Dumkrieger, Gina

Abstract Post-traumatic headache (PTH) is the most common consequence of mild traumatic brain injury (mTBI) and often resembles migraine. In 20% to 30% of migraine patients, headaches are preceded by aura, transient visual or sensory disturbances, which are caused by cortical spreading depression (CSD) that can induce headache pain. However, whether mTBI promotes the development of aura and CSD and their possible contributions to PTH remains unclear. We examined the incidence of new-onset aura in individuals with PTH who had no history of aura. Thirty-two percent (35/108) developed new aura symptoms within 60 days of injury, suggesting that mTBI can sensitize the cortex to the induction of CSD. To test this, we combined electrophysiology, neuroimaging, and behavioral assays in a mouse model of mTBI-induced PTH. After closed-head mTBI, injured mice exhibited a 160% increase in CSD induction probability (ie, lowered threshold) and became sensitized to CSD induction by brief exposure to stress, a common headache trigger. When evoked optogenetically, CSD produced prolonged periorbital and hind paw allodynia, consistent with headache-like pain. Subcutaneous onabotulinumtoxinA administered to the scalp after mTBI did not alter CSD susceptibility but completely abolished optogenetic CSD-induced allodynia. These findings suggest that mTBI elicits sensitization to CSD induction and promotes the emergence of aura in humans, likely contributing to PTH. It is important that, even after mTBI-induced sensitization, onabotulinumtoxinA decoupled CSD from pain behaviors, suggesting efficacy in treating PTH associated with aura.

Abstract To enhance the prediction of traumatic brain injury (mTBI) outcomes, we propose a deep learning approach that integrates brain computed tomography (CT) scans with corresponding synthetic T1-weighted magnetic resonance imaging (T1-MRI). Our method significantly outperforms the prediction using CT scans alone. TRACK-TBI Pilot dataset, which includes imaging and clinical outcome data from patients with TBI, is studied. The hypothesis is brain CT and T1-MRI complement each other and together will improve TBI prognosis compared to using either CT or T1-MRI alone. Since CT and T1-MRI may not be available for the same individual, we employed a specialized version of a generative adversarial network (GAN), known as fixed-point GAN (FP-GAN). FP-GAN was trained using unpaired CT and T1-MRI scans to generate synthetic T1-MRIs from real CT scans. This process produced pseudo-paired CT-MRI data, which was then used to train a deep learning classifier for outcome prediction. The classifier consists of dual parallel 3D ResNet-18 models, each independently processing T1-MRI and CT scans. We used Glasgow Outcome Scale-Extended (GOSE) scores at 3 months post-TBI as the measure of patient outcomes. To avoid data leakage, the subjects used in FP-GAN and ResNet-18 model have no overlap. We further divided the paired data, allocating 69 samples for 5-fold cross-validation and 17 samples for testing. Prognostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score (the harmonic mean of precision and recall), sensitivity (true positive rate) and specificity (true negative rate). For binary classification, we defined good recovery as GOSE ≥ 7 (positive) and poor recovery (negative) as 3 ≤ GOSE ≤ 6. Accordingly, our training set consists of 24 subjects with poor recovery and 45 subjects with good recovery, while the testing set includes 5 subjects with poor recovery and 12 subjects with good recovery. A DeLong test on AUC confirms that the improvement from incorporating synthetic T1-MRI (AUC = 0.76 ± 0.10) is statistically significant (P < 0.05) compared to using CT alone (AUC = 0.68 ± 0.13). The significant improvement from using the combination of real CT and synthetic T1-MRI in sensitivity (SEN = 0.95 ± 0.07) and overall performance metrics, such as F1-score (F1 = 0.84 ± 0.03), suggests that the proposed approach provides a robust and effective prognostic approach compared to using CT alone (SEN = 0.83 ± 0.18 and F1 = 0.76 ± 0.07). This pilot research demonstrates the potential of a deep learning-based harmonization model to bridge the gap between CT and T1-MRI in TBI assessment. By integrating synthetic T1-MRI with CT, prediction performance is substantially enhanced.

