Neuroscience and intelligence
This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these template messages)(Learn how and when to remove this template message)
Neuroscience and intelligence refers to the various neurological factors that are partly responsible for the variation of intelligence within a species or between different species. A large amount of research in this area has been focused on the neural basis of human intelligence. Historic approaches to study the neuroscience of intelligence consisted of correlating external head parameters, for example head circumference, to intelligence. Post-mortem measures of brain weight and brain volume have also been used. More recent methodologies focus on examining correlates of intelligence within the living brain using techniques such as magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), positron emission tomography and other non-invasive measures of brain structure and activity.
Researchers have been able to identify correlates of intelligence within the brain and its functioning. These include overall brain volume, grey matter volume, white matter volume, white matter integrity, cortical thickness and neural efficiency. Although the evidence base for our understanding of the neural basis of human intelligence has increased greatly over the past 30 years, even more research is needed to fully understand it.
One of the main methods used to establish a relationship between intelligence and the brain is to use measures of Brain volume. The earliest attempts at estimating brain volume were done using measures of external head parameters, such as head circumference, however, such approximations proved to be inaccurate when estimating brain size. More recent methodologies that were employed to study this relationship are post-mortem measures of brain weight and volume. However, such measures also proved to be somewhat inconclusive, yielding diverging results depending on sex, which hemisphere was examined and on the type of intelligence measured.
The most widespread methodology in contemporary neuroscience to measure brain volume and size is MRI. MRI is a non-invasive technique used to study the brain structure and function (using fMRI) of living subjects. Overall, larger brain size and volume is associated with better cognitive functioning and higher intelligence. The correlations range from 0.0 to as high as 0.6, and are predominantly positive. The specific regions that show the most robust correlation between volume and intelligence are the frontal, temporal and parietal lobes of the brain. Therefore, it can be safely concluded that larger brains predict greater intelligence.
However, researchers have cautioned against oversimplifying this view. A meta-analytic review by McDaniel found that the correlation between Intelligence and in vivo brain size was larger for females (0.40) than for males (0.25). The same study also found that the correlation between brain size and Intelligence differed for age within sex, with children showing smaller correlations. Furthermore, the hypothesis has been put forward that the relationship between larger brain volumes and higher intelligence is facilitated not by the global increase of brain volume, but instead by the enlargement of selective parts of the brain associated with specific tasks. For example, monolingual adolescents learning new words is displayed growth in gray matter density in bilateral posterior supramarginal gyri directly related to the number of words learned. Similarly, learning to juggle increased grey matter volume in the occipito-temporal cortex for subjects who could not juggle previously, indicating that brain volume is dependent on a large variety of things and not a perfect measure for intelligence.
Another point of caution is that while larger brain volume is associated with higher intelligence, this relationship only explains a modest amount of variance within the distribution of intelligence itself. The correlations reported between brain volume, brain size and intelligence only explain 12% to 36% of the variance in the distribution of intelligence. The amount of variance explained by brain volume also depends on the type of intelligence measured. Up to 36% of variance in verbal intelligence can be explained by brain volume, while only approximately 10% of variance in visuospatial intelligence can be explained by brain volume. These caveats imply that there are other major factors influencing how intelligent an individual is apart from brain size. In a large meta-analysis consisting of 88 studies Pietschnig et al. (2015) estimated the correlation between brain volume and intelligence to be about correlation coefficient of 0.24 which equates to 6% variance. Researcher Jakob Pietschnig showed that this strength of the positive association of brain volume and IQ has been overestimated in the literature, but still remains robust. He has stated that "It is tempting to interpret this association in the context of human cognitive evolution and species differences in brain size and cognitive ability, we show that it is not warranted to interpret brain size as an isomorphic proxy of human intelligence differences". Another 2015 study by researcher Stuart J Ritchie found that brain size explained 12% of the variance in intelligence among individuals.
Grey matter has been examined as a potential biological foundation for differences in intelligence. Similarly to brain volume, global grey matter volume is positively associated with intelligence. More specifically, higher intelligence has been associated with larger cortical grey matter in the prefrontal and posterior temporal cortex in adults. Furthermore, both verbal and nonverbal intelligence have been shown to be positively correlated with grey matter volume across the parietal, temporal and occipital lobes in young healthy adults, implying that intelligence is associated with a wide variety of structures within the brain.
There appear to be sex differences between the relationship of grey matter to intelligence between men and women. Men appear to show more intelligence to grey matter correlations in the frontal and parietal lobes, while the strongest correlations between intelligence and grey matter in women can be found in the frontal lobes and Broca's area. However, these differences do not seem to impact overall Intelligence, implying that the same cognitive ability levels can be attained in different ways.
One specific methodology used to study grey matter correlates of intelligence in areas of the brain is known as voxel-based morphometry (VBM). VBM allows researchers to specify areas of interest with great spatial resolution, allowing the examination of grey matter areas correlated with intelligence with greater special resolution. VBM has been used to correlate grey matter positively with intelligence in the frontal, temporal, parietal, and occipital lobes in healthy adults. VBM has also been used to show that grey matter volume in the medial region of the prefrontal cortex and the dorsomedial prefrontal cortex correlate positively with intelligence in a group of 55 healthy adults. VBM has also been successfully used to establish a positive correlation between grey matter volumes in the anterior cingulate and intelligence in children aged 5 to 18 years old.
Grey matter has also been shown to positively correlate with intelligence in children. Reis and colleagues have found that grey matter in the prefrontal cortex contributes most robustly to variance in Intelligence in children between 5 and 17, while subcortical grey matter is related to intelligence to a lesser extent. Frangou and colleagues examined the relationship between grey matter and intelligence in children and young adults aged between 12 and 21, and found that grey matter in the orbitofrontal cortex, cingulate gyrus, cerebellum and thalamus was positively correlated to intelligence, while grey matter in the caudate nucleus is negatively correlated with intelligence. However, the relationship between grey matter volume and intelligence only develops over time, as no significant positive relationship can be found between grey matter volume and intelligence in children under 11.
An underlying caveat to research into the relationship of grey matter volume and intelligence is demonstrated by the hypothesis of neural efficiency. The findings that more intelligent individuals are more efficient at using their neurons might indicate that the correlation of grey matter to intelligence reflects selective elimination of unused synapses, and thus a better brain circuitry.
Similar to grey matter, white matter has been shown to correlate positively with intelligence in humans. White matter consists mainly of myelinated neuronal axons, responsible for delivering signals between neurons. The pinkish-white color of white matter is actually a result of these myelin sheaths that electrically insulate neurons that are transmitting signals to other neurons. White matter connects different regions of grey matter in the cerebrum together.These interconnections make transport more seamless and allow us to perform tasks easier. Significant correlations between intelligence and the corpus callosum have been found, as larger callosal areas have been positively correlated with cognitive performance. However, there appear to be differences in importance for white matter between verbal and nonverbal intelligence, as although both verbal and nonverbal measures of intelligence correlate positively with the size of the corpus callosum, the correlation for intelligence and corpus callosum size was larger (.47) for nonverbal measures than that for verbal measures (.18). Anatomical mesh-based geometrical modelling has also shown positive correlations between the thickness of the corpus callosum and Intelligence in healthy adults.
White matter integrity has also been found to be related to Intelligence. White matter tract integrity is important for information processing speed, and therefore reduced white matter integrity is related to lower intelligence. The effect of white matter integrity is mediate entirely through information processing speed. These findings indicate that the brain is structurally interconnected and that axonal fibres are integrally important for fast information process, and thus general intelligence.
Contradicting the findings described above, VBM failed to find a relationship between the corpus callosum and intelligence in healthy adults. This contradiction can be viewed to signify that the relationship between white matter volume and intelligence is not as robust as that of grey matter and intelligence.
Cortical thickness has also been found to correlate positively with intelligence in humans. However, the rate of growth of cortical thickness is also related to intelligence. In early childhood, cortical thickness displays a negative correlation with intelligence, while by late childhood this correlation has shifted to a positive one. More intelligent children were found to develop cortical thickness more steadily and over longer periods of time than less bright children. Studies have found cortical thickness to explain 5% in the variance of intelligence among individuals. In a study conducted to find associations between cortical thickness and general intelligence between different groups of people, sex did not play a role in intelligence. Although it is hard to pin intelligence on age based on cortical thickness due to different socioeconomic circumstances and education levels, older subjects (17 - 24) tended to have less variances in terms of intelligence than when compared to younger subjects (19 - 17).
Cortical convolution has increased the folding of the brain’s surface over the course of human evolution. It has been hypothesized that the high degree of cortical convolution may be a neurological substrate that supports some of the human brain's most distinctive cognitive abilities. Consequently, individual intelligence within the human species might be modulated by the degree of cortical convolution.
The neural efficiency hypothesis postulates that more intelligent individuals display less activation in the brain during cognitive tasks, as measured by Glucose metabolism. A small sample of participants (N=8) displayed negative correlations between intelligence and absolute regional metabolic rates ranging from -0.48 to -0.84, as measured by PET scans, indicating that brighter individuals were more effective processors of information, as they use less energy. According to an extensive review by Neubauer & Fink a large number of studies (N=27) have confirmed this finding using methods such as PET scans, EEG and fMRI..
fMRI and EEG studies have revealed that task difficulty is an important factor affecting neural efficiency. More intelligent individuals display neural efficiency only when faced with tasks of subjectively easy to moderate difficulty, while no neural efficiency can be found during difficult tasks. In fact, more able individuals appear to invest more cortical resources in tasks of high difficulty. This appears to be especially true for the Prefrontal Cortex, as individuals with higher intelligence displayed increased activation of this area during difficult tasks compared to individuals with lower intelligence. It has been proposed that the main reason for the neural efficiency phenomenon could be that individuals with high intelligence are better at blocking out interfering information than individuals with low intelligence.
Some scientists prefer to look at more qualitative variables to relate to the size of measurable regions of known function, for example relating the size of the primary visual cortex to its corresponding functions, that of visual performance.
In a study of the head growth of 633 term-born children from the Avon Longitudinal Study of Parents and Children cohort, it was shown that prenatal growth and growth during infancy were associated with subsequent IQ. The study’s conclusion was that the brain volume a child achieves by the age of 1 year helps determine later intelligence. Growth in brain volume after infancy may not compensate for poorer earlier growth.
Parieto-frontal integration theory
In 2007, Behavioral and Brain Sciences published a target article that put forth a biological model of intelligence based on 37 peer-reviewed neuroimaging studies (Jung & Haier, 2007). Their review of a wealth of data from functional imaging (functional magnetic resonance imaging and positron emission tomography) and structural imaging (diffusion MRI, voxel-based morphometry, in vivo magnetic resonance spectroscopy) argues that human intelligence arises from a distributed and integrated neural network comprising brain regions in the frontal and parietal lobes.
Brain injuries at an early age isolated to one side of the brain typically results in relatively spared intellectual function and with IQ in the normal range.
Another theory of brain size in vertebrates is that it may relate to social rather than mechanical skill. Cortical size relates directly to a pairbonding life style and among primates cerebral cortex size varies directly with the demands of living in a large complex social network. Compared to other mammals, primates have significantly larger brain size. Additionally, most primates are found to be polygynandrous, having many social relationships with others. Although inconclusive, some studies have shown that this polygnandrous statue correlates to brain size.
Several environmental factors related to health can lead to significant cognitive impairment, particularly if they occur during pregnancy and childhood when the brain is growing and the blood–brain barrier is less effective. Developed nations have implemented several health policies regarding nutrients and toxins known to influence cognitive function. These include laws requiring fortification of certain food products and laws establishing safe levels of pollutants (e.g. lead, mercury, and organochlorides). Comprehensive policy recommendations targeting reduction of cognitive impairment in children have been proposed.
