Subsequent and simultaneous electrophysiological investigation of the retina and the visual cortex in neurodegenerative and psychiatric diseases: what are the forecasts for the medicine of tomorrow?

Visual electrophysiological deficits have been reported in neurodegenerative disorders as well as in mental disorders. Such alterations have been mentioned in both the retina and the cortex, notably affecting the photoreceptors, retinal ganglion cells (RGCs) and the primary visual cortex. Interestingly, such impairments emphasize the functional role of the visual system. For this purpose, the present study reviews the existing literature with the aim of identifying key alterations in electroretinograms (ERGs) and visual evoked potentials electroencephalograms (VEP-EEGs) of subjects with neurodegenerative and psychiatric disorders. We focused on psychiatric and neurodegenerative diseases due to similarities in their neuropathophysiological mechanisms. Our research focuses on decoupled and coupled ERG/VEP-EEG results obtained with black-and-white checkerboards or low-level visual stimuli. A decoupled approach means recording first the ERG, then the VEP-EEG in the same subject with the same visual stimuli. The second method means recording both ERG and VEP-EEG simultaneously in the same participant with the same visual stimuli. Both coupled and decoupled results were found, indicating deficits mainly in the N95 ERG wave and the P100 VEP-EEG wave in Parkinson's, Alzheimer's, and major depressive disorder. Such results reinforce the link between the retina and the visual cortex for the diagnosis of psychiatric and neurodegenerative diseases. With that in mind, medical devices using coupled ERG/VEP-EEG measurements are being developed in order to further investigate the relationship between the retina and the visual cortex. These new techniques outline future challenges in mental health and the use of machine learning for the diagnosis of mental disorders, which would be a crucial step toward precision psychiatry.

A Bayesian approach for simultaneous spike/LFP separation and spike sorting.

Objective.The aim of this paper is to present a novel method for simultaneous spike waveforms extraction and sorting from the raw recorded signal. The objective is twofold: on the one hand, to enhance spike sorting performance by extracting the spike waveforms of each spike and, on the other hand, to improve the analysis of the multi-scale relationships between spikes and local field potentials (LFP) by offering an accurate separation of these two components constitutive of the raw micro recordings.Approach.The method, based on a Bayesian approach, is fully automated and provides a mean spike shape for each cluster, but also an estimate for each singular spike waveform, as well as the LFP signal cleaned of spiking activity.Main results.The performance of the algorithm is evaluated on simulated and real data, for which both the clustering and spike removal aspects are analyzed. Clustering performance significantly increases when compared to state-of-the-art methods, taking benefit from the separation of the spikes from the LFP handled by our model. Our method also performs better in removing the spikes from the LFP when compared to previously proposed methodologies, especially in the high frequency bands. The method is finally applied on real data (ClinicalTrials.gov Identifier: NCT02877576) and confirm the results obtained on benchmark signals.Significance.By separating more efficiently the spikes from the LFP background, our method allows both a better spike sorting and a more accurate estimate of the LFP, facilitating further analysis such as spike-LFP relationships.

On the localization of oscillatory sources from (S)EEG recordings.

Electrophysiological brain source localization consists in estimating the source positions and activities responsible for the (S)EEG measurements. The localization procedure is usually carried out in the time domain, however in specific situations the activities of interest can be located at well defined frequencies, e.g. in response to a rhythmic stimulation. This paper addresses the problem of sparse localization of multiple sources oscillating at the same frequency. In particular the non-unicity of the solution is emphasized, as alternative source maps involving equivalent or less number of sources can be found, challenging source localization methods based on sparsity. These limitations are illustrated under a realistic SEEG simulation framework, and the usefulness to perform localization for this modality is strengthen out.

Visual electrophysiology and neuropsychology in bipolar disorders: A review on current state and perspectives.

Bipolar disorder is a lifelong condition. Today, there is a urgent need to find indicators of the disease. Specifically, they could be useful to improve the diagnosis and the early detection, the prognosis, to estimate the treatment response and to create homogeneous subgroups of patients based on similar pathophysiological mechanisms. Here, we assume that visual electrophysiology in combination with a neuropsychological assessment can give additional data to routine practice, especially to precise specific damages and pathophysiological characteristics of these patients. Visual electrophysiology is characterized by an electroretinogram and the delivery of visual evoked potentials, which measure retinal and visual cortical neuronal functioning in response to visual stimulations. This review highlights the interest of visual electrophysiology and neuropsychology performed in isolation and to present the benefits of combining these measures. We will review the results based on these measures in patients with bipolar disorders. Finally, we argue for the use of innovative techniques such as signal processing and artificial intelligence techniques for routine care and precision medicine in bipolar disorders.

