SMARTSNP, an R package for fast multivariate analyses of big genomic data

Abstract Principal component analysis (PCA) is a powerful tool for the analysis of population structure, a genetic property that is essential to understand the evolutionary processes driving biological diversification and (pre)historical colonizations, migrations and extinctions. In the current era of high‐throughput sequencing technologies, population structure can be quantified from scores of genetic markers across hundreds to thousands of genomes. However, these big genomic datasets pose substantial computing and analytical challenges. We present the r package smartsnp for fast and user‐friendly computation of PCA on single‐nucleotide polymorphism (SNP) data. Inspired by the current field‐standard software EIGENSOFT, smartsnp includes appropriate SNP scaling for genetic drift and allows projection of ancient samples onto a modern genetic space while also providing permutation‐based multivariate tests for population differences in genetic diversity (both location and dispersion). Our extensive benchmarks show that smartsnp's PCA is 2–4 times faster than EIGENSOFT's SMARTPCA algorithm across a wide range of sample and SNP sizes. All four smartsnp functions (smart_pca, smart_permanova, smart_permdisp and smart_mva) process datasets with up to 100 samples and 1 million simulated SNPs in less than 30 s and accurately recreate previously published SMARTPCA of ancient‐human and wolf genotypes. The package smartsnp provides fast and robust multivariate ordination and hypothesis testing for big genomic data that is also suitable for ancient and low‐coverage modern DNA. The simple implementation should appeal to biological conservation, evolutionary, ecological and (palaeo)genomic researchers, and be useful for phenotype, ancestry and lineage studies

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data

Far Ultraviolet Spectra of a Non-Radiative Shock Wave in the Cygnus Loop

Spatial and spectral profiles of O VI emission behind a shock wave on the northern edge of the Cygnus Loop were obtained with the FUSE satellite. The velocity width of the narrowest O VI profile places a tight constraint on the electron-ion and ion-ion thermal equilibration in this 350 $\rm km s^{-1}$ collisionless shock. Unlike faster shocks in SN1006 and in the heliosphere, this shock brings oxygen ions and protons to within a factor of 2.5 of the same temperature. Comparison with other shocks suggests that shock speed, rather than Alfv\'{e}n Mach number, may control the degree of thermal equilibration. We combine the O VI observations with a low resolution far UV spectrum from HUT, an H$\alpha$ image and ROSAT PSPC X-ray data to constrain the pre-shock density and the structure along the line-of-sight. As part of this effort, we model the effects of resonance scattering of O VI photons within the shocked gas and compute time-dependent ionization models of the X-ray emissivity. Resonance scattering affects the O VI intensities at the factor of 2 level, and the soft spectrum of the X-ray rim can be mostly attributed to departures from ionization equilibrium. The pre-shock density is about twice the canonical value for the Cygnus Loop X-ray emitting shocks.Comment: To appear in Astrophysical Journal, Vol. 584, Feb. 20 200

Conditional Mutual Information Maps as Descriptors of Net Connectivity Levels in the Brain

There is a growing interest in finding ways to summarize the local connectivity properties of the brain through single brain maps. Here we propose a method based on the conditional mutual information (CMI) in the frequency domain. CMI maps quantify the amount of non-redundant covariability between each site and all others in the rest of the brain, partialling out the joint variability due to gross physiological noise. Average maps from a sample of 45 healthy individuals scanned in the resting state show a clear and symmetric pattern of connectivity maxima in several regions of cortex, including prefrontal, orbitofrontal, lateral–parietal, and midline default mode network components; and in subcortical nuclei, including the amygdala, thalamus, and basal ganglia. Such cortical and subcortical hotspots of functional connectivity were more clearly evident at lower frequencies (0.02–0.1 Hz) than at higher frequencies (0.1–0.2 Hz) of endogenous oscillation. CMI mapping can also be easily applied to perform group analyses. This is exemplified by exploring effects of normal aging on CMI in a sample of healthy controls and by investigating correlations between CMI and positive psychotic symptom scores in a sample of 40 schizophrenic patients. Both the normative aging and schizophrenia studies reveal functional connectivity trends that converge with reported findings from other studies, thus giving further support to the validity of the proposed method

Multivariate brain functional connectivity through regularized estimators

Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models

Autobiographical memory and default mode network function in schizophrenia : an fMRI study

