Brain Structure and IQ: How Myelin Elevates Intelligence
How does brain structure create different levels of intelligence? I was somewhat surprised to find that the leading researcher of the relationship between neurobiology and IQ, Richard Haier, is not mentioned anywhere on LessWrong, at least according to the search function. The following post reviews current leading academic literature on the neurobiology of intelligence which is highly suggestive of the role of white matter tracts in the deep brain for elevating psychometric g. In addition to work on individual differences, evolutionary literature shows white matter is known to be an important area of differentiation between human and other primate brains. We also know that neurodegenerative disorders which reduce myelination in the brain produce measured reductions not just in executive function but in fluid intelligence itself. Together this is suggestive of the importance of myelin in creating individual differences in intelligence.
Haier: The NEH and P-FIT
The two major contributions of Haier are his “Neural Efficiency Hypothesis,” sometimes called the brain efficiency hypothesis, and his Parietal-Frontal Integration Theory of intelligence (P-FIT).
Haier et al. (1988) was the first study to combine modern brain imaging techniques with psychological intelligence testing. The surprising result he found was that people with higher intelligence were using less brain energy, somewhat counterintuitive to the idea of more intelligent people having more mental horsepower or brain power. This led to a major insight into the nature of intelligence: intelligent brains are efficient brains. This is the Neural Efficiency Hypothesis. In essence: smarter brains have higher signal to noise ratios. In the original paper Haier speculates:
This inefficiency may be due to the use of more energy by each neuron and/or the use of more neurons to perform the task. The inefficient neural circuits are used intensively to try to solve the problem ~ are unable to do so, possibly because extraneous, irrelevant circuits are used.
Haier 1988 page 214
This was largely confirmed in Haier (1992) and Graham (2010). Haier (1992) showed that while learning a new task there is wider activation which settles into a narrower pattern as expertise is acquired, with more intelligent subjects settling into a narrower pattern more quickly and with higher end efficiency (lower brainpower usage). Graham (2010) showed that the additional inefficient neural activity of less intelligent subjects was happening in inhibitory frontal task-relevant areas like the anterior cingulate cortex. Together they seem to paint a picture of an experimental novice phase where incorrect solutions are pursued and then inhibited. In the less intelligent brain it seems that these incorrect solutions continue to be suggested for a longer period than in a more intelligent brain.
Jung and Haier’s (2007) review of the literature on brain imaging and IQ formulates the Parietal-Frontal Integration Theory of human intelligence, arguably the leading theory of the physical embodiment of IQ differences. They argue that the literature showed correlation of IQ with grey matter in particular areas of the frontal and parietal lobes including Broca’s area, which processes speech in the frontal lobe, and parietal areas like the angular gyrus, which is involved in mathematical reasoning functions and arithmetic. As important or possibly more so than these increases of grey matter were increases in the myelinated white matter connections between these areas. Jung and Haier (2007) put it this way:
The relative contribution of white matter to higher cogni-tive functioning has remained relatively understudied compared to gray matter research linking particular corti-cal regions to performance. However, several lines of inquiry would suggest that the integrity of myelinated axons plays a critical role in intellectual attainment (Miller 1994). For example, myelin thickness is correlated to axonal size (Bishop & Smith 1964; Friede & Samorajski 1967; Mathews 1968), and larger axonal diameter is associ-ated with increased nerve conduction speed (Aboitiz 1992). The simultaneous increases in myelination and axonal diameter have been hypothesized to play a critical role in cognitive development. For example, one group has found significant age-related increases in white matter density within the bilateral internal capsule and the posterior aspects of the left arcuate fasciculus (which links anterior and posterior language cortices) in a young (age range ¼ 4–17 years) normal cohort (Paus et al. 1999). At the other end of the developmental continuum, age-related cognitive decline has been linked to general slowing of brain processes (Hale et al. 1987), with concordant linear decreases in myelination initiated around the fourth decade (Bartzokis et al. 2003). Indeed, reviews of the research literature have found that nearly the entire decline in intellectual functioning observed among the elderly may be accounted for by reductions in processing speed (Lindenberger et al. 1993; Salthouse & Coon 1993).
