Browsing by Author "West, John"

Now showing 1 - 6 of 6
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    Building a Surface Atlas of Hippocampal Subfields from MRI Scans using FreeSurfer, FIRST and SPHARM
    (Institute of Electrical and Electronics Engineers, 2014-08) Cong, Shan; Rizkalla, Maher; Du, Eliza Y.; West, John; Risacher, Shannon; Saykin, Andrew J.; Shen, Li; Alzheimer's Disease Neuroimaging Initiative; Department of Medicine, IU School of Medicine
    The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for brain disorders such as Alzheimer's disease and epilepsy. However, its complex folding anatomy often presents analytical challenges. In particular, the critical hippocampal subfield information is usually ignored by hippocampal registration in detailed morphometric studies. Such an approach is thus inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To bridge this gap, we present our initial effort towards building a computational framework for subfield-guided hippocampal morphometry. This initial effort is focused on surface-based morphometry and aims to build a surface atlas of hippocampal subfields. Using the FreeSurfer software package, we obtain valuable hippocampal subfield information. Using the FIRST software package, we extract reliable hippocampal surface information. Using SPHARM, we develop an approach to create an atlas by mapping interpolated subfield information onto an average surface. The empirical result using ADNI data demonstrates the promise and good reproducibility of the proposed method.
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    Externalizing personality traits, empathy, and gray matter volume in healthy young drinkers
    (Elsevier, 2016-02-28) Charpentier, Judith; Dzemidzic, Mario; West, John; Oberlin, Brandon G.; Eiler, William J. A. II; Saykin, Andrew J.; Kareken, David A.; Department of Neurology, IU School of Medicine
    Externalizing psychopathology has been linked to prefrontal abnormalities. While clinically diagnosed subjects show altered frontal gray matter, it is unknown if similar deficits relate to externalizing traits in non-clinical populations. We used voxel-based morphometry (VBM) to retrospectively analyze the cerebral gray matter volume of 176 young adult social to heavy drinkers (mean age=24.0±2.9, male=83.5%) from studies of alcoholism risk. We hypothesized that prefrontal gray matter volume and externalizing traits would be correlated. Externalizing personality trait components-Boredom Susceptibility-Impulsivity (BS/IMP) and Empathy/Low Antisocial Behaviors (EMP/LASB)-were tested for correlations with gray matter partial volume estimates (gmPVE). Significantly large clusters (pFWE<0.05, family-wise whole-brain corrected) of gmPVE correlated with EMP/LASB in dorsolateral and medial prefrontal regions, and in occipital cortex. BS/IMP did not correlate with gmPVE, but one scale of impulsivity (Eysenck I7) correlated positively with bilateral inferior frontal/orbitofrontal, and anterior insula gmPVE. In this large sample of community-dwelling young adults, antisocial behavior/low empathy corresponded with reduced prefrontal and occipital gray matter, while impulsivity correlated with increased inferior frontal and anterior insula cortical volume. These findings add to a literature indicating that externalizing personality features involve altered frontal architecture.
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    Identifying Connectome Module Patterns via New Balanced Multi-Graph Normalized Cut.
    (Springer, 2015-10) Gao, Hongchang; Cai, Chengtao; Yan, Jingwen; Yan, Lin; Cortes, Joaquin Goni; Wang, Yang; Nie, Feiping; West, John; Saykin, Andrew J.; Shen, Li; Huang, Heng; Department of Radiology and Imaging Sciences, IU School of Medicine
    Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks.
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    Neurobiological mechanisms associated with facial affect recognition deficits after traumatic brain injury
    (Springer, 2015-06) Neumann, Dawn; McDonald, Brenna C.; West, John; Keiski, Michelle A.; Wang, Yang; Department of Physical Medicine and Rehabilitation, IU School of Medicine
    The neurobiological mechanisms that underlie facial affect recognition deficits after traumatic brain injury (TBI) have not yet been identified. Using functional magnetic resonance imaging (fMRI), study aims were to 1) determine if there are differences in brain activation during facial affect processing in people with TBI who have facial affect recognition impairments (TBI-I) relative to people with TBI and healthy controls who do not have facial affect recognition impairments (TBI-N and HC, respectively); and 2) identify relationships between neural activity and facial affect recognition performance. A facial affect recognition screening task performed outside the scanner was used to determine group classification; TBI patients who performed greater than one standard deviation below normal performance scores were classified as TBI-I, while TBI patients with normal scores were classified as TBI-N. An fMRI facial recognition paradigm was then performed within the 3T environment. Results from 35 participants are reported (TBI-I = 11, TBI-N = 12, and HC = 12). For the fMRI task, TBI-I and TBI-N groups scored significantly lower than the HC group. Blood oxygenation level-dependent (BOLD) signals for facial affect recognition compared to a baseline condition of viewing a scrambled face, revealed lower neural activation in the right fusiform gyrus (FG) in the TBI-I group than the HC group. Right fusiform gyrus activity correlated with accuracy on the facial affect recognition tasks (both within and outside the scanner). Decreased FG activity suggests facial affect recognition deficits after TBI may be the result of impaired holistic face processing. Future directions and clinical implications are discussed.
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    A New Statistical Image Analysis Approach and Its Application to Hippocampal Morphometry
    (Springer, 2016) Inlow, Mark; Cong, Shan; Risacher, Shannon L.; West, John; Rizkalla, Maher; Salama, Paul; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of Medicine
    In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer’s disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods.
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    Surface-Based Morphometric Analysis of Hippocampal Subfields in Mild Cognitive Impairment and Alzheimer's Disease
    (IEEE Xplore, 2015-08) Cong, Shan; Rizkalla, Maher; Salama, Paul; West, John; Risacher, Shannon; Saykin, Andrew; Shen, Li; Department of Electrical and Computer Engineering, School of Engineering and Technology
    The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for Alzheimer's disease (AD). Its complex folding anatomy often presents analytical challenges. In particular, the critical subfield information is typically not addressed by the existing hippocampal shape studies. To bridge this gap, we present a computational framework for surface-based morphometric analysis of hippocampal subfields. The major strengths of this framework are as follows: (a) it performs detailed hippocampal shape analysis, (b) it embraces, rather than ignores, the important hippocampal subfield information, and (c) it analyzes regular magnetic resonance imaging scans and is applicable to large scale studies. We demonstrate its effectiveness by applying it to the identification of regional hippocampal subfield atrophy patterns associated with mild cognitive impairment and AD.