Brain Wiring and Life Trajectory

Brain Connections and Destiny

Smith and his colleagues ran a massive computer analysis to look at how these traits varied among the volunteers, and how the traits correlated with different brain connectivity patterns. The team was surprised to find a single, stark difference in the way brains were connected. People with more ‘positive’ variables, such as more education, better physical endurance and above-average performance on memory tests, shared the same patterns. Their brains seemed to be more strongly connected than those of people with ‘negative’ traits such as smoking, aggressive behaviour or a family history of alcohol abuse. __ http://www.nature.com/news/wiring-diagrams-link-lifestyle-to-brain-function-1.18442


The research discussed above shows the potential to improve on current methods of measuring brain intelligence, and of monitoring the effects of various drugs — such as marijuana, Adderall, Ritalin, alcohol, etc. — on the adolescent and adult brains. The method also holds promise for the monitoring of brain rehabilitation after stroke, traumatic brain injury, and after treatment for various brain disorders. In fact, scientists have barely scratched the surface of what this approach to brain imaging will be able to do.

Human Connectome Project

Human Connectome Project


The human brain connectome is the object of very active study. In the beginning, the Human Connectome Project had more limited goals:

This project is presently working to achieve the following goals: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials. ___ http://www.humanconnectomeproject.org/about/

But after many early successes, the goals of human brain connectome research are becoming more ambitious.

We must always remember that the development of brain connections is critically dependent upon a person’s genome, epigenome, and experiential environment from before conception onward. The genetics of brain development is quite complex. Rather subtle genetic and epigenetic alterations appear to make an incredible difference between the brain development of different animal species. Similar subtle differences are likely to underlie the human biodiversity of intelligence separating both population group averages, and individuals.

Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods—such as genome-wide association studies (GWAS), linkage and candidate gene studies—that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. Studies that emphasized the genetic influences on brain connectivity. Some of these analyses of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of the genomic and network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity. __ https://www.sciencedirect.com/science/article/pii/S1053811913005077

Understanding the genetic bases of the human brain connectome is in the very earlist stages.

The mechanisms by which experiences work through gene expression to affect brain development are likewise complex.

Images from the Nature Neurosciences study discussed above:

(a) The 30 brain connections most strongly associated with the CCA mode of population variability. To aid interpretation, the CCA edge modulation weights are multiplied by the sign of the population mean correlation; hence red indicates stronger connections and blue weaker, for high-scoring subjects (and vice versa for low-scoring subjects). (b) Map of CCA connection strength increases (each node's parcel map is weighted by CCA edge-strength increases, summed across edges involving the node). (c) Group-mean functional clustering: four clusters from a hierarchical analysis of all 200 nodes' population-average full correlation (Supplementary Fig. 3). These fall into two groups: one cluster (blue) contains sensory, motor, insula and dorsal attention regions, and a group of three correlated clusters (brown, red, yellow) primarily covering the default mode network and subcortical/cerebellar regions. (d) Data presented as in b, but showing CCA connection strength decreases. Maps in d and b are largely non-overlapping except in insula. Map in b has spatial correlation of +0.40 with the default-mode areas shown in c (that is, high overlap), whereas the map in d has negative correlation (−0.12). The average connectivity strength increase was approximately double that of the average decrease (as reflected in the predominance of red edges in a; also, a single map averaging across all 200 edges for each node showed a pattern of overall increase highly similar to that in b; finally, both the maps in b and d were thresholded at the 80th percentile of their respective distributions, and if the threshold applied to b were applied to d, none of the strength reductions shown would survive). http://www.nature.com/neuro/journal/vaop/ncurrent/fig_tab/nn.4125_F2.html

(a) The 30 brain connections most strongly associated with the CCA mode of population variability. To aid interpretation, the CCA edge modulation weights are multiplied by the sign of the population mean correlation; hence red indicates stronger connections and blue weaker, for high-scoring subjects (and vice versa for low-scoring subjects). (b) Map of CCA connection strength increases (each node’s parcel map is weighted by CCA edge-strength increases, summed across edges involving the node). (c) Group-mean functional clustering: four clusters from a hierarchical analysis of all 200 nodes’ population-average full correlation (Supplementary Fig. 3). These fall into two groups: one cluster (blue) contains sensory, motor, insula and dorsal attention regions, and a group of three correlated clusters (brown, red, yellow) primarily covering the default mode network and subcortical/cerebellar regions. (d) Data presented as in b, but showing CCA connection strength decreases. Maps in d and b are largely non-overlapping except in insula. Map in b has spatial correlation of +0.40 with the default-mode areas shown in c (that is, high overlap), whereas the map in d has negative correlation (−0.12). The average connectivity strength increase was approximately double that of the average decrease (as reflected in the predominance of red edges in a; also, a single map averaging across all 200 edges for each node showed a pattern of overall increase highly similar to that in b; finally, both the maps in b and d were thresholded at the 80th percentile of their respective distributions, and if the threshold applied to b were applied to d, none of the strength reductions shown would survive).
http://www.nature.com/neuro/journal/vaop/ncurrent/fig_tab/nn.4125_F2.html

