Brain Entropy and Neurocognition in Children: An ABCD Study
Poster No:
1013
Submission Type:
Abstract Submission
Authors:
Donovan Roediger1, Jessica Butts1, Kelly Duffy1, Kirsten McKone1, Andrea Wiglesworth1, Bonnie Klimes-Dougan1, Mark Fiecas1, Monica Luciana1, Bryon Mueller1, Kathryn Cullen1
Institutions:
1University of Minnesota, Minneapolis, MN
First Author:
Co-Author(s):
Introduction:
The entropy of BOLD signal fluctuations in fMRI offers a glimpse into the complexity and variability of the underlying neural signals. However, the clinical and cognitive implications of brain entropy remain largely unexplored, particularly in the context of child development. Here we apply a recently-developed method for measuring sample entropy (SampEn) in fMRI data to a large neuroimaging dataset focused on adolescent development. We examine the relationship between brain entropy and neurocognitive performance across the whole brain during the developmental stage of late childhood.
Methods:
We analyzed baseline data from 6898 participants (ages 9-11) in the Adolescent Brain Cognitive Development (ABCD) dataset. Participants included in the analysis met two criteria: 1) summary neurocognitive scores were available in the form of Bayesian Probabilistic Principal Components Analysis (BPPCA) scores (Thompson, 2019), and 2) resting-state fMRI data met minimum quality requirements (at least 20 low-motion windows consisting of 20 consecutive volumes with a respiratory-filtered framewise displacement <0.3mm were available across all runs). Imaging data were preprocessed using the ABCD-HCP pipelines (Feczko, 2021), modified to include a wider bandpass filter (0.009-0.25Hz) and additional spatial smoothing (4mm gaussian, separately in surface and volume space). SampEn values were then calculated at each vertex using the powseR package in R (Roediger, 2024), which allowed for data-driven selection of optimal template length (m=3) and tolerance factor (r=0.325) parameters.
The relationship between the grayordinate-wise entropy maps and each of the three principal component (PC) scores (representing general ability, executive function, and learning/memory) was assessed using the Permutation Analysis of Linear Models (PALM) software package (Winkler, 2014), adjusting for sex assigned at birth and pubertal stage (Pubertal Development Scale scores).
The relationship between the grayordinate-wise entropy maps and each of the three principal component (PC) scores (representing general ability, executive function, and learning/memory) was assessed using the Permutation Analysis of Linear Models (PALM) software package (Winkler, 2014), adjusting for sex assigned at birth and pubertal stage (Pubertal Development Scale scores).
Results:
For each of the three PC scores, the relationship between SampEn and neurocognitive performance varied across the brain. Unthresholded maps of t-statistics are shown in Figure 1. While the strength of the relationship differed across the three PCs, the direction of the relationships were markedly similar. Across PCs, SampEn in sensorimotor, insular, and limbic structures had a positive relationship with neurocognitive performance while frontal, parietal, middle temporal, and cerebellar regions exhibited a negative relationship.
Conclusions:
In late childhood, neurocognitive ability appears to be supported by a combination of lower brain entropy in some regions (especially frontoparietal, default mode areas, and the cerebellum) and higher entropy in others (especially sensorimotor areas, insula, and limbic structures). The direction of these relationships being broadly consistent across different categories of neurocognition (PCs) suggests that this general pattern may be foundational to a broad array of neurocognitive skills. Higher entropy in sensorimotor areas might indicate a more adaptable and flexible network, which could enhance performance on various neurocognitive tasks by allowing for more flexible sensory processing and motor responses. On the other hand, lower entropy in frontal and parietal areas might reflect a more stable and efficient neural state, which could improve performance on tasks requiring executive functions and attentional control.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Learning and Memory:
Learning and Memory Other
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Keywords:
Cognition
Development
FUNCTIONAL MRI
PEDIATRIC
Other - Entropy
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
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.
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:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Roediger D.J. (2024). Optimizing the measurement of sample entropy in resting-state fMRI data. Frontiers in Neurology, 15, 1331365.
Thompson W.K. (2019). The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: Findings from the ABCD study’s baseline neurocognitive battery. Developmental Cognitive Neuroscience, 36, 100606.
Winkler A.M. (2014). Permutation inference for the general linear model. Neuroimage, 92(100), 381–397.