Michel J.A.M. van Putten

University of Twente | UT · Group of Clinical Neurophysiology

Objective: To quantify the effects of propofol on the EEG after cardiac arrest and to assess their influence on predictions of outcome. Methods: In a prospective multicenter cohort study, we analyzed EEG recordings within the first 72 h after cardiac arrest. At six time points, EEGs were classified as favorable (continuous background), unfavorable...

Objective Outcome prediction in patients after cardiac arrest (CA) is challenging. EEG reactivity (EEG‐R) might be a reliable predictor. We aimed to determine the prognostic value of EEG‐R using a standardized assessment. Methods In a prospective cohort study, a strictly defined EEG‐R assessment protocol was executed twice a day in adult patients...

Objective To provide evidence that early EEG allows for reliable prediction of poor or good outcome after cardiac arrest. Methods In a five‐center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to five days. Five‐minute EEG epochs...

Objectives: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent. Design: Prospective cohort...

Objective Postictal symptoms may result from cerebral hypoperfusion which is possibly a consequence of seizure‐induced vasoconstriction. Longer seizures have previously been shown to cause more severe postictal hypoperfusion in rats and epilepsy patients. We studied cerebral perfusion after generalized seizures elicited by electroconvulsive therapy...

Electroconvulsive therapy (ECT) is an effective treatment for major depression, but its working mechanisms are poorly understood. Modulation of excitation/inhibition (E/I) ratios may be a driving factor. Here, we estimate cortical E/I ratios in depressed patients and study whether these ratios change over the course of ECT in relation to clinical e...

OBJECTIVES To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. DESIGN Multicenter cohort, partly prospective and partly retrospective. SETTING Seven academic or teaching hospital...

Objective: To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. Design: Multicenter cohort, partly prospective and partly retrospective. Setting: Seven academic or teaching hospitals from the U.S. and Europe. Patients: Individu...

Objective: Deep learning methods have shown potential in automating interictal epileptiform discharge (IED) detection in electroencephalograms (EEGs). While it is known that these algorithms are dependent on the type of data used for training, this has not been explicitly explored in EEG analysis applications. We study the difference in performance...

Background and Objectives Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury following cardiac arrest. We aimed to delineate the evolution of coma neurophysiology features ensembles associated with recovery from coma after cardiac arrest. Methods Adult subjects in acute coma following cardi...

Human induced pluripotent stem cell (hiPSC)-derived neuronal networks on multi-electrode arrays (MEAs) provide a unique phenotyping tool to study neurological disorders. However, it is difficult to infer cellular mechanisms underlying these phenotypes. Computational modeling can utilize the rich dataset generated by MEAs, and advance understanding...

Objective: Interictal epileptiform discharges (IED) are hallmark biomarkers of epilepsy which are typically detected through visual analysis. Deep learning has shown potential in automating IED detection, which could reduce the burden of visual analysis in clinical practice. This is particularly relevant for ambulatory electroencephalograms (EEGs)...

Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing and is believed to be involved in motor and cognitive control and decision-making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual...

Aim: Rhythmic and periodic patterns (RPPs) on the electroencephalogram (EEG) in comatose patients after cardiac arrest have been associated with high case fatality rates. A good neurological outcome according to the Cerebral Performance Categories (CPC) has been reported in up to 10% of cases. Data on cognitive, emotional, and quality of life outc...

Objective: Absences affect visual attention and eye movements variably. Here, we explore whether the dissimilarity of these symptoms during absences is reflected in differences in electroencephalographic (EEG) features, functional connectivity, and activation of the frontal eye field. Methods: Pediatric patients with absences performed a compute...

Normal brain function depends on continuous cerebral blood flow for the supply of oxygen and glucose, and is quickly compromised in conditions where the metabolic demand cannot be met. Insufficient cerebral perfusion can result in ischemic stroke, with symptoms ranging from loss of motor or language function to coma, depending on the brain areas af...

Continuous EEG monitoring contributes to prediction of neurological outcome in comatose cardiac arrest survivors. While the phenomenology of EEG abnormalities in postanoxic encephalopathy is well-known, the pathophysiology, especially the presumed role of selective synaptic failure is less understood. To further this understanding, we estimate biop...

Objective: We aim to provide a quantitative description of the relation between seizure duration and the postictal state using features extracted from the postictal electroencephalogram (EEG). Methods: Thirty patients with major depressive disorder treated with electroconvulsive therapy (ECT) were studied with continuous EEG before, during, and...

The electrographic manifestation of neural activity can reflect the relationship between the faster action potentials of individual neurons and the slower fluctuations of the local field potential (LFP). This relationship is typically examined in the temporal domain using the spike-triggered average. In this study, we add a spatial component to thi...

