How does brain development vary from person to person?

Why are longitudinal datasets necessary compared to cross-sectional datasets for studying individual brain development?

Only longitudinal datasets can track within-person changes over time. Cross-sectional data can establish normative ranges for age groups but cannot estimate individual rates of change.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

How many time points of high-quality MRI scans were required for participants to be included in this study's analysis?

Participants were required to have exactly three high-quality anatomical brain scans. This allowed the researchers to model individual-level slopes.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What were the names and locations of the three longitudinal datasets combined for this study?

The study combined BrainTime (Leiden University, Netherlands), Neurocognitive Development (University of Oslo, Norway), and LunaCog (University of Pittsburgh, USA).

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

How did the MRI scanner field strengths differ among the three datasets used in the study?

The BrainTime and LunaCog datasets utilized 3-T MRI scanners, whereas the NCD dataset used a 1.5-T MRI scanner.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Which software suite and specific processing stream were used to process the structural brain images?

Images were processed using FreeSurfer 6.0, specifically utilizing its longitudinal processing stream to create an unbiased within-participant template.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Which subcortical structure was intentionally excluded from the final reports of this study and why?

The nucleus accumbens was excluded due to insufficient information regarding its test-retest reliability using the FreeSurfer longitudinal pipeline.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Why did the researchers calculate both annualized change and annualized percent change?

They calculated annualized percent change to assess how a given structure changed relative to its overall size, allowing for better comparison across different brain structures.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What is a primary advantage of using a Generalized Additive Mixed Model (GAMM) over polynomial-based linear mixed models?

GAMM replaces linear slope parameters with ‘smooth’ functions, allowing flexible modeling of non-linear developmental patterns without assuming a predetermined shape.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

How did the researchers categorize individual brain structure changes as "increasing," "decreasing," or "stable"?

Changes were classified as increasing if change was greater than or equal to 1 SD of the sample average, decreasing if less than or equal to negative 1 SD, and stable if in between.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

How were the developmental periods "transition into adolescence," "mid-adolescence," and "transition into early adulthood" defined by age?

Transition into adolescence was defined as a midpoint age under or equal to 13, mid-adolescence was between 14-18, and transition into early adulthood was over 18.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI

Why did the researchers use the average of an individual's brain measurements across all three scans instead of using the initial scan as a baseline for size?

Using the average across all observations avoids a negative statistical bias in slope estimates that measurement error from a single initial scan can introduce.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What overall developmental pattern did cortical measurements (volume, thickness, surface area) display during mid-adolescence?

The majority of individuals demonstrated decreases in total gray matter volume, cortex volume, mean cortical thickness, and white matter surface area during mid-adolescence.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What developmental trajectory did cerebral white matter volume display across the studied age range?

The majority of individuals showed increases in white matter volume during the transition into adolescence, followed by stability starting in mid-adolescence.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What was the general developmental trend for subcortical gray matter structures across the studied age span?

Most subcortical structures and overall subcortical gray matter volume showed little to no substantial change, with high individual variability across the age range.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What specific developmental pattern was identified for the pallidum?

Most individuals showed either an increase or stability in the transition into adolescence, followed by stability (no substantial change) from mid-adolescence onward.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

For which structural brain measures did the researchers find sex differences in the overall magnitude or pattern of annualized percent change?

Sex differences were found in total gray matter volume, white surface area, cerebral white matter volume, subcortical gray matter volume, pallidum, and cortex volume.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What relationship was found between brain size and the rate of cortical change in females?

Females with larger cortical measurements (e.g., larger gray matter, cortex volume, thickness, and surface area) showed steeper decreases (larger negative rates of change) over time.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

How did the rate of cortical change relate to overall brain size in male participants?

Unlike females, males showed similar rates of cortical change regardless of how large or small their brain volumes, thickness, or surface areas were.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Did subcortical structures show any relationship between overall volume and individual rates of change?

No relationship was found between an individual's rate of change and the volume of cerebral white matter, subcortical gray matter, or any specific subcortical structures.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

During which developmental periods was inter-individual variability in the direction of cortical change the greatest?

The greatest inter-individual variability in the direction of cortical change occurred during the transition periods into adolescence and into early adulthood.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Why does inter-individual variability challenge group-level neurodevelopmental models of psychopathology?

Group-level sex differences in maturation rates may fail to identify specific individuals who will develop psychopathology, as individual trajectories vary widely in direction and rate.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What age-range constraint do the authors recommend for studies wanting to examine linear developmental processes in cortical measurements?

They recommend constraining the age range to the teen years, as this is when direction of change (decreases) is highly consistent across individuals.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Why might researchers choose to examine cortical volume over cortical thickness or surface area in longitudinal studies?

Cortical volume demonstrates the most consistency in the direction of change (decrease) during the teenage years.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What major sociodemographic limitation did the authors note regarding the combined datasets?

There was a lack of sociodemographic information across the three samples, limiting the ability to assess how environmental factors influence structural brain variation and behavior.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What is a limitation of how "developmental change" was thresholded in this study?

The threshold was a sample-wide statistical estimate (based on SD of change) rather than an absolute rule, which could affect the conclusions regarding change versus stability.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What study design recommendation do the authors make for future research to separate measurement error from true developmental change?

Future studies should include multiple scans per individual at a single time point to directly distinguish scan-to-scan measurement error from actual growth or reduction.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What key spatial comparison did this study omit that limits its conclusions about cortical maturation?

It did not compare inter-individual variability across different cortical regions (e.g., frontal vs. occipital lobes).

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Why would having at least four longitudinal time points per participant improve future modeling of individual trajectories?

It would allow researchers to estimate non-linear individual-level slopes, which better represent the complex curved nature of structural brain development.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What common inference in cross-sectional neuroimaging literature did this study seek to test longitudinally?

The inference that having a thinner cortex than peers of the same age indicates a more mature or faster-maturing brain.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Under the hypothesis that a thinner cortex reflects more mature development, what pattern of change would be expected in individuals with thinner cortices?

They would be expected to show less overall change in cortical thickness, as stabilizing (lack of change) is a hallmark of structural brain maturity.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What was the full age range of the combined participant sample examined in this study?

The study quantified developmental measurements in individuals aged 8.0 to 26.0 years.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

What did the authors' previous work establish regarding individual differences in subcortical maturation?

It demonstrated that subcortical structures like the amygdala and nucleus accumbens vary in the age at which they reach structural maturity across individuals.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified

Why did the researchers exclude research site as a random factor in their final statistical models?

Models with and without site as a random factor were practically equivalent, showing that scanner/site differences had a negligible impact.

Source: Inter-individual variability in structural brain development from late childhood to young adulthood · DOI · quote verified