Biology subjects

Sanchez-Rodriguez, L. M.

Publications and source records attributed to Sanchez-Rodriguez, L. M..

3 recordsLinked to original sources

bioRxiv2023

Transcriptomic signatures of Abeta- and tau-induced neuronal dysfunction reveal inflammatory processes at the core of Alzheimer's disease pathophysiology

Molecular mechanisms enabling pathology-induced neuronal dysfunction in Alzheimers disease (AD) remain elusive. Here, we use mechanistic computational models to infer the combined influence of PET-measured A{beta} and tau burdens on fMRI-derived neuronal activity and to subsequently identify the transcriptomic spatial correlates of AD pathophysiology. Our results reveal overrepresented genes and biological processes that participate in synaptic degeneration and interact with A{beta} and tau deposits. Furthermore, we confirmed the central role of the immune system and neuroinflammatory pathways within AD pathogenesis; microglia were significantly enriched in the gene set associated with A{beta} and tau synergistic influences on neuronal activity. Lastly, our computational approach unveiled drug candidates with the potential to halt or reduce the observed pathological effects on neuronal activity, including existing medication for cancer, immune disorders, and cardiovascular diseases, many currently under clinical evaluation in AD. Overall, these findings support the notion that the AD brain experiences functional changes intricately associated with a diverse spectrum of molecular processes.

neuroscience↗

bioRxiv2023

Revealing the combined roles of Abeta and tau in Alzheimer's disease via a pathophysiological activity decoder

Neuronal dysfunction and cognitive deterioration in Alzheimers disease (AD) are likely caused by multiple pathophysiological factors. However, evidence in humans remains scarce, necessitating improved non-invasive techniques and integrative mechanistic models. Here, we introduce personalized brain activity models incorporating functional MRI, amyloid-{beta} (A{beta}) and tau-PET from AD-related participants (N=132). Within the model assumptions, electrophysiological activity is mediated by toxic protein deposition. Our integrative subject-specific approach uncovers key patho-mechanistic interactions, including synergistic A{beta} and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP). Furthermore, our results reproduce hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with A{beta}-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

neuroscience↗

bioRxiv2019

A method for multiscale community detection in brain networks

The identification of community structure in graphs continues to attract great interest in several fields. Network neuroscience is particularly concerned with this problem considering the key roles communities play in brain processes and functionality. Most methods used for community detection in brain graphs are based on the maximization of a parameter-dependent modularity function that often obscures the physical meaning and hierarchical organization of the partitions of network nodes. In this work, we present a new method able to detect communities at different scales in a natural, unrestricted way. First, to obtain an estimation of the information flow in the network we release random walkers to freely move over it. The activity of the walkers is separated into oscillatory modes by using empirical mode decomposition. After grouping nodes by their co-occurrence at each time scale, k-modes clustering returns the desired partitions. Our algorithm was first tested on benchmark graphs with favorable performance. Next, it was applied to real and simulated anatomical and/or functional connectomes in the macaque and human brains. We found a clear hierarchical repertoire of community structures in both the anatomical and the functional networks. The observed partitions range from the evident division in two hemispheres -in which all processes are managed globally- to specialized communities seemingly shaped by physical proximity and shared function. Our results stimulate the research of hierarchical community organization in terms of temporal scales of information flow in the brain network. Highlights- Oscillatory modes of networks signals carry information on architectural rules. - Meaningful partitions of the brain networks are found over different temporal scales. - The multiscale organization of the brain responds to the function of its components.

neuroscience↗