Shift-invariant multilinear decomposition of neuroimaging data

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Shift-invariant multilinear decomposition of neuroimaging data. / Mørup, Morten; Hansen, Lars Kai; Arnfred, Sidse Marie; Lim, Lek-Heng; Madsen, Kristoffer Hougaard.

I: NeuroImage, Bind 42, Nr. 4, 01.10.2008, s. 1439-50.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Mørup, M, Hansen, LK, Arnfred, SM, Lim, L-H & Madsen, KH 2008, 'Shift-invariant multilinear decomposition of neuroimaging data', NeuroImage, bind 42, nr. 4, s. 1439-50. https://doi.org/10.1016/j.neuroimage.2008.05.062

APA

Mørup, M., Hansen, L. K., Arnfred, S. M., Lim, L-H., & Madsen, K. H. (2008). Shift-invariant multilinear decomposition of neuroimaging data. NeuroImage, 42(4), 1439-50. https://doi.org/10.1016/j.neuroimage.2008.05.062

Vancouver

Mørup M, Hansen LK, Arnfred SM, Lim L-H, Madsen KH. Shift-invariant multilinear decomposition of neuroimaging data. NeuroImage. 2008 okt. 1;42(4):1439-50. https://doi.org/10.1016/j.neuroimage.2008.05.062

Author

Mørup, Morten ; Hansen, Lars Kai ; Arnfred, Sidse Marie ; Lim, Lek-Heng ; Madsen, Kristoffer Hougaard. / Shift-invariant multilinear decomposition of neuroimaging data. I: NeuroImage. 2008 ; Bind 42, Nr. 4. s. 1439-50.

Bibtex

@article{febe3e375cc04bcf884bf08eb026be86,
title = "Shift-invariant multilinear decomposition of neuroimaging data",
abstract = "We present an algorithm for multilinear decomposition that allows for arbitrary shifts along one modality. The method is applied to neural activity arranged in the three modalities space, time, and trial. Thus, the algorithm models neural activity as a linear superposition of components with a fixed time course that may vary across either trials or space in its overall intensity and latency. Its utility is demonstrated on simulated data as well as actual EEG, and fMRI data. We show how shift-invariant multilinear decompositions of multiway data can successfully cope with variable latencies in data derived from neural activity--a problem that has caused degenerate solutions especially in modeling neuroimaging data with instantaneous multilinear decompositions. Our algorithm is available for download at www.erpwavelab.org.",
keywords = "Algorithms, Brain Mapping/methods, Computer Simulation, Electroencephalography/methods, Evoked Potentials, Visual/physiology, Image Enhancement/methods, Image Interpretation, Computer-Assisted/methods, Linear Models, Magnetic Resonance Imaging/methods, Models, Neurological, Reproducibility of Results, Sensitivity and Specificity, Visual Cortex/physiology",
author = "Morten M{\o}rup and Hansen, {Lars Kai} and Arnfred, {Sidse Marie} and Lek-Heng Lim and Madsen, {Kristoffer Hougaard}",
year = "2008",
month = oct,
day = "1",
doi = "10.1016/j.neuroimage.2008.05.062",
language = "English",
volume = "42",
pages = "1439--50",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Elsevier",
number = "4",

}

RIS

TY - JOUR

T1 - Shift-invariant multilinear decomposition of neuroimaging data

AU - Mørup, Morten

AU - Hansen, Lars Kai

AU - Arnfred, Sidse Marie

AU - Lim, Lek-Heng

AU - Madsen, Kristoffer Hougaard

PY - 2008/10/1

Y1 - 2008/10/1

N2 - We present an algorithm for multilinear decomposition that allows for arbitrary shifts along one modality. The method is applied to neural activity arranged in the three modalities space, time, and trial. Thus, the algorithm models neural activity as a linear superposition of components with a fixed time course that may vary across either trials or space in its overall intensity and latency. Its utility is demonstrated on simulated data as well as actual EEG, and fMRI data. We show how shift-invariant multilinear decompositions of multiway data can successfully cope with variable latencies in data derived from neural activity--a problem that has caused degenerate solutions especially in modeling neuroimaging data with instantaneous multilinear decompositions. Our algorithm is available for download at www.erpwavelab.org.

AB - We present an algorithm for multilinear decomposition that allows for arbitrary shifts along one modality. The method is applied to neural activity arranged in the three modalities space, time, and trial. Thus, the algorithm models neural activity as a linear superposition of components with a fixed time course that may vary across either trials or space in its overall intensity and latency. Its utility is demonstrated on simulated data as well as actual EEG, and fMRI data. We show how shift-invariant multilinear decompositions of multiway data can successfully cope with variable latencies in data derived from neural activity--a problem that has caused degenerate solutions especially in modeling neuroimaging data with instantaneous multilinear decompositions. Our algorithm is available for download at www.erpwavelab.org.

KW - Algorithms

KW - Brain Mapping/methods

KW - Computer Simulation

KW - Electroencephalography/methods

KW - Evoked Potentials, Visual/physiology

KW - Image Enhancement/methods

KW - Image Interpretation, Computer-Assisted/methods

KW - Linear Models

KW - Magnetic Resonance Imaging/methods

KW - Models, Neurological

KW - Reproducibility of Results

KW - Sensitivity and Specificity

KW - Visual Cortex/physiology

U2 - 10.1016/j.neuroimage.2008.05.062

DO - 10.1016/j.neuroimage.2008.05.062

M3 - Journal article

C2 - 18625324

VL - 42

SP - 1439

EP - 1450

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 4

ER -

ID: 193668219