Stable brain PET metabolic networks using a multiple sampling scheme

Interregional communication within the human brain is essential for maintaining functional integrity. A promising approach for investigating how brain regions communicate relies on the assumption that the brain operates as a complex network. In this context, positron emission tomography (PET) images have been suggested as a valuable source for understanding brain networks. However, such networks are typically assembled through direct computation without accounting for outliers, impacting the reliability of group representative networks. In this study, we used brain [<sup>18</sup>F]fluoro-2-deoxyglucose PET data from 1,227 individuals in the Alzheimer's disease (AD) continuum from the Alzheimer's Disease Neuroimaging Initiative cohort to develop a novel method for constructing stable metabolic brain networks that are resilient to spurious data points. Our multiple sampling scheme generates brain networks with greater stability compared with conventional approaches. The proposed method is robust to imbalanced datasets and requires 50% fewer subjects to achieve stability than the conventional method. We further validated the approach in an independent AD cohort (<i>n</i> = 114) from São Paulo, Brazil (Faculdade de Medicina da Universidade de São Paulo). This innovative method is flexible and improves the robustness of metabolic brain network analyses, supporting better insights into brain connectivity and resilience to data variability across multiple radiotracers for both health and disease.

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Publication Details

Journal
Network Neuroscience
Published
2025-01-01
DOI
https://doi.org/10.1162/netn.a.23
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Stable brain PET metabolic networks using a multiple sampling scheme

Guilherme Schu, Tharick Ali Pascoal, Daniele de Paula Faria, Artur Martins Coutinho et al.
Network Neuroscience
Medical Imaging Techniques and Applications
article

Stable brain PET metabolic networks using a multiple sampling scheme

Guilherme Schu, Tharick Ali Pascoal, Daniele de Paula Faria, Artur Martins Coutinho, Jorge Almeida, Christian Limberger, Wagner S. Brum, Julio Cesar de Azeredo, Yuri Elias Rodrigues, Sulantha Mathotaarachchi, Marco Antônio De Bastiani, Geraldo F. Busatto, Andréa Lessa Benedet, Fábio Duran, Carlos Alberto Buchpiguel, Pedro Rosa‐Neto, Eduardo R. Zimmer
article en

Abstract

Interregional communication within the human brain is essential for maintaining functional integrity. A promising approach for investigating how brain regions communicate relies on the assumption that the brain operates as a complex network. In this context, positron emission tomography (PET) images have been suggested as a valuable source for understanding brain networks. However, such networks are typically assembled through direct computation without accounting for outliers, impacting the reliability of group representative networks. In this study, we used brain [<sup>18</sup>F]fluoro-2-deoxyglucose PET data from 1,227 individuals in the Alzheimer's disease (AD) continuum from the Alzheimer's Disease Neuroimaging Initiative cohort to develop a novel method for constructing stable metabolic brain networks that are resilient to spurious data points. Our multiple sampling scheme generates brain networks with greater stability compared with conventional approaches. The proposed method is robust to imbalanced datasets and requires 50% fewer subjects to achieve stability than the conventional method. We further validated the approach in an independent AD cohort (<i>n</i> = 114) from São Paulo, Brazil (Faculdade de Medicina da Universidade de São Paulo). This innovative method is flexible and improves the robustness of metabolic brain network analyses, supporting better insights into brain connectivity and resilience to data variability across multiple radiotracers for both health and disease.

Network NeuroscienceVol. 9(3)
Montreal Neurological Institute and Hospital (CA), Universidade Federal do Rio Grande do Sul (BR), University of Pittsburgh (US), Universidade de São Paulo (BR), University of Strathclyde (GB), Douglas Mental Health University Institute (CA), Instituto do Cérebro (BR), Sociedade Brasileira de Neurociências e Comportamento (BR), Centrus Diagnósticos por Imagem (BR), Southwestern Medical Center (US), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (BR), Hospital Sírio-Libanês (BR), McGill University (CA), École Polytechnique Fédérale de Lausanne (CH), Idiap Research Institute (CH), University of Coimbra (PT), The University of Texas Southwestern Medical Center (US), University of Gothenburg (SE)
Alzheimer's Association, Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, Instituto Serrapilheira
Openalex Percentile: Top 79%
Medical Imaging Techniques and Applications
DOIOpen Access PDFOpenAlex
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