Early fluid intelligence linked to faster gray matter refinement

Early intelligence levels in children can predict the pace of their physical and functional brain development as they progress through adolescence. A large study found that children with higher cognitive abilities experience faster gray matter shrinkage and a more gradual maturation of white matter pathways. The findings were published in Developmental Cognitive Neuroscience.

During adolescence, the human brain undergoes immense physical and functional remodeling. Gray matter, which contains the main cell bodies of neurons, initially grows during childhood and then shrinks in a process of refinement known as synaptic pruning. At the same time, white matter, which acts as the communication cables connecting different brain regions, typically strengthens as protective sheaths of myelin wrap around nerve fibers.

As the brain physically changes, its functional networks also reorganize. The synchronization between different neural networks, measured while the brain is at rest, evolves to balance integration and specialization. Researchers wanted to understand how individual differences in early childhood intelligence relate to these long-term developmental trajectories.

Previous research on cognitive ability and brain maturation has often relied on cross-sectional data, taking a snapshot of a single moment in time. Longitudinal studies tracking the same individuals over many years have been relatively limited in size and scope. To address this gap, researchers Phoebe Thomson, Philip Shaw, and Divyangana Rakesh from King’s College London conducted a detailed investigation into how early intelligence tracks with multiple brain changes over time.

The investigators analyzed data from the Adolescent Brain Cognitive Development study. This is an ongoing, large study following thousands of children across the United States. For this analysis, the researchers reviewed data from 10,495 participants. The data spanned four time points, beginning when the children were around nine or ten years old and continuing into middle adolescence.

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At the start of the study, participants completed a battery of tests to assess different facets of their general cognitive ability. The researchers separated these scores into three distinct categories. The first category was fluid cognition, which involves problem-solving, memory, and processing speed.

The second category was crystallized cognition, which measures accumulated knowledge, such as reading comprehension and vocabulary. The third metric was matrix reasoning, a specific type of visual and spatial abstract reasoning. Treating these abilities as separate constructs allowed the research team to see if different types of intelligence corresponded to different biological maturation patterns.

To measure brain development, the team examined three types of magnetic resonance imaging scans. Structural imaging provided data on the volume, surface area, and thickness of gray matter. Diffusion imaging tracked changes in white matter microstructure. Finally, resting-state functional imaging recorded how different functional networks in the brain communicated with one another.

Because family environment can influence both cognitive test scores and biological development, the researchers adjusted their mathematical models for household socioeconomic status. They also accounted for participant sex and the technical variations among different magnetic resonance imaging scanners used at different research sites.

The researchers first looked at gray matter structure across the whole brain. They found that baseline fluid cognition predicted how cortical surface area and volume changed over time. Specifically, teenagers who started with higher fluid cognition scores experienced a faster rate of gray matter contraction.

This faster decrease in gray matter volume and surface area was especially prominent in the frontal lobe. The frontal lobe is heavily involved in higher-order thinking and decision-making. These structural changes align with theories that children with higher cognitive abilities might undergo an accelerated period of neural refinement following childhood growth.

Cortical thickness, a different structural metric, did not show a statistically significant association with any of the cognitive measures. Crystallized cognition and matrix reasoning scores also did not relate to long-term changes in gray matter structure.

Next, the researchers examined white matter development using a metric known as fractional anisotropy. This metric evaluates how uniformly water molecules travel along neural pathways. Higher fractional anisotropy generally indicates thicker myelin sheathing and more tightly packed nerve fibers, which typically increase over the course of adolescence as brain regions become better connected.

The researchers found that early matrix reasoning scores predicted the rate of this white matter maturation. Children with higher matrix reasoning scores started with higher baseline white matter integrity, but they exhibited a slower, more protracted increase in fractional anisotropy over time. This pattern was visible in several major white matter tracts, including those connecting the frontal lobe to other areas.

Fluid and crystallized cognition did not show a statistically significant association with white matter development.

Finally, the team evaluated resting-state functional connectivity, measuring the communication strength between distinct brain networks. They focused on how often separate networks synchronized their activity. Early scores across all three cognitive measures predicted the trajectory of this between-network communication.

Children with lower baseline cognitive scores exhibited a longitudinal increase in connectivity between different functional networks. This general increase was absent in participants with higher initial cognitive scores. Instead, youth with higher intelligence scores showed a faster decrease in connectivity between certain networks that normally decouple during adolescence, such as those governing auditory processing and cognitive control.

When evaluating resting-state functional connectivity at a closer level, the team looked at exactly how specialized networks interact. They noticed that higher crystallized cognition and matrix reasoning scores were linked to a stronger separation between the default mode network, active during internal thought, and higher-order attention networks. Over the course of adolescence, these systems became more distinctly compartmentalized in youths with higher cognitive abilities.

This decoupling process is thought to help distinct brain networks operate more independently. The findings suggest that higher childhood intelligence might relate to an accelerated specialization of these functional networks.

The study provides an observational account of brain development and does not demonstrate that high cognitive ability directly causes these physical or functional changes. The findings could be influenced by shared underlying variables, such as genetics or environmental stimulation, which might independently shape both intelligence and brain maturation.

Because data from the final measurement time point was incomplete at the time of the analysis, the researchers primarily utilized linear models to chart developmental changes. Brain maturation often follows non-linear patterns, speeding up or slowing down at different ages. Future longitudinal tracking covering a wider age range could help map these physical changes with even greater precision.

The study, “Cognitive ability in childhood predicts adolescent structural and functional brain development: A longitudinal study,” was authored by Phoebe Thomson, Philip Shaw, and Divyangana Rakesh.