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Generative models of the human connectome

2015· article· en· 343 citations· W2182367256 on OpenAlex· 10.1016/j.neuroimage.2015.09.041

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.160
GPT teacher head0.308
Teacher spread
0.148 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

The record

Venue
NeuroImage
Topic
Functional Brain Connectivity Studies
Field
Neuroscience
Canadian institutions
Funders
Division of Graduate EducationNational Institute of Mental HealthNIH Blueprint for Neuroscience ResearchNational Institute on AgingNational Key Research and Development Program of ChinaNatural Sciences and Engineering Research Council of CanadaMedical Research CouncilMcDonnell Center for Systems NeuroscienceFondation LeenaardsNational Institute for Health and Care ResearchNederlandse Organisatie voor Wetenschappelijk OnderzoekWellcome TrustSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Institutes of HealthNational Science FoundationJames S. McDonnell Foundation
Keywords
ConnectomeGenerative grammarHuman Connectome ProjectGenerative modelComputer scienceTopology (electrical circuits)Function (biology)Artificial intelligenceMachine learningNeuroscienceFunctional connectivityMathematicsPsychologyBiologyEvolutionary biology
Has abstract in OpenAlex
yes