An adaptive h-refinement method for the boundary element fast multipole method for quasi-static electromagnetic modeling.

Objective.In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems.Approach.We propose, describe, and systematically investigate an AMR method using the boundary element method with fast multipole acceleration (BEM-FMM) as the base numerical solver. The goal is to efficiently allocate additional unknowns to critical areas of the model, where they will best improve solution accuracy. The implemented AMR method's accuracy improvement is measured on head models constructed from 16 Human Connectome Project subjects under problem classes of TES, TMS, and EEG. Errors are computed between three solutions: an initial non-adaptive solution, a solution found after applying AMR with a conservative refinement rate, and a 'silver-standard' solution found by subsequent 4:1 global refinement of the adaptively-refined model.Main results.Excellent agreement is shown between the adaptively-refined and silver-standard solutions for standard head models. AMR is found to be vital for accurate modeling of TES and EEG forward problems for standard models: an increase of less than 25% (on average) in number of mesh elements for these problems, efficiently allocated by AMR, exposes electric field/potential errors exceeding 60% (on average) in the solution for the unrefined models.Significance.This error has especially important implications for TES dosing prediction-where the stimulation strength plays a central role-and for EEG lead fields. Though the specific form of the AMR method described here is implemented for the BEM-FMM, we expect that AMR is applicable and even required for accurate electromagnetic simulations by other numerical modeling packages as well.

Software Package for Transcranial Magnetic Stimulation Coil and Coil Array Analysis and Design.

Objective: This study aims to describe a MATLAB software package for transcranial magnetic stimulation (TMS) coil analysis and design. Approach: Electric and magnetic fields of the coils as well as their self- and mutual (for coil arrays) inductances are computed, with or without a magnetic core. Solid and stranded (Litz wire) conductors are also taken into consideration. The starting point is the centerline of a coil conductor(s), which is a 3D curve defined by the user. Then, a wire mesh and a computer aided design (CAD) mesh for the volume conductor of a given cross-section (circular, elliptical, or rectangular) are automatically generated. Self- and mutual inductances of the coil(s) are computed. Given the conductor current and its time derivative, electric and magnetic fields of the coil(s) are determined anywhere in space.Computations are performed with the fast multipole method (FMM), which is the most efficient way to evaluate the fields of many elementary current elements (current dipoles) comprising the current carrying conductor at a large number of observation points. This is the major underlying mathematical operation behind both inductance and field calculations. Main Results: The wire-based approach enables precise replication of even the most complex physical conductor geometries, while the FMM acceleration quickly evaluates large quantities of elementary current filaments. Agreement to within 0.74% was obtained between the inductances computed by the FMM method and ANSYS Maxwell 3D for the same coil model. Although not provided in this study, it is possible to evaluate non-linear magnetic cores in addition to the linear core exemplified. An experimental comparison was carried out against a physical MagVenture C-B60 coil; the measured and simulated inductances differed by only 1.25%, and nearly perfect correlation was found between the measured and computed E-field values at each observation point. Significance: The developed software package is applicable to any quasistatic inductor design, not necessarily to the TMS coils only.

An Adaptive H-Refinement Method for the Boundary Element Fast Multipole Method for Quasi-static Electromagnetic Modeling.

Objective: In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems. Approach: We propose, describe, and systematically investigate an AMR method using the Boundary Element Method with Fast Multipole Acceleration (BEM-FMM) as the base numerical solver. The goal is to efficiently allocate additional unknowns to critical areas of the model, where they will best improve solution accuracy.The implemented AMR method's accuracy improvement is measured on head models constructed from 16 Human Connectome Project subjects under problem classes of TES, TMS, and EEG. Errors are computed between three solutions: an initial non-adaptive solution, a solution found after applying AMR with a conservative refinement rate, and a "silver-standard" solution found by subsequent 4:1 global refinement of the adaptively-refined model. Main Results: Excellent agreement is shown between the adaptively-refined and silver-standard solutions for standard head models. AMR is found to be vital for accurate modeling of TES and EEG forward problems for standard models: an increase of less than 25% (on average) in number of mesh elements for these problems, efficiently allocated by AMR, exposes electric field/potential errors exceeding 60% (on average) in the solution for the unrefined models. Significance: This error has especially important implications for TES dosing prediction - where the stimulation strength plays a central role - and for EEG lead fields. Though the specific form of the AMR method described here is implemented for the BEM-FMM, we expect that AMR is applicable and even required for accurate electromagnetic simulations by other numerical modeling packages as well.

