Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Functional Evaluation of Causal Mutations Identified in Human Genetic Studies

Human genetics has been experiencing a wave of genetic discoveries thanks to the development of several technologies, such as genome-wide association studies (GWAS), whole-exome sequencing, and whole genome sequencing. Despite the massive genetic discoveries of new variants associated with human diseases, several key challenges emerge following the genetic discovery. GWAS is known to be good at identifying the locus associated with the patient phenotype. However, the actually causal variants responsible for the phenotype are often elusive. Another challenge in human genetics is that even the causal mutations are already known, the underlying biological effect might remain largely ambiguous. Functional evaluation plays a key role to solve these key challenges in human genetics both to identify causal variants responsible for the phenotype, and to further develop the biological insights from the disease-causing mutations.

We adopted various methods to characterize the effects of variants identified in human genetic studies, including patient genetic and phenotypic data, RNA chemistry, molecular biology, virology, and multi-electrode array and primary neuronal culture systems. Chapter 1 is a broader introduction for the motivation and challenges for functional evaluation in human genetic studies, and the background of several genetics discoveries, such as hepatitis C treatment response, in which we performed functional characterization.

Chapter 2 focuses on the characterization of causal variants following the GWAS study for hepatitis C treatment response. We characterized a non-coding SNP (rs4803217) of IL28B (IFNL3) in high linkage disequilibrium (LD) with the discovery SNP identified in the GWAS. In this chapter, we used inter-disciplinary approaches to characterize rs4803217 on RNA structure, disease association, and protein translation.

Chapter 3 describes another avenue of functional characterization following GWAS focusing on the novel transcripts and proteins identified near the IL28B (IFNL3) locus. It has been recently speculated that this novel protein, which was named IFNL4, may affect the HCV treatment response and clearance. In this chapter, we used molecular biology, virology, and patient genetic and phenotypic data to further characterize and understand the biology of IFNL4. The efforts in chapter 2 and 3 provided new insights to the candidate causal variant(s) responsible for the GWAS for HCV treatment response, however, more evidence is still required to make claims for the exact causal roles of these variants for the GWAS association.

Chapter 4 aims to characterize a mutation already known to cause a disease (seizure) in a mouse model. We demonstrate the potential use of multi-electrode array (MEA) system for the functional characterization and drug testing on mutations found in neurological diseases, such as seizure. Functional characterization in neurological diseases is relatively challenging and available systematic tools are relatively limited. This chapter shows an exploratory research and example to establish a system for the broader use for functional characterization and translational opportunities for mutations found in neurological diseases.

Overall, this dissertation spans a range of challenges of functional evaluations in human genetics. It is expected that the functional characterization to understand human mutations will become more central in human genetics, because there are still many biological questions remaining to be answered after the explosion of human genetic discoveries. The recent advance in several technologies, including genome editing and pluripotent stem cells, is also expected to make new tools available for functional studies in human diseases.

Combined Gene Therapy and Functional Tissue Engineering for the Treatment of Osteoarthritis

The pathogenesis of osteoarthritis is mediated in part by inflammatory cytokines including interleukin-1 (IL-1), which promote degradation of articular cartilage and prevent human mesenchymal stem cell (hMSC) chondrogenesis. We combined gene therapy and functional tissue engineering to develop engineered cartilage with immunomodulatory properties that allow chondrogenesis in the presence of pathologic levels of IL-1 by inducing overexpression of IL-1 receptor antagonist (IL-1Ra) in hMSCs via scaffold-mediated lentiviral gene delivery. A doxycycline-inducible vector was used to transduce hMSCs in monolayer or within 3D woven PCL scaffolds to enable tunable IL-1Ra production. In the presence of IL-1, IL-1Ra-expressing engineered cartilage produced cartilage-specific extracellular matrix, while resisting IL-1-induced upregulation of matrix metalloproteinases and maintaining mechanical properties similar to native articular cartilage. The ability of functional engineered cartilage to deliver tunable anti-inflammatory cytokines to the joint may enhance the long-term success of therapies for cartilage injuries or osteoarthritis.

Following this, we modified this anti-inflammatory engineered cartilage to incorporate rabbit MSCs and evaluated this therapeutic strategy in a pilot study in vivo in rabbit osteochondral defects. Rabbits were fed a custom doxycycline diet to induce gene expression in engineered cartilage implanted in the joint. Serum and synovial fluid were collected and the levels of doxycycline and inflammatory mediators were measured. Rabbits were euthanized 3 weeks following surgery and tissues were harvested for analysis. We found that doxycycline levels in serum and synovial fluid were too low to induce strong overexpression of hIL-1Ra in the joint and hIL-1Ra was undetectable in synovial fluid via ELISA. Although hIL-1Ra expression in the first few days local to the site of injury may have had a beneficial effect, overall a higher doxycycline dose and more readily transduced cell population would improve application of this therapy.

In addition to the 3D woven PCL scaffold, cartilage-derived matrix scaffolds have recently emerged as a promising option for cartilage tissue engineering. Spatially-defined, biomaterial-mediated lentiviral gene delivery of tunable and inducible morphogenetic transgenes may enable guided differentiation of hMSCs into both cartilage and bone within CDM scaffolds, enhancing the ability of the CDM scaffold to provide chondrogenic cues to hMSCs. In addition to controlled production of anti-inflammatory proteins within the joint, in situ production of chondro- and osteo-inductive factors within tissue-engineered cartilage, bone, or osteochondral tissue may be highly advantageous as it could eliminate the need for extensive in vitro differentiation involving supplementation of culture media with exogenous growth factors. To this end, we have utilized controlled overexpression of transforming growth factor-beta 3 (TGF-β3), bone morphogenetic protein-2 (BMP-2) or a combination of both factors, to induce chondrogenesis, osteogenesis, or both, within CDM hemispheres. We found that TGF-β3 overexpression led to robust chondrogenesis in vitro and BMP-2 overexpression led to mineralization but not accumulation of type I collagen. We also showed the development of a single osteochondral construct by combining tissues overexpressing BMP-2 (hemisphere insert) and TGF-β3 (hollow hemisphere shell) and culturing them together in the same media. Chondrogenic ECM was localized in the TGF-β3-expressing portion and osteogenic ECM was localized in the BMP-2-expressing region. Tissue also formed in the interface between the two pieces, integrating them into a single construct.

Since CDM scaffolds can be enzymatically degraded just like native cartilage, we hypothesized that IL-1 may have an even larger influence on CDM than PCL tissue-engineered constructs. Additionally, anti-inflammatory engineered cartilage implanted in vivo will likely affect cartilage and the underlying bone. There is some evidence that osteogenesis may be enhanced by IL-1 treatment rather than inhibited. To investigate the effects of an inflammatory environment on osteogenesis and chondrogenesis within CDM hemispheres, we evaluated the ability of IL-1Ra-expressing or control constructs to undergo chondrogenesis and osteogenesis in the prescence of IL-1. We found that IL-1 prevented chondrogenesis in CDM hemispheres but did not did not produce discernable effects on osteogenesis in CDM hemispheres. IL-1Ra-expressing CDM hemispheres produced robust cartilage-like ECM and did not upregulate inflammatory mediators during chondrogenic culture in the presence of IL-1.