Theoria

I heal through meditation. Upon turning 13 years old, I began the practice of meditation under the tutelage of Hindu priests in Chennai, India. The expectation of this experience was to learn to clear out all distractions, focus intently on breathing and recite certain passages of Scripture. At the time, meditation involved sitting cross-legged and reciting certain phrases 1008 times. The hot, muggy weather and mosquitos that plagued summers in Chennai made this task even harder. While difficult and seemingly purposeless at first, I began to appreciate and better understand the power that meditative practices provided in the following summer months. Specifically, I learned to become more cognizant of the daily avalanche of extraneous thoughts that barreled through my head, which allowed me to better understand and control my subconscious mind. As I grew older, distractions in the form of friends, school, and sports took up a bulk of my time and my once daily meditative practices became increasingly infrequent. During my college years, I hardly meditated at all. Interestingly enough, the anticipatory stress induced by Block 1 exams led me to revisit the idea of meditation. I began using it once again, but now as tool to cope with the rigors and anxiety of medical school. “You should sit in meditation for twenty minutes every day — unless you’re too busy. Then you should sit for an hour.” -Buddhist prover

A Sparse Reconstruction Algorithm for Multi-Frequency Radio Images

In radio interferometry, every pair of antennas in an array defines one sampling point in the Fourier domain of the sky image. By combining information from different wavelengths, sample coverage - and therefore reconstruction quality - can be increased. However, the images at different wavelengths can be dramatically dissimilar; this fact must be taken into account when reconstructing multi-frequency images. In this paper, we present a novel reconstruction algorithm based on the assumption that the spectrum is continuous. In contrast to prior work, we allow for sparse deviations from this assumption: this allows, for example, for accurate reconstruction of line spectra superimposed on a continuum. Using simulated measurements on synthetic multi-frequency images, we show that the proposed approach provides significant improvements over a comparable method based solely on a continuity assumption

Genetic variation in CFH predicts phenytoin-induced maculopapular exanthema in European-descent patients

Objective To characterize, among European and Han Chinese populations, the genetic predictors of maculopapular exanthema (MPE), a cutaneous adverse drug reaction common to antiepileptic drugs. Methods We conducted a case-control genome-wide association study of autosomal genotypes, including Class I and II human leukocyte antigen (HLA) alleles, in 323 cases and 1,321 drug-tolerant controls from epilepsy cohorts of northern European and Han Chinese descent. Results from each cohort were meta-analyzed. Results We report an association between a rare variant in the complement factor H–related 4 (CFHR4) gene and phenytoin-induced MPE in Europeans (p = 4.5 × 10–11; odds ratio [95% confidence interval] 7 [3.2–16]). This variant is in complete linkage disequilibrium with a missense variant (N1050Y) in the complement factor H (CFH) gene. In addition, our results reinforce the association between HLA-A*31:01 and carbamazepine hypersensitivity. We did not identify significant genetic associations with MPE among Han Chinese patients. Conclusions The identification of genetic predictors of MPE in CFHR4 and CFH, members of the complement factor H–related protein family, suggest a new link between regulation of the complement system alternative pathway and phenytoin-induced hypersensitivity in European-ancestral patients

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment