general intelligence; myelin water fraction; neurite density; orientation dispersion; polygenic scores; Humans; Female; Male; Young Adult; Adult; Adolescent; Diffusion Tensor Imaging; Neurites/physiology; Intelligence/genetics; Intelligence/physiology; White Matter/diagnostic imaging; White Matter/physiology; Multifactorial Inheritance; Myelin Sheath/physiology; Brain/diagnostic imaging; Brain; Intelligence; Myelin Sheath; Neurites; White Matter; Cognitive Neuroscience; Cellular and Molecular Neuroscience
Abstract :
[en] White matter is fundamental for efficient information transfer and thus crucial for intelligence. Previous studies demonstrated associations between fractional anisotropy and intelligence, but it remains unclear whether this relation is due to greater axon density, parallel, homogenous fiber orientation distributions, or greater myelination, as all influence fractional anisotropy. Using neurite orientation dispersion and density imaging and myelin water fraction imaging, we analyzed the microstructural architecture of intelligence in 500 healthy young adults. We were also interested whether specific white matter indices mediate the pathway linking genetic disposition for intelligence to phenotype. For the first time, we conducted mediation analyses investigating whether neurite density index, orientation dispersion index, and myelin water fraction of 64 white matter tracts mediate the effects of polygenic scores for intelligence on general intelligence (g). Our results showed that neurite density index, but not orientation dispersion index or myelin water fraction of white matter tracts, was significantly associated with g and that neurite density index of six fiber tracts mediated the relation between genetic variability and g. These findings are a crucial step toward decoding the neurogenetic underpinnings of general intelligence, as they identify that neurite density of specific fiber tracts relates polygenic variation to g, whereas orientation dispersion and myelination did not.
Disciplines :
Neurosciences & behavior
Author, co-author :
Stammen, Christina; Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
Penate, Javier Schneider; Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany
Metzen, Dorothea; Institute of Psychology, Department of Educational Sciences and Psychology, TU Dortmund University, Emil-Figge-Straße 50, 44227 Dortmund, Germany
Hönscher, Maurice J; Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany
Fraenz, Christoph; Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
Schlüter, Caroline; Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany
Güntürkün, Onur; Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany ; German Center for Mental Health (DZPG), Partner site Bochum/Marburg, Massenbergstraße 9-13, 44787 Bochum, Germany
KUMSTA, Robert ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) > Health and Behaviour ; German Center for Mental Health (DZPG), Partner site Bochum/Marburg, Massenbergstraße 9-13, 44787 Bochum, Germany ; Department of Genetic Psychology, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany
Genç, Erhan ; Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
External co-authors :
no
Language :
English
Title :
Neurite density but not myelination of specific fiber tracts links polygenic scores to general intelligence.
We used 64 white matter fiber tracts provided by the population-based HCP-1065 probabilistic tract atlas (; ), from the official website ( https://brain.labsolver.org/hcp_trk_atlas.html ) as NIfTI files. This atlas displays for 64 different fiber tracts for each voxel the probability of being part of the respective white matter fiber tract compiled from the tractography of 1,065 subjects (), with underlying data (\u201C1200 Subjects Data Release\u201D) provided by the Human Connectome Project (HCP), WU-Minn Consortium (Principal Investigators: David van Essen and Kamil Ugurbil; 1U54MH091657), funded by the 16 United States National Institutes of Health (NIH) and Centers supporting the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University (). In a first step, fiber tracts\u2019 NIfTI files were processed using DSI Studio ( https://dsi-studio.labsolver.org ; ; ). We resized the dimensions to match the International Consortium for Brain Mapping (ICBM) 2009a Nonlinear Asymmetric NIfTI template file (). The threshold for the fiber tracts\u2019 probability was set at 0.50 to include only voxels that were part of major white matter tracts in at least half of the sample and exclude peripheral voxels that are more susceptible to intra- and intersubjective variability. We then binarized the fiber tracts and transformed them into a common space via FMRIB\u2019s Linear Image Registration Tool (FLIRT; ; ; ). We chose the template MNI152_T1_1mm_brain within FSL, which is derived from 152 structural images that have been nonlinearly registered into the common Montreal Neurologic Institute (MNI) 152 standard space (1\u2009\u00D7\u20091\u2009\u00D7\u20091 mm). Starting from the MNI 152 standard space, we used FMRIB\u2019s Nonlinear Image Registration Tool () to nonlinearly transform the fiber tracts into the native space of the diffusion-weighted images as well as into the 3D ME-GRASE image space. Each participant\u2019s aligned fiber tracts served as anatomical references from which NODDI coefficients and MWF coefficients were extracted (see , upper-right corner).This work was supported by the Deutsche Forschungsgemeinschaft (GU 227/16-1). The authors would like to thank Wendy Johnson for providing theg factor scores, all research assistants for their support during thebehavioral measurements, PHILIPS Germany (Burkhard M\u00E4dler)for the scientific support with the MRI measurements as well asTobias Otto for technical assistance.This work was supported by the Deutsche Forschungsgemeinschaft (GU 227/16-1).
