Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes.

Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used.

Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes.

Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Predicting seizures in untreated temporal lobe epilepsy using point-process nonlinear models of heartbeat dynamics

Symptoms of temporal lobe epilepsy (TLE) are frequently associated with autonomic dysregulation, whose underlying biological processes are thought to strongly contribute to sudden unexpected death in epilepsy (SUDEP). While abnormal cardiovascular patterns commonly occur during ictal events, putative patterns of autonomic cardiac effects during pre-ictal (PRE) periods (i.e. periods preceding seizures) are still unknown. In this study, we investigated TLE-related heart rate variability (HRV) through instantaneous, nonlinear estimates of cardiovascular oscillations during inter-ictal (INT) and PRE periods. ECG recordings from 12 patients with TLE were processed to extract standard HRV indices, as well as indices of instantaneous HRV complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra) obtained through definition of inhomogeneous point-process nonlinear models, employing Volterra-Laguerre expansions of linear, quadratic, and cubic kernels. Experimental results demonstrate that the best INT vs. PRE classification performance (balanced accuracy: 73.91%) was achieved only when retaining the time-varying, nonlinear, and non-stationary structure of heartbeat dynamical features. The proposed approach opens novel important avenues in predicting ictal events using information gathered from cardiovascular signals exclusively.

CSF lactate levels, τ proteins, cognitive decline: a dynamic relationship in Alzheimer's disease

OBJECTIVES

To investigate, in patients with Alzheimer's Disease (AD), the possible interplay linking alteration of neuronal energy metabolism, as measured via cerebrospinal fluid (CSF) lactate concentration, to severity of AD neurodegenerative processes and impairment of cognitive abilities.

METHODS

In this study we measured and correlated CSF lactate concentrations, AD biomarker levels (τ-proteins and β-amyloid) and Mini-Mental State Examination (MMSE) score in a population of drug-naïve patients with AD ranging from mild (MMSE≥21/30) to moderate-severe (MMSE<21/30) cognitive decline. They were compared to healthy controls and patients with vascular dementia (VaD).

RESULTS

Patients with AD (n=145) showed a significant increase of CSF lactate concentration compared to controls (n=80) and patients with VaD (n=44), which was higher in mild (n=67) than in patients with moderate-severe AD (n=78). Moreover, we found, in either the whole AD population or both subgroups, a CSF profile in which higher CSF levels of t-τ and p-τ proteins corresponded to lower concentrations of lactate.

CONCLUSIONS

We verified the occurrence of high CSF lactate levels in patients with AD, which may be ascribed to mitochondria impairment. Hypothesising that τ proteins may exert a detrimental effect on the entire cellular energy metabolism, the negative correlation found between lactate and τ-protein levels may allow speculation that τ toxicity, already demonstrated to have affected mitochondria, could also impair glycolytic metabolism with a less evident increase of lactate levels in more severe AD. Thus, we suggest a dynamic relationship between neuronal energy metabolism, τ proteins and cognitive decline in AD and propose the clinical potential of assessing CSF lactate levels in patients with AD to better define the neuronal brain metabolism damage.

Anterior Microsurgical Approach to Ventral Lower Cervical Spine Meningiomas: Indications, Surgical Technique and Long Term Outcome

Ventral lower cervical spinal meningiomas with posterior displacement of the spinal cord are rare and anterior approach has been rarely reported in the literature. The authors present their experience about eight patients operated through anterior microsurgical approach. Exposure of meningiomas was achieved through one or two corpectomies, according to meningioma extension. Tumour removal was performed thanks to the aid of a dedicated ultrasonic aspirator, and intraoperative evoked potentials were employed. Particular care was taken with the materials adopted for reconstruction of the anterior dural plane, to avoid postoperative cerebrospinal fluid leak. Vertebral fusion and stabilization were achieved by tantalum cage or titanium graft in case of one or two corpectomies respectively; anterior titanium plate fixed with screws was applied in all patients. Extent of tumour removal was related to the presence of a conserved arachnoidal plane between the tumour and the spinal cord: total removal was achieved in 2 patients, while gross total removal in the other six ones. Postoperative neurological outcome, which was favourable in all patients, was related mostly to preoperative neurologic status. No recurrence after total removal and no remnant growth after gross total removal occurred during an average follow-up period of 6, 7 years.

