Editing Neuroscience and intelligence (section)

==Humans==

===Brain volume===
One of the main methods used to establish a relationship between intelligence and the brain is to use measures of brain volume.<ref name="Luders2009" /> The earliest attempts at estimating brain volume were done using measures of external head parameters, such as head circumference as a proxy for brain size.<ref name="Luders2009" /> More recent methodologies employed to study this relationship include post-mortem measures of brain weight and volume. These have their own limitations and strengths.<ref name="Witelson2006">{{cite journal | last1 = Witelson | first1 = S. F. | last2 = Beresh | first2 = H. | last3 = Kigar | first3 = D. L. | year = 2006 | title = Intelligence and brain size in 100 postmortem brains: sex, lateralization and age factors | journal = Brain | volume = 129 | issue = 2| pages = 386–398 | doi=10.1093/brain/awh696 | pmid=16339797| doi-access = free }}</ref> The advent of MRI as a non-invasive highly-accurate measure of living brain structure and function (using fMRI) made this the pre-dominant and preferred method for measuring brain volume.<ref name="Luders2009" />

Overall, larger brain size and volume is associated with better cognitive functioning and higher intelligence.<ref name=Luders2009 /> The specific regions that show the most robust correlation between volume and intelligence are the frontal, temporal and parietal lobes of the brain.<ref name="Andreasen1993">{{cite journal | last1 = Andreasen | first1 = N. C. | last2 = Flaum | first2 = M. | last3 = Victor Swayze | first3 = I. I. | last4 = O'Leary | first4 = D. S. | last5 = Alliger | first5 = R. | last6 = Cohen | first6 = G. | year = 1993 | title = Intelligence and brain structure in normal individuals | journal = Am J Psychiatry | volume = 150 | issue = 1| pages = 130–4 | doi=10.1176/ajp.150.1.130| pmid = 8417555 }}</ref><ref name="Flashman1997">{{cite journal | last1 = Flashman | first1 = L. A. | last2 = Andreasen | first2 = N. C. | last3 = Flaum | first3 = M. | last4 = Swayze | first4 = V. W. | year = 1997 | title = Intelligence and regional brain volumes in normal controls | journal = Intelligence | volume = 25 | issue = 3| pages = 149–160 | doi=10.1016/s0160-2896(97)90039-8}}</ref><ref name="MacLullich2002">{{cite journal | last1 = MacLullich | first1 = A. M. J. | last2 = Ferguson | first2 = K. J. | last3 = Deary | first3 = I. J. | last4 = Seckl | first4 = J. R. | last5 = Starr | first5 = J. M. | last6 = Wardlaw | first6 = J. M. | year = 2002 | title = Intracranial capacity and brain volumes are associated with cognition in healthy elderly men | journal = Neurology | volume = 59 | issue = 2| pages = 169–174 | doi=10.1212/wnl.59.2.169| pmid = 12136052 | s2cid = 46043963 }}</ref> A large number of studies have been conducted with uniformly positive correlations, leading to the generally safe conclusion that larger brains predict greater intelligence.<ref name="GrayThompson2004">{{cite journal | last1 = Gray | first1 = J. R. | last2 = Thompson | first2 = P. M. | year = 2004 | title = Neurobiology of intelligence: science and ethics | journal = Nature Reviews Neuroscience | volume = 5 | issue = 6| pages = 471–482 | doi=10.1038/nrn1405 | pmid=15152197| s2cid = 2430677 }}</ref><ref name="TogaThompson2005">{{cite journal | last1 = Toga | first1 = A. W. | last2 = Thompson | first2 = P. M. | year = 2005 | title = Genetics of brain structure and intelligence | journal = Annu. Rev. Neurosci. | volume = 28 | pages = 1–23 | doi=10.1146/annurev.neuro.28.061604.135655| pmid = 15651931 | s2cid = 780961 }}</ref> In healthy adults, the correlation of total brain volume and IQ is approximately 0.4 when high-quality tests are used.<ref name=":1">{{Cite journal|last1=Gignac|first1=Gilles E.|last2=Bates|first2=Timothy C.|title=Brain volume and intelligence: The moderating role of intelligence measurement quality|journal=Intelligence|volume=64|pages=18–29|doi=10.1016/j.intell.2017.06.004|year=2017|hdl=20.500.11820/a61135a7-6389-4f5c-9a4e-24403ba7e873|s2cid=84839916 |url=https://www.pure.ed.ac.uk/ws/files/39346142/GignacBatesI2017BrainVolumeAndIntelligence2.pdf|hdl-access=free}}</ref> A large scale study (n = 29k) using [[UK Biobank|the UK Biobank]] found a correlation of .275. The strength of this relationship did not depend on sex, contradicting some earlier studies.<ref>{{Cite journal|last1=Cox|first1=S. R.|last2=Ritchie|first2=S. J.|last3=Fawns-Ritchie|first3=C.|last4=Tucker-Drob|first4=E. M.|last5=Deary|first5=I. J.|date=2019-09-01|title=Structural brain imaging correlates of general intelligence in UK Biobank|journal=Intelligence|volume=76|pages=101376|doi=10.1016/j.intell.2019.101376|pmid=31787788|pmc=6876667|issn=0160-2896}}</ref> A study using a [[Sibling design|sibling-design]] in two medium sized samples found evidence of causality with an effect size of .19.<ref>{{Cite journal|last1=Lee|first1=James J.|last2=McGue|first2=Matt|last3=Iacono|first3=William G.|last4=Michael|first4=Andrew M.|last5=Chabris|first5=Christopher F.|date=July 2019|title=The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling|journal=Intelligence|volume=75|pages=48–58|doi=10.1016/j.intell.2019.01.011|pmid=32831433|pmc=7440690}}</ref> This study design rules out [[Confounding|confounders]] that vary between families, but not those that vary within families.

Less is known about variation on scales less than total brain volume. 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).<ref name="McDaniel2005">{{cite journal | last1 = McDaniel | first1 = M. A. | year = 2005 | title = Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence | journal = Intelligence | volume = 33 | issue = 4| pages = 337–346 | doi=10.1016/j.intell.2004.11.005}}</ref> The same study also found that the correlation between brain size and Intelligence increased with age, with children showing smaller correlations.<ref name=McDaniel2005 /> It has been suggested that the link between larger brain volumes and higher intelligence is related to variation in specific brain regions: a whole-brain measure would under-estimate these links.<ref name=Andreasen1993 /> For functions more specific than general intelligence, regional effects may be more important. For instance evidence suggests that in adolescents learning new words, vocabulary growth is associated with gray matter density in bilateral posterior [[Supramarginal gyrus|supramarginal gyri]].<ref name="Lee2007">{{cite journal | last1 = Lee | first1 = H. | last2 = Devlin | first2 = J. T. | last3 = Shakeshaft | first3 = C. | last4 = Stewart | first4 = L. H. | last5 = Brennan | first5 = A. | last6 = Glensman | first6 = J. | last7 = Price | first7 = C. J. | year = 2007 | title = Anatomical traces of vocabulary acquisition in the adolescent brain | url =http://discovery.ucl.ac.uk/11653/1/11653.pdf | journal = The Journal of Neuroscience | volume = 27 | issue = 5| pages = 1184–1189 | doi=10.1523/jneurosci.4442-06.2007 | pmid=17267574| pmc = 6673201 }}</ref> Small studies have shown transient changes in gray-matter associated with developing a new physical skill (juggling) occipito-temporal cortex <ref name="Driemezer2008">{{cite journal | last1 = Driemeyer | first1 = J. | last2 = Boyke | first2 = J. | last3 = Gaser | first3 = C. | last4 = Büchel | first4 = C. | last5 = May | first5 = A. | year = 2008 | title = Changes in gray matter induced by learning—revisited | journal = PLOS ONE | volume = 3 | issue = 7| page = e2669 | doi=10.1371/journal.pone.0002669 | pmid=18648501 | pmc=2447176| bibcode = 2008PLoSO...3.2669D | doi-access = free }}</ref>

