Brain Controllability Analysis of Neuropsychiatric Symptoms Associated with Cognitive Impairment
Poster No:
623
Submission Type:
Abstract Submission
Authors:
Jared Cammon1, Thiago Macedo e Cordeiro2, Antonio Teixeira Jr3, Yingchun Zhang1
Institutions:
1University of Houston, Houston, TX, 2UTHealth Houston, Houston , TX, 3UTHealth Houston, Houston, TX
First Author:
Co-Author(s):
Introduction:
Alzheimer's disease (AD) is the most common cause of dementia, representing 60% to 80% of cases in the U.S. (2023 Alzheimer's disease facts and figures). Biomarkers are increasingly important as tools which can be utilized to identify Mild Cognitive Impairment (MCI), the prodromal stage of AD, and measure its progression into dementia stage. Neuropsychiatric Symptoms (NPS) occur in up to 85% of adults with MCI and have been shown to be diagnostic and prognostic indicators of AD (Martin et al., 2020,; Gallagher et al., 2017). Brain controllability analysis has recently been used to characterize the neural dynamics underlying neurocognitive deficits in the brain (Fang et al., 2021) and may serve as a more quantitative measurement of NPS, compared to qualitative clinical assessments. The goal of this research was to investigate the association between brain controllability and NPS in MCI, to determine if controllability analysis could serve as a biomarker for MCI.
Methods:
Data from 19 MCI subjects and 15 Cognitive Normal (CN) subjects were selected from Phase ADNI2 of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. All MCI subjects experienced NPS, evidenced by a Neuropsychiatric Inventory Questionnaire (NPI-Q) total score of at least 5. Diffusion Tensor Imaging (DTI) data was preprocessed and reconstructed in DSI Studio and anatomical scans were parcellated according to the Brainnetome Atlas, featuring 210 cortical brain regions of interest. Tractography was then performed and utilized to create a structural connectivity matrix, from which the modal controllability of each region of interest was calculated and ranked according to previous research (Fang et al., 2022). Region of interest controllability rankings were grouped based on anatomical location to obtain the structural controllability of large brain networks including the Default Mode Network (DMN), Central Executive Network (CEN), and Salience Network (SN). These brain networks make up the triple network model, which posits that all or some of these three networks are affected variably in psychiatric disorders (Menon et al., 2019). Controllability of these large brain networks was then compared to NPI-Q total scores and the spearman correlation was calculated to determine the degree and direction of association.
Results:
Figure 1 shows a trend of increasing modal controllability with increasing NPI Score for the CEN. Spearman correlation between the large brain networks and NPI scores revealed a significant and strong positive correlation between CEN and NPS (P=.01, rs=.77). Though the SN had a moderate negative correlation with NPS, it was not significant (P=.17, rs=-.48 ). The DMN had the weakest correlation which was also not significant (P=.97, rs=.02).
Conclusions:
Controllability analysis shows promise as a potential biomarker for NPS in MCI, which are known to be prognostic and diagnostic indicators of the progression of AD. Particularly, CEN modal controllability shows a strong correlation with NPS severity. Areas of high modal controllability map to networks like the CEN, which is involved with executive functions and cognitive control (Gu et al., 2015). Controllability analysis is therefore a likely tool which can be used to help understand underlying impairments associated with NPS in MCI. A larger number of subjects and a multimodal approach, incorporating functional controllability as well, may provide a clearer picture as to the use of controllability analysis as a biomarker for NPS in MCI.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Other Methods 2
Keywords:
Modeling
MRI
Psychiatric Disorders
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
2. Martin, E., & Velayudhan, L. (2020). Neuropsychiatric Symptoms in Mild Cognitive Impairment: A Literature Review. Dementia and geriatric cognitive disorders, 49(2), 146–155. https://doi.org/10.1159/000507078
3. Gallagher, D., Fischer, C. E., & Iaboni, A. (2017). Neuropsychiatric Symptoms in Mild Cognitive Impairment. Canadian journal of psychiatry. Revue canadienne de psychiatrie, 62(3), 161–169. https://doi.org/10.1177/0706743716648296
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