Literature DB >> 15046247

A modified temporal self-correlation method for analysis of fMRI time series.

Yingli Lu1, Yufeng Zang, Tianzi Jiang.   

Abstract

Temporal self-correlation has recentlybeen proposed as a measure for fMRI-activation detection. In this paper, a modified temporal self-correlation method is introduced. The modified temporal self-correlation is based on the expectation value and standard deviation of the correlation coefficients between all pairs of epochs, while the original temporal self-correlation method is only based on the expectation value. Performance of the proposed method is evaluated on both simulated and in vivo fMRI data. Compared with the original temporal self-correlation method, the proposed method shows a significant improvement. In addition, a technique for quantitative comparison of different fMRI data analysis methods is proposed.

Mesh:

Year:  2003        PMID: 15046247     DOI: 10.1385/NI:1:3:259

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  9 in total

1.  Detecting activations in event-related fMRI using analysis of variance.

Authors:  S Clare; M Humberstone; J Hykin; L D Blumhardt; R Bowtell; P Morris
Journal:  Magn Reson Med       Date:  1999-12       Impact factor: 4.668

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Authors:  S H Lai; M Fang
Journal:  Magn Reson Imaging       Date:  1999-07       Impact factor: 2.546

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Authors:  C Goutte; P Toft; E Rostrup; F Nielsen; L K Hansen
Journal:  Neuroimage       Date:  1999-03       Impact factor: 6.556

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Authors:  S C Ngan; W F Auffermann; S Sarkar; X Hu
Journal:  Magn Reson Imaging       Date:  2001-11       Impact factor: 2.546

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6.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.

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Journal:  Proc Natl Acad Sci U S A       Date:  1992-06-15       Impact factor: 11.205

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Journal:  Magn Reson Med       Date:  1992-06       Impact factor: 4.668

8.  Functional mapping of the human visual cortex by magnetic resonance imaging.

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Journal:  Science       Date:  1991-11-01       Impact factor: 47.728

9.  Analysis of fMRI data by blind separation into independent spatial components.

Authors:  M J McKeown; S Makeig; G G Brown; T P Jung; S S Kindermann; A J Bell; T J Sejnowski
Journal:  Hum Brain Mapp       Date:  1998       Impact factor: 5.038

  9 in total
  1 in total

1.  Spatially regularized machine learning for task and resting-state fMRI.

Authors:  Xiaomu Song; Lawrence P Panych; Nan-kuei Chen
Journal:  J Neurosci Methods       Date:  2015-10-16       Impact factor: 2.390

  1 in total

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