Improved characterization of visual evoked potentials in multiple sclerosis by topographic analysis

Visual evoked potentials in multiple sclerosis: P100 latency and visual pathway damage including the lateral geniculate nucleus

OBJECTIVE

To explore associations of the main component (P100) of visual evoked potentials (VEP) to pre- and postchiasmatic damage in multiple sclerosis (MS).

METHODS

31 patients (median EDSS: 2.5), 13 with previous optic neuritis (ON), and 31 healthy controls had VEP, optical coherence tomography and magnetic resonance imaging. We tested associations of P100-latency to the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell/inner plexiform layers (GCIPL), lateral geniculate nucleus volume (LGN), white matter lesions of the optic radiations (OR-WML), fractional anisotropy of non-lesional optic radiations (NAOR-FA), and to the mean thickness of primary visual cortex (V1). Effect sizes are given as marginal R2 (mR2).

RESULTS

P100-latency, pRNFL, GCIPL and LGN in patients differed from controls. Within patients, P100-latency was significantly associated with GCIPL (mR2 = 0.26), and less strongly with OR-WML (mR2 = 0.17), NAOR-FA (mR2 = 0.13) and pRNFL (mR2 = 0.08). In multivariate analysis, GCIPL and NAOR-FA remained significantly associated with P100-latency (mR2 = 0.41). In ON-patients, P100-latency was significantly associated with LGN volume (mR2 = -0.56).

CONCLUSIONS

P100-latency is affected by anterior and posterior visual pathway damage. In ON-patients, damage at the synapse-level (LGN) may additionally contribute to latency delay.

SIGNIFICANCE

Our findings corroborate post-chiasmatic contributions to the VEP-signal, which may relate to distinct pathophysiological mechanisms in MS.

Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance

Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain-computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio.Approach.Using the principle of nonlinear aperiodic FitzHugh-Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times.Results:A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise.Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.

Reliability analysis of individual visual P1 and N1 maps indicates the heterogeneous topographies involved in early visual processing among human subjects

There is a lack of studies regarding the reliability of the event-related components (ERPs) of an electroencephalogram (EEG) used to assess cognitive processing in human subjects. To explore the reliability scores for the P1 and N1 components in two sessions (separated by an average of 116 days), twenty subjects performed a visual lateralized detection paradigm and EEG recording (58 channels) were employed. The session factor did not modulate the P1/N1 latencies. The visual field factor (left (LVF) or right (RVF)) was a determinant for the P1 and N1 topographical distributions as shown in previous studies. Moreover, topographical maps of the grand average showed a very strong correlation level between sessions (>0.9). Finally, individual maps demonstrated that the classic contralateral pattern for the P1 and N1 components was not always present in all subjects. In particular, compared to the N1 component, the P1 component exhibited a more complex set of individual topographical distributions, revealing that some steps are more heterogeneous among human subjects in early visual processing.

Deficient processing of alcohol cues in the addicted brain: Evidence from event-related potential microstates

OBJECTIVE

Patients with alcohol use disorder (AUD) show altered brain responses to alcohol cues as compared to healthy controls. Event-related potential (ERP) studies mostly focus on the P3, which is usually diminished in AUD patients. The few studies that have investigated earlier components have yielded inconsistent results. The present study aimed at identifying the onset of impaired alcohol cue processing in AUD patients, as well as the association between neurophysiological processing and subjective craving.

METHODS

A sample of 15 inpatients with AUD and 15 healthy controls completed a cue reactivity task with alcohol-related, neutral, and scrambled pictures. Multichannel-EEG was recorded from 70 scalp electrodes, and ERP microstates were analyzed.

RESULTS

Patients displayed impaired neurophysiological processing, as indexed by a weaker P3 and a weaker, insensitive P1. The blunted P1 was associated with higher subjective craving.

CONCLUSIONS

Impaired alcohol cue processing in AUD emerges early, at the stage of sensory processing. Such deficient initial processing seems crucial to understanding cue reactivity processes in the brain and in the subjective experience of craving.

SIGNIFICANCE

The results highlight the importance of investigating early ERP components preceding the P3, and contribute to the debate on the onset of information-processing dysfunction in the addicted brain.

Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis

Background

Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of motor EP that uses the whole time series, instead of a few variables, to predict disability progression after two years. Obtaining realistic performance estimates of this task has been difficult because of small data set sizes. We recently extracted a dataset of EPs from the Rehabiliation & MS Center in Overpelt, Belgium. Our data set is large enough to obtain, for the first time, a performance estimate on an independent test set containing different patients.

Methods

We extracted a large number of time series features from the motor EPs with the highly comparative time series analysis software package. Mutual information with the target and the Boruta method are used to find features which contain information not included in the features studied in the literature. We use random forests (RF) and logistic regression (LR) classifiers to predict disability progression after two years. Statistical significance of the performance increase when adding extra features is checked.

Results

Including extra time series features in motor EPs leads to a statistically significant improvement compared to using only the known features, although the effect is limited in magnitude (ΔAUC = 0.02 for RF and ΔAUC = 0.05 for LR). RF with extra time series features obtains the best performance (AUC = 0.75±0.07 (mean and standard deviation)), which is good considering the limited number of biomarkers in the model. RF (a nonlinear classifier) outperforms LR (a linear classifier).

Conclusions

Using machine learning methods on EPs shows promising predictive performance. Using additional EP time series features beyond those already in use leads to a modest increase in performance. Larger datasets, preferably multi-center, are needed for further research. Given a large enough dataset, these models may be used to support clinicians in their decision making process regarding future treatment.

Developing therapeutic strategies to promote myelin repair in multiple sclerosis

Introduction: Approved disease-modifying therapies for multiple sclerosis (MS) lessen inflammatory disease activity that causes relapses and MRI lesions. However, chronic inflammation and demyelination lead to axonal degeneration and neuronal loss, for which there currently is no effective treatment. There has been increasing interest in developing repair-promoting strategies, but there are important unanswered questions regarding the mechanisms and appropriate methods to evaluate these treatments. Areas covered: The rationale for remyelinating agents in MS is discussed, with an overview of both myelin physiology and endogenous repair mechanisms. This is followed by a discussion of the identification and development of potential remyelinating drugs. Potential biomarkers of remyelination are reviewed, including considerations regarding measuring remyelination in clinical trials. Information and data were obtained from a search of recent literature through PubMed. Peer-reviewed original articles and review articles were included. Expert opinion: There are several obstacles to the translation of potential remyelinating agents to clinical trials, particularly uncertainty regarding the most appropriate study population and method to monitor remyelination. Refinements in clinical trial design and outcome measurement, potentially via advanced imaging techniques, are needed to optimize detection of repair in patients with MS.

Clinical evoked potentials in neurology: a review of techniques and indications

Evoked potentials (EPs) are a powerful and cost-effective tool for evaluating the integrity and function of the central nervous system. Although imaging techniques, such as MRI, have recently become increasingly important in the diagnosis of neurological diseases, over the past 30 years, many neurologists have continued to employ EPs in specific clinical applications. This review presents an overview of the recent evolution of 'classical' clinical applications of EPs in terms of early diagnosis and disease monitoring and is an extension of a previous review published in this journal in 2005 by Walsh and collaborators. We also provide an update on emerging EPs based on gustatory, olfactory and pain stimulation that may be used as clinically relevant markers of neurodegenerative disorders such as Parkinson's disease, Alzheimer's disease and cortical or peripheral impaired pain perception. EPs based on multichannel electroencephalography recordings, known as high-density EPs, help to better differentiate between healthy subjects and patients and, moreover, they provide valuable spatial information regarding the site of the lesion. EPs are reliable disease-progression biomarkers of several neurological diseases, such as multiple sclerosis and other demyelinating disorders. Overall, EPs are excellent neurophysiological tools that will expand standard clinical practice in modern neurology.

Mortality trends for multiple sclerosis patients in Sweden from 1968 to 2012

OBJECTIVE

To assess trends in mortality and causes of death for patients with multiple sclerosis (MS) relative to those without MS in Sweden.

METHODS

Patients with an MS diagnosis in Sweden between 1964 and 2012 were identified with the Patient Register and the Multiple Sclerosis Register. For this cohort study, each patient with MS (n = 29,617) was matched with 10 individuals without MS (n = 296,164) on sex, year of birth, vital status, and region of residence at the time of MS diagnosis with the Total Population Register. The Causes of Death Register was used to identify causes of death. Cox proportional hazard models were constructed to assess whether risk of mortality was increased for patients with MS.

RESULTS

The hazard ratio (HR) for patients with MS was 2.92 (95% confidence interval [CI] 2.86-2.99) for all-cause mortality over the entire study period. The largest differences between the cohorts were death resulting from respiratory (HR 5.07, 95% CI 4.87-5.26) and infectious (HR 4.07, 95% CI 3.70-4.47) diseases. Overall and for each specific cause, there have been improvements for the MS group and a subsequent reduction in the HR. The HR decreased from 6.52 (95% CI 5.79-7.34) for the period of 1968 to 1980 to 2.08 (95% CI 1.95-2.22) for the time period of 2001 to 2012. An interaction between time period and MS exposure showed that the decrease in mortality over time was statistically significant, with a larger decrease for patients with MS than their matched comparators.

CONCLUSIONS

There has been a substantial improvement in mortality overall and for each specified cause of death for patients with MS compared with individuals without MS; however, large differences still remain.

