Network communication models improve the behavioral and functional predictive utility of the human structural connectome
The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-functio...
Saved in:
| Published in: | Network neuroscience (Cambridge, Mass.) Vol. 4; no. 4; pp. 980 - 1006 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
One Rogers Street, Cambridge, MA 02142-1209, USA
MIT Press
01.11.2020
MIT Press Journals, The The MIT Press |
| Subjects: | |
| ISSN: | 2472-1751, 2472-1751 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The connectome provides the structural substrate facilitating communication
between brain regions. We aimed to establish whether accounting for polysynaptic
communication in structural connectomes would improve prediction of
interindividual variation in behavior as well as increase structure-function
coupling strength. Connectomes were mapped for 889 healthy adults participating
in the Human Connectome Project. To account for polysynaptic signaling,
connectomes were transformed into communication matrices for each of 15
different network communication models. Communication matrices were (a) used to
perform predictions of five data-driven behavioral dimensions and (b) correlated
to resting-state functional connectivity (FC). While FC was the most accurate
predictor of behavior, communication models, in particular communicability and
navigation, improved the performance of structural connectomes. Communication
also strengthened structure-function coupling, with the navigation and shortest
paths models leading to 35–65% increases in association strength
with FC. We combined behavioral and functional results into a single ranking
that provides insight into which communication models may more faithfully
recapitulate underlying neural signaling patterns. Comparing results across
multiple connectome mapping pipelines suggested that modeling polysynaptic
communication is particularly beneficial in sparse high-resolution connectomes.
We conclude that network communication models can augment the functional and
behavioral predictive utility of the human structural connectome.
Brain network communication models aim to describe the patterns of large-scale
neural signaling that facilitate functional interactions between brain regions.
While information can be directly communicated between anatomically connected
regions, signaling between disconnected areas must occur via a sequence of
intermediate regions. We investigated a number of candidate models of connectome
communication and found that they improved structure-function coupling and the
extent to which structural connectomes can predict interindividual variation in
behavior. Comparing the behavioral and functional predictive utility of
different models provided initial insight into which conceptualizations of
network communication may more faithfully recapitulate biological neural
signaling. Our results suggest network communication models as a promising
avenue to unite our understanding of brain structure, brain function, and human
behavior. |
|---|---|
| Bibliography: | November, 2020 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Handling Editor: Andrea Avena-Koenigsberger Competing Interests: The authors have declared that no competing interests exist. |
| ISSN: | 2472-1751 2472-1751 |
| DOI: | 10.1162/netn_a_00161 |