The structural basis of inter-individual differences in human behaviour and cognition

Nature Reviews Neuroscience

Volumn 12, Issue 4, 2011, Pages 231-242

  Kanai, Ryota ^a   Rees, Geraint ^a,b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ATTENTION; ATTENTION NETWORK TEST; AWARENESS; BEHAVIOR; BRAIN; BRAIN CORTEX; BRAIN REGION; COGNITION; DECISION MAKING; DIFFUSION TENSOR IMAGING; DIZYGOTIC TWINS; ECONOMIC ASPECT; ELECTROENCEPHALOGRAPHY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GENETIC ANALYSIS; GRAY MATTER; HUMAN; INDIVIDUALITY; INFORMATION PROCESSING; INTELLIGENCE; INTRAPARIETAL SULCUS; MENTAL HEALTH; MONOZYGOTIC TWINS; MOTOR PERFORMANCE; NERVE CELL PLASTICITY; NEUROANATOMY; NEUROLOGIC EXAMINATION; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PARIETAL LOBE; PERCEPTION; PERSONALITY; POSITRON EMISSION TOMOGRAPHY; PREFRONTAL CORTEX; PRIORITY JOURNAL; REVIEW; SENSORY STIMULATION; SOCIAL COGNITION; SUPERIOR PARIETAL LOBE; TASK PERFORMANCE; TEMPORAL CORTEX; TRANSCRANIAL MAGNETIC STIMULATION; VISION; VISUAL FIELD; WHITE MATTER;

ATTENTION; AWARENESS; BEHAVIOR; BRAIN; BRAIN MAPPING; COGNITION; DECISION MAKING; HUMANS; INDIVIDUALITY; MAGNETIC RESONANCE IMAGING; NEUROPSYCHOLOGICAL TESTS; PERCEPTION; PERSONALITY; REACTION TIME; STATISTICS AS TOPIC;

EID: 79952898677     PISSN: 1471003X     EISSN: 14710048     Source Type: Journal    
DOI: 10.1038/nrn3000     Document Type: Review

References (191)

1. Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61-83; discussion 83-135 (2010).
2. Vogel, E. K. & Awh, E. How to exploit diversity for scientific gain. Curr. Dir. Pscyhol. Sci. 17, 171-176 (2008).
3. Bishop, S., Duncan, J., Brett, M. & Lawrence, A. D. Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli. Nature Neurosci. 7, 184-188 (2004). (Pubitemid 38129795)
4. Bishop, S. Trait anxiety and impoverished prefrontal control of attention. Nature Neurosci. 12, 92-98 (2008).
5. Osaka, M. et al. The neural basis of individual differences in working memory capacity: an fMRI study. Neuroimage 18, 789-797 (2003). (Pubitemid 36349428)
6. Wig, G. S. et al. Medial temporal lobe BOLD activity at rest predicts individual differences in memory ability in healthy young adults. Proc. Natl Acad. Sci. USA 105, 18555-18560 (2008).
7. McNab, F. & Klingberg, T. Prefrontal cortex and basal ganglia control access to working memory. Nature Neurosci. 11, 103-107 (2008).
8. Haruno, M. & Frith, C. D. Activity in the amygdala elicited by unfair divisions predicts social value orientation. Nature Neurosci. 13, 160-161 (2010).
9. Koten, J. W. Jr et al. Genetic contribution to variation in cognitive function: an fMRI study in twins. Science 323, 1737-1740 (2009).
10. Cacioppo, J. T. et al. In the eye of the beholder: individual differences in perceived social isolation predict regional brain activation to social stimuli. J. Cogn. Neurosci. 21, 83-92 (2009).
11. Gray, J. R., Chabris, C. F. & Braver, T. S. Neural mechanisms of general fluid intelligence. Nature Neurosci. 6, 316-322 (2003). (Pubitemid 36278291)
12. Tom, S. M., Fox, C. R., Trepel, C. & Poldrack, R. A. The neural basis of loss aversion in decision-making under risk. Science 315, 515-518 (2007). . (Pubitemid 46175755)
13. Vogel, E. K. & Machizawa, M. G. Neural activity predicts individual differences in visual working memory capacity. Nature 428, 748-751 (2004). (Pubitemid 38508284)
14. Vogel, E. K., McCollough, A. W. & Machizawa, M. G. Neural measures reveal individual differences in controlling access to working memory. Nature 438, 500-503 (2005). (Pubitemid 41742021)
15. Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29, 169-195 (1999). (Pubitemid 29191338)
16. Amodio, D. M., Jost, J. T., Master, S. L. & Yee, C. M. Neurocognitive correlates of liberalism and conservatism. Nature Neurosci. 10, 1246-1247 (2007). (Pubitemid 47480301)
17. Drew, T. & Vogel, E. K. Neural measures of individual differences in selecting and tracking multiple moving objects. J. Neurosci. 28, 4183-4191 (2008).