- Brain-to-body mass ratio
- Encephalization quotient
- General intelligence
- Impact of health on intelligence
- Outline of human intelligence
- Albert Einstein's brain
- Luders, E.; Narr, K. L.; Thompson, P. M.; Toga, A. W. (2009). "Neuroanatomical correlates of intelligence". Intelligence. 37 (2): 156–163. PMC . PMID 20160919. doi:10.1016/j.intell.2008.07.002.
- 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?". Neuroscience & Biobehavioral Reviews. 57: 411–32. PMID 26449760. doi:10.1016/j.neubiorev.2015.09.017.
- Narr, K. L.; Woods, R. P.; Thompson, P. M.; Szeszko, P.; Robinson, D.; Dimtcheva, T.; Bilder, R. M. (2007). "Relationships between IQ and regional cortical gray matter thickness in healthy adults". Cerebral Cortex. 17 (9): 2163–2171. PMID 17118969. doi:10.1093/cercor/bhl125.
- Gur, R. C.; Turetsky, B. I.; Matsui, M.; Yan, M.; Bilker, W.; Hughett, P.; Gur, R. E. (1999). "Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance". Journal of Neuroscience. 19 (10): 4065–4072.
- Penke, L.; Maniega, S. M.; Bastin, M. E.; Hernandez, M. V.; Murray, C.; Royle, N. A.; Deary, I. J. (2012). "Brain white matter tract integrity as a neural foundation for general intelligence". Molecular Psychiatry. 17 (10): 1026–1030. PMID 22614288. doi:10.1038/mp.2012.66.
- Haier, R. J.; Siegel, B. V.; Nuechterlein, K. H.; Hazlett, E.; Wu, J. C.; Paek, J.; Buchsbaum, M. S. (1988). "Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography". Intelligence. 12 (2): 199–217. doi:10.1016/0160-2896(88)90016-5.
- Dunbar, R. I.; Shultz, S. (2007). "Evolution in the social brain". Science. 317 (5843): 1344–1347. PMID 17823343. doi:10.1126/science.1145463.
- Witelson, S. F.; Beresh, H.; Kigar, D. L. (2006). "Intelligence and brain size in 100 postmortem brains: sex, lateralization and age factors". Brain. 129 (2): 386–398. PMID 16339797. doi:10.1093/brain/awh696.
- Andreasen, N. C.; Flaum, M.; Victor Swayze, I. I.; O'Leary, D. S.; Alliger, R.; Cohen, G. (1993). "Intelligence and brain structure in normal individuals". Am J Psychiatry. 1: 50. doi:10.1176/ajp.150.1.130.
- Flashman, L. A.; Andreasen, N. C.; Flaum, M.; Swayze, V. W. (1997). "Intelligence and regional brain volumes in normal controls". Intelligence. 25 (3): 149–160. doi:10.1016/s0160-2896(97)90039-8.
- MacLullich, A. M. J.; Ferguson, K. J.; Deary, I. J.; Seckl, J. R.; Starr, J. M.; Wardlaw, J. M. (2002). "Intracranial capacity and brain volumes are associated with cognition in healthy elderly men". Neurology. 59 (2): 169–174. doi:10.1212/wnl.59.2.169.
- Gray, J. R.; Thompson, P. M. (2004). "Neurobiology of intelligence: science and ethics". Nature Reviews Neuroscience. 5 (6): 471–482. PMID 15152197. doi:10.1038/nrn1405.
- Toga, A. W.; Thompson, P. M. (2005). "Genetics of brain structure and intelligence". Annu. Rev. Neurosci. 28: 1–23. doi:10.1146/annurev.neuro.28.061604.135655.