Aim Post-traumatic headache (PTH) attributed to mild traumatic brain injury (mTBI) frequently co-occurs with other physical, cognitive, and emotional symptoms. This study assessed associations between PTH improvement and these symptoms in individuals with acute headaches (APTH) and compared symptoms between those who had recently developed persistent PTH (PPTH) to those who had long-standing PPTH. Methods This study included 105 individuals with APTH (mean age = 43.4, SD = 15.8; 70 females, 35 males) and 46 individuals with PPTH (mean age = 38.2, SD = 10.9; 16 females, 30 males) with an average duration of 11.3 years from two prospective longitudinal studies. For those who had APTH, PTH improvement was assessed at three months post-enrollment using an electronic headache diary. Participants completed questionnaires evaluating physical (including headache symptoms, photosensitivity, hyperacusis, insomnia, autonomic symptoms, and disability), emotional (including depression, anxiety, pain catastrophizing, and post-traumatic stress disorder screening), and cognitive symptoms (including memory and attention). The APTH cohort completed questionnaires at baseline, follow-up #1 (three to four months from baseline), and follow-up #2 (six to seven months from baseline). Mixed Effects Models were used to analyze the temporal trend of symptoms among the APTH improved and non-improved groups. Results Among the 105 individuals with APTH, 60 experienced PTH improvement and 45 did not. At baseline, compared to those who had PTH improvement, those who did not have PTH improvement had higher SCAT total scores and physical subscores, greater levels of hyperacusis, pain catastrophizing, anxiety and depression. At follow-up #1 (three to four months), these individuals continued to show more pronounced physical and emotional symptoms and worse cognitive function and insomnia. Most of these group differences persisted at follow-up #2 (six to seven months). Most symptoms showed progressive improvement over three months for individuals who had PTH improvement but not for those without PTH improvement. Furthermore, individuals with long-standing PPTH showed more severe insomnia, pain catastrophizing, and PTSD compared to those who had recently developed PPTH. Conclusion Individuals with APTH who did not experience PTH improvement at three months had more severe physical, cognitive, and emotional symptoms at baseline compared to those who had PTH improvement. While most symptoms normalized in individuals with PTH improvement, symptoms overall persisted at three and at six months for those with PTH persistence. Individuals who had long-standing PPTH had more severe insomnia and more negative cognitive/emotional responses to pain compared to those who had recently developed PPTH.

Introduction: Emerging evidence highlights the increased white matter hyperintensity (WMH) burden in APOE4 homozygotes, the greatest genetic risk factor for late-onset Alzheimer's disease (AD). The link between WMH and cerebral amyloid-beta (Aβ) accumulation remains unclear, especially in presymptomatic AD phases. We aimed to determine whether WMH burden (global or regional) mediates the association between APOE4 homozygosity and amyloid-PET Aβ deposition in a population of subjects with intact cognition. Methods: WMH were segmented using a deep learning model applied to T1 and T2-FLAIR MRI scans from 49 cognitively intact individuals older than 50. The global and regional (periventricular, lobar, basal ganglia, infratentorial) WMH volumes were adjusted for total intracranial volume and log-transformed to normalize distribution. Amyloid-PET cerebral Aβ burden was quantified in centiloid units; individuals with centiloid > 20 were classified as Aβ-positive. To examine our mediation hypothesis, we tested three pathways using logistic and linear regression models: 1) Path a, the association between APOE4 and global or regional WMH volumes (outcome); 2) Path b, the association between WMH volumes and Aβ positivity (outcome); 3) Path c (total) and c' (direct) effects of APOE4on Aβ positivity (outcome), before and after adjusting for WMH volumes. All models incorporated age and vascular risk factors (hypertension, diabetes, hypercholesterolemia, cigarette smoking). Results: We included 11 APOE4 homozygotes, of whom 7 were Aβ-positive and 38 non-carriers (35 3/3 and 3 3/2) (6 Aβ-positive). Compared to APOE4 non-carriers, APOE4 homozygotes had significantly greater WMH volumes in the posterior periventricular region only (p= 0.0453) (path a). Homozygotes were significantly more likely to be Aβ-positive compared to non-carriers (total effect, path c p=0.0019). This association remained significant after adjusting for WMH (direct effect, path c'= 0.0027). However, neither global (p=0.72) nor posterior periventricular (p=0.84) WMH volumes predicted Aβ positivity (path b) (Figure). Conclusions: In our cohort of cognitively normal individuals including preclinical AD, neither global nor regional WMH burden mediated the relationship between APOE4 homozygosity and Aβ positivity. Larger studies are needed to further evaluate the mechanistic relationship between WMH and neurodegeneration in APOE4 carriers.

