Spinal cord injury (SCI) results in persistent neurological deficits and significant long-term disability. Stimulation of peripheral afferents by epidural electrical stimulation (EES) has been reported to reduce spasticity by reorganizing spared and disrupted descending pathways and local circuits. However, a current barrier to the field is that the plasticity mechanisms that underly improved recovery is unknown. Using the power of hM3Dq Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), we aim to accelerate the dissection of the mechanisms underlying enhanced recovery. In these studies, we identified the effect of clozapine-N-oxide (CNO) on the H-reflex of naïve animals; investigated the baseline influence of hM3Dq DREADDs in peripheral afferents in the intact animal using a novel behavioral tool, an addition of angled rungs to the horizontal ladder walking task; and began to uncover the neural and behavioral changes that accompany hM3Dq DREADDs activation in peripheral afferents after SCI. We observed no significant differences in the H-reflex with 4 mg/kg dosage of CNO administration (pre-CNO vs. CNO-active: p=0.82; CNO-active vs. CNO wash-out: p=0.98; n=6). On our novel ladder, we found significant differences in correct hind paw placement (p=0.0002, n=7) and incorrect placement (p=0.01) when DREADDs were activated with CNO (4 mg/kg). In our SCI study, we report that acute and chronic DREADDs activation may activate extensor muscles about the hip (32 cm/s: p=0.047; controls: n=6; DREADDs: n=8 and hereafter unless otherwise stated) as well as induce sprouting and synaptogenesis within motor pools and Clarke’s column in the lumbar spinal cord (motor pool: p=0.00053; Clarke’s column: p=0.021; controls: n=4; DREADDs: n=6). This muscle recruitment may have long-term effects such as increased hindquarter heights (e.g., 16 cm/s: p=0.017) and more frequent hindlimb coordination (p=0.002). Results from this study suggest hM3Dq DREADDs may have the potential to recapitulate EES-activation of afferents as well as provide a platform with which to functionally map changes that occur both within targeted afferents and second order neurons they effect. Future work, such as using C-Fos to examine and map changes in interneuronal networks, could seek to more directly tie changes in kinematics to observed changes in plasticity. / Bioengineering

Every year, 250,000 people worldwide suffer a spinal cord injury (SCI) that leaves them with chronic paraplegia - permanent loss of ability to move their legs. SCI interrupts axons passing along the spinal cord, thereby isolating motor neurons from brain inputs. To date, there are no effective treatments that can reconnect these interrupted axons. In a recent breakthrough, .NeuroRestore developed the STIMO neuroprosthesis that can restore walking after paralyzing SCI using Epidural Electrical Stimulation (EES) of the lumbar spinal cord. Yet, the calibration of EES requires highly trained personnel and a vast amount of time, and the mechanism by which EES restores movement is not fully understood. In this master thesis, we propose to address this issue using modeling combined with Artificial Neural Networks (ANNs). To do so, we introduce the CyberSpine model to predict EES-induced motor response. The implementation of the model relies on the construction of a multipolar basis of solution of the Poisson equation which is then coupled to an ANN trained against actual data of an implanted STIMO user. Furthermore, we show that our CyberSpine model is particularly well adapted to extract biologically relevant information regarding the efficient connectivity of the patient’s spine. Finally, a user-friendly interactive visualization software is built. / Varje år drabbas 250 000 människor i hela världen av en ryggmärgsskada som ger dem kronisk paraplegi - permanent förlust av förmågan att röra benen. Vid en ryggmärgsskada bryts axonerna som passerar längs ryggmärgen, vilket isolerar de motoriska neuronpoolerna från hjärnans ingångar. Hittills finns det inga effektiva behandlingar som kan återansluta dessa avbrutna axoner. NeuroRestore utvecklade nyligen neuroprotesen STIMO som kan återställa gångförmågan efter förlamande ryggmärgsskada med hjälp av epidural elektrisk stimulering (EES) av ländryggmärgen. Kalibreringen av EES-stimuleringar kräver dock högutbildad personal och mycket tid, och den mekanism genom vilken EES återställer rörelse är inte helt klarlagd. I denna masteruppsats föreslår vi att vi tar itu med denna fråga med hjälp av modellering i kombination med artificiell intelligens. För att göra detta introducerar vi CyberSpine-modellen, en modell som kan förutsäga EES-inducerad motorisk respons. Implementeringen av modellen bygger på konstruktionen av en multipolär bas för lösning av Poisson-ekvationen som sedan kopplas till ett artificiellt neuralt nätverk som tränas mot faktiska data från en implanterad STIMO-deltagare. Dessutom visar vi att vår CyberSpine-modell är särskilt väl anpassad för att extrahera biologiskt relevant information om den effektiva anslutningen av patientens ryggrad. Slutligen bygger vi en användarvänlig interaktiv visualiseringsprogramvara.