Which Of The Following Is Most Likely To Draw On Fluid Intelligence?

Hum Brain Mapp. 2022 Mar; 41(iv): 906–916.

Crystallized and fluid intelligence are predicted past microstructure of specific white‐matter tracts

Daylín Góngora, ^{1 , 2} Mayrim Vega‐Hernández, ^two Marjan Jahanshahi, ^{1 , 3} Pedro A. Valdés‐Sosa, ^{1 , 2} Maria L. Bringas‐Vega, ^{1 , two} and CHBMP ^{2 , 4 , five}

Daylín Góngora

^one The Clinical Hospital of Chengdu Brain Scientific discipline Establish, MOE Key Laboratory for Neuroinformation, Academy of Electronic Science and Technology of China, Chengdu China,

² Cuban Neuroscience Center, Havana Cuba,

Mayrim Vega‐Hernández

² Cuban Neuroscience Heart, Havana Cuba,

Marjan Jahanshahi

^one The Clinical Hospital of Chengdu Encephalon Science Institute, MOE Fundamental Laboratory for Neuroinformation, Academy of Electronic Scientific discipline and Technology of People's republic of china, Chengdu China,

³ UCL Queen Square Constitute of Neurology, London UK,

Pedro A. Valdés‐Sosa

¹ The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Academy of Electronic Science and Technology of Prc, Chengdu Mainland china,

² Cuban Neuroscience Center, Havana Cuba,

Maria L. Bringas‐Vega

^ane The Clinical Hospital of Chengdu Brain Scientific discipline Found, MOE Key Laboratory for Neuroinformation, Academy of Electronic Science and Engineering of Prc, Chengdu China,

² Cuban Neuroscience Center, Havana Cuba,

CHBMP

² Cuban Neuroscience Centre, Havana Republic of cuba,

^four Ministry of Scientific discipline, Technology and Environment of Cuba, Havana Cuba,

⁵ Ministry of Public Wellness of Republic of Cuba, Havana Cuba,

Find articles by CHBMP

Received 2022 April 17; Revised 2022 Sep xix; Accepted 2022 Oct 17.

Supplementary Materials

Tabular array S1 Cognitive test simply model estimates from Watershed model including only 1 latent variable (g factor) set up from the cognitive indexes.

GUID: 0EBE7701-BF1E-48A3-A335-4C8821CEA88F

Table S2 Model estimates from MIMIC which included two latent variables set up from the cognitive tests and regressions among structural variables (FA measures) and including the age every bit additional regressor.

GUID: 4F462890-6C1F-4A49-BA1C-D7466F7CD087

Data Availability Statement

Imaging data is available at http://cbmp-ccc.cneuro.cu. The dataset employed here, including behavioral cess and tractography estimations, is available at doi: ten.6084/m9.figshare.8959529.

Abstract

Studies of the neural footing of intelligence have focused on comparison brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Hither, the relation between hateful tract‐based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest arroyo for ten white‐matter fascicles along which the mTBFA was calculated. The report sample included 83 healthy individuals from the 2nd wave of the Cuban Homo Brain Mapping Project, whose WAIS‐III intelligence quotients and indices were obtained. Inspired past the "Watershed model" of intelligence, nosotros employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, every bit mediated past latent variables summarizing the indices. Regularized MIMIC, used due to the express sample size, selected relevant mTBFA by means of an rubberband net penalty and achieved good fits to the data. Two latent variables were necessary to depict the indices: Fluid intelligence (Perceptual System and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Retentivity indices). Regularized MIMIC revealed effects of the forceps small-scale tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant consequence of historic period on both latent variables.

Keywords: crystallized intelligence, fluid intelligence, fractional anisotropy, MIMIC model, white‐matter tracts

1. INTRODUCTION

Intelligence has been divers in many ways over the years, leading to still intense controversy in psychology. Definitions take ranged from the operational argument that "intelligence is what Intelligence Quotient (IQ) tests measure" to the proposal of a latent variable reflecting a very full general capability that, amongst other things, involves the ability to reason, plan, solve issues, think abstractly, empathize circuitous ideas, learn quickly, and larn from experience (Gottfredson, 1997). In whatever instance, irrespective of the definition, measures of intelligence accept potent correlations with brain imaging and genetic measures (Deary, Penke, & Johnson, 2022), prompting the apply of several neuroimaging techniques to try to sympathize the neural ground of intelligence. The relationship between IQ and brain structure has been explored using many techniques including voxel‐based morphometry, cortical thickness, spectroscopy, and diffusion‐weighted imaging (DWI) (Basten, Hilger, & Fiebach, 2022; Haier, 2009; Joshi et al., 2022; Reiss, Abrams, Singer, Ross, & Denckla, 1996; Yu et al., 2008). These studies suggest that structural and functional encephalon imaging can express individual differences in brain pathways, especially the parietofrontal structures (Jung & Haier, 2007) which correlate positively with intelligence (Deary et al., 2022).

