Citations

Abstract Objectives/Background To investigate disruptions in resting state networks in participants with acute posttraumatic headache (PTH) due to mild traumatic brain injury (mTBI). Neuroimaging studies have demonstrated alterations in functional connectivity (FC) within core resting‐state networks in participants with headache versus healthy controls (HCs), which may provide insights into disease mechanisms; however, the extent to which these alterations relate to PTH remains incompletely understood. Methods In this comparative cross‐sectional study with an embedded prospective cohort, participants with acute PTH and HCs were enrolled from the Mayo Clinic Arizona and Minnesota, and the Phoenix Veterans Affairs Health Care between 2020 and 2024. Participants with PTH underwent resting‐state magnetic resonance imaging within 2 months following mTBI. We investigated differences within and between functional brain networks in participants with PTH compared to HCs. Associations between altered FC with headache frequency, alterations of mood, post‐mTBI symptoms, and concussion history were determined and corrected for age and sex. Relationships between PTH outcomes at 3 months and baseline FC were investigated. Results Ninety‐six participants with acute PTH (35 male, 61 female) and 84 age‐balanced HCs were included. Decreased resting state network connectivity in participants with PTH compared to HCs (False Discovery Rate (FDR) corrected <0.05) was observed in two default mode network (DMN) regions encompassing the bilateral frontal and anterior cingulate cortex (ACC), as well as the left postcentral gyrus. Within those DMN regions, exploratory analyses revealed that FC was positively associated with headache frequency in the bilateral ACC, right precuneus and frontal gyrus, and with trait anxiety in the right ACC. In contrast, negative associations were observed between FC and number of mTBIs, as well as state anxiety, in the left ACC (uncorrected, p <0.05). No significant associations with clinical headache variables were observed in the DMN comprising the left postcentral gyrus. Baseline group differences in network connectivity between recovered and nonrecovered participants were found in visual, sensorimotor, default mode, executive control and salience networks. Alterations in baseline connectivity between independent components in recovered and nonrecovered participants were not statistically significant after corrections for multiple comparisons. Conclusion In participants with PTH, the DMN regions showing decreased connectivity also exhibited associations with headache frequency, state and trait anxiety, and number of lifetime mTBIs, suggesting that these changes relate to the severity and persistence of post‐mTBI symptoms. The baseline differences in network connectivity between recovered and nonrecovered participants were not statistically significant after corrections for multiple comparisons, indicating that baseline network connections may not play a significant role in the recovery process.

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.

White matter hyperintensities (WMHs) are common MRI findings and established markers of cerebral small-vessel disease associated with stroke, cognitive decline, and vascular risk factors. Several studies have suggested that individuals with migraine, particularly migraine with aura (MA), may have an increased burden of WMHs compared with individuals without migraine. Early population-based studies and meta-analyses reported a higher prevalence of WMHs in migraine, whereas more recent large neuroimaging cohorts have reported conflicting findings or no association. Differences in study design, migraine ascertainment, MRI acquisition parameters, WMH quantification methods, and adjustment for vascular risk factors complicate interpretation of the existing literature. An updated systematic review focusing on methodologically comparable population-based cohorts is therefore warranted. The primary objective of this systematic review and meta-analysis is to determine whether adults with migraine have a higher prevalence or burden of WMHs on MRI than non-migraine controls. Secondary objectives include evaluating associations according to migraine subtype (MA vs. migraine without aura and episodic vs. chronic migraine), sex, WMH location, age, and MRI acquisition or WMH quantification methods, as well as examining longitudinal WMH progression where available. We will include population-based cross-sectional or longitudinal cohort studies of adults undergoing brain MRI with sequences suitable for WMH detection. Migraine exposure may be defined using International Classification of Headache Disorders criteria, structured interviews, validated questionnaires, physician diagnosis, or ICD codes. Controls must originate from the same source population. Searches will be conducted in PubMed/MEDLINE, EMBASE, Web of Science, and Scopus. Study selection, data extraction, and risk-of-bias assessment will be performed independently by two reviewers using Covidence, and risk of bias will be evaluated using the ROBINS-E tool. Because WMH burden is reported using heterogeneous metrics across studies, the primary synthesis will use a precision-weighted direction-of-effect approach, in which each study contributes the direction of the association, weighted by statistical precision. Where sufficiently comparable outcomes are available, random-effects meta-analysis will be performed. Sensitivity analyses will explore the influence of diagnostic criteria, MRI field strength, and confounder adjustment. This review will provide an updated and methodologically rigorous synthesis of the evidence on the association between migraine and WMHs.

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 &lt; 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.

ABSTRACT Magnetic Resonance Spectroscopy Imaging (MRSI) offers spatially-resolved, neurometabolic information, acquired non-invasively at whole-brain scales from human subjects. Analysis of MRSI however, is extremely challenging. The metabolic information is highly convolved, and sparsely distributed across millions of spatial-spectral datapoints, allowing for little direct human interpretation. Conversely, the overall low signal-to-noise with high-intensity artifacts can confound unsupervised machine learning approaches. These technical barriers have left much of the potential of MRSI unrealized. We acquired MRSI data from 4 human subjects with a diagnosis of multiple sclerosis (MS), incorporating experimental design into an informed machine learning approach. MRSI acquisitions were registered to anatomical MRI to label 105k spectra from brain tissue and 162 spectra from white matter hyperintensities (WMHs), an imaging biomarker associated with MS lesions. Spectral labels were then used in contrastive principal component analysis (cPCA) to filter artifacts and background features in the MRSI data from lesion salient features and clustered into statistically significant states based on features that could be interpreted from the original data. Our approach renders MRSI data into testable representations of neurometabolism, enabling the method for fundamental and clinical research. Graphical Abstract Analysis workflow for neurometabolic profiling of MS lesions. MRSI and anatomical MRI is acquired and processed in parallel for spectral data and anatomical labels. Spectra are then labeled and separated into experimental vs background data for contrastive PCA. Spectra are clustered for similarity, further labeled, and projected onto a brain atlas for a neurometabolic view.

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.