More information on methods:

(a) The set of SMs most strongly associated with the CCA mode of population variability. SMs included in the CCA are colored blue, whereas others (gray) were correlated with the CCA mode post hoc. Vertical position is according to correlation with the CCA mode and font size indicates SM variance explained by the CCA mode. We do not report 'secondary' SMs that are highly redundant with those shown here (Supplementary Table 1 shows the complete set of SMs that correlate highly with the CCA mode). See https://wiki.humanconnectome.org/display/PublicData/HCP+Data+Dictionary+Public-+500+Subject+Release for details of the SMs. (b) The principal CCA mode, a scatter plot of SM weights versus connectome weights, with one point per subject, and an example subject measure (fluid intelligence) indicated with different colors. The high correlation visualized here indicates significant co-variation between the two data sets. (c) The total variance explained of the original data matrices (shown separately for connectomes and subject measures) is plotted for the first 20 CCA modes. The mean and the 5th to 95th percentiles of the null distribution of the same measures, estimated via permutation testing, are shown in black and gray. Using the null distributions to normalize variance explained accounts for the fact that the initial modes are expected to have higher correlations, even in the null scenario, but, as can be seen from the nulls, this is a very small effect in any case. http://www.nature.com/neuro/journal/vaop/ncurrent/fig_tab/nn.4125_F1.html

(a) The set of SMs most strongly associated with the CCA mode of population variability. SMs included in the CCA are colored blue, whereas others (gray) were correlated with the CCA mode post hoc. Vertical position is according to correlation with the CCA mode and font size indicates SM variance explained by the CCA mode. We do not report ‘secondary’ SMs that are highly redundant with those shown here (Supplementary Table 1 shows the complete set of SMs that correlate highly with the CCA mode). See https://wiki.humanconnectome.org/display/PublicData/HCP+Data+Dictionary+Public-+500+Subject+Release for details of the SMs. (b) The principal CCA mode, a scatter plot of SM weights versus connectome weights, with one point per subject, and an example subject measure (fluid intelligence) indicated with different colors. The high correlation visualized here indicates significant co-variation between the two data sets. (c) The total variance explained of the original data matrices (shown separately for connectomes and subject measures) is plotted for the first 20 CCA modes. The mean and the 5th to 95th percentiles of the null distribution of the same measures, estimated via permutation testing, are shown in black and gray. Using the null distributions to normalize variance explained accounts for the fact that the initial modes are expected to have higher correlations, even in the null scenario, but, as can be seen from the nulls, this is a very small effect in any case.
http://www.nature.com/neuro/journal/vaop/ncurrent/fig_tab/nn.4125_F1.html

Although it is not a politically correct topic at this time, the type of research mentioned above should help us to better understand why some people are more intelligent than others, and why some population groups have higher IQ test scores and higher levels of personal and societal achievement.

Economic Success Correlates With National Average IQ More at VDare

Economic Success Correlates With National Average IQ
More at VDare

Solving this problem (the coming dysgenic Idiocracy) is far more urgent to the human future than the ginned up crises of climate apocalypse, resource scarcity Armageddon, overpopulation catastrophe, and all the other faux calamities that our overlords want us to panic over.

The human future revolves around the human brain — the problems it can solve, and the opportunities it can exploit. Many governments of the modern world are growing more and more obstructive to an expansive and prosperous human future. We are going to need a lot more guillotines! 😉

Free Online Neuroscience courses These free online video course may help many readers to understand more of the concepts presented on brain research, intelligence studies, and other neuroscience and psychology related topics.

Seminal Cognitive Science and Neuroscience videos These videos include talks by some of the great neuro researchers — some of whom have since died. Check them out.

If we do not gain a better understanding of human brains — our own and those of others — we will be more vulnerable to being misled by those who would be our overlords.

Hope for the best. Prepare for the worst. Best become resilient and Dangerous in mind, body, and spirit.

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