Luiz A. Baccalá

Bio: Luiz A. Baccalá is an academic researcher from University of São Paulo. The author has contributed to research in topics: Coherence (statistics) & Somatosensory system. The author has an hindex of 23, co-authored 81 publications receiving 4437 citations. Previous affiliations of Luiz A. Baccalá include University of Pennsylvania.

Partial directed coherence: a new concept in neural structure determination

Abstract: This paper introduces a new frequency-domain approach to describe the relationships (direction of information flow) between multivariate time series based on the decomposition of multivariate partial coherences computed from multivariate autoregressive models. We discuss its application and compare its performance to other approaches to the problem of determining neural structure relations from the simultaneous measurement of neural electrophysiological signals. The new concept is shown to reflect a frequency-domain representation of the concept of Granger causality.

Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system

Abstract: Neural ensemble processing of sensorimotor information during behavior was investigated by simultaneously recording up to 48 single neurons at multiple relays of the rat trigeminal somatosensory system. Cortical, thalamic, and brainstem neurons exhibited widespread 7- to 12-hertz synchronous oscillations, which began during attentive immobility and reliably predicted the imminent onset of rhythmic whisker twitching. Each oscillatory cycle began as a traveling wave of neural activity in the cortex that then spread to the thalamus. Just before the onset of rhythmic whisker twitching, the oscillations spread to the spinal trigeminal brainstem complex. Thereafter, the oscillations at all levels were synchronous with whisker protraction. Neural structures manifesting these rhythms also exhibited distributed spatiotemporal patterns of neuronal ensemble activity in response to tactile stimulation. Thus, multilevel synchronous activity in this system may encode not only sensory information but also the onset and temporal domain of tactile exploratory movements.

Comparison of different cortical connectivity estimators for high-resolution EEG recordings.

Abstract: The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF.

Thalamic bursting in rats during different awake behavioral states.

Abstract: Thalamic neurons have two firing modes: tonic and bursting. It was originally suggested that bursting occurs only during states such as slow-wave sleep, when little or no information is relayed by the thalamus. However, bursting occurs during wakefulness in the visual and somatosensory thalamus, and could theoretically influence sensory processing. Here we used chronically implanted electrodes to record from the ventroposterior medial thalamic nucleus (VPM) and primary somatosensory cortex (SI) of awake, freely moving rats during different behaviors. These behaviors included quiet immobility, exploratory whisking (large-amplitude whisker movements), and whisker twitching (small-amplitude, 7- to 12-Hz whisker movements). We demonstrated that thalamic bursting appeared during the oscillatory activity occurring before whisker twitching movements, and continued throughout the whisker twitching. Further, thalamic bursting occurred during whisker twitching substantially more often than during the other behaviors, and a neuron was most likely to respond to a stimulus if a burst occurred ≈120 ms before the stimulation. In addition, the amount of cortical area activated was similar to that during whisking. However, when SI was inactivated by muscimol infusion, whisker twitching was never observed. Finally, we used a statistical technique called partial directed coherence to identify the direction of influence of neural activity between VPM and SI, and observed that there was more directional coherence from SI to VPM during whisker twitching than during the other behaviors. Based on these findings, we propose that during whisker twitching, a descending signal from SI triggers thalamic bursting that primes the thalamocortical loop for enhanced signal detection during the whisker twitching behavior.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

Abstract: This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

Rhythms of the brain

Abstract: Prelude. Cycle 1. Introduction. Cycle 2. Structure defines function. Cycle 3. Diversity of cortical functions is provided by inhibition. Cycle 4. Windows on the brain. Cycle 5. A system of rhythms: from simple to complex dynamics. Cycle 6. Synchronization by oscillation. Cycle 7. The brain's default state: self-organized oscillations in rest and sleep. Cycle 8. Perturbation of the default patterns by experience. Cycle 9. The gamma buzz: gluing by oscillations in the waking brain. Cycle 10. Perceptions and actions are brain state-dependent. Cycle 11. Oscillations in the "other cortex:" navigation in real and memory space. Cycle 12. Coupling of systems by oscillations. Cycle 13. The tough problem. References.