- McDaniel, M. A. (2005). "Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence". Intelligence. 33 (4): 337–346. doi:10.1016/j.intell.2004.11.005.
- Lee, H.; Devlin, J. T.; Shakeshaft, C.; Stewart, L. H.; Brennan, A.; Glensman, J.; Price, C. J. (2007). "Anatomical traces of vocabulary acquisition in the adolescent brain". The Journal of Neuroscience. 27 (5): 1184–1189. PMID 17267574. doi:10.1523/jneurosci.4442-06.2007.
- Driemeyer, J.; Boyke, J.; Gaser, C.; Büchel, C.; May, A. (2008). "Changes in gray matter induced by learning—revisited". PLOS ONE. 3 (7): e2669. PMC . PMID 18648501. doi:10.1371/journal.pone.0002669.
- Pietschnig, Jakob; Penke, Lars; Wicherts, Jelte M.; Zeiler, Michael; Voracek, Martin (2015-10-01). "Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?". Neuroscience & Biobehavioral Reviews. 57: 411–432. PMID 26449760. doi:10.1016/j.neubiorev.2015.09.017.
- Ritchie, Stuart J.; Booth, Tom; Valdés Hernández, Maria del C.; Corley, Janie; Maniega, Susana Muñoz; Gow, Alan J.; Royle, Natalie A.; Pattie, Alison; Karama, Sherif (2015-01-01). "Beyond a bigger brain: Multivariable structural brain imaging and intelligence". Intelligence. 51: 47–56. ISSN 0160-2896. PMC . PMID 26240470. doi:10.1016/j.intell.2015.05.001.
- Colom, R.; Jung, R. E.; Haier, R. J. (2006). "Distributed brain sites for the g-factor of intelligence". NeuroImage. 31 (3): 1359–1365. PMID 16513370. doi:10.1016/j.neuroimage.2006.01.006.
- Haier, R. J.; Jung, R. E.; Yeo, R. A.; Head, K.; Alkire, M. T. (2005). "The neuroanatomy of general intelligence: sex matters". NeuroImage. 25 (1): 320–327. PMID 15734366. doi:10.1016/j.neuroimage.2004.11.019.
- Haier, R. J.; Jung, R. E.; Yeo, R. A.; Head, K.; Alkire, M. T. (2004). "Structural brain variation and general intelligence". NeuroImage. 23 (1): 425–433. PMID 15325390. doi:10.1016/j.neuroimage.2004.04.025.
- Gong, Q. Y.; Sluming, V.; Mayes, A.; Keller, S.; Barrick, T.; Cezayirli, E.; Roberts, N. (2005). "Voxel-based morphometry and stereology provide convergent evidence of the importance of medial prefrontal cortex for fluid intelligence in healthy adults". NeuroImage. 25 (4): 1175–1186. PMID 15850735. doi:10.1016/j.neuroimage.2004.12.044.
- Wilke, M.; Sohn, J. H.; Byars, A. W.; Holland, S. K. (2003). "Bright spots: correlations of gray matter volume with IQ in a normal pediatric population". NeuroImage. 20 (1): 202–215. doi:10.1016/s1053-8119(03)00199-x.
- Frangou, S.; Chitins, X.; Williams, S. C. (2004). "Mapping IQ and gray matter density in healthy young people". NeuroImage. 23 (3): 800–805. doi:10.1016/j.neuroimage.2004.05.027.
- Reiss, A. L.; Abrams, M. T.; Singer, H. S.; Ross, J. L.; Denckla, M. B. (1996). "Brain development, gender and IQ in children A volumetric imaging study". Brain. 119 (5): 1763–1774. doi:10.1093/brain/119.5.1763.
- Haier, R. J.; Siegel, B.; Tang, C.; Abel, L.; Buchsbaum, M. S. (1992). "Intelligence and changes in regional cerebral glucose metabolic rate following learning". Intelligence. 16 (3): 415–426. doi:10.1016/0160-2896(92)90018-m.
