Publications by Year: 2016

Short-acting β2-agonist bronchodilators are the most common medications used in treating chronic obstructive pulmonary disease (COPD). Genetic variants determining bronchodilator responsiveness (BDR) in COPD have not been identified. We performed a genome-wide association study (GWAS) of BDR in 5789 current or former smokers with COPD in one African-American and four white populations. BDR was defined as the quantitative spirometric response to inhaled β2-agonists. We combined results in a meta-analysis. In the meta-analysis, single-nucleotide polymorphisms (SNPs) in the genes KCNK1 (P=2.02 × 10(-7)) and KCNJ2 (P=1.79 × 10(-7)) were the top associations with BDR. Among African Americans, SNPs in CDH13 were significantly associated with BDR (P=5.1 × 10(-9)). A nominal association with CDH13 was identified in a gene-based analysis in all subjects. We identified suggestive association with BDR among COPD subjects for variants near two potassium channel genes (KCNK1 and KCNJ2). SNPs in CDH13 were significantly associated with BDR in African Americans.The Pharmacogenomics Journal advance online publication, 27 October 2015; doi:10.1038/tpj.2015.65.

Post-mortem studies reveal a high rate of cavum septi pellucidi (CSP) in chronic traumatic encephalopathy (CTE). It remains, however, to be determined whether or not the presence of CSP may be a potential in vivo imaging marker in populations at high risk to develop CTE. The aim of this study was to evaluate CSP in former professional American football players presenting with cognitive and behavioral symptoms compared with noncontact sports athletes. Seventy-two symptomatic former professional football players (mean age 54.53 years, standard deviation [SD] 7.97) as well as 14 former professional noncontact sports athletes (mean age 57.14 years, SD 7.35) underwent high-resolution structural 3T magnetic resonance imaging. Two raters independently evaluated the CSP, and interrater reliability was calculated. Within National Football League players, an association of CSP measures with cognitive and behavioral functioning was evaluated using a multivariate mixed effects model. The measurements of the two raters were highly correlated (CSP length: rho = 0.98; Intraclass Correlation Coefficient [ICC] 0.99; p 0.0001; septum length: rho = 0.93; ICC 0.96; p 0.0001). For presence versus absence of CSP, there was high agreement (Cohen kappa = 0.83, p 0.0001). A higher rate of CSP, a greater length of CSP, as well as a greater ratio of CSP length to septum length was found in symptomatic former professional football players compared with athlete controls. In addition, a greater length of CSP was associated with decreased performance on a list learning task (Neuropsychological Assessment Battery List A Immediate Recall, p = 0.04) and decreased test scores on a measure of estimate verbal intelligence (Wide Range Achievement Test Fourth Edition Reading Test, p = 0.02). Given the high prevalence of CSP in neuropathologically confirmed CTE in addition to the results of this study, CSP may serve as a potential early in vivo imaging marker to identify those at high risk for CTE. Future research is needed to investigate the pathomechanism underlying the development of CSP after repetitive head impacts, and its potential association with neuropathologically confirmed CTE.

Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.

Dyslexia is characterized by a deficit in language processing which mainly affects word decoding and spelling skills. In addition, children with dyslexia also show problems in mathematics. However, for the latter, the underlying structural correlates have not been investigated. Sixteen children with dyslexia (mean age 9.8 years [0.39]) and 24 typically developing children (mean age 9.9 years [0.29]) group matched for age, gender, IQ, and handedness underwent 3 T MR diffusion tensor imaging as well as cognitive testing. Tract-Based Spatial Statistics were performed to correlate behavioral data with diffusion data. Children with dyslexia performed worse than controls in standardized verbal number tasks, such as arithmetic efficiency tests (addition, subtraction, multiplication, division). In contrast, the two groups did not differ in the nonverbal number line task. Arithmetic efficiency, representing the total score of the four arithmetic tasks, multiplication, and division, correlated with diffusion measures in widespread areas of the white matter, including bilateral superior and inferior longitudinal fasciculi in children with dyslexia compared to controls. Children with dyslexia demonstrated lower performance in verbal number tasks but performed similarly to controls in a nonverbal number task. Further, an association between verbal arithmetic efficiency and diffusion measures was demonstrated in widespread areas of the white matter suggesting compensatory mechanisms in children with dyslexia compared to controls. Taken together, poor fact retrieval in children with dyslexia is likely a consequence of deficits in the language system, which not only affects literacy skills but also impacts on arithmetic skills.

