Compensatory Arm Reactions in Individuals with Parkinson’s Disease

This study examined how perturbation-evoked compensatory arm reactions in individuals with Parkinson’s disease (PD) are influenced by explicit verbal instruction. Ten individuals with PD and 15 older adults without PD responded to surface translations with or without specific instruction to reach for and grasp the handrail. Electromyographic (EMG) and kinematic recordings were taken from the reaching arm. Results showed that individuals with and without PD benefitted similarly from explicit instruction. Explicit instruction resulted in earlier (p=0.005) and larger (p<0.001) medial deltoid EMG responses in comparison to no specific instructions. Compensatory arm reactions also occurred with a higher peak medio-lateral wrist velocity (p<0.001) and higher peak shoulder abduction angular velocity (p<0.001) with explicit instruction. Explicit instruction positively influenced compensatory arm reactions in individuals with and without PD. Future research is needed to determine whether the benefits of instruction persist over time and translate to a loss of balance in real life.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

Effects of Instruction on Walking and Turning Performance in Individuals with Parkinson's Disease

Parkinson’s disease (PD) is characterized by postural instability and gait impairment. Verbal instructions can reduce postural sway and improve gait performance in PD. For gait, this evidence is limited to unobstructed straight-path walking. As falls in PD often occur when turning, the purpose of this thesis was to determine if instructions can benefit turning performance in this population. Twelve individuals with PD performed two walking tasks (normal walking, walking with a 180 degree turn) under four instruction conditions (no instruction, take big steps, make larger trunk movements, focus on end and/or turn point). Task duration and trunk yaw and roll sway were calculated. In general, the results demonstrated that the instruction to take big steps improved performance for both tasks compared to providing no instruction or externally based instruction. These results suggest that instructions related to step amplitude may facilitate walking and turning performance in PD.

The influence of drug-induced dyskinesias on manual tracking in Parkinson's disease

The influence of peak-dose drug-induced dyskinesia (DID) on manual tracking (MT) was examined in 10 dyskinetic patients (OPO), and compared to 10 age/gendermatched non-dyskinetic patients (NDPD) and 10 healthy controls. Whole body movement (WBM) and MT were recorded with a 6-degrees of freedom magnetic motion tracker and forearm rotation sensors, respectively. Subjects were asked to match the length of a computer-generated line with a line controlled via wrist rotation. Results show that OPO patients had greater WBM displacement and velocity than other groups. All groups displayed increased WBM from rest to MT, but only DPD and NDPO patients demonstrated a significant increase in WBM displacement and velocity. In addition, OPO patients exhibited excessive increase in WBM suggesting overflow DID. When two distinct target pace segments were examined (FAST/SLOW), all groups had slight increases in WBM displacement and velocity from SLOW to FAST, but only OPO patients showed significantly increased WBM displacement and velocity from SLOW to FAST. Therefore, it can be suggested that overflow DID was further increased with increased task speed. OPO patients also showed significantly greater ERROR matching target velocity, but no significant difference in ERROR in displacement, indicating that significantly greater WBM displacement in the OPO group did not have a direct influence on tracking performance. Individual target and performance traces demonstrated this relatively good tracking performance with the exception of distinct deviations from the target trace that occurred suddenly, followed by quick returns to the target coherent in time with increased performance velocity. In addition, performance hand velocity was not correlated with WBM velocity in DPO patients, suggesting that increased ERROR in velocity was not a direct result of WBM velocity. In conclusion, we propose that over-excitation of motor cortical areas, reported to be present in DPO patients, resulted in overflow DID during voluntary movement. Furthermore, we propose that the increased ERROR in velocity was the result of hypermetric voluntary movements also originating from the over-excitation of motor cortical areas.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.