INS NYC 2024 Program

Poster	Poster Session 04 Program Schedule 02/15/2024 12:00 pm - 01:15 pm Room: Majestic Complex (Posters 61-120) Poster Session 04: Neuroimaging \| Neurostimulation/Neuromodulation \| Teleneuropsychology/Technology Final Abstract #106 Resting-state Functional Connectivity and Intelligence in Adolescents and Young Adults Kylie Szymanski, Georgia State University, Atlanta, United States Jordan Pincus, Georgia State University, Atlanta, United States Zening Fu, Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, United States Armin Iraji, Georgia State University, Atlanta, United States Vince Calhoun, Georgia State University, Atlanta, United States William Mahle, Children’s Healthcare of Atlanta, Atlanta, United States Thomas Burns, Children’s Healthcare of Atlanta, Atlanta, United States Tricia King, Georgia State University, Atlanta, United States Category: Neuroimaging Keyword 1: intelligence Keyword 2: neuroimaging: functional connectivity Keyword 3: adolescence Objective: Intelligence remains one of the most robust predictors of educational attainment, job performance, socioeconomic success, and overall health, thus increasing the demand to understand the brain systems that underpin intelligence in adolescents and young adults (AYAs). Converging research suggests that general intelligence relies on the function of large-scale brain networks, and specifically on the functional connectivity between the frontal and parietal regions the cognitive control (CC) domain; however, whether resting-state functional network connectivity (rsFNC) of the CC domain relates differently to crystallized and fluid intelligence remains largely unexplored. In this work, we investigated the CC-rsFNC in relation to fluid intelligence (Matrix Reasoning) and crystallized intelligence (Vocabulary) performance in AYAs. Participants and Methods: Resting-state functional magnetic resonance imaging (fMRI) data was obtained from a mixed sample of 62 participants, including 21 AYAs with congenital heart disease (Mage(SD) = 17.90(1.64), 28.6% females) and 41 healthy controls (Mage(SD) = 19.19(1.91), 56.1% females). Intelligence was measured by performance on the Wechsler Abbreviated Scale of Intelligence-II, Matrix Reasoning (MR; fluid intelligence) and Vocabulary (crystallized intelligence). We applied a novel Neuromark framework to the preprocessed fMRI data to extract the regions-of-interest, or intrinsic connectivity networks (ICNs), to estimate rsFNC. This method is based on the spatial-constrained independent component analysis, which estimates comparable ICNs across subjects while retaining more single-subject variability than seed-based methods. For each participant, time courses were taken from 17 CC ICNs defined by the Neuromark template. Pearson's correlation between the time courses of two ICNs was computed to measure the pairwise rsFNC. Pairwise rsFNC was then averaged across all possible combinations of two ICNs within the CC domain, resulting in one CC-rsFNC value for each subject. The Pearson's correlations were then converted into z-scores for analysis. Multiple linear regressions individually assessed the relationship of rsFNC with MR and Vocabulary z-scores. Results: Greater CC-rsFNC was associated with higher MR scores (R²_semipartial= 0.42, β = 7.57, p = <.001). The relationship between CC-rsFNC and the Vocabulary subtest scores was not significant (p = 0.664). Conclusions: Higher CC-rsFNC is associated with better fluid intelligence scores (MR), aligning with prior research emphasizing the importance of connectivity of the frontoparietal network for intelligence and the essential role of frontal-parietal integration in problem-solving. One predominant theory of the neurological basis of intelligence suggests that the parietal cortex integrates somatosensory input and interacts with prefrontal regions, which then apply cognitive skills, like analogical reasoning and attentional control, to solve cognitive problems given sensory information. This theory posits that heightened general intelligence emerges from enhanced communication between the frontal and parietal regions. However, the result that CC-rsFNC was not associated with crystallized intelligence suggests there may be different neurological underpinnings specific to fluid and crystallized intelligence, warranting further investigation. The specific mechanisms through which the CC domain contributes to different aspects of intelligence may hold implications for optimizing educational attainment, socioeconomic success, career achievement, and overall health.

