Large and fast human pyramidal neurons associate with intelligence
- Vrije Universiteit Amsterdam, Netherlands
- University of Antwerp, Belgium
- Stichting Epilepsie Instellingen Nederland (SEIN), Netherlands
- VU Medical Center, Netherlands
Abstract
It is generally assumed that human intelligence relies on efficient processing by neurons in our brain. Although grey matter thickness and activity of temporal and frontal cortical areas correlate with IQ scores, no direct evidence exists that links structural and physiological properties of neurons to human intelligence. Here, we find that high IQ scores and large temporal cortical thickness associate with larger, more complex dendrites of human pyramidal neurons. We show in silico that larger dendritic trees enable pyramidal neurons to track activity of synaptic inputs with higher temporal precision, due to fast action potential kinetics. Indeed, we find that human pyramidal neurons of individuals with higher IQ scores sustain fast action potential kinetics during repeated firing. These findings provide the first evidence that human intelligence is associated with neuronal complexity, action potential kinetics and efficient information transfer from inputs to output within cortical neurons.
Data availability
Numerical data for fall figures are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.83dv5j7 (doi number 10.5061/dryad.83dv5j7).All customized Matlab scripts used for physiological data analysis are available at https://github.com/INF-Rene/Morphys
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Data from: Large and fast human pyramidal neurons associate with intelligenceDryad Digital Repository, 10.5061/dryad.83dv5j7.
Article and author information
Author details
Natalia A Goriounova
Funding
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VENI grant)
- Natalia A Goriounova
H2020 European Research Council (Human Brain Project)
- Huib D Mansvelder
Fonds Wetenschappelijk Onderzoek (G0F1517N)
- Michele Giugliano
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VICI grant)
- Huib D Mansvelder
H2020 European Research Council (ERC StG)
- Huib D Mansvelder
H2020 European Research Council (Human Brain Project)
- Michele Giugliano
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All procedures were performed with the approval of the Medical Ethical Committee of the VU University Medical Centre (2012/362), and in accordance with Dutch license procedures and the Declaration of Helsinki. Written informed consent was provided by all subjects for data and tissue use for scientific research. All data were anonymized.
Copyright
© 2018, Goriounova et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Large and fast human pyramidal neurons associate with intelligence
eLife 7:e41714.
https://doi.org/10.7554/eLife.41714
Further reading
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Larger neurons seem to lead to higher IQs.
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- Neuroscience
Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.
