Schwedt, Todd J

Abstract Objectives/Background This narrative review summarizes evidence implicating a role for the hypothalamus in the prodrome phase of a migraine attack. Prodrome is the earliest phase of the migraine attack. Understanding the migraine prodrome could lead to a better description of how migraine attacks are initiated and identification of targets for acute and preventive migraine treatment. The hypothalamus has been implicated in migraine prodrome via (1) localization of common prodrome symptoms to the hypothalamus; (2) identification of neurotransmitters, peptides, and hormones important in migraine pathophysiology for which the hypothalamus influences their production or release; and (3) brain neuroimaging studies identifying changes in hypothalamic activity and functional connectivity during the prodrome and preheadache phases of the migraine attack. Methods For this narrative review, PubMed was searched for relevant English language articles using the terms “hypothalamus, prodrome,” “hypothalamus, premonitory,” “hypothalamus, migraine,” “migraine, prodrome,” and “migraine, premonitory.” The PubMed search was performed on October 29, 2025. Full articles were chosen for review based on their relevance to the three areas defined just above: symptom localization, neuropeptide/neurotransmitter release, and brain imaging. Results Those with migraine commonly, and often consistently, experience prodrome symptoms, including hypersensitivities to visual and auditory stimuli, neck pain, fatigue, sleep–wake disturbances, changes in appetite, mood changes, and alterations in thermoregulation perception. Many of these symptoms can be localized to functions of hypothalamic subregions and nuclei. Several neuropeptides, neurotransmitters, and hormones that contribute to prodrome and other migraine attack symptoms, including calcitonin gene‐related peptide, dopamine, orexins, and pituitary adenylate cyclase‐activating polypeptide, are either produced by the hypothalamus or their release is mediated by the hypothalamus. Functional neuroimaging studies of spontaneous and triggered migraine attacks have identified increased hypothalamic activity and altered hypothalamic functional connectivity before migraine headache onset, including during the prodrome. Conclusion There is compelling evidence that the hypothalamus plays a role in the migraine prodrome and likely in migraine attack initiation. This is supported by many migraine prodrome symptoms localizing to hypothalamic function and functional neuroimaging studies demonstrating increased activity and altered connectivity of the hypothalamus during the prodrome phase. Further evidence and research are required to understand the hypothalamus' contribution relative to other brain regions.

Background Photophobia is experienced by most individuals with migraine, and it is frequently the most bothersome symptom other than headache. Light is not perceived uniformly; spectral composition and intensity influence discomfort and can exacerbate symptoms. While monochromatic green light shows therapeutic potential, it appears visually unnatural and is impractical for daily use. This study aimed to determine whether spectrally tailored white light, more visually acceptable in everyday environments, reduces light sensitivity among those with migraine. Methods Twenty adults (18–44 years) with episodic migraine completed two research sessions: one during an acute migraine attack and one between migraine attacks. Participants were exposed to four spectrally distinct white light conditions (red-, green-, cyan-, and blue-enriched) in a randomized order delivered at ten intensity levels (50–590 lux). After each exposure, visual discomfort was rated on a 0–10 scale. Pupil size was measured using a pupillometer. Repeated-measures ANOVAs examined the effects of light spectrum, intensity, and migraine state on visual discomfort and pupil response. Results Migraine attack status significantly increased visual discomfort ( F (1,19) = 25.84, p < .001, η 2 = .576). White light spectrum ( F (3,57) = 12.47, p < .001, η 2 = .396) and light intensity ( F (9171) = 124.30, p < .001, η 2 = .867) were associated with intensity of visual discomfort. Post-hoc analyses indicated that visual discomfort increased with increasing light intensity, especially at higher light intensities. Among all tested spectra, green-enriched white light resulted in the least visual discomfort; blue-, red-, and cyan-enriched white light produced significantly higher discomfort (all p < .001). Pupil size varied by spectrum ( F (3,57) = 14.13, p < .001, η 2 = .426), with green-enriched white light producing the largest pupil size. Conclusion Spectral composition, light intensity, and migraine state jointly influence both subjective and physiological responses to light. Across lighting spectra, lower light intensity was consistently associated with reduced visual discomfort, indicating greater visual comfort at lower intensities. These results identify green-enriched white light at lower intensities as a visually acceptable, migraine-conscious lighting option with potential for real-world application.

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.

