The motor system consists of multiple regions of the central nervous system involved in the control of movement. Because each component of the motor system contributes to a specific motor function, clinical signs and symptoms of motor impairment can often be used to deduce the nature and location of a neurological lesion. In this way, a better understanding of neuroanatomical pathways and functional connections between motor areas leads directly to improvements in the diagnosis and treatment of motor system dysfunction. The purpose of this dissertation was to utilize electrophysiological and computational techniques to study the motor outputs of the pontomedullary reticular formation (PMRF) and the computer-aided diagnosis (CAD) of parkinsonism.Electrophysiological techniques are of particular usefulness in the study of motor function. In a research setting, electrical stimulation can be used to evoke neuronal action potentials. In the clinic, electroencephalography (EEG), nerve conduction studies, and electromyography (EMG) are used to assess motor system function for diagnosing disease and tracking its progression. Additionally, procedures such as transcranial direct-current stimulation and deep brain stimulation (DBS) can be used to modify brain activity during the treatment of certain disorders of the motor system. Computational methods are important in signal processing of electrophysiological recordings and modeling of motor pathways. Furthermore, machine learning algorithms are used in the CAD of neurological disorders.The first study in this dissertation used electrophysiological techniques to study the motor outputs of the PMRF. The PMRF is the origin of the reticulospinal tract, one of the major descending motor pathways. The reticulospinal system is of particular importance following damage to the corticospinal tract. Unilateral cortical injury and motor cortex stroke, which cause corticospinal neuron death, classically result in contralateral hemiparesis. However, stroke patients also exhibit a loss of fractionated control of joints, abnormal flexion synergies, and a reemergence of the asymmetric tonic neck reflex. These additional motor symptoms indicate an increased reliance on reticulospinal pathways following damage to the corticospinal tract.To better characterize the function of reticulospinal neurons in stroke patients and healthy subjects, the motor output of the PMRF was investigated by electrically stimulating PMRF neurons or recording spontaneous spiking. Stimulus-triggered averaging (StimulusTA) and spike-triggered averaging (SpikeTA) of EMG and force recordings were performed to identify event-related changes in motor output. EMG was recorded from 12 pairs of upper limb muscles in two monkeys (M. fascicularis) and forces were detected using two isometric force-sensitive handles. The majority of stimulation sites produced significant force and EMG responses, with an electromechanical delay (EMD) consistent with previous measurements in primates. The magnitude of force responses was correlated with the average post-stimulus change in EMG activity. A multivariate linear regression analysis was used to estimate the contribution of each muscle to force generation, with flexors and extensors exhibiting antagonistic effects.A predominant force output pattern of ipsilateral flexion and contralateral extension was observed in response to PMRF stimulation, with the majority of significant ipsilateral force responses directed medially and posteriorly and the majority of contralateral responses directed laterally and anteriorly. This novel approach permits direct measurement of force outputs evoked by CNS microstimation. Despite the small magnitude of post-stimulus EMG effects, low-intensity single-pulse microstimulation of the PMRF evoked detectable forces. The forces, showing the combined effect of all muscle activity in the arms, were consistent with reciprocal pattern of force outputs from the PMRF detectable with StimulusTA of EMG.In one monkey, the neural activity of PMRF neurons was recorded simultaneously with EMG activity and force output from arm and shoulder muscles. For some of these PMRF neurons, significant post-spike EMG and force effects were detected. These post-spike force effects were significantly correlated with post-spike EMG activity for the same recording site. Consistent with previous findings, PMRF neurons facilitated ipsilateral flexors and contralateral extensors, while suppressing ipsilateral extensors and contralateral flexors. Additionally, EMG and force effects of SpikeTA and StimulusTA obtained from the nearest stimulation site were positively correlated in all significant cases. These findings demonstrate that single PMRF neurons can directly influence force outputs of the upper limbs.The second study in this dissertation used computational techniques to study the CAD of parkinsonism. Parkinson's disease (PD) is a movement disorder caused by degeneration of dopamine-producing neurons in the basal ganglia. It presents with characteristic parkinsonian motor symptoms - tremor, hypokinesia, rigidity, postural instability. Accurate diagnosis is difficult because other disorders like atypical parkinsonian syndromes (APS) also present with parkinsonian motor symptoms. It was recently discovered that about 10% of people diagnosed with PD do not have dopaminergic neuron loss. These subjects without evidence of dopaminergic deficits (SWEDDs) are thought to have a disorder known as dopa-responsive dystonia instead of PD. In order to differentiate between PD, SWEDDs, and healthy controls, an enhanced probabilistic neural network (EPNN) was implemented to classify subjects based on clinical exams and neuroimaging data.The EPNN model diagnosed between all three classes with 92.5% accuracy. The Motor Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale Part III was shown to be the most effective clinical exam at differentiated between PD subjects and healthy controls. The putamen striatal-binding ratio of ioflupane (123I), a radioactive compound that binds to dopamine transporters, was shown to be the most effective measurement at differentiating between PD and SWEDDs subjects. Clinical screening for SWEDDs using EPNN exhibited a sensitivity of 59.0% and specificity was 85.9%. Additionally, the results identify olfactory function, which was measured by the University of Pennsylvania Smell Identification Test, as a potential clinical indicator of SWEDDs, supporting the hypothesis that SWEDDs has a different pathology than PD that disproportionately affects olfactory function.