» Articles » PMID: 39424929

Aging-dependent Loss of Functional Connectivity in a Mouse Model of Alzheimer's Disease and Reversal by MGluR5 Modulator

Abstract

Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (App/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923/ALX001) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

References
1.
Sheline Y, Morris J, Snyder A, Price J, Yan Z, DAngelo G . APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aβ42. J Neurosci. 2010; 30(50):17035-40. PMC: 3023180. DOI: 10.1523/JNEUROSCI.3987-10.2010. View

2.
Wang J, Zuo X, Dai Z, Xia M, Zhao Z, Zhao X . Disrupted functional brain connectome in individuals at risk for Alzheimer's disease. Biol Psychiatry. 2012; 73(5):472-81. DOI: 10.1016/j.biopsych.2012.03.026. View

3.
Marquez F, Yassa M . Neuroimaging Biomarkers for Alzheimer's Disease. Mol Neurodegener. 2019; 14(1):21. PMC: 6555939. DOI: 10.1186/s13024-019-0325-5. View

4.
Wang X, Huang W, Su L, Xing Y, Jessen F, Sun Y . Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease. Mol Neurodegener. 2020; 15(1):55. PMC: 7507636. DOI: 10.1186/s13024-020-00395-3. View

5.
Chetelat G . Multimodal Neuroimaging in Alzheimer's Disease: Early Diagnosis, Physiopathological Mechanisms, and Impact of Lifestyle. J Alzheimers Dis. 2018; 64(s1):S199-S211. PMC: 6004909. DOI: 10.3233/JAD-179920. View