Advanced Data Analysis - Lecture Notes

Lecture notes for Advanced Data Analysis (ADA1 Stat 427/527 and ADA2 Stat 428/528), Department of Mathematics and Statistics, University of New Mexico, Fall 2016-Spring 2017. Additional material including RMarkdown templates for in-class and homework exercises, datasets, R code, and video lectures are available on the course websites: https://statacumen.com/teaching/ada1 and https://statacumen.com/teaching/ada2 . Contents I ADA1: Software 0 Introduction to R, Rstudio, and ggplot II ADA1: Summaries and displays, and one-, two-, and many-way tests of means 1 Summarizing and Displaying Data 2 Estimation in One-Sample Problems 3 Two-Sample Inferences 4 Checking Assumptions 5 One-Way Analysis of Variance III ADA1: Nonparametric, categorical, and regression methods 6 Nonparametric Methods 7 Categorical Data Analysis 8 Correlation and Regression IV ADA1: Additional topics 9 Introduction to the Bootstrap 10 Power and Sample size 11 Data Cleaning V ADA2: Review of ADA1 1 R statistical software and review VI ADA2: Introduction to multiple regression and model selection 2 Introduction to Multiple Linear Regression 3 A Taste of Model Selection for Multiple Regression VII ADA2: Experimental design and observational studies 4 One Factor Designs and Extensions 5 Paired Experiments and Randomized Block Experiments 6 A Short Discussion of Observational Studies VIII ADA2: ANCOVA and logistic regression 7 Analysis of Covariance: Comparing Regression Lines 8 Polynomial Regression 9 Discussion of Response Models with Factors and Predictors 10 Automated Model Selection for Multiple Regression 11 Logistic Regression IX ADA2: Multivariate Methods 12 An Introduction to Multivariate Methods 13 Principal Component Analysis 14 Cluster Analysis 15 Multivariate Analysis of Variance 16 Discriminant Analysis 17 Classificationhttps://digitalrepository.unm.edu/unm_oer/1002/thumbnail.jp

A morphometric analysis of Actaea racemosa L. (Ranunculaceae)

Actaea racemosa L. (syn. Cimicifuga racemosa [L.] Nutt.), Ranunculaceae, commonly known as black cohosh, is an herbaceous, perennial, medicinal plant native to the deciduous woodlands of eastern North America. Historical texts and current sales data indicate the continued popularity of this plant as an herbal remedy for over 175 years. Much of the present supply of A. racemosa is harvested from the wild. Diversity within and between populations of the species has not been well characterized. The purpose of this study was to assess the morphological variation of A. racemosa and identify patterns of variation at the population and species levels. A total of 26 populations representative of a significant portion of the natural range of the species were surveyed and plant material was collected for the morphological analysis of 37 leaflet, flower, and whole plant characteristics. In total, 511 leaflet samples and 83 flower samples were examined. Several of the populations surveyed had sets of relatively unique characteristics (large leaflet measurements, tall leaves and flowers, and a large number of stamen), and Tukey-Kramer multiple comparisons revealed significant differences between specific populations for 20 different characteristics. No unique phenotype, however, was found. Considerable morphological plasticity was noted in the apices of the staminodia. Cluster analyses showed that the morphological variation within populations was not smaller than between population and that this variation in not influenced by their geographic distribution

Correspondence between structure and function in the human brain at rest

To further understanding of basic and complex cognitive functions, previous connectome research has identified functional and structural connections of the human brain. Functional connectivity is often measured by using resting-state functional magnetic resonance imaging (rs-fMRI) and is generally interpreted as an indirect measure of neuronal activity. Gray matter (GM) primarily consists of neuronal and glia cell bodies; therefore, it is surprising that the majority of connectome research has excluded GM measures. Therefore, we propose that by exploring where GM corresponds to function would aid in the understanding of both structural and functional connectivity and in turn the human connectome. A cohort of 603 healthy participants underwent structural and functional scanning on the same 3 T scanner at the Mind Research Network. To investigate the spatial correspondence between structure and function, spatial independent component analysis (ICA) was applied separately to both GM density (GMD) maps and to rs-fMRI data. ICA of GM delineates structural components based on the covariation of GMD regions among subjects. For the rs-fMRI data, ICA identified spatial patterns with common temporal features. These decomposed structural and functional components were then compared by spatial correlation. Basal ganglia components exhibited the highest structural to resting-state functional spatial correlation (r = 0.59). Cortical components generally show correspondence between a single structural component and several resting-state functional components. We also studied relationships between the weights of different structural components and identified the precuneus as a hub in GMD structural network correlations. In addition, we analyzed relationships between component weights, age, and gender; concluding that age has a significant effect on structural components

Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State

Neuroimaging studies have shown that functional brain networks composed from select regions of interest have a modular community structure. However, the organization of functional network connectivity (FNC), comprising a purely data-driven network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain networks in this mental illness

a report from the Children's Oncology Group and the Utah Population Database

Relatively little is known about the epidemiology and factors underlying susceptibility to childhood rhabdomyosarcoma (RMS). To better characterize genetic susceptibility to childhood RMS, we evaluated the role of family history of cancer using data from the largest case–control study of RMS and the Utah Population Database (UPDB). RMS cases (n = 322) were obtained from the Children's Oncology Group (COG). Population-based controls (n = 322) were pair-matched to cases on race, sex, and age. Conditional logistic regression was used to evaluate the association between family history of cancer and childhood RMS. The results were validated using the UPDB, from which 130 RMS cases were identified and matched to controls (n = 1300) on sex and year of birth. The results were combined to generate summary odds ratios (ORs) and 95% confidence intervals (CI). Having a first-degree relative with a cancer history was more common in RMS cases than controls (ORs = 1.39, 95% CI: 0.97–1.98). Notably, this association was stronger among those with embryonal RMS (ORs = 2.44, 95% CI: 1.54–3.86). Moreover, having a first-degree relative who was younger at diagnosis of cancer (<30 years) was associated with a greater risk of RMS (ORs = 2.37, 95% CI: 1.34–4.18). In the largest analysis of its kind, we found that most children diagnosed with RMS did not have a family history of cancer. However, our results indicate an increased risk of RMS (particularly embryonal RMS) in children who have a first-degree relative with cancer, and among those whose relatives were diagnosed with cancer at <30 years of age

Multidimensional Frequency Domain Analysis of Full-Volume fMRI Reveals Significant Effects of Age, Gender, and Mental Illness on the Spatiotemporal Organization of Resting-State Brain Activity

Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender—among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still “big enough” to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function

MODEL PENGELOLAAN PASCA TANGKAP SEBAGAI UPAYA PENGENTASAN KEMISKINAN MASYARAKAT KAMPUNG NELAYAN DI PULAU ENGGANO

Relatively little is known about the epidemiology and factors underlying susceptibility to childhood rhabdomyosarcoma (RMS). To better characterize genetic susceptibility to childhood RMS, we evaluated the role of family history of cancer using data from the largest case-control study of RMS and the Utah Population Database (UPDB). RMS cases (n=322) were obtained from the Children's Oncology Group (COG). Population-based controls (n=322) were pair-matched to cases on race, sex, and age. Conditional logistic regression was used to evaluate the association between family history of cancer and childhood RMS. The results were validated using the UPDB, from which 130 RMS cases were identified and matched to controls (n=1300) on sex and year of birth. The results were combined to generate summary odds ratios (ORs) and 95% confidence intervals (CI). Having a first-degree relative with a cancer history was more common in RMS cases than controls (ORs=1.39, 95% CI: 0.97-1.98). Notably, this association was stronger among those with embryonal RMS (ORs=2.44, 95% CI: 1.54-3.86). Moreover, having a first-degree relative who was younger at diagnosis of cancer (&lt;30years) was associated with a greater risk of RMS (ORs=2.37, 95% CI: 1.34-4.18). In the largest analysis of its kind, we found that most children diagnosed with RMS did not have a family history of cancer. However, our results indicate an increased risk of RMS (particularly embryonal RMS) in children who have a first-degree relative with cancer, and among those whose relatives were diagnosed with cancer at &lt;30years of age

A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease

Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro Imaging genetics through meta analysis (ENIGMA) Consortium

BACKGROUND: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. METHODS: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide. RESULTS: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. CONCLUSIONS: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia

Reproducibility in the absence of selective reporting: An illustration from large‐scale brain asymmetry research

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p‐hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left–right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta‐analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an “ideal publishing environment,” that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically‐used sample sizes