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

Disinhibition is an important symptom in neurodegenerative diseases. However, the clinico-anatomical underpinnings remain controversial. We explored the anatomical correlates of disinhibition in neurodegenerative disease using the perspective of grey and white matter imaging. Disinhibition was assessed with a neuropsychological test and a caregiver information-based clinical rating scale in 21 patients with prefrontal syndromes due to behavioural variant frontotemporal dementia (n = 12) or progressive supranuclear palsy (n = 9), and healthy controls (n = 25). Cortical thickness was assessed using the Freesurfer software on 3T MRI data. The integrity of selected white matter tracts was determined by the fractional anisotropy (FA) from Diffusion Tensor Imaging. Disinhibition correlated with the cortical thickness of the right parahippocampal gyrus, right orbitofrontal cortex and right insula and the FA of the right uncinate fasciculus and right anterior cingulum. Notably, no relationship was seen with the thickness of ventromedial prefrontal cortex. Our results support an associative model of inhibitory control, distributed in a medial temporal lobe-insular-orbitofrontal network, connected by the intercommunicating white matter tracts. This reconciles some of the divergences among previous studies, but also questions the current conceptualisation of the "prefrontal" syndrome and the central role attributed to the ventromedial prefrontal cortex in inhibitory control.

PURPOSE: Compressed sensing methods with motion estimation and compensation techniques have been proposed for the reconstruction of accelerated dynamic MRI. However, artifacts that naturally arise in compressed sensing reconstruction procedures hinder the estimation of motion from reconstructed images, especially at high acceleration factors. This work introduces a robust groupwise nonrigid motion estimation technique applied to the compressed sensing reconstruction of dynamic cardiac cine MRI sequences. THEORY AND METHODS: A spatio-temporal regularized, groupwise, nonrigid registration method based on a B-splines deformation model and a least squares metric is used to estimate and to compensate the movement of the heart in breath-hold cine acquisitions and to obtain a quasistatic sequence with highly sparse representation in temporally transformed domains. RESULTS: Short axis in vivo datasets are used for validation, both original multicoil as well as DICOM data. Fully sampled data were retrospectively undersampled with various acceleration factors and reconstructions were compared with the two well-known methods k-t FOCUSS and MASTeR. The proposed method achieves higher signal to error ratio and structure similarity index for medium to high acceleration factors. CONCLUSIONS: Reconstruction methods based on groupwise registration show higher quality reconstructions for cardiac cine images than the pairwise counterparts tested.

PURPOSE: The delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) method can be used to assess the content of glycosaminoglycan in cartilage. In in vitro and model studies, the content of glycosaminoglycan is often expressed in terms of a fixed charge density (FCD). Values of the fixed charge density obtained using the dGEMRIC method differs from values obtained using other methods. The purpose of this work was to further clarify the origin of this discrepancy. METHODS: dGEMRIC experiments were performed in a μMRI setup on a custom-designed, well-defined model system capturing the relevant ionic features of cartilage. The model system allows for good control over and systematic variation of relevant parameters. The experimental data was compared with results from Monte Carlo simulations on a coarse-grained model. RESULTS: Application of ideal Donnan theory on data obtained from experiments as well as simulations lead to underestimation of the fixed charge density, in agreement with previous studies. CONCLUSION: To obtain more accurate estimates of the fixed charge density using the dGEMRIC method, interionic interactions need to be taken into account in the Donnan analysis. Furthermore, the results suggest that the combination of μMRI dGEMRIC experiments and Monte Carlo simulations are useful tools for an improved understanding of these effects. Magn Reson Med 76:500-509, 2016. © 2015 Wiley Periodicals, Inc.

Registration of multiple 3D ultrasound sectors in order to provide an extended field of view is important for the appreciation of larger anatomical structures at high spatial and temporal resolution. In this paper, we present a method for fully automatic spatio-temporal registration between two partially overlapping 3D ultrasound sequences. The temporal alignment is solved by aligning the normalized cross correlation-over-time curves of the sequences. For the spatial alignment, corresponding 3D Scale Invariant Feature Transform (SIFT) features are extracted from all frames of both sequences independently of the temporal alignment. A rigid transform is then calculated by least squares minimization in combination with random sample consensus. The method is applied to 16 echocardiographic sequences of the left and right ventricles and evaluated against manually annotated temporal events and spatial anatomical landmarks. The mean distances between manually identified landmarks in the left and right ventricles after automatic registration were (mean ± SD) 4.3 ± 1.2 mm compared to a reference error of 2.8 ± 0.6 mm with manual registration. For the temporal alignment, the absolute errors in valvular event times were 14.4 ± 11.6 ms for Aortic Valve (AV) opening, 18.6 ± 16.0 ms for AV closing, and 34.6 ± 26.4 ms for mitral valve opening, compared to a mean inter-frame time of 29 ms.

Parkinson’s disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).