Spasticity as an ictal pattern due to excitotoxic upper motor neuron damage

We describe the case of a man who presented with spasticity and aphasia related to continuous electroencephalographic epileptic activity in the left frontal-temporal regions. Magnetic resonance imaging (MRI) documented in diffusion-weighted images (DWI) two areas of restricted diffusion in the left frontal and temporal cortex. After starting treatment with levetiracetam 3000 mg/day there was progressive recovery of the clinical picture as well as the gradual disappearance of the electroencephalographic seizure activity and the vanishing of areas of restricted diffusion in brain MRI. Based on the clinical, EEG and MRI data, we hypothesized that both aphasia and spasticity represented ictal signs. To our knowledge, this is the first case report of ictal spasticity.

Evaluation of the performances of different P300 based brain-computer interfaces by means of the efficiency metric

The aim of this paper is to show how to use the Efficiency, a brain-computer interface (BCI) performance indicator, to evaluate the performances of a wide range of BCI systems. Unlike the most used metrics in the BCI research field, the Efficiency takes into account the penalties and the strategies to recover errors and this makes it a reliable instrument to describe the behavior of real BCIs. The Efficiency is compared with the accuracy and the information transfer rate, both in the Wolpaw and Nykopp definitions. The comparison covers four widely used classifiers and different stimulation sequences. Results show that the Efficiency is able to predict if the communication will not be possible, because the time spent to correct mistakes is longer than the time needed to generate a correct selection, and therefore it provides a much more realistic evaluation of a system. It can also be easily adapted to evaluate different applications, so it reveals a more general and versatile indicator for BCI systems.

Sleep disorders in adult-onset myotonic dystrophy type 1: a controlled polysomnographic study

BACKGROUND

  Sleep disturbances and excessive daytime somnolence are common and disabling features in adult-onset myotonic dystrophy type 1 (DM1).

METHODS

  Our study used questionnaires, ambulatory polysomnography and the multiple sleep latency test to evaluate sleep-wake cycle and daytime sleepiness in unselected adult-onset DM1 patients. We recruited 18 patients affected by adult-onset DM1 and 18 matched controls.

RESULTS

  Sleep efficiency was <90% in 16/18 patients, and it was significantly reduced when compared with controls. Reduced sleep efficiency was associated with abnormal respiratory events (5/18 patients) and/or periodic limb movements (11/18 patients). The Periodic Limb Movement Index was significantly increased in DM1 versus controls. A significantly lower mean MSLT sleep latency was detected in DM1 versus controls, but it did not reach pathological levels.

CONCLUSIONS

  Our controlled study demonstrated sleep alterations in unselected consecutive DM1 patients. Periodic limb movements in sleep are commonly associated with sleep disturbance in adult-onset DM1, and it may represent a marker of CNS neurodegenerative processes in DM1.

Estimation of the cortical activity from simultaneous multi-subject recordings during the prisoner's dilemma

One of the most challenging questions open in Neuroscience today is the characterization of the brain responses during social interaction. A major limitation of the approaches used in most of the studies performed so far is that only one of the participating brains is measured each time. The "interaction" between cooperating, competing or communicating brains is thus not measured directly, but inferred by independent observations aggregated by cognitive models and assumptions that link behavior and neural activation. In this paper, we present the results of the simultaneous neuroelectric recording of 5 couples of subjects engaged in cooperative games (EEG hyperscanning). The simultaneous recordings of couples of interacting subjects allows to observe and model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. We used a paradigm called Prisoner's dilemma derived from the game theory. Results collected in a population of 10 subjects suggested that the most consistently activated structure in social interaction paradigms is the orbitofrontal region (roughly described by the Brodmann area 10) during the condition of competition.

Study of the time-varying cortical connectivity changes during the attempt of foot movements by spinal cord injured and healthy subjects

In this study we estimated time-varying cortical connectivity patterns from a group of Spinal Cord Injured (SCI) patients during the attempt to move a paralyzed limb. These data were compared with the time-varying connectivity patterns estimated in a control group during the real execution of the movement by using time-varying Partial Directed Coherence. Connectivity was estimated from high resolution EEG recordings with the use of realistic head modelling and the linear inverse estimation of the cortical activity in a series of Regions of Interest of the cortex (ROIs). The experimental evidences obtained support the conclusion that the SCI population involved a larger cortical network than those generated by the healthy subjects during the task performance. Such network differs for the involvement of the parietal cortices, which increases in strength near to the movement imagination onset for the SCI when compared to the normal population. Such details about the temporal evolution of the connectivity patterns cannot be obtained with the application of the standard estimators of connectivity.

