PEAR: PEriodic And fixed Rank separation for fast fMRI

In functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial-temporal resolution trade-off and increase statistical robustness through increased degrees-of-freedom. High quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements. We decompose the fMRI signal into a component which a has fixed rank and a component consisting of a sum of periodic signals which is sparse in the temporal Fourier domain. Data reconstruction is performed by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI. Experimental results include purely synthetic simulation, a simulation with real timecourses and retrospective undersampling of a real fMRI dataset. Evaluation was performed both quantitatively and visually versus ground truth, comparing PEAR to two additional recent methods for fMRI reconstruction from undersampled measurements. Results demonstrate PEAR's improvement in estimating the timecourses and activation maps versus the methods compared against at acceleration ratios of R=8,16 (for simulated data) and R=6.66,10 (for real data). PEAR results in reconstruction with higher fidelity than when using a fixed-rank based model or a conventional Low-rank+Sparse algorithm. We have shown that splitting the functional information between the components leads to better modeling of fMRI, over state-of-the-art methods

Coupled Dictionary Learning for Multi-contrast MRI Reconstruction

Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and Fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled k-space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing k-space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the k-space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variety of sampling schemes, demonstrate that CDLMRI is capable of capturing structural dependencies between different contrasts. The learned priors indicate notable advantages in multi-contrast MR imaging and promising applications in quantitative MR imaging such as MR fingerprinting

Alcohol metabolizing genes and alcohol phenotypes in an Israeli household sample

BACKGROUND: Alcohol dehydrogenase 1B and 1C (ADH1B and ADH1C) variants have been robustly associated with alcohol phenotypes in East Asian populations, but less so in non-Asian populations where prevalence of the most protective ADH1B allele is low (generally <5%). Further, the joint effects of ADH1B and ADH1C on alcohol phenotypes have been unclear. Therefore, we tested the independent and joint effects of ADH1B and ADH1C on alcohol phenotypes in an Israeli sample, with higher prevalence of the most protective ADH1B allele than other non-Asian populations. METHODS: A structured interview assessed lifetime drinking and alcohol use disorders (AUDs) in adult Israeli household residents. Four single nucleotide polymorphisms (SNPs) were genotyped: ADH1B (rs1229984, rs1229982, and rs1159918) and ADH1C (rs698). Regression analysis examined the association between alcohol phenotypes and each SNP (absence vs. presence of the protective allele) as well as rs698/rs1229984 diplotypes (also indicating absence or presence of protective alleles) in lifetime drinkers (n = 1,129). RESULTS: Lack of the ADH1B rs1229984 protective allele was significantly associated with consumption- and AUD-related phenotypes (OR = 1.77 for AUD; OR = 1.83 for risk drinking), while lack of the ADH1C rs698 protective allele was significantly associated with AUD-related phenotypes (OR = 2.32 for AUD). Diplotype analysis indicated that jointly ADH1B and ADH1C significantly influenced AUD-related phenotypes. For example, among those without protective alleles for ADH1B or ADH1C, OR for AUD was 1.87 as compared to those without the protective allele for ADH1B only and was 3.16 as compared to those with protective alleles for both ADH1B and ADH1C. CONCLUSIONS: This study adds support for the relationship of ADH1B and ADH1C and alcohol phenotypes in non-Asians. Further, these findings help clarify the mixed results from previous studies by showing that ADH1B and ADH1C jointly effect AUDs, but not consumption. Studies of the association between alcohol phenotypes and either ADH1B or ADH1C alone may employ an oversimplified model, masking relevant information

Coupled Dictionary Learning for Multi-contrast MRI Reconstruction

Medical imaging tasks often involve multiple contrasts, such as T1-and T2-weighted magnetic resonance imaging (MRI) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities. In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast. Our approach consists of three stages: coupled dictionary learning, coupled sparse denoising, and k-space consistency enforcing. The first stage learns a group of dictionaries that capture correlations among multiple contrasts. By capitalizing on the learned adaptive dictionaries, the second stage performs joint sparse coding to denoise the corrupted target image with the aid of a guidance contrast. The third stage enforces consistency between the denoised image and the measurements in the k-space domain. Numerical experiments on the retrospective under-sampling of clinical MR images demonstrate that incorporating additional guidance contrast via our design improves MRI reconstruction, compared to state-of-the-art approaches