Poster

02/15/2024
12:00 pm - 01:15 pm
Room: Majestic Complex (Posters 61-120)

Poster Session 04: Neuroimaging | Neurostimulation/Neuromodulation | Teleneuropsychology/Technology

Final Abstract #106

Resting-state Functional Connectivity and Intelligence in Adolescents and Young Adults

Kylie Szymanski, Georgia State University, Atlanta, United States
Jordan Pincus, Georgia State University, Atlanta, United States
Zening Fu, Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, United States
Armin Iraji, Georgia State University, Atlanta, United States
Vince Calhoun, Georgia State University, Atlanta, United States
William Mahle, Children’s Healthcare of Atlanta, Atlanta, United States
Thomas Burns, Children’s Healthcare of Atlanta, Atlanta, United States
Tricia King, Georgia State University, Atlanta, United States

Category: Neuroimaging

Keyword 1: intelligence
Keyword 2: neuroimaging: functional connectivity
Keyword 3: adolescence

Objective:

Intelligence remains one of the most robust predictors of educational attainment, job performance, socioeconomic success, and overall health, thus increasing the demand to understand the brain systems that underpin intelligence in adolescents and young adults (AYAs). Converging research suggests that general intelligence relies on the function of large-scale brain networks, and specifically on the functional connectivity between the frontal and parietal regions the cognitive control (CC) domain; however, whether resting-state functional network connectivity (rsFNC) of the CC domain relates differently to crystallized and fluid intelligence remains largely unexplored. In this work, we investigated the CC-rsFNC in relation to fluid intelligence (Matrix Reasoning) and crystallized intelligence (Vocabulary) performance in AYAs.

Participants and Methods:

Resting-state functional magnetic resonance imaging (fMRI) data was obtained from a mixed sample of 62 participants, including 21 AYAs with congenital heart disease (Mage(SD) = 17.90(1.64), 28.6% females) and 41 healthy controls (Mage(SD) = 19.19(1.91), 56.1% females). Intelligence was measured by performance on the Wechsler Abbreviated Scale of Intelligence-II, Matrix Reasoning (MR; fluid intelligence) and Vocabulary (crystallized intelligence). We applied a novel Neuromark framework to the preprocessed fMRI data to extract the regions-of-interest, or intrinsic connectivity networks (ICNs), to estimate rsFNC.
This method is based on the spatial-constrained independent component analysis, which estimates comparable ICNs across subjects while retaining more single-subject variability than seed-based methods. For each participant, time courses were taken from 17 CC ICNs defined by the Neuromark template. Pearson's correlation between the time courses of two ICNs was computed to measure the pairwise rsFNC. Pairwise rsFNC was then averaged across all possible combinations of two ICNs within the CC domain, resulting in one CC-rsFNC value for each subject. The Pearson's correlations were then converted into z-scores for analysis.
Multiple linear regressions individually assessed the relationship of rsFNC with MR and Vocabulary z-scores.

Results:

Greater CC-rsFNC was associated with higher MR scores (R²_semipartial= 0.42, β = 7.57, p = <.001). The relationship between CC-rsFNC and the Vocabulary subtest scores was not significant (p = 0.664).

Conclusions:

Higher CC-rsFNC is associated with better fluid intelligence scores (MR), aligning with prior research emphasizing the importance of connectivity of the frontoparietal network for intelligence and the essential role of frontal-parietal integration in problem-solving. One predominant theory of the neurological basis of intelligence suggests that the parietal cortex integrates somatosensory input and interacts with prefrontal regions, which then apply cognitive skills, like analogical reasoning and attentional control, to solve cognitive problems given sensory information. This theory posits that heightened general intelligence emerges from enhanced communication between the frontal and parietal regions. However, the result that CC-rsFNC was not associated with crystallized intelligence suggests there may be different neurological underpinnings specific to fluid and crystallized intelligence, warranting further investigation. The specific mechanisms through which the CC domain contributes to different aspects of intelligence may hold implications for optimizing educational attainment, socioeconomic success, career achievement, and overall health.