An interesting strand of these studies, one that nosotros volition follow in this commodity, suggests links of white‐affair microstructural properties (such as partial anisotropy or FA) to cognitive data‐processing speed and thus to the neural foundation of general intelligence (Penke et al., 2022). FA quantifies the dispersion of water molecules, and information technology is constrained by the system of white‐matter structures. While sometimes interpreted as a mensurate of white‐matter integrity, FA is a very circuitous and indirect measure with various limitations, and its human relationship to white‐matter health is not even so fully understood (Bough, Prindle, Brandmaier, & Raz, 2022; Jones, Knösche, & Turner, 2022). Nevertheless, FA is widely used, as it has been shown to be associated with individual differences in a range of cognitive domains, especially in quondam age (Madden et al., 2009). As an example, at that place are significant correlations betwixt h2o diffusion parameters and intelligence beyond the life bridge, from childhood (Deary et al., 2022) to onetime machismo (Charlton et al., 2006; Deary et al., 2006). Other studies take provided evidence that all measures of information‐processing speed, as well as a general speed gene equanimous from these tests (g‐speed), were significantly associated with FA (Kuznetsova et al., 2022).

Many of these studies suffer from iii main shortcomings:

1. The relation of neuroimaging with intelligence measures is usually based on simple correlations. While useful in exploratory analyses, they do not provide data about causal pathways that crave more complex multivariate analyses. Specific statistical methods for causal analysis must be used.

2. At that place is an inconsistent exploration of cerebral domains. Some studies rely on specially designed cerebral scales, then summarized to reflect a single latent variable. Even when widely available scales, such as the Wechsler Adult Intelligence Scale (WAIS) (Wechsler, Sierra, & Blanca, 2003), are used, they are ofttimes also reduced to a single score, such as the general or "m" factor (Spearman, 1904), or "fluid intelligence" using different procedures. It is better to explore the private indices of the WAIS, those related to operation intelligence: Perceptual Organization (PO) and Processing Speed (PS), also as exact Intelligence: Verbal Comprehension (VC) and Working Memory (WM).

3. The anatomical accuracy in the definition of the tracts has been variable across studies. Some examples with progressive accuracy are as follows:

   1. Initial studies using voxel‐based averages of FA values without close matching to the tract they belonged to voxel based partial anisotropy.

   2. The mean FA values over each tract, where these are divers by the projection fasciculi from a population‐averaged tractography atlas to the subject'due south native infinite (mean tract‐based partial anisotropy [mTBFA]‐atlas).

   3. FA averages along tracts obtained from each subject's DWI in native infinite (mTBFA‐individual).

We now review the solution to these iii issues and clarify recent papers which dealt with them.

Regarding the beginning problem virtually statistical issues, appropriate multivariate methods to determine causal pathways are now emerging. Specifically, the Structural Equation Modeling (SEM) framework has proven to be particularly useful (Bollen & Hoyle, 2022). These models approximate a set of regression equations which may exist interpreted in terms of a directed Bayesian network in which the variables studied are considered as nodes of a graph with each valid regression equation a directed border. This graph fulfills a Markov property that can be explained as follows: If variable A is connected to B and B to C, and there is no other direct or indirect path from A to C, B "totally mediates" the influence of A on C. In other words, B "screens off" A from C. Under appropriate conditions (Pearl, 2000), the resulting graphs allow some inference most mechanistic causal relations. For a discussion of these concepts in encephalon networks, see Valdes‐Sosa, Roebroeck, Daunizeau, and Friston (2011). A especially useful type of SEM is the Multiple Indicator, Multiple Causes (MIMIC) introduced by Jöreskog and Goldberger (2006) in which latent variables are introduced as mediators betwixt two sets of observed variables. This framework was leveraged by Kievit et al. (2012, 2022) and Kievit, Fuhrmann, Borgeest, Simpson‐Kent, and Henson (2018) to provide an SEM specification for the study of the relation of FA with intelligence, work that is worth summarizing in the next paragraph.

In Kievit et al. (2012), a MIMIC model was estimated with the following three levels to the data of 80 subjects: (a) Voxel‐based region of interest (ROI) measures for 4 FA (VBFA) tracts and four Gy thing, (b) a single grand (WAIS) latent variable, (c) the 4 WAIS indices (WM, VC, PO, and PS). This model was practical and showed a good fit but considered just a single cognitive domain based on a sample with a limited age range (18–29 years) and a limited small number of FA measures that were non actually tract based.

The former study was followed by a seminal paper by Kievit et al. (2016) that embodied statistically the "watershed model" proposed past Cannon and Keller (2006) within the MIMIC framework. That model postulated that multiple causes act through latent variables (endophenotypes) to produce the observable phenotypes. This MIMIC/watershed model was fit to a cantankerous‐sectional sample of 555 subjects anile from xviii to 87 years of historic period (Cam‐CAN; Shafto et al., 2022). The model comprised four levels, the get-go level consisting of x mBTFA‐atlas measures (Table ​ 1). The 2nd level was six speed of processing (CAM/Can). The third level was a unique latent variable identified with "fluid intelligence" (FI‐CAM/CAM). Hereon we will identify psychometric scales and latent variable by the initials of the written report. Finally, the last level consisted of the four subtests of fluid intelligence of Cattell'due south Civilisation Off-white, Scale 2, Form A (Cattell, 1971). This model was built upwardly past a successive exploration of increasingly more complex models verified with confirmatory factor analyses. The overall model gave an excellent fit to such a large sample, verifying the utilize of the MIMIC arroyo. Note that age was not included as an explicit variable at level one.

Table 1

White‐affair tracts included in the analysis

Full name of white‐matter tract	Abridgement
Anterior thalamic radiation	ATR
Cingulum associated to cingulate gyrus	CGC
Cingulum associated to hippocampal gyrus	CGH
Corticospinal tract	CST
Forceps major	Fmj
Forceps minor	Fmn
Inferior fronto‐occipital fasciculus	IFO
Junior longitudinal fasciculus	ILF
Superior longitudinal fasciculus	SLF
Uncinate fasciculus	UNC

Subsequent to the Kievit et al. (2016) proposal of the watershed model for intelligence regularized regression methods were integrated into SEM/MIMIC modeling (Jacobucci, Brandmaier, & Kievit, 2022). Regularization (Zou & Hastie, 2005) imposes restrictions on the relations betwixt coefficients allowing the exploration of large sets of variables with built‐in selection of those that are relevant. This model was used to study subjects from the U.k. Biobank (Kievit et al., 2022) in a longitudinal written report with 3 waves, with 185,317, 9,719, and 870 subjects, respectively. Equally acknowledged by the authors, the cognitive domains explored were express by the UK Biobank pattern with a conflation of fluid and crystal intelligence items. Interestingly, a iii‐level MIMIC model was integrated with a longitudinal one to clarify individual variable trajectories. This time, a three‐level model was assumed:

1. Fifteen mTBFA private for a set up that includes those described in Table ​ 1;

2. Coefficients of the age regression (two latent variables);

3. Fluid intelligence scores (FI‐U.k. Biobank).

In this very large longitudinal written report, the employ of MIMIC models is once more validated, the effect of crumbling is discussed, and the use of personalized (mTBFA‐private) and non atlas‐based FA measures was introduced. As the authors state, the "suboptimal task design" restricted the exploration of cognitive estimation. Besides, findings from the same group (Kievit et al., 2022) showed a weak just significant negative association between age and fluid intelligence.

Our study reported here will try to fill in some of the gaps of previous studies. We will take reward of one of the rare population‐based studies based in a country in Latin America: the second wave of the Cuban Human being Encephalon Mapping Project (CHBMP; Hernandez‐Gonzalez et al., 2022). This cross‐sectional study evaluated more than 95% of a random sample of 2,109 subjects with medical, cognitive, and neuroimaging studies (T1, T2, DWI, electroencephalogram (EEG)) to yield a final group of 240 "functionally healthy" subjects, of which 83 (anile 18–69 years) were used in this study. Non but does this allow u.s. to reexamine some of the unanswered questions from previous studies just also to construct an MIMIC (watershed model) that integrates previous approaches by the following:

1. Exploring a wider range of cognitive domains namely, the iv WAIS indices for intelligence as in Kievit et al. (2012).

2. Division for the kickoff time, the variance of these indices amid the optimal number of latent variables.

3. Using individualized average FA tract‐based measures of white‐thing microstructure as in Kievit et al. (2018).

4. Amalgam a watershed model of the influence of white‐matter microstructure, mediated by latent factors, on the full set up of indices of the WAIS.

2. Fabric AND METHODS

two.i. Participants

The sample included 83 healthy correct‐handed participants with an average historic period of 35.03 ± 10.27 years and 12.12 ± 2.46 years of education. The recruitment was based on a completely randomized sampling using the identity card database stratified by age, gender, and outward ethnic features of ii,109 subjects of the whole population of La Lisa municipality (more than 30,000) in La Habana. Information technology is important to note that this municipality was selected considering its demography closely matched those of the full general Cuban population co-ordinate to the national census of the Democracy of Cuba: http://world wide web.ane.cu/.

The present study was carried out in accordance with The Code of Ideals of the World Medical Association, Declaration of Helsinki (World Medical Arrangement, 2022), and the experimental protocols were approved by the Ethics Commission of the Cuban Neuroscience Middle. The recruitment process did not involve whatsoever kind of reward, simply just feedback about the results and participants were included in the report after accepting and signing the informed consent.

2.ii. Assessments

Each participant underwent an interview and medical examination with specialists in Neurology and Psychiatry, in guild to rule out chronic diseases (due east.grand., addictions, including smoking) or any disorders of the nervous system that would invalidate their participation in the study. Neurological examination was performed following the procedure described in the guidelines published by the U.S. Department of Wellness and Human being Services in 2003 (Neurological Single System Examination in http://world wide web.cms.gov/MLNEdWebGuide/25_EMDOC.asp). The Mini‐International Psychiatric Interview was used for psychiatric evaluation (Sheehan et al., 1998). Intelligence was assessed using the fully validated and translated to Spanish language version of the WAIS‐III (Wechsler et al., 2003), printed and distributed in Mexico past The Manual Moderno (https://www.worldcat.org/title/wais-three-escala-weschler-de-inteligencia-para-adultos-iii/oclc/54053545). This calibration provided scores for a Full Scale IQ (FSIQ), Verbal IQ (VIQ), and Operation IQ (PIQ) forth with iv secondary indices: PO, PS, VC, and WM. The subtests included in each index were as follows: PO: picture completion, block design, matrix reasoning; PS: digit‐symbol coding and symbol search; VC: vocabulary, similarities, information, comprehension; and WM: arithmetic, digit span, letter‐number sequencing. The raw measures were scored according to the official normative data included in the printed version of WAIS‐Three. However, to avoid culture bias, they were after standardized with information from the Cuban sample to produce scores of the specific operation, adjusted for age for our population.

