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Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces.
Pourmotabbed, Haatef; de Jongh Curry, Amy L; Clarke, Dave F; Tyler-Kabara, Elizabeth C; Babajani-Feremi, Abbas.
Afiliación
  • Pourmotabbed H; Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.
  • de Jongh Curry AL; Magnetoencephalography Laboratory, Dell Children's Medical Center, Austin, Texas, USA.
  • Clarke DF; Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA.
  • Tyler-Kabara EC; Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA.
  • Babajani-Feremi A; Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.
Hum Brain Mapp ; 43(4): 1342-1357, 2022 03.
Article en En | MEDLINE | ID: mdl-35019189
Prior studies have used graph analysis of resting-state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test-retest reliability and sensor versus source association of global graph measures. Atlas-based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage-corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Magnetoencefalografía / Conectoma / Red Nerviosa Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Hum Brain Mapp Asunto de la revista: CEREBRO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Magnetoencefalografía / Conectoma / Red Nerviosa Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Hum Brain Mapp Asunto de la revista: CEREBRO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos