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Salvador, R.

Publications and source records attributed to Salvador, R..

2 recordsLinked to original sources

Realistic modeling of ephaptic fields in the human brain

Several decades of research suggest that weak electric fields may influence neural processing, including those induced by neuronal activity and recently proposed as substrate for a potential new cellular communication system, i.e., ephaptic transmission. Here we aim to map ephaptic activity in the human brain and explore its trajectory during aging by characterizing the macroscopic electric field generated by cortical dipoles using realistic finite element modeling. We find that modeled endogenous field magnitudes are comparable to those in measurements of weak but functionally relevant endogenous fields and to those generated by noninvasive transcranial brain stimulation, therefore possibly able to modulate neuronal activity. Then, to evaluate the role of self-generated ephaptic fields in the human cortex, we adapt an interaction approximation that considers the relative orientation of neuron and field to derive the membrane potential perturbation in pyramidal cells. Building on this, we define a simplified metric (EMOD1) that weights dipole coupling as a function of distance and relative orientation between emitter and receiver and evaluate it in a sample of 401 realistic human brain models from subjects aged 16-83. Results reveal that ephaptic modulation follows gyrification patterns in the human brain, and significantly decreases with age, with higher involvement of sensorimotor regions and medial brain structures. By providing the means for fast and direct interaction between neurons, ephaptic modulation likely contributes to the complexity of human function for cognition and behavior, and its modification across the lifespan and in response to pathology.

neuroscience

Personalization of hybrid brain models from neuroimaging and electrophysiology data

Personalization is rapidly becoming standard practice in medical diagnosis and treatment. This study is part of an ambitious program towards computational personalization of neuromodulatory interventions in neuropsychiatry. We propose to model the individual human brain as a network of neural masses embedded in a realistic physical matrix capable of representing measurable electrical brain activity. We call this a hybrid brain model (HBM) to highlight that it encodes both biophysical and physiological characteristics of an individual brain. Although the framework is general, we provide here a pipeline for the integration of anatomical, structural and functional connectivity data obtained from magnetic resonance imaging (MRI), diffuse tensor imaging (DTI connectome) and electroencephalography (EEG). We personalize model parameters through a comparison of simulated cortical functional connectivity with functional connectivity profiles derived from cortically-mapped, subject-specific EEG. We show that individual information can be represented in model space through the proper adjustment of two parameters (global coupling strength and conduction velocity), and that the underlying structural information has a strong impact on the functional outcome of the model. These findings provide a proof of concept and open the door for further advances, including the model-driven design of non-invasive brain-stimulation protocols.

neuroscience