| Examining the validity of the use of ratio IQs in psychological assessments | IQ tests are amongst the most used psychological assessments, both in research and clinical settings. For participants who cannot complete IQ tests normed for their age, ratio IQ scores (RIQ) are routinely computed and used as a proxy of IQ, especially in large research databases to avoid missing data points. However, because it has never been scientifically validated, this practice is questionable. In the era of big data, it is important to examine the validity of this widely used practice. In this paper, we use the case of autism to examine the differences between standard full-scale IQ (FSIQ) and RIQ. Data was extracted from four databases in which ages, FSIQ scores and subtests raw scores were available for autistic participants between 2 and 17 years old. The IQ tests included were the MSEL (N=12033), DAS-II early years (N=1270), DAS-II school age (N=2848), WISC-IV (N=471) and WISC-V (N=129). RIQs were computed for each participant as well as the discrepancy (DSC) between RIQ and FSIQ. We performed two linear regressions to respectively assess the effect of FSIQ and of age on the DSC for each IQ test, followed by additional analyses comparing age subgroups as well as FSIQ subgroups on DSC. Participants at the extremes of the FSIQ distribution tended to have a greater DSC than participants with average FSIQ. Furthermore, age significantly predicted the DSC, with RIQ superior to FSIQ for younger participants while the opposite was found for older participants. These results question the validity of this widely used alternative scoring method, especially for individuals at the extremes of the normal distribution, with whom RIQs are most often employed. | 554/17423 | Secondary Analysis | Shared |
| Intelligence, brain size, and brain morphometry: caveats in brain-behavior associations | It is well-established that brain size is associated with intelligence. But beyond brain size, things are less clear. There are disturbing inconsistencies in the literature on the relation between brain morphometric measures and intelligence.
Some papers have claimed significant and strong correlations between intelligence and morphometric measures such as cortical thickness. Other studies have found either conflicting results, or no significant relations between intelligence and any morphometric measures.
Researchers have suggested a variety of possible reasons for these conflicting results;
we add to that list the conjecture that these discrepancies may have been due to a failure to fully account for the relation between brain size and intelligence when assessing the relation between the morphometric measures and intelligence, or to multicollinearity amongst the independent variables in a multivariate regression analysis.
We show that neither cortical thickness nor peri-cortical contrast significantly improve IQ prediction accuracy beyond what is achieved with brain volume alone. | 383/4344 | Secondary Analysis | Shared |
| A foundation model for generalized brain MRI analysis | Artificial intelligence (AI) applied to brain magnetic resonance imaging (MRI) has the potential to improve disease diagnosis and management but requires algorithms with generalizable knowledge that can perform well in a variety of clinical scenarios. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. Foundation models, by leveraging self-supervised learning, pretraining, and targeted adaptation, present a promising paradigm to overcome these limitations. Here, we present Brain Imaging Adaptive Core (BrainIAC), a novel foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,519 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data settings and high-difficulty tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and AI clinical translation. | 1/2079 | Secondary Analysis | Shared |
| Diffusion Deep Learning for Brain Age Prediction and Longitudinal Tracking in Children Through Adulthood | Deep learning (DL)-based prediction of biological age in the developing human from a brain magnetic resonance image (MRI) (“brain age”) may have important diagnostic and therapeutic applications as a non-invasive biomarker of brain health, aging, and neurocognition. While previous deep learning tools for predicting brain age have shown promising capabilities using single-institution, cross-sectional datasets, our work aims to advance the field by leveraging multi-site, longitudinal data with externally validated and independently implementable code to facilitate clinical translation and utility. This builds on prior foundational efforts in brain age modeling to enable broader generalization and individual’s longitudinal brain development. Here, we leveraged 32,851 T1-weighted MRI scans from healthy children and adolescents aged 3 to 30 from 16 multisite datasets to develop and evaluate several DL brain age frameworks, including a novel regression