Abel S et al. 2019. Myelin damage in normal appearing white matter contributes to impaired cognitive processing speed in multiple sclerosis. J Neuroimaging. 30:205–211. 10.1111/jon.12679.
Abel S et al. 2020. Associations between findings from myelin water imaging and cognitive performance among individuals with multiple sclerosis. JAMA Netw Open. 3:e2014220. 10.1001/jamanetworkopen.2020.14220.
Aboitiz F, Scheibel AB, Fisher RS, Zaidel E. 1992. Fiber composition of the human corpus callosum. Brain Res. 598:143–153. 10.1016/0006-8993(92)90178-C.
Akalu Y et al. 2023. Identifying the role of the reticulospinal tract for strength and motor recovery: a scoping review of nonhuman and human studies. Physiol Rep. 11:e15765 10.14814/phy2.15765.
Ammerman BA et al. 2018. Exploratory analysis of mediators of the relationship between childhood maltreatment and suicidal behavior. J Adolesc. 69:103–112. 10.1016/j.adolescence.2018.09.004.
Andersson JLR, Graham MS, Zsoldos E, Sotiropoulos SN. 2016. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. Neuroimage. 141:556–572. 10.1016/j.neuroimage.2016.06.058.
Andersson JLR, Jenkinson M, Smith SM. 2007. Non-linear registration aka spatial normalisation, FMRIB technical report TR07JA2.
Andersson JL, Skare S, Ashburner J. 2003. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 20:870–888. 10.1016/S1053-8119(03)00336-7.
Andersson JLR, Sotiropoulos SN. 2016. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 125:1063–1078. 10.1016/j.neuroimage.2015.10.019.
Assaf Y, Pasternak O. 2008. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J Mol Neurosci. 34:51–61. 10.1007/s12031-007-0029-0.
Baadsvik EL et al. 2023. Mapping the myelin bilayer with short-T2 MRI: methods validation and reference data for healthy human brain. Magn Reson Med. 89:665–677. 10.1002/mrm.29481.
Babyak MA. 2004. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 66:411–421.
Basser PJ, Pierpaoli C. 1996. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson Series B. 111:209–219. 10.1006/jmrb.1996.0086.
Beaulieu C. 2002. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed. 15:435–455. 10.1002/nbm.782.
Benear SL, Ngo CT, Olson IR. 2020. Dissecting the fornix in basic memory processes and neuropsychiatric disease: a review. Brain Connect. 10:331–354. 10.1089/brain.2020.0749.
Bentler PM, Bonett G. 1980. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull. 88:588–606. 10.1037/0033-2909.88.3.588.
Billiet T et al. 2015. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol Aging. 36:2107–2121. 10.1016/j.neurobiolaging.2015.02.029.
Booth T et al. 2013. Brain white matter tract integrity and cognitive abilities in community-dwelling older people: the Lothian birth cohort, 1936. Neuropsychol. 27:595–607. 10.1037/a0033354.
Borich MR, Mackay AL, Vavasour IM, Rauscher A, Boyd LA. 2013. Evaluation of white matter myelin water fraction in chronic stroke. Neuroimage Clin. 2:569–580. 10.1016/j.nicl.2013.04.006.
Bouhrara M et al. 2020. Adult brain aging investigated using BMC-mcDESPOT-based myelin water fraction imaging. Neurobiol Aging. 85:131–139. 10.1016/j.neurobiolaging.2019.10.003.
Bunea F et al. 2011. Penalized least squares regression methods and applications to neuroimaging. Neuroimage. 55:1519–1527. 10.1016/j.neuroimage.2010.12.028.
Buyanova IS, Arsalidou M. 2021. Cerebral white matter myelination and relations to age, gender, and cognition: a selective review. Front Hum Neurosci. 15:662031. 10.3389/fnhum.2021.662031.
Callow DD, Purcell JJ, Won J, Smith JC. 2022. Neurite dispersion and density mediates the relationship between cardiorespiratory fitness and cognition in healthy younger adults. Neuropsychologia. 169:108207. 10.1016/j.neuropsychologia.2022.108207.
Calvin CM et al. 2017. Childhood intelligence in relation to major causes of death in 68 year follow-up: prospective population study. BMJ. 357:j2708. 10.1136/bmj.j2708.
Cao C et al. 2024. PGS-depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods. Nucleic Acids Res. 52:D963–D971. 10.1093/nar/gkad1029.
Catani M, Thiebaut de Schotten M. 2008. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex. 44:1105–1132. 10.1016/j.cortex.2008.05.004.
Chang CC et al. 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 4:7. 10.1186/s13742-015-0047-8.
