Poster No:
975
Submission Type:
Abstract Submission
Authors:
Shi TANG1
Institutions:
1The chinese university of hong kong, Hong Kong, 0
First Author:
Shi TANG
The chinese university of hong kong
Hong Kong, 0
Introduction:
Understanding brain networks is crucial for neural and cognitive insights. The DMN, once thought inactive at rest, now reveals functions. Directly interpreting DMN function is challenging; thus, studying its maturation is key. In infants, structure precedes function. Connectivity evolves from separation to integration, being weak in 7-9-year-olds and strengthening with age. The MPFC-PCC connection is detectable in kids, correlating with age in 9-13-year-olds. During peri-puberty, DMN connectivity progresses rapidly. This research uses ABCD Study fMRI to quantify age-related DMN changes, aiming to boost understanding of DMN maturation and its impact on adolescent cognition, potentially aiding in comprehending brain development and disorders.
Methods:
Data collection and preprocessing begin with subject selection, excluding those with DSM-5 diagnoses, brain trauma, and addiction records. Quality-controlled rs-fMRI data from the ABCD study is used. Images go through motion and slice timing correction, spatial normalization to MNI 152 2mm, and smoothing. Parcellation with the Gordon 333 (2014) atlas extracts DMN regions, and the Desikan-Killiany atlas identifies anatomical regions.
In the General Linear Model (GLM) analysis, age and sex are independent variables, and the Pearson correlation coefficients of DMN ROI time series data are the dependent variable. Using Python 3.11, the GLM is fitted and hypothesis testing is carried out to determine the significance of age and sex on DMN connectivity.
The machine learning approach extracts features of DMN connectivity data. Age and sex are encoded numerically and binarily. Linear and polynomial regression models predict connectivity, and random forests classify subjects by age or sex groups. Evaluation uses mean squared error for regression and accuracy for classification, and feature importances are examined.
Finally, statistical inference and correction involve applying T-tests, F-tests, and FDR correction to handle false positives in multiple statistical tests. This ensures the reliability of results regarding DMN connectivity changes across age and sex, providing a comprehensive and reliable framework for understanding the relationship between age, sex, and DMN connectivity.
Results:
Males had more alterations and a centered distribution; females' was wider. Females had greater correlation increases, and significant correlation dropped from baseline to 2-year follow-up. At 2-year follow-up, females had a prominent high correlation and males more high-correlation node pairs.
In Age Group 1, gender connectivity differences were clear. Males had positive changes (e.g., L_superiorfrontal-L_precuneus), strengthening neural connections, while females had negative ones (e.g., R_medialorbitofrontal-L_precuneus), weakening links. In Age Group 2, both genders had negative changes. Overall, correlations decreased slightly in both genders. From baseline to 2-year follow-up, the significant correlation distribution declined. In Age Group 1 (9.5-year-olds), males had positive, females negative changes. In Age Group 2 (10.5-year-olds), both had negative changes.
Conclusions:
Typical DMN connectivity development varies by gender. Males have more centered alterations, females a wider distribution. Correlation changes differ, with females showing more pronounced increases. In different age groups, both genders have specific positive or negative changes, highlighting the complexity and gender specificity of DMN development.
Lifespan Development:
Early life, Adolescence, Aging 1
Normal Brain Development: Fetus to Adolescence 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Data analysis
Development
Modeling
Multivariate
NORMAL HUMAN
1|2Indicates the priority used for review
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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):
Healthy subjects
Was this research conducted in the United States?
No
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.
Yes
Please indicate which methods were used in your research:
Functional MRI
Which processing packages did you use for your study?
FSL
Provide references using APA citation style.
Fair, D. A., Cohen, A. L., Power, J. D., Dosenbach, N. U. F., Church, J. A., Miezin, F. M., Schlaggar, B. L., & Petersen, S. E. (2009). Functional brain networks develop from a “local to distributed” organization. PLoS Computational Biology, 5(5), 14–23.
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Gordon, E. M., Lee, P. S., Maisog, J. M., Foss-Feig, J., Billington, M. E., VanMeter, J., & Vaidya, C. J. (2011). Strength of default mode resting-state connectivity relates to white matter integrity in children. Developmental Science, 14(4), 738–751.
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 100(1).
Miŝic, B., Betzel, R. F., De Reus, M. A., Van Den Heuvel, M. P., Berman, M. G., McIntosh, A. R., & Sporns, O. (2016). Network-level structure-function relationships in human neocortex. Cerebral Cortex, 26(7), 3285–3296.
Raichle, M. E., & Snyder, A. Z. (2007). A default mode of brain function: a brief history of an evolving idea. Neuroimage, 37(4), 1083–1090.
No