Jesús Cabrera-Álvarez

Abstract The alpha rhythm is a hallmark of electrophysiological resting-state brain activity, that serves as a biomarker in health and disease. Alpha power is far from uniform over time, exhibiting dynamic fluctuations. The likelihood of those power values can be captured by a decreasing exponential function, which in certain cases becomes bimodal. While alpha rhythm is usually evaluated through the averaged power spectra across entire recordings, its dynamic fluctuations have received less attention. In this study, we investigate the dynamic nature of alpha power, its relationship with functional connectivity (FC) within the default mode network (DMN), and the ability of the Jansen-Rit (JR) neural mass model to replicate these fluctuations. Using MRI and MEG data from 42 participants in resting state with eyes-closed and eyes-open, we evaluated the shape of the exponential distributions for alpha power fluctuations, and their relationship with other spectral variables as frequency, power, and the aperiodic exponent. Additionally, we assessed the temporal relationship between alpha power and FC using phase-based (ciPLV) and amplitude-based (cAEC) metrics. Finally, we employed diffusion-weighted MRI to construct brain network models incorporating JR neural masses to reproduce and characterize alpha fluctuations. Our results indicate that alpha power predominantly follows unimodal exponential distributions, with bimodalities associated to high-power in posterior regions. FC analyses revealed that ciPLV and cAEC were directly correlated with alpha power within the DMN in alpha and beta bands, whereas only theta-band ciPLV showed an inverse relationship with alpha power. JR model simulations suggested that post- supercritical fixed points better replicated alpha power fluctuations compared to limit cycle parameterizations and pre-saddle node fixed points. These results deepen our understanding of the dynamics of alpha rhythm and its intricate relationship with FC patterns, offering novel insights to refine biologically plausible brain simulations and advance computational models of neural dynamics.

The thalamus is a central brain structure that serves as a relay station for sensory inputs from the periphery to the cortex and regulates cortical arousal. Traditionally, it has been regarded as a passive relay that transmits information between brain regions. However, recent studies have suggested that the thalamus may also play a role in shaping functional connectivity (FC) in a task-based context. Based on this idea, we hypothesized that due to its centrality in the network and its involvement in cortical activation, the thalamus may also contribute to resting-state FC, a key neurological biomarker widely used to characterize brain function in health and disease. To investigate this hypothesis, we constructed ten in-silico brain network models based on neuroimaging data (MEG, MRI, and dwMRI), and simulated them including and excluding the thalamus, and raising the noise into thalamus to represent the afferences related to the reticular activating system (RAS) and the relay of peripheral sensory inputs. We simulated brain activity and compared the resulting FC to their empirical MEG counterparts to evaluate model’s performance. Results showed that a parceled version of the thalamus with higher noise, able to drive damped cortical oscillators, enhanced the match to empirical FC. However, with an already active self-oscillatory cortex, no impact on the dynamics was observed when introducing the thalamus. We also demonstrated that the enhanced performance was not related to the structural connectivity of the thalamus, but to its higher noisy inputs. Additionally, we highlighted the relevance of a balanced signal-to-noise ratio in thalamus to allow it to propagate its own dynamics. In conclusion, our study sheds light on the role of the thalamus in shaping brain dynamics and FC in resting-state and allowed us to discuss the general role of criticality in the brain at the mesoscale level.

The accumulation of amyloid-β(Aβ) and hyperphosphorylated-tau (hp-tau) are two classical histopathological biomarkers in Alzheimer’s disease (AD). However, their detailed interactions with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic multiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation and a brain network model of Jansen-Rit neural masses derived from human neuroimaging data whose parameters varied due to neurotoxicity. Results showed that changes in inhibition guided the electrophysiological alterations found in AD, and these changes were mainly attributed to Aβeffects. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony contrary to previous beliefs. Finally, we demonstrated that early Aβand hp-tau depositions’ location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of both Aβand hp-tau together with a mechanistic protein propagation model and network effects within a closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution.

The alpha rhythm is often associated with relaxed wakefulness or idling and is altered by various factors. Abnormalities in the alpha rhythm have been linked to several neurological and psychiatric disorders, including Alzheimer’s disease. Transcranial alternating current stimulation (tACS) has been proposed as a potential tool to restore a disrupted alpha rhythm in the brain by stimulating at the individual alpha frequency (IAF), although some research has produced contradictory results. In this study, we applied an IAF-tACS protocol over parieto-occipital areas to a sample of healthy subjects and measured its effects over the power spectra. Additionally, we used computational models to get a deeper understanding of the results observed in the experiment. Both experimental and numerical results showed an increase in alpha power of 8.02% with respect to the sham condition in a widespread set of regions in the cortex, excluding some expected parietal regions. This result could be partially explained by taking into account the orientation of the electric field with respect to the columnar structures of the cortex, showing that the gyrification in parietal regions could generate effects in opposite directions (hyper-/depolarization) at the same time in specific brain regions. Additionally, we used a network model of spiking neuronal populations to explore the effects that these opposite polarities could have on neural activity, and we found that the best predictor of alpha power was the average of the normal components of the electric field. To sum up, our study sheds light on the mechanisms underlying tACS brain activity modulation, using both empirical and computational approaches. Non-invasive brain stimulation techniques hold promise for treating brain disorders, but further research is needed to fully understand and control their effects on brain dynamics and cognition. Our findings contribute to this growing body of research and provide a foundation for future studies aimed at optimizing the use of non-invasive brain stimulation in clinical settings.