A regression framework for brain network distance metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Feasible Generalized Least Squares (FGLS), and Permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing Multivariate Distance Matrix Regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

Neural Correlates of Hiccups in Patients with Lateral Medullary Infarction

Background Hiccups is a known presentation of lateral medullary infarction. However, the region in the medulla associated with this finding is not clearly known. In this study, we aimed to study the neural correlates of hiccups in patients with lateral medullary infarction (LMI). Materials and Methods This retrospective study included all patients who presented with lateral medullary infarction between January 2008 and May 2018. Patients with hiccups following LMI were identified as cases and those with no hiccups but who had LMI were taken as controls. The magnetic resonance imaging of the brain was done viewed and individual lesions were mapped manually to the template brain. Voxel-based lesion-symptom mapping employing nonparametric permutation testing was performed using MRIcron. Results There were a total of 31 patients with LMI who presented to the hospital during the study period. There were 11 (35.5%) patients with hiccups. Using the voxel-based lesion-symptom mapping analysis, the dorso-lateral region of the middle medulla showed significant association with hiccups. Conclusion In patients with LMI, we postulate that damage to the dorsolateral aspect on the middle medulla could result in hiccups.

Structure and Evolution of Non-Lake-Effect Snow Producing Alberta Clippers

Alberta Clippers (clippers) have long been associated with lake-effect snow (LES) events due to their frequent passage over the Great Lakes basin. However, not all clippers produce LES, and no research has inquired into which synoptic fields most influence LES formation. This study analyzes clippers during non-LES situations to further knowledge on which atmospheric variables most regulate LES development on the synoptic scale. As no such database currently exists, a clipper repository is developed using National Centers for Environmental Prediction Reanalysis data. The repository is then cross referenced with a previously developed LES repository to identify clippers responsible for LES. Composite synoptic-scale patterns were then constructed on the remaining non-LES clippers to identify synoptic conditions that ultimately inhibited LES formation. This analysis is supplemented by an assessment of lake surface conditions in each composite to evaluate how influential the lake characteristics were in the suppression of LES activity. In total, 51 non-LES clippers were identified, tracked, and separated into three composite map types that exhibited unique storm track and spatial characteristics. Permutation testing revealed that lake surface conditions were not significantly (p ≤ 0.05) different between LES and non-LES associated clippers implying the main LES inhibition factors were meteorological.

Preen gland microbiota covary with major histocompatibility complex genotype in a songbird

Pathogen-mediated selection at the major histocompatibility complex (MHC) is thought to promote MHC-based mate choice in vertebrates. Mounting evidence implicates odour in conveying MHC genotype, but the underlying mechanisms remain uncertain. MHC effects on odour may be mediated by odour-producing symbiotic microbes whose community structure is shaped by MHC genotype. In birds, preen oil is a primary source of body odour and similarity at MHC predicts similarity in preen oil composition. Hypothesizing that this relationship is mediated by symbiotic microbes, we characterized MHC genotype, preen gland microbial communities and preen oil chemistry of song sparrows ( Melospiza melodia ). Consistent with the microbial mediation hypothesis, pairwise similarity at MHC predicted similarity in preen gland microbiota. Counter to this hypothesis, overall microbial similarity did not predict chemical similarity of preen oil. However, permutation testing identified a maximally predictive set of microbial taxa that best reflect MHC genotype, and another set of taxa that best predict preen oil chemical composition. The relative strengths of relationships between MHC and microbes, microbes and preen oil, and MHC and preen oil suggest that MHC may affect host odour both directly and indirectly. Thus, birds may assess MHC genotypes based on both host-associated and microbially mediated odours.

Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent Pattern Mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

Somatic mutations in benign breast disease tissues and association with breast cancer risk

Background Benign breast disease (BBD) is a risk factor for breast cancer (BC); however, little is known about the genetic alterations present at the time of BBD diagnosis and how these relate to risk of incident BC. Methods A subset of a long-term BBD cohort was selected to examine DNA variation across three BBD groups (42 future estrogen receptor-positive (ER+) BC, 36 future estrogen receptor-negative (ER−) BC, and 42 controls cancer-free for at least 16 years post-BBD). DNA extracted from archival formalin fixed, paraffin-embedded (FFPE) tissue blocks was analyzed for presence of DNA alterations using a targeted panel of 93 BC-associated genes. To address artifacts frequently observed in FFPE tissues (e.g., C>T changes), we applied three filtering strategies based on alternative allele frequencies and nucleotide substitution context. Gene-level associations were performed using two types of burden tests and adjusted for clinical and technical covariates. Results After filtering, the variant frequency of SNPs in our sample was highly consistent with population allele frequencies reported in 1 KG/ExAC (0.986, p < 1e−16). The top ten genes found to be nominally associated with later cancer status by four of 12 association methods(p < 0.05) were MED12, MSH2, BRIP1, PMS1, GATA3, MUC16, FAM175A, EXT2, MLH1 and TGFB1, although these were not statistically significant in permutation testing. However, all 10 gene-level associations had OR < 1 with lower mutation burden in controls compared to cases, which was marginally statistically significant in permutation testing (p = 0.04). Comparing between the three case groups, BBD ER+ cases were closer to controls in mutation profile, while BBD ER− cases were distinct. Notably, the variant burden was significantly higher in controls than in either ER+ or ER− cases. CD45 expression was associated with mutational burden (p < 0.001). Conclusions Somatic mutations were more frequent in benign breast tissue from women who did not develop cancer, opening questions of clonal diversity or immune-mediated restraint on future cancer development. CD45 expression was positively associated with mutational burden, most strongly in controls. Further studies in both normal and premalignant tissues are needed to better understand the role of somatic gene mutations and their contribution to future cancer development.

ML of Striatum rs-fMRI Features Predicts TLE Diagnosis

Objective: To determine whether patients with temporal lobe epilepsy (TLE) exhibit aberrant resting-state functional magnetic resonance imaging (rs-fMRI)-functional connectivity and build an individualized TLE prediction model using ML (ML). Methods: Sixty TLE patients and fifty-one controls underwent rs-fMRI scanning. The striatum was divided into 12 striatal seeds. rs-FC was compared between groups to enable TLE classification based on striatal FC using the SPM12, SVM and PRONTO softwares. Bilateral striatal FC values were extracted and significance values were obtained using leave-one-out (LOO) SVM analysis and permutation testing (2,000) for cross-validation.Results: Patients with TLE exhibited a significantly decreased rs-FC between the left inferior ventral striatum and the right posterior central gyrus, left superior frontal gyrus;and between the left dorsal rostral putamen and right superior parietal lobule, right middle frontal gyrus. And between right dorsal caudate And left prefrontal lobe, and right middle temporal gyrus. rs-fMRI analysis a revealed significantly increased FC between the left inferior ventral striatum seed and right anterior cingulate in TLE patients (p<0.05). Right dorsal caudate FC may distinguish individuals with TLE from controls with 79.08% Accuracy, including a 72.77% Sensitivity and 76.44% Specificity, resulting in an AUC of 0.71 (p <0 .01). The areas informing classification included left prefrontal lobe, right middle temporal gyrus, and left superior parietal lobule.Conclusion: Our findings demonstrate aberrant FC in certain brain regions, such as the right dorsal caudate, may play an important role as potential biomarkers of TLE and highlight the utility of ML-based models for clinical decision making.

Topography of Movement-Related Delta and Theta Brain Oscillations

The aim of this study was to analyse the high density EEG during movement execution guided by visual attention to reveal the detailed topographic distributions of delta and theta oscillations. Twenty right-handed young subjects performed a finger tapping task, paced by a continuously transited repeating visual stimuli. Baseline corrected power of scalp current density transformed EEG was statistically assessed with cluster-based permutation testing. Delta and theta activities revealed differences in their spatial properties at the time of finger tapping execution. Theta synchronization showed a contralateral double activation in the parietal and fronto-central regions, while delta activity appeared in the central contralateral channels. Differences in the spatiotemporal topography between delta and theta activity in the course of movement execution were identified on high density EEG.