Jean-Michel Pignat

The behavioral significance of coherent resting-state oscillations after stroke

Stroke lesions induce not only loss of local neural function, but disruptions in spatially distributed areas. However, it is unknown whether they affect the synchrony of electrical oscillations in neural networks and if changes in network coherence are associated with neurological deficits. This study assessed these questions in a population of patients with subacute, unilateral, ischemic stroke. Spontaneous cortical oscillations were reconstructed from high-resolution electroencephalograms (EEG) with adaptive spatial filters. Maps of functional connectivity (FC) between brain areas were created and correlated with patient performance in motor and cognitive scores. In comparison to age matched healthy controls, stroke patients showed a selective disruption of FC in the alpha frequency range. The spatial distribution of alpha band FC reflected the pattern of motor and cognitive deficits of the individual patient: network nodes that participate normally in the affected functions showed local decreases in FC with the rest of the brain. Interregional FC in the alpha band, but not in delta, theta, or beta frequencies, was highly correlated with motor and cognitive performance. In contrast, FC between contralesional areas and the rest of the brain was negatively associated with patient performance. Alpha oscillation synchrony at rest is a unique and specific marker of network function and linearly associated with behavioral performance. Maps of alpha synchrony computed from a single resting-state EEG recording provide a robust and convenient window into the functionality and organization of cortical networks with numerous potential applications.

Two Intrinsic Coupling Types for Resting-State Integration in the Human Brain

Recent findings indicate that synchronous neural activity at rest influences human performance in subsequent tasks. Synchronization can occur in form of phase coupling or amplitude correlation. It is unknown whether these coupling types have differing behavioral significance at rest. To address this, we performed resting-state electroencephalography (EEG) and source connectivity analysis in several populations of healthy subjects and patients with brain lesions. We systematically compared different types and frequencies of neural synchronization and investigated their association with behavioral performance in verbal and spatial attention tasks. Behavioral performance could be consistently predicted by two distinct resting-state coupling patterns: (1) amplitude envelope correlation of beta activity between homologous areas of both hemispheres, (2) lagged phase synchronization in EEG alpha activity between a brain area and the entire cortex. A disruption of these coupling patterns was also associated with neurological deficits in patients with stroke lesions. This suggests the existence of two distinct network systems responsible for resting-state integration. Lagged phase synchronization in the alpha band is associated with global interaction across networks while amplitude envelope correlation seems to be behaviorally relevant for interactions within networks and between hemispheres. These two coupling types may therefore provide complementary insights on brain physiology and pathology.

Outcome Prediction of Consciousness Disorders in the Acute Stage Based on a Complementary Motor Behavioural Tool

Introduction Attaining an accurate diagnosis in the acute phase for severely brain-damaged patients presenting Disorders of Consciousness (DOC) is crucial for prognostic validity; such a diagnosis determines further medical management, in terms of therapeutic choices and end-of-life decisions. However, DOC evaluation based on validated scales, such as the Revised Coma Recovery Scale (CRS-R), can lead to an underestimation of consciousness and to frequent misdiagnoses particularly in cases of cognitive motor dissociation due to other aetiologies. The purpose of this study is to determine the clinical signs that lead to a more accurate consciousness assessment allowing more reliable outcome prediction. Methods From the Unit of Acute Neurorehabilitation (University Hospital, Lausanne, Switzerland) between 2011 and 2014, we enrolled 33 DOC patients with a DOC diagnosis according to the CRS-R that had been established within 28 days of brain damage. The first CRS-R assessment established the initial diagnosis of Unresponsive Wakefulness Syndrome (UWS) in 20 patients and a Minimally Consciousness State (MCS) in the remaining13 patients. We clinically evaluated the patients over time using the CRS-R scale and concurrently from the beginning with complementary clinical items of a new observational Motor Behaviour Tool (MBT). Primary endpoint was outcome at unit discharge distinguishing two main classes of patients (DOC patients having emerged from DOC and those remaining in DOC) and 6 subclasses detailing the outcome of UWS and MCS patients, respectively. Based on CRS-R and MBT scores assessed separately and jointly, statistical testing was performed in the acute phase using a non-parametric Mann-Whitney U test; longitudinal CRS-R data were modelled with a Generalized Linear Model. Results Fifty-five per cent of the UWS patients and 77% of the MCS patients had emerged from DOC. First, statistical prediction of the first CRS-R scores did not permit outcome differentiation between classes; longitudinal regression modelling of the CRS-R data identified distinct outcome evolution, but not earlier than 19 days. Second, the MBT yielded a significant outcome predictability in the acute phase (p<0.02, sensitivity>0.81). Third, a statistical comparison of the CRS-R subscales weighted by MBT became significantly predictive for DOC outcome (p<0.02). Discussion The association of MBT and CRS-R scoring improves significantly the evaluation of consciousness and the predictability of outcome in the acute phase. Subtle motor behaviour assessment provides accurate insight into the amount and the content of consciousness even in the case of cognitive motor dissociation.

