A number of cortical structures are reported to have elevated single unit firing rates sustained throughout the memory period of a working memory task. How the nervous system forms and maintains these memories is unknown but reverberating neuronal network activity is thought to be important. We studied the temporal structure of single unit (SU) activity and simultaneously recorded local field potential (LFP) activity from area LIP in the inferior parietal lobe of two awake macaques during a memory-saccade task. Using multitaper techniques for spectral analysis, which play an important role in obtaining the present results, we find elevations in spectral power in a 50-90Hz (gamma) frequency band during the memory period in both SU and LFP activity. The activity is tuned to the direction of the saccade providing evidence for temporal structure that codes for movement plans during working memory. We also find SU and LFP activity are coherent during the memory period in the 50-90Hz gamma band and no consistent relation is present during simple fixation. Finally, we find organized LFP activity in a 15-25Hz frequency band that may be related to movement execution and preparatory aspects of the task. Neuronal activity could be used to control a neural prosthesis but SU activity can be hard to isolate with cortical implants. As the LFP is easier to acquire than SU activity, our finding of rich temporal structure in LFP activity related to movement planning and execution may accelerate the development of this medical application.Keywords: parietal, prosthesis, local field potential, gamma band, coherence, temporal structure.Pesaran et. al. 3Working memory is a brain system requiring the temporary storage and manipulation of information necessary for the performance of complex cognitive tasks (Baddeley, 1992). The neurophysiological basis of working memory is studied in non-human primates by recording neural activity during delayed-response tasks (Fuster, 1995). Cue-selective elevated single unit firing rates have been recorded during the delay period in many brain areas during different versions of the task (Fuster and Jervey, 1982;Bruce and Goldberg, 1985;Gnadt and Andersen, 1988;Miyashita and Chang, 1988;Funahashi et al., 1989;Koch and Fuster, 1989;Miller et al., 1996;Zhou and Fuster, 1996). How this neural activity is sustained is unknown but may be important to understanding the neural basis of working memory (Goldman-Rakic, 1995). Converging evidence points to the importance of a distributed recurrent neuronal network (Goldman-Rakic, 1988) and reverberating network activity has long been suggested as a possible mechanism for short-term memory (Lorente de No, 1938;Hebb, 1949;Amit, 1995;Seung, 1996;Wang, 1999).Measures with the potential to capture correlated neural activity on a millisecond time scale may be needed to resolve reverberating memory activity. The dynamical structure of neuronal activity has been the source of much interest as a temporal code (for a review see Singer and Gray (1995) ) however ...

Modern imaging techniques for probing brain function, including functional magnetic resonance imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques for analysis and visualization of such imaging data to separate the signal from the noise and characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging, and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: "noise" characterization and suppression, and "signal" characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for nonstationarity in the data. Of particular note are 1) the development of a decomposition technique (space-frequency singular value decomposition) that is shown to be a useful means of characterizing the image data, and 2) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.

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New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.