diffusion DL network (AgeDiffuse). In a multisite external validation (5 datasets), we found that AgeDiffuse outperformed conventional DL frameworks, with a mean absolute error (MAE) of 2.78 years (IQR:[1.2-3.9]). In a second, separate external validation (3 datasets), AgeDiffuse yielded an MAE of 1.97 years (IQR: [0.8-2.8]). We found that AgeDiffuse brain age predictions reflected age-related brain structure volume changes better than biological age (R2=0.48 vs R2=0.37). Finally, we found that longitudinal predicted brain age tracked closely with chronological age at the individual level. To enable independent validation and application, we made AgeDiffuse publicly available and usable for the research community. | 556/586 | Secondary Analysis | Shared |
| Automated temporalis muscle quantification and growth charts for children through adulthood | Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making. | 555/585 | Secondary Analysis | Shared |
| Development of sex differences in the human brain | Sex differences in brain anatomy have been described from early childhood through late adulthood, but without any clear consensus among studies. Here, we applied a machine learning approach to estimate ‘brain sex’ using a continuous (rather than binary) classifier in 162 boys and 185 girls aged between 5 and 18 years. Changes in the estimated sex differences over time at different age groups were subsequently calculated using a sliding window approach. We hypothesized that males and females would differ in brain structure already during childhood, but that these differences will become even more pronounced with increasing age, particularly during puberty. Overall, the classifier achieved a good performance, with an accuracy of 80.4% and an AUC of 0.897 across all age groups. Assessing changes in the estimated sex with age revealed a growing difference between the sexes with increasing age – starting with a very large effect size of d=1.2 during childhood which increased even further from age 11 onward to an effect size of d=1.6 at age 17. Overall these findings suggest a systematic sex difference in brain structure already during childhood, and a subsequent increase of this difference during puberty, matching well current models of sexual differentiation of the brain. | 556/556 | Secondary Analysis | Shared |
| Chronic Environmental Stress Impact on DHEA(S) Levels and Executive Function in Children | Though we know the function of stress on many of the body’s natural resources, we remain uncertain of the neurological function of the steroid precursor dehydroepiandrosterone (DHEA) and its’ sulfated component. Some studies have shown DHEA to have a neuroprotective effect on cognitive functions, such as executive functions, though limited work has been done exploring this relationship in children. Cortisol is a well-established neurodegenerative factor associated with stress. The relationship between chronic stress factors of socioeconomic status (SES) and marginalized racial backgrounds with biological correlates is understudied. The purpose of the current study was to explore the relationship between chronic environmental stress factors of marginalized racial status or lower SES and its association with DHEA to cortisol ratios, as well as the relationship of DHEA to cortisol ratios with indirect and direct measures of executive function in children, as executive function is a cognitive domain that remains particularly sensitive to chronic stress. Analysis of a sample of 345 children from the NIH Pediatric MRI Database found that neither marginalized racial status, nor SES was associated with lower DHEA to cortisol ratios. Analysis of children (samples varying between 212 and 345 cases) found that DHEA to cortisol ratios did not predict performance on indirect or direct measures of executive function. Limitations, future directions, and clinical implications are discussed. | 554/554 | Primary Analysis | Shared |
| Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI Scans | Purpose: To predict the corresponding age of myelin maturation from brain MRI scans in infants and young children by using a deep learning algorithm and to build upon previously published models.
Materials and Methods: Brain MRI scans acquired between January 1, 2011, and March 17, 2021, in our institution in patients aged 0–3 years were retrospectively retrieved from the archive. An ensemble of two-dimensional (2D) and three-dimensional (3D) convolutional neural network models was trained and internally validated in 710 patients to predict myelin maturation age on the basis of radiologist-generated labels. The model ensemble was tested on an internal dataset of 123 patients and two external datasets of 226 (0–25 months of age) and 383 (0–2 months of age) healthy children and infants, respectively. Mean absolute error (MAE) and Pearson correlation coefficients were used to assess model performance.
Results: The 2D, 3D, and 2D-plus-3D ensemble models showed an MAE value of 1.43, 2.55, and 1.77 months, respectively, on the internal test set, values of 2.26, 2.27, and 1.22 months on the first external test set, and values of 0.44, 0.27, and 0.31 months on the second external test set. The ensemble model outperformed the performance of the previous state-of-the-art model on the same external test set (MAE = 1.22 vs 2.09 months).
Conclusion: The proposed deep learning model accurately predicted myelin maturation age using pediatric brain MRI scans and may help reduce the time needed to complete this task, as well as interobserver variability in radiologist predictions. | 553/553 | Secondary Analysis | Shared |
| A new template to study callosal growth shows specific growth in anterior and posterior regions of the corpus callosum in early childhood | Most of the studies conducted on the development of the corpus callosum (CC) have been limited to a relatively simple assessment
of callosal area, providing an estimation of the size of the CC in two dimensions rather than its actual measurement. The
goal of this study was to revisit callosal development in childhood and adolescence by using a three-dimensional (3D) magnetic
resonance imaging template of the CC that considers the horizontal width of the CC and compares this with the two-dimensional
(2D) callosal area. We mapped callosal growth in a large sample of youths followed longitudinally (N = 370 at T1; N = 304 at T2;
and N = 246 at T3). Both techniques were based on a five-section subdivision of the CC. The results obtained with the 3D
method revealed that the rate of CC growth over a 4-year period in the rostrum, the genu, the anterior body and the splenium
was significantly higher in the youngest age group (< 7 years) than in older groups, indicating an intense period of development
in early childhood for the anterior and posterior parts of the CC. Similar results were obtained when 2D callosal area was used
for the anterior and posterior parts of the CC. However, divergent results were found in the mid-body and the caudal body of the
CC. As shown by differences between 2D estimations and actual 3D measurements of callosal growth, our study highlights the
importance of considering the horizontal width in measuring developmental changes in the CC. | 427/427 | Secondary Analysis | Shared |
| T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance | Knowing the maturational schedule of typical brain development is critical to our ability to identify deviations from it; such deviations have been related to cognitive performance and even developmental disorders. Chronological age can be predicted from brain images with considerable accuracy, but with limited spatial specificity, particularly in the case of the cerebral cortex. Methods using multi-modal data have shown the greatest accuracy, but have made limited use of cortical measures. Methods using complex measures derived from voxels throughout the brain have also shown great accuracy, but are difficult to interpret in terms of cortical development. Measures based on cortical surfaces have yielded less accurate predictions, suggesting that perhaps cortical maturation is less strongly related to chronological age than is maturation of deep white matter or subcortical structures. We question this suggestion. We show that a simple metric based on the white/gray contrast at the inner border of the cortex is a good predictor of chronological age. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Further, our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are not merely random errors, but are strongly related to IQ, suggesting that this metric is sensitive to aspects of brain development that reflect cognitive performance. | 401/401 | Primary Analysis | Shared |
| Trajectories of cortical thickness maturation in normal brain development | Several reports have described cortical thickness (CTh) developmental trajectories, with conflicting results. Some studies have reported inverted-U shape curves with peaks of CTh in late childhood to adolescence, while others suggested predominant monotonic decline after age 6. In this study, we reviewed CTh developmental trajectories in the NIH MRI Study of Normal Brain Development, and in a second step, evaluated the impact of post-processing quality control (QC) procedures on identified trajectories. The quality-controlled sample included 384 individual subjects with repeated scanning (1-3 per subject, total scans n=753) from 4.9 to 22.3years of age. The best-fit model (cubic, quadratic, or first-order linear) was identified at each vertex using mixed-effects models. The majority of brain regions showed linear monotonic decline of CTh. There were few areas of cubic trajectories, mostly in bilateral temporo-parietal areas and the right prefrontal cortex, in which CTh peaks were at, or prior to, age 8. When controlling for total brain volume, CTh trajectories were even more uniformly linear. The only sex difference was faster thinning of occipital areas in boys compared to girls. The best-fit model for whole brain mean thickness was a