Jung and Haier 2007 page 140
Myelination and IQ Research
Myelin sheaths are cells with fatty proteins in them that wrap themselves around a neuron’s axon in order to insulate the neuron. This has been understood to allow faster and lower energy transmission of signals; essentially the myelinated areas of the cell membrane do not exchange ions with the interstitial fluid during an action potential wave and allow the positive charge of the action potential to more easily perpetuate down the axon without having to push against the positive charges on the outside of the cell membrane (because the myelin forms a barrier which keeps these positive ions far from the membrane). But I think another role of myelin may be more important than the increase in speed: prevention of ephaptic coupling, but more on that later.
The white-matter underneath the thin gray matter surface of the cortex is composed of highly myelinated axons connecting various brain areas. And while my quoted paragraph from Jung and Haier (2007) discusses white matter’s association with mental ability during aging it also has been shown to have strong association with individual differences, perhaps most compellingly in children. Schmithorst et al. (2005) is an example of a study finding a positive association between myelination as measured by Fractional Anisotropy and IQ on the Wechsler. Similarly Navas-Sanchez (2014) finds an association between mathematical giftedness and myelination as measured by FA. I think both studies are compelling due to being done in pediatric populations, which while not a guarantee is suggestive that this trait (increased myelination) is causal of fluid intelligence rather than a result of the accumulated effects of high intelligence.
Chiang et al. (2009) is perhaps most compelling as in a twin study they very directly show a genetic basis for myelination (specifically white matter integrity) particularly as it supports high IQ:
White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a2 0.55, p 0.04, left; a2 0.74, p 0.006, right), bilateral parietal (a2 0.85, p − 0.001, left; a2 0.84, p − 0.001, right), and left occipital (a2 0.76, p 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto-occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata ( p 0.04 for FIQ and p 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination.
Chiang et al. 2009 abstract
Here “FIQ” is full IQ on the Multidimensional Aptitude Battery, PIQ is “performance” (spatial and object assembly) IQ.
One early source of suspicions around the relationship between intelligence and myelination was Diamond et al.’s (1985) study of Albert Einstein’s preserved brain. They find:
The results of the analysis suggest that in left area 39, the neuronal:glial ratio for the Einstein brain is significantly smaller than the mean for the control population (t = 2.62, df 9, p < 0.05, two-tailed).
Oligodendrocytes are glial cells responsible for myelination in the brain. Area 39 is the aforementioned angular gyrus responsible for math and arithmetic and is one of the main endpoints of the white matter tract known as the arcuate fasciculus; Broca’s area is the other main endpoint. These are grey matter areas implicated in P-FIT and the arcuate fasciculus connection between the two is Jung and Haier’s (2007) favored explanatory piece of white matter. This is similar to the findings in Navas-Sanchez (2014), Schmithorst et al. (2005), and Chiang (2009) which find additional white matter or white matter integrity (a measure of how thoroughly myelinated the cells are) associated with these parietal and frontal areas is associated with higher IQ.
Myelination and Human Evolution
Additional myelination along the white matter tracts linking the frontal lobe with rear areas is a known difference in the brain structure between humans and other primates. Schoenemann et al. (2005) shows that while the human frontal lobe experienced most of the allometric growth relative to our ape cousins this increase in size was due to an increase in white matter tracts leading to the frontal areas.
Berto et al. (2019) traces particular gene differences between humans and comparison chimpanzee and rhesus macaque DNA and find that human myelination cells have undergone more rapid evolution than neurons:
Surprisingly though, we find that gene expression in oligodendrocytes has undergone a more dramatic acceleration in the human lineage compared with neurons.
Berto et al. (2019) page 24338
In most primates the arcuate fasciculus connects the angular gyrus with frontal areas but Rilling et al. (2008) and Eichert et al. (2019) show that the arcuate fasciculus in humans has extensions leading into the temporal gyri which are not present in our ape relatives.
The arcuate fasciculus in rhesus macaques connects to the area highlighted in the leftmost image. The central image accounts for the allometric expansion of those areas of the cortex in the evolutionary differences between rhesus and homo using coritcal landmarks. The rightmost image highlights the actual cortical areas of connection to the arcuate fasciculus tract in humans; the cortical areas analogous to rhesus area highlighted in the central image are outlined in black in the rightmost image. Cortical areas actually connected to the arcuate fasciculus in humans seems to have greatly expanded from the analogous areas in rhesus.
Reaction Time and IQ
As previously mentioned, one of the main functions of myelination is increased transmission speed and we know reaction times have inverse correlation with measures of intelligence (smarter is faster). Reaction time’s inverse correlation with IQ had been well established enough to be mentioned alongside things like job performance and low social deviancy as a correlate of intelligence in the APA’s public statement clarifying their position on intelligence research, Intelligence Knowns and Unknowns (Neisser et al. 1996):
Many recent studies show that the speeds with which people perform very simple perceptual and cognitive tasks are correlated with psychometric intelligence (for reviews see Ceci, 1990; Deary, 1995; Vernon, 1987). In general, people with higher intelligence test scores tend to apprehend, scan, retrieve, and respond to stimuli more quickly than those who score lower.
Neisser et al. 1996 page 83
One of the more compelling studies published after Neisser et al. (1996) was Rijsdijk et al.’s (1998) twin study showing reaction time’s association with IQ is genetic:
At age 16 years heritabilities for a simple reaction time (SRT) and choice reaction time (CRT) were 64 and 62% and the average phenotypic correlations between the RTs and IQ, assessed by the Raven standard progressive matrices, was −0.21. At the second test occasion lower heritabilities were observed for the RTs, probably due to modifications in administration procedures. The mean correlations between the RTs and WAIS verbal and per formal subtests were −0.18 and −0.16. Multivariate genetic analyses at both ages showed that the RT-IQ correlations were explained by genetic influences.
Rijsdijk et al. 1998 abstract
The “simple” reaction test was hitting a key when a screen displayed a letter or number. The “choice” reaction test was hitting a certain key when a letter displayed and a different key when a number displayed. Both showed inverse correlation with Rijsdijk’s et al. (1998) measures of intelligence.
These studies showing an association between reaction speed and IQ are some of the oldest and most consistent indicators of the role of myelin in intelligence.
Ephaptic Coupling
But what may be an even more important function for myelin in increasing intelligence than increasing speed may be lessening or preventing ephaptic nerve cross-talk or transmission noise.
Ephaptic coupling refers to the phenomenon whereby nearby axons may trigger an action potential in each other with no synaptic connection whatsoever, purely through the changes in the ambient intercellular magnetic fields. A phenomenon very much like signal bleed between two audio cables.
Ephaptic cross-talk in the brain, particularly in the deeper subcortical white matter tracts, is impossible to directly observe with present technology, at least ethically in humans. Rasminsky (1980) directly observes ephaptic coupling in a line of mice bred for poor myelination. Asmedi et al. (2018) use an electrodiagnostic technique to show ephaptic signaling in de-myelinated peripheral sensory nerves of diabetic patients causes painful diabetic neuropathy.
While it is not currently possible to observe an ephaptic event deep in the brain, computational models show ephaptic signaling events likely happen. Bokil et al. (2001) shows in a computational model of the typically unmyelinated olfactory tract that ephaptic coupling is an important part of olfactory signal processing. Sheheitli and Jirsa (2020) show in a computational model that ephaptic coupling can happen even in the deep white matter tracts of the brain.
#De-Myelinating Disease, Ephaptic Coupling, and Intelligence
Multiple sclerosis is a disease where a patient’s immune system attacks their own myelin sheathing cells, resulting in de-myelination in both the peripheral and central nervous systems. Ostermann and Westerberg (1975) had speculated ephaptic cross-talk was responsible for some symptoms in MS patients:
It is suggested that the paroxysmal phenomena in MS are caused by a transversely spreading ephaptic activation of axons within a partially demyelinated lesion in fibre tracts somewhere in the central nervous system.
