Stimulus-independent data analysis for fMRI

被引：0
|
作者：
Dodel, S [1 ]
Herrmann, JM [1 ]
Geisel, T [1 ]
机构：
[1] Max Planck Inst Stromungsforsch, D-37073 Gottingen, Germany
来源：
EMERGENT NEURAL COMPUTATIONAL ARCHITECTURES BASED ON NEUROSCIENCE: TOWARDS NEUROSCIENCE-INSPIRED COMPUTING | 2001年 / 2036卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and correlation analysis versus stimulus-independent methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) with respect to their capabilities of separating noise sources from functional activity. The methods are applied to a finger tapping fMRI experiment and it is shown that the stimulus-independent methods in addition to the extraction of the stimulus can reveal several non-stimulus related influences such as head movements or breathing.
引用
收藏
页码：39 / 52
页数：14
相关论文
共 50 条
  • [41] When the Mind Wanders: Distinguishing Stimulus-Dependent From Stimulus-Independent Thoughts During Incidental Encoding in Young and Older Adults
    Maillet, David
    Schacter, Daniel L.
    [J]. PSYCHOLOGY AND AGING, 2016, 31 (04) : 370 - 379
  • [42] Forcing the Issue: Aromatic Tuning Facilitates Stimulus-Independent Modulation of a Two-Component Signaling Circuit
    Norholm, Morten H. H.
    von Heijne, Gunnar
    Draheim, Roger R.
    [J]. ACS SYNTHETIC BIOLOGY, 2015, 4 (04): : 474 - 481
  • [43] Parallel independent component analysis for multimodal analysis: Application to fMRI and EEG data
    Liu, Jingyu
    Calhoun, Vince
    [J]. 2007 4TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING : MACRO TO NANO, VOLS 1-3, 2007, : 1028 - 1031
  • [44] An affective computing approach to physiological emotion specificity: Toward subject-independent and stimulus-independent classification of film-induced emotions
    Kolodyazhniy, Vitaliy
    Kreibig, Sylvia D.
    Gross, James J.
    Roth, Walton T.
    Wilhelm, Frank H.
    [J]. PSYCHOPHYSIOLOGY, 2011, 48 (07) : 908 - 922
  • [45] An independent component analysis of fMRI data of grapheme-colour synaesthesia
    Specht, Karsten
    Laeng, Bruno
    [J]. JOURNAL OF NEUROPSYCHOLOGY, 2011, 5 : 203 - 213
  • [46] Comparison of separation performance of independent component analysis algorithms for fMRI data
    Sariya, Yogesh Kumar
    Anand, R. S.
    [J]. JOURNAL OF INTEGRATIVE NEUROSCIENCE, 2017, 16 (02) : 157 - 175
  • [47] Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data
    Vanello, N.
    Ricciardi, E.
    Landini, L.
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2016, 2016
  • [48] Toward a neurometric foundation for probabilistic independent component analysis of fMRI data
    Poppe, Andrew B.
    Wisner, Krista
    Atluri, Gowtham
    Lim, Kelvin O.
    Kumar, Vipin
    MacDonald, Angus W., III
    [J]. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE, 2013, 13 (03) : 641 - 659
  • [49] Toward a neurometric foundation for probabilistic independent component analysis of fMRI data
    Andrew B. Poppe
    Krista Wisner
    Gowtham Atluri
    Kelvin O. Lim
    Vipin Kumar
    Angus W. MacDonald
    [J]. Cognitive, Affective, & Behavioral Neuroscience, 2013, 13 : 641 - 659
  • [50] Parallel group independent component analysis for massive fMRI data sets
    Chen, Shaojie
    Huang, Lei
    Qiu, Huitong
    Nebel, Mary Beth
    Mostofsky, Stewart H.
    Pekar, James J.
    Lindquist, Martin A.
    Eloyan, Ani
    Caffo, Brian S.
    [J]. PLOS ONE, 2017, 12 (03):
12345