Retinal electroretinogram features can detect depression state and treatment response in adults: A machine learning approach.

BACKGROUND: Major depressive disorder (MDD) is a major public health problem. The retina is a relevant site to indirectly study brain functioning. Alterations in retinal processing were demonstrated in MDD with the pattern electroretinogram (PERG). Here, the relevance of signal processing and machine learning tools applied on PERG was studied. METHODS: PERG - whose stimulation is reversible checkerboards - was performed according to the International Society for Clinical Electrophysiology of Vision (ISCEV) standards in 24 MDD patients and 29 controls at the inclusion. PERG was recorded every 4 weeks for 3 months in patients. Amplitude and implicit time of P50 and N95 were evaluated. Then, time/frequency features were extracted from the PERG time series based on wavelet analysis. A statistical model has been learned in this feature space and a metric aiming at quantifying the state of the MDD patient has been derived, based on minimum covariance determinant (MCD) mahalanobis distance. RESULTS: MDD patients showed significant increase in P50 and N95 implicit time (p = 0,006 and p = 0,0004, respectively, Mann-Whitney U test) at the inclusion. The proposed metric extracted from the raw PERG provided discrimination between patients and controls at the inclusion (p = 0,0001). At the end of the follow-up at week 12, the difference between the metrics extracted on controls and patients was not significant (p = 0,07), reflecting the efficacy of the treatment. CONCLUSIONS: Signal processing and machine learning tools applied on PERG could help clinical decision in the diagnosis and the follow-up of MDD in measuring treatment response.

Sparse EEG source localization in frequency domain.

This work presents an approach for EEG source localization when strong priors on predominant frequencies in the activities of the source are available. We describe the fundamentals of the used source reconstruction method based on a greedy approach, which can be applied indifferently in the time or frequency domain. The method is evaluated using simulated data reproducing realistic recorded activities in the context of fast periodic visual stimulation. In particular the advantage of reconstructing the source in the frequency domain against time domain is quantified in a realistic setup. Finally, the performances of the method are illustrated on real EEG signals recorded during a fast periodic visual stimulation task.

Fast simulation of extracellular action potential signatures based on a morphological filtering approximation.

Simulating extracellular recordings of neuronal populations is an important and challenging task both for understanding the nature and relationships between extracellular field potentials at different scales, and for the validation of methodological tools for signal analysis such as spike detection and sorting algorithms. Detailed neuronal multicompartmental models with active or passive compartments are commonly used in this objective. Although using such realistic NEURON models could lead to realistic extracellular potentials, it may require a high computational burden making the simulation of large populations difficult without a workstation. We propose in this paper a novel method to simulate extracellular potentials of firing neurons, taking into account the NEURON geometry and the relative positions of the electrodes. The simulator takes the form of a linear geometry based filter that models the shape of an action potential by taking into account its generation in the cell body / axon hillock and its propagation along the axon. The validity of the approach for different NEURON morphologies is assessed. We demonstrate that our method is able to reproduce realistic extracellular action potentials in a given range of axon/dendrites surface ratio, with a time-efficient computational burden.

A unified weighted minimum norm solution for the reference inverse problem in EEG.

A well known problem in EEG recordings deals with the unknown potential of the reference electrode. In the last years several authors presented comparisons among the most popular solutions, the global conclusion being that the traditional Average Reference (AR) and the Reference Standardization Technique (REST) are the best approximations (Nunez, 2010; Kayser and Tenke, 2010; Liu et al., 2015; Chella et al., 2016). In this work we do not aim to further compare these techniques but to support the fact that both solutions can be derived from a general inverse problem formalism for reference estimation (Hu et al., 2019; Hu et al., 2018; Salido-Ruiz et al., 2011). Using the alternative approach of least squares, our findings are consistent with the theoretical findings in Hu et al. (2019) and Hu et al. (2018) showing that the AR is the minimum norm solution, while REST is a weighted minimum norm including some approximate propagation model. AR is thus a particular case of REST, which itself uses a particular formulation of the source estimation inverse problem. With a different derivation, we provide the additional powerful evidences to reinforce the cited findings.

A multi-channel approach for cortical stimulation artefact suppression in depth EEG signals using time-frequency and spatial filtering.

OBJECTIVE: The stereo electroencephalogram (SEEG) recordings are the sate of the art tool used in pre-surgical evaluation of drug-unresponsive epileptic patients. Coupled with SEEG, electrical cortical stimulation (CS) offer a complementary tool to investigate the lesioned/healthy brain regions and to identify the epileptic zones with precision. However, the propagation of this stimulation inside the brain masks the cerebral activity recorded by nearby multi-contact SEEG electrodes. The objective of this paper is to propose a novel filtering approach for suppressing the CS artifact in SEEG signals using time, frequency as well as spatial information. METHODS: The method combines spatial filtering with tunable-Q wavelet transform (TQWT). SEEG signals are spatially filtered to isolate the CS artifacts within a few number of sources/components. The artifacted components are then decomposed into oscillatory background and sharp varying transient signals using tunable-Q wavelet transform (TQWT). The CS artifact is assumed to lie in the transient part of the signal. Using prior known time-frequency information of the CS artifacts, we selectively mask the wavelet coefficients of the transient signal and extract out any remaining significant electrophysiological activity. RESULTS: We have applied our proposed method of CS artifact suppression on simulated and real SEEG signals with convincing performance. The experimental results indicate the effectiveness of the proposed approach. CONCLUSION: The proposed method suppresses CS artifacts without affecting the background SEEG signal. SIGNIFICANCE: The proposed method can be applied for suppressing both low and high frequency CS artifacts and outperforms current methods from the literature.

Assessing human brain impedance using simultaneous surface and intracerebral recordings.

Most of the literature on the brain impedance proposes a frequency-independent resistive model. Recently, this conclusion was tackled by a series of papers (Bédard et al., 2006; Bédard and Destexhe, 2009; Gomes et al., 2016), based on microscopic sale modeling and measurements. Our paper aims to investigate the impedance issue using simultaneous in vivo depth and surface signals recorded during intracerebral electrical stimulation of epileptic patients, involving a priori different tissues with different impedances. Our results confirm the conclusions from Logothethis et al. (2007): there is no evidence of frequency dependence of the brain tissue impedance (more precisely, there is no difference, in terms of frequency filtering, between the brain and the skull bone), at least at a macroscopic scale. In order to conciliate findings from both microscopic and macroscopic scales, we recall different neural/synaptic current generators' models from the literature and we propose an original computational model, based on fractional dynamics.

A Space-Time-Frequency Dictionary for Sparse Cortical Source Localization.

OBJECTIVE: Cortical source imaging aims at identifying activated cortical areas on the surface of the cortex from the raw electroencephalogram (EEG) data. This problem is ill posed, the number of channels being very low compared to the number of possible source positions. METHODS: In some realistic physiological situations, the active areas are sparse in space and of short time durations, and the amount of spatio-temporal data to carry the inversion is then limited. In this study, we propose an original data driven space-time-frequency (STF) dictionary which takes into account simultaneously both spatial and time-frequency sparseness while preserving smoothness in the time frequency (i.e., nonstationary smooth time courses in sparse locations). Based on these assumptions, we take benefit of the matching pursuit (MP) framework for selecting the most relevant atoms in this highly redundant dictionary. RESULTS: We apply two recent MP algorithms, single best replacement (SBR) and source deflated matching pursuit, and we compare the results using a spatial dictionary and the proposed STF dictionary to demonstrate the improvements of our multidimensional approach. We also provide comparison using well-established inversion methods, FOCUSS and RAP-MUSIC, analyzing performances under different degrees of nonstationarity and signal to noise ratio. CONCLUSION: Our STF dictionary combined with the SBR approach provides robust performances on realistic simulations. From a computational point of view, the algorithm is embedded in the wavelet domain, ensuring high efficiency in term of computation time. SIGNIFICANCE: The proposed approach ensures fast and accurate sparse cortical localizations on highly nonstationary and noisy data.

Combining bayesian source imaging with equivalent dipole approach to solve the intracranial EEG source localization problem.

The brain source localization problem has been extensively studied in the past years, yielding a large panel of methodologies, each bringing their own strengths and weaknesses. Combining several of these approaches might help in enhancing their respective performance. Our study is carried out in the particular context of intracranial recordings, with the objective to explain the measurements based on a reduced number of dipolar activities. We take benefit of the sparse nature of the Bayesian approaches to separate the noise from the source space, and to distinguish between several source contributions on the electrodes. This first step provides accurate estimates of the dipole projections, which can be used as an entry to an equivalent current dipole fitting procedure. We demonstrate on simulations that the localization results are significantly enhanced by this post-processing step when up to five dipoles are activated simultaneously.

Sparse cortical source localization using spatio-temporal atoms.

