This study presents a finite element (FE) investigation of intervertebral disc (IVD) degeneration in the human lumbar spine (L1–L3 segment). The model, based on CT-derived geometry and isotropic hyperelastic representation of disc tissues, incorporates controlled simplifications, detailed in the limitations section. Degenerative changes were parametrically simulated across healthy, mild, moderate, and severe stages by reducing disc height (up to 60%), nucleus pulposus volume (up to 70%), and adjusting tissue stiffness to reflect dehydration and fibrosis. Displacement-controlled compressive loading was applied to assess von Mises stress distributions, reaction forces, and load transfer mechanisms. Results indicate significant load redistribution: annulus fibrosus stresses increased by up to 175% in severe degeneration, while nucleus pulposus stresses decreased by ~70%, indicating a diminished compressive load-bearing contribution of the nucleus. Model predictions were validated against cadaveric and in vivo data, confirming trends in intradiscal pressure (IDP) reductions (40–70%) and stress elevations. The parametric framework elucidates interactions between geometric and material changes, providing clinicians with insights into degeneration progression and guiding biomedical engineers in implant design and interventions.

Non-invasive Spinal Stimulation (NISS) is of increasing interest for clinicians addressing spinal dysfunctions, such as spasticity, chronic pain and hypotonia. NISS can be an alternative when surgically-implanted stimulators or pharmacological therapy are not compatible nor viable. Trans-spinal direct current stimulation (tsDCS) is one NISS strategy that delivers direct currents (DC) of low intensity (1–4 mA) through large electrodes (8–25 cm ² ) placed over selected targets in the vertebral column. Since 2008, tsDCS researchers have build-up evidence regarding modulation of spinal reflexes and sensorimotor responses measured by electromyography. Biophysical constructs based on computational numerical methods provide a well-grounded framework to determine the most effective protocols designed according to each patient’s needs. Additionally, models can work as theoretical labs to investigate how the EF profile induced by stimulation can relate with the changes observed in spinal responses. The accuracy of predictions in tsDCS biophysical constructs strongly rely on how realistic are the digital twins of the spine. The main strategy used is to adapt accurate template models to have a fine description of spinal structures down to the millimeter resolution. However, this strategy lacks the personalized approach of MRI-based realistic models. This is due to the fact that development of pipelines for semi-automatic segmentation of the spinal cord is still in its early stages. This work aims to discuss the current state-of-the art regarding computational constructs of tsDCS, what is known on its effects on spinal networks, based on combined modeling-experimental approaches, and what lies ahead for a more targeted and personalized application.