Biology subjects

Susi, G.

Publications and source records attributed to Susi, G..

3 recordsLinked to original sources

bioRxiv2023

A multiscale closed-loop neurotoxicity model of Alzheimer's disease progression explains functional connectivity alterations

While the accumulation of amyloid-beta (A{beta}) and hyperphosphorylated-tau (hp-tau) as two classical histopathological biomarkers are crucial in Alzheimers disease (AD), their detailed interaction with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic mequltiscale 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 network of Jansen-Rit electrical oscillators whose model parameters varied due to neurotoxicity. Changes in inhibition guided the electrophysiological alterations found in AD, and both A{beta} and hp-tau-related inhibition changes were able to produce similar effects independently. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony. Finally, we demonstrated that early A{beta} and hp-tau depositions location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of bothA{beta} and hp-tau together with a mechanistic protein propagation model and network effects within a unique 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. Significance StatementThis research presents a groundbreaking closed-loop model of AD mechanisms, bridging the gap between protein distribution and neural activity. Contrary to prior assumptions, the study reveals that interregional hyper-synchrony and cellular hyperactivity are not directly linked. Notably, the model identifies neural inhibition as a potential causal factor in neurophysiological AD alterations and posits early depositions of A{beta} as a determinant of the spatiotemporal profile of proteinopathy. The significance of this mechanistic disease framework lies in its potential to produce insights into AD evolution and to guide novel treatment strategies. It underscores the importance of further experiments and modelling efforts to refine our understanding of AD, offering hope for more effective treatments and personalized care in the fight against dementia.

neuroscience↗

bioRxiv2023

Modeling the role of the thalamus in resting-state functional connectivity: nature or structure

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 models 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. Author summarySynchrony between brain regions is an essential aspect of coordinated brain function and serves as a biomarker of health and disease. The thalamus, due to its centrality and widespread connectivity with the cortex, is a crucial structure that may contribute to this synchrony by allowing distant brain regions to work together. In this study, we used computational models to investigate the thalamuss role in generating brain synchrony at rest. Our findings suggest that the structural connectivity of the thalamus is not its primary contribution to brain synchrony. Instead, we found that the thalamus plays a critical role in driving cortical activity, and when it is not driving this activity, its impact on brain synchrony is null. Our study provides valuable insights into the thalamocortical networks role in shaping brain dynamics and FC in resting state, laying the groundwork for further research in this area.

neuroscience↗

bioRxiv2022

E/I unbalance and aberrant oscillation dynamics predict preclinical Alzheimer's disease

Alzheimers disease (AD) is a chronic, nonlinearly progressive neurodegenerative disease that affects multiple domains of behaviour and is the most common form of dementia. However, there is scarce understanding of its biological basis nor there are reliable markers for the earliest disease stages preceding AD. Here we investigated whether AD progression is predicted by increasingly aberrant critical brain dynamics driven by underlying E/I imbalance using magnetoencephalography (MEG) data from cross-sectional (N=343) and longitudinal (N=45) cohorts. As a hallmark of brain criticality, we quantified long-range temporal correlations (LRTCs) in neuronal oscillations and tracked changes in neuronal excitability. We demonstrate that attenuation and progressive changes of LRTCs characterize the earliest stages of disease progression and yield accurate classification to individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). Our data indicate that pathological brain critical dynamics in AD progression provide a clinical marker for targeting specific treatments to individuals at increased risk.

neuroscience↗