Faster Family-wise Error Control for Neuroimaging with a Parametric Bootstrap

In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most common family-wise error (FWE) controlling procedures in imaging, which rely on classical mathematical inequalities or Gaussian random field theory, yield FWE rates that are far from the nominal level. Depending on the approach used, the FWER can be exceedingly small or grossly inflated. Given the widespread use of neuroimaging as a tool for understanding neurological and psychiatric disorders, it is imperative that reliable multiple testing procedures are available. To our knowledge, only permutation joint testing procedures have been shown to reliably control the FWER at the nominal level. However, these procedures are computationally intensive due to the increasingly available large sample sizes and dimensionality of the images, and analyses can take days to complete. Here, we develop a parametric bootstrap joint testing procedure. The parametric bootstrap procedure works directly with the test statistics, which leads to much faster estimation of adjusted \emph{p}-values than resampling-based procedures while reliably controlling the FWER in sample sizes available in many neuroimaging studies. We demonstrate that the procedure controls the FWER in finite samples using simulations, and present region- and voxel-wise analyses to test for sex differences in developmental trajectories of cerebral blood flow

CAD in mammography: lesion-level versus case-level analysis of the effects of prompts on human decisions

Object: To understand decision processes in CAD-supported breast screening by analysing how prompts affect readers’ judgements of individual mammographic features (lesions). To this end we analysed hitherto unexamined details of reports completed by mammogram readers in an earlier evaluation of a CAD tool. Material and methods: Assessments of lesions were extracted from 5,839 reports for 59 cancer cases. Statistical analyses of these data focused on what features readers considered when recalling a cancer case and how readers reacted to CAD prompts. Results: About 13.5% of recall decisions were found to be caused by responses to features other than those indicating actual cancer. Effects of CAD: lesions were more likely to be examined if prompted; the presence of a prompt on a cancer increased the probability of both detection and recall especially for less accurate readers in subtler cases; lack of prompts made cancer features less likely to be detected; false prompts made non-cancer features more likely to be classified as cancer. Conclusion: The apparent lack of impact reported for CAD in some studies is plausibly due to CAD systematically affecting readers’ identification of individual features, in a beneficial way for certain combinations of readers and features and a damaging way for others. Mammogram readers do not ignore prompts. Methodologically, assessing CAD by numbers of recalled cancer cases may be misleading

Genetic Overlap Profiles of Cognitive Ability in Psychotic and Affective Illnesses::A Multi-Site Study of Multiplex Pedigrees

BACKGROUND: Cognitive impairment is a key feature of psychiatric illness, making cognition an important tool for exploring of the genetics of illness risk. It remains unclear which measures should be prioritized in pleiotropy-guided research. Here, we generate profiles of genetic overlap between psychotic and affective disorders and cognitive measures in Caucasian and Hispanic groups. METHODS: Data were from four samples of extended pedigrees (N = 3046). Coefficient of relationship analyses were used to estimate genetic overlap between illness risk and cognitive ability. Results were meta-analyzed. FINDINGS: Psychosis was characterized by cognitive impairments on all measures with a generalized profile of genetic overlap. General cognitive ability shared greatest genetic overlap with psychosis risk (average Endophenotype Ranking Value (ERV) across samples from a random-effects meta-analysis = 0.32) followed by Verbal Memory (ERV = 0.24), Executive Function (ERV = 0.22), and Working Memory (ERV = 0.21). For bipolar disorder, there was genetic overlap with Processing Speed (ERV = 0.05) and Verbal Memory (ERV = 0.11), but these were confined to select samples. Major depression was characterized by enhanced Working and Face Memory performance, as reflected in significant genetic overlap in two samples. INTERPRETATION: There is substantial genetic overlap between risk for psychosis and a range of cognitive abilities (including general intelligence). Most of these effects are largely stable across of ascertainment strategy and ethnicity. Genetic overlap between affective disorders and cognition, on the other hand, tend to be specific to ascertainment strategy, ethnicity, and cognitive test battery