Brain volume is not a perfect account of intelligence: the relationship explains a modest amount of variance in intelligence – 12% to 36% of the variance.<ref name=Witelson2006 /><ref name=Andreasen1993 /> The amount of variance explained by brain volume may also depend on the type of intelligence measured.<ref name=Witelson2006 /> 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.<ref name=Witelson2006 /> A 2015 study by researcher [[Stuart J. Ritchie]] found that brain size explained 12% of the variance in intelligence among individuals.<ref name=":0">{{Cite journal|last1=Ritchie|first1=Stuart J.|last2=Booth|first2=Tom|last3=Valdés Hernández|first3=Maria del C.|last4=Corley|first4=Janie|last5=Maniega|first5=Susana Muñoz|last6=Gow|first6=Alan J.|last7=Royle|first7=Natalie A.|last8=Pattie|first8=Alison|last9=Karama|first9=Sherif|date=2015-01-01|title=Beyond a bigger brain: Multivariable structural brain imaging and intelligence|journal=Intelligence|volume=51|pages=47–56|doi=10.1016/j.intell.2015.05.001|issn=0160-2896|pmc=4518535|pmid=26240470}}</ref> These caveats imply that there are other major factors influencing how intelligent an individual is apart from brain size.<ref name=Luders2009 /> 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.<ref name="Pietschnig 411–432">{{Cite journal|title = Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?|journal = Neuroscience & Biobehavioral Reviews|date = 2015-10-01|pages = 411–432|volume = 57|doi = 10.1016/j.neubiorev.2015.09.017|first1 = Jakob|last1 = Pietschnig|first2 = Lars|last2 = Penke|first3 = Jelte M.|last3 = Wicherts|first4 = Michael|last4 = Zeiler|first5 = Martin|last5 = Voracek|pmid=26449760|s2cid = 23180321}}</ref> Taking into account measurement quality, and sample type and IQ-range, the meta-analytic association of brain volume in appears to be ~ .4  in normal adults.<ref name=":1" /> Researcher Jakob Pietschnig argued that the strength of the positive association of brain volume and IQ remains robust, but has been overestimated in the literature. 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''".<ref name="Pietschnig 411–432"/>

===Grey matter===

[[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.<ref name=Luders2009 /> More specifically, higher intelligence has been associated with larger cortical grey matter in the prefrontal and posterior temporal cortex in adults.<ref name=Narr2007 /> 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.<ref name="Colom2006">{{cite journal | last1 = Colom | first1 = R. | last2 = Jung | first2 = R. E. | last3 = Haier | first3 = R. J. | year = 2006 | title = Distributed brain sites for the g-factor of intelligence | journal = NeuroImage | volume = 31 | issue = 3| pages = 1359–1365 | doi=10.1016/j.neuroimage.2006.01.006 | pmid=16513370| s2cid = 33222604 }}</ref>

There appear to be [[Sex differences in psychology|sex differences]] between the relationship of grey matter to intelligence between men and women.<ref name="Haier2005">{{cite journal | last1 = Haier | first1 = R. J. | last2 = Jung | first2 = R. E. | last3 = Yeo | first3 = R. A. | last4 = Head | first4 = K. | last5 = Alkire | first5 = M. T. | year = 2005 | title = The neuroanatomy of general intelligence: sex matters | journal = NeuroImage | volume = 25 | issue = 1| pages = 320–327 | doi=10.1016/j.neuroimage.2004.11.019 | pmid=15734366| s2cid = 4127512 }}</ref> 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]].<ref name=Haier2005 /> However, these differences do not seem to impact overall Intelligence, implying that the same cognitive ability levels can be attained in different ways.<ref name=Haier2005 />

