Multimodal fusion analysis of structural connectivity and gray matter morphology in migraine

Shifts in structural connectome organization in the limbic and sensory systems of patients with episodic migraine

Migraine is a complex neurological condition characterized by recurrent headaches, which is often accompanied by various neurological symptoms. Magnetic resonance imaging (MRI) is a powerful tool for investigating whole-brain connectivity patterns; however, systematic assessment of structural connectome organization has rarely been performed. In the present study, we aimed to examine the changes in structural connectivity in patients with episodic migraines using diffusion MRI. First, we computed structural connectivity using diffusion MRI tractography, after which we applied dimensionality reduction techniques to the structural connectivity and generated three low-dimensional eigenvectors. We subsequently calculated the manifold eccentricity, defined as the Euclidean distance between each data point and the center of the data in the manifold space. We then compared the manifold eccentricity between patients with migraines and healthy controls, revealing significant between-group differences in the orbitofrontal cortex, temporal pole, and sensory/motor regions. Between-group differences in subcortico-cortical connectivity further revealed significant changes in the amygdala, accumbens, and caudate nuclei. Finally, supervised machine learning effectively classified patients with migraines and healthy controls using cortical and subcortical structural connectivity features, highlighting the importance of the orbitofrontal and sensory cortices, in addition to the caudate, in distinguishing between the groups. Our findings confirmed that episodic migraine is related to the structural connectome changes in the limbic and sensory systems, suggesting its potential utility as a diagnostic marker for migraine.

Increased MRI-based Brain Age in chronic migraine patients

INTRODUCTION

Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants.

METHODS

We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74).

RESULTS

CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine.

CONCLUSION

The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.

HYDI-DSI revisited: Constrained non-parametric EAP imaging without q-space re-gridding

Hybrid Diffusion Imaging (HYDI) was one of the first attempts to use multi-shell samplings of the q-space to infer diffusion properties beyond Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI). HYDI was intended as a flexible protocol embedding both DTI (for lower b-values) and HARDI (for higher b-values) processing, as well as Diffusion Spectrum Imaging (DSI) when the entire data set was exploited. In the latter case, the spherical sampling of the q-space is re-gridded by interpolation to a Cartesian lattice whose extent covers the range of acquired b-values, hence being acquisition-dependent. The Discrete Fourier Transform (DFT) is afterwards used to compute the corresponding Cartesian sampling of the Ensemble Average Propagator (EAP) in an entirely non-parametric way. From this lattice, diffusion markers such as the Return To Origin Probability (RTOP) or the Mean Squared Displacement (MSD) can be numerically estimated. We aim at re-formulating this scheme by means of a Fourier Transform encoding matrix that eliminates the need for q-space re-gridding at the same time it preserves the non-parametric nature of HYDI-DSI. The encoding matrix is adaptively designed at each voxel according to the underlying DTI approximation, so that an optimal sampling of the EAP can be pursued without being conditioned by the particular acquisition protocol. The estimation of the EAP is afterwards carried out as a regularized Quadratic Programming (QP) problem, which allows to impose positivity constraints that cannot be trivially embedded within the conventional HYDI-DSI. We demonstrate that the definition of the encoding matrix in the adaptive space allows to analytically (as opposed to numerically) compute several popular descriptors of diffusion with the unique source of error being the cropping of high frequency harmonics in the Fourier analysis of the attenuation signal. They include not only RTOP and MSD, but also Return to Axis/Plane Probabilities (RTAP/RTPP), which are defined in terms of specific spatial directions and are not available with the former HYDI-DSI. We report extensive experiments that suggest the benefits of our proposal in terms of accuracy, robustness and computational efficiency, especially when only standard, non-dedicated q-space samplings are available.

Microstructural white matter alterations associated with migraine headaches: a systematic review of diffusion tensor imaging studies

The pathophysiology of migraine as a headache disorder is still undetermined. Diffusion tensor imaging (DTI) has significantly improved our knowledge about brain microstructure in this disease. Here, we aimed to systematically review DTI studies in migraine and survey the sources of heterogeneity by investigating diffusion parameter changes associated with clinical characteristics and migraine subtypes. Microstructural changes, as revealed by widespread alteration of diffusion metrics in white matter (WM) tracts, subcortical and cortical regions, were reported by several migraine DTI studies. Specifically, we reported changes in the corpus callosum, thalamic radiations, corona radiata, and brain stem. These alterations showed high variability across migraine cycle phases. Additionally, migraine associated with depressive/anxiety symptoms revealed significant changes in the corpus callosum, internal capsule, and superior longitudinal fasciculus. No significant WM microstructural differences were observed between migraine patients with and without aura. Overall, differences between chronic and episodic migraine showed inconsistency across studies. Migraine is associated with microstructural changes in widespread regions including thalamic radiations, corpus callosum, and brain stem. These alterations can highlight neuronal damage and neuronal plasticity mechanisms either following pain stimulations occurring in migraine cycle or as a compensatory response to pain in chronic migraine. Longitudinal studies applying advanced modalities may shed new light on the underlying microstructural changes in migraine subtypes.

Gray matter alteration in medication overuse headache: a coordinates-based activation likelihood estimation meta-analysis

Medication overuse headache (MOH) is a prevalent secondary headache, bringing heavy economic burden and neuropsychological damage. Neuroimaging studies on the disease reported divergent results. To merge the reported neuroimaging alterations in MOH patients and explore a pathophysiological mechanism of this disorder. A meta-analytic activation likelihood estimation (ALE) analysis method was used. We systematically searched English and Chinese databases for both morphological and functional neuroimaging studies published before Nov 18, 2021. Reported altered brain regions and the stereotactic coordinates of their peaks were extracted and pooled by GingerALE using Gaussian probability distribution into brain maps, illustrating converged regions of alteration among studies. We identified 927 articles, of which five studies on gray matter changes, using voxel-based morphometry (VBM) were eventually included for ALE analysis, with 344 subjects and 54 coordinates put into GingerALE. No functional magnetic resonance imaging (fMRI) or positron emission topography (PET) studies were included for pooling. Compared with healthy controls (HCs), MOH featured increased gray matter density in midbrain, striatum, cingulate, inferior parietal cortex and cerebellum (P < 0.001 uncorrected), whereas decreased gray matter density in orbitofrontal cortex (P < 0.05, family-wise error), frontal, insular and parietal cortices (P < 0.001 uncorrected). Withdrawal of analgesics led to decreased gray matter density in superior temporal gyrus, cuneus, midbrain and cerebellum (P < 0.001 uncorrected). This meta-analysis confirmed that medication overuse headache is associated with morphologic alteration in the reward system, the prefrontal cortex and a reversible modification in the pain network. Further functional imaging paradigms and longitudinal studies are required for a more definite conclusion and a causal mechanism.