Chiang MC et al. 2009. Genetics of brain fiber architecture and intellectual performance. J Neurosci. 29:2212–2224. 10.1523/JNEUROSCI.4184-08.2009.
Choi SW, Mak TS, O’Reilly PF. 2020. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 15:2759–2772. 10.1038/s41596-020-0353-1.
Chou MY et al. 2019. Role of gait speed and grip strength in predicting 10-year cognitive decline among community-dwelling older people. BMC Geriatr. 19:186. 10.1186/s12877-019-1199-7.
Coad BM et al. 2020. Precommissural and postcommissural fornix microstructure in healthy aging and cognition. Brain Neurosci Adv. 4:239821281989931–239821281989912. 10.1177/2398212819899316.
Cox SR, Ritchie SJ, Fawns-Ritchie C, Tucker-Drob EM, Deary IJ. 2019. Structural brain imaging correlates of general intelligence in UK biobank. Intelligence. 76:101376. 10.1016/j.intell.2019.101376.
Cox SR et al. 2016. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat Commun. 7:13629. 10.1038/ncomms13629.
Cremers LG et al. 2016. Altered tract-specific white matter microstructure is related to poorer cognitive performance: the Rotterdam study. Neurobiol Aging. 39:108–117. 10.1016/j.neurobiolaging.2015.11.021.
Daducci A et al. 2015. Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. Neuroimage. 105:32–44. 10.1016/j.neuroimage.2014.10.026.
Das S et al. 2016. Next-generation genotype imputation service and methods. Nat Genet. 48:1284–1287. 10.1038/ng.3656.
Davies G et al. 2018. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun. 9:2098. 10.1038/s41467-018-04362-x.
Deary IJ. 2014. The stability of intelligence from childhood to old age. Curr Dir Psychol Sci. 23:239–245. 10.1177/0963721414536905.
Deary IJ, Cox SR, Hill WD. 2022. Genetic variation, brain, and intelligence differences. Mol Psychiatry. 27:335–353. 10.1038/s41380-021-01027-y.
Dunst B, Benedek M, Koschutnig K, Jauk E, Neubauer AC. 2014. Sex differences in the IQ-white matter microstructure relationship: a DTI study. Brain Cogn. 91:71–78. 10.1016/j.bandc.2014.08.006.
Dvorak AV et al. 2021. An atlas for human brain myelin content throughout the adult life span. Sci Rep. 11:269. 10.1038/s41598-020-79540-3.
Elliott ML et al. 2019. A polygenic score for higher educational attainment is associated with larger brains. Cereb Cortex. 29:3496–3504. 10.1093/cercor/bhy219.
Erdodi LA et al. 2017. Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) processing speed scores as measures of noncredible responding: the third generation of embedded performance validity indicators. Psychol Assess. 29:148–157. 10.1037/pas0000319.
Fan CC et al. 2022. Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain. Nat Commun. 13:2423. 10.1038/s41467-022-30110-3.
Faulkner ME et al. 2024. Harnessing myelin water fraction as an imaging biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination: a review. J Neurochem. 168:2243–2263. 10.1111/jnc.16170.
Flanagan DP, Dixon SG. 2013. The Cattell-Horn-Carroll theory of cognitive abilities. In: Encyclopedia of special education Reynolds CR, Vannest KJ, Fletcher-Janzen E (eds). John Wiley & Sons, Hoboken, New Jersey.
Fonov V et al. 2011. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 54:313–327. 10.1016/j.neuroimage.2010.07.033.
Fraenz C et al. 2021. Interindividual differences in matrix reasoning are linked to functional connectivity between brain regions nominated by parieto-frontal integration theory. Intelligence. 87:101545. 10.1016/j.intell.2021.101545.
Friedrich P et al. 2020. The relationship between axon density, myelination, and fractional anisotropy in the human corpus callosum. Cereb Cortex. 30:2042–2056. 10.1093/cercor/bhz221.
Froeling M, Tax CMW, Vos SB, Luijten PR, Leemans A. 2017. "MASSIVE" brain dataset: multiple acquisitions for standardization of structural imaging validation and evaluation. Magn Reson Med. 77:1797–1809. 10.1002/mrm.26259.
Gareau PJ, Rutt BK, Karlik SJ, Mitchell JR. 2000. Magnetization transfer and multicomponent T2 relaxation measurements with histopathologic correlation in an experimental model of MS. J Magn Reson Imaging. 11:586–595. 10.1002/1522-2586(200006)11:6<586::AID-JMRI3>3.0.CO;2-V.
Genç E, Fraenz C. 2021. Diffusion-weighted imaging of intelligence. In: Barbey AK, Karama S, Haier RJ, editors. The Cambridge handbook of intelligence and cognitive neuroscience. Cambridge University Press, Cambridge, United Kingdom, p. 191–209 10.1017/9781108635462.014.
Genç E et al. 2018. Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nat Commun. 9:1905. 10.1038/s41467-018-04268-8.
Genç E et al. 2019. The neural architecture of general knowledge. EJP. 33:589–605. 10.1002/per.2217.
Genç E et al. 2021. Polygenic scores for cognitive abilities and their association with different aspects of general intelligence—a deep phenotyping approach. Mol Neurobiol. 58:4145–4156. 10.1007/s12035-021-02398-7.
Genç E et al. 2023. Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp. 44:3359–3376. 10.1002/hbm.26286.
Gong Z, Bilgel M, Resnick SM, An Y, Bouhrara M. 2023a. White matter myelination is associated with longitudinal changes in processing speed in normative aging. Alzheimers Dement. 19:e076956. 10.1002/alz.076956.
Gong Z et al. 2023b. Lower myelin content is associated with more rapid cognitive decline among cognitively unimpaired individuals. Alzheimers Dement. 19:3098–3107. 10.1002/alz.12968.
Góngora D, Vega-Hernández M, Jahanshahi M, Valdés-Sosa PA, Bringas-Vega ML. 2020. Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts. Hum Brain Mapp. 41:906–916. 10.1002/hbm.24848.
Gozdas E, Fingerhut H, Dacorro L, Bruno JL, Hosseini SMH. 2021. Neurite imaging reveals widespread alterations in gray and white matter neurite morphology in healthy aging and amnestic mild cognitive impairment. Cereb Cortex. 31:5570–5578. 10.1093/cercor/bhab180.
Grasby KL et al. 2020. The genetic architecture of the human cerebral cortex. Science. 367:eaay6690. 10.1126/science.aay6690.
Greve DN, Fischl B. 2009. Accurate and robust brain image alignment using boundary-based registration. Neuroimage. 48:63–72. 10.1016/j.neuroimage.2009.06.060.
Grussu F et al. 2017. Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology? Ann Clin Transl Neurol. 4:663–679. 10.1002/acn3.445.
Haier RJ et al. 1988. Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence. 12:199–217. 10.1016/0160-2896(88)90016-5.
Harms RL, Fritz FJ, Tobisch A, Goebel R, Roebroeck A. 2017. Robust and fast nonlinear optimization of diffusion MRI microstructure models. Neuroimage. 155:82–96. 10.1016/j.neuroimage.2017.04.064.
Harms RL, Roebroeck A. 2018. Robust and fast Markov chain Monte Carlo sampling of diffusion MRI microstructure models. Front Neuroinform. 12:97. 10.3389/fninf.2018.00097.
Haworth CM et al. 2010. The heritability of general cognitive ability increases linearly from childhood to young adulthood. Mol Psychiatry. 15:1112–1120. 10.1038/mp.2009.55.
Hennig J. 1988. Multiecho imaging sequences with low refocusing flip angles. J Magn Reson. 78:397–407. 10.1016/0022-2364(88)90128-X.
Hennig J, Weigel M, Scheffler K. 2003. Multiecho sequences with variable refocusing flip angles: optimization of signal behavior using smooth transitions between pseudo steady states (TRAPS). Magn Reson Med. 49:527–535. 10.1002/mrm.10391.
Herlin B, Uszynski I, Chauvel M, Dupont S, Poupon C. 2024. Sex-related variability of white matter tracts in the whole HCP cohort. Brain Struct Funct. 229:1713–1735. 10.1007/s00429-024-02833-0.
Hidese S et al. 2020. Correlation between the Wechsler Adult Intelligence Scale- 3rd edition metrics and brain structure in healthy individuals: a whole-brain magnetic resonance imaging study. Front Hum Neurosci. 14:211. 10.3389/fnhum.2020.00211.
Hill WD et al. 2019. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol Psychiatry. 24:169–181. 10.1038/s41380-017-0001-5.
Holleran L et al. 2020. The relationship between white matter microstructure and general cognitive ability in patients with schizophrenia and healthy participants in the ENIGMA consortium. Am J Psychiatry. 177:537–547. 10.1176/appi.ajp.2019.19030225.
Hossiep R, Hasella M, Turck D. 2001. BOMAT-advanced-short version: Bochumer Matrizentest. Hogrefe, Göttingen (Germany).
Hu L, Bentler PM. 1999. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model. 6:1–55. 10.1080/10705519909540118.
Hua K et al. 2008. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage. 39:336–347. 10.1016/j.neuroimage.2007.07.053.
Hunt BA et al. 2016. Relationships between cortical myeloarchitecture and electrophysiological networks. Proc Natl Acad Sci USA. 113:13510–13515. 10.1073/pnas.1608587113.
Jacobucci R et al. 2022. Regsem: Regularized Structural Equation Modeling. R package version 1.9.3. https://CRAN.Rproject.org/package=regsem.
Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ. 2008. Improving fluid intelligence with training on working memory. Proc Natl Acad Sci USA. 105:6829–6833. 10.1073/pnas.0801268105.
James SN et al. 2023. Neuroimaging, clinical and life course correlates of normal-appearing white matter integrity in 70-year-olds. Brain Commun. 5:fcad225. 10.1093/braincomms/fcad225.
Jang SH, Lee SJ. 2019. Corticoreticular tract in the human brain: a mini review. Front Neurol. 10:1188. 10.3389/fneur.2019.01188.
Jenkinson M, Bannister P, Brady M, Smith S. 2002. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 17:825–841. 10.1006/nimg.2002.1132.
Jenkinson M, Smith SM. 2001. A global optimisation method for robust affine registration of brain images. Med Image Anal. 5:143–156. 10.1016/S1361-8415(01)00036-6.
Jespersen SN, Leigland LA, Cornea A, Kroenke CD. 2012. Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging. IEEE Trans Med Imaging. 31:16–32. 10.1109/TMI.2011.2162099.
Jespersen SN et al. 2010. Neurite density from magnetic resonance diffusion measurements at ultrahigh field: comparison with light microscopy and electron microscopy. Neuroimage. 49:205–216. 10.1016/j.neuroimage.2009.08.053.
Jitsuishi T et al. 2020. White matter dissection and structural connectivity of the human vertical occipital fasciculus to link vision-associated brain cortex. Sci Rep. 10:820. 10.1038/s41598-020-57837-7.
Johnson W, Bouchard TJ, Krueger RF, McGue M, Gottesman II. 2004. Just one g: consistent results from three test batteries. Intelligence. 32:95–107. 10.1016/S0160-2896(03)00062-X.
Johnson W, te Nijenhuis J, Bouchard TJ. 2008. Still just 1 g: consistent results from five test batteries. Intelligence. 36:81–95. 10.1016/j.intell.2007.06.001.
Jones DK, Knösche TR, Turner R. 2013. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage. 73:239–254. 10.1016/j.neuroimage.2012.06.081.
Jöreskog KG. 1969. A general approach to confirmatory maximum likelihood factor analysis. Psychometrika. 34:183–202. 10.1007/BF02289343.
Jung RE, Haier RJ. 2007. The parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav Brain Sci. 30:135–154. 10.1017/S0140525X07001185.
Kemerdere R et al. 2016. Role of the left frontal aslant tract in stuttering: a brain stimulation and tractographic study. J Neurol. 263:157–167. 10.1007/s00415-015-7949-3.
Kevenaar JT, Hoogenraad CC. 2015. The axonal cytoskeleton: from organization to function. Front Mol Neurosci. 8:44. 10.3389/fnmol.2015.00044.
Khelfaoui H, Ibaceta-Gonzalez C, Angulo MC. 2024. Functional myelin in cognition and neurodevelopmental disorders. Cell Mol Life Sci. 81:181. 10.1007/s00018-024-05222-2.
Kievit RA, Fuhrmann D, Borgeest GS, Simpson-Kent IL, Henson RNA. 2018. The neural determinants of age-related changes in fluid intelligence: a pre-registered, longitudinal analysis in UK Biobank. Wellcome Open Res. 3:38 https://doi.org/http://dx.doi.org/10.12688/wellcomeopenres.14241.2.
Kievit RA et al. 2014. Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking. Nat Commun. 5:5658. 10.1038/ncomms6658.
Kievit RA et al. 2016. A watershed model of individual differences in fluid intelligence. Neuropsychologia. 91:186–198. 10.1016/j.neuropsychologia.2016.08.008.
Kodiweera C, Alexander AL, Harezlak J, McAllister TW, Wu YC. 2016. Age effects and sex differences in human brain white matter of young to middle-aged adults: a DTI, NODDI, and q-space study. Neuroimage. 128:180–192. 10.1016/j.neuroimage.2015.12.033.
Koirala N, Perdue MV, Su X, Grigorenko EL, Landi N. 2021. Neurite density and arborization is associated with reading skill and phonological processing in children. Neuroimage. 241:118426. 10.1016/j.neuroimage.2021.118426.
Latini F et al. 2020. New insights into the anatomy, connectivity and clinical implications of the middle longitudinal fasciculus. Front Neuroanat. 14:610324.
Laule C et al. 2006. Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult Scler. 12:747–753. 10.1177/1352458506070928.
Laule C et al. 2007. Magnetic resonance imaging of myelin. Neurotherapeutics. 4:460–484. 10.1016/j.nurt.2007.05.004.
Laule C et al. 2008. Myelin water imaging of multiple sclerosis at 7 T: correlations with histopathology. Neuroimage. 40:1575–1580. 10.1016/j.neuroimage.2007.12.008.
Laule C et al. 2016. High-resolution myelin water imaging in post-mortem multiple sclerosis spinal cord: a case report. Mult Scler. 22:1485–1489. 10.1177/1352458515624559.
Lawrence KE et al. 2021. Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: a UK Biobank study. Brain Imaging Behav. 15:2813–2823. 10.1007/s11682-021-00548-y.
Le Bihan D. 2003. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci. 4:469–480. 10.1038/nrn1119.
Lee JJ et al. 2018. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 50:1112–1121. 10.1038/s41588-018-0147-3.
Leemans A, Jeurissen B, Sijbers J, Jones DK. 2009. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proc Intl Soc Mag Reson Med. 17:3537.
Lett TA et al. 2020. Cortical surfaces mediate the relationship between polygenic scores for intelligence and general intelligence. Cereb Cortex. 30:2707–2718. 10.1093/cercor/bhz270.
Liepmann D, Beauducel A, Brocke B, Amthauer R. 2007. Intelligenz-Struktur-test 2000 R (I-S-T 2000 R). Manual. Hogrefe, Göttingen (Germany).
Liewald D, Miller R, Logothetis N, Wagner HJ, Schuz A. 2014. Distribution of axon diameters in cortical white matter: an electron-microscopic study on three human brains and a macaque. Biol Cybern. 108:541–557. 10.1007/s00422-014-0626-2.
MacKay AL, Laule C. 2016. Magnetic resonance of myelin water: an in vivo marker for myelin. Brain Plast. 2:71–91. 10.3233/BPL-160033.
Malpas CB et al. 2016. MRI correlates of general intelligence in neurotypical adults. J Clin Neurosci. 24:128–134. 10.1016/j.jocn.2015.07.012.
McDaniel M. 2005. Big-brained people are smarter: a meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence. 33:337–346. 10.1016/j.intell.2004.11.005.
McNeish DM. 2015. Using lasso for predictor selection and to assuage overfitting: a method long overlooked in behavioral sciences. Multivariate Behav Res. 50:471–484. 10.1080/00273171.2015.1036965.
Meinert S et al. 2022. Association of brain white matter microstructure with cognitive performance in major depressive disorder and healthy controls: a diffusion-tensor imaging study. Mol Psychiatry. 27:1103–1110. 10.1038/s41380-021-01330-8.
Mendez Colmenares A et al. 2024. Testing the structural disconnection hypothesis: myelin content correlates with memory in healthy aging. Neurobiol Aging. 141:21–33. 10.1016/j.neurobiolaging.2024.05.013.
Meyers SM et al. 2013. Multicenter measurements of myelin water fraction and geometric mean T2: intra- and intersite reproducibility. J Magn Reson Imaging. 38:1445–1453. 10.1002/jmri.24106.
Miller EM. 1994. Intelligence and brain myelination: a hypothesis. Pers Individ Differ. 17:803–832. 10.1016/0191-8869(94)90049-3.
Mitchell BL et al. 2020. Educational attainment polygenic scores are associated with cortical total surface area and regions important for language and memory. Neuroimage. 212:116691. 10.1016/j.neuroimage.2020.116691.
Mori S, Wakana S, van Zijl PCM, Nagae-Poetscher LM. 2005. MRI atlas of human white matter. Elsevier B. V.
Nave KA. 2010. Myelination and support of axonal integrity by glia. Nature. 468:244–252. 10.1038/nature09614.
Neisser U et al. 1996. Intelligence: knowns and unknowns. Am Psychol. 51:77–101. 10.1037/0003-066X.51.2.77.
Neubauer AC, Fink A. 2009. Intelligence and neural efficiency. Neurosci Biobehav Rev. 33:1004–1023. 10.1016/j.neubiorev.2009.04.001.
Ocklenburg S, Güntürkün O. 2018. Structural hemispheric asymmetries. The lateralized brain: the neuroscience and evolution of hemispheric asymmetries. London (United Kingdom): Academic Press. p. 239–262. 10.1016/B978-0-12-803452-1.00009-6.
Ocklenburg S et al. 2019. Myelin water fraction imaging reveals hemispheric asymmetries in human white matter that are associated with genetic variation in PLP1. Mol Neurobiol. 56:3999–4012. 10.1007/s12035-018-1351-y.
Oelhafen S et al. 2013. Increased parietal activity after training of interference control. Neuropsychologia. 51:2781–2790. 10.1016/j.neuropsychologia.2013.08.012.
Oldfield RC. 1971. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 9:97–113. 10.1016/0028-3932(71)90067-4.
Oswald WD, Roth E. 1987. Der Zahlen-Verbindungs-test (ZVT). Hogrefe, Göttingen (Germany).
Ouellette R et al. 2020. Validation of rapid magnetic resonance myelin imaging in multiple sclerosis. Ann Neurol. 87:710–724. 10.1002/ana.25705.
Page D et al. 2024. Examining the neurostructural architecture of intelligence: The Lothian Birth Cohort 1936 study. Cortex. 178:269–286. 10.1016/j.cortex.2024.06.007.
Pakkenberg B, Gundersen HJ. 1997. Neocortical neuron number in humans: effect of sex and age. J Comp Neurol. 384:312–320. 10.1002/(SICI)1096-9861(19970728)384:2<312::AID-CNE10>3.0.CO;2-K.
