Multimodal neurophysiological evaluation of primary progressive multiple sclerosis – An increasingly valid biomarker, with limits

Individual differences in visual evoked potential latency are associated with variance in brain tissue volume in people with multiple sclerosis: An analysis of brain function-structure correlates

Visual evoked potentials (VEP) index visual pathway functioning, and are often used for clinical assessment and as outcome measures in people with multiple sclerosis (PwMS). VEPs may also reflect broader neural disturbances that extend beyond the visual system, but this possibility requires further investigation. In the present study, we examined the hypothesis that delayed latency of the P100 component of the VEP would be associated with broader structural changes in the brain in PwMS. We obtained VEP latency for a standard pattern-reversal checkerboard stimulus paradigm, in addition to Magnetic Resonance Imaging (MRI) measures of whole brain volume (WBV), gray matter volume (GMV), white matter volume (WMV), and T2-weighted fluid attenuated inversion recovery (FLAIR) white matter lesion volume (FLV). Correlation analyses indicated that prolonged VEP latency was significantly associated with lower WBV, GMV, and WMV, and greater FLV. VEP latency remained significantly associated with WBV, GMV, and WMV even after controlling for the variance associated with inter-ocular latency, age, time between VEP and MRI assessments, and other MRI variables. VEP latency delays were most pronounced in PwMS that exhibited low volume in both white and gray matter simultaneously. Furthermore, PwMS that had delayed VEP latency based on a clinically relevant cutoff (VEP latency ≥ 113 ms) in both eyes had lower WBV, GMV, and WMV and greater FLV in comparison to PwMS that had normal VEP latency in one or both eyes. The findings suggest that PwMS that have delayed latency in both eyes may be particularly at risk for exhibiting greater brain atrophy and lesion volume. These analyses also indicate that VEP latency may index combined gray matter and white matter disturbances, and therefore broader network connectivity and efficiency. VEP latency may therefore provide a surrogate marker of broader structural disturbances in the brain in MS.

A comprehensive review of transcranial magnetic stimulation in secondary dementia

Although primary degenerative diseases are the main cause of dementia, a non-negligible proportion of patients is affected by a secondary and potentially treatable cognitive disorder. Therefore, diagnostic tools able to early identify and monitor them and to predict the response to treatment are needed. Transcranial magnetic stimulation (TMS) is a non-invasive neurophysiological technique capable of evaluating in vivo and in “real time” the motor areas, the cortico-spinal tract, and the neurotransmission pathways in several neurological and neuropsychiatric disorders, including cognitive impairment and dementia. While consistent evidence has been accumulated for Alzheimer’s disease, other degenerative cognitive disorders, and vascular dementia, to date a comprehensive review of TMS studies available in other secondary dementias is lacking. These conditions include, among others, normal-pressure hydrocephalus, multiple sclerosis, celiac disease and other immunologically mediated diseases, as well as a number of inflammatory, infective, metabolic, toxic, nutritional, endocrine, sleep-related, and rare genetic disorders. Overall, we observed that, while in degenerative dementia neurophysiological alterations might mirror specific, and possibly primary, neuropathological changes (and hence be used as early biomarkers), this pathogenic link appears to be weaker for most secondary forms of dementia, in which neurotransmitter dysfunction is more likely related to a systemic or diffuse neural damage. In these cases, therefore, an effort toward the understanding of pathological mechanisms of cognitive impairment should be made, also by investigating the relationship between functional alterations of brain circuits and the specific mechanisms of neuronal damage triggered by the causative disease. Neurophysiologically, although no distinctive TMS pattern can be identified that might be used to predict the occurrence or progression of cognitive decline in a specific condition, some TMS-associated measures of cortical function and plasticity (such as the short-latency afferent inhibition, the short-interval intracortical inhibition, and the cortical silent period) might add useful information in most of secondary dementia, especially in combination with suggestive clinical features and other diagnostic tests. The possibility to detect dysfunctional cortical circuits, to monitor the disease course, to probe the response to treatment, and to design novel neuromodulatory interventions in secondary dementia still represents a gap in the literature that needs to be explored.

Multimodal Evoked Potentials as Candidate Prognostic and Response Biomarkers in Clinical Trials of Multiple Sclerosis

SUMMARY

Evoked potentials (EPs) measure quantitatively and objectively the alterations of central signal propagation in multiple sclerosis and have long been used for diagnosis. More recently, their utility for prognosis has been demonstrated in several studies, summarizing multiple EP modalities in a single score. In particular, visual, somatosensory, and motor EPs are useful because of their sensitivity to pathology in the frequently affected optic nerve, somatosensory tract, and pyramidal system. Quantitative EP scores show higher sensitivity to change than clinical assessment and may be used to monitor disease progression. Visual EP and the visual system have served as a model to study remyelinating therapies in the setting of acute and chronic optic neuritis. This review presents rationale and evidence for using multimodal EP as prognostic and response biomarkers in clinical trials, targeting remyelination or halting disease progression in multiple sclerosis.

