Shooting a high-density electroencephalographic picture on sleep in children with attention-deficit/hyperactivity disorder.

STUDY OBJECTIVES: Sleep-related slow-wave activity (SWA) has been recognized as a marker of synaptic plasticity. In children affected by attention deficit hyperactivity disorder (ADHD), SWA is mainly located in the central rather than frontal regions, reflecting a maturational delay. A detailed subjective and objective sleep investigation, including a full night video-polysomnography (PSG-HD-EEG), was performed on 30 consecutive drug naïve outpatients with a diagnosis of ADHD. They received a diagnosis of sleep disorders in 29/30 cases, and most of them had a past history of sleep problems. They had a higher apnea-hypopnea index at PSG, and slept less than 9 hr at actigraphy. We aimed to describe the SWA behavior in the same group of children with ADHD. MATERIALS AND METHODS: The full-night PSG-HD EEG of children with ADHD was compared with the one of the 25 healthy controls. The scalp SWA mapping, the decrease of SWA during the night, and the EEG source of SWA were analyzed. RESULTS: At scalp topography, the focus of SWA was observed over the centro-parietal-occipital regions in participants with ADHD (p < 0.01), which remained significant in the subgroups divided between subgroups according to the sleep diagnosis (p < 0.01). The physiological decrease in SWA was more evident in control participants. The source analysis revealed a greater delta power over the posterior cingulate in participants with ADHD (p < 0.01). CONCLUSIONS: Our results confirm static and dynamic changes in SWA behavior in children with ADHD, which may reflect a maturational delay occurring at a vulnerable age, as a consequence of chronic sleep deprivation.

From physiological neck myoclonus to sleep related head jerk.

Neck myoclonus (NM) is a frequent recently described sleep-related motor phenomenon occurring mainly during REM sleep with uncertain effect on sleep continuity. To better describe this phenomenon we studied 11 consecutive drug-free patients undergoing a video-polysomnographic (V-PSG) study who present at least 5 NM events in one single night of recording. All events were measured and checked for their association with rapid eye and leg movements, EEG arousals, awakenings and Bereitschaftspotential. One hundred and eighty-two motor events from 11 subjects were analyzed. Motor events were approximately 0.5 s in duration and occurred during REM sleep in 79.7% of the cases. Only 14.8% of the events were associated with rapid eye movements, 52.2% with leg movements, while approximately 80% of them were accompanied by an arousal or awakening. No EEG abnormalities and Bereitschaftspotential were observed. For its duration and its segregation in sleep, NM could be more appropriately named "sleep-related head jerks" (SRHJ). SRHJ should be recognized and its effect on sleep stability should be re-considered.

A new visual feed-back modality for the reduction of artifacts in mu-rhythm based brain-computer interfaces.

A common problem in EEG recording sessions is that results can be heavily contaminated by artifacts. One of the main reasons is that eyes movements generate a noise signal that superimpose to the data. In some BCI protocols the user has generally to control the movement of a cursor on a PC screen by self-regulating his/her mu-rhythm. In general this requires the user to move the eyes to follow the same cursor, thus intrinsically generating a huge amount of noise. To overcome this problem a new feedback modality has been developed, which is able to dramatically reduce the artifacts as it does not require subjects to move their eyes.

A UML model for the description of different brain-computer interface systems.

BCI research lacks a universal descriptive language among labs and a unique standard model for the description of BCI systems. This results in a serious problem in comparing performances of different BCI processes and in unifying tools and resources. In such a view we implemented a Unified Modeling Language (UML) model for the description virtually of any BCI protocol and we demonstrated that it can be successfully applied to the most common ones such as P300, mu-rhythms, SCP, SSVEP, fMRI. Finally we illustrated the advantages in utilizing a standard terminology for BCIs and how the same basic structure can be successfully adopted for the implementation of new systems.

Brain computer interface research at the neuroscience department of the "Tor Vergata" University of Rome, Italy.

Brain Computer Interface (BCI) systems have gained great visibility in the last years as they represent a quite innovative way of communication and a new instrument aimed at exploring brain functions. A lot of research labs are developing their own BCI system, everyone being involved in some particular aspects of them. At the "Tor Vergata" University our purpose is to develop tools for the evaluation and the optimization of the performances of BCI systems and to delineate some criteria for the analysis and implementation of different BCI systems; also we have defined file formats for BCI data in order to allow the sharing of tools among groups and to create models for the generalization and therefore the unification of the resources. All the tools and routines mentioned are part of the Body Language Framework++ 2.0.

How the NPX data format handles EEG data acquired simultaneously with fMRI.

There is a growing interest in combining EEG and (f)MRI data as they provide complementary information: EEG is characterized by a high temporal resolution but poor spatial one, while fMRI is characterized by a high spatial resolution but low temporal one. However, while a standard file format for storing EEG data is available since over a decade, it does not fulfill the needs of modern protocols and devices such as those involved in simultaneous EEG and fMRI recordings. The main reasons are the limited bit resolution, some difficulties encountered in handling and storing acquisition events or trace markers for off-line analyses and the impossibility to add some protocol-specific information that is not considered in the actual data formats. This, among others, hinders the release of free analysis software and makes it difficult to share data across different laboratories as every research unit develops its own tools according to its needs, stores data in proprietary formats and a lot of time is spent building software applications for converting data from one format to another. The NPX (NeuroPhysiological signals in eXtensible Markup Language) data format was defined to overcome these and other limitations, and here its main characteristics are reported as well as how some typical problems occurring in simultaneous EEG-fMRI recordings are also treated. Many tools based on the NPX technology can be freely downloaded, including a tool for removing artifacts occurring during simultaneous EEG-fMRI recordings.

An independent component analysis-based approach on ballistocardiogram artifact removing.

Interest about simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data acquisition has rapidly increased during the last years because of the possibility that the combined method offers to join temporal and spatial resolution, providing in this way a powerful tool to investigate spontaneous and evoked brain activities. However, several intrinsic features of MRI scanning become sources of artifacts on EEG data. Noise sources of a highly predictable nature such as those related to the pulse MRI sequence and those determined by magnetic gradient switching during scanning do not represent a major problem and can be easily removed. On the contrary, the ballistocardiogram (BCG) artifact, a large signal visible on all EEG traces and related to cardiac activity inside the magnetic field, is determined by sources that are not fully stereotyped and causing important limitations in the use of artifact-removing strategies. Recently, it has been proposed to use independent component analysis (ICA) to remove BCG artifact from EEG signals. ICA is a statistical algorithm that allows blind separation of statistically independent sources when the only available information is represented by their linear combination. An important drawback with most ICA algorithms is that they exhibit a stochastic behavior: each run yields slightly different results such that the reliability of the estimated sources is difficult to assess. In this preliminary report, we present a method based on running the FastICA algorithm many times with slightly different initial conditions. Clustering structure in the signal space of the obtained components provides us with a new way to assess the reliability of the estimated sources.

Simultaneous EEG-fMRI acquisition: how far is it from being a standardized technique?

Simultaneous EEG-fMRI is a powerful tool to study spontaneous and evoked brain activity because of the complementary advantages of the two techniques in terms of temporal and spatial resolution. In recent years, a significant number of scientific works have been published on this subject. However, many technical problems related to the intrinsic incompatibility of EEG and MRI methods are still not fully solved. Furthermore, simultaneous acquisition of EEG and event-related fMRI requires precise synchronization of all devices involved in the experimental setup. Thus, timing issue must be carefully considered in order to avoid significant methodological errors. The aim of the present work is to highlight and discuss some of technical and methodological open issues associated with the combined use of EEG and fMRI. These issues are presented in the context of preliminary data regarding simultaneous acquisition of event-related evoked potentials and BOLD images during a visual odd-ball paradigm.