Spatial discriminant ICA for RS-fMRI characterisation

This source preferred by Emili Balaguer-Ballester

Authors: Tabas-Diaz, A., Balaguer-Ballester, E., Pressnitzer, D., Siebert, A. and Rupp, A.

Publisher: IEEE Conference Publishing Services, In press

DOI: 10.1109/PRNI.2014.6858547

Communication sounds are typically asymmetric in time and human listeners are highly sensitive to short-term temporal asymmetry. Nevertheless neurophysiological correlates of perceptual asymmetry remain largely elusive to current approaches. Physiological recordings suggested that perceptual asymmetry is based on multiple scales of temporal integration within the auditory processing hierarchy. To test this hypothesis, we used magneto-encephalographic recordings to perform a model-driven analysis of auditory evoked fields (AEF) elicited by asymmetric sounds characterised by rising or decreasing envelopes (ramped and damped, respectively), using a hierarchical model of pitch perception with top-down modulation. We found a strong correlation between the perceived salience of ramped and damped stimuli and the AEFs, as quantified by the amplitude of the N100m component. Furthermore, the N100m magnitude is closely mirrored by a hierarchical model with stimulus-driven temporal integration windows of auditory nerve activity patterns. This strong correlation of AEFs, perception and modelling suggest that temporal asymmetry is processed in a hierarchical manner where integration windows are top-down modulated, in line with reversal hierarchical theory principles.

This source preferred by Emili Balaguer-Ballester

Authors: Tabas-Diaz, A., Balaguer-Ballester, E. and Igual, E.

Publisher: IEEE Conference Publishing Services, In press

Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups.

In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher's Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.

This data was imported from Scopus:

Authors: Tabas, A., Balaguer-Ballester, E. and Igual, L.

ISBN: 9781479941506

DOI: 10.1109/PRNI.2014.6858546

Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher's Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments. © 2014 IEEE.

This data was imported from Web of Science (Lite):

Authors: Tabas, A., Balaguer-Ballester, E., Igual, L. and IEEE

The data on this page was last updated at 05:10 on February 17, 2020.