2.3. Magnetic resonance imaging acquisition protocol

A Siemens 1.5 T Magnetom Symphony system with a standard birdcage head coil for signal transmission/reception (Siemens, Erlangen, Federal republic of germany) was used to acquire images, including a high‐resolution Tone‐weighted anatomical image and a standard diffusion sequence.

The T1‐weighted structural image (ane × one × 1 mm resolution) was caused with the post-obit parameters: echo time (TE) = iii.93 ms, repetition fourth dimension (TR) = 3,000 ms, flip bending = 8°, and field of view (FOV) = 256 × 256. This yielded 160 face-to-face ane‐mm‐thick slices in a sagittal orientation. Axial diffusion weighted images were acquired forth 12 independent directions, in l slices spaced at 3 mm, with ii × 2 mm in‐plane resolution, and a improvidence weighting b value of 1,200 south/mmⁱⁱ. The following parameters were used: FOV = 128 × 128, TE = 160 ms, TR = 7,000 ms, flip angle = xc°. A reference image (b0 image) with no diffusion weighting was as well obtained (b = 0 south/mmⁱⁱ).

In club to correct the distortions caused past magnetic field inhomogeneities in the series of diffusion‐weighted images, stage and magnitude maps were obtained. The parameters used were voxel size of 3.5 mm, ET_one = 7.71 ms, ET_two = 12.47 ms, and RT = 672 ms. The Diffusion Tensor Imaging images were movement, eddy‐current, and distortion corrected. Using the magnitude and phase images and the unwarping functionality (Anderson, 2001), the effects of the master inhomogeneities of magnetic fields were corrected. Afterwards, the improvidence tensor and the FA were determined in each voxel (Pierpaoli & Basser, 1996).

2.iv. Fiber tracking computation

Computation of the diffusion tensor and fiber tracking was performed using DTI&FiberTools five.3.0 (http://www.uniklinik-reiburg.de/mr/live/arbeitsgruppen/diffusion_en.html; Kreher, Hennig, & Il'yasov, 2006) and implemented in Matlab (The MathWorks, 2022). Co-ordinate to the formulation of Basser, Mattiello, and LeBihan (1994), and past diagonalizing the diffusion tensor for each voxel, the toolbox generates as output half dozen components of a improvidence tensor, iii eigenvectors that characterize the direction of diffusion, and three eigenvalues that narrate the magnitude of the improvidence in the corresponding eigenvector calculated (Basser et al., 1994).

Iii‐dimensional reconstruction of the tracts was performed using the deterministic tractography method Fiber Consignment by Continuous Tracking algorithm and a beast‐force reconstruction arroyo (Mori, Crain, Chacko, & Van Zijl, 1999). Cobweb tracking was initiated by specifying six parameters: the minimum FA threshold for starting tracking, the minimum FA for stopping tracking, the maximum trace (Tr) for starting tracking, the maximum trace for stopping tracking, the critical angle threshold for stopping tracking in example the algorithm encounters a precipitous turn in the fiber management, and a minimum fiber length. The offset criteria used in the reconstruction of the tracts were FA = 0.15, Tr = 0.0016, and a end criteria FA = 0.ten, Tr = 0.002. A turning angle threshold of 53.1° and minimum fiber length of five voxels were used. The DTI&Fiber Tools v.iii.0 used these parameters to generate the coordinates of all fibers in the brain from which the tract trajectory is reconstructed after drawing an ROI in a user‐divers region of the brain.

2.5. Definition of tract‐based partial anisotropy (mTBFA)

A multiple ROIs arroyo was used for the reconstruction of the tracts of interest because it has been shown that the two‐ROI and brute‐force approaches could effectively reduce the sensitivity to the noise and ROI placement (Huang, Zhang, van Zijl, & Mori, 2004). The fiber tracking was performed on every voxel of the encephalon, and fibers that penetrated the previously defined ROIs were assigned to the specific tracts associated with each pair of ROIs.

Definition of ROIs for studied tracts was made by replicating a set of predefined ROI by Mori et al. (2002) that was employed successfully in subsequent work (Góngora, Domínguez, & Bobes, 2022; Mori et al., 2008; Wakana et al., 2007; Wakana, Jiang, Nagae‐Poetscher, Van Zijl, & Mori, 2004). The following process replicated the methodology published past Góngora et al. (2016). These ROIs were drawn using the programme MRIcron (http://world wide web.mricron.com) on a reference anatomical image with a spatial resolution of ane × ane × one mm³ in stereotactic space of the Montreal Neurological Establish (Evans et al., 1993). The ROIs were then transformed to each private encephalon infinite automatically using the SPM toolbox functionalities (Friston, Ashburner, Kiebel, Nichols, & Penny, 2007). In this routine, the high‐resolution anatomical Tane image was realigned to the standard position on the AC–PC plane and normalized using the procedures of SPM. The unnormalized T1 was rigidly co‐registered with the b0 paradigm using a mutual information cost function (Collignon et al., 1995). The ROIs were defined for the following tracts defined in Table ​ 1. The resulting path of these tracts was visually inspected and corrected in cases where necessary by the exclusion of fibers that did not belong anatomically to tracts. The mean tract‐based FA (mTBFA) was obtained as an estimate of the average along each tract, which resulted from the superposition of the specific coordinates for each tract on the corresponding maps of FA. For the statistical assay, this parameter was averaged between the respective bilateral tracts.

