Back

Slow Cortical Waves through Cyclicity Analysis

Abraham, I.; Shahsavarani, B.; Zimmerman, B.; Husain, F.; baryshnikov, y.

2021-07-14 neuroscience
10.1101/2021.05.16.444387 bioRxiv
Show abstract

Fine-grained understanding of dynamics in cortical networks is crucial in unpacking brain function. Here, we introduce a novel analytical method to characterize the dynamic interaction between distant brain regions, and apply it to data from the Human Connectome Project. Resting-state fMRI results in time series recordings of the activity of different brain regions, which are aperiodic and lacking a base frequency. Cyclicity Analysis, a novel technique robust with respect to time-reparametrizations, is effective in recovering temporal ordering of such time series along a circular trajectory without assuming any time-scale. Our analysis detected slow cortical waves of activity propagating across the brain with consistent lead-lag relationships between specific brain regions. We also observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain. Our results suggest the possible role played by slow waves of information transmission between brain regions that underlie emergent cognitive function.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
NeuroImage
○ 903 papers in training set
Top 0.7%
18.3%
Similar papers in this journal
2
PLOS Computational Biology
● 1863 papers in training set
Top 3%
10.9%
Similar papers in this journal
3
Network Neuroscience
● 126 papers in training set
Top 0.2%
8.8%
Similar papers in this journal
4
PLOS ONE
● 5266 papers in training set
Top 19%
8.8%
Similar papers in this journal
5
Neural Computation
● 39 papers in training set
Top 0.1%
6.7%
Similar papers in this journal
50% of probability mass above
6
Scientific Reports
○ 3612 papers in training set
Top 26%
4.0%
7
Cognitive Neurodynamics
○ 18 papers in training set
Top 0.1%
2.7%
8
Frontiers in Neuroscience
○ 256 papers in training set
Top 2%
2.6%
9
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.2%
2.4%
10
Neurocomputing
○ 13 papers in training set
Top 0.1%
2.1%
11
Brain Topography
○ 29 papers in training set
Top 0.2%
1.7%
12
eneuro
● 439 papers in training set
Top 5%
1.4%
13
Human Brain Mapping
○ 329 papers in training set
Top 3%
1.3%
14
Royal Society Open Science
● 214 papers in training set
Top 5%
1.1%
15
Frontiers in Computational Neuroscience
○ 60 papers in training set
Top 0.9%
1.1%
16
Physical Review E
● 112 papers in training set
Top 1.0%
1.1%
17
eLife
● 5828 papers in training set
Top 59%
1.1%
18
Journal of Neuroscience Methods
○ 122 papers in training set
Top 1%
1.1%
19
Frontiers in Human Neuroscience
○ 77 papers in training set
Top 2%
1.0%
20
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.7%
1.0%
21
Neural Networks
○ 35 papers in training set
Top 0.5%
1.0%
22
The Journal of Neuroscience
● 1025 papers in training set
Top 9%
1.0%
23
Brain Structure and Function
○ 93 papers in training set
Top 1%
0.8%
24
Neuroinformatics
○ 46 papers in training set
Top 0.8%
0.8%
25
Entropy
○ 21 papers in training set
Top 0.3%
0.8%
26
Frontiers in Systems Neuroscience
○ 22 papers in training set
Top 0.3%
0.8%
27
Imaging Neuroscience
● 282 papers in training set
Top 4%
0.8%
28
Journal of Neural Engineering
○ 221 papers in training set
Top 2%
0.8%
29
IEEE Transactions on Neural Systems and Rehabilitation Engineering
● 49 papers in training set
Top 1%
0.6%
30
Medical Image Analysis
○ 35 papers in training set
Top 0.8%
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.