- Shaw, P.; Greenstein, D.; Lerch, J.; Clasen, L.; Lenroot, R.; Gogtay, N.; Giedd, J. (2006). "Intellectual ability and cortical development in children and adolescents". Nature. 440 (7084): 676–679. PMID 16572172. doi:10.1038/nature04513.
- Fletcher, J. M.; Bohan, T. P.; Brandt, M. E.; Brookshire, B. L.; Beaver, S. R.; Francis, D. J.; Miner, M. E. (1992). "Cerebral white matter and cognition in hydrocephalic children". Archives of neurology. 49 (8): 818–824. doi:10.1001/archneur.1992.00530320042010.
- Thompson, P. M.; Schwartz, C.; Lin, R. T.; Khan, A. A.; Toga, A. W. (1996). "Three-dimensional statistical analysis of sulcal variability in the human brain". The Journal of Neuroscience. 16 (13): 4261–4274.
- Thompson, P. M.; Schwartz, C.; Toga, A. W. (1996). "High-resolution random mesh algorithms for creating a probabilistic 3D surface atlas of the human brain". NeuroImage. 3 (1): 19–34. PMID 9345472. doi:10.1006/nimg.1996.0003.
- Thompson, P. M.; MacDonald, D.; Mega, M. S.; Holmes, C. J.; Evans, A. C.; Toga, A. W. (1997). "Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces". Journal of Computer Assisted Tomography. 21 (4): 567–581. doi:10.1097/00004728-199707000-00008.
- Luders, E.; Narr, K. L.; Bilder, R. M.; Thompson, P. M.; Szeszko, P. R.; Hamilton, L.; Toga, A. W. (2007). "Positive correlations between corpus callosum thickness and intelligence". NeuroImage. 37 (4): 1457–1464. doi:10.1016/j.neuroimage.2007.06.028.
- Menary, Kyle; Collins, Paul F.; Porter, James N.; Muetzel, Ryan; Olson, Elizabeth A.; Kumar, Vipin; Steinbach, Michael; Lim, Kelvin O.; Luciana, Monica (2013-01-01). "Associations between cortical thickness and general intelligence in children, adolescents and young adults". Intelligence. 41 (5): 597–606. ISSN 0160-2896. PMC . PMID 24744452. doi:10.1016/j.intell.2013.07.010.
- Luders, Eileen; Narr, Katherine L.; Thompson, Paul M.; Toga, Arthur W. (2009-03-01). "Neuroanatomical Correlates of Intelligence". Intelligence. 37 (2): 156–163. ISSN 0160-2896. PMC . PMID 20160919. doi:10.1016/j.intell.2008.07.002.
- Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews, 33(7), 1004-1023.
- Andreasen, N.C.; O'Leary, D.S.; Arndt, S.; Cizadlo, T.; Rezai, K.; Watkins, G.L.; Boles Ponto, L.L.; Hichwa, R.D. (1995). "PET studies of memory: novel and practiced free recall of complex narratives". NeuroImage. 2: 284–295. doi:10.1006/nimg.1995.1036.
- Doppelmayr, M.; Klimesch, W.; Schwaiger, J.; Auinger, P.; Winkler, T. (1998). "Theta synchronization in the human EEG and episodic retrieval". Neurosci. Lett. 257: 41–44. doi:10.1016/s0304-3940(98)00805-2.
- Rypma, B.; D'Esposito, M. (1999). "The roles of prefrontal brain regions in components of working memory: effects of memory load and individual differences". PNAS. 96: 6558–6563. doi:10.1073/pnas.96.11.6558.
- Neubauer, A.C., Sange, G., Pfurtscheller, G., 1999. Psychometric intelligence and event-related desynchronisation during performance of a letter matching task. In: Pfurtscheller, G., Lopes da Silva, F.H. (Eds.),Event-Related Desynchronization (ERD) and Related Oscillatory EEG-Phenomena of the Awake Brain. Elsevier, Amsterdam, pp. 219–231.