Assessing the memorization of TV commercials with the use of high resolution EEG: a pilot study

The present work intends to evaluate the functional characteristics of the cerebral network during the successful memory encoding of TV commercials. We estimated the functional networks in the frequency domain from a set of high-resolution EEG data. High resolution EEG recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). Summarizing the main results of the present study, a sign of the memorization of a particular set of TV commercials have been found in a group of investigated subjects with the aid of advanced modern tools for the acquisition and the processing of EEG data. The cerebral processes involved during the observation of TV commercials that were remembered successively by the population examined (RMB dataset) are generated by the posterior parietal cortices and the prefrontal areas, rather bilaterally and are irrespective of the frequency bands analyzed. Such results are compatible with previously results obtained from EEG recordings with superficial electrodes as well as with the brain activations observed with the use of MEG and fMRI devices.

Community structure of cortical networks in spinal cord injured patients

In the present study, we estimated the cortical networks were from high-resolution EEG recordings in a group of spinal cord injured patients and in a group of healthy subjects, during the preparation of a limb movement. Then, we use the Markov Clustering method to analyse the division of the network into community structures. The results indicate large differences between the injured patients and the healthy subjects. In particular, the networks of spinal cord injured patient exhibited a higher density of clusters. In the Alpha (7-12 Hz) frequency band, the two observed largest communities were mainly composed by the cingulate motor areas with the supplementary motor areas, and by the pre-motor areas with the right primary motor area of the foot. This functional separation could reflect the partial alteration in the primary motor areas because of the effects of the spinal cord injury.

Brain network analysis from high-resolution EEG recordings by the application of theoretical graph indexes

The extraction of the salient characteristics from brain connectivity patterns is an open challenging topic since often the estimated cerebral networks have a relative large size and complex structure. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach would extract significant information from the functional brain networks estimated through different neuroimaging techniques. The present work intends to support the development of the "brain network analysis:" a mathematical tool consisting in a body of indexes based on the graph theory able to improve the comprehension of the complex interactions within the brain. In the present work, we applied for demonstrative purpose some graph indexes to the time-varying networks estimated from a set of high-resolution EEG data in a group of healthy subjects during the performance of a motor task. The comparison with a random benchmark allowed extracting the significant properties of the estimated networks in the representative Alpha (7-12 Hz) band. Altogether, our findings aim at proving how the brain network analysis could reveal important information about the time-frequency dynamics of the functional cortical networks.

Study of the time-varying cortical connectivity during the attempt of a foot movement by Spinal Cord Injured patients

In this study we estimated time-varying cortical connectivity patterns from a group of Spinal Cord Injured (SCI) patients during the attempt to move a paralyzed limb. This data were compared with the time-varying connectivity patterns estimated in a control group during the effective execution of the movement. Connectivity was estimated from high resolution EEG recordings with the use of realistic head modelling and the linear inverse estimation of the cortical activity. Time-varying PDC was obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Such estimator is able to follow rapid changes in the connectivity between cortical areas during an experimental task. The obtained experimental evidences support the conclusion that the SCI population involved a larger cortical network than those generated by the healthy subjects during the task performance. Such network differs for the involvement of the parietal cortices, which increases in strength near to the EMG onset.

Estimation of effective and functional cortical connectivity from neuroelectric and hemodynamic recordings

In this paper, different linear and nonlinear methodologies for the estimation of cortical connectivity from neuroelectric and hemodynamic measurements are reviewed and applied on common data set in order to highlight similarities and differences in the results. Different effective and functional connectivity methods were applied to motor and cognitive data sets, including structural equation modeling (SEM), directed transfer function (DTF), partial directed coherence (PDC), and direct directed transfer function (dDTF). Comparisons were made between the results in order to understand if, for a same dataset, effective and functional connectivity estimators can return the same cortical connectivity patterns. An application of a nonlinear method [phase synchronization index (PSI)] to similar executed and imagined movements was also reviewed. Connectivity patterns estimated with the use of the neuroelectric information and of the information from the multimodal integration of neuroelectric and hemodynamic data were also compared. Results suggests that the estimation of the cortical connectivity patterns performed with the linear methods (SEM, DTF, PDC, dDTF) or with the nonlinear method (PSI) on movement related potentials returned similar cortical networks. Differences in cortical connectivity were noted between the patterns estimated with the use of multimodal integration and those estimated by using only the neuroelectric data.

Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.

Time-varying cortical connectivity by adaptive multivariate estimators applied to a combined foot-lips movement

In this paper we propose the use of an adaptive multivariate approach to define time-varying multivariate estimators based on the Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC). DTF and PDC are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. Time-varying DTF and PDC were obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Such estimators are able to follow rapid changes in the connectivity between cortical areas during an experimental task. We provide an application to the cortical estimations obtained from high resolution EEG data, recorded from a group of healthy subject during a combined foot-lips movement, and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected, one constant across the task and the other evolving during the preparation of the joint movement.

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies

The Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC) are frequency-domain estimators, based on the multivariate autoregressive modelling (MVAR) of time series, that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods requires the stationary of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR). This approach will allow the observation of transient influences between the cortical areas during the execution of a task. Time-varying DTF and PDC were obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Simulations were performed under different levels of Signal to Noise Ratio (SNR), number of trials (TRIALS) and frequency bands (BAND), and of different values of the RLS adaptation factor adopted (factor C). The results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of SNR ad number of trials. Moreover, the capability of follow the rapid changes in connectivity is highly increased by the number of trials at disposal, and by the right choice of the value adopted for the adaptation factor C. The results of the simulation study indicate that DTF and PDC computed on adaptive MVAR can be effectively used to estimate time-varying patterns of functional connectivity between cortical activations, under general conditions met in practical EEG recordings.

Cognitive and psychiatric effects of topiramate monotherapy in migraine treatment: an open study

Few data are available on cognitive and psychiatric effects of topiramate (TPM) monotherapy in migraine. Twenty patients affected by migraine were treated with TPM monotherapy. At the same time, twenty control subjects were selected. A comprehensive neuropsychological and behavioural battery of tests were performed at baseline (T0), at titration (T1) and in maintenance period (T2). Topiramate serum levels were also investigated at T1 and T2. On comparison with the control group, no cognitive and psychiatric differences were detected at baseline. A significant reduction of word fluency score (P < 0.05) was evident after TPM treatment, both at T1 and T2. No patient developed psychiatric adverse events. TPM induced an impairment of verbal fluency and no psychiatric adverse events, demonstrating selective negative cognitive profile in migraine therapy. Slow titration, low doses, lack of previous psychiatric disorders and/or familial history may explain our data.

Cortical network topology during successful memory encoding in a lifelike experiment

In the present work, we estimated the functional networks in the frequency domain from a set of high-resolution EEG data in a group of healthy subjects during the showing of commercial spots within a neutral documentary. Then, we evaluated the differences in the cortical network associated with later remembered and not-remembered commercials by calculating the global- E(g) and local-efficiency E(l) indexes. During the visualization of the video-clips that will be forgotten (FRG), the cortical network exhibited high values of global- and local-efficiency, reflecting a small-world configuration. During the visualization of the video-clips that will be remembered (RMB), the same indexes appeared significantly lower. Such a difference seems not depending on the spectral content of the cortical activity. This result shows how the network communication efficiency would be affected by the presence of attentional and semantic processes that are behind a successful memory encoding in a lifelike situation.

Imaging functional brain connectivity patterns from high-resolution EEG and fMRI via graph theory

We describe a set of computational tools able to estimate cortical activity and connectivity from high-resolution EEG and fMRI recordings in humans. These methods comprise the estimation of cortical activity using realistic geometry head volume conductor models and distributed cortical source models, followed by the evaluation of cortical connectivity between regions of interest coincident with the Brodmann areas via the use of Partial Directed Coherence. Connectivity patterns estimated on the cortical surface in different frequency bands are then imaged and interpreted with measures based on graph theory. These computational tools were applied on a set of EEG and fMRI data from a Stroop task to demonstrate the potential of the proposed approach. The present findings suggest that the methodology is able to identify differences in functional connectivity patterns elicited by different experimental tasks or conditions.

Neuroelectrical source imaging of mu rhythm control for BCI applications

In the last decade, the possibility to noninvasively estimate cortical activity has been highlighted by the application of the techniques known as high resolution EEG. These techniques include a subject's multi-compartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multi-dipole source model, and regularized linear inverse source estimates of cortical current density. The aim of this paper is to demonstrate that the use of cortical activity estimated from noninvasive EEG recordings of motor imagery is useful in the context of a brain computer interface as compared with others scalp spatial filters usually used on-line.

