A simple view of the brain through a frequency-specific functional connectivity measure

被引:200
作者
Salvador, R.
Martinez, A.
Pomarol-Clotet, E.
Gomar, J.
Vila, F.
Sarro, S.
Capdevila, A.
Bullmore, Edward T.
机构
[1] CASM, Barcelona 08830, Spain
[2] SJD SSM, Barcelona, Spain
[3] Univ Cambridge, Addenbrookes Hosp, Dept Psychiat, Brain Mapping Unit, Cambridge CB2 2QQ, England
基金
英国医学研究理事会;
关键词
brain connectivity; mutual information; frequency domain; resting state; default mode network; N-back; coherence;
D O I
10.1016/j.neuroimage.2007.08.018
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Here we develop a measure of functional connectivity describing the degree of covariability between a brain region and the rest of the brain. This measure is based on previous formulas for the mutual information (MI) between clusters of regions in the frequency domain. Under the current scenario, the MI can be given as a simple monotonous function of the multiple coherence and it leads to an easy visual representation of connectivity patterns. Computationally efficient formulas, adequate for short time series, are presented and applied to functional magnetic resonance imaging (fMRI) data measured in subjects (N=34) performing a working memory task or being at rest. While resting state coherence in high (0.17-0.25 Hz) and middle (0.08-0.17 Hz) frequency intervals is bilaterally salient in several limbic and temporal areas including the insula, the amygdala, and the primary auditory cortex, low frequencies (<0.08 Hz) have greatest connectivity in frontal structures. Results from the comparison between resting and N-back conditions show enhanced low frequency coherence in many of the areas previously reported in standard fMRI activation studies of working memory, but task related reductions in high frequency connectivity are also found in regions of the default mode network. Finally, potentially confounding effects of head movement and regional volume on MI are identified and addressed. (C) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:279 / 289
页数:11
相关论文
共 39 条
[1]   Neural systems for recognizing emotion [J].
Adolphs, R .
CURRENT OPINION IN NEUROBIOLOGY, 2002, 12 (02) :169-177
[2]  
[Anonymous], 1981, Time series data analysis and theory, DOI 10.1201/b15288-24
[3]   Chronic pain and the emotional brain: Specific brain activity associated with spontaneous fluctuations of intensity of chronic back pain [J].
Baliki, Marwan N. ;
Chialvo, Dante R. ;
Geha, Paul Y. ;
Levy, Robert M. ;
Harden, R. Norman ;
Parrish, Todd B. ;
Apkarian, A. Vania .
JOURNAL OF NEUROSCIENCE, 2006, 26 (47) :12165-12173
[4]   Probabilistic independent component analysis for functional magnetic resonance imaging [J].
Beckmann, CF ;
Smith, SA .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2004, 23 (02) :137-152
[5]   FUNCTIONAL CONNECTIVITY IN THE MOTOR CORTEX OF RESTING HUMAN BRAIN USING ECHO-PLANAR MRI [J].
BISWAL, B ;
YETKIN, FZ ;
HAUGHTON, VM ;
HYDE, JS .
MAGNETIC RESONANCE IN MEDICINE, 1995, 34 (04) :537-541
[6]   How good is good enough in path analysis of fMRI data? [J].
Bullmore, ET ;
Horwitz, B ;
Honey, G ;
Brammer, M ;
Williams, S ;
Sharma, T .
NEUROIMAGE, 2000, 11 (04) :289-301
[7]   Functional magnetic resonance image analysis of a large-scale neurocognitive network [J].
Bullmore, ET ;
RabeHesketh, S ;
Morris, RG ;
Williams, SCR ;
Gregory, L ;
Gray, JA ;
Brammer, MJ .
NEUROIMAGE, 1996, 4 (01) :16-33
[8]   A method for comparing group fMRI data using independent component analysis: application to visual, motor and visuomotor tasks [J].
Calhoun, VD ;
Adali, T ;
Pekar, JJ .
MAGNETIC RESONANCE IMAGING, 2004, 22 (09) :1181-1191
[9]  
Cordes D, 2001, AM J NEURORADIOL, V22, P1326
[10]   Neural mechanisms of prefrontal cortical function: implications for cognitive rehabilitation [J].
D'Esposito, Mark ;
Chen, Anthony J. -W. .
REPROGRAMMING THE BRAIN, 2006, 157 :123-+