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Description
In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.
ContributorsPadmanaban, Subash (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Crook, Sharon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Non-invasive visualization of the trigeminal nerve through advanced MR sequences and methods like tractography is important for studying anatomical and microstructural changes due to pathology like trigeminal neuralgia (TN), facial dystonia, multiple sclerosis, and for surgical pre-planning. The use of specific anatomical markers from CT, MPRAGE and cranial nerve imaging (CRANI) sequences, enabled successful tractography of patient-specific trajectory of the frontal, nasociliary, infraorbital, and mandibular nerve branches extending beyond the cisternal brain stem region and leading to the face. Performance of MPRAGE sequence together with the advanced T2-weighted CRANI sequence with and without a gadolinium contrast agent, was studied to characterize identification efficiency in smaller nerve structures in the extremities. A large FOV nerve visualization exam inclusive of the anatomy of all trigeminal nerve distal branches can be obtained within an acquisition time of 20 minutes using pre-contrast CRANI and MPRAGE. Post-processing with MPR and MIP images improved nerve visualization.Transcranial electrical stimulation techniques (TES) have been used for the treatment of multiple neurodegenerative diseases. These techniques involve placing electrodes on the scalp with multiple peripheral branches of the trigeminal nerve crossing directly under that may be stimulated. This was studied through hybrid computational realistic axon models. These models also facilitated studying the effects of electrode drift during experiments on the recruitment of peripheral nerves. An optimal point of lowest threshold was found while displacing the nerve horizontally i.e., the activation thresholds of both myelinated and unmyelinated axons increased when the electrodes were displaced medially and decreased to a certain extend when the electrodes were displaced laterally, after which further lateral displacement led to increase of thresholds. Inclusion of unmyelinated axons in the modeling provided the capability of finding maximum stimulation amplitude below which side effects like pain sensation may be avoided. In the case of F3 – F4 electrode montage the maximum amplitude was 2.39 mA and in case of RS – LS montage the maximum amplitude was 2.44 mA. Such modeling studies may be useful for personalization of TES devices for finding optimal positioning of electrodes with respect to target and stimulation amplitude range that minimizes side effects.
ContributorsSahu, Sulagna (Author) / Sadleir, Rosalind (Thesis advisor) / Tillery, Stephen H (Committee member) / Crook, Sharon (Committee member) / Beeman, Scott (Committee member) / Abbas, James (Committee member) / Arizona State University (Publisher)
Created2023