Deep learning methods have shown potential in automating interictal epileptiform discharge (IED) detection in electroencephalograms (EEGs). To implement this in a clinical setting, it needs to have similar performance to visual assessment. We aim to compare a neural network trained for IED detection with a group of experts for validation and assess...

Objective Deep learning methods have shown potential in automating interictal epileptiform discharge (IED) detection in electroencephalograms (EEGs). However, it is known that these algorithms are dependent on the type of data used for training and this is not currently explored in EEG analysis applications. We aim to explore the difference in perf...

There is a need for reliable predictors in patients with moderate to severe traumatic brain injury to assist clinical decision making. We assess the ability of early continuous EEG monitoring at the intensive care unit (ICU) in patients with traumatic brain injury (TBI) to predict long term clinical outcome and evaluate its complementary value to c...

Objective: To clarify the significance of any form of myoclonus in comatose patients after cardiac arrest with rhythmic and periodic EEG patterns (RPPs) by analyzing associations between myoclonus and EEG pattern, response to anti-seizure medication and neurological outcome. Design: Post hoc analysis of the prospective randomized Treatment of EL...

Continuous EEG monitoring contributes to prediction of neurological outcome in comatose cardiac arrest survivors. While the phenomenology of EEG abnormalities in postanoxic encephalopathy is well-known, the pathophysiology, especially the presumed role of selective synaptic failure is less understood. To further this understanding, we estimate biop...

Attention is an important aspect of human brain function and often affected in neurological disorders. Objective assessment of attention may assist in patient care, both for diagnostics and prognostication. We present a compact test using a combination of a choice reaction time task, eye-tracking and EEG for assessment of visual attention in the cl...

Purpose: Absence seizures often affect attention. In particular, complaints related to unresponsiveness, visual allocation and maintenance of attention are consistent in patients with absences. Defining the relation between absence seizures characteristics and neurophysiological markers of visuospatial attention can help explaining clinical symptom...

Here is the link to "TH-104. Reliable prediction of poor outcome in postanoxic coma using EEG in a four-electrode frontotemporal montage": https://www.sciencedirect.com/science/article/pii/S1388245722006101

Introduction: Mild traumatic brain injury (mTBI) often affects attention. In particular, complaints related to visual allocation and maintenance of attention are consistent in mTBI patients. Objective assessment of visuospatial attention in mTBI can help explaining clinical symptoms and may assist in prognostication. Methods: We employed a multi-g...

Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are...

The relationship between action potentials and the associated local field potential (LFP) in neural recordings is typically studied only in the temporal domain using the spike-triggered average (STA). In this study, we present a novel approach, termed the spatiotemporal spike-centered average (st-SCA), that allows for visualization of the spike-LFP...

Background Postictal phenomena as delirium, headache, nausea, myalgia, and anterograde and retrograde amnesia are common manifestations after seizures induced by electroconvulsive therapy (ECT). Comparable postictal phenomena also contribute to the burden of patients with epilepsy. The pathophysiology of postictal phenomena is poorly understood and...

In the penumbra of a brain infarct, neurons initially remain structurally intact, but perfusion is insufficient to maintain neuronal activity at physiological levels. Improving neuronal recovery in the penumbra has large potential to advance recovery of stroke patients, but penumbral pathology is incompletely understood, and treatments are scarce....

Background: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. Methods: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of...

Background: Whether the treatment of rhythmic and periodic electroencephalographic (EEG) patterns in comatose survivors of cardiac arrest improves outcomes is uncertain. Methods: We conducted an open-label trial of suppressing rhythmic and periodic EEG patterns detected on continuous EEG monitoring in comatose survivors of cardiac arrest. Patien...

Objectives To assess neurological outcome after targeted temperature management (TTM) at 33°C vs. 36°C, stratified by the severity of encephalopathy based on EEG-patterns at 12 and 24h. Design Post hoc analysis of prospective cohort study. Setting Five Dutch Intensive Care units. Patients 479 adult comatose post-cardiac arrest patients. Interve...

Objective: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. Methods: We pr...

Objective: Automatic detection and analysis of respiratory events in sleep using a single respiratory effort belt and deep learning. Methods: Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort relat...

Objective Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest...

High water permeabilities permit rapid adjustments of glial volume upon changes in external and internal osmolarity, and pathologically altered intracellular chloride concentrations ([Cl – ] int ) and glial cell swelling are often assumed to represent early events in ischemia, infections, or traumatic brain injury. Experimental data for glial [Cl –...

Objective Standardized investigation of epileptic seizures and the postictal state may contribute to a better understanding of ictal and postictal phenomena. This comparative case study aims to assess whether electrically-induced seizures in electroconvulsive therapy (ECT) show sufficient similarities with spontaneous seizures to serve as a human e...

