Plomin R, von Stumm S. The new genetics of intelligence[J]. Nat Rev Genet, 2018, 19(3): 148-159.

Gottfredson LS. Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography[J]. Intelligence, 1997, 24(1): 13-23.

Sternberg RJ. Intelligence[J]. Dialogues Clin Neurosci, 2012, 14(1): 19-27.

Yang W, Liu P, Wei D, et al. Females and males rely on different cortical regions in Raven's Matrices reasoning capacity: evidence from a voxel-based morphometry study[J]. PLoS One, 2014, 9(3): e93104.

Ohtani T, Nestor PG, Bouix S, et al. Medial frontal white and gray matter contributions to general intelligence[J]. PLoS One, 2014, 9(12): e112691.

Ouyang LR, Liao WH, Zhou GF, et al. Resting-state fMRI analysis based on Chinese brain template Chinese2020 in patients with Alzheimer disease[J]. Chin J Med Imaging Technol, 2019, 35(1): 9-14.

Qiu J, Wang BX, Wang L, et al. Research on regional homogeneity of resting state functional magnetic resonance imaging in first-episode depressive disorder patients[J]. Chin J Magn Reson Imaging, 2020, 11(9): 721-725.

Li C, Yang G, Li M, et al. Fluid intelligence relates to the resting state amplitude of low-frequency fluctuation and functional connectivity: a multivariate pattern analysis[J]. Neuroreport, 2018, 29(1): 8-12.

Li YM, Yang HG, Fan GG. Resting state amplitude of lowfrequency fluctuation alterations of mild cognitive impairment in patients with multiple system atrophy[J]. Chin J Magn Reson Imaging, 2020, 11(4): 246-252.

Shen YR, Li ZL, Wu XL, et al. Resting-state fMRI observation on functional connectivity of insula in patients with chronic insomnia based on Chinese brain template[J]. Chin J Med Imaging Technol, 2019, 35(1): 15-19.

Dryburgh E, McKenna S, Rekik I. Predicting full-scale and verbal intelligence scores from functional connectomic data in individuals with autism spectrum disorder[J]. Brain Imaging Behav, 2020, 14(5): 1769-1778.

Khundrakpam BS, Lewis JD, Reid A, et al. Imaging structural covariance in the development of intelligence[J]. Neuroimage, 2017, 144(Pt A): 227-240.

Nusbaum F, Hannoun S, Kocevar G, et al. Hemispheric differences in white matter microstructure between two profiles of children with high intelligence quotient vs. controls: A tract-based spatial statistics study[J]. Front Neurosci, 2017, 11: 173.

Dubois J, Galdi P, Paul LK, et al. A distributed brain network predicts general intelligence from resting-state human neuroimaging data[J]. Philos Trans R Soc Lond B Biol Sci, 2018, 373(1756): 20170284.

Yoon YB, Shin WG, Lee TY, et al. Brain structural networks associated with intelligence and visuomotor ability[J]. Sci Rep, 2017, 7(1): 2177.

Suprano I, Delon-Martin C, Kocevar G, et al. Topological modification of brain networks organization in children with high intelligence quotient: A resting-state fMRI study[J]. Front Hum Neurosci, 2019, 13: 241.

Jiang R, Calhoun VD, Cui Y, et al. Multimodal data revealed different neurobiological correlates of intelligence between males and females[J]. Brain Imaging Behav, 2020, 14(5): 1979-1993.

Xiao L, Stephen JM, Wilson TW, et al. Alternating diffusion map based fusion of multimodal brain connectivity networks for IQ prediction[J]. IEEE Trans Biomed Eng, 2019, 66(8): 2140-2151.

Finn ES, Shen X, Scheinost D, et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity[J]. Nat Neurosci, 2015, 18(11): 1664-1671. DOI: 10.1038/nn.4135.

Jiang R, Calhoun VD, Fan L, et al. Gender differences in connectome-based predictions of individualized intelligence quotient and sub-domain scores[J]. Cereb Cortex, 2020, 30(3): 888-900.

Authorloop JH, Siegel B, Tang C, et al. Intelligence and changes in regional cerebral glucose metabolic rate following learning[J]. Intelligence, 1992, 16(3-4): 415-426.

Neubauer AC, Grabner RH, Fink A, et al. Intelligence and neural efficiency: Further evidence of the influence of task content and sex on the brain-IQ relationship[J]. Cognitive Brain Res, 2005, 25(1): 217-225.

Tschernegg M, Neuper C, Schmidt R, et al. FMRI to probe sex-related differences in brain function with multitasking[J]. PLoS One, 2017, 12(7): e0181554.

Schultz DH, Cole MW. Higher intelligence is associated with less task-related brain network reconfiguration[J]. J Neurosci, 2016, 36(33): 8551-8561.

Schlagenhauf F, Rapp MA, Huys QJ, et al. Ventral striatal prediction error signaling is associated with dopamine synthesis capacity and fluid intelligence[J]. Hum Brain Mapp, 2013, 34(6): 1490-1499.

Neubauer AC, Wammerl M, Benedek M, et al. The influence of transcranial alternating current stimulation (tACS) on fluid intelligence: An fMRI study[J]. Pers Individ Dif, 2017, 118: 50-55.

