Authors: Razvan V. Marinescu
arXiv
In order to find effective treatments for Alzheimer's disease (AD), we need to identify subjects at risk of AD as early as possible. To this end, recently developed disease progression models can be u...
Authors: Sabine Plancoulaine, Camille Stagnara, Sophie Flori, Flora Bat-Pitault, Jian-Sheng Lin, Hugues Patural, Patricia Franco
arXiv
Background. Few studies on the relations between sleep quantity and/or quality and cognition were conducted among pre-schoolers from healthy general population. We aimed at identifying, among 3 years...
Authors: Suprateek Kundu, Jin Ming, Jennifer Stevens
arXiv
Recently, the potential of dynamic brain networks as a neuroimaging biomarkers for mental illnesses is being increasingly recognized. However, there are several unmet challenges in developing such bio...
Authors: Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, James S. Duncan
arXiv
Recurrent neural networks (RNNs) were designed for dealing with time-series data and have recently been used for creating predictive models from functional magnetic resonance imaging (fMRI) data. Howe...
Authors: Jorge Antonio Gaxiola Tirado
arXiv
The use of time- and frequency-based features has proven effective in the process of classifying mental tasks in Brain Computer Interfaces (BCIs). Still, most of those methods provide little insight a...
Authors: Ixavier A Higgins, Ying Guo, Suprateek Kundu, Ki Sueng Choi, Helen Mayberg
arXiv
Recently, graph theory has become a popular method for characterizing brain functional organization. One important goal in graph theoretical analysis of brain networks is to identify network differenc...
Authors: Moo K. Chung, Hyekyoung Lee, Andrey Gritsenko, Alex DiChristofano, Dustin Pluta, Hernando Ombao, Victor Solo
arXiv
Existing brain network distances are often based on matrix norms. The element-wise differences in the existing matrix norms may fail to capture underlying topological differences. Further, matrix norm...
Authors: Sergi Gomez, Mark O'Sullivan, Emanuel Popovici, Sean Mathieson, Geraldine Boylan, Andriy Temko
arXiv
Significant training is required to visually interpret neonatal EEG signals. This study explores alternative sound-based methods for EEG interpretation which are designed to allow for intuitive and qu...
Authors: Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Pamela Ventola, James S. Duncan
arXiv
Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome. Early diagnosis and precise treatment are essential for ASD patients. Although researchers have built many analytical models, t...
Authors: Catalina Obando, Charlotte Rosso, Joshua Siegel, Maurizio Corbetta, Fabrizio De Vico Fallani
arXiv
Plasticity after stroke is a complex phenomenon initiated by the functional reorganization of the brain, especially in the perilesional tissue. At macroscales, the reestablishment of segregation withi...
Authors: Marie Roald, Suchita Bhinge, Chunying Jia, Vince Calhoun, Tülay Adalı, Evrim Acar
arXiv
Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functi...
Authors: Natalie Klein, Joshua H. Siegle, Tobias Teichert, Robert E. Kass
arXiv
Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As...
Authors: Mariya Toneva, Jennifer Williams, Anand Bollu, Christoph Dann, Leila Wehbe
arXiv
To study information processing in the brain, neuroscientists manipulate experimental stimuli while recording participant brain activity. They can then use encoding models to find out which brain "zon...
Authors: Kenneth D. Harris, Alex E. Yuan
arXiv
We describe two families of statistical tests to detect partial correlation in vectorial timeseries. The tests measure whether an observed timeseries Y can be predicted from a second series X, even af...
Authors: James Wilsenach, Katie Warnaby, Charlotte M. Deane, Gesine Reinert
arXiv
As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust...