Functional brain networks in schizophrenia: a review

被引:152
作者
Calhoun, Vince D. [1 ,2 ,3 ,4 ]
Eichele, Tom [5 ]
Pearlson, Godfrey [3 ,4 ]
机构
[1] Mind Res Network, Albuquerque, NM 87106 USA
[2] Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
[3] Olin Neuropsychiat Res Ctr, Hartford, CT USA
[4] Yale Univ, Sch Med, Dept Psychiat, New Haven, CT USA
[5] Univ Bergen, Dept Biol & Med Psychol, Bergen, Norway
来源
FRONTIERS IN HUMAN NEUROSCIENCE | 2009年 / 3卷
基金
美国国家卫生研究院;
关键词
fMRI; schizophrenia; independent component analysis; functional connectivity; functional network connectivity; INDEPENDENT COMPONENT ANALYSIS; RESTING-STATE NETWORKS; DEFAULT-MODE NETWORK; MONKEY VISUAL-CORTEX; BOLD SIGNAL; ALZHEIMERS-DISEASE; ACTIVITY FLUCTUATIONS; PREFRONTAL CORTEX; NEURAL MECHANISMS; BLIND SEPARATION;
D O I
10.3389/neuro.09.017.2009
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia.
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页数:12
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