Carlos Pedreira

Past, present and future of spike sorting techniques.

Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future.

Realistic simulation of extracellular recordings.

In this paper we present an efficient method to generate realistic simulations of extracellular recordings. The method uses a hybrid and computationally simple approach, where the features of the background noise arise naturally from its biophysical process of generation. The generated data resemble the characteristics of real recordings, as quantified by the amplitude and frequency distributions. Moreover, we reproduce real features that are specially challenging for the analysis of extracellular data, such as the presence of sparse firing neurons and multi-unit activity. We compare our simulations with real recordings from the human medial temporal lobe and exemplify their use for testing spike detection and sorting algorithms. These results show that this technique provides an optimal scenario for generating realistic simulations of extracellular recordings.

How many neurons can we see with current spike sorting algorithms

Highlights ► Spike sorting algorithms are limited in the number of single units they can detect. ► The maximum number of correctly identified neurons stands between 8 and 10. ► Sparse neurons are strongly affected by this limitation. ► Further development of algorithms is needed to address sparse neurons detection.

Responses of human medial temporal lobe neurons are modulated by stimulus repetition.

Recent studies have reported the presence of single neurons with strong responses to visual inputs in the human medial temporal lobe. Here we show how repeated stimulus presentation--photos of celebrities and familiar individuals, landmark buildings, animals, and objects--modulates the firing rate of these cells: a consistent decrease in the neural activity was registered as images were repeatedly shown during experimental sessions. The effect of repeated stimulus presentation was not the same for all medial temporal lobe areas. These findings are consistent with the view that medial temporal lobe neurons link visual percepts to declarative memory.

How Do We See Art: An Eye-Tracker Study

We describe the pattern of fixations of subjects looking at figurative and abstract paintings from different artists (Molina, Mondrian, Rembrandt, della Francesca) and at modified versions in which different aspects of these art pieces were altered with simple digital manipulations. We show that the fixations of the subjects followed some general common principles (e.g., being attracted to saliency regions) but with a large variability for the figurative paintings, according to the subject’s personal appreciation and knowledge. In particular, we found different gazing patterns depending on whether the subject saw the original or the modified version of the painting first. We conclude that the study of gazing patterns obtained by using the eye-tracker technology gives a useful approach to quantify how subjects observe art.

Single‐cell recordings in the human medial temporal lobe

Recordings from individual neurons in patients who are implanted with depth electrodes for clinical reasons have opened the possibility to narrow down the gap between neurophysiological studies in animals and non‐invasive (e.g. functional magnetic resonance imaging, electroencephalogram, magnetoencephalography) investigations in humans. Here we provide a description of the main procedures for electrode implantation and recordings, the experimental paradigms used and the main steps for processing the data. We also present key characteristics of the so‐called ‘concept cells’, neurons in the human medial temporal lobe with selective and invariant responses that represent the meaning of the stimulus, and discuss their proposed role in declarative memory. Finally, we present novel results dealing with the stability of the representation given by these neurons, by studying the effect of stimulus repetition in the strength of the responses. In particular, we show that, after an initial decay, the response strength reaches an asymptotic value after approximately 15 presentations that remains above baseline for the whole duration of the experiment.

A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers

Highlights • We created a validation method for the evaluation of automated classification of interictal spikes.• We used a modified version of Wave_clus (WC) to automatically classify the data of 5 patients.• WC classification was similar to EEG reviewers providing an unbiased evaluation of the clinical data.

BOLD mapping of human epileptic spikes recorded during simultaneous intracranial EEG-fMRI: The impact of automated spike classification

Objectives: Simultaneous intracranial EEG and functional MRI (icEEG‐fMRI) can be used to map the haemodynamic (BOLD) changes associated with the generation of IEDs. Unlike scalp EEG‐fMRI, in most patients who undergo icEEG‐fMRI, IEDs recorded intracranially are numerous and show variability in terms of field amplitude and morphology. Therefore, visual marking can be highly subjective and time consuming. In this study, we applied an automated spike classification algorithm, Wave_clus (WC), to IEDs marked visually on icEEG data acquired during simultaneous fMRI acquisition. The motivation of this work is to determine whether using a potentially more consistent and unbiased automated approach can produce more biologically meaningful BOLD patterns compared to the BOLD patterns obtained based on the conventional, visual classification. Methods: We analysed simultaneous icEEG‐fMRI data from eight patients with severe drug resistant epilepsy, and who subsequently underwent resective surgery that resulted in a good outcome: confirmed epileptogenic zone (EZ). For each patient two fMRI analyses were performed: one based on the conventional visual IED classification and the other based on the automated classification. We used the concordance of the IED‐related BOLD maps with the confirmed EZ as an indication of their biological meaning, which we compared for the automated and visual classifications for all IED originating in the EZ. Results: Across the group, the visual and automated classifications resulted in 32 and 24 EZ IED classes respectively, for which 75% vs 83% of the corresponding BOLD maps were concordant. At the single‐subject level, the BOLD maps for the automated approach had greater concordance in four patients, and less concordance in one patient, compared to those obtained using the conventional visual classification, and equal concordance for three remaining patients. These differences did not reach statistical significance. Conclusion: We found automated IED classification on icEEG data recorded during fMRI to be feasible and to result in IED‐related BOLD maps that may contain similar or greater biological meaning compared to the conventional approach in the majority of the cases studied. We anticipate that this approach will help to gain significant new insights into the brain networks associated with IEDs and in relation to postsurgical outcome. HighlightsIcEEG‐fMRI provides a unique insight into the generators of IEDs.Visual IED marking can be highly subjective and time consuming.An automated spike classification algorithm, Wave_clus, can minimise subjectivity.The BOLD maps associated with IEDs classified using Wave_clus may commonly have equal or greater biological meaning than those obtained using conventional, visual classification.