Ostermann and Westerberg 1975
Ephaptic signaling continues to be a favored explanation for MS symptoms (Bruno et al. 2022).
I speculate that this demyelination in deep white matter tracts results in a similar lower intelligence to that of many otherwise healthy lower intelligence people. Until recently, there has been a distinct separation between the therapeutic literature discussing MS patients in terms of lower executive function rather than explicitly in terms of lower IQ or intelligence. However, a couple of recent studies clarify this connection. Recent findings indicate that while MS patients retain their crystallized intelligence and largely their existing memories at onset, the disease impacts their fluid intelligence (Goitia et al. 2020, Kapanci et al. 2019). Kapanci et al. (2019) Is able to explain all the difference in executive function among MS patients by controlling for the difference in fluid intelligence, providing a powerful argument that the reduction in executive functions in MS is mediated by a reduction in fluid intelligence.
Notably these studies counterindicate two more widely known functions of myelin in creating intelligence. Kapanci et al. (2019) note MS patients completed the same number of items as controls on the intelligence task, counterindicating the role of myelin in speeding up processing:
The difference in intelligence scores was due to a higher number of incorrectly solved CFT20-R items in patients with MS than healthy controls. The number of omitted and unreached items did not differ between the two groups, so that time limitation of the CFT20-R is an unlikely explanation of the IQ differences.
Kapanci et al. 2019
Goitia et al. (2020) seems to counterindicate the role of myelin in reducing energy costs for nerve signaling by reporting that MS patients’ fatigue levels did not explain the greater impairment:
Our results show that fatigue levels could not explain the impairments observed, while fluid intelligence losses could. This is in agreement with several studies [16–18,73,79,80] that reported no effect of fatigue on cognitive tasks.
Goitia et al. 2020
Discussion
This accumulated evidence seems to indicate we may connect Jung and Haier’s (2007) P-FIT theory to Haier’s (1988) Neural Efficiency Hypothesis through the ability of thicker white matter fibers to reduce or eliminate dysfunctional ephaptic cross-talk in task relevant areas.
In essence: a lack of insulating myelin causes what in a more intelligent brain would be precise sniper-rifle-like signals traveling between the endpoints of white matter tracts like the arcuate fasciculus to instead spread into an imprecise shotgun blast of signals through ephaptic excitation of densely bundled fibers due to poorer myelination. This would explain Graham et al.’s (2010) finding that inhibitory prefrontal areas accounted for large parts of the increased brain activity in their normal IQ subjects compared to high IQ subjects. As Graham et al. (2010) puts it:
If the Average IQ participants were to have experienced greater conflict (e.g., from competing response alternatives) then this could explain their relatively greater activation in these regions observed compared with the High IQ participants.
Graham 2010
These inappropriate response suggestions which are filtered by the additional brain activity may be caused by ephaptic cross-talk.
And there is some direct evidence of a link between greater myelination and neural efficiency in Burzynska et al. (2013):
Our results indicate that greater microstructural integrity of the major WM tracts was negatively related to condition-related blood oxygenation level-dependent (BOLD) signal in task-positive GM regions. This negative relationship suggests that better quality of structural connections allows for more efficient use of task-related GM processing resources.
Burzynska et al. 2013 abstract
In summary: we have seen that white matter and myelination were important evolutionary distinctions between humans and other primates, that white matter and myelination are influenced by genetics in ways that are further known to influence intelligence, lack of myelination results in lower fluid intelligence likely through ephaptic cross-talk, and that this seems to fit well with observations that higher intelligence results in lower brain activations in task relevant areas. This seems to suggest preventing ephaptic cross-talk through myelination is an important contributor to differences in intellectual ability between individuals.
It may be counterintuitive to think of higher levels of intelligence as manifesting not as a broad creative explosion of alternatives which are then narrowed down to the correct answer but rather that high intelligence is avoiding thinking of wrong answers.
References
Amato, M. P., et al. “Cognitive and psychosocial features of childhood and juvenile MS.” Neurology 70.20 (2008): 1891-1897.
