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Functional ultrasound imaging and prewhitening analysis reveal MK-801-induced disruption of brain network connectivity

Affiliation
Department of Neuroscience ,University of California Riverside ,Riverside ,CA ,United States
Hakopian, Erik;
Affiliation
Department of Neuroscience ,University of California Riverside ,Riverside ,CA ,United States
Stepanian, Argishti E.;
Affiliation
Department of Neuroscience ,University of California Riverside ,Riverside ,CA ,United States
Zhong, Shan;
Affiliation
Department of Biomedical Engineering ,University of Southern California ,Los Angeles ,CA ,United States
Agyeman, Kofi A.;
Affiliation
Department of Neurological Surgery ,Keck School of Medicine ,University of Southern California ,Los Angeles ,CA ,United States
Zepeda, Nancy;
Affiliation
Department of Neurological Surgery ,Keck School of Medicine ,University of Southern California ,Los Angeles ,CA ,United States
Wu, Kevin;
Affiliation
Department of Biomedical Engineering ,University of Southern California ,Los Angeles ,CA ,United States
Liu, Charles;
Affiliation
Department of Biomedical Engineering ,University of Southern California ,Los Angeles ,CA ,United States
Lee, Darrin J.;
Affiliation
Department of Biomedical Engineering ,University of Southern California ,Los Angeles ,CA ,United States
Christopoulos, Vassilios

Background Disruption of N-methyl-D-aspartate receptor (NMDAR) activity within the septohippocampal network - a critical circuit that includes the hippocampus, medial prefrontal cortex (mPFC) and other nuclei - is believed to contribute to learning and memory impairments. Although animal models using the NMDAR antagonist Dizocilpine (MK-801) replicate cognitive deficits associated with memory and learning disorders, the direct effects of MK-801 on brain network connectivity have not been well characterized. Objective This study aims to explore the effects of MK-801 on brain network connectivity using functional ultrasound imaging (fUSI) and apply time series analysis methods to mitigate potential statistical confounds in functional connectivity assessments. Methods fUSI was employed to assess changes in cerebral blood volume (CBV) and network connectivity in MK-801-treated mice. To account for the nonstationarity and autocorrelation inherent in fUSI time series, an AutoRegressive Integrated Moving Average (ARIMA) model was applied to stabilize the mean and remove autocorrelation, ensuring more reliable signal analysis. Results Our analysis revealed that MK-801 significantly disrupts functional connectivity (FC) across key brain regions, including the hippocampus, mPFC, and striatum. We also demonstrated that removing autocorrelation from the fUSI time series mitigates the risk of spurious associations, enhancing the reliability of network analysis. Conclusion This study demonstrates the importance of accounting for nonstationarity in fUSI time series to improve the accuracy of brain network connectivity analysis. Our findings indicate that MK-801-induced NMDAR inhibition disrupts connectivity both within and outside the septohippocampal circuit, offering new insights into the neural mechanisms underlying cognitive deficits in disorders affecting memory and learning.

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License Holder: Copyright © 2025 Hakopian, Stepanian, Zhong, Agyeman, Zepeda, Wu, Liu, Lee and Christopoulos.

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