Penke L et al. 2010. A general factor of brain white matter integrity predicts information processing speed in healthy older people. J Neurosci. 30:7569–7574. 10.1523/JNEUROSCI.1553-10.2010.
Penke L et al. 2012. Brain white matter tract integrity as a neural foundation for general intelligence. Mol Psychiatry. 17:1026–1030. 10.1038/mp.2012.66.
Pietschnig J, Penke L, Wicherts JM, Zeiler M, Voracek M. 2015. Meta-analysis of associations between human brain volume and intelligence differences: how strong are they and what do they mean? Neurosci Biobehav Rev. 57:411–432. 10.1016/j.neubiorev.2015.09.017.
Plomin R, von Stumm S. 2018. The new genetics of intelligence. Nat Rev Genet. 19:148–159. 10.1038/nrg.2017.104.
Polderman TJ et al. 2015. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 47:702–709. 10.1038/ng.3285.
Powell MJD. 1964. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J. 7:155–162. 10.1093/comjnl/7.2.155.
Prasloski T et al. 2012. Rapid whole cerebrum myelin water imaging using a 3D GRASE sequence. Neuroimage. 63:533–539. 10.1016/j.neuroimage.2012.06.064.
Price AL et al. 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 38:904–909. https://doi.org/10.1038/ng1847.
Purcell S, Chang C. PLINK 1.9 [Internet]. www.cog-genomics.org/plink/1.9/.
Qian W, Khattar N, Cortina LE, Spencer RG, Bouhrara M. 2020. Nonlinear associations of neurite density and myelin content with age revealed using multicomponent diffusion and relaxometry magnetic resonance imaging. Neuroimage. 223:117369. 10.1016/j.neuroimage.2020.117369.
R Core Team . 2022. R: a language and environment for statistical computing, 4.2.2 edn. R Foundation for Statistical Computing, Vienna, Austria.
Raghavan S et al. 2021. Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition. Brain Commun. 3:fcab106. 10.1093/braincomms/fcab106.
Raven JC, Court JH, Raven J. 1990. Coloured progressive matrices. Manual for Raven’s progressive matrices and vocabulary scales. Oxford Psychologists Press, Oxford (United Kingdom).
Riccomagno MM, Kolodkin AL. 2015. Sculpting neural circuits by axon and dendrite pruning. Annu Rev Cell Dev Biol. 31:779–805. 10.1146/annurev-cellbio-100913-013038.
Rosseel Y. 2012. Lavaan: an R package for structural equation modeling. J Stat Softw. 48:1–36 http://dx.doi.org/10.18637/jss.v048.i02.
Roth B et al. 2015. Intelligence and school grades: a meta-analysis. Intelligence. 53:118–137. 10.1016/j.intell.2015.09.002.
RStudio Team . 2022. RStudio: integrated development environment for R, 2022.12.0.353 edn. Posit Software, PBC, Boston, MA.
Savage JE et al. 2018. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet. 50:912–919. 10.1038/s41588-018-0152-6.
Schlüter C, Fraenz C, Friedrich P, Güntürkün O, Genç E. 2022. Neurite density imaging in amygdala nuclei reveals interindividual differences in neuroticism. Hum Brain Mapp. 43:2051–2063. 10.1002/hbm.25775.
Schmahmann JD, Guell X, Stoodley CJ, Halko MA. 2019. The theory and neuroscience of cerebellar cognition. Annu Rev Neurosci. 42:337–364. 10.1146/annurev-neuro-070918-050258.
Schmidt FL, Hunter J. 2004. General mental ability in the world of work: occupational attainment and job performance. J Pers Soc Psychol. 86:162–173. 10.1037/0022-3514.86.1.162.
Schmitt JE, Raznahan A, Liu S, Neale MC. 2020. The genetics of cortical myelination in young adults and its relationships to cerebral surface area, cortical thickness, and intelligence: a magnetic resonance imaging study of twins and families. Neuroimage. 206:116319. 10.1016/j.neuroimage.2019.116319.
Schneider WJ, McGrew KS. 2012. The Cattell-Horn-Carroll model of intelligence. In: Contemporary intellectual assessment: theories, tests, and issues Flanagan DP, Harrison PL (eds), 3rd edn. Guilford Press, New York, p. 99–144.
Serang S, Jacobucci R. 2020. Exploratory mediation analysis of dichotomous outcomes via regularization. Multivariate Behav Res. 55:69–86. 10.1080/00273171.2019.1608145.
Serrano-Sponton L et al. 2024. Harnessing the frontal aslant tract’s structure to assess its involvement in cognitive functions: new insights from 7-T diffusion imaging. Sci Rep. 14:17455. 10.1038/s41598-024-67013-w.
Smith SM et al. 2004. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 23:S208–S219. 10.1016/j.neuroimage.2004.07.051.
