Microglial activation mediates progression of neuronal dysfunction and cognitive impairment in AD
Poster No:
139
Submission Type:
Abstract Submission
Authors:
Yaoyu Zhang1,2, Wenqi Zhang1, Jialin Hu1, Miao Zhang3, Yibo Zhao4, Yudu Li4,5,6, Wen Jin4,7, Wenli Li1, Xu-Feng Jiang3, Zhi-Pei Liang4,7, Biao Li3, Yao Li1,2
Institutions:
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China, 3Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 4Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 5Department of bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 6National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 7Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
First Author:
Yaoyu Zhang
School of Biomedical Engineering, Shanghai Jiao Tong University|Institute of Medical Robotics, Shanghai Jiao Tong University
Shanghai, China|Shanghai, China
School of Biomedical Engineering, Shanghai Jiao Tong University|Institute of Medical Robotics, Shanghai Jiao Tong University
Shanghai, China|Shanghai, China
Co-Author(s):
Miao Zhang
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Yibo Zhao
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
Urbana, IL, USA
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
Urbana, IL, USA
Yudu Li
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of bioengineering, University of Illinois at Urbana-Champaign|National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
Urbana, IL, USA|Urbana, IL, USA|Urbana, IL, USA
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of bioengineering, University of Illinois at Urbana-Champaign|National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
Urbana, IL, USA|Urbana, IL, USA|Urbana, IL, USA
Wen Jin
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Urbana, IL, USA|Urbana, IL, USA
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Urbana, IL, USA|Urbana, IL, USA
Xu-Feng Jiang
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Zhi-Pei Liang
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Urbana, IL, USA|Urbana, IL, USA
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Urbana, IL, USA|Urbana, IL, USA
Biao Li
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Yao Li
School of Biomedical Engineering, Shanghai Jiao Tong University|Institute of Medical Robotics, Shanghai Jiao Tong University
Shanghai, China|Shanghai, China
School of Biomedical Engineering, Shanghai Jiao Tong University|Institute of Medical Robotics, Shanghai Jiao Tong University
Shanghai, China|Shanghai, China
Introduction:
In Alzheimer's disease (AD), the accumulation of Aβ in the brain initiates a cascade of pathological events, including the propagation of tau across Braak stages(Rossano et al., 2024). This progression is associated with neuronal dysfunction and worsening of cognitive impairments. Neuroinflammation, especially microglial activation, plays multi-facet roles in AD progression. Recent evidence indicates that microglial activation propagates jointly with tau across Braak stages(Pascoal et al., 2021) and mediates key steps linking tau spread, neurodegeneration and cognitive impairment(Rossano et al., 2024). Despite this, inter-subject heterogeneity and stage-dependent microglial functions contribute to mixed findings in current research. To advance therapeutic strategies and effectively assess clinical outcomes, it is essential to investigate effects of microglial activation on neuronal metabolism. This relationship may elucidate mechanisms linking different microglial activation states to cognitive performance in AD.
N-acetylaspartate (NAA) is a known marker of neuronal integrity and mitochondrial function and may be affected by neuroinflammatory processes in AD(Chaney et al., 2021). Using MR spectroscopic imaging (MRSI), a label-free molecular imaging method, we could map NAA distributions noninvasively. However, traditional MRSI techniques are constrained by low spatial resolution, narrow brain coverage and long scan time, hindering comprehensive whole-brain analyses of associations between microglial activation, neuronal metabolism and cognitive decline in AD.
In this study, we applied SPICE, a high-resolution, whole-brain, 3D-MRSI technique(Guo et al., 2021; Lam et al., 2020; Peng et al., 2018), to map NAA levels in Aβ+, cognitively-impaired subjects. This method achieves metabolites mapping at a nominal resolution of 2×3×3mm3 with whole brain coverage within 10 minutes. Using a hybrid PET/MR system, we simultaneously measured microglial activation with [18F]DPA-714. In Braak stage-defined anatomical regions, we analyzed associations between microglial activation, NAA levels and cognitive performance. Furthermore, we investigated the role of microglial activation in AD progression, emphasizing its contribution to the spread of neuronal dysfunction across Braak stages and its relationship to multi-domain cognitive impairments.
N-acetylaspartate (NAA) is a known marker of neuronal integrity and mitochondrial function and may be affected by neuroinflammatory processes in AD(Chaney et al., 2021). Using MR spectroscopic imaging (MRSI), a label-free molecular imaging method, we could map NAA distributions noninvasively. However, traditional MRSI techniques are constrained by low spatial resolution, narrow brain coverage and long scan time, hindering comprehensive whole-brain analyses of associations between microglial activation, neuronal metabolism and cognitive decline in AD.