EEG Alpha Band Synchrony Predicts Cognitive and Motor Performance in Patients with Ischemic Stroke

Functional brain networks are known to be affected by focal brain lesions. However, the clinical relevance of these changes remains unclear. This study assesses resting-state functional connectivity (FC) with electroencephalography (EEG) and relates observed topography of FC to cognitive and motor deficits in patients three months after ischemic stroke. Twenty patients (mean age 61.3 years, range 37–80, 9 females) and nineteen age-matched healthy participants (mean age 66.7 years, range 36–88, 13 females) underwent a ten-minute EEG-resting state examination. The neural oscillations at each grey matter voxel were reconstructed using an adaptive spatial filter and imaginary component of coherence (IC) was calculated as an index of FC. Maps representing mean connectivity value at each voxel were correlated with the clinical data. Compared to healthy controls, alpha band IC of stroke patients was locally reduced in brain regions critical to observed behavioral deficits. A voxel-wise Pearson correlation of clinical performances with FC yielded maps of the neural structures implicated in motor, language, and executive function. This correlation was again specific to alpha band coherence. Ischemic lesions decrease the synchrony of alpha band oscillations between affected brain regions and the rest of the brain. This decrease is linearly related to cognitive and motor deficits observed in the patients.

Isolated prospective confabulation in Wernicke–Korsakoff syndrome: a case for reality filtering

A 57-year-old man suffered severe amnesia and disorientation, accompanied by content-specific confabulation, due to an alcoholic Wernicke–Korsakoff syndrome. For months, he was deeply concerned about a single obligation that he thought he had to respond to, but which he had already assumed 20 years previously. This monothematic, prospective confabulation was associated with failures of reality filtering as previously documented in behaviorally spontaneous confabulation and disorientation: the patient failed to suppress the interference of currently irrelevant memories and to abandon anticipations that were no longer valid (impaired extinction capacity). Magnetic resonance imaging showed damage to the mamillary bodies and the dorsomedial thalamic nucleus. Positron emission tomography (FDG-PET) showed extended orbitofrontal hypometabolism. We suggest that isolated prospective confabulation shares the core feature (acts and thoughts based on currently irrelevant memory), mechanism (failure of reality filtering), and anatomical basis (orbitofrontal dysfunction) with behaviorally spontaneous confabulations.

From disorders of consciousness to early neurorehabilitation using assistive technologies in patients with severe brain damage.

PURPOSE OF REVIEW The purpose of this review is to provide an update on the latest challenges addressed by neurorehabilitation initiated very early after the brain damage, such as dealing with disorders of consciousness in terms of diagnosis, prognosis and rehabilitative treatment, or determining best timing for first rehabilitative intervention, best therapeutic approaches and best modalities. RECENT FINDINGS Early management of patients with severe brain damage requires a multidisciplinary rehabilitative approach that encompasses clinical skills in various fields, standard therapies, and assistive technologies.Despite a high rate of misdiagnosis and poor outcome prediction in disorders of consciousness, the observation of subtle motor signs may be a promising way to reach accurate diagnosis and better outcome prediction. Neurosensory stimulation remains the current treatment to promote emergence from disorders of consciousness.Early timing of neurological rehabilitation is definitively efficient, but a safety period should be respected. Some standard therapies and assistive technologies have demonstrated explicit evidence in neurological recovery and high treatment dose is needed to emphasize the therapeutic effect, but several controversies persist in treatment evidence. SUMMARY Current advancements have provided growing evidence for early neurorehabilitation, which should be definitively applied, but further studies are explicitly needed to diminish persistent controversies in the field.

Modulation of environmental reduplicative paramnesia by perceptual experience

Environmental reduplicative paramnesia (ERP) is characterized by the involuntary attribution of a false identity to a place. ERP has rarely been examined experimentally; its mechanisms therefore remain speculative. Here, we describe a patient with extended traumatic right fronto-temporal damage and severe persistent ERP, in whom we were able to modulate ERP by exposing him to various typical landmarks of the town where he was hospitalized. When landmarks were ambiguous as regards location (e.g., unknown buildings), the patient erroneously localized himself in his hometown, which was more than two thousand kilometers away. In contrast, when he visited distinct landmarks of the place where he actually resided, his ERP was immediately corrected, and spatial orientation was restored. These findings indicate that ERP may be temporarily modifiable through perception of unequivocal topographic information.

Non-stationary Gaussian spatio-temporal modeling of fMRI data

In functional magnetic resonance imaging (fMRI), modeling of the complex link between neuronal activity and its hemodynamic expression via the neurovascular coupling usually requires the use of elaborated models. To avoid linear assumptions and a priori modeling of this expected hemodynamic signal, Bayesian approaches have recently improved their accuracy in estimating and detecting brain activation. Recent studies, using Markov random field (MRF) to represent activated brain voxels and likelihoods to find the maximum a posteriori (MAP) estimation of model parameters, provided superior efficiency in comparison with the context-free and the statistical parametric mapping (SPM) approaches. We propose another approach for detecting brain activity by introducing non-Gaussian and non-stationary Gaussian image models that exploit local subband image statistics in the non-decimated wavelet domain. These statistical models, being very simple and tractable, have demonstrated state-of-the-art performance in a number of applications including lossy image compression, denoising and digital watermarking. Such an approach yields close form analytical solutions with low computational complexity that makes it very attractive in neuropsychological research allowing to obtain theoretical performance limits. These models in the scope of the Bayesian estimation framework are applied on synthetic fMRI data corrupted by an additive white Gaussian noise (AWGN) with varying variance and on real fMRI data obtained from a motor preparation task. Comparison of the results with those obtained using SPM maps demonstrates the high efficiency of the proposed method.