No abstract

The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

BackgroundAntiretroviral therapy (ART) has significantly reduced HIV-related morbidity and mortality. However, therapeutic benefit of ART is often limited by delayed drug-associated toxicity. Nucleoside reverse transcriptase inhibitors (NRTIs) are the backbone of ART regimens. NRTIs compete with endogenous deoxyribonucleotide triphosphates (dNTPs) in incorporation into elongating DNA chain resulting in their cytotoxic or antiviral effect. Thus, the efficacy of NRTIs could be affected by direct competition with endogenous dNTPs and/or feedback inhibition of their metabolic enzymes. In this paper, we assessed whether the levels of ribonucleotides (RN) and dNTP pool sizes can be used as biomarkers in distinguishing between HIV-infected patients with ART-induced mitochondrial toxicity and HIV-infected patients without toxicity.MethodsWe used data collected through a case-control study from 50 subjects. Cases were defined as HIV-infected individuals with clinical and/or laboratory evidence of mitochondrial toxicity. Each case was age, gender, and race matched with an HIV-positive without evidence of toxicity. We used a range of machine learning procedures to distinguish between patients with and without toxicity. Using resampling methods like Monte Carlo k-fold cross validation, we compared the accuracy of several machine learning algorithms applied to our data. We used the algorithm with highest classification accuracy rate in evaluating the diagnostic performance of 12 RN and 14 dNTP pool sizes as biomarkers of mitochondrial toxicity.ResultsWe used eight classification algorithms to assess the diagnostic performance of RN and dNTP pool sizes distinguishing HIV patients with and without NRTI-associated mitochondrial toxicity. The algorithms resulted in cross-validated classification rates of 0.65–0.76 for dNTP and 0.72–0.83 for RN, following reduction of the dimensionality of the input data. The reduction of input variables improved the classification performance of the algorithms, with the most pronounced improvement for RN. Complex tree-based methods worked the best for both the deoxyribose dataset (Random Forest) and the ribose dataset (Classification Tree and AdaBoost), but it is worth noting that simple methods such as Linear Discriminant Analysis and Logistic Regression were very competitive in terms of classification performance.ConclusionsOur finding of changes in RN and dNTP pools in participants with mitochondrial toxicity validates the importance of dNTP pools in mitochondrial function. Hence, levels of RN and dNTP pools can be used as biomarkers of ART-induced mitochondrial toxicity.

Background Infection with the Human Immunodeficiency Virus (HIV) dramatically increases the risk of developing active tuberculosis (TB). Several studies have indicated that co-infection with TB increases the risk of HIV progression and death. Sub-Saharan Africa bears the brunt of these dual epidemics, with about 2.4 million HIV-infected people living with TB. The main objective of our study was to assess whether the pre-HAART CD4+ T-lymphocyte counts and percentages could serve as biomarkers for post-HAART treatment immune-recovery in HIV-positive children with and without TB co-infection. Methods The data analyzed in this retrospective study were collected from a cohort of 305 HIV-infected children being treated with HAART. A Lehmann family of ROC curves were used to assess the diagnostic performance of pre- HAART treatment CD4+ T-lymphocyte count and percentage as biomarkers for post-HAART immune recovery. The Kaplan–Meier estimator was used to compare differences in post-HAART recovery times between patients with and without TB co-infection. Results We found that the diagnostic performance of both pre-HARRT treatment CD4+ T-lymphocyte count and percentage was comparable and achieved accuracies as high as 74%. Furthermore, the predictive capability of pre-HAART CD4+ T-lymphocyte count and percentage were slightly better in TB-negative patients. Our analyses also indicate that TB-negative patients have a shorter recovery time compared to the TB-positive patients. Conclusions Pre-HAART CD4+ T-lymphocyte count and percentage are stronger predictors of immune recovery in TB-negative pediatric patients, suggesting that TB co-infection complicates the treatment of HIV in this cohort. These findings suggest that the detection and treatment of TB is essential for the effectiveness of HAART in HIV-infected pediatric patients.

MRI provides an opportunity to link neuroanatomic phenotypes to genetic expression. Genome-wide associative studies in the ENIGMA consortium and the UK Biobank have demonstrated significant links between brain structure and specific genes. Similar studies in rodents are challenging because of the scale. We report whole brain diffusion connectomes of four strains of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than the human connectome. We derived volumes and scalar diffusion metrics for 322 regions of the brain. Volume was the most heritable trait followed by FA, RD, and AD. These traits were heritable in > 60% of the regions when comparing all four strains. Many were also highly heritable when the BTBR was not included. Using a unique statistical approach to limit false discovery allowed us to identify a number of specific brain nodes in which connectivity was highly heritable.