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Abstract #1116

Multilevel clinical connectome fingerprinting: uncovering functional connectivity changes across the migraine cycle

Inês Esteves1, Ana R. Fouto1, Amparo Ruiz-Tagle1, Gina Caetano1, Rita G. Nunes1, Nuno A. Silva2, Pedro Vilela3, Raquel Gil-Gouveia4, Isabel Pavão Martins5, César Caballero-Gaudes6, and Patrícia Figueiredo1
1ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisboa, Portugal, 2Learning Health, Hospital da Luz, Lisboa, Portugal, 3Neurology Department, Hospital da Luz, Lisboa, Portugal, 4Center for Interdisciplinary Research in Health, Universidade Católica Portuguesa, Lisboa, Portugal, 5Centro de Estudos Egas Moniz e Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa (FMUL), Lisboa, Portugal, 6Basque Center on Cognition, Brain and Language, Donostia, Spain

Synopsis

Keywords: Functional Connectivity, Brain, Migraine, Longitudinal, Multilevel Clinical Connectome Fingerprinting

Motivation: Case-control fMRI studies spanning the entire migraine cycle are lacking, precluding a complete assessment of brain functional connectivity in migraine. Such studies are essential for understanding the inherent changes in the brain of migraine patients as well as transient changes along the cycle.

Goal(s): Our goal was to determine the influence of the migraine cycle on individual functional connectome fingerprints.

Approach: Functional connectivity (FC) was longitudinally studied for migraine patients (across the four different cycle phases) and matched healthy controls.

Results: We observed greater heterogeneity in FC patterns of migraine patients and significant changes in FC across the cycle compared to controls.

Impact: This work represents the first case-control fMRI longitudinal study across the whole migraine cycle. Building upon clinical connectome fingerprinting, applied for the first time to migraine, it tackles a major cause of disability worldwide, contributing to developing connectome-based disease biomarkers.

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Keywords