Biology subjects

Fouto, A. R.

Publications and source records attributed to Fouto, A. R..

4 recordsLinked to original sources

bioRxiv2025

Disrupted salience network dynamics during the imagery of migraine attacks

Moderate to severe head pain is a hallmark of recurring migraine attacks. However, it is challenging to study patients during spontaneous attacks and most research on the brain mechanisms of pain in migraine patients has been limited to the processing of painful stimuli between attacks. Here, we hypothesize that the experience of a migraine attack extends beyond the response to painful stimuli and is associated with specific impairments of the salience network (SN), which integrates sensory, emotional and cognitive information in relation to salient stimuli. To test this hypothesis, we analysed the SN dynamics of a group of patients with episodic migraine in three distinct conditions: at rest during a spontaneous migraine attack (ictal phase); while performing an imagery task aiming to elicit the experience of a previous attack, during the interictal phase; and at rest, during the interictal phase. For comparison, we also studied a group of healthy controls in three matching conditions, including rest as well as an imagery task of a (non-migraine) head pain experience. We collected functional magnetic resonance imaging (fMRI) data and used a dynamic functional connectivity (dFC) analysis to examine the temporal features of the SN from a total of 78 samples. Compared to healthy controls, the SN had a significantly shorter lifetime in patients during the pain imagery task, but not during a migraine attack or interictal resting state. Our results support the disruption of the SN in migraine, and indicate that pain imagery may be a useful paradigm for isolating the emotional and cognitive aspects of pain and investigating SN dynamics.

neuroscience↗

bioRxiv2024

Multilevel clinical fingerprinting: uncovering longitudinal changes in the functional connectome of the brain along the migraine cycle

Migraine is a common neurological disorder characterized by recurrent headache episodes alternating with symptom-free periods, which has been associated with alterations across large-scale functional brain networks albeit with variable findings. Critically, despite the cyclic nature of the disorder, longitudinal studies spanning the various phases of the migraine cycle are scarce. Here, we leverage the identifiability of individual functional connectomes (FC) to investigate changes along the migraine cycle. For this purpose, we employ a case-control longitudinal design to study a group of 10 patients with episodic menstrual or menstrual-related migraine without aura, in the 4 phases of their spontaneous migraine cycle (preictal, ictal, postictal, interictal), and a group of 14 healthy controls in corresponding phases of the menstrual cycle, using resting-state fMRI. We propose a novel multilevel clinical fingerprinting approach to analyse the differential FC identifiability within-subject, as well as within-session and within-group. The individual FC matrices are then reconstructed with 19 principal components maximizing identifiability at all levels, and analyzed with Network-Based Statistic to identify significant changes in FC strength. We observe decreased FC identifiability for patients in the preictal phase relative to controls, which increases with the progression of the attack and becomes comparable to controls in the interictal phase. Regarding the FC strength, is increased in the ictal and postictal phases relative to controls across several networks. Our novel multilevel clinical fingerprinting approach captures FC variations along the migraine cycle in a case-control longitudinal study, bringing new insights into the cyclic nature of the disorder.

bioengineering↗

bioRxiv2024

Structural connectome changes in episodic migraine: the role of the cerebellum

BackgroundThe pathophysiology of migraine remains poorly understood, yet a growing number of studies have shown structural connectivity disruptions across large-scale brain networks. Although both structural and functional changes have been found in the cerebellum of migraine patients, the cerebellum has barely been assessed in previous structural connectivity studies of migraine. Our objective is to investigate the structural connectivity of the entire brain, including the cerebellum, in individuals diagnosed with episodic migraine without aura during the interictal phase, compared with healthy controls. MethodsTo that end, 14 migraine patients and 15 healthy controls were recruited (all female), and diffusion-weighted and T1-weighted MRI data were acquired. The structural connectome was estimated for each participant based on two different whole-brain parcellations, including cortical and subcortical regions as well as the cerebellum. The structural connectivity patterns, as well as global and local graph theory metrics, were compared between patients and controls, for each of the two parcellations, using network-based statistics and a generalized linear model (GLM), respectively. We also compared the number of connectome streamlines within specific white matter tracts using a GLM. ResultsWe found increased structural connectivity in migraine patients relative to healthy controls with a distinct involvement of cerebellar regions, using both parcellations. Specifically, the node degree of the posterior lobe of the cerebellum was greater in patients than in controls and patients presented a higher number of streamlines within the anterior limb of the internal capsule. Moreover, the connectomes of patients exhibited greater global efficiency and shorter characteristic path length, which correlated with the age onset of migraine. ConclusionsA distinctive pattern of heightened structural connectivity and enhanced global efficiency in migraine patients compared to controls was identified, which distinctively involves the cerebellum. These findings provide evidence for increased integration within structural brain networks in migraine and underscore the significance of the cerebellum in migraine pathophysiology.

bioengineering↗

bioRxiv2024

Cerebrovascular reactivity mapping using breath-hold BOLD-fMRI: comparison of signal models combined with voxelwise lag optimization

Cerebrovascular reactivity (CVR) can be mapped noninvasively using blood oxygenation level dependent (BOLD) fMRI during a breath-hold (BH) task. Previous studies showed that the BH BOLD response is best modeled as the convolution of the partial pressure of end-tidal CO2 (PetCO2) with a canonical hemodynamic response function (HRF). However, previous model comparisons employed a global bulk time lag, which is now well accepted to provide only a rough approximation of the heterogeneous distribution of response latencies across the brain. Here, we investigate the best modeling approach for mapping CVR based on BH BOLD-fMRI data, when using a lagged general linear model approach combined with voxelwise lag optimization. In a group of fourteen healthy participants, we compared models considering: two types of regressors (PetCO2 and Block), three convolution models (no convolution; convolution with a single gamma HRF; and convolution with a double gamma HRF), and a variable HRF delay (3-11s). We found no significant improvements in model fit with other delays, and hence selected the canonical delay of 6s. Although the two regressor types yielded similar model fits, PetCO2 produced significantly greater CVR values than Block models. Interestingly, a single gamma HRF yielded the greatest CVR values in PetCO2 models, while block models benefited from convolution with a double gamma HRF. In conclusion, when modeling BH BOLD-fMRI signals with voxelwise lag optimization, PetCO2 regressors convolved with a single gamma HRF should be preferentially used. In case good quality PetCO2 recordings are unavailable, a block-based model convolved with the canonical HRF may be a good alternative. In conclusion, our manuscript reports the first systematic signal model comparison, providing evidence to support the use of specific modeling approaches for CVR mapping based on BH BOLD-fMRI.

bioengineering↗