Rodrigo Quian Quiroga

Nonlinear multivariate analysis of neurophysiological signals

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependence between signals. We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.

Extracting information from neuronal populations: information theory and decoding approaches.

To a large extent, progress in neuroscience has been driven by the study of single-cell responses averaged over several repetitions of stimuli or behaviours. However,the brain typically makes decisions based on single events by evaluating the activity of large neuronal populations. Therefore, to further understand how the brain processes information, it is important to shift from a single-neuron, multiple-trial framework to multiple-neuron, single-trial methodologies. Two related approaches — decoding and information theory — can be used to extract single-trial information from the activity of neuronal populations. Such population analysis can give us more information about how neurons encode stimulus features than traditional single-cell studies.

Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex.

Local field potentials (LFPs) arise largely from dendritic activity over large brain regions and thus provide a measure of the input to and local processing within an area. We characterized LFPs and their relationship to spikes (multi and single unit) in monkey inferior temporal cortex (IT). LFP responses in IT to complex objects showed strong selectivity at 44% of the sites and tolerance to retinal position and size. The LFP preferences were poorly predicted by the spike preferences at the same site but were better explained by averaging spikes within approximately 3 mm. A comparison of separate sites suggests that selectivity is similar on a scale of approximately 800 microm for spikes and approximately 5 mm for LFPs. These observations imply that inputs to IT neurons convey selectivity for complex shapes and that such input may have an underlying organization spanning several millimeters.

Learning driver-response relationships from synchronization patterns

We test recent claims that causal (driver-response) relationships can be deduced from interdependencies between simultaneously measured time series. We apply two recently proposed interdependence measures that should give results similar to cross predictabilities used by previous authors. The systems that we study are asymmetrically coupled simple models (Lorenz, Roessler, and Hénon models), the couplings being such that they lead to generalized synchronization. If the data were perfect (noise-free, infinitely long), we should be able to detect, at least in some cases, which of the coupled systems is the driver and which the response. This might no longer be true if the time series has finite length. Instead, estimated interdependencies depend strongly on which of the systems has a higher effective dimension at the typical neighborhood sizes used to estimate them, and causal relationships are more difficult to detect. We also show that slightly different variants of the interdependence measure can have quite different sensitivities.

Antigenic variation in Giardia lamblia is regulated by RNA interference

Giardia lamblia (also called Giardia intestinalis) is one of the most common intestinal parasites of humans. To evade the host’s immune response, Giardia undergoes antigenic variation—a process that allows the parasite to develop chronic and recurrent infections. From a repertoire of ∼190 variant-specific surface protein (VSP)-coding genes, Giardia expresses only one VSP on the surface of each parasite at a particular time, but spontaneously switches to a different VSP by unknown mechanisms. Here we show that regulation of VSP expression involves a system comprising RNA-dependent RNA polymerase, Dicer and Argonaute, known components of the RNA interference machinery. Clones expressing a single surface antigen efficiently transcribe several VSP genes but only accumulate transcripts encoding the VSP to be expressed. Detection of antisense RNAs corresponding to the silenced VSP genes and small RNAs from the silenced but not for the expressed vsp implicate the RNA interference pathway in antigenic variation. Remarkably, silencing of Dicer and RNA-dependent RNA polymerase leads to a change from single to multiple VSP expression in individual parasites.

Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe

Neurons in the temporal lobe of both monkeys and humans show selective responses to classes of visual stimuli and even to specific individuals. In this study, we investigate the latency and selectivity of visually responsive neurons recorded from microelectrodes in the parahippocampal cortex, entorhinal cortex, hippocampus, and amygdala of human subjects during a visual object presentation task. During 96 experimental sessions in 35 subjects, we recorded from a total of 3278 neurons. Of these units, 398 responded selectively to one or more of the presented stimuli. Mean response latencies were substantially larger than those reported in monkeys. We observed a highly significant correlation between the latency and the selectivity of these neurons: the longer the latency the greater the selectivity. Particularly, parahippocampal neurons were found to respond significantly earlier and less selectively than those in the other three regions. Regional analysis showed significant correlations between latency and selectivity within the parahippocampal cortex, entorhinal cortex, and hippocampus, but not within the amygdala. The later and more selective responses tended to be generated by cells with sparse baseline firing rates and vice versa. Our results provide direct evidence for hierarchical processing of sensory information at the interface between the visual pathway and the limbic system, by which increasingly refined and specific representations of stimulus identity are generated over time along the anatomic pathways of the medial temporal lobe.

Movement intention is better predicted than attention in the posterior parietal cortex

We decoded on a trial-by-trial basis the location of visual targets, as a marker of the locus of attention, and intentions to reach and to saccade in different directions using the activity of neurons in the posterior parietal cortex of two monkeys. Predictions of target locations were significantly worse than predictions of movement plans for the same target locations. Moreover, neural signals in the parietal reach region (PRR) gave better predictions of reaches than saccades, whereas signals in the lateral intraparietal area (LIP) gave better predictions of saccades than reaches. Taking together the activity of both areas, the prediction of either movement in all directions became nearly perfect. These results cannot be explained in terms of an attention effect and support the idea of two segregated populations in the posterior parietal cortex, PRR and LIP, that are involved in different movement plans.

Sparse Representation in the Human Medial Temporal Lobe

Recent experiments characterized individual neurons in the human medial temporal lobe with remarkably selective, invariant, and explicit responses to images of famous individuals or landmark buildings. Here, we used a probabilistic analysis to show that these data are consistent with a sparse code in which neurons respond in a selective manner to a small fraction of stimuli.

Stationarity of the EEG series

The authors introduce a routine for the analysis of stationarity, which is based on the weak stationarity criteria. From the application of the weak stationarity criteria, one see that only in some cases or in some epochs of a series will the information obtained from these algorithms be reliable. Therefore, one can only obtain dynamic information in those cases. As a complement to this finding, the correlation dimension and the Lyapunov exponents were calculated for the different EEG series analyzed. >

On-line, voluntary control of human temporal lobe neurons.

Daily life continually confronts us with an exuberance of external, sensory stimuli competing with a rich stream of internal deliberations, plans and ruminations. The brain must select one or more of these for further processing. How this competition is resolved across multiple sensory and cognitive regions is not known; nor is it clear how internal thoughts and attention regulate this competition. Recording from single neurons in patients implanted with intracranial electrodes for clinical reasons, here we demonstrate that humans can regulate the activity of their neurons in the medial temporal lobe (MTL) to alter the outcome of the contest between external images and their internal representation. Subjects looked at a hybrid superposition of two images representing familiar individuals, landmarks, objects or animals and had to enhance one image at the expense of the other, competing one. Simultaneously, the spiking activity of their MTL neurons in different subregions and hemispheres was decoded in real time to control the content of the hybrid. Subjects reliably regulated, often on the first trial, the firing rate of their neurons, increasing the rate of some while simultaneously decreasing the rate of others. They did so by focusing onto one image, which gradually became clearer on the computer screen in front of their eyes, and thereby overriding sensory input. On the basis of the firing of these MTL neurons, the dynamics of the competition between visual images in the subject’s mind was visualized on an external display.