Learning from multimodal data sets can leverage complementary information and lead to improved performance for prediction tasks. A commonly used strategy to account for feature correlations in high-dimensional data sets is the latent variable approach. Several latent variable methods have been proposed for multimodal data sets; however, these methods either focus on extracting a shared component across all modalities or extracting a shared component and individual components specific to each modality, overlooking correlations within partial subsets of modalities. We propose multimodal fission learning (MMFL), the first supervised latent variable model that adopts a generalizable decomposition into globally joint, partially joint, and individual components from multimodal data sets. A key strength of MMFL is a natural extension to incorporate incomplete multimodal data in either training and test phases by leveraging the learned modality structure. Through simulation studies, we demonstrate that MMFL outperforms a variety of existing multimodal algorithms under both complete modality and incomplete modality settings. We applied MMFL to two real-world case studies: early prediction of Alzheimer’s disease using neuroimaging and genetic data and predicting posttraumatic headache improvement using clinical data collected via questionnaires and brain neuroimaging data. MMFL achieved improved predictive accuracy and enhanced interpretability offering insights for within- and cross-modal relationships of multimodal data sets. History: Rema Padman served as the senior editor for this article. Funding: This research was supported by the National Institutes of Health [Grants 2R42AG053149-02A1, R01AG069453, 30AG072980, R01AG069453, and 1R61NS113315-01], the National Science Foundation [Grant DMS-2053170], and the Department of Defense [Award W81XWH-19-0534]. The grantee organization is the Northern California Institute for Research and Education. In the past, ADNI has also received funding from the National Institute of Biomedical Imaging and Bioengineering, the Canadian Institutes of Health Research, and private sector contributions through the Foundation for the National Institutes of Health including generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. Data Ethics & Reproducibility Note: The code capsule is available at https://github.com/lingchm/MMFL and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2024.0059 ).

Background Migraine is a highly prevalent neurological disorder with many treatment options, both pharmacological and non-pharmacological. Artificial intelligence (AI) has great potential to optimize treatment selection strategies for individual patients. This review provides an overview of AI models and the techniques used to predict migraine treatment outcomes. Methods We conducted a literature search in PubMed and examined studies that reported employing AI models to predict migraine preventive and acute treatment outcomes. We also explored incorporating AI/machine learning to enhance personalized migraine treatment strategies, including forecasting migraine attacks. Additionally, we summarized future research directions, including incorporating multimodality data, using AI frameworks for the discovery of novel treatment targets, and advancing the field with innovative AI techniques such as digital twins, conversational AI and virtual AI agents. Results Studies have employed ML and deep learning on a combination of clinical features and imaging data to predict acute or preventive migraine treatment outcomes with reported success. Continued model optimization, validation, and prospective assessment of the clinical utility of deploying ML models in real-world settings are crucial. Conclusions While AI has demonstrated success in predicting migraine treatment responses, future research incorporating novel AI techniques and diverse data sources could pave the way to advance personalized migraine treatment.

Migraine is one of the most disabling diseases worldwide, especially when it transforms into chronic migraine, which is often associated with medication overuse and can become resistant or even refractory to treatments. Molecular, neuroimaging and neurophysiological changes have been described in chronic migraine, some of which might not be fully reversible with preventive treatment. For these reasons, we should aim to prevent this transition, and initiate preventive treatment before disease becomes refractory and burden increases. Preventive migraine treatments are often delayed because of access to care, stigma leading to undertreatment and patients’ reluctance as a result of fear of side effects and, in some cases, fear of being labeled as chronically ill. With the availability of effective and well-tolerated preventive treatments, we must shift our mindset and take advantage of new opportunities to initiate preventive treatment earlier. In this International Headache Society position statement, we propose a migraine preventive strategy under the idea of shifting from reactive treatment once disability is established (prevention of attacks), to proactive, individualized prevention initiated early with safe, effective and tolerable therapies (prevention of disease progression). This approach is based on 1) promoting the early initiation of effective and tolerable preventive therapies, starting from two to four monthly migraine days in line with the majority of current guidelines and recommendations and 2) fostering longitudinal studies to gather more evidence on the potential benefit of early prevention, with the final goal of improving patient outcomes, promoting excellent migraine care, enhancing individual and social well-being, and, ultimately, preventing migraine progression and preserving brain health.

Background This systematic review and meta-analysis synthesized migraine-related disability outcomes according to headache frequency subclassifications, including low-frequency episodic migraine (LFEM), high-frequency episodic migraine (HFEM) and chronic migraine (CM). Methods We searched the PubMed and Cochrane Library (CENTRAL) up to 16 October 2024 for peer-reviewed non-interventional studies reporting migraine-related disability outcomes in CM and subclassifications of episodic migraine (e.g. LFEM and HFEM). Eligible studies with an HFEM subgroup were grouped by headache frequency measure (monthly migraine days [MMD] or monthly headache days [MHD]), HFEM subgroup, disability parameter and study setting. Random-effects meta-analyses were conducted on groups with three or more studies, with the results presented in forest plots. Risk of bias was assessed using Joanna Briggs Institute tools. Results Of the 32 included studies, 27 were grouped, yielding five meta-analysis groups containing three or more studies. All five groups had an HFEM subgroup of 8–14 MHD. Accordingly, we classified LFEM as 0–7 MHD and CM as ≥15 MHD. Ten studies contributed data to the five meta-analysis groups. Estimated pooled values are reported by headache frequency subgroup for each meta-analysis group: LFEM (95% confidence interval [CI]), HFEM (95% CI) and CM (95% CI). For meta-analysis group 1, four population-based studies reported Migraine Disability Assessment (MIDAS) Grade IV proportions, with pooled values of 11.1% (7.1–17.1), 43.9% (31.8–56.9) and 57.5% (42.7–71.1), respectively. For meta-analysis group 2, five population-based studies documented Work Productivity and Activity Impairment (WPAI) outcomes. Pooled mean overall work productivity impairment (OWPI) scores were 36.9% (30.8–43.1), 44.7% (38.4–51.0) and 52.0% (47.7–56.3), respectively. Pooled mean activity impairment (AI) scores were 36.4% (33.7–39.1), 46.4% (42.8–50.0) and 53.5% (52.1–54.8), respectively. For meta-analysis group 3, three clinic-based studies presented MIDAS Grade IV proportions. Pooled values were 15.0% (4.4–40.5), 42.4% (17.1–72.4) and 65.7% (30.3–89.4), respectively. For meta-analysis group 4, three clinic-based studies reported WPAI outcomes. Pooled mean OWPI scores were 28.8% (21.6–35.9), 40.2% (36.2–44.2) and 49.6% (46.3–52.9), respectively. Pooled mean AI scores were 29.4% (21.9–36.9), 43.5% (39.0–48.1) and 51.6% (48.8–54.4), respectively. For meta-analysis group 5, three clinic-based studies detailed MIDAS scores. Pooled mean scores were 10.7 (3.9–17.5), 23.9 (9.4–38.4) and 49.6 (15.8–83.3), respectively. Conclusions This study showed a pattern of increasing migraine-related disability with rising headache frequency. Regardless of study setting, our meta-analyses also suggested a severe level of disability in many individuals (42%–44%) with HFEM, highlighting an unmet need for more effective migraine management. Disability burden and headache frequency should both be considered when determining treatment needs and therapy access, particularly for patients with HFEM. Study Registration: INPLASY2024120039.

Background Headache is a common symptom following mild traumatic brain injury (mTBI). Post-traumatic headache (PTH), a secondary headache disorder that develops after mTBI, often persists for months or years. To identify potential recovery mechanisms and prognostic biomarkers, the present study investigated whether longitudinal changes in pain-induced brain activation differ between healthy controls (HC) and PTH participants showing headache improvement and those without improvement. Methods Thirty-three participants who met International Classification of Headache Disorders, 3rd edition, criteria for acute PTH within 59 days post-mTBI and 33 HC participants were included with no significant differences in demographics. All participants underwent functional magnetic resonance imaging scans at baseline, four weeks, and 16 weeks post-enrollment using a thermal stimulation paradigm with noxious and non-painful heat stimuli. ‘Painful vs. Non-Painful Heat’ contrasts were generated using SPM12. PTH improvement was assessed at three months post-enrollment via electronic headache diaries. Two-sample t -tests compared the brain activation between HC and PTH at baseline. Linear mixed-effects models examined longitudinal changes for HC, PTH improvement and non-improvement groups across visits. Generalized linear models compared these groups within visits. Results Baseline analysis revealed several regions with significantly higher activation in acute PTH compared to HC, including bilateral postcentral gyrus, right superior temporal gyrus, right middle temporal gyrus, left inferior parietal gyrus, right superior parietal gyrus, left ventral striatum, left olfactory cortex, left gyrus rectus, and left middle occipital gyrus. Over time, the PTH improvement group demonstrated progressive normalization across all identified brain regions, whereas the non-improvement group showed only partial normalization in left ventral striatum, left olfactory cortex, and left gyrus rectus. Sustained elevated activation in specific regions distinguished PTH participants without headache improvement from those with headache improvement, suggesting potential biomarkers for persistent PTH. Conclusions Our findings demonstrate significantly altered pain-induced brain activations in participants with acute PTH compared to HC. Longitudinal analysis revealed distinct recovery trajectories: progressive normalization in the improvement group versus persistent alterations in the non-improvement group. These neuroimaging patterns may serve as biomarkers for identifying individuals at risk for persistent PTH, with implications for early intervention and personalized treatment approaches.

We here present evidence-based guidelines for the pharmacological treatment of migraine. These guidelines, created by the Italian Society for the Study of Headache and the International Headache Society, aim to offer clear, actionable recommendations to healthcare professionals. They incorporate evidence-based recommendations from randomized controlled trials and expert-based opinions. The guidelines follow the GRADE approach for assessing the quality of evidence. The guideline development involved a systematic review of literature across multiple databases, adherence to Cochrane review methods, and a structured framework for data extraction and interpretation. Although the guidelines provide a robust foundation for migraine treatment, they also highlight gaps in current research, such as the paucity of head-to-head drug comparisons and the need for long-term outcome studies. These guidelines serve as a resource to standardize migraine treatment and promote high-quality care across different healthcare settings.