Sleep spindle deficits in antipsychotic-naïve early course schizophrenia and in non-psychotic first-degree relatives

Introduction: Chronic medicated patients with schizophrenia have marked reductions in sleep spindle activity and a correlated deficit in sleep-dependent memory consolidation. Using archival data, we investigated whether antipsychotic-naïve early course patients with schizophrenia and young non-psychotic first-degree relatives of patients with schizophrenia also show reduced sleep spindle activity and whether spindle activity correlates with cognitive function and symptoms. Method: Sleep spindles during Stage 2 sleep were compared in antipsychotic-naïve adults newly diagnosed with psychosis, young non-psychotic first-degree relatives of schizophrenia patients and two samples of healthy controls matched to the patients and relatives. The relations of spindle parameters with cognitive measures and symptom ratings were examined. Results: Early course schizophrenia patients showed significantly reduced spindle activity relative to healthy controls and to early course patients with other psychotic disorders. Relatives of schizophrenia patients also showed reduced spindle activity compared with controls. Reduced spindle activity correlated with measures of executive function in early course patients, positive symptoms in schizophrenia and IQ estimates across groups. Conclusions: Like chronic medicated schizophrenia patients, antipsychotic-naïve early course schizophrenia patients and young non-psychotic relatives of individuals with schizophrenia have reduced sleep spindle activity. These findings indicate that the spindle deficit is not an antipsychotic side-effect or a general feature of psychosis. Instead, the spindle deficit may predate the onset of schizophrenia, persist throughout its course and be an endophenotype that contributes to cognitive dysfunction

Analysis of very low frequency oscillations in electromagnetic brain signal recordings

Spontaneous very low frequency oscillations (&lt;0.5 Hz), previously regarded as physiological noise, have of late been increasingly analysed in neuroimaging studies. These slow oscillations, which occur within widely distributed neuroanatomical systems and are unrelated to cardiac and respiratory events, are thought to arise from variations in metabolic demands in the resting brain. However, they also persist during active goal-directed processing, where they predict inter-trial variability in evoked responses and may present a potential source of attention deficit during task performance. This work presents a series of new approaches for investigating: (i) the slow waves in electromagnetic (EM) brain signal recordings, (ii) their contribution in brain function, and (iii) the changes that the slow wave mechanisms undergo during cognitive processing versus resting states. State-of-the-art blind source separation methodologies, including single-channel and spacetime independent component analysis (SC-ICA and ST-ICA), are employed for denoising and dimensionality reduction of multi-channel EM data, and to extract neurophysiologically meaningful brain sources from the recordings. Particularly, magnetoencephalographic (MEG) data of attention-deficit/hyperactivity disorder (ADHD) and control children, and electroencephalographic (EEG) data recorded from healthy adult controls, are analysed. The key analytical challenges and techniques available for the analysis of the slow waves in EM brain signal recordings are discussed, and specific solutions proposed.Core results demonstrate that the inter-trial variability in the amplitude and latency of the eventrelated fields sensory component, the M100 (in MEG), exhibits a slow wave pattern, which is indicative of the intrinsic slow waves modulating underlying brain processes. In a separate study, phase synchronisation in the slow wave band was observed between fronto-central, central and parietal brain regions, and the level of synchrony varied between rest and task conditions, and as a function of ADHD. Furthermore, a new EEG experimental framework and a multistage signal processing methodology have been designed and implemented in order to investigate brain activity during task performance in contrast with that during rest. Here, the brain has been envisaged as an oscillatory system onto which a graded load was imposed to yield a variable output response – the P300. Specifically, results show that the amplitude and phase of the brain sources in the slow wave band share essential similarities during rest and task conditions, but are distinct enough to be classified separately. This is in keeping with the view that the intrinsic slow waves are continuously influencing active brain sources and they are in turn affected by external stimulation. These slow wave variations are also significantly correlated with the level of cognitive attention assessed by performance measures (such as reaction time and error rates). Moreover, the power ofthe sources in the slow wave band is attenuated during task, and the level of attenuation drops as the task difficulty level is increased, whilst their phase undergoes a change in structure (measured through entropy).These new methodologies, developed for gaining insight into the neurophysiological role of the slow waves, could be used for assessing changes in the brain electrical oscillators as a function of various psychiatric and/or neurobehavioural disorders such as ADHD. This could ultimately lead towards a more scientific (and accurate) approach for the prognosis and diagnosis of these disorders.<br/

Space-time independent component analysis: the definitive BSS technique to use in biomedical signal processing?

Independent Component Analysis (ICA) is a very common instantiation of the Blind Source Separation (BSS) problem. In the context of biomedical signal analysis, ICA is generally applied to multi-channel recordings of physiological phenomena in order to de-noise and extract meaningful information underlying the recordings. This paper assesses the Spatio-Temporal ICA (ST-ICA) framework, which uses both spatial and temporal information derived from multi-channel time-series to extract underlying sources. In contrast, the standard implementation of the ICA algorithm generally uses only limited spatial information to inform the separation process. One of the major steps in the implementation of any ICA algorithm is the selection of relevant components from the many ICA usually returns. With ST-ICA there is a rich data-set of components exhibiting spatial as well as temporal/spectral information that could be used to identify the underlying process subspaces extracted by the ST-ICA algorithm. This paper highlights the methodology for performing ST-ICA and assesses the possible ways in which process subspace identification may take place

Issues on the design of a variable LINC amplifier system in SiGe operating at 2.4GHz

An increasing number of wireless communication systems require linear transmitters that can achieve high efficiency. Good linearity is necessary for bandwidth efficient modulation while poor efficiency directly affects operational costs and causes thermal losses in the base station. This paper examines the implementation of the outphasing technique, also known as Linear Amplification with Nonlinear Components (LINC) using a 0.25 mm SiGe BICMOS process. Adaptation of the amplifier in order to achieve different power levels, via the use of Class-F amplifying sections working in parallel is also examined. The effects of the input and output mismatch introduced when changing the power level are discussed and possible solutions presented. Furthermore, specific problems which arise when using a bipolar technology are addressed. This work also discusses the adaptation of a tunable phase shifter for a 1 V supply and wide-linear range. The 2.4 GHz amplifier system presented in this work achieved a 3-level power control with a maximum PAE in the range of 58-73

Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals-2

<p><b>Copyright information:</b></p><p>Taken from "Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals"</p><p>http://www.behavioralandbrainfunctions.com/content/3/1/62</p><p>Behavioral and brain functions : BBF 2007;3():62-62.</p><p>Published online 10 Dec 2007</p><p>PMCID:PMC2235870.</p><p></p> (b) The inverse filter frequency response obtained from the corresponding 6th order MA model

Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals-3

<p><b>Copyright information:</b></p><p>Taken from "Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals"</p><p>http://www.behavioralandbrainfunctions.com/content/3/1/62</p><p>Behavioral and brain functions : BBF 2007;3():62-62.</p><p>Published online 10 Dec 2007</p><p>PMCID:PMC2235870.</p><p></p>inal EEG signal (amplitude in log scale) and of the filtered signal (amplitude in linear scale) showing a dominant peak at 0.1 Hz

Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals-5

<p><b>Copyright information:</b></p><p>Taken from "Distinguishing low frequency oscillations within the 1/spectral behaviour of electromagnetic brain signals"</p><p>http://www.behavioralandbrainfunctions.com/content/3/1/62</p><p>Behavioral and brain functions : BBF 2007;3():62-62.</p><p>Published online 10 Dec 2007</p><p>PMCID:PMC2235870.</p><p></p>nding spectrograms