18. Martens, S., Munneke, J., Smid, H. & Johnson, A. Quick minds don't blink: electrophysiological correlates of individual differences in attentional selection. J. Cogn. Neurosci. 18, 1423-1438 (2006). (Pubitemid 44511742)
19. Herzmann, G., Kunina, O., Sommer, W. & Wilhelm, O. Individual differences in face cognition: brain-behavior relationships. J. Cogn. Neurosci. 22, 571-589 (2010).
20. Kienast, T. et al. Dopamine in amygdala gates limbic processing of aversive stimuli in humans. Nature Neurosci. 11, 1381-1382 (2008).
21. Zald, D. H. et al. Midbrain dopamine receptor availability is inversely associated with novelty-seeking traits in humans. J. Neurosci. 28, 14372-14378 (2008).
22. Gjedde, A. et al. Inverted-U-shaped correlation between dopamine receptor availability in striatum and sensation seeking. Proc. Natl Acad. Sci. USA 107, 3870-3875 (2010).
23. Gerretsen, P. et al. Is desire for social relationships mediated by serotonergic system in the prefrontal cortex? An [18F] setoperone PET study. Soc. Neurosci. 5, 375-383 (2010).
24. Boy, F. et al. Individual differences in subconscious motor control predicted by GABA concentration in SMA. Curr. Biol. 20, 1779-1785 (2010).
25. Sumner, P. et al. More GABA, less distraction: a neurochemical predictor of motor decision speed. Nature Neurosci. 13, 825-827 (2010).
26. Muthukumaraswamy, S. D. et al. Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans. Proc. Natl Acad. Sci. USA 106, 8356-8361 (2009).
27. Gaser, C. & Schlaug, G. Brain structures differ between musicians and non-musicians. J. Neurosci. 23, 9240-9245 (2003). (Pubitemid 37243825)
28. Sluming, V. et al. Voxel-based morphometry reveals increased gray matter density in Broca's area in male symphony orchestra musicians. NeuroImage 17, 1613-1622 (2002). (Pubitemid 35333879)
29. Maguire, E. A. et al. Navigation-related structural change in the hippocampi of taxi drivers. Proc. Natl Acad. Sci. USA 97, 4398-4403 (2000). (Pubitemid 30226144)
30. Mechelli, A. et al. Neurolinguistics: structural plasticity in the bilingual brain. Nature 431, 757 (2004). (Pubitemid 39434062)
31. Westerhausen, R. et al. Interhemispheric transfer time and structural properties of the corpus callosum. Neurosci. Lett. 409, 140-145 (2006). (Pubitemid 44634969)
32. Whitford, T. J. et al. Predicting inter-hemispheric transfer time from the diffusion properties of the corpus callosum in healthy individuals and schizophrenia patients: a combined ERP and DTI study. Neuroimage 54, 2318-2329 (2011).
33. Tuch, D. S. et al. Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. Proc. Natl Acad. Sci. USA 102, 12212-12217 (2005). (Pubitemid 41291154)
34. Johansen-Berg, H. et al. Integrity of white matter in the corpus callosum correlates with bimanual co-ordination skills. Neuroimage 36, Suppl. 2, T16-T21 (2007). (Pubitemid 46702723)
35. Van Gaal, S. et al. Pre-SMA gray-matter density predicts individual differences in action selection in the face of conscious and unconscious response conflict. J. Cogn. Neurosci. 23, 382-390 (2011).
36. Pew, R. W. The speed-accuracy operating characteristic. Acta Psychol. 30, 16-26 (1969).
37. Bogacz, R., Wagenmakers, E. J., Forstmann, B. U. & Nieuwenhuis, S. The neural basis of the speed-accuracy tradeoff. Trends Neurosci. 33, 10-16 (2010).