ii.6. Statistical analysis

In the present study, we first used the MIMIC model to decide the number of latent variables necessary to explain the four indices of WAIS‐III. For this purpose, nosotros outset fitted a single latent g (WAIS‐3) model every bit in Kievit et al. (2012) as a composite of PO and PS, and VC and WM. Subsequently, a two‐latent variable model was fitted to these same indices respective to verbal and functioning intelligence scales.

Subsequently, the sample was fitted with a modification of the watershed model proposed by Kievit et al. (2016). In our model, anatomical variables (mTBFA) bear on the two intelligence latent variables that in plow mediate the WAIS‐Iii indices. Historic period is also included every bit affecting the latent variables every bit in Kievit et al. (2018). Exploratory analysis regarding possible confounds (gender, handedness, and educational level) in the estimations was performed but without relevant effects in the estimations and will not be further considered in this article.

Estimates may be imprecise when using maximum likelihood estimation with big numbers of variables and a limited sample size. We therefore used the regularized SEM model (Jacobucci et al., 2022) implemented in the cv_regsem function (regsem package in R) (Jacobucci, Grimm, & McArdle, 2022) as a postprocessing of a laavan model. We used an Rubberband‐net regularization (equal proportion of Lasso and Ridge regularization). The regularization parameter λ, which determines the number of variables to keep in the model, was explored across 35 values of λ ranging from 0 to 0.35. The elastic‐internet method provides a compromise between sparsity and variable pick of cluster of related variables (Zou & Hastie, 2005). To choose a final model among the 35 models run, the Bayesian data benchmark (BIC; Schwartz, 1978) was used, which approximates the Bayesian model prove, thus providing a trade‐off metric of model fit and model complexity in which the all-time model achieves the everyman value. The concluding model is shown as that selected past the regularized SEM bundle.

3. RESULTS

Analysis of the IQ measures

The application of the WAIS‐III resulted in the estimation of a mean FSIQ of 101.75 ± thirteen.25, a mean VIQ of 96.58 ± 13.73, and a hateful PIQ of 107.94 ± 11.64, which were in the average range and were normally distributed (Figure ​ 1). The means for the four indices were PO: 27.71 ± 6.77, PS: 16.63 ± 4.97, VC: 29.39 ± 7.42, and WM: 26.17 ± 5.77.

Histogram of Wechsler Adult Intelligence Scale 3 (Wechsler et al., 2003). FSIQ, Full Scale Intelligence Quotient; PIQ, Performance Intelligence Quotient; VIQ, Exact Intelligence Caliber

As a offset pace for the full MIMIC model, we first congenital upward the measurement model using only the WAIS‐III indices. Nosotros tested the adequacy of the single latent variable g‐WAIS. This model showed an almost acceptable, merely not good, fit, with a χ² = v.589, df = 2, p = .061, Root Mean Square Error of Approximation (RMSEA) = 0.161 (0.000–0.326), Comparative Fix Index (CFI) = 0.963, Standardised Root Mean Foursquare Rest (SRMR) = 0.044, and Satorra–Bentler scaling factor = 1.196. The BIC of this model was 831.075. All the indexes contribute significantly to their corresponding latent variables with a p < .001.

We subsequently fitted a 2nd model for the indices, now considering ii latent variables: one corresponding to the verbal (VC and WM) and the other to the functioning (PO and PS)‐related indices. This model as well fits the data very well: χ² = 0.189, df = 1, p = .664, RMSEA = 0.000 (0.000–0.226), CFI = one, SRMR = 0.008, and Satorra–Bentler scaling factor = 1.045. The BIC of this model was lower than the unmarried latent variable 1 with an estimate of 825.854 indicating a ameliorate fit. Note that all the indices contributed significantly to their corresponding latent variables with a p < .001. For the residue of the commodity, nosotros used these two latent factors identifying the exact 1 with "crystallized intelligence" and the performance 1 with "fluid intelligence" according to Cattell (1963)), denoted in this article equally CI and FI without whatever farther clarification. The detailed breakdown of the contribution of the indices to latent factors is every bit follows. For FI, PO has an R ² of 0.856 while PS: 0.408. For CI, the R ⁱⁱ of VC is 0.756 and WM 0.483.

3.one. MIMIC (watershed) model of integrated WM and cognitive measures

We fitted a regularized SEM/MIMIC/Watershed model with an rubberband‐cyberspace penalty (Jacobucci et al., 2022), introducing the following hierarchical levels:

1. The most upstream variables were the 10 major white‐matter tract mTBFA, which were modeled as influencing only the two latent variables. Note that age was also added at this level as in Kievit et al. (2018).

2. A next level where the ii latent variables obtained in the previous section: Fluid and Crystallized intelligence.

3. Finally, at the lowest level, the four WAIS‐Three indices (VC, PO, WM, and PS) mediated by the latent variable of their corresponding cognitive domain.