Spearman C. 1904. ``General intelligence,'' objectively determined and measured. Am J Psychol. 15:201–292. 10.2307/1412107.
Sporns O, Tononi G, Edelman GM. 2000. Connectivity and complexity: the relationship between neuroanatomy and brain dynamics. Neural Netw. 13:909–922. 10.1016/S0893-6080(00)00053-8.
Stammen C et al. 2023. Robust associations between white matter microstructure and general intelligence. Cereb Cortex. 33:6723–6741. 10.1093/cercor/bhac538.
Strenze T. 2007. Intelligence and socioeconomic success: a meta-analytic review of longitudinal research. Intelligence. 35:401–426. 10.1016/j.intell.2006.09.004.
Tamnes CK et al. 2010. Intellectual abilities and white matter microstructure in development: a diffusion tensor imaging study. Hum Brain Mapp. 31:1609–1625. 10.1002/hbm.20962.
Tang CY et al. 2010. Brain networks for working memory and factors of intelligence assessed in males and females with fMRI and DTI. Intelligence. 38:293–303. 10.1016/j.intell.2010.03.003.
Tian Y, Zhang Y. 2022. A comprehensive survey on regularization strategies in machine learning. Inf Fusion. 80:146–166. 10.1016/j.inffus.2021.11.005.
Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J R Statist Soc B. 58:267–288. 10.1111/j.2517-6161.1996.tb02080.x.
Van Essen DC et al. 2013. The WU-Minn human connectome project: an overview. Neuroimage. 80:62–79. 10.1016/j.neuroimage.2013.05.041.
Vargas WS et al. 2015. Measuring longitudinal myelin water fraction in new multiple sclerosis lesions. Neuroimage Clin. 9:369–375. 10.1016/j.nicl.2015.09.003.
Vavasour IM, Clark CM, Li DK, Mackay AL. 2006. Reproducibility and reliability of MR measurements in white matter: clinical implications. Neuroimage. 32:637–642. 10.1016/j.neuroimage.2006.03.036.
Vavasour IM, Laule C, Li DK, Traboulsee AL, MacKay AL. 2011. Is the magnetization transfer ratio a marker for myelin in multiple sclerosis? J Magn Reson Imaging. 33:713–718. 10.1002/jmri.22441.
Vos SB et al. 2017. The importance of correcting for signal drift in diffusion MRI. Magn Reson Med. 77:285–299. 10.1002/mrm.26124.
Wakana S et al. 2007. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage. 36:630–644. 10.1016/j.neuroimage.2007.02.049.
Wang Z et al. 2023. Association of motor function with cognitive trajectories and structural brain differences: a community-based cohort study. Neurol. 101:e1718–e1728. 10.1212/WNL.0000000000207745.
Whittall KP et al. 1997. In vivo measurement of T2 distributions and water contents in normal human brain. Magn Reson Med. 37:34–43. 10.1002/mrm.1910370107.
Wilcox RR. 1997. Introduction to robust estimation and hypothesis testing. Academic Press, San Diego.
Williams CM, Peyre H, Ramus F. 2023. Brain volumes, thicknesses, and surface areas as mediators of genetic factors and childhood adversity on intelligence. Cereb Cortex. 33:5885–5895. 10.1093/cercor/bhac468.
Wu Y, Sun D, Wang Y, Wang Y, Ou S. 2016. Segmentation of the cingulum bundle in the human brain: a new perspective based on DSI tractography and fiber dissection study. Front Neuroanat. 10:84. 10.3389/fnana.2016.00084.
Yeh FC. 2022. Population-based tract-to-region connectome of the human brain and its hierarchical topology. Nat Commun. 13:4933. 10.1038/s41467-022-32595-4.
Yeh FC et al. 2018. Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage. 178:57–68. 10.1016/j.neuroimage.2018.05.027.
Yeung HW et al. 2023. Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes. Hum Brain Mapp. 44:1913–1933. 10.1002/hbm.26182.
Yin H et al. 2023. The language-related cerebro-cerebellar pathway in humans: a diffusion imaging-based tractographic study. Quant Imaging Med Surg. 13:1399–1416 https://doi.org/http://dx.doi.org/10.21037/qims-22-303.
Zagorsky JL. 2007. Do you have to be smart to be rich? The impact of IQ on wealth, income and financial distress. Intelligence. 35:489–501. 10.1016/j.intell.2007.02.003.
Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. 2012. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 61:1000–1016. 10.1016/j.neuroimage.2012.03.072.
Zhao B et al. 2021a. Common genetic variation influencing human white matter microstructure. Science. 372:eabf3736. 10.1126/science.abf3736.
Zhao B et al. 2021b. Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706). Mol Psychiatry. 26:3943–3955. 10.1038/s41380-019-0569-z.
Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol. 67:301–320. 10.1111/j.1467-9868.2005.00503.x.
