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Diaz-Pier, S.

Publications and source records attributed to Diaz-Pier, S..

2 recordsLinked to original sources

Virtual prototyping of non-invasive spinal cord electrical stimulation targeting upper limb motor function

Transcutaneous spinal cord stimulation (tSCS) applied over the cervical or lumbar spinal cord facilitates motor function after paralysis. However, emerging electrophysiological evidence indicates mechanistic differences between cervical and lumbar tSCS and across different cervical tSCS paradigms. To clarify these discrepancies, we developed and validated a multi-scale, whole-body computational model of tSCS-induced volume conduction, axonal recruitment, and synaptic transmission. Across 24 cervical and four lumbar tSCS paradigms, simulations showed that somatosensory afferents consistently exhibit lower stimulation thresholds than motor efferents. In turn, region-specific synaptic transmission differences may explain electrophysiological discrepancies between cervical and lumbar tSCS. Across cervical tSCS paradigms, substantial volume conduction and axonal recruitment differences were observed that explain electrophysiological discrepancies. Specifically, clinically-prevalent paradigms, including those with anodes placed over the clavicles or iliac crests, engaged peripheral nerves in addition to spinal roots. This effect was amplified by multiphasic waveforms, which introduced recruitment sites near the anodes. By integrating simulations with electrophysiological recordings in 14 able-bodied individuals, we investigated previously unexplored cervical tSCS paradigms on their capacity to recruit somatosensory afferents relative to motor threshold.

neuroscience↗

Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes

The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activity between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC well aligns with the observed FC when compared to that simulated with traditional structural connectome. Simulations were performed using the open source framework The Virtual Brain on High Performance Computing infrastructure.

neuroscience↗