monotonic decline of 0.027mm per year. QC procedures had a significant impact on identified trajectories, with a clear shift toward more complex trajectories (i.e., quadratic or cubic) when including all scans without QC (n=954). Trajectories were almost exclusively linear when using only scans that passed the most stringent QC (n=598). The impact of QC probably relates to decreasing the inclusion of scans with CTh underestimation secondary to movement artifacts, which are more common in younger subjects. In summary, our results suggest that CTh follows a simple linear decline in most cortical areas by age 5, and all areas by age 8. This study further supports the crucial importance of implementing post-processing QC in CTh studies of development, aging, and neuropsychiatric disorders. | 379/379 | Secondary Analysis | Shared |
| Prediction of brain maturity based on cortical thickness at different spatial resolutions | Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n = 308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R = 0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity. | 341/341 | Secondary Analysis | Shared |
| A human craniofacial life-course: cross-sectional morphological covariations during postnatal growth, adolescence, and aging | Covariations between anatomical structures are fundamental to craniofacial ontogeny, maturation and aging and yet are rarely studied in such a cognate fashion. Here we offer a comprehensive investigation of the human craniofacial complex using freely
available software and MRI datasets representing 575 individuals from 0 to 79 years old. We employ both standard craniometrics methods as well as Procrustes based analyses to capture and document cross-sectional trends. Findings suggest that
anatomical structures behave primarily as modules, and manifest integrated patterns of shape change as they compete for space, particularly with relative expansions of the brain during early postnatal life and of the face during puberty. Sexual dimorphism was detected in infancy and intensified during adolescence with gender differences in the magnitude and pattern of morphological covariation as well as of aging. These findings partly support the spatial-packing hypothesis and reveal important insights into phenotypic adjustments to deep-rooted, and presumably genetically defined, trajectories of morphological size and shape change that characterise the normal human craniofacial life-course. | 78/308 | Secondary Analysis | Shared |
| Development and validation of a brain maturation index using longitudinal neuroanatomical scans | Background
Major psychiatric disorders are increasingly being conceptualized as ‘neurodevelopmental’, because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of neuroanatomical measurements may reliably identify individual subjects deviating from a normative neurodevelopmental trajectory. However, while recent studies have shown great promise in developing accurate brain maturation indices using neuroimaging data and multivariate machine learning techniques, this approach has not been validated using a large sample of longitudinal data from children and adolescents.
Methods
T1-weighted scans from 303 healthy subjects aged 4.88 to 18.35 years were acquired from the National Institute of Health (NIH) pediatric repository (http://www.pediatricmri.nih.gov). Out of the 303 subjects, 115 subjects were re-scanned after 2 years. The least absolute shrinkage and selection operator algorithm (LASSO) was ‘trained’ to integrate neuroanatomical changes across chronological age and predict each individual's brain maturity. The resulting brain maturation index was developed using first-visit scans only, and was validated using second-visit scans.
Results
We report a high correlation between the first-visit chronological age and brain maturation index (r = 0.82, mean absolute error or MAE = 1.69 years), and a high correlation between the second-visit chronological age and brain maturation index (r = 0.83, MAE = 1.71 years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27 years.
Conclusions
The brain maturation index developed in this study accurately predicted individual subjects' brain maturation longitudinally. Due to its strong clinical potentials in identifying individuals with an abnormal brain maturation trajectory, the brain maturation index may allow timely clinical interventions for individuals at risk for psychiatric disorders.
| 303/303 | Secondary Analysis | Shared |
| When does the youthfulness of the female brain emerge? | Goyal et al., report in the February issue of PNAS that the female adult
brain has a persistently lower metabolic brain age compared with the male brain
at the same chronological age (1). In interpreting this remarkable finding, the
authors propose that sex-related differences in brain development may in part
play a role in “setting” the female brain at a younger initial brain age at puberty,
allowing them to maintain a younger brain throughout adulthood. We argue that
may not be the case and provide evidence to show that, in fact, the opposite may
be true during childhood and adolescence.
