Volume 12 Supplement 1

Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access

Different LFP frequency bands convey complementary information about the BOLD signal

  • Cesare Magri1Email author,
  • Ulrich Schridde1,
  • Stefano Panzeri3,
  • Yusuke Murayama1 and
  • Nikos K Logothetis1, 2
Contributed equally
BMC Neuroscience201112(Suppl 1):P204

DOI: 10.1186/1471-2202-12-S1-P204

Published: 18 July 2011

Blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most widely used noninvasive imaging technique for investigating brain activity. However, the BOLD signal is only indirectly coupled to the underlying neural activity and the relationship between the two signals is not fully understood [1]. Recordings in anaesthetized and awake monkeys have shown that hemodynamic responses are strongly related to local field potentials (LFPs) [2, 3]. LFPs are thought to represent the input and intracortical processing in a cortical area and are usually separated into different frequency bands that reflect different neural processes [4]. Previous studies have shown that different LFP bands correlate differently with the BOLD signal [3, 5, 6]. However little is known about which property of the BOLD signal is reflected by each band and whether different bands convey different information about the BOLD signal. To address this question we performed simultaneous recordings of neural activity and BOLD fMRI in early visual areas V1 and V2 in 4 anesthetized monkeys. All measurements were performed with the monkeys sitting in complete darkness while no stimulus was being presented. We computed mutual information between LFP power and BOLD fMRI to determine which frequencies in the LFPs were most informative about the BOLD signal. We found three highly informative bands, namely the alpha band [8-12Hz], the gamma band [40-100Hz] and the [18-35 Hz] “nMod” band that was previously found to be unrelated to visual stimuli and was thus suggested to primarily reflect neuromodulatory input [4]. We found that gamma power was the most informative about BOLD fMRI and reflected well changes in the amplitude of the BOLD signal. In particular, an increase in gamma power above its median value was followed, on average, by an increase in BOLD signal, and the BOLD signal decreased, instead, following a decrease in gamma power below its median. Moreover, we found that gamma and nMod power were complementary, i.e. that by combining nMod power together with gamma power we could extract 30% more information than could be extracted from gamma power alone. We investigated the origin of this complementarity and we found that the power in the nMod band reflected the timing with which changes in BOLD signal occurred following changes in gamma power. Finally, we found that, as suggested by previous theoretical work [7], an increase in alpha power without a change in total LFP power was followed by a decrease in BOLD signal and vice versa. These results indicate that distinct neural processes are reflected differently in the BOLD signal and that, consequently, it may be possible to retrieve information about the different contributions from the recorded BOLD time course.




* These authors contributed equally to the work.

Authors’ Affiliations

Max Planck Institute for Biological Cybernetics
Imaging Science and Biomedical Engineering University of Manchester
Italian Institute of Technology, Department of Robotics, Brain and Cognitive Sciences


  1. Logothetis NK: What we can do and what we cannot do with fMRI. Nature. 2008, 453 (7197): 869-878. 10.1038/nature06976.View ArticlePubMedGoogle Scholar
  2. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A: Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001, 412 (6843): 150-157. 10.1038/35084005.View ArticlePubMedGoogle Scholar
  3. Goense JBM, Logothetis NK: Neurophysiology of the BOLD fMRI signal in awake monkeys. Curr Biol. 2008, 18 (9): 631-640. 10.1016/j.cub.2008.03.054.View ArticlePubMedGoogle Scholar
  4. Belitski A, Gretton A, Magri C, Murayama Y, Montemurro MA, Logothetis NK, Panzeri S: Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information. J Neurosci. 2008, 28 (22): 5696-5709. 10.1523/JNEUROSCI.0009-08.2008.View ArticlePubMedGoogle Scholar
  5. Scholvinck ML, Maier A, Ye FQ, Duyn JH, Leopold DA: Neural basis of global resting-state fMRI activity. P Natl Acad Sci USA. 2010, 107 (22): 10238-10243. 10.1073/pnas.0913110107.View ArticleGoogle Scholar
  6. Scheeringa R, Fries P, Petersson KM, Oostenveld R, Grothe I, Norris DG, Hagoort P, Bastiaansen MCM: Neuronal Dynamics Underlying High- and Low-Frequency EEG Oscillations Contribute Independently to the Human BOLD Signal. Neuron. 2011, 69 (3): 572-583. 10.1016/j.neuron.2010.11.044.View ArticlePubMedGoogle Scholar
  7. Kilner JM, Mattout J, Henson R, Friston KJ: Hemodynamic correlates of EEG: A heuristic. Neuroimage. 2005, 28 (1): 280-286. 10.1016/j.neuroimage.2005.06.008.View ArticlePubMedGoogle Scholar


© Magri et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.