Note that for this implementation of regularized SEM, p values are not estimated. Instead, in that location is a selection of regression coefficients (or edges in the SEM graph) thus finessing the demand for multiple comparisons. The parameter trajectory plot of this model is shown in Effigy ​ two, which shows the value of the coefficients for each value of the regularization parameter which controls the trade‐off between model fit and the penalisation imposed. The elastic net imposes a balance (in our case, half and half) between an L _one sparseness penalty and an Fifty ₂ ridge penalty (see Jacobucci et al. 2022 and for a more detailed word Valdes‐Sosa et al., 2005) for a discussion of this and other similar models in the detection of neural networks.

Parameter trajectory plot from regularized MIMIC. The graph shows the values of the regression coefficients every bit a function of the penalty value. The dashed vertical line highlights the penalization value yielding the model with the all-time fit (i.e., the lowest Bayesian information criterion)

The value of the regularization parameter lambda selected was 0.3 respective to the lowest BIC value of 0.836. The resulting model is depicted in Figure ​ 3. The latent variables are represented as circles in the resulting diagrams and the observed variables as squares (Schreiber, Stage, Male monarch, Nora, & Barlow, 2006). From Figure ​ three, it can be seen that the edges selected by the regularized MIMIC (with the coefficient values in parenthesis) were as follows:

1. Age to FI (0.069) and CI (−0.043)

2. Forceps Minor (Fmn) to CI (0.019)

3. Superior longitudinal fasciculus (SLF) to FI (−0.004)

A regularized MIMIC model of the relationship between 2 latent variables (CI and FI) and white‐affair tracts where nonzero model estimates edges are by solid lines and the color indicates whether the consequence is positive (green) or negative (red). The zero estimates edges are represented by dotted lines. CI, Crystallized Intelligence; FI, Fluid intelligence; MIMIC, Multiple Indicators, Multiple Causes

As noted in Jacobucci et al. (2019), this type of variable selection may non exist familiar to some researchers who are more used to p values or R‐squared statistics.

4. Word

4.1. Construction of cognitive variables

Previous studies using MIMIC accept postulated a single latent variable mediating between FA and cognitive indexes. In fact, there are many studies, focusing only on relations betwixt intelligence indices, that have demonstrated that rather than a single general factor, there are several relatively contained factors needed to explain intelligence in terms of dissimilar cognitive domains. Specially important is the confirmatory factor analysis of a sample of half dozen,832 individuals from 33 cross‐sectional studies where five correlated first‐guild factors were identified: reasoning, spatial ability, retentivity, PS, and vocabulary (Salthouse, 2004), which are very closely related only not identical to the four independent indexes of the WAIS‐III: PO or Reasoning (PO), PS, VC, and WM. Thus, the single‐cistron summarization of intelligence scales in Kievit et al. (2016, Kievit et al., 2022) lead to a misallocation of variance of the intelligence measures that volition bear upon the full model.

By contrast, here we leveraged the availability of the full WAIS‐III recorded with DWI from the CHBMP. Our analysis showed that 2 latent factors, fluid intelligence or FI and crystalized intelligence or CI, provide better fits than a single general or thousand (WAIS) latent variable. This is in understanding with the ascertainment of Kievit et al. (2016) who suggested that fitting more than than one latent variable, with the exploration of more cerebral domains, would increment the explained variance of the models.

iv.2. Full MIMIC model relating white‐matter microstructure to intelligence indexes, mediated by latent variables

As mentioned before, the model produced by regsem is not accompanied past p values or confidence intervals, which may seem surprising. Jacobucci et al. (2019) signal out that this type of model incurs acceptable bias when using regularization, just the more than important aim is the holdout sample generalization, which is accomplished by reducing variance and preferring models of a complication that is afforded past the observed data. In this framework, the resulting edges left in the MIMIC model are important if they are simply not prepare to nothing. This is the estimation we will follow here.

The regularized MIMIC model indicated an historic period effect on both FI and CI. This points to the effectiveness of this method to unravel more subtle relations in a set up of predictors since historic period was non meaning in nonregularized versions of MIMIC models. This method of regularization is supported by previous studies that showed its power to model the relation between cerebral performance and imaging metrics, taking a high‐dimensional set of predictors and reducing this ready to create a relatively parsimonious representation of central tracts previously implicated in specific tasks, for example, visual brusque‐term retentiveness operation (Jacobucci et al., 2022).

The regularized MIMIC model revealed 2 tracts that are connected directly to the latent variables. We draw these two paths of putative causation next.

I tract, Fmn is an interhemispheric tract whose fibers connect left and right frontal lobes through the knee of the corpus callosum (Mori, Wakana, Van Zijl, & Nagae‐Poetscher, 2005), the enhanced anatomical connectivity of which may underlie the greater fluid reasoning, visuospatial WM, and creative capabilities appreciated in mathematically gifted children (Navas‐Sánchez et al., 2022). The microstructural characteristics of interhemispheric connections have been positively correlated to some intelligence variables in females just negatively correlated in males (Tang et al., 2022).