Berto, Stefano, et al. “Accelerated evolution of oligodendrocytes in the human brain.” Proceedings of the National Academy of Sciences 116.48 (2019): 24334-24342.
Bokil, Hemant, et al. “Ephaptic interactions in the mammalian olfactory system.” The Journal of neuroscience 21.20 (2001): RC173.
Bruno, Antonio, Ettore Dolcetti, and Diego Centonze. “Theoretical and therapeutic implications of the spasticity-plus syndrome model in multiple sclerosis.” Frontiers in neurology 12 (2022): 802918.
Chiang, Ming-Chang, et al. “Genetics of brain fiber architecture and intellectual performance.” Journal of Neuroscience 29.7 (2009): 2212-2224.
Eichert, Nicole, et al. “What is special about the human arcuate fasciculus? Lateralization, projections, and expansion.” cortex 118 (2019): 107-115.
Goitia, Belén, et al. “The relationship between executive functions and fluid intelligence in multiple sclerosis.” PLoS One 15.4 (2020): e0231868.
Graham, Steven, et al. “IQ-related fMRI differences during cognitive set shifting.” Cerebral Cortex 20.3 (2010): 641-649.
Haier, Richard J., et al. “Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography.” Intelligence 12.2 (1988): 199-217.
Haier, Richard J., et al. “Regional glucose metabolic changes after learning a complex visuospatial/motor task: a positron emission tomographic study.” Brain research 570.1-2 (1992): 134-143.
Jung, Rex E., and Richard J. Haier. “The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence.” Behavioral and brain sciences 30.2 (2007): 135-154
Kapanci, Tugba, et al. “Evaluating the relationship between psychometric intelligence and cognitive functions in paediatric multiple sclerosis.” Multiple Sclerosis Journal–Experimental, Translational and Clinical 5.4 (2019): 2055217319894365.
Navas‐Sánchez, Francisco J., et al. “White matter microstructure correlates of mathematical giftedness and intelligence quotient.” Human brain mapping 35.6 (2014): 2619-2631.
Neisser, Ulric, et al. “Intelligence: knowns and unknowns.” American psychologist 51.2 (1996): 77.
Rasminsky, Michael. “Ephaptic transmission between single nerve fibres in the spinal nerve roots of dystrophic mice.” The Journal of Physiology 305.1 (1980): 151-169.
Rilling, James K., et al. “The evolution of the arcuate fasciculus revealed with comparative DTI.” Nature neuroscience 11.4 (2008): 426-428.
Schmithorst, Vincent J., et al. “Cognitive functions correlate with white matter architecture in a normal pediatric population: a diffusion tensor MRI study.” Human brain mapping 26.2 (2005): 139-147.
Schoenemann, P. Thomas, Michael J. Sheehan, and L. Daniel Glotzer. “Prefrontal white matter volume is disproportionately larger in humans than in other primates.” Nature neuroscience 8.2 (2005): 242-252
Sheheitli, Hiba, and Viktor K. Jirsa. “A mathematical model of ephaptic interactions in neuronal fiber pathways: Could there be more than transmission along the tracts?.” Network Neuroscience 4.3 (2020): 595-610.
Nice, thanks! A few follow-up points:
Another piece of general evidence for myelination being somehow very important for cognition is just the massive amount of metabolic and anatomical investment in white matter (~half of the brain).
Plausibly this is provided in your citations, but, to convince me more of ephaptic coupling being a limiting factor and a primary function of myelination would be BOTECs showing how much cross-talk there would be for long-range axons with less myelination—especially, some lower level of myelination that still gets most of the benefits for speed+efficiency. (I see you have a citation that it “can” happen, but the quantities would matter here, at least within an OOM, say.)
I’m not sure I strongly buy the relevance of your citations for showing that a / the main effect of myelination on intelligence is reducing cross-talk. They’re suggestive though, so I’m updating some. (I haven’t read any of the cited studies.)