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.<ref name="Haier2004">{{cite journal | last1 = Haier | first1 = R. J. | last2 = Jung | first2 = R. E. | last3 = Yeo | first3 = R. A. | last4 = Head | first4 = K. | last5 = Alkire | first5 = M. T. | year = 2004 | title = Structural brain variation and general intelligence | journal = NeuroImage | volume = 23 | issue = 1| pages = 425–433 | doi=10.1016/j.neuroimage.2004.04.025 | pmid=15325390| s2cid = 29426973 }}</ref> 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.<ref name="Gong2005">{{cite journal | last1 = Gong | first1 = Q. Y. | last2 = Sluming | first2 = V. | last3 = Mayes | first3 = A. | last4 = Keller | first4 = S. | last5 = Barrick | first5 = T. | last6 = Cezayirli | first6 = E. | last7 = Roberts | first7 = N. | year = 2005 | title = Voxel-based morphometry and stereology provide convergent evidence of the importance of medial prefrontal cortex for fluid intelligence in healthy adults | journal = NeuroImage | volume = 25 | issue = 4| pages = 1175–1186 | doi=10.1016/j.neuroimage.2004.12.044 | pmid=15850735| s2cid = 6986485 }}</ref> 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.<ref name="Wilke2003">{{cite journal | last1 = Wilke | first1 = M. | last2 = Sohn | first2 = J. H. | last3 = Byars | first3 = A. W. | last4 = Holland | first4 = S. K. | year = 2003 | title = Bright spots: correlations of gray matter volume with IQ in a normal pediatric population | journal = NeuroImage | volume = 20 | issue = 1| pages = 202–215 | doi=10.1016/s1053-8119(03)00199-x| pmid = 14527581 | s2cid = 14583968 }}</ref>

Grey matter has also been shown to positively correlate with intelligence in children.<ref name=Wilke2003 /><ref name="Frangou2006">{{cite journal | last1 = Frangou | first1 = S. | last2 = Chitins | first2 = X. | last3 = Williams | first3 = S. C. | year = 2004 | title = Mapping IQ and gray matter density in healthy young people | journal = NeuroImage | volume = 23 | issue = 3| pages = 800–805 | doi=10.1016/j.neuroimage.2004.05.027| pmid = 15528081 | s2cid = 16808023 }}</ref><ref name="Reis2006">{{cite journal | last1 = Reiss | first1 = A. L. | last2 = Abrams | first2 = M. T. | last3 = Singer | first3 = H. S. | last4 = Ross | first4 = J. L. | last5 = Denckla | first5 = M. B. | year = 1996 | title = Brain development, gender and IQ in children A volumetric imaging study | journal = Brain | volume = 119 | issue = 5| pages = 1763–1774 | doi=10.1093/brain/119.5.1763| pmid = 8931596 | doi-access = free }}</ref> Reis and colleagues<ref name=Reis2006 /> 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<ref name=Frangou2006 /> 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.<ref name=Wilke2003 />

An underlying caveat to research into the relationship of grey matter volume and intelligence is demonstrated by the hypothesis of [[#Neural efficiency|neural efficiency]].<ref name="Haier1988"/><ref name="Haier1992">{{cite journal | last1 = Haier | first1 = R. J. | last2 = Siegel | first2 = B. | last3 = Tang | first3 = C. | last4 = Abel | first4 = L. | last5 = Buchsbaum | first5 = M. S. | year = 1992 | title = Intelligence and changes in regional cerebral glucose metabolic rate following learning | journal = Intelligence | volume = 16 | issue = 3| pages = 415–426 | doi=10.1016/0160-2896(92)90018-m}}</ref> 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.<ref name="Shaw2006">{{cite journal | last1 = Shaw | first1 = P. | last2 = Greenstein | first2 = D. | last3 = Lerch | first3 = J. | last4 = Clasen | first4 = L. | last5 = Lenroot | first5 = R. | last6 = Gogtay | first6 = N. | last7 = Giedd | first7 = J. | year = 2006 | title = Intellectual ability and cortical development in children and adolescents | url = https://zenodo.org/record/1233291| journal = Nature | volume = 440 | issue = 7084| pages = 676–679 | doi=10.1038/nature04513 | pmid=16572172| bibcode = 2006Natur.440..676S | s2cid = 3079565 }}</ref>

