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Mark A Espeland,
R Nick Bryan,
Joseph S Goveas,
Jennifer G Robinson,
Mustafa S Siddiqui,
Simin Liu,
Patricia E Hogan, Ramon Casanova,
Laura H Coker,
Kristine Yaffe,
Kamal Masaki,
Rebecca Rossom,
Susan M Resnick
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ABSTRACT: OBJECTIVE
To study how type 2 diabetes adversely affects brain volumes, changes in volume, and cognitive function.RESEARCH DESIGN AND METHODS
Regional brain volumes and ischemic lesion volumes in 1,366 women, aged 72-89 years, were measured with structural brain magnetic resonance imaging (MRI). Repeat scans were collected an average of 4.7 years later in 698 women. Cross-sectional differences and changes with time between women with and without diabetes were compared. Relationships that cognitive function test scores had with these measures and diabetes were examined.RESULTSThe 145 women with diabetes (10.6%) at the first MRI had smaller total brain volumes (0.6% less; P = 0.05) and smaller gray matter volumes (1.5% less; P = 0.01), but not white matter volumes, both overall and within major lobes. They also had larger ischemic lesion volumes (21.8% greater; P = 0.02), both overall and in gray matter (27.5% greater; P = 0.06), in white matter (18.8% greater; P = 0.02), and across major lobes. Overall, women with diabetes had slightly (nonsignificant) greater loss of total brain volumes (3.02 cc; P = 0.11) and significant increases in total ischemic lesion volumes (9.7% more; P = 0.05) with time relative to those without diabetes. Diabetes was associated with lower scores in global cognitive function and its subdomains. These relative deficits were only partially accounted for by brain volumes and risk factors for cognitive deficits.CONCLUSIONS
Diabetes is associated with smaller brain volumes in gray but not white matter and increasing ischemic lesion volumes throughout the brain. These markers are associated with but do not fully account for diabetes-related deficits in cognitive function.
Diabetes care 08/2012; · 8.09 Impact Factor
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ABSTRACT: To investigate the effect of resting-state (RS) functional magnetic resonance (MR) imaging blood oxygen level-dependent (BOLD) signal acquisition duration on stability of computed graph theory metrics of brain network connectivity.
An institutional ethics committee approved this study, and informed consent was obtained. BOLD signal (7.5 minutes worth) was obtained from 30 subjects and truncated into 30-second time bins that ranged from 1.5 to 7.5 minutes. A binarized adjacency matrix for each subject and acquisition duration was generated at network costs between 0.1 and 0.5, where network cost is defined as the ratio of the number of edges (connections) in a network to the maximum possible number of edges. Measures of correlation coefficient stability associated with functional connectivity matrices (correlation coefficient standard deviation [SD] and correlation threshold) and associated graph theory metrics (small worldness, local efficiency, and global efficiency) were computed for each subject at each BOLD signal acquisition duration. Computations were implemented with a 15-node 30-core computer cluster to enable analysis of the approximately 2000 resulting brain networks. Analysis of variance and posthoc analyses were conducted to identify differences between time bins for each measure.
Small worldness, local efficiency, and global efficiency stabilized after 2 minutes of BOLD signal acquisition, whereas correlation coefficient data from functional connectivity matrices (correlation coefficient SD and cost-associated threshold) stabilized after 5 minutes of BOLD signal acquisition.
Graph theory metrics of brain network connectivity (small worldness, local efficiency, and global efficiency) may be accurately computed from as little as 1.5-2.0 minutes of RS functional MR imaging BOLD signal. As such, implementation of these methods in the context of time-constrained clinical imaging protocols may be feasible and cost-effective. Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101708/-/DC1.
Radiology 03/2011; 259(2):516-24. · 5.73 Impact Factor
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Ramon Casanova,
Mark A Espeland,
Joseph S Goveas,
Christos Davatzikos,
Sarah A Gaussoin,
Joseph A Maldjian,
Robert L Brunner,
Lewis H Kuller,
Karen C Johnson,
W Jerry Mysiw,
Benjamin Wagner,
Susan M Resnick
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ABSTRACT: Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years; however, it is unknown whether this results in a broad-based characteristic pattern of effects. Structural magnetic resonance imaging was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L(1) penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and nonusers.
Magnetic Resonance Imaging 02/2011; 29(4):546-53. · 1.99 Impact Factor
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ABSTRACT: In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.
Frontiers in Neuroinformatics 01/2011; 5:22.
