Publications by Year: 2011

Parametric statistical methods, such as Z-, t-, and F-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level.

BACKGROUND: Schizophrenia is believed to result from abnormal functional integration of neural processes thought to arise from aberrant brain connectivity. However, evidence for anatomical dysconnectivity has been equivocal, and few studies have examined axonal fiber connectivity in schizophrenia at the level of whole-brain networks. METHODS: Cortico-cortical anatomical connectivity at the scale of axonal fiber bundles was modeled as a network. Eighty-two network nodes demarcated functionally specific cortical regions. Sixty-four direction diffusion tensor-imaging coupled with whole-brain tractography was performed to map the architecture via which network nodes were interconnected in each of 74 patients with schizophrenia and 32 age- and gender-matched control subjects. Testing was performed to identify pairs of nodes between which connectivity was impaired in the patient group. The connectional architecture of patients was tested for changes in five network attributes: nodal degree, small-worldness, efficiency, path length, and clustering. RESULTS: Impaired connectivity in the patient group was found to involve a distributed network of nodes comprising medial frontal, parietal/occipital, and the left temporal lobe. Although small-world attributes were conserved in schizophrenia, the cortex was interconnected more sparsely and up to 20% less efficiently in patients. Intellectual performance was found to be associated with brain efficiency in control subjects but not in patients. CONCLUSIONS: This study presents evidence of widespread dysconnectivity in white-matter connectional architecture in a large sample of patients with schizophrenia. When considered from the perspective of recent evidence for impaired synaptic plasticity, this study points to a multifaceted pathophysiology in schizophrenia encompassing axonal as well as putative synaptic mechanisms.

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on B\ ezier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.

AIM: The sulcogyral pattern of the orbitofrontal cortex (OFC) is characterized by a remarkable inter-individual variability that likely reflects neurobehavioral traits and genetic aspects of neurodevelopment. The aim of the present study was to evaluate the OFC sulcogyral pattern of patients with schizophrenia (SZ) and healthy controls (HC) to determine group differences in OFC sulcogyral pattern as well as gender differences between groups. METHODS: Forty-seven SZ patients (M/F, 23/24) and forty-seven HC (M/F, 17/30), matched on age and gender, were analyzed using magnetic resonance imaging. The sulcogyral pattern was classified into type I, II, or III based on the guidelines set by Chiavaras and Petrides in a previous paper. Chi-squared analysis was used to investigate group and gender differences in the sulcogyral pattern distribution, and categorical regression was used to explore clinical correlations.

The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 × 512 × 445 × 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT-based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT-based filtering. The short processing time increases the clinical value of true 4D image denoising significantly.

User interaction is required for reliable segmentation of brain tumors in clinical practice and in clinical research. By incorporating current research tools, 3D Slicer provides a set of interactive, easy to use tools that can be efficiently used for this purpose. One of the modules of 3D Slicer is an interactive editor tool, which contains a variety of interactive segmentation effects. Use of these effects for fast and reproducible segmentation of a single glioblastoma from magnetic resonance imaging data is demonstrated. The innovation in this work lies not in the algorithm, but in the accessibility of the algorithm because of its integration into a software platform that is practical for research in a clinical setting.

BACKGROUND: Patients with schizophrenia have deficits in facial affect expression and detection that hinder social interactions. The goal of this study was to examine whether or not epidemiologically-related antipsychotic-na ıve schizotypal personality disorder (SPD) subjects would have similar deficits as patients with schizophrenia.

One of the most intriguing characteristics of Williams Syndrome individuals is their hypersociability. The amygdala has been consistently implicated in the etiology of this social profile, particularly given its role in emotional and social behavior. This study examined amygdala volume and symmetry in WS individuals and in age and sex matched controls. Magnetic resonance imaging scans were obtained on a GE 1.5-T magnet with 1.5-mm contiguous slices and were used to measure whole gray matter, white matter and cerebrospinal fluid volumes, as well as amygdala volume (right and left). Results revealed significantly reduced intracranial volume in individuals with WS, compared with controls. There were no differences between groups in absolute amygdalae volume, although there was a relative increase in amygdalae volumes, when adjusted for total intracranial content. There were no inter-hemispheric differences in amygdalae volumes in both groups. These results suggest a relative increase in amygdala volume in WS compared with healthy controls that likely reflects abnormal neurodevelopmental processes of midline brain structures.

The current report used structural magnetic resonance imaging (MRI) to objectively measure olfactory bulb volume and olfactory sulcal depth in patients diagnosed with chronic schizophrenia and healthy controls. Additional measures were obtained to assess olfactory function. The olfactory bulb and sulcus were manually traced on structural 3T MRIs for 25 right-handed male patients diagnosed with chronic schizophrenia and 25 matched male healthy controls. A sub-set of subjects received the University of Pennsylvania Smell Identification Test (UPSIT). Olfactory bulb volume was significantly decreased in patients with schizophrenia compared to healthy controls, as was their performance on the UPSIT. Additionally, a positive correlation was seen in patients between right bulb volume and UPSIT scores. Overall, our findings support earlier research studies showing morphometric and functional changes in the olfactory system in patients with schizophrenia.

PURPOSE: To investigates the impact of nonrigid motion correction on pixel-wise pharmacokinetic analysis of free-breathing DCE-MRI in patients with solitary pulmonary nodules (SPNs). Misalignment of focal lesions due to respiratory motion in free-breathing dynamic contrast-enhanced MRI (DCE-MRI) precludes obtaining reliable time-intensity curves, which are crucial for pharmacokinetic analysis for tissue characterization. MATERIALS AND METHODS: Single-slice 2D DCE-MRI was obtained in 15 patients. Misalignments of SPNs were corrected using nonrigid B-spline image registration. Pixel-wise pharmacokinetic parameters K(trans) , v(e) , and k(ep) were estimated from both original and motion-corrected DCE-MRI by fitting the two-compartment pharmacokinetic model to the time-intensity curve obtained in each pixel. The "goodness-of-fit" was tested with χ(2) -test in pixel-by-pixel basis to evaluate the reliability of the parameters. The percentages of reliable pixels within the SPNs were compared between the original and motion-corrected DCE-MRI. In addition, the parameters obtained from benign and malignant SPNs were compared.