In this study, we applied SPICE, a high-resolution, whole-brain, 3D-MRSI technique(Guo et al., 2021; Lam et al., 2020; Peng et al., 2018), to map NAA levels in Aβ+, cognitively-impaired subjects. This method achieves metabolites mapping at a nominal resolution of 2×3×3mm3 with whole brain coverage within 10 minutes. Using a hybrid PET/MR system, we simultaneously measured microglial activation with [18F]DPA-714. In Braak stage-defined anatomical regions, we analyzed associations between microglial activation, NAA levels and cognitive performance. Furthermore, we investigated the role of microglial activation in AD progression, emphasizing its contribution to the spread of neuronal dysfunction across Braak stages and its relationship to multi-domain cognitive impairments.
Methods:
88 Aβ+, cognitively impaired patients were included: 29 MCI and 59 AD patients. MMSE, Complex Figure Test (CFT) and Stroop Test were assessed. Imaging was performed on a 3T PET/MR system. PET imaging parameters are: 127 slices, voxel size=2.1×2.1×2.0mm3. 3D-MRSI were acquired using the SPICE sequence: FOV=240×240×160mm3, voxel size=2×3×3mm3, repetition time=160ms, echo time=1.6ms, scan time=9m55s. DPA-714 uptake value ratios (SUVR) and NAA levels were computed by normalizing each voxel to the mean cerebellar gray matter and water signals, respectively. In MNI space, voxel-wise correlations between SUVR and NAA were analyzed using biological parametric mapping (BPM). A composite SUVR value was extracted from significantly negatively-correlated BPM regions. Mean NAA values were extracted from Braak stage-defined ROIs. Correlations between composite SUVR, NAA levels at early, middle and late Braak stages, and cognitive scores were analyzed. To explore roles of microglial activation in AD progression, mediation analyses were performed using structural equation modelling. p<0.05 is considered statistically significant.
Results:
As shown in Fig. 1, higher composite SUVR is correlated with lower NAA levels in all Braak stages and scores of MMSE, CFT and Stroop inferences in patients. Moreover, composite SUVR mediates the spread of NAA reduction from early to middle Braak regions, and associations between decreased NAA and worsen visuospatial, executive and general performance.
·Figure 1
·Figure 2
Conclusions:
Microglial activation is associated with and may mediate interactions between neuronal dysfunction and cognitive impairment during AD progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Novel Imaging Acquisition Methods:
MR Spectroscopy 2
Keywords:
Cognition
Magnetic Resonance Spectroscopy (MRS)
MR SPECTROSCOPY
Positron Emission Tomography (PET)
1|2Indicates the priority used for review
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Guo, R., Zhao, Y., Li, Y., Wang, T., Li, Y., Sutton, B., & Liang, Z. P. (2021). Simultaneous QSM and metabolic imaging of the brain using SPICE: further improvements in data acquisition and processing. Magn Reson Med, 85(2), 970-977. https://doi.org/10.1002/mrm.28459
Lam, F., Li, Y., Guo, R., Clifford, B., & Liang, Z. P. (2020). Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces. Magn Reson Med, 83(2), 377-390. https://doi.org/10.1002/mrm.27980
Pascoal, T. A., Benedet, A. L., Ashton, N. J., Kang, M. S., Therriault, J., Chamoun, M., Savard, M., Lussier, F. Z., Tissot, C., Karikari, T. K., Ottoy, J., Mathotaarachchi, S., Stevenson, J., Massarweh, G., Schöll, M., de Leon, M. J., Soucy, J.-P., Edison, P., Blennow, K., Zetterberg, H., Gauthier, S., & Rosa-Neto, P. (2021). Microglial activation and tau propagate jointly across Braak stages. Nature Medicine, 27(9), 1592-1599. https://doi.org/10.1038/s41591-021-01456-w
Peng, X., Lam, F., Li, Y., Clifford, B., & Liang, Z. P. (2018). Simultaneous QSM and metabolic imaging of the brain using SPICE. Magn Reson Med, 79(1), 13-21. https://doi.org/10.1002/mrm.26972
Rossano, S. M., Johnson, A. S., Smith, A., Ziaggi, G., Roetman, A., Guzman, D., Okafor, A., Klein, J., Tomljanovic, Z., Stern, Y., Brickman, A. M., Lee, S., Kreisl, W. C., & Lao, P. (2024). Microglia measured by TSPO PET are associated with Alzheimer's disease pathology and mediate key steps in a disease progression model. Alzheimer's & Dementia, 20(4), 2397-2407. https://doi.org/10.1002/alz.13699