Christos Papadelis | Harvard Medical School (original) (raw)

Papers by Christos Papadelis

Coordination between vision and action relies on a fronto-parietal network that receives visual a... more Coordination between vision and action relies on a fronto-parietal network that receives visual and proprioceptive sensory input in order to compute motor control signals. Here, we investigated with magnetoencephalography (MEG) which cortical areas are functionally coupled on the basis of synchronization during visuomotor integration. MEG signals were recorded from twelve healthy adults while performing a unimanual visuomotor (VM) task and control conditions. The VM task required the integration of pinch motor commands with visual sensory feedback. By using a beamformer, we localized the neural activity in the frequency range of 1–30 Hz during the VM compared to rest. Virtual sensors were estimated at the active locations. A multivariate autoregressive model was used to estimate the power and coherence of estimated activity at the virtual sensors. Event-related desynchronisation (ERD) during VM was observed in early visual areas, the rostral part of the left inferior frontal gyrus (IFG), the right IFG, the superior parietal lobules, and the left hand motor cortex (M1). Functional coupling in the alpha frequency band bridged the regional activities observed in motor and visual cortices (the start and the end points in the visuomotor loop) through the left or right IFG. Coherence between the left IFG and left M1 correlated inversely with the task performance. Our results indicate that an occipital-prefrontal-motor functional network facilitates the modulation of instructed motor responses to visual cues. This network may supplement the mechanism for guiding actions that is fully incorporated into the dorsal visual stream.

Introduction The primary goal of neuroimaging is to help determine the etiology of neonatal seizu... more Introduction The primary goal of neuroimaging is to help determine the etiology of neonatal seizures, which in turn helps to guide clinical management and prognosis. Neuroimaging serves as a complement to the clinical examination, electroencepahalography (EEG) evaluation and laboratory investigations and must be interpreted in this context. The most common neuroimaging modalities used in the evaluation of neonatal seizures are ultrasound and magnetic resonance imaging (MRI). Ultrasound is a non-invasive, inexpensive , bedside screening tool typically used to search for hemorrhage or hydrocephalus when a neonate, especially a preterm or unstable neonate, presents with seizures. Computed tomography (CT) may be performed to provide an urgent assessment when there is concern for fractures or hemorrhage. Otherwise, CT is rarely performed unless MRI is unavailable because of the exposure to ionizing radiation as well as the lower sensitivity and specificity for diagnosing brain disorders associated with seizures compared to MRI. MRI is the modality of choice when seizures are identified because of the multiple contrast mechanisms and rich physiological information it provides. In most institutions, neonatal MRI can be performed without sedation or anesthesia, making it an insignificant risk study. In this chapter we will provide an overview of ultrasound and MRI technologies as well as imaging findings in the most common disorders associated with neonatal seizures. We conclude with a brief discussion on the emerging role of near infrared spectroscopy (NIRS) and magnetoencephalography (MEG).

Multichannel real time system that automatically identifies burst-suppression. Estimates burst-su... more Multichannel real time system that automatically identifies burst-suppression. Estimates burst-suppression index using neural network technology. Excellent agreement between automated and manual classification of burst-suppression. a b s t r a c t Objective: To develop a real-time monitoring system that has the potential to guide the titration of anesthetic agents in the treatment of pediatric status epilepticus (SE). Methods: We analyzed stored multichannel electroencephalographic (EEG) data collected from 12 pedi-atric patients with generalized SE. EEG recordings were initially segmented in 500 ms time-windows. Features characterizing the power, frequency, and entropy of the signal were extracted from each segment. The segments were annotated as bursts (B), suppressions (S), or artifacts (A) by two electroen-cephalographers. The EEG features together with the annotations were inputted in a three-layer feed forward neural network (NN). The sensitivity and specificity of NNs with different architectures and training algorithms to classify segments into B, S, or A were estimated. Results: The maximum sensitivity (95.96% for B, 89.25% for S, and 75% for A) and specificity (89.36 for B, 96.26% for S, and 99.8% for A) was observed for the NN with 10 nodes in the hidden layer. By using this NN, we designed a real-time system that estimates the burst-suppression index (BSI). Conclusions: Our system provides a reliable real-time estimate of multichannel BSI requiring minimal memory and computation time. Significance: The system has the potential to assist intensive care unit attendants in the continuous EEG monitoring.

