Matching Items (6)
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
Description
There is preclinical evidence that the detrimental cognitive effects of hormone loss can be ameliorated by estrogen therapy (Bimonte, Acosta, & Talboom, 2010), however, one of the primary concerns with current hormone therapies is that they are nonselective, leading to increased risk of breast and endometrial cancers as well as heart disease. Thus, in order to achieve a successful and clinically relevant long-term hormone therapy option, it is optimal to find an estrogen therapy regimen that is selective to its target tissue. Recently, phytoestrogens have been found to exert selective, beneficial effects on cognition and brain. For example, genistein and diadzein produce neuroprotective effects in cognitive brain regions (Zhao, Chen, & Diaz Brinton, 2002). The purpose of this study was threefold: 1) to examine the cognitive impact of phytoestrogens in young ovariectomized rats, 2) to replicate the dose effects found in the Luine study (Luine et al., 2006), while controlling for manufacturer differences, and 3) to assess if the rodent diet used in our laboratory has an estrogenic-like cognitive impact.The current findings suggest that, at least for object memory, diets containing varying amounts of phytoestrogens can alter cognition, with diets containing high amounts of phytoestrogens showing potential benefits to this type of memory.
ContributorsWhitton, Elizabeth Nicole (Author) / Bimonte-Nelson, Heather (Thesis director) / Presson, Clark (Committee member) / Baxter, Leslie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2013-05
Description
Adults with autism spectrum disorder (ASD) commonly have co-morbid psychiatric symptoms which can decrease quality of life. Although many adults with ASD are achieving greater independence, including attending college, psychiatric symptoms are generally not well controlled in this group. Mindfulness Based Stress Reduction (MBSR) is a program that has successfully been used to reduce the stress, depression, and anxiety symptoms in many clinical and non-clinical groups and may also be effective for college-aged students with ASD. The present investigation assessed the demand, practicality, implementation, adaptation, and acceptability of an MBSR course for college students with ASD. A total of 22 participants completed the questionnaire containing 53 questions and were between the ages of 18 to 64. We found that the MBSR therapy is in high demand for individuals with ASD, and that the participants would be willingly complete the intervention techniques. Participants generally stated that a therapy course like MBSR may help reduce their symptoms, and that they were eager to enroll. Participants were willing to attend all 8 classes during the summer, with a preference for afternoons. Also, modifications including yoga and background music would be accepted by each participant as well as any additional modifications made to the course to meet the needs of the individuals with ASD. Next steps include enrolling and randomizing students into the MBSR course or control group, as well as collect pre- and post-intervention data. We hypothesize MBSR will reduce the psychiatric symptoms and stress levels of individuals in college with ASD, demonstrating its effectiveness in this vulnerable population.
ContributorsJones, Rachel Michelle (Author) / Braden, Blair (Thesis director) / Baxter, Leslie (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Department of Speech and Hearing Science (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Background: Gait disturbance, clumsiness, and other mild movement problems are often observed in children with autism spectrum disorder (ASD) (Maurer and Damasio 1982). As the brain ages, these symptoms may persist or worsen in late adulthood in those diagnosed with ASD. This study focused on older adults with ASD to study motor behavior and underlying brain integrity. Using a finger tapping task, motor performance was measured in a cross-sectional study comparing older adults with ASD and age-matched typically developing (TD) controls. We hypothesized that older adults with ASD would show poorer motor performance (slower finger tapping speed). We also hypothesized that underlying brain differences, measured using MRI, in regions associated with motor function including the primary motor cortex, basal ganglia, and cerebellum, as well as the white matter connecting tracts would exist between groups and be associated with the proposed disparity in motor performance.
Method: A finger oscillation (Finger Tapping) test was administered to both ASD (n=21) and TD (n=20) participants aged 40-70 year old participants as a test of fine motor speed. Magnetic resonance (MR) images were collected using a Philips 3 Tesla scanner. 3D T1-weighted and diffusion tensor images (DTI) were obtained to measure gray and white matter volume and white matter integrity, respectively. FreeSurfer, an automated volumetric measurement software, was used to determine group volumetric differences. Mean, radial, and axial diffusivity, fractional anisotropy, and local diffusion homogeneity were measured from DTI images using PANDA software in order to evaluate white matter integrity.