===White matter===

Similar to grey matter, [[white matter]] has been shown to correlate positively with intelligence in humans.<ref name=Luders2009 /><ref name=Gur1999 /> 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.<ref name=Luders2009 /> 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).<ref name="Fletcher1992">{{cite journal | last1 = Fletcher | first1 = J. M. | last2 = Bohan | first2 = T. P. | last3 = Brandt | first3 = M. E. | last4 = Brookshire | first4 = B. L. | last5 = Beaver | first5 = S. R. | last6 = Francis | first6 = D. J. | last7 = Miner | first7 = M. E. | year = 1992 | title = Cerebral white matter and cognition in hydrocephalic children | journal = Archives of Neurology | volume = 49 | issue = 8| pages = 818–824 | doi=10.1001/archneur.1992.00530320042010| pmid = 1524514 }}</ref> Anatomical mesh-based geometrical modelling<ref name="Thompson1996a">{{cite journal | last1 = Thompson | first1 = P. M. | last2 = Schwartz | first2 = C. | last3 = Lin | first3 = R. T. | last4 = Khan | first4 = A. A. | last5 = Toga | first5 = A. W. | year = 1996 | title = Three-dimensional statistical analysis of sulcal variability in the human brain | journal = The Journal of Neuroscience | volume = 16 | issue = 13| pages = 4261–4274 | doi = 10.1523/JNEUROSCI.16-13-04261.1996 | pmid = 8753887 | pmc = 6578992 | doi-access = free }}</ref><ref name="Thompson1996b">{{cite journal | last1 = Thompson | first1 = P. M. | last2 = Schwartz | first2 = C. | last3 = Toga | first3 = A. W. | year = 1996 | title = High-resolution random mesh algorithms for creating a probabilistic 3D surface atlas of the human brain | journal = NeuroImage | volume = 3 | issue = 1| pages = 19–34 | doi=10.1006/nimg.1996.0003 | pmid=9345472| s2cid = 15940105 }}</ref><ref name="Thompson1997">{{cite journal | last1 = Thompson | first1 = P. M. | last2 = MacDonald | first2 = D. | last3 = Mega | first3 = M. S. | last4 = Holmes | first4 = C. J. | last5 = Evans | first5 = A. C. | last6 = Toga | first6 = A. W. | year = 1997 | title = Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces | journal = Journal of Computer Assisted Tomography | volume = 21 | issue = 4| pages = 567–581 | doi=10.1097/00004728-199707000-00008| pmid = 9216760 }}</ref> has also shown positive correlations between the thickness of the corpus callosum and Intelligence in healthy adults.<ref name="Luders2007">{{cite journal | last1 = Luders | first1 = E. | last2 = Narr | first2 = K. L. | last3 = Bilder | first3 = R. M. | last4 = Thompson | first4 = P. M. | last5 = Szeszko | first5 = P. R. | last6 = Hamilton | first6 = L. | last7 = Toga | first7 = A. W. | year = 2007 | title = Positive correlations between corpus callosum thickness and intelligence | journal = NeuroImage | volume = 37 | issue = 4| pages = 1457–1464 | doi=10.1016/j.neuroimage.2007.06.028| pmid = 17689267 | pmc = 2754582 }}</ref>

White matter integrity has also been found to be related to intelligence.<ref name=Penke2012 /> White matter tract integrity is important for information processing speed, and therefore reduced white matter integrity is related to lower intelligence.<ref name=Penke2012 /> The effect of white matter integrity is mediate entirely through information processing speed.<ref name=Penke2012 /> These findings indicate that the brain is structurally interconnected and that axonal fibres are integrally important for fast information process, and thus general intelligence.<ref name=Penke2012 />

Contradicting the findings described above, VBM failed to find a relationship between the corpus callosum and intelligence in healthy adults.<ref name=Haier2004 /> 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.<ref name=Luders2009 />

===Cortical thickness===

[[Cerebral cortex#Thickness|Cortical thickness]] has also been found to correlate positively with intelligence in humans.<ref name=Narr2007 /> However, the rate of growth of cortical thickness is also related to intelligence.<ref name=Shaw2006 /> In early childhood, cortical thickness displays a negative correlation with intelligence, while by late childhood this correlation has shifted to a positive one.<ref name=Shaw2006 /> More intelligent children were found to develop cortical thickness more steadily and over longer periods of time than less bright children.<ref name=Shaw2006 /> Studies have found cortical thickness to explain 5% in the variance of intelligence among individuals.<ref name=":0" /> 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.<ref name="Menary 597–606">{{Cite journal|last1=Menary|first1=Kyle|last2=Collins|first2=Paul F.|last3=Porter|first3=James N.|last4=Muetzel|first4=Ryan|last5=Olson|first5=Elizabeth A.|last6=Kumar|first6=Vipin|last7=Steinbach|first7=Michael|last8=Lim|first8=Kelvin O.|last9=Luciana|first9=Monica|date=2013-01-01|title=Associations between cortical thickness and general intelligence in children, adolescents and young adults|journal=Intelligence|volume=41|issue=5|pages=597–606|doi=10.1016/j.intell.2013.07.010|issn=0160-2896|pmc=3985090|pmid=24744452}}</ref> 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).<ref name="Menary 597–606"/>{{dubious|Sloppy_extraction_from_cite?|date=May 2018}}

=== Cortical convolution ===
The folding of the brain’s surface, known as [[Gyrification|cortical convolution]], has become more pronounced throughout human evolution. It has been suggested that this increased folding serves as a neurological foundation for some of the brain’s most unique cognitive functions. As a result, the extent of cortical convolution may play a role in shaping individual intelligence within the human species. <ref>{{Cite journal|title = Neuroanatomical Correlates of Intelligence |journal = Intelligence|date = 2009-03-01|issn = 0160-2896|pmc = 2770698|pmid = 20160919|pages = 156–163|volume = 37|issue = 2|doi = 10.1016/j.intell.2008.07.002|first1 = Eileen|last1 = Luders|first2 = Katherine L.|last2 = Narr|first3 = Paul M.|last3 = Thompson|first4 = Arthur W.|last4 = Toga}}</ref>

An analysis published in 2019 found the contours of 677 children and adolescent (mean age 12.72 years) brains had a genetic correlation of almost 1 between IQ and surface area of the [[supramarginal gyrus]] on the left side of the brain.<ref>{{Cite web|url=https://www.the-scientist.com/news-opinion/brain-surface-area-reveals-overlap-in-genes--intelligence--evolution-65559|title=Brain Surface Area Reveals Overlap in Genes, Intelligence, Evolution|website=The Scientist Magazine®|access-date=2019-07-17}}</ref><ref>{{Cite journal|last1=Raznahan|first1=Armin|last2=Giedd|first2=Jay N.|last3=Lee|first3=Nancy Raitano|last4=Wallace|first4=Gregory L.|last5=Chu|first5=Alan|last6=Pritikin|first6=Joshua N.|last7=Seidlitz|first7=Jakob|last8=Liu|first8=Siyuan|last9=Clasen|first9=Liv S.|date=2019-04-17|title=A Comprehensive Quantitative Genetic Analysis of Cerebral Surface Area in Youth|journal=Journal of Neuroscience|volume=39|issue=16|pages=3028–3040|doi=10.1523/JNEUROSCI.2248-18.2019|issn=0270-6474|pmid=30833512|pmc=6468099}}</ref>

===Neural efficiency===

The neural efficiency hypothesis postulates that more intelligent individuals display less activation in the brain during cognitive tasks, as measured by Glucose metabolism.<ref name=Haier1988 /> 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.<ref name=Haier1988 /> According to an extensive review by Neubauer & Fink<ref name="Neubauer2009">Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. ''Neuroscience & Biobehavioral Review''s, 33(7), 1004-1023.</ref> a large number of studies (N=27) have confirmed this finding using methods such as PET scans,<ref name="Andreasen1995">{{cite journal | last1 = Andreasen | first1 = N.C. | last2 = O'Leary | first2 = D.S. | last3 = Arndt | first3 = S. | last4 = Cizadlo | first4 = T. | last5 = Rezai | first5 = K. | last6 = Watkins | first6 = G.L. | last7 = Boles Ponto | first7 = L.L. | last8 = Hichwa | first8 = R.D. | year = 1995 | title = PET studies of memory: novel and practiced free recall of complex narratives | journal = NeuroImage | volume = 2 | issue = 4| pages = 284–295 | doi=10.1006/nimg.1995.1036| pmid = 9343613 | s2cid = 19745366 }}</ref> EEG<ref name="Doppelmayer1998">{{cite journal | last1 = Doppelmayr | first1 = M. | last2 = Klimesch | first2 = W. | last3 = Schwaiger | first3 = J. | last4 = Auinger | first4 = P. | last5 = Winkler | first5 = T. | year = 1998 | title = Theta synchronization in the human EEG and episodic retrieval | journal = Neurosci. Lett. | volume = 257 | issue = 1| pages = 41–44 | doi=10.1016/s0304-3940(98)00805-2| pmid = 9857961 | s2cid = 11307011 }}</ref> and fMRI.<ref name="Rypma1999">{{cite journal | last1 = Rypma | first1 = B. | last2 = D'Esposito | first2 = M. | year = 1999 | title = The roles of prefrontal brain regions in components of working memory: effects of memory load and individual differences | journal = PNAS | volume = 96 | issue = 11| pages = 6558–6563 | doi=10.1073/pnas.96.11.6558| pmid = 10339627 | pmc = 26921 | bibcode = 1999PNAS...96.6558R | doi-access = free }}</ref>

fMRI and EEG studies have revealed that task difficulty is an important factor affecting neural efficiency.<ref name=Neubauer2009 /> 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.<ref name="Neubauer1999">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.</ref> In fact, more able individuals appear to invest more cortical resources in tasks of high difficulty.<ref name=Neubauer2009 /> 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.<ref name="Callicott1999">{{cite journal | last1 = Callicott | first1 = J. H. | last2 = Mattay | first2 = V. S. | last3 = Bertolino | first3 = A. | last4 = Finn | first4 = K. | last5 = Coppola | first5 = R. | last6 = Frank | first6 = J. A. | year = 1999 | title = Physiological characteristics of capacity constraints in working memory as revealed by functional MRI | journal = Cerebral Cortex | volume = 9 | issue = 1| pages = 20–26 | doi=10.1093/cercor/9.1.20| pmid = 10022492 | doi-access = free }}</ref><ref name="Rympa2006">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.</ref> 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.<ref name="Gray2003">{{cite journal | last1 = Gray | first1 = J. R. | last2 = Chabris | first2 = C. F. | last3 = Braver | first3 = T. S. | year = 2003 | title = Neural mechanisms of general fluid intelligence | journal = Nature Neuroscience | volume = 6 | issue = 3| pages = 316–322 | doi=10.1038/nn1014 | pmid=12592404| s2cid = 10492067 }}</ref>

===Further research===
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.<ref>{{cite journal |title=Brain size does not predict cognitive abilities within families |journal=PNAS |volume=97 |issue=9 |pages=4932–4937 |doi=10.1073/pnas.97.9.4932 |pmid=10781101 |pmc=18335 |date=April 2000 | last1 = Schoenemann | first1 = PT | last2 = Budinger | first2 = TF | last3 = Sarich | first3 = VM | last4 = Wang | first4 = WS | bibcode=2000PNAS...97.4932S|doi-access=free }}</ref><ref>[https://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=neurosci.box.1833 Brain size and intelligence]</ref>