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ABSTRACT: In fMRI data analysis it has been shown that for a wide range of situations the hemodynamic response function (HRF) can be reasonably characterized as the impulse response function of a linear and time invariant system. An accurate and robust extraction of the HRF is essential to infer quantitative information about the relative timing of the neuronal events in different brain regions. When no assumptions are made about the HRF shape, it is most commonly estimated using time windowed averaging or a least squares estimated general linear model based on either Fourier or delta basis functions. Recently, regularization methods have been employed to increase the estimation efficiency of the HRF; typically these methods produce more accurate HRF estimates than the least squares approach [Goutte, C., Nielsen, F.A., Hansen, L.K., 2000. Modeling the Haemodynamic Response in fMRI Using Smooth FIR Filters. IEEE Trans. Med. Imag. 19(12), 1188-1201.]. Here, we use simulations to clarify the relative merit of temporal regularization based methods compared to the least squares methods with respect to the accuracy of estimating certain characteristics of the HRF such as time to peak (TTP), height (HR) and width (W) of the response. We implemented a Bayesian approach proposed by Marrelec et al. [Marrelec, G., Benali, H., Ciuciu, P., Pelegrini-Issac, M., Poline, J.-B., 2003. Robust Estimation of the Hemodynamic Response Function in Event-Related BOLD fMRI Using Basic Physiological Information. Hum. Brain Mapp. 19, 1-17., Marrelec, G., Benali, H., Ciuciu, P., Poline, J.B. Bayesian estimation of the hemodynamic of the hemodynamic response function in functional MRI. In: R. F, editor; 2001; Melville. p 229-247.] and its deterministic counterpart based on a combination of Tikhonov regularization [Tikhonov, A.N., Arsenin, V.Y., 1977. Solution of ill-posed problems. Washington DC: W.H. Winston.] and generalized cross-validation (GCV) [Wahba, G., 1990. Spline Models for Observational Data. Philadelphia: SIAM.] for selecting the regularization parameter. The performance of both methods is compared with least square estimates as a function of temporal resolution, color and strength of the noise, and the type of stimulus sequences used. In almost all situations, under the considered assumptions (e.g. linearity, time invariance and smooth HRF), the regularization-based techniques more accurately characterize the HRF compared to the least-squares method. Our results clarify the effects of temporal resolution, noise color, and experimental design on the accuracy of HRF estimation.
NeuroImage 06/2008; 40(4):1606-18. · 5.89 Impact Factor
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ABSTRACT: Age-related alterations in white matter have the potential to profoundly affect cognitive functioning. In fact, magnetic resonance imaging (MRI) studies using fractional anisotropy (FA) to measure white matter integrity reveal a positive correlation between FA and behavioral performance in older adults. Confounding these results are imaging studies demonstrating age-related white matter atrophy in some areas displaying altered FA, suggesting changes in diffusion may be simply an epiphenomenon of tissue loss. In the current study, structural MRI techniques were used to identify the relationship between white matter integrity and decreased volume in healthy aging adults. The data demonstrated that white matter atrophy did in fact account for differences in some areas, but significant FA decreases remained across much of the white matter after adjusting for atrophy. Results suggest a complex relationship between changes in white matter integrity and volume. FA appears to be more sensitive than volume loss to changes in normal appearing tissue, and these FA changes may actually precede white matter atrophy in some brain areas. As such, the ability to detect early white matter alterations may facilitate development of targeted treatments that prevent or slow age-related white matter degradation and associated cognitive sequelae.
Cerebral Cortex 03/2008; 18(2):433-42. · 6.54 Impact Factor
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ABSTRACT: Blood oxygen-level-dependent signal decreases relative to baseline (deactivations) can occur with stimulation of an opposing sensory modality. Here, we show the importance of the difficulty of an auditory task on the deactivation of visual cortical areas. Participants performed an auditory temporal-order judgment task in conjunction with sparse-sampling functional MRI at both moderate and high levels of difficulty (adjusted for each individual's own threshold). With moderate difficulty, small deactivations were observed not only in parietal and cingulate cortex, but occipital cortex as well. When the same task was more difficult, deactivations increased significantly to include a greater extent of functionally defined visual cortex. Together, these results suggest that cross-modal deactivations occur in compensation for task difficulty, perhaps acting as an intrinsic filter for nonrelevant information.
Neuroreport 02/2008; 19(2):151-4. · 1.66 Impact Factor
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ABSTRACT: Even the healthiest older adults experience changes in cognitive and sensory function. Studies show that older adults have reduced neural responses to sensory information. However, it is well known that sensory systems do not act in isolation but function cooperatively to either enhance or suppress neural responses to individual environmental stimuli. Very little research has been dedicated to understanding how aging affects the interactions between sensory systems, especially cross-modal deactivations or the ability of one sensory system (e.g., audition) to suppress the neural responses in another sensory system cortex (e.g., vision). Such cross-modal interactions have been implicated in attentional shifts between sensory modalities and could account for increased distractibility in older adults. To assess age-related changes in cross-modal deactivations, functional MRI studies were performed in 61 adults between 18 and 80 years old during simple auditory and visual discrimination tasks. Results within visual cortex confirmed previous findings of decreased responses to visual stimuli for older adults. Age-related changes in the visual cortical response to auditory stimuli were, however, much more complex and suggested an alteration with age in the functional interactions between the senses. Ventral visual cortical regions exhibited cross-modal deactivations in younger but not older adults, whereas more dorsal aspects of visual cortex were suppressed in older but not younger adults. These differences in deactivation also remained after adjusting for age-related reductions in brain volume of sensory cortex. Thus, functional differences in cortical activity between older and younger adults cannot solely be accounted for by differences in gray matter volume.
Human Brain Mapping 01/2008; 30(1):228-40. · 5.88 Impact Factor
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ABSTRACT: In recent years, multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality-specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in Matlab with a user-friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely used T-field, has been implemented in the correlation analysis for more accurate results. An example with in vivo data is presented, demonstrating the potential of the BPM methodology as a tool for multimodal image analysis.
NeuroImage 02/2007; 34(1):137-43. · 5.89 Impact Factor
