Neuronal spatiotemporal pattern discrimination

The dynamical evolution of seizures

Steven Schiff, Tim Sauer, Rohit Kumar, Steven L. Weinstein

Research output: Contribution to journalArticle

88 Citations (Scopus)

Abstract

We developed a modern numerical approach to the multivariate linear discrimination of Fisher from 1936 based upon singular value decomposition that is sufficiently stable to permit widespread application to spatiotemporal neuronal patterns. We demonstrate this approach on an old problem in neuroscience-whether seizures have distinct dynamical states as they evolve with time. A practical result was the first demonstration that human seizures have distinct initiation and termination dynamics, an important characterization as we seek to better understand how seizures start and stop. Our approach is broadly applicable to a wide variety of neuronal data, from multichannel EEG or MEG, to sequentially acquired optical imaging data or fMRI.

Original languageEnglish (US)
Pages (from-to)1043-1055
Number of pages13
JournalNeuroImage
Volume28
Issue number4
DOIs
StatePublished - Dec 1 2005

Fingerprint

Seizures
Optical Imaging
Neurosciences
Electroencephalography
Magnetic Resonance Imaging

All Science Journal Classification (ASJC) codes

  • Neurology
  • Cognitive Neuroscience

Cite this

Schiff, Steven ; Sauer, Tim ; Kumar, Rohit ; Weinstein, Steven L. / Neuronal spatiotemporal pattern discrimination : The dynamical evolution of seizures. In: NeuroImage. 2005 ; Vol. 28, No. 4. pp. 1043-1055.
@article{375b2a45e99549b9ac1c8bd89f89b7bb,
title = "Neuronal spatiotemporal pattern discrimination: The dynamical evolution of seizures",
abstract = "We developed a modern numerical approach to the multivariate linear discrimination of Fisher from 1936 based upon singular value decomposition that is sufficiently stable to permit widespread application to spatiotemporal neuronal patterns. We demonstrate this approach on an old problem in neuroscience-whether seizures have distinct dynamical states as they evolve with time. A practical result was the first demonstration that human seizures have distinct initiation and termination dynamics, an important characterization as we seek to better understand how seizures start and stop. Our approach is broadly applicable to a wide variety of neuronal data, from multichannel EEG or MEG, to sequentially acquired optical imaging data or fMRI.",
author = "Steven Schiff and Tim Sauer and Rohit Kumar and Weinstein, {Steven L.}",
year = "2005",
month = "12",
day = "1",
doi = "10.1016/j.neuroimage.2005.06.059",
language = "English (US)",
volume = "28",
pages = "1043--1055",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "4",

}

Neuronal spatiotemporal pattern discrimination : The dynamical evolution of seizures. / Schiff, Steven; Sauer, Tim; Kumar, Rohit; Weinstein, Steven L.

In: NeuroImage, Vol. 28, No. 4, 01.12.2005, p. 1043-1055.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Neuronal spatiotemporal pattern discrimination

T2 - The dynamical evolution of seizures

AU - Schiff, Steven

AU - Sauer, Tim

AU - Kumar, Rohit

AU - Weinstein, Steven L.

PY - 2005/12/1

Y1 - 2005/12/1

N2 - We developed a modern numerical approach to the multivariate linear discrimination of Fisher from 1936 based upon singular value decomposition that is sufficiently stable to permit widespread application to spatiotemporal neuronal patterns. We demonstrate this approach on an old problem in neuroscience-whether seizures have distinct dynamical states as they evolve with time. A practical result was the first demonstration that human seizures have distinct initiation and termination dynamics, an important characterization as we seek to better understand how seizures start and stop. Our approach is broadly applicable to a wide variety of neuronal data, from multichannel EEG or MEG, to sequentially acquired optical imaging data or fMRI.

AB - We developed a modern numerical approach to the multivariate linear discrimination of Fisher from 1936 based upon singular value decomposition that is sufficiently stable to permit widespread application to spatiotemporal neuronal patterns. We demonstrate this approach on an old problem in neuroscience-whether seizures have distinct dynamical states as they evolve with time. A practical result was the first demonstration that human seizures have distinct initiation and termination dynamics, an important characterization as we seek to better understand how seizures start and stop. Our approach is broadly applicable to a wide variety of neuronal data, from multichannel EEG or MEG, to sequentially acquired optical imaging data or fMRI.

UR - http://www.scopus.com/inward/record.url?scp=28244488692&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=28244488692&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2005.06.059

DO - 10.1016/j.neuroimage.2005.06.059

M3 - Article

VL - 28

SP - 1043

EP - 1055

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 4

ER -