Migraine research: AI finds patterns throughout the body

Researchers at the Norwegian University of Science and Technology (NTNU) used artificial intelligence to analyze health data from more than 43,000 participants in the Trøndelag Health Study (HUNT) and found that migraines leave a distinct biological signature throughout the body, enabling AI to identify the condition without headache symptom data. The AI model, which used 60 variables including age, sex, and general health information, distinguished people with migraine from those without headaches and revealed four previously hidden subgroups, including an all-male group and a group with prominent neck pain. Anker Stubberud, a physician and headache researcher at NTNU, said the findings suggest migraines have traces that extend far beyond the attacks and could help improve diagnostic accuracy.

Migraine research: AI finds patterns throughout the body AI can trawl vast amounts of health data which is not specifically related to migraines, but which can be used to identify people predisposed to the condition. A new study from NTNU reveals that migraines may leave a biological pattern throughout the body, traces of which can be identified by artificial intelligence. “There are many inconsistencies with the diagnosis of migraines. It appears that AI could help us make the correct diagnosis,” says Anker Stubberud, a physician and headache researcher at NTNU. Researchers used AI to analyse information gathered from more than 43,000 participants during a county-wide health study, Helseundersøkelsen i Trøndelag HUNT https://www.ntnu.edu/hunt . The study shows that the condition can be identified through a complex interplay between genetics, clinical characteristics and environmental factors. “Migraines are a major health problem worldwide and have a considerable impact on quality of life. Accurate diagnoses are important so that people can receive the best possible treatment,” says Stubberud. A major public health problem Half of the world’s population experiences headaches. For some, the headaches are so severe that they are categorised as a migraine. Migraine is characterised by symptoms including throbbing headaches, sensitivity to light and sound, vomiting and nausea. The condition affects around 15 per cent of the Norwegian population and is the leading cause of disability among women under the age of 50. One challenge in identifying migraines is that the diagnosis is based entirely on symptoms. “There are no blood tests or other biological tests that can be used as part of the diagnostic process. All we have is the person’s description of their symptoms. If the diagnosis is incorrect, the treatment may also miss the mark. We are therefore exploring whether we can identify biological markers that could help practicioners make the correct diagnosis,” says Stubberud. This is where AI comes in. Migraines without the headache The researchers developed an AI model that could distinguish people with migraine from people without headaches. The model was not given access to information about the participants’ headache symptoms, instead relying on data such as age, sex and general health information. “The information included everything from constipation and medication use to back pain. We included 60 different variables. The fact that AI could identify migraine so accurately without knowing anything about the headache itself suggests that the condition leaves traces that extend far beyond the attacks,” says Stubberud. Not only did the research indicate that migraines have a distinct and traceable biological signature, it also revealed several previously hidden subgroups of migraine. Four different forms of migraine Using a method in which AI independently searches for patterns in the data, the researchers analysed more than 12,000 people with different forms of headache. The analysis initially identified two distinct main groups. The first group consisted of 1,425 people, more than 90 per cent of whom met the criteria for migraine. The remainder 10,760 people had other types of headache which only partially macthed the criteria for migraine, or had other forms of headache altogether. This suggests that AI was able to identify a distinct migraine profile independently of traditional diagnostic criteria. The most interesting discovery came when the researchers took a closer look at the migraine group and found four subgroups with different characteristics: 1. An all-male group This subgroup stood out clearly because all of the participants were men. The finding may indicate that migraine in men has distinctive characteristics that are often obscured when researchers analyse women and men together. 2. A group with prominent neck pain People in this group reported significant neck pain in addition to migraine. This could provide new insights into the well-known but still debated relationship between neck pain and migraine. 3. A group with extensive musculoskeletal pain, anxiety and depression This subgroup had a higher prevalence of widespread pain, mental health problems and other health challenges. This could represent a more complex form of migraine in which several biological systems are involved simultaneously. 4. A group with classic migraine The final group had symptoms that more closely matched the established definition of migraine, including migraine aura, without the pronounced additional health problems that characterised the other groups. Migraine aura is a set of temporary neurological symptoms which can occur before or during an attack. Most commonly these are visual disturbances such as flashing lights, zigzag patterns, blind spots or shimmering areas, but they can also involve tingling or numbness, difficulty speaking, or other neurological symptoms. Read also: Why people with migraine cope with sleep deprivation less well than others https://norwegianscitechnews.com/2025/06/heres-why-migraine-symptoms-are-worse-in-patients-who-get-little-sleep/ Genetics supported the differences The researchers also compared genetic patterns between the groups to investigate whether the subgroups were biologically different. Traditionally, an established risk score is used to calculate the genetic risk of disease based on many small genetic variants. In the NTNU study, the researchers also tested newer AI-based genetic risk models. The results showed that AI was best at distinguishing between the different migraine groups. “This strengthens the hypothesis that migraine is not a single disease, but rather a diverse group of different biological conditions,” says Stubberud. Paving the way for personalised treatment The study suggests that diagnosis in the future could become more data-driven, providing an additional tool alongside the doctor’s clinical judgement and the patient’s description of their symptoms. “So far, much of the research on AI in healthcare has been based solely on numbers and statistics. AI ought to be tested on patients who seek actual medical help for headaches. This research is still lacking. These AI tools have not yet been tested in clinical practice,” says Stubberud. He emphasises that if different forms of migraine do indeed have different biological causes, this could also have implications for treatment. “Some patients may respond better to certain medications or preventive measures than others,” says Stubberud. Sources: Antonios Danelakis, Håkon Kvisle Abildsnes, Fahim Faisal, Marte-Helene Bjørk, Dominic Giles, Knut Hagen for the International Headache Genetics Consortium, Tjaša Kumelj, Manjit Matharu, Parashkev Nachev, Erling Tronvik, Bendik S. Winsvold and Anker Stubberud. Machine Diagnostics and Machine Phenotyping of Migraine: A HUNT Study.Neurology 2026;107. doi:10.1212/WNL.0000000000218076 https://www.neurology.org/doi/full/10.1212/WNL.0000000000218076 Stubberud, A. Artificial intelligence in headache care. Nature Reviews Neurology 22, 473–474 2026 . https://doi.org/10.1038/s41582-026-01226-7 https://doi.org/10.1038/s41582-026-01226-7