In a study of the head growth of 633 term-born children from the Avon Longitudinal Study of Parents and Children [[cohort study|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.<ref>{{Cite journal
|url=http://pediatrics.aappublications.org/cgi/content/short/118/4/1486
|title=The Influence of Head Growth in Fetal Life, Infancy, and Childhood on Intelligence at the Ages of 4 and 8 Years
|access-date=August 6, 2006 |date=October 4, 2006
|author= 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
|journal= Pediatrics |volume= 118 |issue= 4|pages= 1486–1492 |doi=10.1542/peds.2005-2629 |pmid=17015539
|s2cid=12447118
|url-access=subscription}}</ref>

There is an association between IQ and [[myopia]]. One suggested explanation is that one or several [[pleiotropic]] gene(s) affect the size of the [[neocortex]] part of the brain and eyes simultaneously.<ref>{{Cite journal 
| pmid = 19127804 
| last1 = Czepita | first1 = D. 
| last2 = Lodygowska | first2 = E. 
| last3 = Czepita | first3 = M. 
| title = Are children with myopia more intelligent? A literature review 
| journal = Annales Academiae Medicae Stetinensis 
| volume = 54 
| issue = 1 
| pages = 13–16; discussion 16 
| year = 2008
}}</ref> <!-- You can also find this correlation in [[Study of Mathematically Precocious Youth]], IIRC. -->

====Parieto-frontal integration theory====
{{main|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 ([[Rex Jung|Jung]] & [[Richard J. Haier|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.<ref>{{Cite journal
|url=http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=1305780
|title=The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence
|access-date=September 28, 2009
|date=July 26, 2007
|author1=Richard Haier  |author2=Rex Jung
 |journal=Behavioral and Brain Sciences
|volume=30
|issue=2
|pages=135–154
|name-list-style=amp |publisher=Cambridge University Press
|doi=10.1017/S0140525X07001185
|pmid=17655784
|s2cid=14699011
|url-access=subscription
}}</ref>

A recent lesion mapping study conducted by [[Aron K. Barbey|Barbey and colleagues]] provides evidence to support the P-FIT theory of intelligence.<ref>{{cite journal|last1=Barbey|first1=Aron K.|last2=Colom|first2=Roberto|last3=Solomon|first3=Jeffrey|last4=Krueger|first4=Frank|last5=Forbes|first5=Chad|last6=Grafman|first6=Jordan|title=An integrative architecture for general intelligence and executive function revealed by lesion mapping|journal=Brain|volume=135|issue=4|pages=1154–1164|doi=10.1093/brain/aws021|pmid=22396393|pmc=3326251|year=2012}}</ref><ref>{{cite news|author1=HealthDay|title=Researchers Map Brain Regions Linked to Intelligence|url=http://health.usnews.com/health-news/news/articles/2012/04/13/researchers-map-brain-regions-linked-to-intelligence|agency=U.S. News & World Report|date=2012-04-13}}</ref><ref>{{cite web|last1=Yates|first1=Diana|title=Researchers Use Brain-Injury Data to Map Intelligence in the Brain|url=http://news.illinois.edu/news/12/0410braininjury_AronBarbey.html|website=University of Illinois News Bureau|publisher=University of Illinois}}</ref>

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.<ref>{{cite journal |last1=Bava |first1=Sunita |last2=Ballantyne |first2=Angela O |last3=Trauner |first3=Doris A |title=Disparity of Verbal and Performance IQ Following Early Bilateral Brain Damage |journal=Cognitive and Behavioral Neurology |volume=18 |issue=3 |pages=163–70 |year=2005 |pmid=16175020 |doi= 10.1097/01.wnn.0000178228.61938.3e|s2cid=30150030 }}</ref>