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Mutual connectivity analysis of resting-state functional MRI data with local models.
DSouza, Adora M; Abidin, Anas Z; Chockanathan, Udaysankar; Schifitto, Giovanni; Wismüller, Axel.
Afiliação
  • DSouza AM; Department of Electrical Engineering, University of Rochester, Rochester, NY, USA. Electronic address: adora.dsouza@rochester.edu.
  • Abidin AZ; Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA.
  • Chockanathan U; Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, NY, USA.
  • Schifitto G; Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, NY, USA.
  • Wismüller A; Department of Electrical Engineering, University of Rochester, Rochester, NY, USA; Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, NY, USA; Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maxi
Neuroimage ; 178: 210-223, 2018 09.
Article em En | MEDLINE | ID: mdl-29777828
ABSTRACT
Functional connectivity analysis of functional MRI (fMRI) can represent brain networks and reveal insights into interactions amongst different brain regions. However, most connectivity analysis approaches adopted in practice are linear and non-directional. In this paper, we demonstrate the advantage of a data-driven, directed connectivity analysis approach called Mutual Connectivity Analysis using Local Models (MCA-LM) that approximates connectivity by modeling nonlinear dependencies of signal interaction, over more conventionally used approaches, such as Pearson's and partial correlation, Patel's conditional dependence measures, etcetera. We demonstrate on realistic simulations of fMRI data that, at long sampling intervals, i.e. high repetition time (TR) of fMRI signals, MCA-LM performs better than or comparable to correlation-based methods and Patel's measures. However, at fast image acquisition rates corresponding to low TR, MCA-LM significantly outperforms these methods. This insight is particularly useful in the light of recent advances in fast fMRI acquisition techniques. Methods that can capture the complex dynamics of brain activity, such as MCA-LM, should be adopted to extract as much information as possible from the improved representation. Furthermore, MCA-LM works very well for simulations generated at weak neuronal interaction strengths, and simulations modeling inhibitory and excitatory connections as it disentangles the two opposing effects between pairs of regions/voxels. Additionally, we demonstrate that MCA-LM is capable of capturing meaningful directed connectivity on experimental fMRI data. Such results suggest that it introduces sufficient complexity into modeling fMRI time-series interactions that simple, linear approaches cannot, while being data-driven, computationally practical and easy to use. In conclusion, MCA-LM can provide valuable insights towards better understanding brain activity.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Encéfalo / Imageamento por Ressonância Magnética / Conectoma / Acoplamento Neurovascular / Modelos Teóricos Tipo de estudo: Prognostic_studies Limite: Adult / Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Encéfalo / Imageamento por Ressonância Magnética / Conectoma / Acoplamento Neurovascular / Modelos Teóricos Tipo de estudo: Prognostic_studies Limite: Adult / Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article