Journal Article
Anastasia Yendiki, Douglas N Greve, Stuart Wallace, Mark Vangel, Jeremy Bockholt, Bryon A Mueller, Vince Magnotta, Nancy Andreasen, Dara S Manoach, and Randy L Gollub. 2010. “Multi-site characterization of an fMRI working memory paradigm: reliability of activation indices.” Neuroimage, 53, 1, Pp. 119-31.Abstract
Neuroimaging studies are facilitated significantly when it is possible to recruit subjects and acquire data at multiple sites. However, the use of different scanners and acquisition protocols is a potential source of variability in multi-site data. In this work we present a multi-site study of the reliability of fMRI activation indices, where 10 healthy volunteers were scanned at 4 different sites while performing a working memory paradigm. Our results indicate that, even with different scanner manufacturers and field strengths, activation variability due to site differences is small compared to variability due to subject differences in this cognitive task, provided we choose an appropriate activation measure.
Stefan Ehrlich, Eric M Morrow, Joshua L Roffman, Stuart R Wallace, Melissa Naylor, Jeremy H Bockholt, Antonia Lundquist, Anastasia Yendiki, Beng-Choon Ho, Tonya White, Dara S Manoach, Vincent P Clark, Vince D Calhoun, Randy L Gollub, and Daphne J Holt. 2010. “The COMT Val108/158Met polymorphism and medial temporal lobe volumetry in patients with schizophrenia and healthy adults.” Neuroimage, 53, 3, Pp. 992-1000.Abstract
Abnormalities of the medial temporal lobe have been consistently demonstrated in schizophrenia. A common functional polymorphism, Val108/158Met, in the putative schizophrenia susceptibility gene, catechol-O-methyltransferase (COMT), has been shown to influence medial temporal lobe function. However, the effects of this polymorphism on volumes of medial temporal lobe structures, particularly in patients with schizophrenia, are less clear. Here we measured the effects of COMT Val108/158Met genotype on the volume of two regions within the medial temporal lobe, the amygdala and hippocampus, in patients with schizophrenia and healthy control subjects. We obtained MRI and genotype data for 98 schizophrenic patients and 114 matched controls. An automated atlas-based segmentation algorithm was used to generate volumetric measures of the amygdala and hippocampus. Regression analyses included COMT met allele load as an additive effect, and also controlled for age, intracranial volume, gender and acquisition site. Across patients and controls, each copy of the COMT met allele was associated on average with a 2.6% increase in right amygdala volume, a 3.8% increase in left amygdala volume and a 2.2% increase in right hippocampus volume. There were no effects of COMT genotype on volumes of the whole brain and prefrontal regions. Thus, the COMT Val108/158Met polymorphism was shown to influence medial temporal lobe volumes in a linear-additive manner, mirroring its effect on dopamine catabolism. Taken together with previous work, our data support a model in which lower COMT activity, and a resulting elevation in extracellular dopamine levels, stimulates growth of medial temporal lobe structures.
Z Kikinis, JH Fallon, M Niznikiewicz, P Nestor, C Davidson, L Bobrow, PE Pelavin, B Fischl, A Yendiki, RW McCarley, R Kikinis, M Kubicki, and ME Shenton. 2010. “Gray matter volume reduction in rostral middle frontal gyrus in patients with chronic schizophrenia.” Schizophr Res, 123, 2-3, Pp. 153-9.Abstract
The dorsolateral prefrontal cortex (DLPFC) is a brain region that has figured prominently in studies of schizophrenia and working memory, yet the exact neuroanatomical localization of this brain region remains to be defined. DLPFC primarily involves the superior frontal gyrus and middle frontal gyrus (MFG). The latter, however is not a single neuroanatomical entity but instead is comprised of rostral (anterior, middle, and posterior) and caudal regions. In this study we used structural MRI to develop a method for parcellating MFG into its component parts. We focused on this region of DLPFC because it includes BA46, a region involved in working memory. We evaluated volume differences in MFG in 20 patients with chronic schizophrenia and 20 healthy controls. Mid-rostral MFG (MR-MFG) was delineated within the rostral MFG using anterior and posterior neuroanatomical landmarks derived from cytoarchitectonic definitions of BA46. Gray matter volumes of MR-MFG were then compared between groups, and a significant reduction in gray matter volume was observed (p<0.008), but not in other areas of MFG (i.e., anterior or posterior rostral MFG, or caudal regions of MFG). Our results demonstrate that volumetric alterations in MFG gray matter are localized exclusively to MR-MFG. 3D reconstructions of the cortical surface made it possible to follow MFG into its anterior part, where other approaches have failed. This method of parcellation offers a more precise way of measuring MR-MFG that will likely be important in further documentation of DLPFC anomalies in schizophrenia.
Kenneth F Koral, Anastasia Yendiki, and Yuni K Dewaraja. 2007. “Recovery of total I-131 activity within focal volumes using SPECT and 3D OSEM.” Phys Med Biol, 52, 3, Pp. 777-90.Abstract
We experimentally investigated the SPECT recovery of I-131 activity in multiple spheres located simultaneously at different locations within a cylindrical phantom that had an elliptical cross section. The sphere volumes ranged from 209 cc down to 4.2 cc. A Prism 3000 camera and two types of parallel-hexagonal-hole collimation were employed: high energy (HE) and ultra high energy (UHE). Using appropriately-different 3D models of the point source response function for the two types of collimation, approximately the same recovery of activity could be achieved with either collimation by 3D OSEM reconstruction. The recovery coefficient was greater with no background activity in the phantom by 0.10, on average, compared to that with background. In the HE collimation case, the activity recovery was considerably better for all volumes using 3D OSEM reconstruction than it had been in the past using 1D SAGE reconstruction. Recovery-coefficient-based correction in a simulated patient case involving spherical tumours moderately improved the activity estimates (average error reduced from 14% to 9% for UHE collimation, and from 15% to 11% for HE collimation). For a test case with HE collimation, increasing the projection-image sampling density while decreasing the image voxel size increased the recovery coefficient by 0.075 on average, and, if used in a full set of calibration measurements of recovery coefficient versus volume, might lead to further improvement in accuracy for the patient case.
Anastasia Yendiki and Jeffrey A Fessler. 2007. “Analysis of observer performance in unknown-location tasks for tomographic image reconstruction.” J Opt Soc Am A Opt Image Sci Vis, 24, 12, Pp. B99-B109.Abstract
Our goal is to optimize regularized image reconstruction for emission tomography with respect to lesion detectability in the reconstructed images. We consider model observers whose decision variable is the maximum value of a local test statistic within a search area. Previous approaches have used simulations to evaluate the performance of such observers. We propose an alternative approach, where approximations of tail probabilities for the maximum of correlated Gaussian random fields facilitate analytical evaluation of detection performance. We illustrate how these approximations, which are reasonably accurate at low probability of false alarm operating points, can be used to optimize regularization with respect to lesion detectability.
Anastasia Yendiki and Jeffrey A Fessler. 2006. “Analysis of observer performance in known-location tasks for tomographic image reconstruction.” IEEE Trans Med Imaging, 25, 1, Pp. 28-41.Abstract
We consider the task of detecting a statistically varying signal of known location on a statistically varying background in a reconstructed tomographic image. We analyze the performance of linear observer models in this task. We show that, if one chooses a suitable reconstruction method, a broad family of linear observers can exactly achieve the optimal detection performance attainable with any combination of a linear observer and linear reconstructor. This conclusion encompasses several well-known observer models from the literature, including models with a frequency-selective channel mechanism and certain types of internal noise. Interestingly, the "optimal" reconstruction methods are unregularized and in some cases quite unconventional. These results suggest that, for the purposes of designing regularized reconstruction methods that optimize lesion detectability, known-location tasks are of limited use.
K. F. Koral, A. Yendiki, Q. Lin, and Y. K. Dewaraja. 2005. “Comparison of 3D OSEM vs. 1D SAGE for focal total-activity quantification in I-131 SPECT with HE collimation.” IEEE Trans Nuc Sci, 52, 1, Pp. 154-158.
K. F. Koral, A. Yendiki, Q. Lin, Y. K. Dewaraja, and J. A. Fessler. 2004. “Determining total I-131 activity within a VoI using SPECT, a UHE Collimator, OSEM, and a constant conversion factor.” IEEE Trans Nuc Sci, 51, 3, Pp. 611-618.
A Yendiki and JA Fessler. 2004. “A comparison of rotation- and blob-based system models for 3D SPECT with depth-dependent detector response.” Phys Med Biol, 49, 11, Pp. 2157-68.Abstract
We compare two different implementations of a 3D SPECT system model for iterative reconstruction, both of which compensate for non-uniform photon attenuation and depth-dependent system response. One implementation performs fast rotation of images represented using a basis of rectangular voxels, whereas the other represents images using a basis of rotationally symmetric volume elements. In our simulations the blob-based approach was found to slightly outperform the rotation-based one in terms of the bias-variance tradeoff in the reconstructed images. Their difference can be significant, however, in terms of computational load. The rotation-based method is faster for many typical SPECT reconstruction problems, but the blob-based one can be better-suited to cases where the reconstruction algorithm needs to process one volume element at a time.
A. Yendiki. 2005. “Detectability in statistically reconstructed tomographic images.” The University of Michigan, Ann Arbor, MI.