Over the past three decades, insights into the role of the cerebellum in emotional processing hav... more Over the past three decades, insights into the role of the cerebellum in emotional processing have substantially increased. Indeed, methodological refinements in cerebellar le-sion studies and major technological advancements in the field of neuroscience are in particular responsible to an exponential growth of knowledge on the topic. It is timely to review the available data and to critically evaluate the current status of the role of the cerebellum in emotion and related domains. The main aim of this article is to present an overview of current facts and ongoing debates relating to clinical, neuroimaging, and neurophysiological findings on the role of the cerebellum in key aspects of emotion. Experts in the field of cerebellar research discuss the range of cerebellar contributions to emotion in nine topics. Topics include the role of the cerebellum in perception and recognition, forwarding and encoding of emotional information, and the experience and regulation of emotional states in relation to motor, cognitive, and social behaviors. In addition, perspectives including cerebellar involvement in emotional learning, pain, emotional aspects of speech, and neuropsychiatric aspects of the cerebellum in mood

Objective: The objective of this study is the development and evaluation of efficient neurophysio... more Objective: The objective of this study is the development and evaluation of efficient neurophysiological signal statistics, which may assess the driver's alertness level and serve as potential indicators of sleepiness in the design of an on-board countermeasure system. Methods: Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects exposed to real field driving conditions. A number of severe driving errors occurred during the experiments. The analysis was performed in two main dimensions: the mac-roscopic analysis that estimates the ongoing temporal evolution of physiological measurements during the driving task, and the microscopic event analysis that focuses on the physiological measurements' alterations just before, during, and after the driving errors. Two independent neurophysiologists visually interpreted the measurements. The EEG data were analyzed by using both linear and non-linear analysis tools. Results: We observed the occurrence of brief paroxysmal bursts of alpha activity and an increased synchrony among EEG channels before the driving errors. The alpha relative band ratio (RBR) significantly increased, and the Cross Approximate Entropy that quantifies the synchrony among channels also significantly decreased before the driving errors. Quantitative EEG analysis revealed significant variations of RBR by driving time in the frequency bands of delta, alpha, beta, and gamma. Most of the estimated EEG statistics, such as the Shannon Entropy, Kullback–Leibler Entropy, Coherence, and Cross-Approximate Entropy, were significantly affected by driving time. We also observed an alteration of eyes blinking duration by increased driving time and a significant increase of eye blinks' number and duration before driving errors. Conclusions: EEG and EOG are promising neurophysiological indicators of driver sleepiness and have the potential of monitoring sleepi-ness in occupational settings incorporated in a sleepiness countermeasure device. Significance: The occurrence of brief paroxysmal bursts of alpha activity before severe driving errors is described in detail for the first time. Clear evidence is presented that eye-blinking statistics are sensitive to the driver's sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.

Journal of Pediatric Epilepsy, 2015

Cognitive functions are the result of the interaction between assemblies of neurons located in th... more Cognitive functions are the result of the interaction between assemblies of neurons located in the brain and organized in networks. The activation of such networks due to a stimulus (e.g. acoustic or visual) can be observed as changes of on-going signals recorded either by EEG or MEG. The most common way to observe these changes is by ERP analysis. However, during the past decade neuroscientists have recognized the importance of oscillatory analysis. The main portion of oscillatory analysis of cognitive processing is focused till now on specific frequency bands (e.g. theta, alpha, but till now analysis of delta oscillations is not very extensive. Consequently, the place of generation of delta activity is not certain. Previous studies indicate a significant increase of delta activity during sexual arousal and orgasm. More precisely, under the above condition there is suppression of alpha activity and increase of delta oscillations even with the view of erotic films. The above changes...

Clinical EEG and neuroscience, 2015

Disturbances of the autonomic nervous system (ANS) are common in neuropsychiatric disorders. Dise... more Disturbances of the autonomic nervous system (ANS) are common in neuropsychiatric disorders. Disease specific alterations of both sympathetic and parasympathetic activity can be assessed by heart rate variability (HRV), whereas electrodermal activity (EDA) can assess sympathetic activity. In posttraumatic stress disorder (PTSD), parasympathetic HRV parameters are typically decreased and EDA is increased, whereas in major depressive disorder (MDD) and dissociation, both parasympathetic and sympathetic markers are decreased. ANS abnormalities have also been identified in psychogenic nonepileptic seizures (PNES) by using HRV, indicating lower parasympathetic activity at baseline. In addition to reviewing the current literature on ANS abnormalities in PTSD, MDD, and disorders with prominent dissociation, including borderline personality disorder (BPD), this article also presents data from a pilot study on EDA in patients with PNES. Eleven patients with PNES, during an admission to our e...

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of ph... more Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multi-channel recordings from both the central and the autonomic nervous systems. Following the bi-directional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, that is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in XML format, thereby accounting for platform independency, easy inter-connectivity and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states differing both in theirarousal and valence dimension. It is therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions and it is hereby discussed how future developments may be steered to serve for affective healthcare applications such as the monitoring of the elderly or chronically ill people.