Purpose White matter hyperintensity (WMH) is associated with heterogeneous pathologies. In neurodegeneration, the prevailing hypothesis is that WMH primarily reflects vascular pathology, exerting an additive effect in AD. Alternatively, a subset of WMH may arise secondary to AD‐related pathophysiology. APOE4 genotype, the greatest genetic risk factor for late‐onset AD, impacts the blood‐brain barrier and WMH burden. Herein, we investigate the association between regional WMH burden and APOE4 allelic dosage in cognitively normal individuals. Methods We used deep learning to automatically segment WMH from T2‐FLAIR MRI from 154 cognitively intact individuals older than 50. We mapped WMH to 4 specific brain regions (lobar, periventricular, basal ganglia and infratentorial, further parcellated into sub‐regions, Table 1) using atlas‐based registration and FastSurfer. WMH volumes were adjusted for total intracranial volume and normalized using log transformation. The APOE4 groups were compared for WMH burden using linear regression, controlling for age and vascular risk factors (hypertension, diabetes, hypercholesterolemia, cigarette smoking). For brain regions where more than 20% of participants had no WMH, a two‐part hurdle model was applied. Results We included 13 APOE4 homozygotes, 50 heterozygotes (ε4/ ε3) and 91 non‐carriers. Compared to non‐carriers, APOEε4 homozygotes exhibited significantly greater WMH burden in the whole brain, periventricular (posterior) and lobar regions (Figure 1a). Homozygotes exhibited a significantly greater WMH burden in the whole brain and posterior periventricular region, when compared to heterozygotes (Figure 1b). No significant whole‐brain or region‐specific differences were observed between heterozygotes and non‐carriers (Figure 1c). Conclusion In cognitively intact individuals at risk for AD, APOE4 homozygosity is associated with significantly greater WMH burden, particularly in the posterior periventricular space, after controlling for age and vascular risk factors. The relationship between regional‐specific WMH burden and neurodegeneration warrants further investigation. image image

Background This narrative review establishes the current state of the art of machine learning approaches for prediction of migraine attacks. Related concepts are highlighted including the identification of triggers or premonitory symptoms and methods for evaluating prediction models. Existing efforts at machine learning prediction of individual migraine headaches and attacks are reviewed in detail. Challenges in this task are discussed. Results A variety of input data and modeling approaches have been used. It is consistently found that individualized models provide better results compared to a generalized model and achievable performance varies considerably between individuals. Patient needs should be assessed to discover what a valuable prediction looks like. The field should develop common standards for evaluating migraine prediction algorithms. Conclusions/Interpretations While the problem is far from solved there is great potential and reason to believe that feasible solutions that improve the quality of life of those with migraine are within our grasp.

Abstract Background Post-traumatic headache (PTH) is a common acute and persistent symptom following mild traumatic brain injury (mTBI). Symptoms of cutaneous allodynia and presence of nociceptive sensitization might be associated with acute PTH and its persistence. The objectives of this study were to compare allodynia symptoms and cutaneous heat pain thresholds amongst males and females with acute PTH to healthy controls (HC) and determine if pain thresholds and allodynia symptoms are associated with PTH outcomes. Methods This prospective longitudinal study enrolled 139 adults with acute PTH attributed to mTBI as defined by the International Classification of Headache Disorders and 95 HC. All PTH participants completed a baseline research visit near PTH onset and a follow-up visit three to four months later. All PTH participants and a subset of HC completed the Allodynia Symptom Checklist (ASC-12) at each research visit. A different subset of the participants underwent quantitative sensory testing (QST) during baseline, 4-week, and 16-week research visits to quantify cutaneous heat pain thresholds at the forehead and forearms. Data from daily headache diaries were used to determine longitudinal PTH improvement versus non-improvement at three months. ASC-12 score and pain threshold comparisons were made between PTH and HC groups, PTH improved versus non-improved cohorts, and between PTH males and females. Results Participants with PTH had an average age of 42.6 years and 64.0% were female. HC had an average age of 40.0 years and 65.3% were female. At the first visit, PTH participant ASC-12 scores averaged 3.6 versus 0.1 amongst HC, p < 0.001. 44.8% of PTH participants had headache improvement at 3 months. ASC-12 scores were higher in the PTH non-improved versus improved group at baseline (4.0 versus 2.4, p = 0.038) and 3-month follow-up (3.4 versus 1.9, p = 0.012). ASC-12 scores were higher in females than males at baseline (4.7 versus 1.6, p < 0.001) and 3-months (3.9 versus 1.2, p < 0.001). Cutaneous heat pain thresholds at the forehead and forearm did not differ between any group. Conclusions PTH attributed to mTBI is associated with symptoms of cutaneous allodynia. Greater allodynia symptoms are present in females with PTH compared to males and may be associated with PTH non-improvement.