Brain computer interface: estimation of cortical activity from non invasive high resolution EEG recordings

The aim of this paper is to analyze whether the use of the cortical activity estimated from non invasive EEG recordings could be useful to detect mental states related to the imagination of limb movements. Estimation of cortical activity was performed on high resolution EEG data related to the imagination of limb movements gathered in five normal healthy subjects by using realistic head models. Cortical activity was estimated in region of interest associated with the subject's Brodmann areas by using depth-weighted minimum norm solutions. Comparisons between surface recorded EEG and the estimated cortical activity were performed. The estimated cortical activity related to the mental imagery of limbs in the five subjects is located mainly over the contralateral primary motor area. The unbalance between brain activity estimated in contralateral and ipsilateral motor cortical areas relative to the finger movement imagination is greater than those obtained in the scalp EEG recordings. Results suggest that the use of the estimated cortical activity for the motor imagery of upper limbs could be potentially superior with respect to the use of surface EEG recordings. This is due to a greater statistically significant unbalance between the activity estimated in the contralateral and ipsilateral hemisphere with respect to those observed with surface EEG. These results are useful in the context of the development of a non invasive brain computer interface.

Repetitive transcranial magnetic stimulation of the motor cortex ameliorates spasticity in multiple sclerosis

OBJECTIVE

To investigate whether repetitive transcranial magnetic stimulation (rTMS) can modify spasticity.

METHODS

We used high-frequency (5 Hz) and low-frequency (1 Hz) rTMS protocols in 19 remitting patients with relapsing-remitting multiple sclerosis and lower limb spasticity.

RESULTS

A single session of 1 Hz rTMS over the leg primary motor cortex increased H/M amplitude ratio of the soleus H reflex, a reliable neurophysiologic measure of stretch reflex. Five hertz rTMS decreased H/M amplitude ratio of the soleus H reflex and increased corticospinal excitability. Single sessions did not induce any effect on spasticity. A significant improvement of lower limb spasticity was observed when rTMS applications were repeated during a 2-week period. Clinical improvement was long-lasting (at least 7 days after the end of treatment) when the patients underwent 5 Hz rTMS treatment during a 2-week protocol. No effect was obtained after a 2-week sham stimulation.

CONCLUSIONS

Repetitive transcranial magnetic stimulation may improve spasticity in multiple sclerosis.

Features extraction from time-varying cortical networks adopting a theoretical graph approach

In this work, a novel approach is proposed in order to capture relevant features related to the structure and organization of the functional brain networks estimated in the time-frequency domain. To achieve this, we used a cascade of computational tools able to estimate first the electrical activity of the cortical surface by using high resolution EEG techniques. Then, on the cortical signals from different regions of interests we estimated the time-varying functional connectivity patterns by means of the adaptive Partial Directed Coherence. Such time-varying connectivity estimation returns a series of causality patterns evolving during the examined task which can be summarized and interpreted with the aid of mathematical indexes based on the graph theory. The combination of all these methods is demonstrated on a set of high resolution EEG data recorded from a healthy subject performing a simple foot movement.

Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data

The aim of this paper is to test a technique called partial directed coherence (PDC) and its modification (squared PDC; sPDC) for the estimation of human cortical connectivity by means of simulation study, in which both PDC and sPDC were studied by analysis of variance. The statistical analysis performed returned that both PDC and sPDC are able to estimate correctly the imposed connectivity patterns when data exhibit a signal-to-noise ratio of at least 3 and a length of at least 27 s of nonconsecutive recordings at 250 Hz of sampling rate, equivalent, more generally, to 6750 data samples.

Effect of cabergoline added to levodopa treatment on sleep-wake cycle in idiopathic Parkinson's disease: an open label 24-hour polysomnographic study

Few studies focused on the effects of cabergoline on sleep-wake cycle in PD. Twelve patients affected by PD treated with levodopa as monotherapy underwent two 24-hour ambulatory polysomnographic (A-PSG) sessions twice: in baseline condition (levodopa as monotherapy) and after addition of cabergoline. In each condition, a subjective evaluation of sleep quality and daytime sleepiness was obtained by means of Parkinson's disease Sleep Scale (PDSS) and the Epworth Sleepiness Scale. The statistical analysis of sleep parameters revealed a significant increase of sleep efficiency and slow wave sleep under cabergoline. The PDSS total score showed a significant improvement of overall sleep quality after cabergoline. No significant changes in daytime sleepiness were observed. No patient referred and/or showed sleep attacks before and after addition of cabergoline. We hypothesize that the long-lasting effect of cabergoline may improve the objective quality of nocturnal sleep in PD patients complaining nocturnal motor disability without inducing daytime sleepiness.

Temporal lobe epileptic activity mimicking dementia: a case report

Epileptic activity is an underdiagnosed cause that can determine a disruption of memory and cognitive performance, leading an incorrect diagnosis of dementia. We report a 68-year-old man, referred with a 5-year history of subtle behavioral changes and a subjective memory impairment, who was admitted to our department because of recurrent episodes of confusional state lasting from 1 week. Neuropsychological evaluation demonstrated a marked impairment of all cognitive domains examined. Electroencephalogram (EEG) recording showed frequent almost continuous sharp waves localized on the bilateral posterior temporal regions with mild right side predominance. Treatment with phenytoin reversed his cognitive dysfunction and behavioral disturbances. We presume that ictal temporal lobe epileptiform activity is the cause of his confusional episodes and cognitive dysfunction, showing an electroclinical picture of complex partial status epilepticus. However, we hypothesize that the interictal discharges and ictal and postictal effects of subclinical seizures could be involved in the behavioral changes and memory impairment complained by our patient in the last years.

Autoregressive spectral analysis in Brain Computer Interface context

Over the past decade, a number of studies have evaluated the possibility that scalp-recorded electroencephalogram (EEG) activity might be the basis for a brain-computer interface (BCI), a system able to determine the intent of the user from a variety of different electrophysiological signals. With our current EEG-based communication system, users learn over a series of training sessions to use EEG to move a cursor on a video screen: to make this possible users must learn to control the EEG features that determines cursor movement and we must improve signal processing methods to extract from background noise the EEG features that the system translates into cursor movement. Non-invasive data acquisition, makes automated feature extraction challenging, since the signals of interest are "hidden" in a highly noisy environment. It was demonstrated that the spatial filtering operations improve the signal-to-noise ratio. On the contrary, autoregressive modeling has been successfully used by many investigators for EEG signals analysis in BCI context, but to our knowledge no clear guidelines exist on how to choose the parameters of the spectral estimation. Here we present an analysis of the dependence of BCI performance on the parameters of the feature extraction algorithm. In order to optimize user performances, we observed that a different model order value had to be chosen correspondently to different EEG features used to control the system, according to the differences in the spectral power content of alpha and/or beta bands.

Sleep episodes and daytime somnolence as result of individual susceptibility to different dopaminergic drugs in a PD patient: a polysomnographic study

The association between excessive daytime somnolence (EDS) and idiopathic Parkinson's disease (PD) is often reported but still debated. The possible role of antiparkinsonian therapy or primarily of PD on excessive diurnal sleepiness is controversial. We describe the case of a 61-year-old patient affected by PD who experienced sleep episodes (SE) occurring during pramipexole plus L-Dopa therapy. Polysomnographic sleep studies and subjective evaluations of daytime sleepiness (Epworth Sleepiness Scale) were carried out under administration of pramipexole plus L-Dopa, L-Dopa monotherapy and cabergoline plus L-Dopa. The polysomnography revealed two sleep events during pramipexole plus L-Dopa. Moreover, the polysomnographic data showed an increase of both diurnal and nocturnal sleep under pramipexole plus L-Dopa compared with cabergoline plus L-Dopa and L-Dopa as monotherapy. In addition, while Epworth Sleepiness Scale (ESS) Score showed a mild sleepiness under pramipexole (ESS score=11), ESS scores were normal under both L-Dopa and cabergoline plus L-Dopa. Sleep episodes also disappeared under both L-Dopa and cabergoline plus L-Dopa (2- and 12-month follow-up). We hypothesize that an individual susceptibility to specific antiparkinsonian drug may play a significant role in the genesis of sleepiness in our PD patient.

Different TMS patterns of intracortical inhibition in early onset Alzheimer dementia and frontotemporal dementia

OBJECTIVE

To investigate putative changes in cortical excitability of patients affected by early-onset mild dementia by means of transcranial magnetic stimulation (TMS) and to verify whether a peculiar neurophysiological profile may contribute to characterise Alzheimer's disease (AD) vs frontotemporal dementia (FTD).

METHODS

Motor threshold and intracortical inhibition (ICI) and facilitation (ICF) after paired-pulse TMS (inter-stimulus intervals from 1 to 20 ms) were studied in two groups of early-onset demented patients with a neuropsychological profile suggestive of AD (n = 12) and FTD (n = 8). Twelve age-matched healthy subjects were considered as control group. In both patient groups, recordings were performed before and after a single oral dose of 4 mg galantamine.

RESULTS

No significant difference in motor threshold was observed among the three studied groups. On the contrary, early-onset AD showed a significant reduction of ICI compared to control group, no changes were detected in FTD patients. No significant changes in ICF were found between both patient groups and healthy subjects. The acute administration of galantamine reversed the modified ICI in AD group.

CONCLUSIONS

The differential pattern of ICI exhibited by early-onset AD vs FTD in the early stage of disease may represent a non-invasive, reproducible electrophysiological tool, which may contribute to early differential diagnosis and, possibly, to monitor therapeutic effectiveness.

SIGNIFICANCE

The present results support the possibility that subtle, early modifications in intracortical circuitry features AD, but not FTD patients.

Bilateral frontal polymicrogyria and epilepsy in a patient with Turner mosaicism: a case report

Turner's syndrome (TS) is rarely associated with serious abnormalities of brain structure or malformations of cortical development. We report a 17-year-old girl with TS and 45,XO/46,XX mosaicism presenting bilateral frontal polymicrogyria (BFP) and epilepsy. To our knowledge, the association between TS and BFP has never been reported to date. Our observation confirms that in humans the X-chromosome plays an important role in the development and specialization of brain structure and function. We hypothesize that the absence or abnormalities of developmental genes localized on the X-chromosome could be involved in the pathogenesis of BFP observed in our patient.

Introducing BF++: AC++ framework for cognitive bio-feedback systems design

OBJECTIVE

This paper addressed the issue of building-up a framework for the realization of several cognitive bio-feedback (CBF) systems. It minimizes the programming effort and maximizes the efficiency and the cross-platform portability so that it can be used with many platforms (either software or hardware).

METHODS

A generic CBF system was decomposed into six modules: acquisition, kernel, feedback rule, patient feedback, operator user interface and persistent storage. The way in which these modules interact was defined by immutable software interfaces in a way that allows to completely substitute a module without the need to modify the others.

RESULTS

Three Brain Computer Interface engines were developed with less than 40 lines of C++ code each. They can also be used under virtually any platform that supports an ANSI C++ compiler.

CONCLUSION

A framework for the implementation of a wide range of CBF systems was developed. Compared to the other approaches that are described in the literature, the proposed one is the most efficient, the most protable across different platforms, the most generic and the one that allows the realization of the cheapest final systems.

Cortical dysplasia, temporal atrophy, mental retardation, dysmorphic facies, and partial epilepsy: an EEG and dynamic susceptibility contrast (DSC) MRI study in a new possible genetic syndrome

We report the case of a 24-year-old female with partial epilepsy, mental retardation, and dysmorphic facies. In EEG, a high spiking rate (HSR) was evident with abnormalities in the right frontal region. Morphological MRI showed a left temporo-mesial sclerosis and a cortical dysplasia localized in the right frontal cortex. Dynamic susceptibility contrast (DSC) MRI showed hyperperfusion in right frontal region and hypoperfusion in left fronto-temporal region. Left fronto-temporal hypoperfusion is consistent with temporo-mesial sclerosis. Right frontal hyperperfusion is related to the cortical dysplasia area with HSR. HSR may resemble both electroencephalographic and perfusional ictal pattern. DSC MRI is useful in the evaluation of regions involved in the genesis of ictal and interictal epileptiform activity. Furthermore, we hypothesize that our patient would be affected by a new possible genetic syndrome.

Classification of EEG mental patterns by using two scalp electrodes and Mahalanobis distance-based classifiers

OBJECTIVES

In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes.

METHODS

Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used.

RESULTS

The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes.

CONCLUSIONS

Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Deep brain stimulation of both subthalamic nucleus and internal globus pallidus restores intracortical inhibition in Parkinson's disease paralleling apomorphine effects: a paired magnetic stimulation study

OBJECTIVE

We investigated the effect of bilateral subthalamic nucleus (STN) and internal globus pallidus (GPi) deep brain stimulation (DBS) on intracortical inhibition (ICI) in patients with advanced Parkinson's disease (PD).

