Migraine attacks as a result of hypothalamic loss of control.

Migraine is a complex neurological disorder affecting approximately 12% of the population. The pathophysiology is not yet fully understood, however the clinical features of the disease, such as the cyclic behaviour of attacks and vegetative symptoms, suggest a prominent role of the hypothalamus. Previous research has observed neuronal alterations at different time points during the migraine interval, specifically just before the headache is initiated. We therefore aimed to assess the trajectory of migraineurs' brain activity over an entire migraine cycle. Using functional magnetic resonance imaging (fMRI) with pseudo-continuous arterial spin labelling (ASL), we designed a longitudinal intra-individual study to detect the rhythmicity of (1) the cerebral perfusion and (2) the hypothalamic connectivity over an entire migraine cycle. Twelve episodic migraine patients were examined in 82 sessions during spontaneous headache attacks with follow-up recordings towards the next attack. We detected cyclic changes of brain perfusion in the limbic circuit (insula and nucleus accumbens), with the highest perfusion during the headache attack. In addition, we found an increase of hypothalamic connectivity to the limbic system over the interictal interval towards the attack, then collapsing during the headache phase. The present data provide strong evidence for the predominant role of the hypothalamus in generating migraine attacks. Due to a genetically-determined cortical hyperexcitability, migraineurs are most likely characterised by an increased susceptibility of limbic neurons to the known migraine trigger. The hypothalamus as a metronome of internal processes is suggested to control these limbic circuits: migraine attacks may occur as a result of the hypothalamus losing control over the limbic system. Repetitive psychosocial stress, one of the leading trigger factors reported by patients, might make the limbic system even more vulnerable and lead to a premature triggering of a migraine attack. Potential therapeutic interventions are therefore suggested to strengthen limbic circuits with dedicated medication or psychological approaches.

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference.

Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.