38. Chittka, L., Dyer, A. G., Bock, F. & Dornhaus, A. Psychophysics: bees trade off foraging speed for accuracy. Nature 424, 388 (2003). (Pubitemid 36917477)
39. Uchida, N. & Mainen, Z. F. Speed and accuracy of olfactory discrimination in the rat. Nature Neurosci. 6, 1224-1229 (2003). (Pubitemid 37346044)
40. Van Veen, V. et al. The neural and computational basis of controlled speed-accuracy tradeoff during task performance. J. Cogn. Neurosci. 20, 1952-1965 (2008).
41. Ivanoff, J., Branning, P. & Marois, R. fMRI evidence for a dual process account of the speed-accuracy tradeoff in decision-making. PLoS ONE 3, e2635 (2008).
42. Forstmann, B. U. et al. Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proc. Natl Acad. Sci. USA 107, 15916-15920 (2010).
43. Forstmann, B. U. et al. Striatum and pre-SMA facilitate decision-making under time pressure. Proc. Natl Acad. Sci. USA 105, 17538-17542 (2008).
44. Andrews, T. J., Halpern, S. D. & Purves, D. Correlated size variations in human visual cortex, lateral geniculate nucleus and optic tract. J. Neurosci. 17, 2859-2868 (1997). (Pubitemid 27155019)
45. Dougherty, R. F. et al. Visual field representations and locations of visual areas V1/2/3 in human visual cortex. J. Vis. 3, 586-598 (2003). (Pubitemid 39047394)
46. Daniel, P. M. & Whitteridge, D. The representation of the visual field on the cerebral cortex in monkeys. J. Physiol. 159, 203-221 (1961).
47. Duncan, R. O. & Boynton, G. M. Cortical magnification within human primary visual cortex correlates with acuity thresholds. Neuron 38, 659-671 (2003). (Pubitemid 36645038)
48. Sterzer, P., Kleinschmidt, A. & Rees, G. The neural bases of multistable perception. Trends Cogn. Sci. 13, 310-318 (2009).
49. Pettigrew, J. D. & Miller, S. M. A 'sticky' interhemispheric switch in bipolar disorder? Proc. R. Soc. B 265, 2141-2148 (1998). (Pubitemid 28528504)
50. Miller, S. M. et al. Genetic contribution to individual variation in binocular rivalry rate. Proc. Natl Acad. Sci. USA 107, 2664-2668 (2009).
51. Kanai, R., Bahrami, B. & Rees, G. Human parietal cortex structure predicts individual differences in perceptual rivalry. Curr. Biol. 20, 1626-1630 (2010).
52. Kanai, R., Carmel, D., Bahrami, B. & Rees, G. Structural and functional fractionation of right superior parietal cortex in bistable perception. Curr. Biol. 21, R106-R107 (2011).
53. Lumer, E., Friston, K. & Rees, G. Neural correlates of perceptual rivalry in the human brain. Science 280, 1930-1934 (1998). (Pubitemid 28299399)
54. Kleinschmidt, A., Bichel, C., Zeki, S. & Frackowiak, R. S. J. Human brain activity during spontaneously reversing perception of ambiguous figures. Proc. R. Soc. B 265, 2427-2433 (1998). (Pubitemid 29015451)
55. Rees, G., Kreiman, G. & Koch, C. Neural correlates of consciousness in humans. Nature Rev. Neurosci. 3, 261-270 (2002). (Pubitemid 135706605)
56. Sterzer, P. & Kleinschmidt, A. A neural basis for inference in perceptual ambiguity. Proc. Natl Acad. Sci. USA 104, 323-328 (2006).
57. Britz, J., Pitts, M. A. & Michel, C. M. Right parietal brain activity precedes perceptual alternation during binocular rivalry. Hum. Brain Mapp. 5 Aug 2010 (doi:10.1002/hbm.21117).
58. Bonneh, Y. S., Pavlovskaya, M., Ring, H. & Soroker, N. Abnormal binocular rivalry in unilateral neglect: evidence for a non-spatial mechanism of extinction. Neuroreport 15, 473-477 (2004). (Pubitemid 38328143)
59. Kanai, R., Muggleton, N. G. & Walsh, V. TMS over the intraparietal sulcus induces perceptual fading. J. Neurophysiol.100, 3343-3350 (2008).
