3D pattern of brain atrophy in HIV/AIDS visualized using tensor-based morphometry

Abstract

35% of HIV-infected patients have cognitive impairment, but the profile of HIV-induced brain damage is still not well understood. Here we used tensor-based morphometry (TBM) to visualize brain deficits and clinical/anatomical correlations in HIV/AIDS. To perform TBM, we developed a new MRI-based analysis technique that uses fluid image warping, and a new α-entropy-based information-theoretic measure of image correspondence, called the Jensen–Rényi divergence (JRD).

Methods

3D T1-weighted brain MRIs of 26 AIDS patients (CDC stage C and/or 3 without HIV-associated dementia; 47.2 ± 9.8 years; 25M/1F; CD4⁺ T-cell count: 299.5 ± 175.7/μl; log₁₀ plasma viral load: 2.57 ±  1.28 RNA copies/ml) and 14 HIV-seronegative controls (37.6 ± 12.2 years; 8M/6F) were fluidly registered by applying forces throughout each deforming image to maximize the JRD between it and a target image (from a control subject). The 3D fluid registration was regularized using the linearized Cauchy–Navier operator. Fine-scale volumetric differences between diagnostic groups were mapped. Regions were identified where brain atrophy correlated with clinical measures.

Results

Severe atrophy (∼ 15–20% deficit) was detected bilaterally in the primary and association sensorimotor areas. Atrophy of these regions, particularly in the white matter, correlated with cognitive impairment (P = 0.033) and CD4⁺ T-lymphocyte depletion (P = 0.005).

Conclusion

TBM facilitates 3D visualization of AIDS neuropathology in living patients scanned with MRI. Severe atrophy in frontoparietal and striatal areas may underlie early cognitive dysfunction in AIDS patients, and may signal the imminent onset of AIDS dementia complex.

Introduction

The hallmark of acquired immune deficiency syndrome (AIDS)/human immunodeficiency virus (HIV) infection is progressive immunosuppression, particularly the depletion of CD4⁺ T-lymphocytes. HIV enters the brain within 2 weeks of initial infection (Paul et al., 2002), and damages neurons primarily by stimulating the production of cytokines that are toxic to neurons, leading to excitotoxic cell death. Thirty-five percent of HIV-infected patients have some signs of neurocognitive dysfunction (White et al., 1995), characterized by difficulties in concentration, psychomotor slowing, and impaired information processing (Becker et al., 1997). In the more advanced stages of the disease, around 15% of AIDS patients have HIV-associated dementia, a complex disorder consisting of psychomotor slowing, behavioral abnormalities, and Parkinsonian features such as bradykinesia and gait disturbance (McArthur et al., 2005). Decline in neurocognitive function predicts and directly contributes to mortality (Sacktor et al., 1996).

HIV encephalopathy is pathologically characterized by diffuse white matter pallor and rarefaction, as well as astrocytic cell death. The blood–brain barrier breaks down and HIV-induced cytokines and neurotoxins are produced, causing dendritic simplification and neuronal loss. Regionally, central white matter and deep gray matter structures – such as the basal ganglia, thalamus, and brainstem – are particularly vulnerable to atrophy (Budka et al., 1987, Price et al., 1988, Thompson et al., 2001, Gray et al., 2003, Bell, 2004, McArthur et al., 2005). In a recent MRI study, we found selective cortical thinning in primary sensorimotor, premotor, and visual areas in AIDS (Thompson et al., 2005). Surface-based anatomical maps also revealed regional atrophy in the caudate and hippocampus (Becker et al., submitted for publication-a) as well as corpus callosum thinning and ventricular expansion (Thompson et al., 2006). However, there is a still a lack of detailed 3D maps that show the profile of HIV-associated changes throughout the brain. More conventional volumetric studies were the first to demonstrate global white matter atrophy, basal ganglia volume loss, and ventricular enlargement (Aylward et al., 1993, Hall et al., 1996, Stout et al., 1998). Nevertheless, volumetric studies are generally labor-intensive and cannot visualize the profile of deficits at the voxel level. Other imaging modalities, such as magnetic resonance spectroscopy (MRS; Chang et al., 1999) or diffusion tensor imaging (DTI; Filippi et al., 2001), reveal early white matter abnormalities that are not visible on structural MRI, but they suffer from limited spatial resolution.

Here we applied tensor-based morphometry (TBM; see Davatzikos et al., 1996, Davatzikos et al., 2001, Thompson et al., 2000, Chung et al., 2001, Chung et al., 2003, Chung et al., 2004, Fox et al., 2001, Shen and Davatzikos, 2003, Studholme et al., 2001, Studholme et al., 2003, Studholme et al., 2004, Teipel et al., 2004, for related work) to detect and automatically quantify the subtle and distributed patterns of brain atrophy in HIV/AIDS.

In a cross-sectional TBM design the most important step in the morphometric analysis is to nonlinearly deform all the images to match a preselected brain image, which acts as a template. Then, the Jacobian determinant (i.e., the local expansion factor) of the deformation fields is used to gauge the local volume differences between the individual images and the template, and these can be analyzed statistically to identify group differences or localized atrophy at the voxel level. Automated image registration, which aligns one image with another, is typically guided by quantitative measures of image similarity based on the statistical dependence of the voxel intensities, such as their correlation (Collins et al., 1994), summed squared intensity differences (Woods et al., 1998, Ashburner and Friston, 1999), or more complex measures derived from the joint histogram of the registered images, such as ratio image uniformity (Woods et al., 1992). Among these different approaches, the mutual information method (MI; Viola and Wells, 1997) has proved popular and highly effective, and assumes that MI of two images is maximal when the images are optimally aligned. The parameters of the alignment transformation are tuned to maximize the MI. The MI method has been successfully applied to rigid (Viola and Wells, 1997), non-rigid (Lorenzen et al., 2004, Studholme et al., 2001), and cross-modality registrations (e.g., MRI to PET or histologic images; Kim et al., 1997). Hermosillo (2002) developed a variational formulation to maximize MI using a regularization functional borrowed from linear elasticity theory. This was further extended (D’Agostino et al., 2003) to deal with large local deformations while maintaining one-to-one topology (Christensen et al., 1996) using a viscous fluid model. Related work on diffeomorphic matching techniques can be found in Miller (2004), and Avants and Gee (2004); these registration methods can handle large deformations without disrupting the image topology.

He et al. (2003) were the first to apply the Jensen–Rényi divergence (JRD) to image registration. The concept of MI was generalized in an information-theoretic framework derived from Rényi’s α-entropy that allows an extra degree of freedom (α) in which MI α = 1 is a special case. They showed that the JRD was more robust than MI for 2D inverse synthetic aperture radar (ISAR) image registrations involving translations, rotations and scaling.

In this paper, we modify the original definition of JRD to drive nonlinear image registration. JRD has been applied before to rigid registration by Pluim et al. (2004) but the nonlinear case has not been studied. We iteratively determine the driving force at each voxel such that the JRD between the deforming source and target images is maximized under the viscous fluid regularization (Christensen et al., 1996). This means that the transforms have the important property of being smooth (i.e., continuous and differentiable) as well as one-to-one – there can be no tearing or folding of the image – as the permissible transformations are constrained to obey the constitutive laws of a compressible fluid, as defined rigorously in the framework of continuum mechanics. In this paper, we first show that our algorithm performs well on 2D and 3D images with different noise and intensity distributions. We then apply the JRD algorithm to a neuroscientific study using TBM, and mapping the profile of brain atrophy in HIV/AIDS. We show that this atrophy is selective and associated, in specific brain regions, with decline in cellular immunity and neurocognitive deterioration, providing a valuable biological measure of the disease process.