METHODS

The activity of intracortical inhibitory circuits was studied in 4 PD patients implanted with stimulating electrodes both in STN and GPi by means of paired-pulse transcranial magnetic stimulation, delivered in a conditioning-test design at short (1-6 ms) interstimulus intervals (ISI). The effect of apomorphine on the same PD patients was also investigated.

RESULTS

We observed that implanted PD patients showed a significant increase in ICI during either bilateral STN or GPi DBS at 3 ms ISI, and during bilateral STN DBS at 2 ms ISI in comparison to their off DBS condition. The same statistical improvement was observed during apomorphine infusion at 3 and 2 ms ISI. In each condition, the electrophysiological changes were associated with a significant clinical improvement as measured by the Unified Parkinson's Disease Rating Scale motor examination.

CONCLUSIONS

These results are consistent with the hypothesis that basal ganglia DBS can mimic the effects of pharmacological dopaminergic therapy on PD patients cortical activity. We propose that in PD patients, the basal ganglia DBS-induced improvement of ICI may be related to a recovery in modulation of thalamo-cortical motor pathway.

Effect of apomorphine on cortical inhibition in Parkinson's disease patients: a transcranial magnetic stimulation study

In this study, transcranial magnetic stimulation (TMS) of the primary motor hand area was used to test cortical excitability in Parkinson's disease (PD) patients. Motor evoked potentials (MEPs) to TMS were studied at rest by utilising distinct paired-pulse TMS protocols. Out of 29 untreated PD patients and 29 healthy subjects, early cortical inhibition (1-6 ms) was studied in a first subgroup of 17 PD patients and 15 healthy subjects, whereas late cortical inhibition (20-200 ms) was studied in a second subgroup of 21 PD patients and 19 healthy subjects. In all PD patients the same TMS protocols were performed before and after 3 h of apomorphine infusion. In comparison to healthy subjects, untreated PD patients showed a significant reduction of both early and late cortical inhibition, which was maximal at 2-3 ms, and at 80-100 ms, respectively. Apomorphine administration consistently reversed all the MEP abnormalities found in PD patients. The lack of TMS effects on the Hoffman's reflex (HR), at those intervals revealing the reduced inhibition in PD patients, is compatible with a supraspinal origin of the observed MEP abnormalities. Our data suggest that the cortical and/or subcortical loss of dopaminergic transmission in PD patients is associated with impaired motor cortical inhibitory mechanisms, as tested by a decreased early and late MEP inhibition.

Ketotic hyperglycemia and epilepsia partialis continua

Epilepsia partialis continua (EPC) may occur during nonketotic hyperglycemia but has not been described with diabetic ketoacidosis. The authors report a patient with EPC associated with ketotic hyperglycemia. Brain MRI showed two areas of abnormal signal intensity in the left precentral gyrus and in the right cerebellar hemisphere. Hyperglycemia may reduce seizure threshold because of the increase in gamma-aminobutyric acid metabolism and may trigger epileptic discharges.

Recognition of imagined hand movements with low resolution surface Laplacian and linear classifiers

EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous EEG signals. In this framework, it was suggested that EEG patterns can be better detected with EEG data transformed with Surface Laplacian computation (SL) than with the unprocessed raw potentials. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used. Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements. Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant. Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and these obtained with the SL computed from 26 electrodes (full resolution SL). More importantly, satisfactorily recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%). These results demonstrated also the utility of linear classifiers for the detection of mental patterns in the BCI field.

Reduced L-dopa absorption and increased clinical fluctuations in Helicobacter pylori-infected Parkinson's disease patients

We report that the area under the curve of L-dopa plasma concentration, following the administration of a single 250 mg L-dopa dose, is augmented after Helicobacter pylori (HP) eradication in six Parkinson's disease (PD) patients showing high IgG antibody titer against HP. A prolongation of L-dopa clinical benefit was also observed. We suggest that HP infection-activated gastric alterations may be responsible, at least in part, for the reported erratic efficacy of oral L-dopa therapy in some advanced PD patients. Given the high percentage of HP-positivity in the age cohorts including the largest prevalence of PD patients, we propose that HP eradication be recommended in all PD patients under L-dopa therapy.