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Open Access Publications from the University of California

Scholarly Works (320 results)

  • Article
  • Peer Reviewed
UC San Diego Previously Published Works (2014)

Purpose

While many recent studies have demonstrated improved detection and characterization of malignant lesions using high b-value diffusion imaging techniques, little is known about the underlying physical characteristics of tumor cells that modulate the restricted water signal at high b on clinical scanners.

Methods

Monte Carlo simulations of diffusion in a synthetic tumor cell environment were used to study the specific effects of tumor cell diameter and nuclear volume fraction (ν) on high b diffusion contrast.

Results

Results indicate that clinical pulsed-gradient spin-echo diffusion-weighted signals measured at high b (∼4000 s/mm(2)), long diffusion time (Δ ∼40-60 ms), and long echo time (TE ∼60-140 ms) are generally insensitive to tumor cell diameter, but increase exponentially with ν. Moreover, these results are predicted by a simple analytic expression for the intracellular restricted water signal with elevated T2 for the intranuclear versus cytosolic compartment.

Conclusion

Nuclear volume fraction is an important characteristic of cancer cells that modulates the apparent restriction of water at high b on clinical scanners. This model offers a possible explanation for the apparent unreliable correlation between tumor cell density (cellularity) and traditional ADC.
    Cover page: Distinct effects of nuclear volume fraction and cell diameter on high b‐value diffusion MRI contrast in tumors
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2018)
    With the availability of high-throughput sequencing data, identification of genetic causal variants accurately requires the efficient incorporation of function annotation data into the optimization routine. This motivates the need for development of novel methods for genome wide association studies with special focus on fine-mapping capabilities. A penalty function method that is simple to implement and capable of integrating functional annotation information into the estimation procedure, is proposed in this work. The idea is to use the prior distribution of the effect sizes explicitly as a penalty function. The estimates obtained are shown to be better correlated with the true effect sizes (in comparison with a few existing techniques). An increase in the positive and negative predictive value is demonstrated using Hapgen2 simulated data.
      Cover page: Determining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2014)
      Genome-wide association studies (GWAS) have identified a large number of gene variants associated with schizophrenia, but these variants explain only a small portion of the heritability. It is becoming increasingly clear that schizophrenia is influenced by many genes, most of which have effects too small to be identified using traditional GWAS statistical methods. By applying recently developed Empirical Bayes statistical approaches, we have demonstrated that functional genic elements show differential contribution to phenotypic variance, with some elements (regulatory regions and exons) showing strong enrichment for association with schizophrenia. Applying related methods, we also showed abundant genetic overlap (pleiotropy) between schizophrenia and other phenotypes, including bipolar disorder, cardiovascular disease risk factors, and multiple sclerosis. We estimated the number of gene variants with effects in schizophrenia and bipolar disorder to be approximately 1.2%. By applying our novel statistical framework, we dramatically improved gene discovery and detected a large number of new gene loci associated with schizophrenia that have not yet been identified with standard GWAS methods. Utilizing independent schizophrenia substudies, we showed that these new loci have high replication rates in de novo samples, indicating that they likely represent true schizophrenia risk genes. The new statistical tools provide a powerful approach for uncovering more of the missing heritability of schizophrenia and other complex disorders. In conclusion, the highly polygenic architecture of schizophrenia strongly suggests the utility of research approaches that recognize schizophrenia neuropathology as a complex dynamic system, with many small gene effects integrated in functional networks.
        Cover page: Boosting the Power of Schizophrenia Genetics by Leveraging New Statistical Tools
        • Thesis
        • Peer Reviewed
        UC San Diego Electronic Theses and Dissertations (2017)

        Searching the genetic basis of human complex traits is an essential tool to illuminating biological processes and predicting risks of diseases. However, with the advance of genotyping technologies, e.g. genome sequencing, and sophistication of phenotypic measurements, e.g. magnetic resonance imaging, two prong challenges have imposed on the endeavor for mapping genotypes to phenotypes. First is the weak genetic signals due to the multifactorial contributions from common genetic variants. The effect sizes of those genetic variants become too small to be detected. The second is the inconsistency of the phenotypic measurements, which the genetically fundamental units, or so called endophenotype, are not apparent. This polygenes-polymeasurements problem become even more prominent in recent surge of large-scale cross-traits genomic studies. This dissertation is a collection of my series studies to develop novel methods to tackle the polygenes-polymeasurement challenges. In particular, I focused on how to extract generalizable signals from diverse genomic databses. The extracted signals can be useful in either predicting disease risks or elucidating biological processes in human brain.

          Cover page: Genotype-Phenotype Mapping of the Human Brain in the Era of Large-Scale Genomics Databases
          • Article
          UC San Diego Previously Published Works (2022)
          Diffusion-weighted imaging (DWI) is routinely used to aid in the detection and characterization of prostate cancer. Given imaging time constraints in a clinical setting, it is important to maximize the statistical efficiency of a DWI examination of the prostate. The objective is to maximize the accuracy with which microstructural information about the prostate can be obtained while minimizing diffusion scan time. In this study, we apply estimation theory to evaluate the statistical efficiency of different DWI acquisitions and methods. Specifically, we show that the variance of DWI parameters estimated using nonlinear multiexponential signal models is considerably higher than the variance observed using linear signal models. We then derive a simple analytical expression for the efficiency of a linear estimator and use it to optimize b -value sampling for DWI of the prostate.
            • Thesis
            • Peer Reviewed
            UC San Diego Electronic Theses and Dissertations (2021)

            Twin studies have established that the influence of genetics on human traits related to brain and behavior are pervasive. For a large majority of complex human traits uncovering which genetic variants are associated with phenotypic variations, by performing Genome Wide Association (GWA) studies, has been difficult due these traits being highly polygenic – many genetic variants with small effects that have a larger effect in aggregate. Conversely, some traits have been shown to have a Mendelian genetic architecture – a single genetic variant imparting a large effect. In this thesis I explore the genetic contribution to variability of traits relating to brain and behavior in large GWA datasets for phenotypes of increasing complexity: a) Mendelian traits, b) polygenic traits and c) polygenic and multi-dimensional traits. First, I present analysis of the neurological impact of hereditary hemochromatosis, a Mendelian disorder that results in an excess of iron being absorbed by the body. Next, I present two projects investigating the genetic propensity/liability of i) cognitive performance and ii) psychopathology in a large sample of typically developing children aged 9-10 years old. Finally, I present a method for analyzing polygenic and multi-dimensional traits and apply it to the phenotype of human cortical morphology (cortical area, thickness and sulcal depth). In the age of large genomic databases this work may prove to be important for early detection of at risk groups as well as understanding the genetic determinants that give rise to complex human traits.

            • 1 supplemental ZIP
            Cover page: UNDERSTANDING THE GENETIC INFLUENCE ON TRAITS RELATED TO BRAIN AND BEHAVIOR IN ADULTS AND CHILDREN
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2017)
            The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations.
              Cover page: Corrected Four-Sphere Head Model for EEG Signals
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2016)
              Laminar population analysis (LPA) is a method for analysis of electrical data recorded by linear multielectrodes passing through all lamina of cortex. Like principal components analysis (PCA) and independent components analysis (ICA), LPA offers a way to decompose the data into contributions from separate cortical populations. However, instead of using purely mathematical assumptions in the decomposition, LPA is based on physiological constraints, i.e., that the observed LFP (low-frequency part of signal) is driven by action-potential firing as observed in the MUA (multi-unit activity; high-frequency part of the signal). In the presently developed generalized laminar population analysis (gLPA) the set of basis functions accounting for the LFP data is extended compared to the original LPA, thus allowing for a better fit of the model to experimental data. This enhances the risk for overfitting, however, and we therefore tested various versions of gLPA on virtual LFP data in which we knew the ground truth. These synthetic data were generated by biophysical forward-modeling of electrical signals from network activity in the comprehensive, and well-known, thalamocortical network model developed by Traub and coworkers. The results for the Traub model imply that while the laminar components extracted by the original LPA method overall are in fair agreement with the ground-truth laminar components, the results may be improved by use of gLPA method with two (gLPA-2) or even three (gLPA-3) postsynaptic LFP kernels per laminar population.
                Cover page: Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2014)
                Clinical research studies generate data that need to be shared and statistically analyzed by their participating institutions. The distributed nature of research and the different domains involved present major challenges to data sharing, exploration, and visualization. The Data Portal infrastructure was developed to support ongoing research in the areas of neurocognition, imaging, and genetics. Researchers benefit from the integration of data sources across domains, the explicit representation of knowledge from domain experts, and user interfaces providing convenient access to project specific data resources and algorithms. The system provides an interactive approach to statistical analysis, data mining, and hypothesis testing over the lifetime of a study and fulfills a mandate of public sharing by integrating data sharing into a system built for active data exploration. The web-based platform removes barriers for research and supports the ongoing exploration of data.
                  Cover page: A web-portal for interactive data exploration, visualization, and hypothesis testing
                  • Article
                  • Peer Reviewed
                  UC San Diego Previously Published Works (2014)
                  Although a crucial role of the fusiform gyrus (FG) in face processing has been demonstrated with a variety of methods, converging evidence suggests that face processing involves an interactive and overlapping processing cascade in distributed brain areas. Here we examine the spatio-temporal stages and their functional tuning to face inversion, presence and configuration of inner features, and face contour in healthy subjects during passive viewing. Anatomically-constrained magnetoencephalography (aMEG) combines high-density whole-head MEG recordings and distributed source modeling with high-resolution structural MRI. Each person's reconstructed cortical surface served to constrain noise-normalized minimum norm inverse source estimates. The earliest activity was estimated to the occipital cortex at ~100 ms after stimulus onset and was sensitive to an initial coarse level visual analysis. Activity in the right-lateralized ventral temporal area (inclusive of the FG) peaked at ~160 ms and was largest to inverted faces. Images containing facial features in the veridical and rearranged configuration irrespective of the facial outline elicited intermediate level activity. The M160 stage may provide structural representations necessary for downstream distributed areas to process identity and emotional expression. However, inverted faces additionally engaged the left ventral temporal area at ~180 ms and were uniquely subserved by bilateral processing. This observation is consistent with the dual route model and spared processing of inverted faces in prosopagnosia. The subsequent deflection, peaking at ~240 ms in the anterior temporal areas bilaterally, was largest to normal, upright faces. It may reflect initial engagement of the distributed network subserving individuation and familiarity. These results support dynamic models suggesting that processing of unfamiliar faces in the absence of a cognitive task is subserved by a distributed and interactive neural circuit.
                    Cover page: Spatio-temporal dynamics and laterality effects of face inversion, feature presence and configuration, and face outline
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