Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts

Predictability of intelligence and age from structural connectomes

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

An Examination of the Motives for Attributing and Interpreting Deception in People with Amnestic Mild Cognitive Impairment

The aim of the present study was to examine how a person with amnestic mild cognitive impairment perceives the phenomenon of deception. Amnestic mild cognitive impairment (aMCI) usually represents the prodromal phase of Alzheimer's disease (AD), with patients showing memory impairment but with normal activities of daily living. It was expected that aMCI patients would face difficulties in the attribution and interpretation of deceptive behavior due to deficits regarding their diagnosis. The main sample of the study consisted of 76 older adults who were patients of a daycare center diagnosed with aMCI. A sample of 55 highly educated young adults was also examined in the same experiment to qualitatively compare their performance with that of aMCI patients. Participants were assigned a scenario where a hypothetical partner (either a friend or a stranger) was engaged in a task in which the partner could lie to boost their earnings at the expense of the participant. The results showed that aMCI patients, even if they understood that something was going wrong, did not invest in interpretations of potential deception and tended to avoid searching for confirmative information related to the hypothetical lie of their partner compared to highly educated young adults. It seems that aMCI patients become somehow "innocent", and this is discussed in terms of cognitive impairment and/or socioemotional selectivity.

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

Blood pressure-related white matter microstructural disintegrity and associated cognitive function impairment in asymptomatic adults

BACKGROUND AND OBJECTIVES

We aimed to investigate the white matter (WM) microstructural/cytostructural disintegrity patterns related to higher systolic blood pressure (SBP), and whether they mediate SBP effects on cognitive performance in middle-aged adults.

METHODS

Using the UK Biobank study of community-dwelling volunteers aged 40-69 years, we included participants without a history of stroke, dementia, demyelinating disease or traumatic brain injury. We investigated the association of SBP with MRI diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (a measure of neurite density), isotropic (free) water volume fraction (ISOVF) and orientation dispersion across WM tracts. Then, we determined whether WM diffusion metrics mediated the effects of SBP on cognitive function.

RESULTS

We analysed 31 363 participants-mean age of 63.8 years (SD: 7.7), and 16 523 (53%) females. Higher SBP was associated with lower FA and neurite density, but higher MD and ISOVF. Among different WM tracts, diffusion metrics of the internal capsule anterior limb, external capsule, superior and posterior corona radiata were most affected by higher SBP. Among seven cognitive metrics, SBP levels were only associated with 'fluid intelligence' (adjusted p<0.001). In mediation analysis, the averaged FA of external capsule, internal capsule anterior limb and superior cerebellar peduncle mediated 13%, 9% and 13% of SBP effects on fluid intelligence, while the averaged MD of external capsule, internal capsule anterior and posterior limbs, and superior corona radiata mediated 5%, 7%, 7% and 6% of SBP effects on fluid intelligence, respectively.

DISCUSSION

Among asymptomatic adults, higher SBP is associated with pervasive WM microstructure disintegrity, partially due to reduced neuronal count, which appears to mediate SBP adverse effects on fluid intelligence. Diffusion metrics of select WM tracts, which are most reflective of SBP-related parenchymal damage and cognitive impairment, may serve as imaging biomarkers to assess treatment response in antihypertensive trials.

Decoding information about cognitive health from the brainwaves of sleep

Sleep electroencephalogram (EEG) signals likely encode brain health information that may identify individuals at high risk for age-related brain diseases. Here, we evaluate the correlation of a previously proposed brain age biomarker, the "brain age index" (BAI), with cognitive test scores and use machine learning to develop and validate a series of new sleep EEG-based indices, termed "sleep cognitive indices" (SCIs), that are directly optimized to correlate with specific cognitive scores. Three overarching cognitive processes were examined: total, fluid (a measure of cognitive processes involved in reasoning-based problem solving and susceptible to aging and neuropathology), and crystallized cognition (a measure of cognitive processes involved in applying acquired knowledge toward problem-solving). We show that SCI decoded information about total cognition (Pearson's r = 0.37) and fluid cognition (Pearson's r = 0.56), while BAI correlated only with crystallized cognition (Pearson's r = - 0.25). Overall, these sleep EEG-derived biomarkers may provide accessible and clinically meaningful indicators of neurocognitive health.

Structural architecture and brain network efficiency link polygenic scores to intelligence

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

Brain reserve theory: Are adults with intellectual disability more vulnerable to age than peers with typical development?

BACKGROUND

Life expectancy is on rise and the intriguing question is: When does cognitive decline occur among adults with intellectual disability, compared to adults with typical development? This cross-sectional study examined cognitive performance of crystallised/fluid intelligence, working and long-term memory of adults with intellectual disability of etiologies other than Down syndrome (IQ 50-68) and adults with typical development (IQ 85-114) in four age cohorts (30-39; 40-49; 50-59; 60-69).