Germline immunomodulatory expression quantitative trait loci (ieQTLs) associated with immune-related toxicity from checkpoint inhibition.

BACKGROUND: Immune checkpoint inhibition (ICI) has improved clinical outcomes for metastatic melanoma patients; however, 65-80% of patients treated with ICI experience immune-related adverse events (irAEs). Given the plausible link of irAEs with underlying host immunity, we explored whether germline genetic variants controlling the expression of 42 immunomodulatory genes were associated with the risk of irAEs in melanoma patients treated with the single-agent anti-CTLA-4 antibody ipilimumab (IPI). METHODS: We identified 42 immunomodulatory expression quantitative trait loci (ieQTLs) most significantly associated with the expression of 382 immune-related genes. These germline variants were genotyped in IPI-treated melanoma patients, collected as part of a multi-institutional collaboration. We tested the association of ieQTLs with irAEs in a discovery cohort of 95 patients, followed by validation in an additional 97 patients. RESULTS: We found that the alternate allele of rs7036417, a variant linked to increased expression of SYK, was strongly associated with an increased risk of grade 3-4 toxicity [odds ratio (OR) = 7.46; 95% confidence interval (CI) = 2.65-21.03; p = 1.43E-04]. This variant was not associated with response (OR = 0.90; 95% CI = 0.37-2.21; p = 0.82). CONCLUSION: We report that rs7036417 is associated with increased risk of severe irAEs, independent of IPI efficacy. SYK plays an important role in B-cell/T-cell expansion, and increased pSYK has been reported in patients with autoimmune disease. The association between rs7036417 and IPI irAEs in our data suggests a role of SYK overexpression in irAE development. These findings support the hypothesis that inherited variation in immune-related pathways modulates ICI toxicity and suggests SYK as a possible future target for therapies to reduce irAEs.

Tumor immunogenomic signatures improve a prognostic model of melanoma survival.

BACKGROUND: Tumor mutation burden (TMB) has been associated with melanoma immunotherapy (IT) outcomes, including survival. We explored whether combining TMB with immunogenomic signatures recently identified by The Cancer Genome Atlas (TCGA) can refine melanoma prognostic models of overall survival (OS) in patients not treated by IT. METHODS: Cox proportional-hazards (Cox PH) analysis was performed on 278 metastatic melanomas from TCGA not treated by IT. In a discovery and two validation cohorts Cox PH models assessed the interaction between TMB and 53 melanoma immunogenomic features to refine prediction of melanoma OS. RESULTS: Interferon-γ response (IFNγRes) and macrophage regulation gene signatures (MacReg) combined with TMB significantly associated with OS (p = 8.80E-14). We observed that patients with high TMB, high IFNγRes and high MacReg had significantly better OS compared to high TMB, low IFNγRes and low MacReg (HR = 2.8, p = 3.55E-08). This association was not observed in low TMB patients. CONCLUSIONS: We report a model combining TMB and tumor immune features that significantly improves prediction of melanoma OS, independent of IT. Our analysis revealed that patients with high TMB, high levels of IFNγRes and MacReg had significantly more favorable OS compared to high TMB patients with low IFNγRes and low MacReg. These findings may substantially improve current melanoma prognostic models.

Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies.

An array-based genotyping approach has been the standard practice for genome-wide association studies (GWASs); however, as sequencing costs plummet over the past years, ultra low-coverage whole-genome sequencing (ulcWGS <0.5× coverage) has emerged as a promising alternative that provides superior genomic coverage with substantial reduction of genotyping cost. To evaluate the potential utility of ulcWGS, we performed a whole-genome sequencing (WGS) of 72 European individuals to a target coverage of 0.4× and compared its performance with the widely used Infinium Global Screening Multi-Disease Array (GSA-MD). We showed that the number of variants captured by ulcWGS is comparable with imputed GSA-MD platform, particularly for low-frequency (95.5%) and common variants (99.9%), with high imputation R2 accuracy (mean 0.93 for SNPs and 0.86 for indels). Using deep-coverage 30× WGS as the "truth" genotypes, we found that ulcWGS has higher overall nonreference genotype concordance compared with imputed GSA-MD for both SNPs (0.90 vs. 0.88) and indels (0.86 vs. 0.83). In addition, ulcWGS proved to be as sensitive as the genotyping-based method in sex imputation and ancestry prediction producing similar principal component (PC) scores. Our findings provide important evidence that the cost efficient ulcWGS of <0.5× generates high genotype accuracy, outperforming the standard genotyping arrays, making it an attractive alternative to the array-based method in next-generation GWAS design.