Monoamines, BDNF, Dehydroepiandrosterone, DHEA-Sulfate, and Childhood Depression—An Animal Model Study

Basal levels of monoamines and DHEA in four main limbic brain regions were measured in prepubertal Wistar Kyoto (WKY) rats (a putative animal model of childhood depression). Basal levels of “Brain-Derived Neurotrophic Factor (BDNF)” were also determined in two regions in the hippocampus, compared with Wistar strain controls. In the second phase, we examined the responsiveness of prepubertal WKY rats to different types of chronic antidepressant treatments: Fluoxetine, Desipramine, and dehydroepiandrosterone sulfate (DHEAS). WKY prepubertal rats exhibited different monoamine levels in the limbic system, reduced DHEA levels in the VTA and lower levels of BDNF in the hippocampus CA3 region compared to controls. In prepubertal WKY rats, only treatment with DHEAS produced a statistically significant decrease in immobility, compared to saline-administered controls in the forced swim test. Wistar controls were not affected by any antidepressant. The results imply that DHEA(S) and BDNF may be involved in the pathophysiology and pharmacotherapy of childhood depression

Alcohol consumption mediates the relationship between ADH1B and DSM-IV alcohol use disorder and criteria

OBJECTIVE: A single nucleotide variation in the alcohol dehydrogenase 1B (ADH1B) gene, rs1229984, produces an ADH1B enzyme with faster acetaldehyde production. This protective variant is associated with lower alcohol consumption and lower risk for alcohol use disorders (AUDs). Based on the premise that faster ADH1B kinetics decreases alcohol consumption, we formally tested if the association between ADH1B variant rs1229984 and AUDs occurs through consumption. We also tested whether the association between rs1229984 and each of the 11 Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), AUD criteria occurs through consumption. METHOD: A total of 1,130 lifetime drinkers from an Israeli household sample were assessed with a structured interview and genotyped for rs1229984 (protective allele frequency = 0.28). Logistic regression evaluated the association between rs1229984 and each phenotype (AUDs, 11 individual DSM-IV criteria). For phenotypes significantly related to rs1229984, the effect through consumption was tested with logistic regression and bootstrapping. RESULTS: ADH1B rs1229984 was significantly associated with AUDs and six criteria, with odds ratios ranging from 1.32 to 1.96. The effect through consumption was significant for these relationships, explaining 23%-74% of the total ADH1B effect. CONCLUSIONS: This is the first study to show that ADH1B rs1229984 is related to 6 of the 11 DSM-IV AUD criteria and that alcohol consumption explained a significant proportion of these associations and the association of ADH1B with AUDs. Better understanding of the relationship between ADH1B and the DSM-IV AUD criteria, including effects through consumption, will enhance our understanding of the etiologic model through which AUDs can occur

Non-state space: The strategic ejection of dangerous and high maintenance urban space

Some commentators have characterised so-called ‘no-go’ areas as sites in which the exercise of authority is prevented. Here we suggest that many such spaces are produced by state, policing and citizen repertoires that aim to minimise the costs and risks of engaging, supporting and servicing such spaces and their populations. In this article we locate strategies of public spending, policing and political action that offer a governing logic in which neighbourhoods are essentially subtracted from the constitution of the city. During such designations the assurances of citizenship, vitality of civic institutions and presence of policing may be partially or wholly suspended. We present a framework for the identification of such strategies in which these forms of social, political and spatial exiting are described as being autotomic in nature – spaces that are ejected in order to avoid losses or further damage to the body politic of the city in ways akin to the response of certain animals that protect themselves from predation by shedding a limb or body part. This term adds force and depth to assessments of the ways in which both temporary and more sustained exits by policing, management and state servicing are designed in order to avoid responsibility over, or engagement with, spaces that are deemed a threat in order to maintain the integrity of the remaining, included city