Decoding auditory EEG responses in healthy and clinical populations: A comparative study

Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data

Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.

Predicting neurological outcome after cardiac arrest by combining computational parameters extracted from standard and deviant responses from auditory evoked potentials

Background

Despite multimodal assessment (clinical examination, biology, brain MRI, electroencephalography, somatosensory evoked potentials, mismatch negativity at auditory evoked potentials), coma prognostic evaluation remains challenging.

Methods

We present here a method to predict the return to consciousness and good neurological outcome based on classification of auditory evoked potentials obtained during an oddball paradigm. Data from event-related potentials (ERPs) were recorded noninvasively using four surface electroencephalography (EEG) electrodes in a cohort of 29 post-cardiac arrest comatose patients (between day 3 and day 6 following admission). We extracted retrospectively several EEG features (standard deviation and similarity for standard auditory stimulations and number of extrema and oscillations for deviant auditory stimulations) from the time responses in a window of few hundreds of milliseconds. The responses to the standard and the deviant auditory stimulations were thus considered independently. By combining these features, based on machine learning, we built a two-dimensional map to evaluate possible group clustering.

Results

Analysis in two-dimensions of the present data revealed two separated clusters of patients with good versus bad neurological outcome. When favoring the highest specificity of our mathematical algorithms (0.91), we found a sensitivity of 0.83 and an accuracy of 0.90, maintained when calculation was performed using data from only one central electrode. Using Gaussian, K-neighborhood and SVM classifiers, we could predict the neurological outcome of post-anoxic comatose patients, the validity of the method being tested by a cross-validation procedure. Moreover, the same results were obtained with one single electrode (Cz).

Conclusion

statistics of standard and deviant responses considered separately provide complementary and confirmatory predictions of the outcome of anoxic comatose patients, better assessed when combining these features on a two-dimensional statistical map. The benefit of this method compared to classical EEG and ERP predictors should be tested in a large prospective cohort. If validated, this method could provide an alternative tool to intensivists, to better evaluate neurological outcome and improve patient management, without neurophysiologist assistance.

Cognitive assessment using ERP in child and adolescent psychiatry: Difficulties and opportunities

Event related potentials (ERPs) represent powerful tools to investigate cognitive functioning in child and adolescent psychiatry. So far, the available body of research has largely focused on advancements in analysis methods, with little attention given to the perspective of assessment. The aim of this brief report is to provide recommendations for cognitive ERPs assessment that can be applied across diagnostic categories in child and adolescent psychiatry research. First, we discuss major issues for ERPs testing using examples from common psychiatric disorders. We conclude by summing up our recommendations for methodological standards and highlighting the potential role of ERPs in the field.

NeuroTec Sitem-Insel Bern: Closing the Last Mile in Neurology

Neurology is focused on a model where patients receive their care through repeated visits to clinics and doctor’s offices. Diagnostic tests often require expensive and specialized equipment that are only available in clinics. However, this current model has significant drawbacks. First, diagnostic tests, such as daytime EEG and sleep studies, occur under artificial conditions in the clinic, which may mask or wrongly emphasize clinically important features. Second, early detection and high-quality management of chronic neurological disorders require repeat measurements to accurately capture the dynamics of the disease process, which is impractical to execute in the clinic for economical and logistical reasons. Third, clinic visits remain inaccessible to many patients due to geographical and economical circumstances. Fourth, global disruptions to daily life, such as the one caused by COVID-19, can seriously harm patients if access to in-person clinical visits for diagnostic and treatment purposes is throttled. Thus, translating diagnostic and treatment procedures to patients’ homes will convey multiple substantial benefits and has the potential to substantially improve clinical outcomes while reducing cost. NeuroTec was founded to accelerate the re-imagining of neurology and to promote the convergence of technological, scientific, medical and societal processes. The goal is to identify and validate new digital biomarkers that can close the last mile in neurology by enabling the translation of personalized diagnostics and therapeutic interventions from the clinic to the patient’s home.

Individual EEG profiling of attention deficits in left spatial neglect: A pilot study

Electrophysiological group studies in brain-damaged patients can be run to capture the EEG correlates of specific cognitive impairments. Nonetheless, this procedure is not adequate to characterize the inter-individual variability present in major neuropsychological syndromes. We tested the possibility of getting a reliable individual EEG characterization of deficits of endogenous orienting of spatial attention in right-brain damaged (RBD) patients with left spatial neglect (N+). We used a single-trial topographical analysis (STTA; [39] of individual scalp EEG topographies recorded during leftward and rightward orienting of attention with central cues in RBD patients with and without (N-) neglect and in healthy controls (HC). We found that the STTA successfully decoded EEG signals related to leftward and rightward orienting in five out of the six N+, five out of the six N- patients and in all the six HC. In agreement with findings from conventional average-group studies, successful classifications of EEG signals in N+ were observed during the 400-800 ms period post-cue-onset, which reflects preserved voluntary engagement of attention resources (ADAN component). These results suggest the possibility of acquiring reliable individual EEG profiles of neglect patients.

An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data

OBJECTIVE

Brain-computer interface (BCI) technology enables people to use direct measures of brain activity for communication and control. The National Center for Adaptive Neurotechnologies and Helen Hayes Hospital are studying long-term independent home use of P300-based BCIs by people with amyotrophic lateral sclerosis (ALS). This BCI use takes place without technical oversight, and users can encounter substantial variation in their day-to-day BCI performance. The purpose of this study is to identify and evaluate features in the electroencephalogram (EEG) that correlate with successful BCI performance during home use with the goal of improving BCI for people with neuromuscular disorders.

APPROACH

Nine people with ALS used a P300-based BCI at home over several months for communication and computer control. Sessions from a routine calibration task were categorized as successful ([Formula: see text]70%) or unsuccessful (<70%) BCI performance. The correlation of temporal and spectral EEG features with BCI performance was then evaluated.

MAIN RESULTS

BCI performance was positively correlated with an increase in alpha-band (8-14 Hz) activity at locations PO8, P3, Pz, and P4; and beta-band (15-30 Hz) activity at occipital locations. In addition, performance was significantly positively correlated with a positive deflection in EEG amplitude around 220 ms at frontal mid-line locations (i.e. Fz and Cz). BCI performance was negatively correlated with delta-band (1-3 Hz) activity recorded from occipital locations.

SIGNIFICANCE

These results highlight the variability found in the EEG and describe EEG features that correlate with successful BCI performance during day-to-day use of a P300-based BCI by people with ALS. These results should inform studies focused on improved BCI reliability for people with neuromuscular disorders.

Predicting risk decisions in a modified Balloon Analogue Risk Task: Conventional and single-trial ERP analyses

Event-related potential (ERP) has the potential to reveal the temporal neurophysiological dynamics of risk decision-making, but this potential has not been fully explored in previous studies. When predicting risk decision with ERPs, most studies focus on between-trial analysis that reflects feedback learning, while within-trial analysis that could directly link option assessment with behavioral output has been largely ignored. Suitable task design is crucial for applying within-trial prediction. In this study, we used a modified version of the classic Balloon Analogue Risk Task (BART). In each trial of the task, participants made multiple rounds of decisions between a risky option (pump up the balloon) and a safe option (cash out). Behavioral results show that as the level of economic risk increased, participants were less willing to make a risky decision and also needed a longer response time to do so. In general, the ERP results showed distinct characteristics compared with previous findings based on between-trial prediction, particularly about the role of the P1 component. Specifically, both the P1 (amplitude and latency) and P3 (amplitude) components evoked by current outcomes predicted subsequent decisions. We suggest that these findings indicate the importance of selective attention (indexed by the P1) and motivational functions (indexed by the P3), which may help clarify the cognitive mechanism of risk decision-making. The theoretical significance of these findings is discussed.

Somatosensory and auditory deviance detection for outcome prediction during postanoxic coma

Objective

Prominent research in patients with disorders of consciousness investigated the electrophysiological correlates of auditory deviance detection as a marker of consciousness recovery. Here, we extend previous studies by investigating whether somatosensory deviance detection provides an added value for outcome prediction in postanoxic comatose patients.

Methods

Electroencephalography responses to frequent and rare stimuli were obtained from 66 patients on the first and second day after coma onset.

Results

Multivariate decoding analysis revealed an above chance‐level auditory discrimination in 25 patients on the first day and in 31 patients on the second day. Tactile discrimination was significant in 16 patients on the first day and in 23 patients on the second day. Single‐day sensory discrimination was unrelated to patients’ outcome in both modalities. However, improvement of auditory discrimination from first to the second day was predictive of good outcome with a positive predictive power (PPV) of 0.73 (CI = 0.52–0.88). Analyses considering the improvement of tactile, auditory and tactile, or either auditory or tactile discrimination showed no significant prediction of good outcome (PPVs = 0.58–0.68).

Interpretation

Our results show that in the acute phase of coma deviance detection is largely preserved for both auditory and tactile modalities. However, we found no evidence for an added value of somatosensory to auditory deviance detection function for coma‐outcome prediction.