Authors: Xilin Shen, François G. Meyer
arXiv
We propose a novel method to embed a functional magnetic resonance imaging (fMRI) dataset in a low-dimensional space. The embedding optimally preserves the local functional coupling between fMRI tim...
Authors: Sieun Lee, Ben Cardoen, Marianne Etherson, Nitish Jawahar, Ellen Townsend, Kapil Sayal, Peter Fonagy, Aja Murray, Joanna Lockwood, Ayan Mahamud, Chris Hollis, Rory O'Connor, Dorothee Auer
arXiv
Understanding the dynamics of suicidal ideation and behaviour in youth and the factors associated with transitions from thoughts to behaviours is critical for early identification, monitoring, and pre...
Authors: Roberto D. Pascual-Marqui, Rolando J. Biscay, Pedro A. Valdes-Sosa, Jorge Bosch-Bayard, Jorge J. Riera-Diaz
arXiv
An important field of research in functional neuroimaging is the discovery of integrated, distributed brain systems and networks, whose different regions need to work in unison for normal functioning....
Authors: Gaël Varoquaux, Alexandre Gramfort, Jean Baptiste Poline, Bertrand Thirion
arXiv
Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In part...
Authors: T. H. A. van der Reep, D. Molenaar, W. Löffler, Y. Pinto
arXiv
We show that quantum detector tomography can be applied to the human visual system to explore human perception of photon number states. In detector tomography, instead of using very hard to produce ph...
Authors: Arnaud P. Fournel, Emanuelle Reynaud, Michael J. Brammer, Andrew Simmons, Cedric E. Ginestet
arXiv
Studies of functional MRI data are increasingly concerned with the estimation of differences in spatio-temporal networks across groups of subjects or experimental conditions. Unsupervised clustering a...
Authors: Wonsang You, Sophie Achard, Jörg Stadler, Bernd Brückner, Udo Seiffert
arXiv
A variety of resting state neuroimaging data tend to exhibit fractal behavior where its power spectrum follows power-law scaling. Resting state functional connectivity is significantly influenced by f...
Authors: Sean L. Simpson, Satoru Hayasaka, Paul J. Laurienti
arXiv
Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on un...
Authors: Rahul Biswas, SuryaNarayana Sripada, Somabha Mukherjee
arXiv
Inferring causation from time series data is of scientific interest in different disciplines, particularly in neural connectomics. While different approaches exist in the literature with parametric mo...
Authors: Jonathan Touboul, Olivier Faugeras
arXiv
In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this...
Authors: Hongtu Zhu, Tengfei Li, Bingxin Zhao
arXiv
The aim of this paper is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learn...
Authors: Noslen Hernández, Antonio Galves, Jesus Garcia, Marcos Dimas Gubitoso, Claudia D. Vargas
arXiv
In this article we address two related issues on the learning of probabilistic sequences of events. First, which features make the sequence of events generated by a stochastic chain more difficult to...
Authors: Johan Medrano, Karl J. Friston, Peter Zeidman
arXiv
A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations...
Authors: Drausin F. Wulsin, Emily B. Fox, Brian Litt
arXiv
Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not...
Authors: Zhi-Qin John Xu, Douglas Zhou, David Cai
arXiv
To understand how neural networks process information, it is important to investigate how neural network dynamics varies with respect to different stimuli. One challenging task is to design efficient...