First, according to the Figure 2A in Goyal et al, surprisingly, the predicted
age between 35-50 y were under-estimated for both males and females. It is
unclear if the bias in this age range could have affected the overall findings or
played a role in only the result from training on males and testing on females
surviving a two-sided t-test. Moreover, it is unclear which age range was
determinative of the significant difference between predicted and chronological
age.
Second, we used cortical thickness, which i) has been validated as a
reliable biomarker for brain age (2, 3) and ii) has shown strong association with
sex hormones during puberty maturation (4, 5) from 265 healthy children and
youth (118 boys, 147 girls) between the ages of 5 and 18 from the NIH MRI
Study of Normal Brain Development (6) and estimated the difference between
brain age and actual chronological age. Similar to Goyal et al., we first trained the
ML algorithm (support vector regression with default parameters, implemented
using LIBSVM toolbox) on the male cohort only and then tested it on the female
cohort, and vice versa. We found that while cortical-thickness-based brain age
correlated strongly with actual chronological age in both cohorts (training on boys
and testing on girls: r = 0.75, p<0.001; training on girls and testing on boys: r =
0.71, p < 0.001; Figure 1A), the mean cortical thickness brain age was on
average 0.42 y older for girls compared with boys (p = 0.02, two-sided t-test;
Figure 1B) when the male data was used as the training set and 0.47 y younger
for boys compared with girls (p = 0.03, two-sided t-test; Figure 1B) when the
female data was used as the training set. In other words, while per Goyal and
colleagues’ investigation adult females may have a younger brain than adult
males during development, this pattern is not the same and in fact seems to be in
the opposite direction during puberty.
While cortical thinning as a biomarker for aging may reflect a different
aspect of aging than what metabolic changes may reflect, given that they are
both strongly predictive of chronological age, it is likely that they may also be
correlated. Therefore, given our finding, we propose that the mechanisms that
are involved in keeping the female brain younger in adulthood may get engaged
at a later point in life and not during puberty. | 265/265 | Secondary Analysis | Shared |
| The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI) | The NIH MRI Study of normal brain development sought to characterize typical brain development in a population
of infants, toddlers, children and adolescents/young adults, covering the socio-economic and ethnic diversity
of the population of the United States. The study began in 1999 with data collection commencing in 2001 and
concluding in 2007. The study was designed with the final goal of providing a controlled-access database;
open to qualified researchers and clinicians,which could serve as a powerful tool for elucidating typical brain development
and identifying deviations associated with brain-based disorders and diseases, and as a resource for
developing computational methods and image processing tools.
This paper focuses on the DTI component of the NIH MRI study of normal brain development. In this work, we
describe the DTI data acquisition protocols, data processing steps, quality assessment procedures, and data
included in the database, along with database access requirements. For more details, visit http://www.
pediatricmri.nih.gov.
This longitudinal DTI dataset includes raw and processed diffusion data from 498 low resolution (3 mm) DTI
datasets from274 unique subjects, and 193 high resolution (2.5mm) DTI datasets from152 unique subjects. Subjects
range in age from10 days (from date of birth) through 22 years. Additionally, a set of age-specific DTI templates
are included. This forms one component of the larger NIHMRI study of normal brain development which
also includes T1-, T2-, proton density-weighted, and proton magnetic resonance spectroscopy (MRS) imaging
data, and demographic, clinical and behavioral data. | 230/230 | Secondary Analysis | Shared |
| Brain structure, cognition and behavior in child and adolescent survivors of leukemia | Pediatric acute lymphoblastic leukemia (ALL) is the most common for of childhood cancer. It is successfully treated in ~90% of cases, primarily based on combination chemotherapy. Survivors of ALL are at elevated risk of cognitive or behavioral problems, which frequently include impairments in processing speed, working memory, attention, and executive function. These changes are accompanied by alterations in brain development, evident by changes in brain structure volume. In our study, data from the Pediatric MRI Data Repository was used as a secondary control data set (supplementing a control data set collected as part of the study). The results were shown as a collective, as well as in two groups matched on an individual basis to participants in the study (based on age, sex, and full-scale IQ). | 175/175 | Secondary Analysis | Shared |
| Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain | Metrics derived from the diffusion tensor, such as fractional anisotropy (FA) and mean diffusivity (MD) have
been used in many studies of postnatal brain development. A common finding of previous studies is that these
tensor-derived measures vary widely even in healthy populations. This variability can be due to inherent interindividual
biological differences as well as experimental noise. Moreover, when comparing different studies,
additional variability can be introduced by different acquisition protocols. In this study we examined scans
of 61 individuals (aged 4–22 years) from the NIH MRI study of normal brain development. Two scans were
collected with different protocols (low and high resolution). Our goal was to separate the contributions of
biological variability and experimental noise to the overall measured variance, as well as to assess potential
systematic effects related to the use of different protocols. We analyzed FA and MD in seventeen regions of
interest. We found that biological variability for both FA and MD varies widely across brain regions; biological
variability is highest for FA in the lateral part of the splenium and body of the corpus callosum along with the
cingulum and the superior longitudinal fasciculus, and for MD in the optic radiations and the lateral part of the
splenium. These regions with high inter-individual biological variability are the most likely candidates for
assessing genetic and environmental effects in the developing brain. With respect to protocol-related effects,
the lower resolution acquisition resulted in higher MD and lower FA values for the majority of regions compared
with the higher resolution protocol. However, the majority of the regions did not show any age–protocol
interaction, indicating similar trajectories were obtained irrespective of the protocol used. | 128/128 | Secondary Analysis | Shared |
| Cross-Lagged Models of Cognitive and Reading Abilities in School-Aged Children: Unexpected Directionality | Objective: Processing speed (PS) and working memory (WM) are domain-general cognitive skills that are associated with single-word reading abilities. The present study used a cross-lagged panel design in a school-aged sample to better characterize the developmental emergence of these cognitive-academic relationships.
Participants and Methods. The sample included 117 typically developing children (8-15 years old) who completed neuropsychological testing every 2 years as part of the NIH MRI Study of Normal Brain Development (publicly available). PS was measured by Coding (WISC-III), WM was measured by Digit Span (WISC-III), and single-word reading was measured by Letter-Word Id (WJ-III). Path analysis was used to test cross-sectional correlations, auto-regressive paths, and cross-lagged paths between cognitive predictors and reading abilities in two separate models (PS and reading, WM and reading).
Results: A cross-lagged pattern emerged in which earlier Letter-Word Id predicted later Coding and Digit Span scores, but not the reverse. For the PS and reading model, Letter-Word Id significantly predicted Coding from 8-9 to 10-11 years (β=.23, p=.034), and this relationship diminished over time (10-11 to 12-13: β=.14, p=.084; 12-13 to 14-15: β=.08, p=.310). Similarly, in the WM and reading model, Letter-Word Id significantly predicted Digit Span from 8-9 to 10-11 years (β=.27, p=.003), and again showed a diminishing relationship over time (10-11 to 12-13: β=.11, p=.198; 12-13 to 14-15: β=.18, p=.023). The cross-lagged paths from Coding/Digit Span to Letter-Word Id never reached significance.
Conclusion: These results reveal an unexpected developmental pattern in which earlier single-word reading abilities predict later PS and WM, but not the reverse. The current results warrant replication with alternative measures to rule out competing explanations. However, if replicated, the results could expand conventional notions of neuropsychological predictors as causal in academic development by showing that domain-general cognitive skills might also be the consequence of academic development, at least in the case of PS and WM with reading.
| 117/117 | Secondary Analysis | Shared |
