Back

Human Connectomes Are Heritable

Chung, J.; Bridgeford, E. W.; Powell, M.; Vogelstein, J. T.

2023-04-03 neuroscience
10.1101/2023.04.02.532875 bioRxiv
Show abstract

A complete understanding of human behavior and disease depends upon our ability to parse genetic and environmental influences in the human brain. The heritability of a trait quantifies the degree of its variability due to genetic influences. Classical approach for quantifying heritability operate on simple traits, and sometimes do not properly control for other potential sources of variation, such as age or sex. We therefore develop Causal Heritability of Networks (CHaiN) to rigorously quantify heritability of human brain networks (i.e., connectomes). We applied CHaiN to 1024 anatomical connectomes derived from the Human Connectome Project. Connectomes appeared to be heritable, but heritability was insignificant once we addressed variability within networks. These results suggest that previous conclusions on connectome heritability may be driven by the shared network structures, and highlights the importance of modeling networks and other sources of variability when studying heritability of connectomes.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Network Neuroscience
● 126 papers in training set
Top 0.1%
32.4%
Similar papers in this journal
2
NeuroImage
○ 903 papers in training set
Top 0.5%
19.3%
Similar papers in this journal
50% of probability mass above
3
PLOS Computational Biology
● 1863 papers in training set
Top 3%
10.2%
Similar papers in this journal
4
PLOS ONE
● 5266 papers in training set
Top 29%
5.1%
Similar papers in this journal
5
Biostatistics
◐ 24 papers in training set
Top 0.1%
4.7%
Similar papers in this journal
6
Scientific Reports
○ 3612 papers in training set
Top 35%
2.9%
7
Human Brain Mapping
○ 329 papers in training set
Top 2%
2.9%
8
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 26%
1.8%
9
eLife
● 5828 papers in training set
Top 49%
1.8%
10
Nature Communications
○ 5641 papers in training set
Top 47%
1.5%
11
Cerebral Cortex
◐ 396 papers in training set
Top 4%
1.2%
12
Imaging Neuroscience
● 282 papers in training set
Top 3%
1.1%
13
Frontiers in Neuroscience
○ 256 papers in training set
Top 6%
0.9%
14
PNAS Nexus
◐ 159 papers in training set
Top 3%
0.9%
15
Communications Biology
○ 993 papers in training set
Top 28%
0.9%
16
The Journal of Neuroscience
● 1025 papers in training set
Top 10%
0.9%
17
eneuro
● 439 papers in training set
Top 8%
0.6%
18
Statistics in Medicine
○ 40 papers in training set
Top 0.7%
0.5%
19
PLOS Genetics
● 862 papers in training set
Top 14%
0.5%
20
Nature Computational Science
○ 55 papers in training set
Top 2%
0.5%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.