The Human Connectome: A Structural Description of the Human Brain
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TLDR
A research strategy to achieve the connection matrix of the human brain (the human “connectome”) is proposed, and its potential impact is discussed.Abstract:
The connection matrix of the human brain (the human “connectome”) represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.read more
Citations
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Weight Consistency Specifies Regularities of Macaque Cortical Networks
Nikola T. Markov,Nikola T. Markov,P. Misery,P. Misery,Arnaud Falchier,Arnaud Falchier,Camille Lamy,Camille Lamy,Julien Vezoli,Julien Vezoli,René Quilodran,René Quilodran,Marie-Alice Gariel,Marie-Alice Gariel,Pascale Giroud,Pascale Giroud,Mária Ercsey-Ravasz,Louis-Jan Pilaz,Louis-Jan Pilaz,C. Huissoud,C. Huissoud,Pascal Barone,Pascal Barone,Colette Dehay,Colette Dehay,Zoltán Toroczkai,D. C. Van Essen,Henry Kennedy,Henry Kennedy,Kenneth Knoblauch,Kenneth Knoblauch +30 more
TL;DR: Results indicate that connection weight heterogeneity plays an important role in determining cortical network specificity, high investment in local projections highlights the importance of local processing, and transmission of information across multiple hierarchy levels mainly involves pathways having low FLN values.
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Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders.
TL;DR: This work reviews methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based healthy network dynamics and how whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales.
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References
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