METHOD

The WAIS III^HEB and the Rey-AVLT were administered to both groups.

RESULTS

Four patterns of cognitive performance were found: (a) Vocabulary (crystallised intelligence), Spatial Span Forward and Retention yielded similar scores across all four age cohorts in participants with typical development and with intellectual disability. (b) Similarities, Raven and Digit Span Backward exhibit lower scores only in 50-59 or 60-69 compared to the 30-39 age cohort in both groups, (c) Digit Span Forward, Spatial Span Backward and Total Leaning (LTM) yielded lower scores in the 50-59 or 60-69 age cohorts in the typical group, but similar scores in participants with intellectual disability along the age cohorts, (d) Block Design (fluid intelligence) yielded a lower score in the 50-59 cohort versus lower scores only at ages 60-69 in participants with typical development.

CONCLUSIONS

Our findings suggest a possible parallel trajectory in age-related cognitive performance for individuals with and without intellectual disability in six measures, and a possible more preserved trajectory in fluid intelligence and some memory measures in adults with intellectual disability compared to their peers. Caution should be exercised regarding Digit and Spatial Span Backwards, which yielded a floor effect in participants with intellectual disability. The Cognitive Reserve Theory, the Safeguard Hypothesis and late maturation might serve as explanations for these findings.

Neuroplasticity enables bio-cultural feedback in Paleolithic stone-tool making

Stone-tool making is an ancient human skill thought to have played a key role in the bio-cultural co-evolutionary feedback that produced modern brains, culture, and cognition. To test the proposed evolutionary mechanisms underpinning this hypothesis we studied stone-tool making skill learning in modern participants and examined interactions between individual neurostructural differences, plastic accommodation, and culturally transmitted behavior. We found that prior experience with other culturally transmitted craft skills increased both initial stone tool-making performance and subsequent neuroplastic training effects in a frontoparietal white matter pathway associated with action control. These effects were mediated by the effect of experience on pre-training variation in a frontotemporal pathway supporting action semantic representation. Our results show that the acquisition of one technical skill can produce structural brain changes conducive to the discovery and acquisition of additional skills, providing empirical evidence for bio-cultural feedback loops long hypothesized to link learning and adaptive change.

Robust associations between white matter microstructure and general intelligence

Few tract-based spatial statistics (TBSS) studies have investigated the relations between intelligence and white matter microstructure in healthy (young) adults, and those have yielded mixed observations, yet white matter is fundamental for efficient and accurate information transfer throughout the human brain. We used a multicenter approach to identify white matter regions that show replicable structure-function associations, employing data from 4 independent samples comprising over 2000 healthy participants. TBSS indicated 188 voxels exhibited significant positive associations between g factor scores and fractional anisotropy (FA) in all 4 data sets. Replicable voxels formed 3 clusters, located around the left-hemispheric forceps minor, superior longitudinal fasciculus, and cingulum-cingulate gyrus with extensions into their surrounding areas (anterior thalamic radiation, inferior fronto-occipital fasciculus). Our results suggested that individual differences in general intelligence are robustly associated with white matter FA in specific fiber bundles distributed across the brain, consistent with the Parieto-Frontal Integration Theory of intelligence. Three possible reasons higher FA values might create links with higher g are faster information processing due to greater myelination, more direct information processing due to parallel, homogenous fiber orientation distributions, or more parallel information processing due to greater axon density.

Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors

Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Health's Research Domain Criteria (RDoC). Following RDoC's integrative approach, we aimed to develop brain-based predictive models for cognitive abilities that (a) are developmentally stable over years during adolescence and (b) account for the relationships between cognitive abilities and socio-demographic, psychological and genetic factors. For this, we leveraged the unique power of the large-scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n ~ 11 k) and combined MRI data across modalities (task-fMRI from three tasks: resting-state fMRI, structural MRI and DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across 2 years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of 'opportunistic stacking' allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto-parietal networks during a working-memory task to drive childhood-cognition prediction. The brain-based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio-demographic and psychological factors (proportion mediated = 18.65% [17.29%-20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated = 15.6% [11%-20.7%]). Thus, our brain-based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoC's integrative framework.

