Probing white matter abnormalities in preclinical and early Alzheimer’s disease
Qing Wang1,2, Yong Wang1,3,4, Joshua S Shimony1, Anne M Fagan2,5, John C Morris5,6, and Tammie L.S. Benzinger1,6,7

1Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States, 2Knight Alzheimer's Disease Research Center, St. Louis, MO, United States, 3Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, MO, United States, 4Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States, 5Neurology, Washington University in St. Louis, St. Louis, MO, United States, 6Knight Alzheimer’s Disease Research Center, St. Louis, MO, United States, 7Neurosurgery, Washington University in St. Louis, St. Louis, MO, United States

Synopsis

Robust neuroimaging biomarker sensitive to the early white matter abnormalities could provide novel insights into the pathogenesis of Alzheimer’s disease (AD), and serve as surrogate measures for disease progression. We demonstrated that novel DBSI white matter abnormality biomarkers strongly correlate with invasive CSF measures of neuronal injuries, and provide specific preclinical measures of WM abnormalities for early diagnostics and accurate assessment of disease-modifying therapies targeting neuro-protection in AD.

Purpose

Although our understanding of preclinical and early Alzheimer’s disease pathology has significantly improved with the development of cerebrospinal fluid (CSF) biomarkers (such as beta-amyloid 42 [Aβ42], tau, phosphorylated tau 181 [ptau181], and visinin-like protein-1 [VILIP-1]), we severely lack robust noninvasive neuroimaging methods to detect and quantify the alterations in preclinical and early AD. Diffusion tensor imaging (DTI) has been used to detect white matter (WM) microstructure changes, but it failed to differentiate between axon/myelin damage and neuroinflammation. To address these limitations, we developed and validated a novel magnetic resonance imaging technique – diffusion basis spectrum imaging (DBSI) – that can specifically image neuronal injury in neurodegeneration diseases 1, 2. This study aims to examine whether DTI- or DBSI-derived indices correlated with CSF levels of each of the neuronal injury markers (tau, ptau181, and VILIP-1) in preclinical and early AD.

Methods

Participants: 175 cognitively normal participants and 82 very mild dementia (CDR0.5)3 participants were selected from a larger population enrolled at Knight Alzheimer’s Disease Research Center (Knight ADRC) of the Washington University School of Medicine (St Louis, MO, USA), in longitudinal studies of memory and aging.

CSF Collection and Analysis: CSF (20-30 mL) was collected by lumbar puncture as previously described 4. Samples were analyzed by ELISA (Innotest; Innogenetics, Ghent, Belgium) after one freeze-thaw for beta amyloid 42 (Aβ42), total tau, and tau phosphorylated at threonine-181 (ptau181) and by ELISA (Quidel, San Diego, CA) after two freeze-thaw cycles for VILIP-1.

NIA-AA preclinical AD stages: Cognitively normal participants were divided into preclinical stage 0 and stage 1 groups according to CSF measures and research criteria proposed by the National Institute on Aging and the Alzheimer’s Association (NIA-AA)5. Participants were classified as preclinical stage 0 (all biomarkers negative [Aβ42 > 500 pg/ml, tau <339 pg/ml, and ptau181 < 80 pg/ml) and preclinical stage 1 (Aβ42 < 500 pg/ml and either tau > 339 pg/ml or ptau 181 > 80 pg/ml).

Imaging Acquisition and Processing: Diffusion MRI images were collected (2x2x2mm voxels, TR=14500ms, TE=102ms, multi-bvalue scheme, 23 directions and bmax = 1400s/mm2). Data were collected in two 6 minute runs on 3T Trio scanner (Siemens, Erlangen, Germany). DTI and DBSI derived mean diffusivity, fractional anisotropy (FA), radial diffusivity and axial diffusivity were computed for all datasets. The whole brain voxel-wise DTI-indices was analyzed using Tract Based Spatial Statistics (TBSS) (available in FSL).

Results

Aβ42, an indicator of amyloid burden, was significantly higher in the preclinical stage 0 group than in the stage 1 and CDR 0.5 groups (Fig. 1A); having a high level of Aβ42 is categorized as being Aβ42-negative. We found no statistical difference in level of Aβ42 between the stage 1 and CDR 0.5 groups (Fig. 1A). There were no significant differences in CSF tau, ptau181, or VILIP-1 between the stage 0 and stage 1 groups (Fig. 1B-D), suggesting that the preclinical stage 1 patients had no neuronal injuries. In contrast, CSF tau, ptau181, and VILIP-1 were significantly higher in the CDR 0.5 group than in the stage 0 and stage 1 groups (Fig. 1B-D), indicating neuronal injury in the CDR 0.5 participants. The CSF ptau181 correlated with only one DTI measure, fractional anisotropy, in 25 WM regions (Fig. 2A). In contrast, the CSF levels of neuronal injury markers correlated with both DBSI-derived fractional anisotropy and radial diffusivity in multiple WM regions. For example, levels of tau negatively correlated with DBSI-derived fractional anisotropy in 14 WM regions (Fig. 2B), and levels of ptau181 positively correlated with DBSI-derived radial diffusivity in 31 WM regions (Fig. 2C). In addition, we observed a significant correlation between levels of VILIP-1 and DBSI-derived fractional anisotropy and radial diffusivity (Fig. 2B) in 36 and 28 WM regions, respectively.

Discussion and Conclusion

CSF measures of tau protein and phosphorylated tau are hypothesized to reflect neurodegeneration and may be the biomarkers that are most relevant to microstructural changes in early AD. VILIP-1, a neuronal calcium-sensor protein, is a marker of neuronal injury, and CSF measures of VILIP-1 are useful for diagnosis and prognosis in early AD. We observed elevated CSF tau, ptau181, and VILIP-1 in the CDR 0.5 participants in this study, indicating that these participants had neuronal injury. DBSI-derived fractional anisotropy and radial diffusivity correlated significantly with CSF tau, ptau181, and VILIP-1, whereas only DTI-derived fractional anisotropy correlated with ptau181 (Fig.2). Unfortunately, DTI-derived fractional anisotropy is sensitive to the mixed contribution from both WM damages and neuroinflammation. By separating the confounding effect from neuroinflammation, DBSI metrics reflect WM abnormalities accurately and specifically.

Acknowledgements

Supported by NIA P01-AG026276, P01AG003991, P50AG05681 and NMSS RG5265 A1.

References

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2. Wang Y, Wang Q, Haldar JP, et al. Quantification of increased cellularity during inflammatory demyelination. Brain 2011;134:3590-3601.

3. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43:2412-2414.

4. Fagan AM, Mintun MA, Mach RH, et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid A beta(42) in humans. Ann Neurol 2006;59:512-519.

5. Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:280-292.

Figures

Figure1. CSF Levels of amyloid burden (Aβ42) and neuronal injury markers (tau, ptau181 and VILIP-1) in the preclinical stage 0, stage 1 and CDR 0.5 participants.

Figure 2. DTI FA skeleton (green) overlaid on the mean FA images of all participants. The skeletal voxels in blue and red represent the significantly (P<0.05) negative and positive, respectively, correlations between CSF levels of neuronal injury markers and diffusion MRI diffusivity indices.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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