No abstract

Although the electroencephalogram (EEG) is widely used in research and clinical settings, its link to the underlying neural activity during sensory processing remains poorly understood. To investigate this, we made simultaneous recordings of surface EEG, intracortical local field potential, and multiunit activity (MUA) in the alert monkey visual cortex during presentation of natural movies. Using a general linear model, we show that in single trials, EEG power in the gamma band (30-100 Hz) and phase in delta band (2-4 Hz) are significant predictors of the MUA response. Specifically, we found that the MUA response was strongest only when increases in EEG gamma power occurred during the negative-going phase of the delta wave, thus revealing a frequency-band coupling mechanism that can be exploited to infer population spiking activity. This finding may open up a new dimension in the use and interpretation of EEG in normal and pathological conditions.

BackgroundInformation theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.ResultsHere we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies.ConclusionThe new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.

For vocal animals, recognizing species-specific vocalizations is important for survival and social interactions. In humans, a voice region has been identified that is sensitive to human voices and vocalizations. As this region also strongly responds to speech, it is unclear whether it is tightly associated with linguistic processing and is thus unique to humans. Using functional magnetic resonance imaging of macaque monkeys (Old World primates, Macaca mulatta) we discovered a high-level auditory region that prefers species-specific vocalizations over other vocalizations and sounds. This region not only showed sensitivity to the 'voice' of the species, but also to the vocal identify of conspecific individuals. The monkey voice region is located on the superior-temporal plane and belongs to an anterior auditory 'what' pathway. These results establish functional relationships with the human voice region and support the notion that, for different primate species, the anterior temporal regions of the brain are adapted for recognizing communication signals from conspecifics.