Computational causal discovery for post-traumatic stress in police officers.

This article reports on a study aimed to elucidate the complex etiology of post-traumatic stress (PTS) in a longitudinal cohort of police officers, by applying rigorous computational causal discovery (CCD) methods with observational data. An existing observational data set was used, which comprised a sample of 207 police officers who were recruited upon entry to police academy training. Participants were evaluated on a comprehensive set of clinical, self-report, genetic, neuroendocrine and physiological measures at baseline during academy training and then were re-evaluated at 12 months after training was completed. A data-processing pipeline-the Protocol for Computational Causal Discovery in Psychiatry (PCCDP)-was applied to this data set to determine a causal model for PTS severity. A causal model of 146 variables and 345 bivariate relations was discovered. This model revealed 5 direct causes and 83 causal pathways (of four steps or less) to PTS at 12 months of police service. Direct causes included single-nucleotide polymorphisms (SNPs) for the Histidine Decarboxylase (HDC) and Mineralocorticoid Receptor (MR) genes, acoustic startle in the context of low perceived threat during training, peritraumatic distress to incident exposure during first year of service, and general symptom severity during training at 1 year of service. The application of CCD methods can determine variables and pathways related to the complex etiology of PTS in a cohort of police officers. This knowledge may inform new approaches to treatment and prevention of critical incident related PTS.

Functional analysis of RPS27 mutations and expression in melanoma.

Next-generation sequencing has enabled genetic and genomic characterization of melanoma to an unprecedent depth. However, the high mutational background plus the limited depth of coverage of whole-genome sequencing performed on cutaneous melanoma samples make the identification of novel driver mutations difficult. We sought to explore the somatic mutation portfolio in exonic and gene regulatory regions in human melanoma samples, for which we performed targeted sequencing of tumors and matched germline DNA samples from 89 melanoma patients, identifying known and novel recurrent mutations. Two recurrent mutations found in the RPS27 promoter associated with decreased RPS27 mRNA levels in vitro. Data mining and IHC analyses revealed a bimodal pattern of RPS27 expression in melanoma, with RPS27-low patients displaying worse prognosis. In vitro characterization of RPS27-high and RPS27-low melanoma cell lines, as well as loss-of-function experiments, demonstrated that high RPS27 status provides increased proliferative and invasive capacities, while low RPS27 confers survival advantage in low attachment and resistance to therapy. Additionally, we demonstrate that 10 other cancer types harbor bimodal RPS27 expression, and in those, similarly to melanoma, RPS27-low expression associates with worse clinical outcomes. RPS27 promoter mutation could thus represent a mechanism of gene expression modulation in melanoma patients, which may have prognostic and predictive implications.

Immunomodulatory germline variation associated with the development of multiple primary melanoma (MPM).

Multiple primary melanoma (MPM) has been associated with a higher 10-year mortality risk compared to patients with single primary melanoma (SPM). Given that 3-8% of patients with SPM develop additional primary melanomas, new markers predictive of MPM risk are needed. Based on the evidence that the immune system may regulate melanoma progression, we explored whether germline genetic variants controlling the expression of 41 immunomodulatory genes modulate the risk of MPM compared to patients with SPM or healthy controls. By genotyping these 41 variants in 977 melanoma patients, we found that rs2071304, linked to the expression of SPI1, was strongly associated with MPM risk reduction (OR = 0.60; 95% CI = 0.45-0.81; p = 0.0007) when compared to patients with SPM. Furthermore, we showed that rs6695772, a variant affecting expression of BATF3, is also associated with MPM-specific survival (HR = 3.42; 95% CI = 1.57-7.42; p = 0.0019). These findings provide evidence that the genetic variation in immunomodulatory pathways may contribute to the development of secondary primary melanomas and also associates with MPM survival. The study suggests that inherited host immunity may play an important role in MPM development.

Brain entropy and human intelligence: A resting-state fMRI study.

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.