Disrupted anatomic networks in the 22q11.2 deletion syndrome

AbstractThe 22q11.2 deletion syndrome (22q11DS) is an uncommon genetic disorder with an increased risk of psychosis. Although the neural substrates of psychosis and schizophrenia are not well understood, aberrations in cortical networks represent intriguing potential mechanisms. Investigations of anatomic networks within 22q11DS are sparse. We investigated group differences in anatomic network structure in 48 individuals with 22q11DS and 370 typically developing controls by analyzing covariance patterns in cortical thickness among 68 regions of interest using graph theoretical models. Subjects with 22q11DS had less robust geographic organization relative to the control group, particularly in the occipital and parietal lobes. Multiple global graph theoretical statistics were decreased in 22q11DS. These results are consistent with prior studies demonstrating decreased connectivity in 22q11DS using other neuroimaging methodologies

d=3 Bosonic Vector Models Coupled to Chern-Simons Gauge Theories

We study three dimensional O(N)_k and U(N)_k Chern-Simons theories coupled to a scalar field in the fundamental representation, in the large N limit. For infinite k this is just the singlet sector of the O(N) (U(N)) vector model, which is conjectured to be dual to Vasiliev's higher spin gravity theory on AdS_4. For large k and N we obtain a parity-breaking deformation of this theory, controlled by the 't Hooft coupling lambda = 4 \pi N / k. For infinite N we argue (and show explicitly at two-loop order) that the theories with finite lambda are conformally invariant, and also have an exactly marginal (\phi^2)^3 deformation. For large but finite N and small 't Hooft coupling lambda, we show that there is still a line of fixed points parameterized by the 't Hooft coupling lambda. We show that, at infinite N, the interacting non-parity-invariant theory with finite lambda has the same spectrum of primary operators as the free theory, consisting of an infinite tower of conserved higher-spin currents and a scalar operator with scaling dimension \Delta=1; however, the correlation functions of these operators do depend on lambda. Our results suggest that there should exist a family of higher spin gravity theories, parameterized by lambda, and continuously connected to Vasiliev's theory. For finite N the higher spin currents are not conserved.Comment: 34 pages, 29 figures. v2: added reference

Genetic influences on externalizing psychopathology overlap with cognitive functioning and show developmental variation

Background: Questions remain regarding whether genetic influences on early life psychopathology overlap with cognition and show developmental variation. Methods: Using data from 9,421 individuals aged 8-21 from the Philadelphia Neurodevelopmental Cohort, factors of psychopathology were generated using a bifactor model of item-level data from a psychiatric interview. Five orthogonal factors were generated: anxious-misery (mood and anxiety), externalizing (attention deficit hyperactivity and conduct disorder), fear (phobias), psychosis-spectrum, and a general factor. Genetic analyses were conducted on a subsample of 4,662 individuals of European American ancestry. A genetic relatedness matrix was used to estimate heritability of these factors, and genetic correlations with executive function, episodic memory, complex reasoning, social cognition, motor speed, and general cognitive ability. Gene × Age analyses determined whether genetic influences on these factors show developmental variation. Results: Externalizing was heritable (h2 = 0.46, p = 1 × 10-6), but not anxious-misery (h2 = 0.09, p = 0.183), fear (h2 = 0.04, p = 0.337), psychosis-spectrum (h2 = 0.00, p = 0.494), or general psychopathology (h2 = 0.21, p = 0.040). Externalizing showed genetic overlap with face memory (ρg = -0.412, p = 0.004), verbal reasoning (ρg = -0.485, p = 0.001), spatial reasoning (ρg = -0.426, p = 0.010), motor speed (ρg = 0.659, p = 1x10-4), verbal knowledge (ρg = -0.314, p = 0.002), and general cognitive ability (g)(ρg = -0.394, p = 0.002). Gene × Age analyses revealed decreasing genetic variance (γg = -0.146, p = 0.004) and increasing environmental variance (γe = 0.059, p = 0.009) on externalizing. Conclusions: Cognitive impairment may be a useful endophenotype of externalizing psychopathology and, therefore, help elucidate its pathophysiological underpinnings. Decreasing genetic variance suggests that gene discovery efforts may be more fruitful in children than adolescents or young adults