60. Schwarzkopf, D. S., Song, C. & Rees, G. The surface area of human V1 predicts the subjective experience of object size. Nature Neurosci. 14, 28-30 (2011).
61. Rajimehr, R. & Tootell, R. B. H. Does retinotopy influence cortical folding in primate visual cortex? J. Neurosci. 29, 11149-11152 (2009).
62. Sereno, M. I. et al. Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268, 889-893 (1995).
63. Pruessner, J. C. et al. Volumetry of hippocampus and amygdala with high-resolution MRI and three-dimensional analysis software: minimizing the discrepancies between laboratories. Cereb. Cortex 10, 433-442 (2000). (Pubitemid 30185342)
64. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341-355 (2002). (Pubitemid 34162530)
65. Jovicich, J. et al. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 45, 177-192 (2009).
66. Weniger G., Lange, C. & Irle, E. Abnormal size of the amygdala predicts impaired emotional memory in major depressive disorder. J. Affect. Disord. 94, 219-229 (2006). (Pubitemid 44109731)
67. Irle. et al. Reuced amygdalar and hippocampal size in adults with generalized social phobia. J. Psychiatry Neurosci. 35, 126-131 (2010). .
68. Bickart, K. C. et al. Amygdala volume and social network size in humans. Nature Neurosci. 14, 163-164 (2011).
69. Rouw, R. & Scholte, H. S. Neural basis of individual differences in synesthetic experiences. J. Neurosci. 30, 6205-6213 (2010).
70. Metcalfe, J. & Shimamura, A. Metacognition: Knowing About Knowing (MIT Press, Cambridge MA, 1994).
71. Kolb, F. C. & Braun, J. Blindsight in normal observers. Nature 377, 336-338 (1995).
72. Persaud, N., McLeod, P. & Cowey, A. Post-decision wagering objectively measures awareness. Nature Neurosci. 10, 257-261 (2007). (Pubitemid 46174851)
73. Lau, H. C. & Passingham, R. E. Relative blindsight in normal observers and the neural correlate of visual consciousness. Proc. Natl Acad. Sci. USA 103, 18763-18768 (2006). (Pubitemid 44913484)
74. Kanai, R., Walsh, V. & Tseng, C.-H. Subjective discriminability of invisibility: a framework for distinguishing perceptual and attentional failures of awareness. Conscious. Cogn. 19, 1045-1057 (2010).
75. Hampton, R. R. Rhesus monkeys know when they remember. Proc. Natl Acad. Sci. USA 98, 5359-5362 (2001). (Pubitemid 32397133)
76. Smith, J. D. The study of animal metacognition. Trends Cogn. Sci. 13, 389-395 (2009).
77. Kunimoto, C., Miller, J. & Pashler, H. Confidence and accuracy of near-threshold discrimination responses. Conscious. Cogn. 10, 294-340 (2001). (Pubitemid 32868442)
78. Galvin, S. J., Podd, J. V., Drga, V. & Whitmore, J. Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions. Psychon. Bull. Rev. 10, 843-876 (2003). (Pubitemid 38483381)
79. Washburn, D. A., Smith, J. D. & Taglialatela, L. A. Individual differences in metacognitive responsiveness: cognitive and personality correlates. J. Gen. Psychol. 132, 446-461 (2005). (Pubitemid 41560727)
80. Fleming, S. M. et al. Relating introspective accuracy to individual differences in brain structure. Science 329, 1541-1543 (2010).
81. Fan, J. et al. Testing the efficiency and independence of attentional networks. J. Cogn. Neurosci. 14, 340-347 (2002). (Pubitemid 34267537)
82. Fan, J. et al. Testing the behavioural interaction and integration of attentional networks. Brain Cogn. 70, 209-220 (2009).
83. Westlye, L. T., Grydeland, H., Walhovd, K. B. & Fjell, A. M. Associations between regional cortical thickness and attentional networks as measured by the attention network test. Cereb. Cortex 21, 345-356 (2011).
