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Genc Figure_4 fewer connections

The ISIR July 2017 meeting in Montreal seems a long time ago, and that feeling is entirely explicable by it being 10 months since I heard the lecture in question. I was chairing the session, which normally diminishes attention to the actual content, but this talk was the exception. It came up with a counter-intuitive finding, and it has been difficult to avoid talking about it. Brighter brains have fewer connections between neurones. Cool.

It has been a real struggle to keep quiet about this remarkable result, and a relief that the embargo has been lifted today, 14 months after receipt of the paper by the publishers. Publish and be damn delayed. Blogging is the future.

As you will see from the author list, particularly the last author, this is a team which has been working on this topic for decades, (with important results from at least 1988) and has always sought to have reliable measures and large sample sizes before publishing anything. In ISIR 2014, tired of reading neuro-bollocks in the media, I lobbed Rex Jung what I thought might be a tricky question: How reliable are your neuro-imaging measures? He replied that he and Rich Haier had always put their subjects into the scanner twice: once briefly so as to get benchmark reliability measures, and then again for the full session. Jung and Haier also held back from publication until they had large sample sizes, although in early years this meant a long wait, since they were mostly working in the odd free spaces between the high priority medical school clinical use of the sole scanner available. Things have got better in recent years.

Another feature of this duo is that when they were offered an celebratory session at ISIR 2017 they chose to invite their critics to knock hell into them. Several did, and I pursue them every now and then to make their P-FIT theory more specific. So, it is great to be able to report some new and very specific findings.

Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Erhan Genç, Christoph Fraenz, Caroline Schlüter, Patrick Friedrich, Rüdiger Hossiep, Manuel C. Voelkle, Josef M. Ling, Onur Güntürkün & Rex E. Jung

Nature Communications volume 9, Article number: 1905 (2018)

The first two authors contributed equally. Take a good look at their reference list, which is a roll-call of the top people in the field, and those one should turn to for further comments on this paper and its implications.

Here is the main finding in full screen size, with the relevant explanations.

Here is the link to the entire paper:

Here is the abstract:

Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning.

“Intelligence is not a function of how hard the brain works but rather how efficiently it works”.

In terms of method, the team collected 259 participants (138 males) between 18 and 40 years of age (M = 24.31, SD = 4.41) which gives the analysis of results sufficient power. Participants had no history of psychiatric or neurological disorders and matched the standard inclusion criteria for fMRI examinations. Each participant completed the matrix-reasoning test and neuroimaging measurements.
To validate the results obtained from sample of 259 subjects, the team downloaded additional data provided by the Human Connectome Project, namely, the “S500 plus MEG2” release. This set includes 506 participants with data suitable for their analyses. The best papers now give what would formerly have been two papers, for the price of one. The first sample is the sample of discovery, the second the sample of validation. Some things in science are getting better.

The measures themselves are a new variant of diffusion imaging analysis. If you will forgive a simplistic analysis: a pipe full of water will show different measures if measured end-on (where all the water in the pipe vibrates with the imposed resonance) as compared to when measured at right angles to the pipe (where only a small amount of water is available for resonance to be detected). In this way you can deduce which way the dendrites run in the brain.

Currently, the most promising technique for the quantification of neurite morphology is a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). This technique is based on a multi-shell high-angular-resolution diffusion imaging protocol and offers a novel way to analyze diffusion-weighted data with regard to tissue microstructure. It features a three-compartment model distinguishing intra-neurite, extra-neurite, and cerebrospinal fluid (CSF) environments. NODDI is based on a diffusion model that was successfully validated by histological examinations utilizing staining methods in gray and white matter of rats and ferrets. In addition, Zhang, Schneider have shown that NODDI is also capable of estimating diffusion markers of neurite density and orientation dispersion by in vivo measurements in humans. Direct validation of NODDI has recently been performed in a study investigating neurite dispersion as a potential marker of multiple sclerosis pathology in post-mortem spinal cord specimens. The authors reported that neurite density obtained from NODDI significantly matched neurite density, orientation dispersion, and myelin density obtained from histology. Furthermore, the authors also found that NODDI neurite dispersion matched the histological neurite dispersion. This indicates that NODDI metrics are closely reflecting their histological conditions.

The point is that this study confirms previous findings, that “measures of neurite density and arborization show negative relationships to measures of intelligence, implicating neural efficiency, particularly within parieto-frontal brain regions, as suggested by the vast majority of neuroimaging studies of intelligence”.

The study also provides a partial confirmation of the P-FIT theory, in that a majority of the observed associations between brain areas and intelligence conform to the predictions from P-FIT as proposed by Haier and Jung, or as further elaborated by Basten. The score could be called a 4 out of 5 area confirmation.

Our results indicate that neurite density and neurite orientation dispersion within the cortex are both negatively associated with intelligence. At first glance, this finding might appear counterintuitive to the central working hypothesis of differential neuroscience, which usually finds that “bigger is better” (i.e., more neuronal mass is associated with higher ability levels). However, our results conform well to findings on the mechanisms of maturation-induced and learning-induced synaptic plasticity.

Brain maturation is associated with a sharp increase of synapse number, followed by a massive activity-dependent synaptic pruning that reduces synaptic density by half, thereby enabling the establishment of typical mature cortical microarchitecture. Maturation-associated synaptic pruning is not only an event of early childhood, but proceeds at a rapid rate at least until the end of the second decade of life. Most importantly, the mechanisms of synaptic growth and pruning during maturation overlap with those of learning in the mature brain.

Consequently, diverse learning tasks are associated with simultaneous growth and retraction of dendritic and synaptic processes in involved neural zones. Microstructural studies with confocal imaging on organotypic brain cultures reveal that long-term potentiation initially induces synaptic growth, followed by an increased loss of connections within 10% of non-stimulated hippocampal spines. Thus, both the ability to produce and prune neural connections constitutes the neurobiological foundation of learning and cognition.

The authors say:

First, our findings confirm an important observation from previous research, namely, that bigger brains with a higher number of neurons are associated with higher intelligence.

Second, we demonstrate that higher intelligence is associated with cortical mantles with sparsely and well-organized dendritic arbor, thereby increasing processing speed and network efficiency.
Importantly, the findings obtained from our experimental sample were confirmed by the analysis of an independent validation sample from the Human Connectome Project. This replication of results is particularly striking given that both data sets are very different on many levels. For example, two different cognitive tests were used in order to measure intelligence, i.e., BOMAT and PMAT24. Both of them are culture-fair matrix-reasoning instruments capable of assessing the construct of fluid intelligence. Nevertheless, both tests tend to produce different results when testing individuals from high-IQ ranges. This might be attributed to the fact that BOMAT, in contrast to PMAT24 and other matrix-reasoning tests, was deliberately designed to avoid ceiling effects in very intelligent samples such as university students or high potentials.
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Both data sets indicate that intelligence is associated with neurite density and orientation dispersion. Equally important, both data sets also show that this association points into a negative direction. This general pattern is clearly visible in both data sets. Moreover, one has to acknowledge that most of the statistically significant cortical areas, despite lacking a perfect match between data sets, show an impressive overlap with regions previously identified as belonging to the P-FIT network (about 70%).

Finally, to the best of our knowledge, these results are the first to offer a neuroanatomical explanation underlying the neural efficiency hypothesis of intelligence.

Higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning.

Let me repeat what I told Erhan Genc at the end of his presentation in July 2017.

“I think that this is a major finding”. It pushes the boundary of what we know about brainpower.

• Category: Science • Tags: Brain Scans, Brighter Brains, Intelligence, IQ 
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