PhD Scientific Days 2026

Mental Health Sciences 4.

Large-scale dynamic causal modeling for resting-state fMRI in women with migraine

Előadó neve

Dr. Gecse, Kinga, PhD

Neptune code

X9LFNV

Előadó munkahelye

Semmelweis University, Faculty of Pharmaceutical Sciences, Department of Pharmacodynamics

Előadó telefonszáma

+36202969397

Előadó e-mail címe

gecse.kinga@semmelweis.hu

Az előadás címe

Large-scale dynamic causal modeling for resting-state fMRI in women with migraine

Szerző(k) neve és munkahelye

Kinga Gecse¹, Anna Nemeth¹, Bernadett Nagy¹, Gyongyi Kokonyei², Csaba Sandor Aranyi³, Miklos Emri³, Gabriella Juhasz¹

1: Semmelweis University, Faculty of Pharmaceutical Sciences, Department of Pharmacodynamics
2: Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary
3: Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary

Bemutatás módja

Szóbeli

Szekció

Mental Health Sciences 4.

Language of the presentation

Hungarian

Preferred session

Mental Health Sciences

Összefoglaló szövege

Introduction: Migraine is three times more common in women than men. Beside hormonal influences, neuronal mechanisms are contributing to migraine attack development. Previous studies showed that female brain networks seem to be more vulnerable to migraine-related disruptions. Even in interictal period, both increased and decreased connections could be detected suggesting a complex reorganization of brain networks.
Aims: Our aim was to identify the migraine-specific alterations in female brain considering large-scale resting-state networks.
Methods: Sixty-six episodic migraine without aura patients and 71 healthy women underwent a resting-state functional magnetic imaging session (3T). We applied spectral dynamic causal modelling (DCM) to estimate effective connectivity of large-scale network with 33 regions of interests. Parametric empirical Bayes (PEB) framework was applied to estimate the group differences with post-hoc Bayesian Model Reduction (BMR) to identify the most optimal model and remove redundant group-level parameters.
Results: In migraine patients, the intrinsic connections of default-mode network (DMN), central executive network (CEN) and periaqueductal grey matter (PAG) were decreased compared to healthy controls. However, we found an increased extrinsic connectivity between DMN and CEN. Additionally, a decreased connection was detected from spinal trigeminal nucleus (STN) to PAG.
Conclusions: Our findings demonstrate that migraine is associated with extensive alterations in resting-state functional connectivity across multiple brain networks. The observed increase in extrinsic and decrease in intrinsic connectivity suggest a reorganization of the 'migraine brain' compared to healthy controls. Furthermore, the connectivity between STN and PAG, a key pathway of pain perception and modulation, exhibits significant impairment in migraine patients. further investigations are needed to determine whether these alterations may contribute to migraine attack generation or may occur as a scar effect of repeated attacks.
Funding: EKÖP-2025-624, 2017-1.2.1-NKP-2017-00002, NAP2022-I-4/2022; TKP2021-EGA-25; NKFIH(K143391).

University

Semmelweis University

Supervisor

Gabriella Juhasz

Publication of my abstract

I do not give consent to the publication of my abstract on the website of the congress.

phd.section.field

after finishing doctoral studies with absolutorium (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

5972

Start

16:15

End

16:25