomalacia fluid-attenuated inversion recovery (FLAIR) hyperintense lesions in neurite density and dispersion. Methods. 26 epilepsy patients associated with FLAIR hyperintensity around the trauma encephalomalacia region were in the epilepsy group. 18 posttraumatic patients with a FLAIR hyperintense encephalomalacia region were in the nonepilepsy group. Neurite density and dispersion affection in FLAIR hyperintense lesions around encephalomalacia were measured by NODDI using intracellular volume fraction (ICVF), and we compare these findings with conventional diffusion MRI parameters, namely, fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Differences were compared between the epilepsy and nonepilepsy groups, as well as in the FLAIR hyperintense part and in the FLAIR hypointense part to try to find neurite density and dispersion differences in these parts. Results. ICVF of FLAIR hyperintense lesions in the epilepsy group was significantly higher than that in the nonepilepsy group ( P < 0.001 ). ICVF reveals more information of FLAIR(+) and FLAIR(-) parts of encephalomalacia than OD and FA and ADC. Conclusion. The FLAIR hyperintense part around encephalomalacia in the epilepsy group showed higher ICVF, indicating that this part may have more neurite density and dispersion and may be contributing to epilepsy. NODDI indicated high neurite density with the intensity of myelin in the FLAIR hyperintense lesion. Therefore, NODDI likely shows that neurite density may be a more sensitive marker of pathology than FA.

An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: Retrospective Cohort Study

Background Posttraumatic epilepsy (PTE) is a common sequela after traumatic brain injury (TBI), and identifying high-risk patients with PTE is necessary for their better treatment. Although artificial neural network (ANN) prediction models have been reported and are superior to traditional models, the ANN prediction model for PTE is lacking. Objective We aim to train and validate an ANN model to anticipate the risks of PTE. Methods The training cohort was TBI patients registered at West China Hospital. We used a 5-fold cross-validation approach to train and test the ANN model to avoid overfitting; 21 independent variables were used as input neurons in the ANN models, using a back-propagation algorithm to minimize the loss function. Finally, we obtained sensitivity, specificity, and accuracy of each ANN model from the 5 rounds of cross-validation and compared the accuracy with a nomogram prediction model built in our previous work based on the same population. In addition, we evaluated the performance of the model using patients registered at Chengdu Shang Jin Nan Fu Hospital (testing cohort 1) and Sichuan Provincial People’s Hospital (testing cohort 2) between January 1, 2013, and March 1, 2015. Results For the training cohort, we enrolled 1301 TBI patients from January 1, 2011, to December 31, 2017. The prevalence of PTE was 12.8% (166/1301, 95% CI 10.9%-14.6%). Of the TBI patients registered in testing cohort 1, PTE prevalence was 10.5% (44/421, 95% CI 7.5%-13.4%). Of the TBI patients registered in testing cohort 2, PTE prevalence was 6.1% (25/413, 95% CI 3.7%-8.4%). The results of the ANN model show that, the area under the receiver operating characteristic curve in the training cohort was 0.907 (95% CI 0.889-0.924), testing cohort 1 was 0.867 (95% CI 0.842-0.893), and testing cohort 2 was 0.859 (95% CI 0.826-0.890). Second, the average accuracy of the training cohort was 0.557 (95% CI 0.510-0.620), with 0.470 (95% CI 0.414-0.526) in testing cohort 1 and 0.344 (95% CI 0.287-0.401) in testing cohort 2. In addition, sensitivity, specificity, positive predictive values and negative predictors in the training cohort (testing cohort 1 and testing cohort 2) were 0.80 (0.83 and 0.80), 0.86 (0.80 and 0.84), 91% (85% and 78%), and 86% (80% and 83%), respectively. When calibrating this ANN model, Brier scored 0.121 in testing cohort 1 and 0.127 in testing cohort 2. Compared with the nomogram model, the ANN prediction model had a higher accuracy (P=.01). Conclusions This study shows that the ANN model can predict the risk of PTE and is superior to the risk estimated based on traditional statistical methods. However, the calibration of the model is a bit poor, and we need to calibrate it on a large sample size set and further improve the model.

The birth of experimental neurosurgery: Wilder Penfield at Montreal’s Royal Victoria Hospital, 1928–1934

Wilder Penfield is well known as the founder of the Montreal Neurological Institute (MNI), the site of his most important contributions to the investigation and treatment of epilepsy and to our understanding of the structure-function relationship of the brain. The seeds of the MNI were sown 6 years before its opening in 1934, when Penfield accepted the position of head of the Subdepartment of Neurosurgery at McGill University’s Royal Victoria Hospital (RVH). Penfield took this position because of the facilities made available to him to pursue the neuropathological research that he had undertaken with Pío del Río Hortega in Madrid, and to continue his investigation into the nature and treatment of posttraumatic epilepsy that he began with Otfrid Foerster in Breslau. Penfield and his first neurosurgical research fellows Joseph Evans, Jerzy Choróbski, Nathan Norcross, Theodore Erickson, Isadore Tarlov, and Arne Torkildsen studied the substrate of focal epilepsy, the innervation of cortical arteries, the function of the diencephalon, the microscopic structure of spinal nerve roots, and the ventricular system in health and disease. In his 6 years at the RVH, Penfield and his fellows effected a paradigm shift that saw neurosurgery pass from empirical practice to scientific discipline.

Portrait of My Father: Frida Kahlo’s Intimate Relation to Epilepsy

The painting <i>Portrait of My Father</i> (1951) by the Mexican painter, Frida Kahlo, is discussed by the author within the context of epilepsy and biographical events in the lives of both Frida and her father, the German Mexican photographer Guillermo Kahlo. The biographical accounts of the photographer’s seizures are suggestive of juvenile absence epilepsy but cannot discount the possibility of posttraumatic epilepsy of mesial frontal origin.