Matching Items (2)
Filtering by
- All Subjects: Bioinformatics
- All Subjects: Functional Magnetic Resonance Imaging
Description
In 1937 Canadian neurosurgeon Wilder Penfield made the first to attempt to map the sensorimotor cortex of the human brain in his paper entitled Somatic Motor and Sensory Representation in the Cerebral Cortex of Man as Studied by Electrical Stimulation. While analogous experimentation had been carried out previously using animal subjects, Penfield sought to understand the delicate and complex neuronal pathways that served as the hidden control mechanisms for human activity. The motor homunculus that followed from his findings has been widely accepted as the standard model for the relative spatial representation of the functionality of the motor cortex, and has been virtually unaltered since its inception. While Penfield took measures to collect cortical data in a manner as accurately as scientifically possible for the time period, his original model is deserving of further analysis using modern techniques. This study uses functional magnetic resonance imaging (fMRI) to quantitatively determine motor function volumes and spatial relationships for four motor tasks: toe, finger, eyebrow, and tongue. Although Penfield's general representation of the superior-to-inferior spatial distribution of the motor cortex was replicated with reasonable accuracy, relative mean task volumes seem to differ from Penfield's original model. The data was first analyzed in each individual patient's native anatomical space for task comparison within a single subject. The volumes of the motor cortex devoted to the eyebrow and toe tasks, which comprise only small portions of the Penfield homunculus, are shown to be relatively large in their fMRI representation compared to finger and tongue. However, these tasks have large deviation values, indicating a lack of consistency in task volume size among patients. Behaviorally, toe movement may include whole foot movement in some individuals, and eyebrows may include face movement, causing distributions that are more widespread. The data was then analyzed in the Montreal Neurological Institute (MNI) space, which is mathematically normalized for task comparison between different subjects. Tongue and finger tasks were the largest in volume, much like Penfield's model. However, they also had substantial deviation, again indicating task volume size inconsistencies. Since the Penfield model is only a qualitative spatial evaluation of motor function along the precentral gyrus, numerical deviation from the model cannot necessarily be quantified. Hence, the results of this study can be interpreted standalone without a current comparison. While future research will serve to further validate these distances and volumes, this quantitative model of the functionality of the motor cortex will be of great utility for future neurological research and during preoperative evaluations of neurosurgical patients.
ContributorsOland, Gabriel Lee (Author) / Frakes, David (Thesis director) / Santello, Marco (Committee member) / Baxter, Leslie (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2013-05
Description
Background: Noninvasive MRI methods that can accurately detect subtle brain changes are highly desirable when studying disease-modifying interventions. Texture analysis is a novel imaging technique which utilizes the extraction of a large number of image features with high specificity and predictive power. In this investigation, we use texture analysis to assess and classify age-related changes in the right and left hippocampal regions, the areas known to show some of the earliest change in Alzheimer's disease (AD). Apolipoprotein E (APOE)'s e4 allele confers an increased risk for AD, so studying differences in APOE e4 carriers may help to ascertain subtle brain changes before there has been an obvious change in behavior. We examined texture analysis measures that predict age-related changes, which reflect atrophy in a group of cognitively normal individuals. We hypothesized that the APOE e4 carriers would exhibit significant age-related differences in texture features compared to non-carriers, so that the predictive texture features hold promise for early assessment of AD. Methods: 120 normal adults between the ages of 32 and 90 were recruited for this neuroimaging study from a larger parent study at Mayo Clinic Arizona studying longitudinal cognitive functioning (Caselli et al., 2009). As part of the parent study, the participants were genotyped for APOE genetic polymorphisms and received comprehensive cognitive testing every two years, on average. Neuroimaging was done at Barrow Neurological Institute and a 3D T1-weighted magnetic resonance image was obtained during scanning that allowed for subsequent texture analysis processing. Voxel-based features of the appearance, structure, and arrangement of these regions of interest were extracted utilizing the Mayo Clinic Python Texture Analysis Pipeline (pyTAP). Algorithms applied in feature extraction included Grey-Level Co-Occurrence Matrix (GLCM), Gabor Filter Banks (GFB), Local Binary Patterns (LBP), Discrete Orthogonal Stockwell Transform (DOST), and Laplacian-of-Gaussian Histograms (LoGH). Principal component (PC) analysis was used to reduce the dimensionality of the algorithmically selected features to 13 PCs. A stepwise forward regression model was used to determine the effect of APOE status (APOE e4 carriers vs. noncarriers), and the texture feature principal components on age (as a continuous variable). After identification of 5 significant predictors of age in the model, the individual feature coefficients of those principal components were examined to determine which features contributed most significantly to the prediction of an aging brain. Results: 70 texture features were extracted for the two regions of interest in each participant's scan. The texture features were coded as 70 initial components andwere rotated to generate 13 principal components (PC) that contributed 75% of the variance in the dataset by scree plot analysis. The forward stepwise regression model used in this exploratory study significantly predicted age, accounting for approximately 40% of the variance in the data. The regression model revealed 5 significant regressors (2 right PC's, APOE status, and 2 left PC by APOE interactions). Finally, the specific texture features that contributed to each significant PCs were identified. Conclusion: Analysis of image texture features resulted in a statistical model that was able to detect subtle changes in brain integrity associated with age in a group of participants who are cognitively normal, but have an increased risk of developing AD based on the presence of the APOE e4 phenotype. This is an important finding, given that detecting subtle changes in regions vulnerable to the effects of AD in patients could allow certain texture features to serve as noninvasive, sensitive biomarkers predictive of AD. Even with only a small number of patients, the ability for us to determine sensitive imaging biomarkers could facilitate great improvement in speed of detection and effectiveness of AD interventions..
ContributorsSilva, Annelise Michelle (Author) / Baxter, Leslie (Thesis director) / McBeath, Michael (Committee member) / Presson, Clark (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05