Back

Controllability analysis of macaque structural connectome from an edge centric perspective

Dey, S.; Bharti, E.; Deng, Z.-D.

2025-03-10 neuroscience
10.1101/2025.03.07.642125 bioRxiv
Show abstract

In this paper, we investigate the edge controllability properties of the macaque structural connectome, which is reconstructed using optimal tractography parameters. We derive the expression of edge modal controllability and edge average controllability, providing a mathematical framework to analyze their roles from a network systems perspective. Further, we establish the relationship between the two controllability measures, providing insights into their functional implications. We also identify the top edges with the highest average controllability values, which may be critical in facilitating state transitions within the macaque brain network. These findings may have implications for neurostimulation interventions.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Network Neuroscience
● 126 papers in training set
Top 0.1%
12.5%
Similar papers in this journal
2
NeuroImage
○ 903 papers in training set
Top 2%
9.8%
Similar papers in this journal
3
PLOS Computational Biology
● 1863 papers in training set
Top 5%
6.7%
Similar papers in this journal
4
PLOS ONE
● 5266 papers in training set
Top 24%
6.7%
Similar papers in this journal
5
Journal of Neural Engineering
○ 221 papers in training set
Top 0.6%
6.3%
Similar papers in this journal
6
Frontiers in Computational Neuroscience
○ 60 papers in training set
Top 0.3%
5.2%
7
Scientific Reports
○ 3612 papers in training set
Top 22%
4.3%
50% of probability mass above
8
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.2%
3.2%
9
Neurocomputing
○ 13 papers in training set
Top 0.1%
3.2%
10
Neuroinformatics
○ 46 papers in training set
Top 0.2%
3.2%
11
IEEE Journal of Biomedical and Health Informatics
● 37 papers in training set
Top 0.4%
2.4%
12
IEEE Transactions on Neural Systems and Rehabilitation Engineering
● 49 papers in training set
Top 0.5%
2.1%
13
Cognitive Neurodynamics
○ 18 papers in training set
Top 0.2%
1.7%
14
Brain Sciences
○ 55 papers in training set
Top 0.7%
1.7%
15
Frontiers in Neuroscience
○ 256 papers in training set
Top 4%
1.5%
16
Neuroscience Letters
○ 32 papers in training set
Top 0.2%
1.4%
17
IEEE Access
● 35 papers in training set
Top 0.8%
1.3%
18
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.4%
1.3%
19
Human Brain Mapping
○ 329 papers in training set
Top 3%
1.3%
20
Neural Computation
● 39 papers in training set
Top 0.6%
1.1%
21
IEEE Transactions on Biomedical Engineering
● 40 papers in training set
Top 0.8%
1.1%
22
Frontiers in Human Neuroscience
○ 77 papers in training set
Top 1%
1.1%
23
Entropy
○ 21 papers in training set
Top 0.2%
1.1%
24
Brain Structure and Function
○ 93 papers in training set
Top 1%
1.0%
25
Frontiers in Systems Neuroscience
○ 22 papers in training set
Top 0.3%
0.8%
26
Brain Topography
○ 29 papers in training set
Top 0.5%
0.8%
27
Computers in Biology and Medicine
○ 128 papers in training set
Top 5%
0.6%
28
Chaos: An Interdisciplinary Journal of Nonlinear Science
● 17 papers in training set
Top 0.4%
0.6%
29
Journal of Neuroscience Methods
○ 122 papers in training set
Top 2%
0.6%
30
Physical Review E
● 112 papers in training set
Top 1%
0.6%

"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.