84. Fan, J. et al. The activation of attentional networks. Neuroimage 26, 471-479 (2005). (Pubitemid 40719825)
85. Bush, G., Luu, P. & Posner, M. I. Cognitive and emotional influences in anterior cingulate cortex. Trends. Cogn. Sci. 4, 215-222 (2000). (Pubitemid 30332147)
86. Aron, A. R., Robbins, T. W. & Poldrack, R. A. Inhibition and the right inferior frontal cortex. Trends Cogn. Sci. 8, 170-177(2004). (Pubitemid 38429927)
87. Putnam M. C. et al. Structural organization of the corpus callosum predicts the extent and impact of cortical activity in the nondominant hemisphere. J. Neurosci. 28, 2912-2918 (2008). (Pubitemid 351398882)
88. Tomassini, V. et al. Structural and functional bases for individual differences in motor learning. Hum. Brain Mapp. 32, 494-508 (2011).
89. Olesen, P. J., Nagy, Z. Westerberg, H. & Klingberg, T. Combined analysis of DTI and fMRI data reveals a joint maturation of white and grey matter in a fronto-parietal network. Cogn. Brain Res. 18, 48-57 (2003). (Pubitemid 37500974)
90. Critchley, H. D. et al. Neural systems supporting interoceptive awareness. Nature Neurosci. 7, 189-195 (2004). (Pubitemid 38129796)
91. Olson, E. A. et al. White matter integrity predicts delay discounting behavior in 9-to 23-year-olds: a diffusion tensor imaging study. J. Cogn. Neurosci. 21, 1406-1421 (2009).
92. McClure, S. M., Laibson, D. I., Loewenstein, G. & Cohen, J. D. Separate neural systems value immediate and delayed monetary rewards. Science 306, 503-507 (2004). (Pubitemid 39372453)
93. Cardinal, R. N. & Ceung, T. H. C. Nucleus accumbens core lesions retard instrumental learning and performance with delayed reinforcement in the rat. BMC Neurosci. 6, 9 (2005).
94. Deary, I. J., Penke, L. & Johnson, W. The neuroscience of human intelligence differences. Nature Rev. Neurosci. 11, 201-211 (2010).
95. Hulshoff Pol, H. E. et al. Genetic contributions to human brain morphology and intelligence. J. Neurosci. 26, 10235-10242 (2006).
96. Rushton, J. P. & Ankney, C. D. Whole brain size and general mental ability: a review. Int. J. Neurosci. 119, 691-731 (2009).
97. Karama, S. et al. Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence 37, 145-155 (2009).
98. Choi, Y. Y. et al. Multiple bases of human intelligence revealed by cortical thickness and neural activation. J. Neurosci. 28, 10323-10329 (2008).
99. Deary, I. J. et al. White matter integrity and cognition in childhood and old age. Neurology 66, 505-512 (2006). (Pubitemid 43739877)
100. Schmithorst, V. J., Wilke, M., Dardzinski, B. J. & Holland, S. K. Cognitive functions correlate with white matter architecture in a normal pediatric population: a diffusion tensor MRI study. Hum. Brain Mapp. 26, 139-147 (2005). (Pubitemid 41434663)
101. Chiang, M. C. et al. Genetics of brain fiber architecture and intellectual performance. J. Neurosci. 29, 2212-2224 (2009).
102. Charlton, R. A. et al. The relationship between white matter brain metabolites and cognition in normal aging: the GENIE study. Brain Res. 1164, 108-116 (2007). (Pubitemid 47198229)
103. Li, Y. et al. Brain anatomical network and intelligence. PLoS Comput. Biol. 5, e1000395 (2009).
104. Jensen. A. R. Clocking the Mind: Mental Chronometry and Individual Differences (Elsevier, Amsterdam, 2006).
105. Deary, I. J., Der, G. & Ford, G. Reaction times and intelligence differences: a population-based cohort study. Intelligence 29, 389-399 (2001).
106. Penke, L. et al. A general factor of brain white matter integrity predicts information processing speed in healthy older people. J. Neurosci. 30, 7569-7574 (2010).
107. Costa, P. T. Jr. & McCrae, R. R. Revised NEO Personality Inventory (NEO?PI?R) and NEO Five-?Factor Inventory (NEO-?FFI) Manual (Psychological Assesments Resources, Odessa, 1992).
108. Paunonen, S. V. & Ashton, M. C. Big five factors and faces and the prediction of behavior. J. Pers. Soc. Psychol. 81, 524-539 (2001). (Pubitemid 33595621)
109. Ashby, F. G., Isen, A. M. & Turken, A. U. A neuropsychological theory of positive affect and its influence on cognition. Psychol. Rev. 106, 529-550 (1999).
