Author

Artur Martins Coutinho

Universidade de São PauloORCID 0000-0002-5555-4583Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Organism:human· 2 Assay:imaging· 2 Approach:statistical model· 2

Frequent Co-Authors

Eduardo Rigon ZimmeriD· 2 shared Marco Antônio De BastianiiD· 2 shared Alzheimer’s Disease Neuroimaging Initiative· 1 shared Andréa Lessa BenedetiD· 1 shared Andrei BiegeriD· 1 shared Carlos Alberto BuchpigueliD· 1 shared Christian LimbergeriD· 1 shared Daniele de Paula FariaiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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2 shared papersNeuroscience

Eduardo Rigon ZimmerORCID

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2 papers

Recent·Most cited

Sources of Variability in Normative Cerebral FDG-PET imaging

Giordana Salvi de Souza, Guilherme Povala, Guilherme G. Schu Peixoto, Artur Martins Coutinho, Andrei Bieger, Mateus Rozalem Aranha, Marco Antônio De Bastiani, Eduardo Rigon Zimmer, Wyllians Vendramini Borelli, Laura Willers Souza

The paper investigates variability in FDG-PET imaging due to scanner manufacturer, reference region, age, and sex, and generates covariate-adjusted normative data.

bioRxiv · Aug 5, 2026 · PreprintNeuroscience

Stable brain PET metabolic networks using a multiple sampling scheme

Guilherme Garcia Schu, Christian Limberger, Wagner Scheeren Brum, Marco Antônio De Bastiani, Yuri Elias Rodrigues, Julio Cesar de Azeredo, Tharick Ali Pascoal, Andréa Lessa Benedet, Sulantha Sanjeewa Mathotaarachchi, Pedro Rosa‐Neto, Jorge Almeida, Daniele de Paula Faria, Fábio Duran, Carlos Alberto Buchpiguel, Artur Martins Coutinho, , Alzheimer’s Disease Neuroimaging Initiative

This paper develops a multiple sampling scheme to construct stable brain metabolic networks from PET data, showing improved stability and reduced subject requirements compared to conventional methods.

Network Neuroscience · Jan 1, 2025Peer-reviewedNeuroscience