In our model, the Fmn was continued to the CI latent variable, therefore besides predicting operation in the WM and VC indices. In contradistinction, a positive association of the Fmn with the FI (Cam‐CAN) has been reported by Kievit et al. (2016). This is not surprising since FI (Cam‐Tin) in the words of the authors they are all subtests of a single cerebral domain (Cattell, 1971). Fifty-fifty more than complicated is the comparing with results of the UK Biobank study (Kievit et al., 2022) since the latent variable is divers with a set up of measures that have been suggested to be difficult to translate (Lyall et al., 2022).

Our model besides identified another white‐matter tract as important, the SLF. The SLF is located over the cingulum running from the dorsal and medial parietal cortex to premotor and prefrontal cortices (Schmahmann et al., 2007). Specifically, it is on the superior lateral portion of the putamen forming a long curvation that emits branches toward the temporal, parietal, and occipital lobes (Mori et al., 2005). This tract connects the caudal part of the junior parietal lobule and intraparietal sulcus, areas that are involved in visuospatial information processing. Also, their fibers get to the posterior prefrontal cortex, which is of great importance in perception and sensation (Petrides & Pandya, 2006; Schmahmann et al., 2007).

In our model, the SLF influences FI with a negative sign, which in turn influences PS and PO. This negative path between SLF and the latent variable FI suggests that those subjects with higher FI scores will accept lower FA values. This result, at beginning sight, is counterintuitive if FA reflects "white‐matter integrity" or "increased speed" of neural activeness forth the corresponding fiber tracts. Similar lack of concordance with the "myelin integrity" hypothesis was found by Braddick et al. (2017) for the correlation of individual children'southward sensitivity to global move coherence with the FA of the left SLF. Hoeft et al. (2007) likewise showed increased FA of SLF was associated with poor visuospatial abilities in Williams' syndrome, which argues against the white‐thing FA identification. In addition, the negative relation between the FA of the SLF and memory tests has been previously reported (Tang et al., 2022), at least for males in the left branch of the SLF.

This apparently counterintuitive result may be viewed equally we stated in the introduction, due to the fact that FA is a highly circuitous measure that must be interpreted with circumspection (Jones et al., 2022). Although information technology is true that FA is direct related to increased myelin thickness, parallelism, and packing of axons, it also depends on other factors such as barriers and obstacles imposed by microstructure, jail cell membranes, myelin sheaths, and microtubules (Beaulieu, 2002). It may decrease with larger axonal diameters due to an increase in the mobility of h2o in the intra‐axonal compartment water mobility (Takahashi et al., 2002). Fiber crossings may be another factor influencing the observed values of FA. This may explicate both positive and negative correlations of FA with reaction time every bit reported and discussed by Tuch et al. (2005). A deeper understanding of white‐affair microstructural determinants of cognitive functions volition require improved diffusion magnetic resonance imaging (MRI) technology and methods (Jelescu & Budde, 2022; Jones et al., 2022; Riffert, Schreiber, Anwander, & Knösche, 2022).

Interestingly, the consequence of the SLF was restricted to FI while we were expecting some influence over CI. At that place are some reports of a relationship of this white‐matter tract with the tests which composed CI. For case, a multiple sclerosis written report by combining the Paced Visual Serial Add-on Test with functional MRI‐guided fiber tractography establish the SLF was the primary white‐matter tract connecting areas active during this attending and WM task (Bonzano, Pardini, Mancardi, Pizzorno, & Roccatagliata, 2009). In addtition, Papagno et al. (2017) found that directly electrical stimulation of the SLF during awake surgery improves exact brusque‐term memory. This as well supports the participation of the SLF in the so‐chosen "phonological loop," which has been described as a crucial component for linguistic communication acquisition (Papagno et al., 2022).

4.iii. Limitations and future piece of work

Several limitations should exist noted in the present research which pertain to report design, measures of encephalon organization, and statistical methodology. We detail them hither and betoken out that the next wave of the CHBMP will take them into consideration.

The sample is relatively small with data gathered cantankerous‐sectionally. A larger sample is foreseen for the 3rd wave of the CHBMP, and collaborative studies with other Latin American countries are being organized. A major difficulty to perform combined analyses with other databases from the United States or Europe is the lack of harmonization of their cognitive studies. In that location are also the following problems related to measures of brain organization:

1. Even when the trajectories obtained agreed with neuro‐anatomic descriptions derived from postmortem and other in vivo tractography studies (Carpenter & Sutin, 1983; Góngora et al., 2022; Mori et al., 2002; Nieuwenhuys, Voogd, & Van Huijzen, 2007; Wakana et al., 2007), the beingness of inaccuracies due to partial volume effects, noise, and crossing fibers involve the visualized pathways practise not necessarily reflect brain connectivity since private axons could be merging and blanching at any signal along the bundle (Wakana et al., 2007).

2. A further limitation is related to the quality of DWI images gathered in the 2nd wave of the CHBMP:

   1. Only 12 diffusion‐sensitizing gradient directions were recorded (besides thin to allow higher club diffusion models);

   2. Too, just express voxel size was possible (2 × two × 3) that could affect the robustness to noise, partial volume upshot, and crossing cobweb regions. These limitations were due to the technology available in 2004 in Cuba, which has been since then modernized.

3. The fiber tracking algorithm (Fiber Assignment by Continuous Tracking) employed is highly susceptible to errors in the orientation of the principal eigenvector, due both to noise and to instances where the direction of the underlying tract beefcake is ambiguous, for instance, assessment of voxels where fiber bundles cross, diverge, or converge. Improved algorithms now be and volition be used in the future.