Emotion Recognition in a Health Continuum: Comparison of Healthy Adults of Advancing Age, Community Dwelling Adults Bearing Vascular Risk Factors and People Diagnosed with Mild Cognitive Impairment

The identification of basic emotions plays an important role in social relationships and behaviors linked to survival. In neurodegenerative conditions such as Alzheimer's disease (AD), the ability to recognize emotions may already be impaired at early stages of the disease, such as the stage of Mild Cognitive Impairment (MCI). However, as regards vascular pathologies related to cognitive impairment, very little is known about emotion recognition in people bearing vascular risk factors (VRF). Therefore, the aim of the present study was to examine emotion recognition ability in the health continuum "healthy advancing age-advancing age with VRF-MCI". The sample consisted of 106 adults divided in three diagnostic groups; 43 adults with MCI, 41 adults bearing one or more VRF, and 22 healthy controls of advancing age (HC). Since HC were more educated and younger than the other two groups, the age-group and level of educational were taken into account in the statistical analyses. A dynamic visual test was administered to examine recognition of basic emotions and emotionally neutral conditions. The results showed only a significant diagnostic group x educational level interaction as regards total emotion recognition ability, F (4, 28.910) = 4.117 p = 0.004 η² = 0.166. High educational level seems to contribute to a high-level-emotion-recognition-performance both in healthy adults of advancing age and in adults bearing vascular risk factors. Medium educational level appears to play the same role only in healthy adults. Neither educational level can help MCI people to enhance their significantly lower emotion recognition ability.

Relationship Between TSH Levels and Cognition in the Young Adult: An Analysis of the Human Connectome Project Data

CONTEXT

The nature of the relationship between serum thyrotropin (TSH) levels and higher cognitive abilities is unclear, especially within the normal reference range and in the younger population.

OBJECTIVE

To assess the relationship between serum TSH levels and mental health and sleep quality parameters (fluid intelligence [Gf], MMSE (Mini-Mental State Examination), depression scores, and, finally, Pittsburgh Sleep Quality Index (PSQI) scores (working memory, processing speed, and executive function) in young adults.

METHODS

This was a retrospective analysis of the data from the Human Connectome Project (HCP). The HCP consortium is seeking to map human brain circuits systematically and identify their relationship to behavior in healthy adults. Included were 391 female and 412 male healthy participants aged 22-35 years at the time of the screening interview. We excluded persons with serum TSH levels outside the reference range (0.4-4.5 mU/L). TSH was transformed logarithmically (log TSH). All the key variables were normalized and then linear regression analysis was performed to assess the relationship between log TSH as a cofactor and Gf as the dependent variable. Finally, a machine learning method, random forest regression, predicted Gf from the dependent variables (including alcohol and tobacco use). The main outcome was normalized Gf (nGf) and Gf scores.

RESULTS

Log TSH was a significant co-predictor of nGF in females (β = 0.31(±0.1), P < .01) but not in males. Random forest analysis showed that the model(s) had a better predictive value for females (r = 0.39, mean absolute error [MAE] = 0.81) than males (r = 0.24, MAE = 0.77).

CONCLUSION

Higher serum TSH levels might be associated with higher Gf scores in young women.

Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study

Brain network analysis represents a powerful technique to gain insights into the connectivity profile characterizing individuals with different levels of fluid intelligence (Gf). Several studies have used diffusion tensor imaging (DTI) and slow-oscillatory resting-state fMRI (rs-fMRI) to examine the anatomical and functional aspects of human brain networks that support intelligence. In this study, we expand this line of research by investigating fast-oscillatory functional networks. We performed graph theory analyses on resting-state magnetoencephalographic (MEG) signal, in addition to structural brain networks from DTI data, comparing degree, modularity and segregation coefficient across the brain of individuals with high versus average Gf scores. Our results show that high Gf individuals have stronger degree and lower segregation coefficient than average Gf participants in a significantly higher number of brain areas with regards to structural connectivity and to the slower frequency bands of functional connectivity. The opposite result was observed for higher-frequency (gamma) functional networks, with higher Gf individuals showing lower degree and higher segregation across the brain. We suggest that gamma oscillations in more intelligent individuals might support higher local processing in segregated subnetworks, while slower frequency bands would allow a more effective information transfer between brain subnetworks, and stronger information integration.

Infant repetition effects and change detection: Are they related to adaptive skills?

Repetition effects and change detection response have been proposed as neuro-electrophysiological correlates of fundamental learning processes. As such, they could be a good predictor of brain maturation and cognitive development. We recorded high density EEG in 71 healthy infants (32 females) aged between 3 and 9 months, while they listened to vowel sequences (standard /a/a/a/i/ [80%] and deviant /a/a/a/a/ [20%]). Adaptive skills, a surrogate of cognitive development, were measured via the parent form of the Adaptive Behavior Assessment System Second Edition (ABAS-II). Cortical auditory-evoked potentials (CAEPs) analyses, time-frequency analyses and a statistical approach using linear mixed models (LMMs) and linear regression models were performed. Age and adaptive skills were tested as predictors. Age modulation of repetition effects and change detection response was observed in theta (3-5 Hz), alpha (5-10 Hz) and high gamma (80-90 Hz) oscillations and in all CAEPs. Moreover, adaptive skills modulation of repetition effects was evidenced in theta (3-5 Hz), high gamma oscillations (80-90 Hz), N250/P350 peak-to-peak amplitude and P350 latency. Finally, adaptive skills modulation of change detection response was observed in the N250/P350 peak-to-peak amplitude. Our results confirm that repetition effects and change detection response evolve with age. Moreover, our results suggest that repetition effects and change detection response vary according to adaptive skills displayed by infants during the first year of life, demonstrating their predictive value for neurodevelopment.