- Callicott, J. H.; Mattay, V. S.; Bertolino, A.; Finn, K.; Coppola, R.; Frank, J. A. (1999). "Physiological characteristics of capacity constraints in working memory as revealed by functional MRI". Cerebral Cortex. 9 (1): 20–26. doi:10.1093/cercor/9.1.20.
- Rypma, B., Berger, J. S., Prabhakaran, V., Bly, B. M., Kimberg, D. Y., & Biswal, B. B. (2006). Neural correlates of cognitive efficiency. NeuroImage, 33(3), 969 –979.
- Gray, J. R.; Chabris, C. F.; Braver, T. S. (2003). "Neural mechanisms of general fluid intelligence". Nature Neuroscience. 6 (3): 316–322. PMID 12592404. doi:10.1038/nn1014.
- Schoenemann, PT; Budinger, TF; Sarich, VM; Wang, WS (April 2000). "Brain size does not predict cognitive abilities within families". PNAS. 97 (9): 4932–4937. PMC . PMID 10781101. doi:10.1073/pnas.97.9.4932.
- Brain size and intelligence
- Catharine R. Gale, Finbar J. O'Callaghan, Maria Bredow, MBChB, Christopher N. Martyn, DPhil and the Avon Longitudinal Study of Parents and Children Study Team (October 4, 2006). "The Influence of Head Growth in Fetal Life, Infancy, and Childhood on Intelligence at the Ages of 4 and 8 Years". Pediatrics. 118: 1486–1492. PMID 17015539. doi:10.1542/peds.2005-2629. Retrieved August 6, 2006.
- Czepita, D.; Lodygowska, E.; Czepita, M. (2008). "Are children with myopia more intelligent? A literature review". Annales Academiae Medicae Stetinensis. 54 (1): 13–16; discussion 16. PMID 19127804.
- Richard Haier & Rex Jung (July 26, 2007). "The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence". Cambridge University Press. Retrieved September 28, 2009.
- Barbey, Aron K.; Colom, Roberto; Solomon, Jeffrey; Krueger, Frank; Forbes, Chad; Grafman, Jordan (2012). "An integrative architecture for general intelligence and executive function revealed by lesion mapping". Brain. 135 (4): 1154–1164. doi:10.1093/brain/aws021.
- HealthDay (2012-04-13). "Researchers Map Brain Regions Linked to Intelligence". US News and World Report.
- Yates, Diana. "Researchers Use Brain-Injury Data to Map Intelligence in the Brain". University of Illinois News Bureau. University of Illinois.
- Bava, Sunita; Ballantyne, Angela O; Trauner, Doris A (2005). "Disparity of Verbal and Performance IQ Following Early Bilateral Brain Damage". Cognitive and Behavioral Neurology. 18 (3): 163–70. PMID 16175020. doi:10.1097/01.wnn.0000178228.61938.3e.
- Dunbar RI, Shultz S; Shultz (2007-09-07). "Evolution in the social brain". Science. 317 (5843): 1344–1347. PMID 17823343. doi:10.1126/science.1145463.
- Olness, K. (2003). "Effects on brain development leading to cognitive impairment: a worldwide epidemic". Journal of Developmental and Behavioral Pediatrics. 24 (2): 120–30. PMID 12692458. doi:10.1097/00004703-200304000-00009.
- Neuroscience for Kids
- Deary, I. J.; Penke, L.; Johnson, W. (2010). "The neuroscience of human intelligence differences" (PDF). Nature Reviews Neuroscience. 11 (3): 201–211. PMID 20145623. doi:10.1038/nrn2793.
- Jeremy R. Gray, Psychology Department, Yale University, and Paul M. Thompson, Laboratory of Nero Imaging, Department of Neurology, University of California, Los Angeles School of Medicine (June 2004). "Neurobiology of Intelligence: Science and Ethics" (PDF). Nature Publishing Group, Volume 5. Retrieved August 6, 2006.