Poster No:
327
Submission Type:
Abstract Submission
Authors:
Henry Bockholt1, Bradley Baker2, Jordan Clemsen1, Vince Calhoun3, Jane Paulsen4
Institutions:
1GSU, Atlanta, GA, 2TReNDs, Atlanta, GA, 3GSU/GATech/Emory, Decatur, GA, 4University of Wisconsin, Madison, WI
First Author:
Co-Author(s):
Introduction:
This study aims to delineate the functional and structural brain network differences in Huntington's Disease (HD) patients with and without recent suicide ideation (SI), utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI).
Methods:
The resting state functional MRI (rs-fMRI) datasets underwent a standardized preprocessing regimen, including motion correction, spatial normalization, and smoothing to reduce artifacts. We applied Spatially Constrained Independent Component Analysis (SC-ICA), following Du et al. (2020), through the GIFT toolbox to segregate functional networks from background noise. This approach enhances the detection of spatially coherent neural activity patterns. Post-ICA, K-Means clustering sorted independent components into distinct brain networks. We then computed dynamic functional network connectivity (dFNC) states using sliding-window Pearson correlation to capture the temporal variability of network interactions. This method permits the assessment of the stability and fluctuation of functional connections over time. Samples were comprised of 90 participants obtained as part of the Prevent-HD study at the University of Wisconsin-Madison. Participants with the gene mutation for Huntington's disease (HD) were separated into two groups according to findings from the Columbia Suicide Severity Rating Scale (C-SSRS), a standardized tool for evaluating the presence and severity of suicidal ideation and behavior. Suicidal ideation and behaviors are documented over a range from passive death wishes to active suicidal plans. Individuals reporting suicidal ideation (SI) within the past three months were compared with those endorsing no SI. Resting fMRI "Eyes Open" data were gathered with a 3T GE Premier (Flip Angle = 50, TE = 0.032, TR = 0.607, Slice Thickness = 2.5mm, Multiband Acceleration Factor = 8).
Results:
The dynamic nature of brain connectivity was quantified, revealing patterns specific to HD participants with recent SI. Comparative analysis between groups demonstrated significant differences in the dFNC states. Specifically, reduced connectivity strength was observed in networks involving the prefrontal and limbic systems in the SI group, suggesting a potential disruption in the neural circuits related to mood regulation and executive function. Additionally, the SI group exhibited alterations in white matter integrity, indicating a possible structural basis for the observed functional discrepancies.
Conclusions:
The findings illustrate the intricate relationship between functional and structural brain network disruptions and SI in persons with the gene mutation for HD. The variability in dFNC states provides a nuanced understanding of the pathophysiological mechanisms underlying SI in HD, which may inform clinical monitoring and intervention strategies. Future research should explore the longitudinal progression of these network changes and their potential as biomarkers for psychiatric comorbidities in neurodegenerative diseases.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Learning and Memory:
Working Memory
Lifespan Development:
Aging
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Aging
Cerebrovascular Disease
Degenerative Disease
DISORDERS
FUNCTIONAL MRI
MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
1. Du, Yuhui, et al. “NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders.” NeuroImage: Clinical 28 (2020): 102375.
2. Rachakonda S, Egolf E, Correa N, Calhoun V. Group ICA of fMRI toolbox (GIFT) manual. Dostupnez [cit 2011-11-5]. 2007 Dec 18.
3. Sakoğlu, Ünal, et al. “A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia.” Magnetic Resonance Materials in Physics, Biology and Medicine 23.5 (2010): 351-366.
4. Wilcoxon, Frank. “Individual comparisons by ranking methods.” Breakthroughs in statistics. Springer, New York, NY, 1992. 196-202.
5. Sakoğlu, Ünal, et al. “A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia.” Magnetic Resonance Materials in Physics, Biology and Medicine 23.5 (2010): 351-366.
6. Friston, Karl J. “Statistical parametric mapping.” Neuroscience databases. Springer, Boston, MA, 2003. 237-250.
7. Posner, K., Brown, G. K., Stanley, B., Brent, D. A., Yershova, K. V., Oquendo, M. A., ... & Mann, J. J. (2011). The Columbia–Suicide Severity Rating Scale: Initial validity and internal consistency findings from three multisite studies with adolescents and adults. American Journal of Psychiatry, 168(12), 1266-1277.
8. Kachian, Z. R., Cohen-Zimerman, S., Bega, D., Gordon, B., & Grafman, J. (2019).
Suicidal ideation and behavior in Huntington’s disease: Systematic review
and recommendations. Journal of Affective Disorders, 250, 319–329.
9. McGarry, A., McDermott, M. P., Kieburtz, K., Fung, W. L. A., McCusker, E., Peng, J., de Blieck, E. A., & Cudkowicz, M. (2019). Risk factors for suicidality in Huntington disease. Neurology, 92(14), e1643–e1651.
10. Radin, A. K., Shaw, J., Brown, S. P., Flint, H., Fouts, T., McCue, E., Skeie, A., Peña, C., Youell, J., Ratzliff, A., Powers, D. M., Biss, M., Lemon, H., Sandoval, D.,
Hartmann, J., Hammar, E., Doty-Jones, A., Wilson, J., Austin, G., ... Comtois, K. A. (2023). Comparative effectiveness of safety planning intervention with
instrumental support calls (ISC) versus safety planning intervention with two-
way text message caring contacts (CC) in adolescents and adults screening
positive for suicide risk in emergency departments and primary care clinics: