A review of fMRI simulation studies

Simulation studies that validate statistical techniques for fMRI data are challenging due to the complexity of the data. Therefore, it is not surprising that no common data generating process is available (i.e. several models can be found to model BOLD activation and noise). Based on a literature search, a database of simulation studies was compiled. The information in this database was analysed and critically evaluated focusing on the parameters in the simulation design, the adopted model to generate fMRI data, and on how the simulation studies are reported. Our literature analysis demonstrates that many fMRI simulation studies do not report a thorough experimental design and almost consistently ignore crucial knowledge on how fMRI data are acquired. Advice is provided on how the quality of fMRI simulation studies can be improved

Head to head comparisons of two modalities of perfusion adenosine stress echocardiography with simultaneous SPECT

<p>Abstract</p> <p>Background</p> <p>Real-time perfusion (RTP) contrast echocardiography can be used during adenosine stress echocardiography (ASE) to evaluate myocardial ischemia. We compared two different types of RTP power modulation techniques, angiomode (AM) and high-resolution grayscale (HR), with ^99mTc-tetrofosmin single-photon emission computed tomography (SPECT) for the detection of myocardial ischemia.</p> <p>Methods</p> <p>Patients with known or suspected coronary artery disease (CAD), admitted to SPECT, were prospectively invited to participate. Patients underwent RTP imaging (SONOS 5500) using AM and HR during Sonovue^®infusion, before and throughout the adenosine stress, also used for SPECT. Analysis of myocardial perfusion and wall motion by RTP-ASE were done for AM and HR at different time points, blinded to one another and to SPECT. Each segment was attributed to one of the three main coronary vessel areas of interest.</p> <p>Results</p> <p>In 50 patients, 150 coronary areas were analyzed by SPECT and RTP-ASE AM and HR. SPECT showed evidence of ischemia in 13 out of 50 patients. There was no significant difference between AM and HR in detecting ischemia (p = 0.08). The agreement for AM and HR, compared to SPECT, was 93% and 96%, with Kappa values of 0.67 and 0.75, respectively (p < 0.001).</p> <p>Conclusion</p> <p>There was no significant difference between AM and HR in correctly detecting myocardial ischemia as judged by SPECT. This suggests that different types of RTP modalities give comparable data during RTP-ASE in patients with known or suspected CAD.</p

Quantitative detection of myocardial ischaemia by stress echocardiography; a comparison with SPECT

<p>Abstract</p> <p>Aims</p> <p>Real-time perfusion (RTP) adenosine stress echocardiography (ASE) can be used to visually evaluate myocardial ischaemia. The RTP power modulation technique angio-mode (AM), provides images for off-line perfusion quantification using Qontrast^®software, generating values of peak signal intensity (A), myocardial blood flow velocity (β) and myocardial blood flow (Axβ). By comparing rest and stress values, their respective reserve values (A-r, β-r, Axβ-r) are generated. We evaluated myocardial ischaemia by RTP-ASE Qontrast^®quantification, compared to visual perfusion evaluation with ^99mTc-tetrofosmin single-photon emission computed tomography (SPECT).</p> <p>Methods and Results</p> <p>Patients admitted to SPECT underwent RTP-ASE (SONOS 5500) using AM during Sonovue^®infusion, before and throughout adenosine stress, also used for SPECT. Visual myocardial perfusion and wall motion analysis, and Qontrast^®quantification, were blindly compared to one another and to SPECT, at different time points off-line.</p> <p>We analyzed 201 coronary territories (left anterior descendent [LAD], left circumflex [LCx] and right coronary [RCA] artery territories) in 67 patients. SPECT showed ischaemia in 18 patients and 19 territories. Receiver operator characteristics and kappa values showed significant agreement with SPECT only for β-r and Axβ-r in all segments: area under the curve 0.678 and 0.665; P < 0.001 and < 0.01, respectively. The closest agreements were seen in the LAD territory: kappa 0.442 for both β-r and Axβ-r; P < 0.01. Visual evaluation of ischaemia showed good agreement with SPECT: accuracy 93%; kappa 0.67; P < 0.001; without non-interpretable territories.</p> <p>Conclusion</p> <p>In this agreement study with SPECT, RTP-ASE Qontrast^®quantification of myocardial ischaemia was less accurate and less feasible than visual evaluation and needs further development to be clinically useful.</p

The GABA transporter 1 (SLC6A1): a novel candidate gene for anxiety disorders

Recent evidence suggests that the GABA transporter 1 (GAT-1; SLC6A1) plays a role in the pathophysiology and treatment of anxiety disorders. In order to understand the impact of genetic variation within SLC6A1 on pathological anxiety, we performed a case–control association study with anxiety disorder patients with and without syndromal panic attacks. Using the method of sequential addition of cases, we found that polymorphisms in the 5′ flanking region of SLC6A1 are highly associated with anxiety disorders when considering the severity of syndromal panic attacks as phenotype covariate. Analysing the effect size of the association, we observed a constant increase in the odds ratio for disease susceptibility with an increase in panic severity (OR ~ 2.5 in severely affected patients). Nominally significant association effects were observed considering the entire patient sample. These data indicate a high load of genetic variance within SLC6A1 on pathological anxiety and highlight GAT-1 as a promising target for treatment of anxiety disorders with panic symptoms

Genome-wide association study of panic disorder reveals genetic overlap with neuroticism and depression

Panic disorder (PD) has a lifetime prevalence of 2-4% and heritability estimates of 40%. The contributory genetic variants remain largely unknown, with few and inconsistent loci having been reported. The present report describes the largest genome-wide association study (GWAS) of PD to date comprising genome-wide genotype data of 2248 clinically well-characterized PD patients and 7992 ethnically matched controls. The samples originated from four European countries (Denmark, Estonia, Germany, and Sweden). Standard GWAS quality control procedures were conducted on each individual dataset, and imputation was performed using the 1000 Genomes Project reference panel. A meta-analysis was then performed using the Ricopili pipeline. No genome-wide significant locus was identified. Leave-one-out analyses generated highly significant polygenic risk scores (PRS) (explained variance of up to 2.6%). Linkage disequilibrium (LD) score regression analysis of the GWAS data showed that the estimated heritability for PD was 28.0-34.2%. After correction for multiple testing, a significant genetic correlation was found between PD and major depressive disorder, depressive symptoms, and neuroticism. A total of 255 single-nucleotide polymorphisms (SNPs) with p < 1 × 10-4 were followed up in an independent sample of 2408 PD patients and 228,470 controls from Denmark, Iceland and the Netherlands. In the combined analysis, SNP rs144783209 showed the strongest association with PD (pcomb = 3.10  × 10-7). Sign tests revealed a significant enrichment of SNPs with a discovery p-value of <0.0001 in the combined follow up cohort (p = 0.048). The present integrative analysis represents a major step towards the elucidation of the genetic susceptibility to PD

Resisting Sleep Pressure:Impact on Resting State Functional Network Connectivity

In today's 24/7 society, sleep restriction is a common phenomenon which leads to increased levels of sleep pressure in daily life. However, the magnitude and extent of impairment of brain functioning due to increased sleep pressure is still not completely understood. Resting state network (RSN) analyses have become increasingly popular because they allow us to investigate brain activity patterns in the absence of a specific task and to identify changes under different levels of vigilance (e.g. due to increased sleep pressure). RSNs are commonly derived from BOLD fMRI signals but studies progressively also employ cerebral blood flow (CBF) signals. To investigate the impact of sleep pressure on RSNs, we examined RSNs of participants under high (19 h awake) and normal (10 h awake) sleep pressure with three imaging modalities (arterial spin labeling, BOLD, pseudo BOLD) while providing confirmation of vigilance states in most conditions. We demonstrated that CBF and pseudo BOLD signals (measured with arterial spin labeling) are suited to derive independent component analysis based RSNs. The spatial map differences of these RSNs were rather small, suggesting a strong biological substrate underlying these networks. Interestingly, increased sleep pressure, namely longer time awake, specifically changed the functional network connectivity (FNC) between RSNs. In summary, all FNCs of the default mode network with any other network or component showed increasing effects as a function of increased 'time awake'. All other FNCs became more anti-correlated with increased 'time awake'. The sensorimotor networks were the only ones who showed a within network change of FNC, namely decreased connectivity as function of 'time awake'. These specific changes of FNC could reflect both compensatory mechanisms aiming to fight sleep as well as a first reduction of consciousness while becoming drowsy. We think that the specific changes observed in functional network connectivity could imply an impairment of information transfer between the affected RSNs

Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data

In recent years, state of the art brain imaging techniques like Functional Magnetic Resonance Imaging (fMRI), have raised new challenges to the statistical community, which is asked to provide new frameworks for modeling and data analysis. Here, motivated by resting state fMRI data, which can be seen as a collection of spatially dependent functional observations among brain regions, we propose a parsimonious but flexible representation of their dependence structure leveraging a Bayesian time-dependent latent factor model. Adopting an assumption of separability of the covariance structure in space and time, we are able to substantially reduce the computational cost and, at the same time, provide interpretable results. Theoretical properties of the model along with identifiability conditions are discussed. For model fitting, we propose a mcmc algorithm to enable posterior inference. We illustrate our work through an application to a dataset coming from the enkirs project, discussing the estimated covariance structure and also performing model selection along with network analysis. Our modeling is preliminary but offers ideas for developing fully Bayesian fMRI models, incorporating a plausible space and time dependence structure