Visual evoked potential latency predicts cognitive function in people with multiple sclerosis

Prior studies have reported an association between visual evoked potentials (VEPs) and cognitive performance in people with multiple sclerosis (PwMS), but the specific mechanisms that account for this relationship remain unclear. We examined the relationship between VEP latency and cognitive performance in a large sample of PwMS, hypothesizing that VEP latency indexes not only visual system functioning but also general neural efficiency. Standardized performance index scores were obtained for the domains of memory, executive function, visual-spatial processing, verbal function, attention, information processing speed, and motor skills, as well as global cognitive performance (NeuroTrax battery). VEP P100 component latency was obtained using a standard checkerboard pattern-reversal paradigm. Prolonged VEP latency was significantly associated with poorer performance in multiple cognitive domains, and with the number of cognitive domains in which performance was ≥ 1 SD below the normative mean. Relationships between VEP latency and cognitive performance were significant for information processing speed, executive function, attention, motor skills, and global cognitive performance after controlling for disease duration, visual acuity, and inter-ocular latency differences. This study provides evidence that VEP latency delays index general neural inefficiency that is associated with cognitive disturbances in PwMS.

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.

Diagnostic ability of multifocal electroretinogram in early multiple sclerosis using a new signal analysis method

PURPOSE

To determine if a novel analysis method will increase the diagnostic value of the multifocal electroretinogram (mfERG) in diagnosing early-stage multiple sclerosis (MS).

METHODS

We studied the mfERG signals of OD (Oculus Dexter) eyes of fifteen patients diagnosed with early-stage MS (in all cases < 12 months) and without a history of optic neuritis (ON) (F:M = 11:4), and those of six controls (F:M = 3:3). We obtained values of amplitude and latency of N1 and P1 waves, and a method to assess normalized root-mean-square error (FNRMSE) between model signals and mfERG recordings was used. Responses of each eye were analysed at a global level, and by rings, quadrants and hemispheres. AUC (area under the ROC curve) is used as discriminant factor.

RESULTS

The standard method of analysis obtains further discrimination between controls and MS in ring R3 (AUC = 0.82), analysing N1 waves amplitudes. In all of the retina analysis regions, FNRMSE value shows a greater discriminating power than the standard method. The highest AUC value (AUC = 0.91) was in the superior temporal quadrant.

CONCLUSION

By analysing mfERG recordings and contrasting them with those of healthy controls it is possible to detect early-stage MS in patients without a previous history of ON.

Fluid biomarker and electrophysiological outcome measures for progressive MS trials

Progressive multiple sclerosis (MS) is characterized by insidious clinical worsening that is difficult to accurately quantify and predict. Biofluid markers and electrophysiological measures are potential candidate outcome measures in clinical trials, allowing the quantification of nervous damage occurring in the disease. Neurofilaments are highly specific neuronal proteins. They may have come closest to such applications by their higher concentrations repeatedly demonstrated in cerebrospinal fluid (CSF) in all stages of MS, during relapses, their responsiveness to disease-modifying treatments in relapsing and progressive MS and their associations with measures of inflammatory and degenerative magnetic resonance imaging (MRI) outcomes. Digital single-molecule array (Simoa) technology improves accuracy of bioassays in the quantification of neurofilament light chain (NfL) in serum and plasma. NfL seems to mark a common final path of neuroaxonal injury independent of specific causal pathways. CSF and blood levels of NfL are highly correlated across various diseases including MS, suggesting that blood measurements may be useful in assessing response to treatment and predicting future disease activity. Other biomarkers like matrix metalloproteinases, chemokines, or neurotrophic factors have not been studied to a similar extent. Such measures, especially in blood, need further validation to enter the trial arena or clinical practice. The broadening armamentarium of highly sensitive assay technologies in the future may shed even more light on patient heterogeneity and mechanisms leading to disability in MS. Evoked potentials (EPs) are used in clinical practice to measure central conduction of central sensorimotor pathways. They correlate with and predict the severity of clinical involvement of their corresponding function. Their validation for use in multicenter studies is still lacking, with the exception of visual EPs. If further validated, EPs and fluid biomarkers would represent useful outcome measures for clinical trials, being related to specific mechanisms of the ongoing pathologic changes.

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.

Do evoked potentials contribute to the functional follow-up and clinical prognosis of multiple sclerosis?

The clinical variability and complexity of multiple sclerosis (MS) challenges the individual clinical course prognostication. This study aimed to find out whether multimodal evoked potentials (EP) correlate with the motor components of multiple sclerosis functional composite (MSFCm) and predict clinically relevant motor functional deterioration. One hundred MS patients were assessed at baseline (T ₀) and about 7.5 years later (T ₁), with visual, somatosensory and motor EP and rated on the Expanded Disability Status Scale (EDSS) and the MSFCm, including the 9 Hole Peg Test and the Timed 25 Foot Walk (T25FW). The Spearman correlation coefficient (r _S) was used to evaluate the cross-sectional and longitudinal relationship between EP Z scores and clinical findings. The predictive value of baseline electrophysiological data for clinical worsening (EDSS, 9-HPT, T25FW, MSFCm) during follow-up was assessed by logistic regression analysis. Unlike longitudinal correlations, cross-sectional correlations between EP Z scores and clinical outcomes were all significant and ranged between 0.22 and 0.67 (p < 0.05). The global EP Z score was systematically predictive of EDSS and MSFCm worsening over time (all p < 0.05). EP latency was a better predictor than amplitude, although weaker than latency and amplitude aggregation in the global EP Z score. The study demonstrates that EP numerical scores can be used for motor function monitoring and outcome prediction in patients with MS.