The Neurobiology and Intelligence

Common IQ tests (e.g. WAIS/WISC) and neurodivergence

Some adjustments can be made

• Colourblind version of some tasks

• Visual/auditory versions

• In WAIS-V, the clinician can skip some tasks

However, tasks are often applied without adjustments

• Neurodivergent individuals may be at a disadvantage

• Other populations (e.g. immigrants) may be as well

Wilson (2023) - Autism

Meta-analysis

• Data from 1,800 neurodivergent people

• Children and adults (WISC and WAIS)

• Autism performance:

  • Verbal and nonverbal reasoning - typical range

  • Processing speed ∼1 SD below the mean

  • WM - Slightly reduced

Wilson (2023) - ADHD

Meta-analysis

• Data from 1,800 neurodivergent people (autism and ADHD)

• Children and adults (WISC and WAIS)

• ADHD performance:

  • Mostly at age-expected levels

  • WM - slightly reduced scores

Courchesne et al.,

• None of the autistic children could complete the WISC-IV

• May be wrongly regarded as having little cognitive potential

Darwin (1871)

• Humans have large brain size in proportion to their body

• Especially in comparison to other primates (e.g. gorilla and orangutang)

• Therefore indicates relation to higher mental powers

Galton and Head Size

• Brains do not stop growing if you attend university

• At 19, men with high honours have a larger brain than others

• By 25, this difference decreases

• Therefore, high honour men are more precocious and gifted

Rushton & Ankey (1996)

• Brain size correlates with cognitive ability = 0.44

• Brain size increases with age, sex, social class and race

• Cognitive ability increases by age, sex, social class and race

Peters (1993)

Evidence is not strong enough to show that intelligence and cranial capacity are positively related

• Brain size only accounts for 2.1% variation in intelligence

• What is the point of emphasising brain size and intelligence across sociodemographic differences? - Establish superiority of white males

• Too little is known about anatomical differences in the cerebral cortex of large and small, and male and female, brains

McDaniel et al. (2005)

Brain Size/IQ debate: 2005 Meta-Analysis

• 37 studies

• Brain volume/IQ correlation for:

  • Males = 0.34

  • Female = 0.40

  • Children and Adults = 0.33

  • Overall = 0.33

Pietsching et al. (2015)

Systematic review of published and unpublished studies.

• 88 studies: 148 healthy and clinical mixed-sex samples (>8000 individuals).

• Significant positive correlation (r = .24) between brain volume and IQ.

• Generalised over age (children vs adults), IQ domain, and sex.

  • Publication bias of studies with strong positive correlations

  • Small and non-significant associations omitted from reports.

Brain Size/IQ debate - Conclusion

The strength of the positive association of brain volume and IQ, although robust, has been overestimated in the literature.

• “It is tempting to interpret this association in the context of human cognitive evolution and species differences in brain size and cognitive ability

• It is not warranted to interpret brain size as an isomorphic proxy of human intelligence differences.”

Number of Brain Cells and IQ

50 postmortem brains from Danish males (aged 20-52 years) for whom there was documentation of premorbid IQ (mean 94, SD 14).

• Found no correlation between IQ and the number of brain cells.

• Only a weak correlation between IQ and brain weight.

• Near zero correlation with other neocortical structures

Sex differences - Ruigrok et al., 2014

• On average, adult male brains have 10.8% larger total brain volume than women

• 2.1 SD (or 131ml) difference

Dyliert et al. (2009); Flynn (2012); Johnson et al. (2009)

• There is also a clear absence of sex differences in IQ

• Hence, large brains do not necessarily translate to higher IQ in humans.

Sex Differences - Haier et al.

High IQ in:

• Women - More gray and white matter in frontal language areas

• Men - More gray matter in posterior sensory integration areas

Sholz et al. (2009)

Changes in white matter as a result of learning an entirely new skill

Maguire et al. (2000)

• London Cab Drivers

• Significantly larger hippocampal volume

  • Responsible for spatial navigation

Woollett & Maguire (2011)

• Qualified cab driver trainees have significantly higher grey matter intensity in the hippocampus

• Not the case for trainees who failed and controls

• Grey matter increase was specifically linked to the intense spatial learning involved in mastering “The Knowledge” of London’s street layout.

Brain efficiency - Haier et al. (1988)

Measured brain glucose activity (PET)

• Correlation between glucose metabolic rate and Raven’s matrices score:

  • Supraventricular = -0.77

  • Midventricular = -0.67

  • Infraventricular= -0.75

Brain Efficiency (PET) - Haier et al. (1992a)

• After learning a complex visuo-spatial task (Tetris)

• Decrease in cerebral metabolism after practice

Brain Efficiency (PET) - Haier et al. (1992b)

• After learning to play Tetris

• More pronounced decrease in cerebral metabolism in higher IQ participants (measured using Raven’s Advanced Progressive Matrices)

Haier and Benbow (1995)

• 22 male and 22 female participants

• Half high and half average SAT-Maths Scores

During the PET scan, each participant completed a new SAT-Maths test.

• Male Participants: significant correlations between the math score and glucose metabolism in the temporal lobes bilaterally (middle, inferior, and posterior; analogous to BAs 20, 21, 22).

• Female Participants: no correlations.

Ryman et al. (2016) - White Matter Efficiency Differences

• Male: no correlation with IQ

• Female: positive correlation with IQ

Ryman et al. (2016) - Grey Matter Efficiency Differences

Male: a significant positive relationship between fronto-parietal grey matter region volumes and intelligence.

Female participants: total grey matter volume did predict intelligence in females, but a regionally specific contribution of the fronto-parietal grey matter volume was not evident.

Ryman et al. (2016) - Conclusion

• Efficiency of white matter organisation and the total grey matter volume were predictive of intelligence in females.

• Intelligence was related primarily to a fronto-parietal grey matter volume in males.

Finn et al. (2015) - Method

• fMRI obtained from 126 participants.

• 6 imaging sessions/participant (mixture of rest and complex cognitive tasks).

• Explored functional connectivity patterns (across the whole brain and within 10 key networks).

  • Individual differences

  • Connectivity analysis for each person in each condition.

Finn et al. (2015) - Findings

• Individual connectivity patterns were stable across all six conditions

• Connectivity patterns in the frontoparietal network were the most distinctive

• Connectivity profiles appeared to be able to predict levels of fluid intelligence (measured using matrix reasoning)

Functional connectivity

• Act as a ‘fingerprint’ that can accurately identify subjects from a large group

• Connectivity profiles that are most discriminating of individuals were also most predictive of cognitive behaviour

Definition of a Connectotype

The distinct pattern of brain activity that characterises the way each person’s mind works

Key features of Connectotyping

• Occur within the brain’s most sophisticated networks i.e. in the frontal and parietal cortices.

• Connectotypes are:

  • Stable over time - Has been evidenced in adults/children, and humans/non-human primates.

  • Familial - e.g. the connectotypes of family members resemble one another more than those of strangers.

  • Appear to also be heritable.