A self-fulfilling prophecy (SFP) in neuroprognostication occurs when a patient in coma is predicted to have a poor outcome, and life-sustaining treatment is withdrawn on the basis of that prediction, thus directly bringing about a poor outcome (viz. death) for that patient. In contrast to the predominant emphasis in the bioethics literature, we loo...

The anatomical and functional organization of neurons and astrocytes at ‘tripartite synapses’ is essential for reliable neurotransmission, which critically depends on ATP. In low energy conditions, synaptic transmission fails, accompanied by a breakdown of ion gradients, changes in membrane potentials and cell swelling. The resulting cellular damag...

Ischemic stroke is a leading cause of mortality and chronic disability. Either recovery or progression towards irreversible failure of neurons and astrocytes occurs within minutes to days, depending on remaining perfusion levels. Initial damage arises from energy depletion resulting in a failure to maintain homeostasis and ion gradients between ext...

Brain function is reflected in both the action potentials of individual neurons and interactions through e.g. synaptic currents reflected in widespread, slow fluctuations of the local field potential (LFP). We analyzed microelectrode array data to determine state-dependent correlations between action potential and LFP during seizure events as well...

The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG. Visual assessment is, however, time consuming and prone to subjectivity, leading to a hig...

The anatomical and functional organization of neurons and astrocytes at ‘tripartite synapses’ is essential for reliable neurotransmission, which critically depends on ATP. In low energy conditions, synaptic transmission fails, accompanied by a breakdown of ion gradients, changes in membrane potentials and cell swelling. The resulting cellular damag...

Objective To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest. Methods Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12h, 24h and 4...

Objective Automating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram (EEG) recordings can reduce the time spent on visual analysis for the diagnosis of epilepsy. Deep learning has shown potential for this purpose, but the scarceness of expert annotated data creates a bottleneck in the process. Methods We used EEGs fr...

Objective: In ischemic stroke, treatments to protect neurons from irreversible damage are urgently needed. Studies in animal models have shown that neuroprotective treatments targeting neuronal silencing improve brain recovery, but in clinical trials none of these were effective in patients. This failure of translation poses doubts on the real eff...

Sleep scoring is an important step for the detection of sleep disorders and usually performed by visual analysis. Since manual sleep scoring is time consuming, machine-learning based approaches have been proposed. Though efficient, these algorithms are black-box in nature and difficult to interpret by clinicians. In this paper, we propose a deep le...

Using pre-treatment biomarkers to guide patients to the preferred antidepressant medication treatment could be a promising approach to enhance its current modest response and remission rates. This open-label prospective study assessed the feasibility of using such pre-treatment biomarkers, by using previously identified EEG features (paroxysmal act...

The gold standard to assess respiration during sleep is polysomnography; a technique that is burdensome, expensive (both in analysis time and measurement costs), and difficult to repeat. Automation of respiratory analysis can improve test efficiency and enable accessible implementation opportunities worldwide. Using 9,656 polysomnography recordings...

Objective Early EEG contains reliable information for outcome prediction of comatose patients after cardiac arrest. We introduce dynamic functional connectivity measures and estimate additional predictive values. Methods We performed a prospective multicenter cohort study on continuous EEG for outcome prediction of comatose patients after cardiac...

All comatose patients after circulatory arrest initially have a severely abnormal disturbed electroencephalogram. The speed of normalisation is a robust contributor to prediction of outcome. Differences between patients with poor and good outcome are largest <24 hours after the arrest. Lasting suppression at ≥12 hours or synchronised patterns with...

The brain is obligatory dependent on sufficient oxygen and glucose. Deprivation of either glucose or oxygen (oxygen-glucose deprivation, OGD) will result in abnormal functioning of neurons, and if sufficiently severe, will ultimately result in irreversible damage.

All models discussed thus far were single cell models, but the brain has many cells that are coupled and exchange information. Connections of a few neurons can perform elementary functions, for instance filtering incoming action potential trains or detecting edges in images. More complex functions require the concerted action of neurons, where part...

We discuss how neurons transmit signals between neurons, focusing on the chemical synapse. After a more phenomenological treatise, we derive expressions to quantify synaptic transfer. A few neurological diseases, characterized by abnormal synaptic transmission, are discussed, too.

This chapter discusses neural mass models and EEG rhythms. We start with a simple model, adding additional components step-by-step, eventually resulting in coupled neural masses that simulate a physiological EEG rhythm with a peak frequency in the 8–13 Hz (\(\alpha \)) frequency range. While this chapter focuses on physiology, we will learn in late...

In this chapter, we introduce the essentials of the generation of the EEG. We discuss current dipole sources to model the ionic currents and associated potentials generated by pyramidal cortical cells. We explain why the EEG mainly reflects synchronous activity from large assemblies of these pyramidal cells. In the second part of the chapter, we gi...