The brain functional correlates of autobiographical recall are well established, but have been little studied in schizophrenia. Additionally, autobiographical memory is one of a small number of cognitive tasks that activates rather than de-activates the default mode network, which has been found to be dysfunctional in this disorder. Twenty-seven schizophrenic patients and 30 healthy controls underwent functional magnetic resonance imaging while viewing cue words that evoked autobiographical memories. Control conditions included both non-memory-evoking cues and a low level baseline (cross fixation). Compared to both non-memory evoking cues and low level baseline, autobiographical recall was associated with activation in default mode network regions in the controls including the medial frontal cortex, the posterior cingulate cortex and the hippocampus, as well as other areas. Clusters of de-activation were seen outside the default mode network. There were no activation differences between the schizophrenic patients and the controls, but the patients showed clusters of failure of de-activation in non-default mode network regions. According to this study, patients with schizophrenia show intact activation of the default mode network and other regions associated with recall of autobiographical memories. The finding of failure of de-activation outside the network suggests that schizophrenia may be associated with a general difficulty in de-activation rather than dysfunction of the default mode network per se

Abnormalities in gray matter volume in patients with borderline personality disorder and their relation to lifetime depression: A VBM study

Background Structural imaging studies of borderline personality disorder (BPD) have found regions of reduced cortical volume, but these have varied considerably across studies. Reduced hippocampus and amygdala volume have also been a regular finding in studies using conventional volumetric measurement. How far comorbid major depression, which is common in BPD and can also affect in brain structure, influences the findings is not clear. Methods Seventy-six women with BPD and 76 matched controls were examined using whole-brain voxel-based morphometry (VBM). The hippocampus and amygdala were also measured, using both conventional volume measurement and VBM within a mask restricted to these two subcortical structures. Lifetime history of major depression was assessed using structured psychiatric interview. Results At a threshold of p = 0.05 corrected, the BPD patients showed clusters of volume reduction in the dorsolateral prefrontal cortex bilaterally and in the pregenual/subgenual medial frontal cortex. There was no evidence of volume reductions in the hippocampus or amygdala, either on conventional volumetry or using VBM masked to these regions. Instead there was evidence of right-sided enlargement of these structures. No significant structural differences were found between patients with and without lifetime major depression. Conclusions According to this study, BPD is characterized by a restricted pattern of cortical volume reduction involving the dorsolateral frontal cortex and the medial frontal cortex, both areas of potential relevance for the clinical features of the disorder. Previous findings concerning reduced hippocampus and amygdala volume in the disorder are not supported. Brain structural findings in BPD do not appear to be explainable on the basis of history of associated lifetime major depression

NRN1 Gene as a Potential Marker of Early-Onset Schizophrenia: Evidence from Genetic and Neuroimaging Approaches

Included in the neurotrophins family, the Neuritin 1 gene (NRN1) has emerged as an attractive candidate gene for schizophrenia (SZ) since it has been associated with the risk for the disorder and general cognitive performance. In this work, we aimed to further investigate the association of NRN1 with SZ by exploring its role on age at onset and its brain activity correlates. First, we developed two genetic association analyses using a family-based sample (80 early-onset (EO) trios (offspring onset ≤ 18 years) and 71 adult-onset (AO) trios) and an independent case control sample (120 healthy subjects (HS), 87 EO and 138 AO patients). Second, we explored the effect of NRN1 on brain activity during a working memory task (N-back task; 39 HS, 39 EO and 39 AO; matched by age, sex and estimated IQ). Different haplotypes encompassing the same three Single Nucleotide Polymorphisms(SNPs, rs3763180 rs10484320 rs4960155) were associated with EO in the two samples (GCT, TCC and GTT). Besides, the GTT haplotype was associated with worse N-back task performance in EO and was linked to an inefficient dorsolateral prefrontal cortex activity in subjects with EO compared to HS. Our results show convergent evidence on the NRN1 association with EO both from genetic and neuroimaging approaches, highlighting the role of neurotrophins in the pathophysiology of SZ

Brain imaging correlates of self- and other-reflection in schizophrenia

An alteration in self/other differentiation has been proposed as a basis for several symptoms in schizophrenia, including delusions of reference and social functioning deficits. Dysfunction of the right temporo-parietal junction (TPJ), a region linked with social cognition, has been proposed as the basis of this alteration. However, imaging studies of self- and other-processing in schizophrenia have shown, so far, inconsistent results. Patients with schizophrenia and healthy controls underwent fMRI scanning while performing a task with three conditions: self-reflection, other-reflection and semantic processing. Both groups activated similar brain regions for self- and other-reflection compared to semantic processing, including the medial prefrontal cortex, the precuneus and the TPJ. Compared to healthy subjects, patients hyperactivated the left lateral frontal cortex during self- and other-reflection. In other-reflection, compared to self-reflection, patients failed to increase right TPJ activity. Altered activity in the right TPJ supports a disturbance in self/other differentiation in schizophrenia, which could be linked with psychotic symptoms and affect social functioning in patients. Hyperactivity of the lateral frontal cortex for self- and other-reflection suggests the presence of greater cognitive demand to perform the task in the patient group