4.7 Article

The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies

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JOURNAL OF NEUROSCIENCE
卷 32, 期 4, 页码 1395-1407

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SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.3985-11.2012

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资金

  1. Max Planck Society
  2. Compagnia di San Paolo
  3. Bernstein Center for Computational Neuroscience, Tubingen, Germany
  4. German Federal Ministry of Education and Research [FKZ: 01GQ1002]
  5. Italian Institute of Technology

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There is growing evidence that several components of the mass neural activity contributing to the local field potential (LFP) can be partly separated by decomposing the LFP into nonoverlapping frequency bands. Although the blood oxygen level-dependent (BOLD) signal has been found to correlate preferentially with specific frequency bands of the LFP, it is still unclear whether the BOLD signal relates to the activity expressed by each LFP band independently of the others or if, instead, it also reflects specific relationships among different bands. We investigated these issues by recording, simultaneously and with high spatiotemporal resolution, BOLD signal and LFP during spontaneous activity in early visual cortices of anesthetized monkeys (Macaca mulatta). We used information theory to characterize the statistical dependency between BOLD and LFP. We found that the alpha (8-12 Hz), beta (18-30 Hz), and gamma (40-100 Hz) LFP bands were informative about the BOLD signal. In agreement with previous studies, gamma was the most informative band. Both increases and decreases in BOLD signal reliably followed increases and decreases in gamma power. However, both alpha and beta power signals carried information about BOLD that was largely complementary to that carried by gamma power. In particular, the relationship between alpha and gamma power was reflected in the amplitude of the BOLD signal, while the relationship between beta and gamma bands was reflected in the latency of BOLD with respect to significant changes in gamma power. These results lay the basis for identifying contributions of different neural pathways to cortical processing using fMRI.

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