This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which uses a data driven spatio-temporal dictionary to reconstruct the measurements using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithms. We tested and compared our methods with the well-known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage over MUSIC-type methods in case of synchronized sources.

Dipolar estimates of the cortical map.

Various methods based on anatomical or mathematical models have been developed to estimate cortical potentials. Among them, the most popular are the surface Laplacians (SL) and the Electrical Source Imaging (ESI) approaches. In this paper, we develop an informed method named dipolar cortical mapping (DCM), aiming to find a balance between ESI methods based on anatomical models and methods without strong anatomical priors, such as surface Laplacians. Our method only uses easily available information on the electrode position and is based on a physiologically parametrized family of interpolating functions. Simulation results show that DCM competes with previously proposed surface Laplacians and with the model based Minimum Norm Estimates (MNE) computed with a Boundary Element Model (BEM).

Influence of the stereo-EEG sensors setup and of the averaging on the dipole localization problem.

While scalp EEG/MEG source imaging have been extensively studied in the last two decades, the case of source localization from invasive measurements has resulted in few works to date. Yet there is a lot to gain from stereo-electroencephalographic (SEEG) recordings, providing high signal to noise ratio measurements of the explored brain structures. The SEEG setup consists in multi-contact electrodes inserted in the brain volume, each containing a dozen of collinear measuring contacts. This particular setup raises the question of the conditioning of the inverse problem. In recent works, we have evaluated the feasibility to localize a single dominant equivalent dipole facing different sensors and noise configurations. We deepen here the analysis by evaluating the influence of the chosen subset of sensors and of the number of averaged time samples on the accuracy of the localization. We conduct experiments on simulated data as well as on real epileptic spikes, illustrating the trade off to be made between these two factors.

Dipolar source localization from intracerebral SEEG recordings.

This paper aims at exploring the feasibility of a brain source localization method from intracerebral stereo-electroencephalography (SEEG) measurements. The SEEG setup consists in multi-contact electrodes inserted in the brain volume, each containing about 10 collinear measuring contacts. In clinical context, these signals are usually observed using a bipolar montage (potential differences between neighbouring contacts of a SEEG electrode). The propagation of distant activity is thus suppressed, resulting in the observation of local activities around the contacts. We propose in this paper to take benefit of the propagation information by considering the original SEEG recordings (common reference montage), with the objective to localize sources possibly distant from the electrode contacts, and whose activities are propagating through the volume. Our method is based on an equivalent dipole model for the source and homogeneous infinite models for the propagation environment. This simple approach shows satisfactory localization performance under appropriate conditions, described in this paper. The proposed method is validated on real SEEG signals for the localisation of an intra-cortical electrical stimulation (ICS) generator.

Hidden Markov chain modeling for epileptic networks identification.

The partial epileptic seizures are often considered to be caused by a wrong balance between inhibitory and excitatory interneuron connections within a focal brain area. These abnormal balances are likely to result in loss of functional connectivities between remote brain structures, while functional connectivities within the incriminated zone are enhanced. The identification of the epileptic networks underlying these hypersynchronies are expected to contribute to a better understanding of the brain mechanisms responsible for the development of the seizures. In this objective, threshold strategies are commonly applied, based on synchrony measurements computed from recordings of the electrophysiologic brain activity. However, such methods are reported to be prone to errors and false alarms. In this paper, we propose a hidden Markov chain modeling of the synchrony states with the aim to develop a reliable machine learning methods for epileptic network inference. The method is applied on a real Stereo-EEG recording, demonstrating consistent results with the clinical evaluations and with the current knowledge on temporal lobe epilepsy.

Smoothness constraint for cortical dipolar sources estimation.

In the last decade, a wide range of approaches have been proposed to estimate the activity of physiological sources from multi-channel electroencephalographic (EEG) data. Two utterly different directions can be distinguished: brain source imaging (BSI) and blind source separation (BSS). While the first approach is based on the inversion of a given forward model, the latter blindly decomposes the EEG mixing by optimization of a contrast function excluding any physiological priors on the problem. All these methods have proven their ability in reconstructing efficiently the source activities in some well adapted situations. Nevertheless, the synthesis of a reliable lead field model for BSI is computationally demanding, and the criterion to be optimized in BSS are often inadequate with regards to the physiology of the problem. In this paper, a compromise between these two methodological trends is introduced. A BSS method is described taking account of physiological knowledge on the projection of the sources on the scalp map in conjunction with strong priors on the localization of the recorded sources. This estimation method is demonstrated to lead to a generalization of the classical Hjorth's laplacian montage, and provides satisfactory simulation results when the appropriate configurations on the sources are met.