Disrupted Energy Landscape in Mild Cognitive Impairment: Insights from Network Control Theory
Poster No:
202
Submission Type:
Abstract Submission
Authors:
Dara Neumann1, Qolamreza Razlighi2, José Luchsinger3, Yaakov Stern3, Davangere Devanand3, Amy Kuceyeski1, Ceren Tozlu2
Institutions:
1Cornell University, Ithaca, NY, 2Weill Cornell Medicine, New York, NY, 3Columbia University Irving Medical Center, New York, NY
First Author:
Co-Author(s):
Introduction:
Mild cognitive impairment (MCI) is a clinical cognitive deficit that is not severe enough to meet the threshold for Alzheimer's Disease (AD) (Lopez, 2013). MCI prevalence ranges from 3% to as high as 42% in population-based studies (Tricco et al., 2012). MCI patients have an increased risk of AD (Morris et al., 2001), so a diagnosis of MCI may represent a critical turning point in the trajectory of developing AD and thus may be an opportunity for early intervention. Establishing neuroimaging signatures of MCI may allow more informed diagnosis, and, more importantly, an understanding of its underlying mechanisms that could pave the way for novel treatments. Therefore, this study aims to explore if brain dynamics and/or entropy of brain activity are different in subjects with MCI compared to healthy controls.
Methods:
499 healthy controls (HC) (mean age: 66.7, sex: 59.9% F) and 55 patients with MCI (mean age: 71.9, sex: 50.9% F) were included in our study. First, brain activity states were identified through k-means clustering of functional MRI (fMRI) time series. Second, Network Control Theory (NCT), which serves as a modeling strategy to capture brain dynamics on the structural connectome, was used to measure the minimum energy required to transition between these brain states or so-called transition energy (TE). TE was computed between every pair of brain states at a global, network, and regional level. Additionally, entropy of fMRI time series was calculated for each brain region. The TE and entropy metrics were compared between the MCI and control groups using an ANCOVA that controlled for age, sex, and intracranial volume. The p-values were corrected for multiple comparisons with the Benjamini-Hochberg method.
Results:
Commonly recurring brain states included those with high and low amplitude activity in limbic, somatomotor, and dorsal attention networks (see Figure 1). Lower global TE (p=0.038) and global entropy (p<0.001) were observed in subjects with MCI compared to HC. Twenty-two brain regions showed significantly higher TE in HC compared to MCI and 15 regions showed significantly higher TE in MCI compared to HC. TE between the two limbic states and TE between visual and limbic states were significantly lower in MCI compared to HC. 199 out of 200 regions had significantly higher entropy in HC compared to MCI.
Conclusions:
This study highlights energetic and entropic alterations in MCI, offering novel insights into the neurobiological mechanisms underlying this disease. Lower TE in the MCI subjects compared to controls may indicate a possible compensation mechanism in this early stage of cognitive impairment, while decreased entropy across almost all regions in the brain could reflect disease processes. Future studies that include Alzheimer's subjects are needed to better characterize the changes in the energetic landscape regarding the later stage of cognitive impairment. These findings could be used to understand how brain dynamics shift early in the path to dementia, potentially leading to applications aimed at alleviating clinical symptoms and improving the quality of life for people with MCI.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI
Multi-Modal Imaging
Keywords:
Aging
Computational Neuroscience
Degenerative Disease
DISORDERS
FUNCTIONAL MRI
MRI
Positron Emission Tomography (PET)
STRUCTURAL MRI
Structures
Systems
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Provide references using APA citation style.
Morris, J. C., Storandt, M., Miller, J. P., McKeel, D. W., Price, J. L., Rubin, E. H., & Berg, L. (2001). Mild cognitive impairment represents early-stage alzheimer disease. Archives of Neurology, 58(3). https://doi.org/10.1001/archneur.58.3.397
Tricco AC, Soobiah C, Lillie E, et al. Use of cognitive enhancers for mild cognitive impairment: protocol for a systematic review and network meta-analysis. Syst Rev. 2012;1:25. doi: 10.1186/2046-4053-1-25