4. The only measure of brain organization analyzed is FA, which as we have discussed previously is not very specific. Improved measures of white‐matter microstructure are to be preferred (Riffert et al., 2022)

5. Instead of exploring only the effects of white‐matter differences on individual intelligence, information technology would be preferable to use a model based on integrating model of DWI, functional Magnetic Resonance Imaging (fMRI), and peradventure EEG every bit previously described (Valdés‐Sosa, Vega‐Hernández, Sánchez‐Bornot, Martínez‐Montes, & Bobes, 2009). This blazon of causal neural connectivity model (Valdes‐Sosa et al., 2022) might and then be used as the upstream construct in an extended MIMIC model of intelligence.

Despite these limitations, nosotros believe this study is valuable due to the completely randomized sampling of participants from the full general Cuban population, the personalized decision of tract‐based FA analysis, and the administration of the complete WAIS‐III. In fact, i of the limitations of previous work was the limited measures of FI employed (four subtests of fluid reasoning) nerveless on the Cam‐Tin project (Shafto et al., 2022). This issue was overcome in our study employing more than WAIS‐III measures, opening a wider spectrum of cerebral domains, and finding other significant associations. On the other hand, the choice of ii latent variables, independent of the evaluator seems to be a reasonable alternative to reduce the random or systematic measurement errors associated with observable variables (IQ scores). The MIMIC model revealed the effect of distinct fiber tracts, Fmn and SFL, on FI and CI, respectively. Work to overcome the limitations and extend this line of enquiry will be pursued in the future.

v. CONCLUSIONS

The nowadays work was inspired by the watershed model of Kievit et al. (2016) who proposed the utilise of a MIMIC hierarchical model, using a regularized SEM version. Nosotros constitute this approach very useful. By exploring the full set of indices of the WAIS‐III, it was possible to observe novel associations of FA with four cognitive domains mediated by latent variables relating to both fluid intelligence (SLF) and crystallized intelligence (Fmn). The relation of the Fmn tract with crystalized intelligence was only discoverable past including in the model indices related to this domain, something that seems to accept been disregarded in many studies. In fact, we believe that a broad range of cognitive functions could exist explored, something apparently not yet envisaged in the current large brain projects.

Conflict OF INTERESTS

The authors declare that the inquiry was conducted in the absence of whatever commercial or fiscal relationships that could be construed as a potential conflict of interest.

Author CONTRIBUTIONS

M.L.B. conceived this and directed this written report as function of the Cuban Human Brain Mapping Project which was coordinated by P.Five.S. D.G. performed the DTI data processing, methodology, assay, software implementation, and writing of the first manuscript under the supervision of M.L.B. and P.Five. 1000.5. contributed in the statistical analysis. M.J. contributed to the final revision and editing of this article.

Supporting information

Table S1 Cognitive test only model estimates from Watershed model including just ane latent variable (g factor) set from the cerebral indexes.

Table S2 Model estimates from MIMIC which included two latent variables fix from the cerebral tests and regressions amid structural variables (FA measures) and including the age as boosted regressor.

ACKNOWLEDGMENT

The authors would like to give thanks the support from the National Natural Science Foundation of Mainland china (NSFC) projects (81861128001, 61871105, 61673090, and 81330032) and the CNS Plan of UESTC (No. Y0301902610100201). Our gratitude to all the participants who volunteered to participate in the CHBM and the continued back up from the Ministry of Public Health and the Ministry building of Science and Technology of the Cuba to CHBM Project since 2004. We would like to thank the contributions of primary investigators, researchers, and staff personnel of CHBM who include the following electric current researchers: Pedro Valdés Sosa, Lidice Galan, Iris Rodriguez Gil, Eduardo Aubert Vazquez, Jorge Bosch Bayard, Lourdes Valdes Urrutia, Evelio Gonzalez Dalmau, Maria L. Bringas Vega, Trinidad Virues Alba, Agustin Lage Castellanos, and Marcia Cobas Ruiz; and previous researchers and founders: Lester Melie, Pedro A. Valdes Hernandez, Gertrudis Hernandez Gonzalez, Yenisleidy Lorenzo Ceballos, Nancy Iglesias Pozo, and Geraldine Tudela. Note that this is a representative list of researchers involved in the 2 waves of the project.

Notes

Góngora D, Vega‐Hernández M, Jahanshahi M, Valdés‐Sosa PA, Bringas‐Vega ML, CHBMP. Crystallized and fluid intelligence are predicted by microstructure of specific white‐matter tracts. Hum Brain Mapp. 2022;41:906–916. 10.1002/hbm.24848 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

Funding information CNS Program of UESTC, Grant/Honour Number: Y0301902610100201; Ministry of Health and Science and Technology of Cuba; National Natural Science Foundation of Prc (NSFC), Grant/Laurels Numbers: 81861128001, 61871105, 61673090, 81330032

DATA AVAILABILITY Statement

Imaging data is available at http://cbmp-ccc.cneuro.cu. The dataset employed hither, including behavioral assessment and tractography estimations, is available at doi: 10.6084/m9.figshare.8959529.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267934/

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