Results: All participants were right-handed and there were no significant differences in demographic variables (ASD/TD, means) including age (51.9/49.1 years), IQ (107/112) and years education (15/16). Total brain volume was not significantly different between groups. No statistically significant group differences were observed in finger tapping speed. ASD participants compared to TDs showed a trend of slower finger tapping (taps/10 seconds) speed on the dominant hand (47.00 (±11.2) vs. (50.5 (±6.6)) and nondominant hand (44.6 (±7.6) vs. (47.2 (±6.6)). However, a large degree of variability was observed in the ASD group, and the Levene’s test for homogeneity of variance approached significance (p=0.053) on the dominant, but not the nondominant, hand. No significant group differences in gray matter regional volume were found for brain regions associated with performing motor tasks. In contrast, group differences were found on several measures of white matter including the corticospinal tract, anterior internal capsule and middle cerebellar peduncle. Brain-behavior correlations showed that dominant finger tapping speed correlated with left hemisphere white matter integrity of the corticospinal tract and right hemisphere cerebellar white matter in the ASD group.
Conclusions: No significant differences were observed between groups in finger tapping speed but the high degree of variability seen in the ASD group. Differences in motor performance appear to be associated with observed brain differences, particularly in the integrity of white matter tracts contributing to motor functioning.
Method: A finger oscillation (Finger Tapping) test was administered to both ASD (n=21) and TD (n=20) participants aged 40-70 year old participants as a test of fine motor speed. Magnetic resonance (MR) images were collected using a Philips 3 Tesla scanner. 3D T1-weighted and diffusion tensor images (DTI) were obtained to measure gray and white matter volume and white matter integrity, respectively. FreeSurfer, an automated volumetric measurement software, was used to determine group volumetric differences. Mean, radial, and axial diffusivity, fractional anisotropy, and local diffusion homogeneity were measured from DTI images using PANDA software in order to evaluate white matter integrity.
Results: All participants were right-handed and there were no significant differences in demographic variables (ASD/TD, means) including age (51.9/49.1 years), IQ (107/112) and years education (15/16). Total brain volume was not significantly different between groups. No statistically significant group differences were observed in finger tapping speed. ASD participants compared to TDs showed a trend of slower finger tapping (taps/10 seconds) speed on the dominant hand (47.00 (±11.2) vs. (50.5 (±6.6)) and nondominant hand (44.6 (±7.6) vs. (47.2 (±6.6)). However, a large degree of variability was observed in the ASD group, and the Levene’s test for homogeneity of variance approached significance (p=0.053) on the dominant, but not the nondominant, hand. No significant group differences in gray matter regional volume were found for brain regions associated with performing motor tasks. In contrast, group differences were found on several measures of white matter including the corticospinal tract, anterior internal capsule and middle cerebellar peduncle. Brain-behavior correlations showed that dominant finger tapping speed correlated with left hemisphere white matter integrity of the corticospinal tract and right hemisphere cerebellar white matter in the ASD group.
Conclusions: No significant differences were observed between groups in finger tapping speed but the high degree of variability seen in the ASD group. Differences in motor performance appear to be associated with observed brain differences, particularly in the integrity of white matter tracts contributing to motor functioning.
ContributorsDeatherage, Brandon R. (Co-author) / Braden, B. Blair (Co-author, Committee member) / Smith, Christopher J. (Co-author) / McBeath, Michael (Co-author, Thesis director) / Thompson, Aimee M. (Co-author) / Wood, Emily G. (Co-author) / McGee, Samuel C. (Co-author) / Sinha, Krishna (Co-author) / Baxter, Leslie (Co-author, Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor) / Department of Information Systems (Contributor)
Created2017-05
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
The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.
ContributorsDi Martino, Adriana (Author) / O'Connor, David (Author) / Chen, Bosi (Author) / Alaerts, Kaat (Author) / Anderson, Jeffrey S. (Author) / Assaf, Michal (Author) / Balsters, Joshua H. (Author) / Baxter, Leslie (Author) / Beggiato, Anita (Author) / Bernaerts, Sylvie (Author) / Blanken, Laura M. E. (Author) / Bookheimer, Susan Y. (Author) / Braden, B. Blair (Author) / Byrge, Lisa (Author) / Castellanos, F. Xavier (Author) / Dapretto, Mirella (Author) / Delorme, Richard (Author) / Fair, Damien A. (Author) / Fishman, Inna (Author) / Fitzgerald, Jacqueline (Author) / Gallagher, Louise (Author) / Keehn, R. Joanne Jao (Author) / Kennedy, Daniel P. (Author) / Lainhart, Janet E. (Author) / Luna, Beatriz (Author) / Mostofsky, Stewart H. (Author) / Muller, Ralph-Axel (Author) / Nebel, Mary Beth (Author) / Nigg, Joel T. (Author) / O'Hearn, Kirsten (Author) / Solomon, Marjorie (Author) / Toro, Roberto (Author) / Vaidya, Chandan J. (Author) / Wenderoth, Nicole (Author) / White, Tonya (Author) / Craddock, R. Cameron (Author) / Lord, Catherine (Author) / Leventhal, Bennett (Author) / Milham, Michael P. (Author) / College of Health Solutions (Contributor)
Created2017-03-14