scholarly journals Gauging Functional Brain Activity: From Distinguishability to Accessibility

2019 ◽  
Vol 10 ◽  
Author(s):  
David Papo
2020 ◽  
Author(s):  
Nikhil Goyal ◽  
Dustin Moraczewski ◽  
Peter Bandettini ◽  
Emily S. Finn ◽  
Adam Thomas

AbstractUnderstanding brain functionality and predicting human behavior based on functional brain activity is a major goal of neuroscience. Numerous studies have been conducted to investigate the relationship between functional brain activity and attention, subject characteristics, autism, psychiatric disorders, and more. By modeling brain activity data as networks, researchers can leverage the mathematical tools of graph and network theory to probe these relationships. In their landmark study, Smith et al. (2015) analyzed the relationship of young adult connectomes and subject measures, using data from the Human Connectome Project (HCP). Using canonical correlation analysis (CCA), Smith et al. found that there was a single prominent CCA mode which explained a statistically significant percentage of the observed variance in connectomes and subject measures. They also found a strong positive correlation of 0.87 between the primary CCA mode connectome and subject measure weights. In this study, we computationally replicate the findings of the original study in both the HCP 500 and HCP 1200 subject releases. The exact computational replication in the HCP 500 dataset was a success, validating our analysis pipeline for extension studies. The extended replication in the larger HCP 1200 dataset was partially successful and demonstrated a dominant primary mode.


2019 ◽  
Vol 15 ◽  
pp. P718-P718
Author(s):  
Rebecca J. Melrose ◽  
Ariana Stickel ◽  
Joseph Veliz ◽  
David L. Sultzer ◽  
Amy Jimenez

2020 ◽  
Vol 137 ◽  
pp. 107275
Author(s):  
Francesca Garbarini ◽  
Fabrizio Calzavarini ◽  
Matteo Diano ◽  
Monica Biggio ◽  
Carola Barbero ◽  
...  

Author(s):  
Pilar Lopez-Garcia ◽  
Alexandra Cristobal-Huerta ◽  
Leslie Young Espinoza ◽  
Patricio Molero ◽  
Felipe Ortuño Sanchez-Pedreño ◽  
...  

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0194388 ◽  
Author(s):  
Jeroen Gielen ◽  
Wietse Wiels ◽  
Jeroen Van Schependom ◽  
Jorne Laton ◽  
Wim Van Hecke ◽  
...  

2018 ◽  
Author(s):  
Marjolein M.L.J.Z. Vandenbosch ◽  
Dennis van’t Ent ◽  
Dorret I. Boomsma ◽  
Andrey P. Anokhin ◽  
Dirk J.A. Smit

AbstractThe human brain shows remarkable development of functional brain activity from childhood to adolescence. Here, we investigated whether electroencephalogram (EEG) recordings are suitable for predicting the age of children and adolescents. Moreover, we investigated whether over-or underestimation of age was stable over longer time periods, as stable prediction error can be interpreted as reflecting individual brain maturational level. Finally, we established whether the age-prediction error was genetically determined. Three minutes eyes-closed resting state EEG data from the longitudinal EEG studies of Netherlands Twin Register (n=836) and Washington University in St. Louis (n = 702) were used at ages 5, 7, 12, 14, 16 and 18. Longitudinal data were available within childhood and adolescence. We calculated power in 1 Hz wide bins (1 to 24 Hz). Random Forest regression and Relevance Vector Machine with 6-fold cross-validation were applied. The best mean absolute prediction error was obtained with Random Forest (1.22 years). Classification of childhood vs. adolescence reached over 94% accuracy. Prediction errors were moderately to highly stable over periods of 1.5 to 2.1 years (0.53 < r < 0.74) and signifcantly affected by genetic factors (heritability between 42% and 79%). Our results show that age prediction from low-cost EEG recordings is comparable in accuracy to those obtained with MRI. Children and adolescents showed stable over- or underestimation of their age, which means that some participants have stable brain activity patterns that reflect those of an older or younger age, and could therefore reflect individual brain maturational level. This prediction error is heritable, suggesting that genes underlie maturational level of functional brain activity. We propose that age prediction based on EEG recordings can be used for tracking neurodevelopment in typically developing children, in preterm children, and in children with neurodevelopmental disorders.


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