Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG.

Evoked responses and oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how most frequently studied EEG signals: the P300-evoked response and alpha oscillations (8-12 Hz) can be linked with the baseline-shift mechanism. This mechanism states that oscillations generate evoked responses if oscillations have a non-zero mean and their amplitude is modulated by the stimulus.

Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study.

Background: Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine learning. We hypothesised that brain age estimated from EEG during general anaesthesia is associated with perioperative risk.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals.

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments.

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging.

Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging.

Population modeling with machine learning can enhance measures of mental health.

Background: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans.

Propofol Requirement and EEG Alpha Band Power During General Anesthesia Provide Complementary Views on Preoperative Cognitive Decline.

Although cognitive decline (CD) is associated with increased post-operative morbidity and mortality, routinely screening patients remains difficult. The main objective of this prospective study is to use the EEG response to a Propofol-based general anesthesia (GA) to reveal CD. 42 patients with collected EEG and Propofol target concentration infusion (TCI) during GA had a preoperative cognitive assessment using MoCA.

Interactive Psychometrics for Autism With the Human Dynamic Clamp: Interpersonal Synchrony From Sensorimotor to Sociocognitive Domains.

The human dynamic clamp (HDC) is a human-machine interface designed on the basis of coordination dynamics for studying realistic social interaction under controlled and reproducible conditions. Here, we propose to probe the validity of the HDC as a psychometric instrument for quantifying social abilities in children with autism spectrum disorder (ASD) and neurotypical development. To study interpersonal synchrony with the HDC, we derived five standardized scores following a gradient from sensorimotor and motor to higher sociocognitive skills in a sample of 155 individuals (113 participants with ASD, 42 typically developing participants; aged 5 to 25 years; IQ > 70).

Inference and Prediction Diverge in Biomedicine.

In the 20 century, many advances in biological knowledge and evidence-based medicine were supported by p values and accompanying methods. In the early 21 century, ambitions toward precision medicine place a premium on detailed predictions for single individuals. The shift causes tension between traditional regression methods used to infer statistically significant group differences and burgeoning predictive analysis tools suited to forecast an individual's future.

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.

Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subject-level, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset.

Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers.

Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability.

Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness.

Severe brain injuries can lead to long-lasting disorders of consciousness (DoC) such as vegetative state/unresponsive wakefulness syndrome (VS/UWS) or minimally conscious state (MCS). While behavioral assessment remains the gold standard to determine conscious state, EEG has proven to be a promising complementary tool to monitor the effect of new therapeutics. Encouraging results have been obtained with invasive electrical stimulation of the brain, and recent studies identified transcranial direct current stimulation (tDCS) as an effective approach in randomized controlled trials.

Distinguishing Social From Private Intentions Through the Passive Observation of Gaze Cues.

Observing others' gaze is most informative during social encounters between humans: We can learn about potentially salient objects in the shared environment, infer others' mental states and detect their communicative intentions. We almost automatically follow the gaze of others in order to check the relevance of the target of the other's attention. This phenomenon called gaze cueing can be conceptualized as a triadic interaction involving a gaze initiator, a gaze follower and a gaze target, i.

Robust EEG-based cross-site and cross-protocol classification of states of consciousness.

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice.

A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices.

Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets. While there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject.

Survival and consciousness recovery are better in the minimally conscious state than in the vegetative state.

Background: The prognosis value of early clinical diagnosis of consciousness impairment is documented by an extremely limited number of studies, whereas it may convey important information to guide medical decisions.

Objective: We aimed at determining if patients diagnosed at an early stage (<90 days after brain injury) as being in the minimally conscious state (MCS) have a better prognosis than patients in the vegetative state/Unresponsive Wakefulness syndrome (VS/UWS), independent of care limitations or withdrawal decisions.

Methods: Patients hospitalized in ICUs of the Pitié-Salpêtrière Hospital (Paris, France) from November 2008 to January 2011 were included and evaluated behaviourally with standardized assessment and with the Coma Recovery Scale-Revised as being either in the VS/UWS or in the MCS.

Brain-heart interactions reveal consciousness in noncommunicating patients.

Objective: We here aimed at characterizing heart-brain interactions in patients with disorders of consciousness. We tested how this information impacts data-driven classification between unresponsive and minimally conscious patients.

Methods: A cohort of 127 patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS; n = 70) and minimally conscious state (MCS; n = 57) were presented with the local-global auditory oddball paradigm, which distinguishes 2 levels of processing: short-term deviation of local auditory regularities and global long-term rule violations.

Autoreject: Automated artifact rejection for MEG and EEG data.

We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold - a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors.

Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines.

Decoding, i.e. prediction from brain images or signals, calls for empirical evaluation of its predictive power.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure.

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model.

MEG and EEG data analysis with MNE-Python.

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions.