RT info:eu-repo/semantics/article
T1 Alternative Microstructural Measures to Complement Diffusion Tensor Imaging in Migraine Studies with Standard MRI Acquisition
A1 Planchuelo Gómez, Álvaro
A1 García Azorín, David
A1 Guerrero Peral, Angel Luis
A1 Luis García, Rodrigo de
A1 Rodríguez Velasco, Margarita
A1 Aja Fernández, Santiago
K1 Migraine
K1 Chronic Migraine
K1 Diffusion tensor imaging
K1 Magnetic resonance imaging (MRI)
K1 Tract-based spatial statistics
K1 Diffusion magnetic resonance imaging
AB The white matter state in migraine has been investigated using diffusion tensor imaging (DTI) measures, but results using this technique are conflicting. To overcome DTI measures, we employed ensemble average diffusion propagator measures obtained with apparent measures using reduced acquisitions (AMURA). The AMURA measures were return-to-axis (RTAP), return-to-origin (RTOP) and return-to-plane probabilities (RTPP). Tract-based spatial statistics was used to compare fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity from DTI, and RTAP, RTOP and RTPP, between healthy controls, episodic migraine and chronic migraine patients. Fifty healthy controls, 54 patients with episodic migraine and 56 with chronic migraine were assessed. Significant differences were found between both types of migraine, with lower axial diffusivity values in 38 white matter regions and higher RTOP values in the middle cerebellar peduncle in patients with a chronic migraine (p < 0.05 family-wise error corrected). Significantly lower RTPP values were found in episodic migraine patients compared to healthy controls in 24 white matter regions (p < 0.05 family-wise error corrected), finding no significant differences using DTI measures. The white matter microstructure is altered in a migraine, and in chronic compared to episodic migraine. AMURA can provide additional results with respect to DTI to uncover white matter alterations in migraine.
PB MDPI
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/70544
UL https://uvadoc.uva.es/handle/10324/70544
LA eng
NO Brain Sciences, 2020, vol. 10, n. 10, p. 711
NO Producción Científica
DS UVaDOC
RD 19-nov-2024