For Kapanci et al. (2019), I think you’re saying, if it was about processing speed, then they should complete fewer questions with degraded myelin? But IDK, is there a reference point that says this should happen (e.g. some other condition that definitely just decreases processing speed, resulting in completing fewer questions but with the unaffected rate of correctness)? It’s plausible to me that degrading processing speed just breaks / degrades a bunch of things in your thinking, e.g. leading to forgetting things, being less careful, not noticing mistakes, etc., without you noticing and slowing down to keep getting things correct; that could result in completing the same number of questions but less correctly.
Similarly for Goitia et al. 2020, would higher energy costs for demyelinated axons obviously translate into notable fatigue, rather than, say, working somewhat less hard to be careful / check your answer / figure out the right strategy / etc.?
For Graham et al. (2010), is the idea that for both people with higher and lower fluid intelligence, they have active areas of the brain that aren’t relevant for solving the problem; and then the higher fluid intelligence guy just doesn’t worry about it because the myelination stops cross-talk, but the lower fluid intelligence guy’s frontal cortex has to work really hard to suppress activity in those other areas to prevent cross-talk? This sounds pretty odd to me, though I’m not sure why. Examples of alternative hypotheses:
You just need more / stronger inhibitory connections in order to inhibit efficiently; smarter people have more of those. This is independent of cross-talk and myelination.
Something else about being smart means that brain areas with the wrong solution to the current task will, autonomously, more quickly go quiet or stop vying for attention.
So I don’t think this post really justifies “lack of myelination results in lower fluid intelligence likely through ephaptic cross-talk”, though you might know more that would justify it. For example, there might be counterevidence for other plausible explanations for why lower myelination causes these effects.
If ephaptic cross-talk is a / the main issue that myelination is about, then I think that’s somewhat bullish for my idea of “prosthetic connectivity”? I think that idea’s justification (metabolic investment in white matter / long range myelinated axons) is largely agnostic as to why that’s important, but if having very-low-noise connections is so crucial, prosthetic connectivity should be able to provide that. (Though there are major problems with brain interfaces in general; cf. https://www.lesswrong.com/posts/jTiSWHKAtnyA723LE/overview-of-strong-human-intelligence-amplification-methods#Brain_brain_electrical_interface_approaches )
Yes. We lack the technical capability to directly show that ephaptic signaling is causing the lower intelligence by, say, correlating ephaptic events in the deep brain with poorer performance. But I think we basically have the next best thing. It just seems logically incongruent that more signaling is done, that more alternatives are suggested, in a brain that is slower and which uses more energy to send a signal. The speed and energy efficiency functions of myelin seem to logically imply that more myelin means more suggestions that can be subsequently quickly cancelled, but we observe the opposite. Only the ephaptic protection properties of myelin logically explain the lower activity in more myelinated higher g individuals.
Perhaps I should have omitted mentioning these indications MS patients are not slower and their fatigue does not correlate with poor performance but I felt it was worth mentioning that there is at least some indication empirically that speed and energy are not the root cause, though these indications are far from conclusive in and of themselves.
Low IQ people are having additional activation during the task, particularly in frontal areas known to function as action inhibitors and conflict resolvers. Perhaps I should have used a longer quote:
Graham et al. 2010 page 3
Yes, but with the caveat that current methods of electrical stimulation are too imprecise (which I believe you mention in your recent post). Imprecision is exactly what we are trying to avoid.
That said: it recently occurred to me that a combination of genetic alteration and cybernetic implants could potentially have greater precision perhaps if we use light-activated neurons in the central nervous system. I wrote a post about a lab using this technique:
https://www.lesswrong.com/posts/kTpvsf3fAuMhzYKBT/on-columbia-university-s-superintelligent-cyborg-mice
Ok, this may be a bit of self-promotion, while Kellendonk’s lab does use mice genetically engineered to have optically activated neurons I don’t discuss that detail of his work in the post (it is about him trying to extend working memory by stimulating the thalamus). Please forgive me. The general technique is called optogenetics for any potential unfamiliar reader:
https://en.wikipedia.org/wiki/Optogenetics
Just a speculation, but we do use optics to make chips with a couple hundred times more precision than the width of even a smaller axon.
The extended quote sounds quite consistent with “the Average IQ participants took longer to learn which was the correct response, in the sense that the various generators of their alternative responses took longer to stop bidding for attention / decision-power”. Or no?
Yes. This is also the finding of Haier et al. (1992).
Ok. (To me that’s an alternative explanation for why the frontal cortex is working harder in Average IQ, as an alternative to “the frontal cortex has to work harder because there’s more cross-process interference due to more signal leakage”.)
I would point out that it is measures of fluid intelligence which drop in the de-myelinated MS patients, which suggests that this is not a matter of training nor remembering an already discovered pattern.
The neural efficiency hypothesis seems to have some more nuance based on later works as explained in Haier’s book The Neuroscience of Intelligence, 4.2 Functional Brain Efficiency: Does Imaging Show that Less Is More? (2nd edition, 2023). Below is an LLM summary of that part of the book:
LLM summary of 4.2 Functional Brain Efficiency: Does Imaging Show that Less Is More?
1. Task Complexity and Difficulty
Inconsistent activation patterns: Whether higher intelligence is associated with more or less brain activity depends on how difficult a task is.
The complexity threshold: Research suggests that for moderate tasks, individuals with higher IQs display lower neural activity (indicating higher efficiency). However, as task complexity increases, higher-IQ individuals show increased signal enhancement in executive areas (like frontal and parietal regions), whereas lower-IQ individuals show decreased activity.
2. Network Type (Task-Positive vs. Task-Negative)
Opposing network behaviors: Brain efficiency differs sharply depending on the type of network analyzed:
Task-positive networks (areas where activation increases during tasks): Higher intelligence is actually associated with less efficiency (more activation).
Task-negative networks (default mode networks where activation decreases during tasks): Higher intelligence is associated with greater efficiency (more effective deactivation or less intrusion).
Because of these opposing regional dynamics, whole-brain analyses often produce confusing or inconsistent results.
3. Sex as a Moderating Variable
Studies indicate that brain efficiency is moderated by sex. For example, in females, higher intelligence has been linked to greater activity in task-related brain areas when solving highly difficult problems, rather than the reduced activity predicted by a simple efficiency model.
4. Network Topology and Reconfiguration
Global vs. Node Efficiency: Global network efficiency (across the entire brain) does not consistently correlate with IQ. However, node efficiency—specifically in localized brain regions responsible for salience processing and filtering out irrelevant information—does correlate with higher intelligence.
Network Stability (Reconfiguration): Individuals with higher general intelligence (
5. Structural Connectivity and Processing Speed
White Matter Integrity: High-fluid-intelligence individuals tend to have stronger white matter structural connectivity (measured via DTI). This stronger structural foundation supports more efficient, faster, and more directed information processing.
Timing and Sequence: Using high-time-resolution tools like MEG, researchers study whether efficient brains use fewer active areas, or engage the same sequence of brain regions (such as the sensory-to-parietal-to-frontal path) at a much faster rate.
6. Cellular and Microstructural Organization
Recent advances at the cellular level reveal a nuanced, dual-nature explanation of efficiency:
Sparse, efficient wiring: Some research suggests that higher intelligence is associated with lower overall density and less branching (arborization) of dendrites across the cortex. This points to a sparser, more direct, and less noisy neural circuitry.
Larger, faster individual neurons: Conversely, other studies show that higher IQ is associated with larger, more complex pyramidal neurons (with larger dendritic trees) in specific regions like the temporal lobe. These larger cells are capable of sustaining faster action potentials and transferring information more efficiently.
Reconciliation: A thicker cortex in higher-IQ individuals does not necessarily contain more neurons, but rather a lower density of larger cells. This suggests that “lower density” on a macro scale can coexist with highly complex, efficient individual cells.
Summary
The brain efficiency hypothesis is not true in a literal, blanket sense of “less brain activity overall.” Instead, the current consensus is that neural efficiency is highly specific: it is characterized by sparser and more stable network wiring, faster individual cellular processing, and the ability to selectively recruit or quiet down specific brain regions depending on the demands of the task.
Key Papers