Greene AS, Gao S, Scheinost D, et al. Task-induced brain state manipulation improves prediction of individual traits[J]. Nat Commun, 2018, 9(1): 2807.

Jiang R, Zuo N, Ford JM, et al. Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships[J]. Neuroimage, 2020, 207: 116370.

Langeslag SJ, Schmidt M, Ghassabian A, et al. Functional connectivity between parietal and frontal brain regions and intelligence in young children: the Generation R study[J]. Hum Brain Mapp, 2013, 34(12): 3299-3307.

Li C, Tian L. Association between resting-state coactivation in the parieto-frontal network and intelligence during late childhood and adolescence[J]. AJNR Am J Neuroradiol, 2014, 35(6): 1150-1156.

Vakhtin AA, Ryman SG, Flores RA, et al. Functional brain networks contributing to the parieto-frontal integration theory of intelligence[J]. Neuroimage, 2014, 103: 349-354.

Han JF, Xu Y, Ge L. MRI-based brain functional connectivity research on healthy sdults intelligence[J]. Chin J Health Psychol, 2019, 27(2): 290-294.

Hearne LJ, Mattingley JB, Cocchi L. Functional brain networks related to individual differences in human intelligence at rest[J]. Sci Rep, 2016, 6: 32328.

Yang S, Zhao Z, Cui H, et al. Temporal variability of cortical gyral-sulcal resting state functional activity correlates with fluid intelligence[J]. Front Neural Circuits, 2019, 13: 36.

Saxe GN, Calderone D, Morales LJ. Brain entropy and human intelligence: A resting-state fMRI study[J]. PLoS One, 2018, 13(2): e0191582.

Hilger K, Ekman M, Fiebach CJ, et al. Intelligence is associated with the modular structure of intrinsic brain networks[J]. Sci Rep, 2017, 7(1): 16088.

Hilger K, Ekman M, Fiebach CJ, et al. Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence[J]. Intelligence, 2017, 60: 10-25.

Kruschwitz JD, Waller L, Daedelow LS, et al. General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set[J]. Neuroimage, 2018, 171: 323-331.

Shen X, Finn ES, Scheinost D, et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity[J]. Nat Protoc, 2017, 12(3): 506-518.

Bajaj S, Raikes A, Smith R, et al. The relationship between general intelligence and cortical structure in healthy individuals[J]. Neuroscience, 2018, 388: 36-44.

Yeo RA, Ryman SG, Pommy J, et al. General cognitive ability and fluctuating asymmetry of brain surface area[J]. Intelligence, 2016, 56: 93-98.

Moodie JE, Ritchie SJ, Cox SR, et al. Fluctuating asymmetry in brain structure and general intelligence in 73-year-olds[J]. Intelligence, 2020, 78: 101407.

Tamnes CK, Østby Y, Walhovd KB, et al. Intellectual abilities and white matter microstructure in development: a diffusion tensor imaging study[J]. Hum Brain Mapp, 2010, 31(10): 1609-1625.

Malpas CB, Genc S, Saling MM, et al. MRI correlates of general intelligence in neurotypical adults[J]. J Clin Neurosci, 2016, 24: 128-134.

Yuan P, Voelkle MC, Raz N. Fluid intelligence and gross structural properties of the cerebral cortex in middle-aged and older adults: A multi-occasion longitudinal study[J]. Neuroimage, 2018, 172: 21-30.

Fu X, Shrestha S, Sun M, et al. Microstructural white matter alterations in mild cognitive impairment and Alzheimer's disease: Study based on neurite orientation dispersion and density imaging (NODDI)[J]. Clin Neuroradiol, 2020, 30(3): 569-579.

Genç E, Fraenz C, Schlüter C, et al. Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence[J]. Nat Commun, 2018, 9(1): 1905.

Kim DJ, Davis EP, Sandman CA, et al. Children's intellectual ability is associated with structural network integrity[J]. Neuroimage, 2016, 124(Pt A): 550-556.

Kenett YN, Medaglia JD, Beaty RE, et al. Driving the brain towards creativity and intelligence: A network control theory analysis[J]. Neuropsychologia, 2018, 118(PtA): 79-90.

Kocevar G, Suprano I, Stamile C, et al. Brain structural connectivity correlates with fluid intelligence in children: A DTI graph analysis[J]. Intelligence, 2019, 72: 67-75.

Hope TR, Selnes P, Rektorová I, et al. Diffusion tensor and restriction spectrum imaging reflect different aspects of neurodegeneration in Parkinson's disease[J]. PLoS One, 2019, 14(5): e0217922.

Ghazi Sherbaf F, Same K, Aarabi MH. High angular resolution diffusion imaging correlates of depression in Parkinson's disease: a connectometry study[J]. Acta Neurol Belg, 2018, 118(4): 573-579.

Arab A, Wojna-Pelczar A, Khairnar A, et al. Principles of diffusion kurtosis imaging and its role in early diagnosis of neurodegenerative disorders[J]. Brain Res Bull, 2018, 139: 91-98.