The effects of hemodynamic lag on functional connectivity and behavior after stroke

Stroke disrupts the brain's vascular supply, not only within but also outside areas of infarction. We investigated temporal delays (lag) in resting state functional magnetic resonance imaging signals in 130 stroke patients scanned two weeks, three months and 12 months post stroke onset. Thirty controls were scanned twice at an interval of three months. Hemodynamic lag was determined using cross-correlation with the global gray matter signal. Behavioral performance in multiple domains was assessed in all patients. Regional cerebral blood flow and carotid patency were assessed in subsets of the cohort using arterial spin labeling and carotid Doppler ultrasonography. Significant hemodynamic lag was observed in 30% of stroke patients sub-acutely. Approximately 10% of patients showed lag at one-year post-stroke. Hemodynamic lag corresponded to gross aberrancy in functional connectivity measures, performance deficits in multiple domains and local and global perfusion deficits. Correcting for lag partially normalized abnormalities in measured functional connectivity. Yet post-stroke FC-behavior relationships in the motor and attention systems persisted even after hemodynamic delays were corrected. Resting state fMRI can reliably identify areas of hemodynamic delay following stroke. Our data reveal that hemodynamic delay is common sub-acutely, alters functional connectivity, and may be of clinical importance

Comparability of Functional MRI Response in Young and Old During Inhibition

When using fMRI to study age-related cognitive changes, it is important to establish the integrity of the hemodynamic response because, potentially, it can be affected by age and disease. However, there have been few attempts to document such integrity and no attempts using higher cognitive rather than perceptual or motor tasks. We used fMRI with 28 healthy young and older adults on an inhibitory control task. Although older and young adults differed in task performance and activation patterns, they had comparable hemodynamic responses. We conclude that activation during cognitive inhibition, which was predominantly increased in elders, was not due to vascular confounds or specific changes in hemodynamic coupling

Goal-directed therapy in intraoperative fluid and hemodynamic management.

Intraoperative fluid management is pivotal to the outcome and success of surgery, especially in high-risk procedures. Empirical formula and invasive static monitoring have been traditionally used to guide intraoperative fluid management and assess volume status. With the awareness of the potential complications of invasive procedures and the poor reliability of these methods as indicators of volume status, we present a case scenario of a patient who underwent major abdominal surgery as an example to discuss how the use of minimally invasive dynamic monitoring may guide intraoperative fluid therapy

Estimating effective connectivity in linear brain network models

Contemporary neuroscience has embraced network science to study the complex and self-organized structure of the human brain; one of the main outstanding issues is that of inferring from measure data, chiefly functional Magnetic Resonance Imaging (fMRI), the so-called effective connectivity in brain networks, that is the existing interactions among neuronal populations. This inverse problem is complicated by the fact that the BOLD (Blood Oxygenation Level Dependent) signal measured by fMRI represent a dynamic and nonlinear transformation (the hemodynamic response) of neuronal activity. In this paper, we consider resting state (rs) fMRI data; building upon a linear population model of the BOLD signal and a stochastic linear DCM model, the model parameters are estimated through an EM-type iterative procedure, which alternately estimates the neuronal activity by means of the Rauch-Tung-Striebel (RTS) smoother, updates the connections among neuronal states and refines the parameters of the hemodynamic model; sparsity in the interconnection structure is favoured using an iteratively reweighting scheme. Experimental results using rs-fMRI data are shown demonstrating the effectiveness of our approach and comparison with state of the art routines (SPM12 toolbox) is provided

HRF estimation improves sensitivity of fMRI encoding and decoding models

Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal. The general linear model (GLM) allows to estimate the activation from a design matrix and a fixed hemodynamic response function (HRF). However, the HRF is known to vary substantially between subjects and brain regions. In this paper, we propose a model for jointly estimating the hemodynamic response function (HRF) and the activation patterns via a low-rank representation of task effects.This model is based on the linearity assumption behind the GLM and can be computed using standard gradient-based solvers. We use the activation patterns computed by our model as input data for encoding and decoding studies and report performance improvement in both settings.Comment: 3nd International Workshop on Pattern Recognition in NeuroImaging (2013

A New Approach in Risk Stratification by Coronary CT Angiography.

