scholarly journals P2-333: ASSOCIATION OF WHITE MATTER INTEGRITY AND FUNCTIONAL BRAIN ACTIVITY IN COGNITIVE AGING

2019 ◽  
Vol 15 ◽  
pp. P718-P718
Author(s):  
Rebecca J. Melrose ◽  
Ariana Stickel ◽  
Joseph Veliz ◽  
David L. Sultzer ◽  
Amy Jimenez
Keyword(s):  
White Matter ◽  
Cognitive Aging ◽  
Brain Activity ◽  
White Matter Integrity ◽  
Functional Brain ◽  
Functional Brain Activity

scholarly journals Computationally replicating the Smith et al. (2015) positive-negative mode linking functional connectivity and subject measures

2020 ◽  
Author(s):  
Nikhil Goyal ◽  
Dustin Moraczewski ◽  
Peter Bandettini ◽  
Emily S. Finn ◽  
Adam Thomas
Keyword(s):  
Brain Activity ◽  
Strong Positive Correlation ◽  
Activity Data ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Using Data ◽  
Functional Brain Activity ◽  
Relationship Of ◽  
The Relationship ◽  
Landmark Study

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.

The effects of chronic tDCS on functional brain activity and sustained attention performance

2019 ◽  
Vol 12 (2) ◽  
pp. 536
Author(s):  
R. Mc Kinley ◽  
M. Sherwood ◽  
C. Mullenger ◽  
L. McIntire
Keyword(s):  
Sustained Attention ◽  
Brain Activity ◽  
Functional Brain ◽  
Attention Performance ◽  
Functional Brain Activity

P3-474: OBJECTIVE CARDIOMETABOLIC DEMENTIA-RELATED RISK FACTORS ASSOCIATED WITH FUNCTIONAL BRAIN ACTIVITY IN OLDER ADULTS: AN EVENT-RELATED POTENTIAL STUDY

2019 ◽  
Vol 15 ◽  
pp. P1149-P1149
Author(s):  
Hannah Keage ◽  
Daniel Feuerriegel ◽  
Danielle Greaves ◽  
Emma Tregoweth ◽  
Scott Coussens ◽  
...  
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Brain Activity ◽  
Event Related Potential ◽  
Functional Brain ◽  
Factors Associated ◽  
Potential Study ◽  
Related Risk ◽  
Functional Brain Activity

Imageability effect on the functional brain activity during a naming to definition task

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

The influence of the COMT genotype in the underlying functional brain activity of context processing in schizophrenia and in relatives

2016 ◽  
Vol 71 ◽  
pp. 176-182 ◽  
Author(s):  
Pilar Lopez-Garcia ◽  
Alexandra Cristobal-Huerta ◽  
Leslie Young Espinoza ◽  
Patricio Molero ◽  
Felipe Ortuño Sanchez-Pedreño ◽  
...  
Keyword(s):  
Brain Activity ◽  
Context Processing ◽  
Comt Genotype ◽  
Functional Brain ◽  
Functional Brain Activity

scholarly journals Episodic memory function is associated with multiple measures of white matter integrity in cognitive aging

2012 ◽  
Vol 6 ◽  
Author(s):  
Samuel N. Lockhart ◽  
Adriane B. V. Mayda ◽  
Alexandra E. Roach ◽  
Evan Fletcher ◽  
Owen Carmichael ◽  
...  
Keyword(s):  
White Matter ◽  
Episodic Memory ◽  
Cognitive Aging ◽  
Memory Function ◽  
White Matter Integrity ◽  
Multiple Measures

scholarly journals The effect of task modality and stimulus frequency in paced serial addition tests on functional brain activity

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0194388 ◽  
Author(s):  
Jeroen Gielen ◽  
Wietse Wiels ◽  
Jeroen Van Schependom ◽  
Jorne Laton ◽  
Wim Van Hecke ◽  
...  
Keyword(s):  
Brain Activity ◽  
Stimulus Frequency ◽  
Functional Brain ◽  
Serial Addition ◽  
Functional Brain Activity ◽  
Task Modality

scholarly journals EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents

2018 ◽  
Author(s):  
Marjolein M.L.J.Z. Vandenbosch ◽  
Dennis van’t Ent ◽  
Dorret I. Boomsma ◽  
Andrey P. Anokhin ◽  
Dirk J.A. Smit
Keyword(s):  
Random Forest ◽  
Children And Adolescents ◽  
Prediction Error ◽  
Prediction Models ◽  
Brain Activity ◽  
Childhood And Adolescence ◽  
Functional Brain ◽  
Eeg Recordings ◽  
Functional Brain Activity ◽  
Age Prediction

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.

Sign in / Sign up

Export Citation Format

Share Document