110. MacLean, M. H. & Arnell, K. M. Personality predicts temporal attention costs in the attentional blink paradigm. Psychon. Bull. Rev. 17, 556-562 (2010).
111. DeYoung, C. G., Peterson, J. B. & Higgins, D. M. Sources of openness/intellect: cognitive and neuropsychological correlates of the fifth factor of personality. J. Pers. 73, 825-858 (2005). (Pubitemid 40937811)
112. Reimann, R., Angleitner, A. & Strelau, J. Genetic and environmental influences on personality: a study of twins reared together using the self-and peer report NEO-FFI scales. J. Pers. 65, 449-476 (1997). (Pubitemid 127128613)
113. Bouchard, T. J. Jr & McGue, M. Genetic and environmental influences on human psychological differences. J. Neurobiol. 54, 4-45 (2003). (Pubitemid 36050067)
114. DeYoung, C. G. & Gray, J. R. in The Cambridge Handbook of Personality Psychology (eds Corr, P. J. & Matthews, G.) 323-346 (Cambridge Univ. Press, New York, 2009).
115. DeYoung, C. G. et al. Testing predictions from personality neuroscience: brain structure and the big five. Psychol. Sci. 21, 820-828 (2010).
116. Matsuo, K. et al. A voxel-based morphometry study of frontal gray matter correlates of impulsivity. Hum. Brain Mapp. 30, 1188-1195 (2009).
117. Welborn, B. L. et al. Variation in orbitofrontal cortex volume: relation to sex, emotion regulation and affect. Soc. Cogn. Affect. Neurosci. 4, 328-339 (2009).
118. Kruger, J. & Dunning, D. Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments. J. Pers. Soc. Psychol. 77, 1121-1134 (1999).
119. Thompson, P. M. et al. Genetic influences on brain structure. Nature Neurosci. 4, 1253-1258 (2001). (Pubitemid 33135632)
120. Glahn, D. C., Thompson, P. M. & Blangero, J. Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function. Hum. Brain Mapp. 28, 488-501 (2007). (Pubitemid 46849366)
121. Schmitt, J. E. et al. A multivariate analysis of neuroanatomic relationships in a genetically informative pediatric sample. Neuroimage 35, 70-82 (2007).
122. Peper, J. S. et al. Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum. Brain Mapp. 28, 464-473 (2007). (Pubitemid 46849363)
123. Panizzon, M. S. et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728-2735 (2009).
124. Devlin, B., Daniels, M. & Roeder, K. The heritability of IQ. Nature 388, 468-471 (1997). (Pubitemid 27328923)
125. Haier, R. J. et al. Structural brain variation and general intelligence. Neuroimage 23, 425-433 (2004). (Pubitemid 39119473)
126. Lange, N., Froimowitz, M. P., Bigler, E. D. & Lainhart, J. E. Associations between IQ, total and regional brain volumes, and demography in a large normative sample of healthy children and adolescents. Dev. Neuropsychol. 35, 296-317 (2010).
127. Duncan, J. et al. A neural basis for general intelligence. Science 289, 457-480 (2000).
128. Bouchard, T. J. & McGue, M. Familial studies of intelligence: a review. Science 212, 1055-1059 (1981). (Pubitemid 11078025)
129. Rubenstein, J. L. & Rakic, P. Genetic control of cortical development. Cereb. Cortex 9, 521-523 (1999). (Pubitemid 29441492)
130. Rubenstein, J. L. et al. Genetic control of cortical regionalization and connectivity. Cereb. Cortex 9, 524-532 (1999). (Pubitemid 29441493)
131. Rakic, P. Specification of cerebral cortical areas. Science 241, 170-176 (1988).
132. Pallas, S. L. Intrinsic and extrinsic factors that shape neocortical specification. Trends Neurosci. 24, 417-423 (2001). (Pubitemid 32938545)
133. Draganski, B. et al. Neuroplasticity: changes in grey matter induced by training. Nature 427, 311-312 (2004). (Pubitemid 38133650)
134. Draganski, B. et al. Temporal and spatial dynamics of brain structure changes during extensive learning. J. Neurosci. 26, 6314-6317 (2006).
135. Driemeyer, J. et al. Changes in gray matter induced by learning-revisited. PLoS ONE 3, e2669 (2008).
136. Haier, R. J., Karama, S., Leyba, L. & Jung, R. E. MRI assessment of cortical thickness and functional activity changes in adolescent girls following three months of practice on a visual-spatial task. BMC Res. Notes 2, 174 (2009).
137. Quallo, M. M. et al. Gray and white matter changes associated with tool-use learning in macaque monkeys. Proc. Natl Acad. Sci. USA 106, 18379-18384 (2009).
138. Scholz, J., Klein, M. C., Behrens, T. E. & Johansen-Berg, H. Training induces changes in white-matter architecture. Nature Neurosci. 12, 1370-1371 (2009).
139. Taubert, M. et al. Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. J. Neurosci. 30, 11670-11677 (2010).
140. Carreira, M. et al. An anatomical signature for literacy. Nature 461, 983-986 (2009).
141. Dehaene, S. et al. How learning to read changes the cortical networks for vision and language. Science 330, 1359-1364 (2010).
142. Turkeltaub, P. E. et al. Development of neural mechanisms for reading. Nature Neurosci. 6, 767-773 (2003). (Pubitemid 36792893)
143. Price, C. J. & Mechelli, A. Reading and reading disturbance. Curr. Opin. Neurobiol. 15, 231-238 (2005). (Pubitemid 41569274)
144. Machizawa, M. G. & Driver, J. Principal component analysis behavioural individual differences suggests that particular aspects of visual working memory may relate to specific aspects of attention. Neuropsychologia 3 Dec 2010 (doi:10.1016/j.neuropsychologia.
145. Garrido, L. VBM reveals reduced gray matter volume in the temporal cortex of developmental prosopagnosics. Brain 132, 3443-3455 (2009).
146. Mechelli, A. Friston, K. J., Frackowiak, R. S. & Price, C. J. Structural covariance in the human cortex. J. Neurosci. 25, 8303-8310 (2005). (Pubitemid 41292125)
147. He, Y., Chen, Z. & Evans, A. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17, 2407-2419.
148. Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159. (2008).
149. Volkmar, F. R. & Greenough, W. T. Rearing complexity affects branching of dendrites in the visual cortex of the rat. Science 176, 1445-1447 (1972).
150. Turner, A. M. & Greenough, W. T. Differential rearing effects on rat visual cortex synapses. I. Synaptic and neuronal density and synapses per neuron. Brain Res. 329, 195-203 (1985). (Pubitemid 15142234)
151. Trachtenberg, J. T. et al. Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature 420, 788-794 (2002). (Pubitemid 36019621)
152. Rakic, P. A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci. 18, 383-388 (1995).
153. Rakic, P. The radial edifice of cortical architecture: from neuronal silhouettes to genetic engineering. Brain Res. Rev. 55, 204-219 (2007). (Pubitemid 47554452)
154. Eriksson S. H. et al. Quantitative grey matter histological measures do not correlate with grey matter probability values from in vivo MRI in the temporal lobe. J. Neurosci. Methods 181, 111-118 (2009). .
155. Logothetis, N. K. et al. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150-157 (2001). (Pubitemid 32674704)
156. Rauch, A., Rainer, G. & Logothetis, N. K. The effect of a serotonin-induced dissociation between spiking and perisynaptic activity on BOLD functional MRI. Proc. Natl Acad. Sci. USA 105, 6759-6764 (2008). (Pubitemid 351754580)
157. Viswanathan, A. & Freeman, R. D. Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nature Neurosci. 10, 1308-1312 (2007). (Pubitemid 47480299)
158. Logothetis, N. K. What we can do and what we cannot do with fMRI. Nature 453, 869-878 (2008). (Pubitemid 351832564)
159. Sirotin, Y. B. & Das, A. Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity. Nature 457, 475-479 (2009).
160. Jiao, Y. et al. Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage 50, 589-599 (2010).
161. Querbes, O. et al. Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve. Brain 132, 2036-2047 (2009).
162. Ashburner, J. & Friston, K. J. Voxel-based morphometry -the methods. Neuroimage 11, 805-821 (2000).
163. Good, C. D. et al. A voxel-based morphometric study of ageing in 465 normal adjult human brain. Neuroimage 14, 21-36 (2001). (Pubitemid 32552740)
164. Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage 38, 95-113 (2007). (Pubitemid 47440133)
165. Wright, I. C. et al. Meta-analysis of regional brain volumes in schizophrenia. Am. J. Psychiatry 157, 16-25 (2000). (Pubitemid 30027416)
166. Raz, N. et al. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb. Cortex 15, 1676-1689 (2005). (Pubitemid 41447032)
167. Fischl, B. & Dale, A. M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl Acad. Sci. USA 97, 11050-11055 (2000).
168. MacDonald, D., Kabani, N., Avis, D. & Evans, A. C. Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 12, 340-356 (2000).
169. Miller, M. I. et al. Bayesian construction of geometrically based cortical thickness metrics. Neuroimage 12, 676-687 (2000).
170. Hutton, C. et al. Voxel-based cortical thickness measurements in MRI. Neuroimage 40, 1701-1710 (2008).
171. Zilles, K. in The Human Nervous System (ed. Paxinos, G.) (Academic Press, San Diego, 2004).
172. Geyer, S., Schleicher, A. & Zilles, K. Areas 3a, 3b and 1 of human primary somatosensory cortex. Neuroimage 10, 63-83 (1999).
173. Collins, C. E. et al. Neuron densities vary across and within cortical areas in primates. Proc. Natl Acad. Sci. USA 107, 15927-15932 (2010).
174. Fields, R. D. White matter in learning, cognition and psychiatric disorders. Trends Neurosci. 31, 361-370 (2008).
175. Johansen-Berg, H. Behavioral relevance of variation in white matter microstructure. Curr. Opin. Neurol. 23, 351-358 (2010).
176. Beaulieu, C. Diffusion MRI: From Quantitative Measurement to In Vivo Neuroanatomy (eds Johansen-Berg, H. & Behrens, T. E. J.) 105-126 (Elsevier, London, 2009).
177. Behrens, T. E. et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neurosci. 6, 750-757 (2003). (Pubitemid 36792891)
178. Kaas, J. H. Why is brain size so important: design problems and solutions as neocortex gets bigger or smaller. Brain Mind 1, 7-23 (2000). (Pubitemid 32780819)
179. Hyde, K. L. et al. Morphometry of the amusic brain: a two-site study. Brain 129, 2562-2570 (2006). (Pubitemid 44522090)
180. Hyde, K. L. et al. Cortical thickness in congenital amusia: when less is better than more. J. Neurosci. 27, 13028-13032 (2007). (Pubitemid 350159569)
181. Dumontheil, I., Hassan, B., Gilbert, S. J. & Blakemore, S.-J. Development of the selection and manipulation of self-generated thoughts in adolescence. J. Neurosci. 30, 7664-7671 (2010).
182. Kanai, R., Dong, M., Bahrami, B. & Rees, G. Distractibility in daily life is reflected in the structure of human parietal cortex. J. Neurosci. (in the press).
183. Huttenlocher, P. R. & Dabholkar, A. S. Regional differences in synaptogenesis in human cerebral cortex. J. Comp. Neurol. 387, 167-178 (1997). (Pubitemid 27430343)
184. Chechik, G., Meilijson, I. & Ruppin, E. Neuronal regulation: a mechanism for synaptic pruning during brain maturation. Neural Comput. 11, 2061-2080 (1999).
185. Gogtay, N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl Acad. Sci. USA 101, 8174-8179 (2004). (Pubitemid 38698073)
186. Abitz, M. et al. Excess of neurons in the human newborn mediodorsal thalamus compared with that of the adult. Cereb. Cortex 17, 2573-2578 (2007). (Pubitemid 47537815)
187. Jernigan, T. L. et al. Maturation of human cerebrum observed in vivo during adolescence. Brain 114, 2037-2049 (1999).
188. Slwell, E. R. et al. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nature Neurosci. 2, 859-861 (1999).
189. Sowell, E. R., Thompson, P. M., Tessner, K. D. & Toga, A. W. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. J. Nerurosci. 21, 8819-8829 (2001). (Pubitemid 33054846)
190. Sowell, E. R. et al. Longitudinal mapping of cortical thickness and brain growth in normal children. J. Neurosci. 24, 8223-8231 (2004). . (Pubitemid 39280801)
191. Toga, A. W., Thompson, P. M. & Sowell, E. R. Mapping brain maturation. Trends Neurosci. 29, 148-159 (2006).