For a decade, coronary computed tomographic angiography (CCTA) has been used as a promising noninvasive modality for the assessment of coronary artery disease (CAD) as well as cardiovascular risks. CCTA can provide more information incorporating the presence, extent, and severity of CAD; coronary plaque burden; and characteristics that highly correlate with those on invasive coronary angiography. Moreover, recent techniques of CCTA allow assessing hemodynamic significance of CAD. CCTA may be potentially used as a substitute for other invasive or noninvasive modalities. This review summarizes risk stratification by anatomical and hemodynamic information of CAD, coronary plaque characteristics, and burden observed on CCTA

The failing heart under stress: echocardiography is an essential monitoring tool in the Intensive Care Unit

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Regularized brain reading with shrinkage and smoothing

Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high dimensional. Hence, direct estimates are inherently imprecise and call for regularization. We compare a suite of approaches which regularize via shrinkage: ridge regression, the elastic net (a generalization of ridge regression and the lasso), and a hierarchical Bayesian model based on small area estimation (SAE). We contrast regularization with spatial smoothing and combinations of smoothing and shrinkage. All methods are tested on functional magnetic resonance imaging (fMRI) data from multiple subjects participating in two different experiments related to reading, for both predicting neural response to stimuli and decoding stimuli from responses. Interestingly, when the regularization parameters are chosen by cross-validation independently for every voxel, low/high regularization is chosen in voxels where the classification accuracy is high/low, indicating that the regularization intensity is a good tool for identification of relevant voxels for the cognitive task. Surprisingly, all the regularization methods work about equally well, suggesting that beating basic smoothing and shrinkage will take not only clever methods, but also careful modeling.Comment: Published at http://dx.doi.org/10.1214/15-AOAS837 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Simultaneous Robotic Manipulation and Functional Magnetic Resonance Imaging: Feasibility in Children with Autism Spectrum Disorders

An unanswered question concerning the neural basis of autism spectrum disorders (ASD) is how sensorimotor deficits in individuals with ASD are related to abnormalities of brain function. We previously described a robotic joystick and video game system that allows us to record functional magnetic resonance images (FMRI) while adult humans make goal- directed wrist motions. We anticipated several challenges in extending this approach to studying goal-directed behaviors in children with ASD and in typically developing (TYP) children. In particular we were concerned that children with autism may express increased levels of anxiety as compared to typically developing children due to the loud sounds and small enclosed space of the MRI scanner. We also were concerned that both groups of children might become restless during testing, leading to an unacceptable amount of head movement. Here we performed a pilot study evaluating the extent to which autistic and typically developing children exhibit anxiety during our experimental protocol as well as their ability to comply with task instructions. Our experimental controls were successful in minimizing group differences in drop-out due to anxiety. Kinematic performance and head motion also were similar across groups. Both groups of children engaged cortical regions (frontal, parietal, temporal, occipital) while making goal- directed movements. In addition, the ASD group exhibited task- related correlations in subcortical regions (cerebellum, thalamus), whereas correlations in the TYP group did not reach statistical significance in subcortical regions. Four distinct regions in frontal cortex showed a significant group difference such that TYP children exhibited positive correlations between the hemodynamic response and movement, whereas children with ASD exhibited negative correlations. These findings demonstrate feasibility of simultaneous application of robotic manipulation and functional imaging to study goal-directed motor behaviors in autistic and typically developing children. The findings also suggest the presence of marked changes in neural activation during a sensorimotor task requiring goal- directed movement

One-year outcomes after transcatheter insertion of an interatrial shunt device for the management of heart failure with preserved ejection fraction

Background—Heart failure with preserved ejection fraction has a complex pathophysiology and remains a therapeutic challenge. Elevated left atrial pressure, particularly during exercise, is a key contributor to morbidity and mortality. Preliminary analyses have demonstrated that a novel interatrial septal shunt device that allows shunting to reduce the left atrial pressure provides clinical and hemodynamic benefit at 6 months. Given the chronicity of heart failure with preserved ejection fraction, evidence of longer-term benefit is required. Methods and Results—Patients (n=64) with left ventricular ejection fraction ≥40%, New York Heart Association class II–IV, elevated pulmonary capillary wedge pressure (≥15 mm Hg at rest or ≥25 mm Hg during supine bicycle exercise) participated in the open-label study of the interatrial septal shunt device. One year after interatrial septal shunt device implantation, there were sustained improvements in New York Heart Association class (P<0.001), quality of life (Minnesota Living with Heart Failure score, P<0.001), and 6-minute walk distance (P<0.01). Echocardiography showed a small, stable reduction in left ventricular end-diastolic volume index (P<0.001), with a concomitant small stable increase in the right ventricular end-diastolic volume index (P<0.001). Invasive hemodynamic studies performed in a subset of patients demonstrated a sustained reduction in the workload corrected exercise pulmonary capillary wedge pressure (P<0.01). Survival at 1 year was 95%, and there was no evidence of device-related complications. Conclusions—These results provide evidence of safety and sustained clinical benefit in heart failure with preserved ejection fraction patients 1 year after interatrial septal shunt device implantation. Randomized, blinded studies are underway to confirm these observations