Converging intracortical signatures of two separated processing timescales in human early auditory cortex

Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fin-gerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal micro -architecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model.

Joao Emanuel Felipe Gerhard, Wulfram Gerstner, Skander Mensi, Richard Naud

Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in neuroprosthetic devices and electrophysiological experiments. Frequently only a few spike trains are available in each class. We derive analytical expressions for the small-sample bias present when comparing estimators of the time-dependent firing intensity. We then exploit analogies between the comparison of firing intensities and previously used spike train metrics and show that improved spike train measures can be successfully used for fitting neuron models to experimental data, for comparisons of spike trains, and classification of spike train data. In classification tasks, the improved similarity measures can increase the recovered information. We demonstrate that when similarity measures are used for fitting mathematical models, all previous methods systematically underestimate the noise. Finally, we show a striking implication of this deterministic bias by reevaluating the results of the single-neuron prediction challenge.

Massachusetts Institute of Technology Press2011

Real-time EEG Feedback on Alpha Power Lateralization Leads to Behavioral Improvements in a Covert Attention Task

Michael Eric Anthony Pereira, Christoph Schneider, Luca Tonin

Visual attention can be spatially oriented, even in the absence of saccadic eye-movements, to facilitate the processing of incoming visual information. One behavioral proxy for this so-called covert visuospatial attention (CVSA) is the validity effect (VE): the reduction in reaction time (RT) to visual stimuli at attended locations and the increase in RT to stimuli at unattended locations. At the electrophysiological level, one correlate of CVSA is the lateralization in the occipital α-band oscillations, resulting from α-power increases ipsilateral and decreases contralateral to the attended hemifield. While this α-band lateralization has been considerably studied using electroencephalography (EEG) or magnetoencephalography (MEG), little is known about whether it can be trained to improve CVSA behaviorally. In this cross-over sham-controlled study we used continuous real-time feedback of the occipital α-lateralization to modulate behavioral and electrophysiological markers of covert attention. Fourteen subjects performed a cued CVSA task, involving fast responses to covertly attended stimuli. During real-time feedback runs, trials extended in time if subjects reached states of high α-lateralization. Crucially, the ongoing α-lateralization was fed back to the subject by changing the color of the attended stimulus. We hypothesized that this ability to self-monitor lapses in CVSA and thus being able to refocus attention accordingly would lead to improved CVSA performance during subsequent testing. We probed the effect of the intervention by evaluating the pre-post changes in the VE and the α-lateralization. Behaviorally, results showed a significant interaction between feedback (experimental–sham) and time (pre-post) for the validity effect, with an increase in performance only for the experimental condition. We did not find corresponding pre-post changes in the α-lateralization. Our findings suggest that EEG-based real-time feedback is a promising tool to enhance the level of covert visuospatial attention, especially with respect to behavioral changes. This opens up the exploration of applications of the proposed training method for the cognitive rehabilitation of attentional disorders.

SPRINGER2020

Spatio-temporal coupling of the electric and hemodynamic brain responses in humans

Roberto Martuzzi

Our comprehension of human brain functions and their dynamics has been dramatically improved by recent developments in non-invasive imaging techniques. These methods can be divided into two different categories, according to the nature of the measured signal: hemodynamic techniques, such as functional magnetic resonance imaging (fMRI) and positiron emission tomography (PET), and electromagnetic techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). These two categories are have complementary characteristics: hemodynamic techniques have a good spatial resolution (on a millimeter spatial scale) but have a poor temporal resolution, which is inherently limited by the rate changes in blood flow and oxygenation. Electromagnetic techniques have sub-millisecond temporal resolution but have a poor spatial resolution, since the analysis of intracranial generators requires the solution of an underdetermined inverse problem (i.e. there are infinite solutions that can explain equally well the same scalp-recorded distribution). The complementarity of the characteristics of these two families of methods allowed researchers to suppose that the understanding of spatio-temporal brain dynamics can be drastically improved by their combination (so-called multimodal imaging). Unfortunately some caveats hinder such combination. First, the nature of neurovascular coupling is still poorly understood. Second, analytical methods for multimodal imaging are largely in their infancy. The first part of this thesis focuses on the analysis of the temporal characteristics of the blood oxygenation level dependent (BOLD) signal and on how they are modulated by stimulus conditions. To analyze the BOLD dynamics, a novel method for synchronizing stimulus delivery and volume acquisition was developed. This method allows for estimating the BOLD signal with a high temporal resolution (in this thesis up to 125 ms) and for studying how the temporal characteristics (in this thesis mainly the BOLD peak latency and slope) are modulated by stimulus conditions (with an approach similar to that used in the analysis of the EEG evoked potentials). We applied this novel technique to a simple reaction time task to lateralized visual stimuli (the so-called Poffenberger paradigm) as well as to a multisensory auditory-visual reaction time task. In the first study (the Poffenberger paradigm) the analysis of BOLD dynamics supported the theory of a bilateral visuo-motor pathway even in the case of a visual stimulus ipsilateral to the responding hand. In the second study, (the auditory-visual multisensory reaction-time task), the analysis showed auditory-visual interactions within both primary auditory and visual cortices that could not be otherwise revealed by traditional fMRI analysis methods since it does not involve changes in signal amplitude. The second part of this thesis focuses on the comparison of the statistical results obtained by the analyses of fMRI and of the intracranial local field potentials (LFPs), estimated by the ELECTRA inverse solution. We first developed a new method for the analysis of EEG data. This method is based on the statistical comparison of the spectral characteristics of the estimated intracranial LFPs of the pre- and post- stimulus onset periods. Each single trial is analyzed independently, without including an averaging step, so that the information carried by high frequencies is preserved. We also propose a new metric, called resemblance, to investigate the relationship between fMRI and the estimated intracranial LFPs. Single-trial analysis and the resemblance metric were applied in an experiment involving separate EEG and fMRI acquisitions during the same passive visual stimulation protocol. This experiment revealed that only a limited set of LFP frequencies shows a spatial correlation with fMRI. This set of frequencies changes across brain areas, such that progression from lower to higher cortical levels of visual processing incorporates at each step new frequencies. In conclusion, in this thesis we show that the estimation and the analysis of the BOLD time course can give an important contribution to better understanding brain functions and brain organization. To fully understand the meaning of changes in BOLD dynamics, we need a better knowledge of the neuro-vascular coupling. To do that, we introduced a new method for evaluating the relationship between EEG and fMRI across frequencies and anatomical regions.

EPFL2006

Real-time EEG Feedback on Alpha Power Lateralization Leads to Behavioral Improvements in a Covert Attention Task

Michael Eric Anthony Pereira, Christoph Schneider, Luca Tonin

SPRINGER2020

Spatio-temporal coupling of the electric and hemodynamic brain responses in humans

Roberto Martuzzi

EPFL2006

Converging intracortical signatures of two separated processing timescales in human early auditory cortex

Improved Similarity Measures for Small Sets of Spike Trains

Real-time EEG Feedback on Alpha Power Lateralization Leads to Behavioral Improvements in a Covert Attention Task

Spatio-temporal coupling of the electric and hemodynamic brain responses in humans

Real-time EEG Feedback on Alpha Power Lateralization Leads to Behavioral Improvements in a Covert Attention Task

Improved Similarity Measures for Small Sets of Spike Trains

Spatio-temporal coupling of the electric and hemodynamic brain responses in humans