Estimates of brain age for gray matter and white matter in younger and older adults: Insights into human intelligence

Aging entails a multifaceted complex of changes in macro- and micro-structural properties of human brain gray matter (GM) and white matter (WM) tissues, as well as in intellectual abilities. To better capture tissue-specific brain aging, we combined volume and distribution properties of diffusivity indices to derive subject-specific age scores for each tissue. We compared age-related variance between younger and older adults for GM and WM age scores, and tested whether tissue-specific age scores could explain different effects of aging on fluid (Gf) and crystalized (Gc) intelligence in younger and older adults. Chronological age was strongly associated with GM (R2 = 0.73) and WM (R2 = 0.57) age scores. The GM age score accounted for significantly more variance in chronological age in younger relative to older adults (p < 0.001), whereas the WM age score accounted for significantly more variance in chronological age in older compared to younger adults (p < 0.025). Consistent with existing literature, younger adults outperformed older adults in Gf while older adults outperformed younger adults in Gc. The GM age score was negatively associated with Gf in younger adults (p < 0.02), whereas the WM age score was negatively associated with Gc in older adults (p < 0.02). Our results provide evidence for differences in the effects of age on GM and WM in younger versus older adults that may contribute to age-related differences in Gf and Gc.

The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset

The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18–68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64–120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca.

Measurement(s)	functional brain measurement
Technology Type(s)	electroencephalography (EEG) • magnetic resonance imaging (MRI) • neuropsychological testing
Factor Type(s)	age of participants • gender of participants • handedness of participants • educational level of participants
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Location	Cuba

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13277348

Fibre-specific laterality of white matter in left and right language dominant people

Language is the most commonly described lateralised cognitive function, relying more on the left hemisphere compared to the right hemisphere in over 90% of the population. Most research examining the structure-function relationship of language lateralisation only included people showing a left language hemisphere dominance. In this work, we applied a state-of-the-art "fixel-based" analysis approach, allowing statistical analysis of white matter micro- and macrostructure on a fibre-specific level in a sample of participants with left and right language dominance (LLD and RLD). Both groups showed a similar extensive pattern of white matter lateralisation including a comparable leftwards lateralisation of the arcuate fasciculus, regardless of their functional language lateralisation. These results suggest that lateralisation of language functioning and the arcuate fasciculus are driven by independent biases. Finally, a significant group difference of lateralisation was detected in the forceps minor, with a leftwards lateralisation in LLD and rightwards lateralisation for the RLD group.

Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

Structural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.

Intelligence trajectories in adolescents and adults with down syndrome: Cognitively stimulating leisure activities mitigate health and ADL problems

GOALS

This study examined: (a) crystallized/fluid intelligence trajectories of adolescents and adults with Down syndrome; and (b) the contribution of endogenous (health, activities of daily living-ADL) and exogenous (cognitively stimulating leisure activities) factors on adults' intelligence with age.

METHOD

Four cohorts (N = 80) with Down syndrome participated: adolescents (ages 16-21) and adults (ages 30-45, 46-60 and 61+). All completed Vocabulary and Similarities (crystallized) and Block Design and Raven (fluid) intelligence tests (WAIS-III^HEB , Wechsler, 2001).

RESULTS

The 30-45 cohort significantly outperformed the 16-21 cohort. Except for Vocabulary, which remained stable, onset of decline was at 40-50. Age-related declining health and ADL correlated with participants' lower fluid intelligence, but cognitive leisure activities mitigated this influence.

CONCLUSIONS

Intelligence development into adulthood supported the continuous trajectory and compensation age theory, rather than accelerated or stable trajectories. Not only endogenous factors but also exogenous factors determined intelligence levels in adults with Down syndrome, supporting cognitive activity theory.

Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts

Studies of the neural basis of intelligence have focused on comparing brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Here, the relation between mean tract-based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest approach for 10 white-matter fascicles along which the mTBFA was calculated. The study sample included 83 healthy individuals from the second wave of the Cuban Human Brain Mapping Project, whose WAIS-III intelligence quotients and indices were obtained. Inspired by the "Watershed model" of intelligence, we employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, as mediated by latent variables summarizing the indices. Regularized MIMIC, used due to the limited sample size, selected relevant mTBFA by means of an elastic net penalty and achieved good fits to the data. Two latent variables were necessary to describe the indices: Fluid intelligence (Perceptual Organization and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Memory indices). Regularized MIMIC revealed effects of the forceps minor tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant effect of age on both latent variables.