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BOLD Signal Variability and Functional Connectivity Alterations in Major Depressive Disorder:

Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM

Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM

Poster No:

1892

Submission Type:

Abstract Submission

Authors:

kaiting ko¹, Chih-Chieh Yang²

Institutions:

¹National Yang Ming Chiao Tung University, TAIPEI, taipei, ²Institution of Brain Science, Taipei, Taipei

First Author:

kaiting ko
National Yang Ming Chiao Tung University
TAIPEI, taipei

Co-Author:

Chih-Chieh Yang
Institution of Brain Science
Taipei, Taipei

Introduction:

Major depressive disorder (MDD) is associated with altered brain activity, yet neuroimaging findings remain inconsistent. Traditional metrics often fail to capture dynamic neural changes, whereas Blood Oxygen Level-Dependent (BOLD) signal variability provides a novel lens to assess moment-to-moment fluctuations in brain activity. This study investigates BOLD signal variability as a biomarker for distinguishing MDD from healthy states, using resting-state functional MRI (rs-fMRI) to identify regional and network-level alterations.

Methods:

Resting-state fMRI data from 45 individuals with MDD and 45 age- and sex-matched healthy controls were analyzed from the Taiwan Aging and Mental Illness (TAMI) cohort. BOLD signal variability was calculated as the voxel-wise standard deviation of the time series. Group differences were assessed using general linear models, controlling for demographic covariates. Functional connectivity analyses examined network disruptions involving regions with altered variability.

Results:

Increased BOLD signal variability in MDD was identified in default mode network (DMN) regions, including the bilateral precuneus, posterior cingulate cortex, and left cuneus. Decreased variability was observed in the temporal lobe (e.g., superior temporal gyrus, temporal pole), frontal lobe (e.g., orbitofrontal cortex, medial frontal gyrus), insula, limbic structures (e.g., amygdala, anterior cingulate cortex), and subcortical areas (e.g., hippocampus, nucleus accumbens, putamen). Hypervariable DMN regions showed reduced connectivity with cognitive and emotional regulatory areas, while hypovariable regions exhibited altered connectivity with frontal executive regions.

·Regional Differences in BOLD Signal Variability in Patients with MDD

Conclusions:

Distinct patterns of BOLD signal variability in MDD reveal region-specific alterations in neural activity and connectivity, particularly in networks involved in self-referential thought, emotion regulation, and cognitive processing. These findings highlight the potential of BOLD signal variability as a biomarker for understanding MDD pathophysiology and guiding targeted interventions.

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ²

Novel Imaging Acquisition Methods:

BOLD fMRI ¹

Keywords:

Affective Disorders

FUNCTIONAL MRI

^1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Please indicate which methods were used in your research:

Functional MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

Other, Please list - MATLAB

Provide references using APA citation style.

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Diagnostic and statistical manual of mental disorders: DSM-5™, 5th ed. (2013). [doi:10.1176/appi.books.9780890425596]. American Psychiatric Publishing, Inc. https://doi.org/10.1176/appi.books.9780890425596
Dinatolo, M. F., Pur, D. R., Eagleson, R., & de Ribaupierre, S. (2023). The Role of Blood Oxygen Level Dependent Signal Variability in Pediatric Neuroscience: A Systematic Review. Life (Basel), 13(7). https://doi.org/10.3390/life13071587
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Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2010). Blood oxygen level-dependent signal variability is more than just noise. Journal of Neuroscience, 30(14), 4914-4921. https://doi.org/10.1523/JNEUROSCI.5166-09.2010
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011). The importance of being variable. Journal of Neuroscience, 31(12), 4496-4503. https://doi.org/10.1523/JNEUROSCI.5641-10.2011
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Garrett, D. D., Samanez-Larkin, G. R., MacDonald, S. W. S., Lindenberger, U., McIntosh, A. R., & Grady, C. L. (2013). Moment-to-moment brain signal variability: A next frontier in human brain mapping? Neuroscience and Biobehavioral Reviews, 37(4), 610-624. https://doi.org/10.1016/j.neubiorev.2013.02.015
Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. (2018). Lancet, 392(10159), 1789-1858. https://doi.org/10.1016/s0140-6736(18)32279-7
Grady, C. L., & Garrett, D. D. (2013). Understanding variability in the BOLD signal and why it matters for aging. Brain Imaging and Behavior, 8(2), 274-283. https://doi.org/10.1007/s11682-013-9253-0
Grady, C. L., Rieck, J. R., Baracchini, G., & DeSouza, B. (2023). Relation o

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