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Neuroimage. 2016 Jan 1;124(Pt A):1054-1064. doi: 10.1016/j.neuroimage.2015.09.041. Epub 2015 Sep 30.

Generative models of the human connectome.

Author information

1
Indiana University, Psychological and Brain Sciences, Bloomington IN, 47405, USA.
2
Indiana University, Psychological and Brain Sciences, Bloomington IN, 47405, USA; Indiana University, Network Science Institute, Bloomington IN 47405, USA.
3
Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
4
Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.
5
Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland; Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
6
Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK.
7
Indiana University, Psychological and Brain Sciences, Bloomington IN, 47405, USA; Indiana University, Network Science Institute, Bloomington IN 47405, USA. Electronic address: osporns@indiana.edu.

Abstract

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.

KEYWORDS:

Connectome; Generative models; Graph theory

PMID:
26427642
PMCID:
PMC4655950
DOI:
10.1016/j.neuroimage.2015.09.041
[Indexed for MEDLINE]
Free PMC Article

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