A new role for evoked potentials in MS? Repurposing evoked potentials as biomarkers for clinical trials in MS

Evoked potentials (EP) characterize signal conduction in selected tracts of the central nervous system in a quantifiable way. Since alteration of signal conduction is the main mechanism of symptoms and signs in multiple sclerosis (MS), multimodal EP may serve as a representative measure of the functional impairment in MS. Moreover, EP have been shown to be predictive for disease course, and thus might help to select patient groups at high risk of progression for clinical trials. EP can detect deterioration, as well as improvement of impulse propagation, independently from the mechanism causing the change. Therefore, they are candidates for biomarkers with application in clinical phase-II trials. Applicability of EP in multicenter trials has been limited by different standards of registration and assessment.

The role of auditory cortices in the retrieval of single-trial auditory-visual object memories

Single-trial encounters with multisensory stimuli affect both memory performance and early-latency brain responses to visual stimuli. Whether and how auditory cortices support memory processes based on single-trial multisensory learning is unknown and may differ qualitatively and quantitatively from comparable processes within visual cortices due to purported differences in memory capacities across the senses. We recorded event-related potentials (ERPs) as healthy adults (n = 18) performed a continuous recognition task in the auditory modality, discriminating initial (new) from repeated (old) sounds of environmental objects. Initial presentations were either unisensory or multisensory; the latter entailed synchronous presentation of a semantically congruent or a meaningless image. Repeated presentations were exclusively auditory, thus differing only according to the context in which the sound was initially encountered. Discrimination abilities (indexed by d') were increased for repeated sounds that were initially encountered with a semantically congruent image versus sounds initially encountered with either a meaningless or no image. Analyses of ERPs within an electrical neuroimaging framework revealed that early stages of auditory processing of repeated sounds were affected by prior single-trial multisensory contexts. These effects followed from significantly reduced activity within a distributed network, including the right superior temporal cortex, suggesting an inverse relationship between brain activity and behavioural outcome on this task. The present findings demonstrate how auditory cortices contribute to long-term effects of multisensory experiences on auditory object discrimination. We propose a new framework for the efficacy of multisensory processes to impact both current multisensory stimulus processing and unisensory discrimination abilities later in time.

Whole-scalp EEG mapping of somatosensory evoked potentials in macaque monkeys

High-density scalp EEG recordings are widely used to study whole-brain neuronal networks in humans non-invasively. Here, we validate EEG mapping of somatosensory evoked potentials (SSEPs) in macaque monkeys (Macaca fascicularis) for the long-term investigation of large-scale neuronal networks and their reorganisation after lesions requiring a craniotomy. SSEPs were acquired from 33 scalp electrodes in five adult anaesthetized animals after electrical median or tibial nerve stimulation. SSEP scalp potential maps were identified by cluster analysis and identified in individual recordings. A distributed, linear inverse solution was used to estimate the intracortical sources of the scalp potentials. SSEPs were characterised by a sequence of components with unique scalp topographies. Source analysis confirmed that median nerve SSEP component maps were in accordance with the somatotopic organisation of the sensorimotor cortex. Most importantly, SSEP recordings were stable both intra- and interindividually. We aim to apply this method to the study of recovery and reorganisation of large-scale neuronal networks following a focal cortical lesion requiring a craniotomy. As a prerequisite, the present study demonstrated that a 300-mm² unilateral craniotomy over the sensorimotor cortex necessary to induce a cortical lesion, followed by bone flap repositioning, suture and gap plugging with calcium phosphate cement, did not induce major distortions of the SSEPs. In conclusion, SSEPs can be successfully and reproducibly recorded from high-density EEG caps in macaque monkeys before and after a craniotomy, opening new possibilities for the long-term follow-up of the cortical reorganisation of large-scale networks in macaque monkeys after a cortical lesion.

A comparative study of experimental mouse models of central nervous system demyelination

Several mouse models of multiple sclerosis (MS) are now available. We have established a mouse model, in which ocular infection with a recombinant HSV-1 that expresses murine IL-2 constitutively (HSV-IL-2) causes CNS demyelination in different strains of mice. This model differs from most other models in that it represents a mixture of viral and immune triggers. In the present study, we directly compared MOG_35–55, MBP_35–47, and PLP_190–209 models of EAE with our HSV-IL-2-induced MS model. Mice with HSV-IL-2-induced and MOG-induced demyelinating diseases demonstrated a similar pattern and distribution of demyelination in their brain, spinal cord, and optic nerves. In contrast, no demyelination was detected in the optic nerves of MBP- and PLP-injected mice. IFN-β injections significantly reduced demyelination in brains of all groups, in the spinal cords of the MOG and MBP groups, and completely blocked it in the spinal cords of the PLP and HSV-IL-2 groups as well as in optic nerves of MOG and HSV-IL-2 groups. In contrast to IFN-β treatment, IL-12p70 protected the HSV-IL-2 group from demyelination, while IL-4 was not effective at all in preventing demyelination. MOG-injected mice showed clinical signs of paralysis and disease-related mortality whereas mice in the other treatment groups did not. Collectively, the results indicate that the HSV-IL-2 model and the MOG model complement each other and, together, provide unique insights into the heterogeneity of human MS.