Globus pallidus internus activity during simultaneous bilateral microelectrode recordings in status dystonicus.

Limited data are available regarding the electrophysiology of status dystonicus (SD). We report simultaneous microelectrode recordings (MERs) from the globus pallidus internus (GPi) of a patient with SD who was treated with bilateral deep brain stimulation (DBS). Mean neuronal discharge rate was of 30.1 ± 10.9 Hz and 38.5 Hz ± 11.1 Hz for the right and left GPi, respectively. On the right side, neuronal electrical activity was completely abolished at the target point, whereas the mean burst index values showed a predominance of bursting and irregular activity along trajectories on both sides. Our data are in line with previous findings of pallidal irregular hypoactivity as a potential electrophysiological marker of dystonia and thus SD, but further electrophysiological studies are needed to confirm our results.

Hemodynamic and EEG Time-Courses During Unilateral Hand Movement in Patients with Cortical Myoclonus. An EEG-fMRI and EEG-TD-fNIRS Study.

Multimodal human brain mapping has been proposed as an integrated approach capable of improving the recognition of the cortical correlates of specific neurological functions. We used simultaneous EEG-fMRI (functional magnetic resonance imaging) and EEG-TD-fNIRS (time domain functional near-infrared spectroscopy) recordings to compare different hemodynamic methods with changes in EEG in ten patients with progressive myoclonic epilepsy and 12 healthy controls. We evaluated O2Hb, HHb and Blood oxygen level-dependent (BOLD) changes and event-related desynchronization/synchronization (ERD/ERS) in the α and ß bands of all of the subjects while they performed a simple motor task. The general linear model was used to obtain comparable fMRI and TD-fNIRS activation maps. We also analyzed cortical thickness in order to evaluate any structural changes. In the patients, the TD-NIRS and fMRI data significantly correlated and showed a significant lessening of the increase in O2Hb and the decrease in BOLD. The post-movement ß rebound was minimal or absent in patients. Cortical thickness was moderately reduced in the motor area of the patients and correlated with the reduction in the hemodynamic signals. The fMRI and TD-NIRS results were consistent, significantly correlated and showed smaller hemodynamic changes in the patients. This finding may be partially attributable to mild cortical thickening. However, cortical hyperexcitability, which is known to generate myoclonic jerks and probably accounts for the lack of EEG ß-ERS, did not reflect any increased energy requirement. We hypothesize that this is due to a loss of inhibitory neuronal components that typically fire at high frequencies.

Role of the EEG Theta Network During Software Production: A Connectivity Study.

Software programming is an acquired evolutionary skill originating from consolidated cognitive functions (i.e., attentive, logical, coordination, mathematic calculation, and language comprehension), but the underlying neurophysiological processes are still not completely known. In the present study, we investigated and compared the brain activities supporting realistic programming, text and code reading tasks, analyzing Electroencephalographic (EEG) signals acquired from 11 experienced programmers. Multichannel spectral analysis and a phase-based effective connectivity study were carried out. Our results highlighted that both realistic programming and reading tasks are supported by modulations of the Theta fronto-parietal network, in which parietal areas behave as sources of information, while frontal areas behave as receivers. Nevertheless, during realistic programming, both an increase in Theta power and changes in network topology emerged, suggesting a task-related adaptation of the supporting network system. This reorganization mainly regarded the parietal area, which assumes a prominent role, increasing its hub functioning and its connectivity in the network in terms of centrality and degree.

Chronic massive fetomaternal hemorrhage in a newborn from immigrants. Clinical and organizational implications.

Fetomaternal hemorrhage (FMH) refers to the entry of fetal blood into the maternal bloodstream before or during delivery. FMH of more than 30 mL occurs with the frequency of about 1/300. Fetal outcomes may be compromised by still births, hydrops fetalis, cardiac complications, and increased rates of postpartum infant death. In most cases, the cause is not identified. Clinical manifestations of FMH depend on the volume of blood lost and the rate that it occurred. We report a case of chronic massive FMH in a newborn of an immigrant mother with a favorable outcome. Medical visits and tests during pregnancy, including ultrasound scans, were not performed. The baby was hemodynamically stable after birth, manifesting only pallor. The complete blood count revealed severe hypochromic anemia (hemoglobin 3,8 g/dl, hematocrit 14,4%) and reticulocytosis (reticulocyte 25,2%). There was no ABO blood type incompatibility and the result of direct Coomb's test was negative. The Kleihauer-Betke test revealed 5% of fetal erythrocytes in the maternal bloodstream equivalent to 180 mL. The fact that FMH can occur without prior risk factors, and the diagnosis is often postnatal, underscores the importance of heightened of medical suspicion particularly in infants born to immigrants where there is often the lack of prenatal visits.

Levodopa-dependent differences in the non-oscillatory activity of the subthalamic nucleus.

The study of local field potentials (LFP) recorded from the basal ganglia of patients with movement disorders led to significant advancement in the understanding the pathophysiology of Parkinson's disease (PD). The possibility of investigating possible changes in the activity of the brain caused by the levodopa administration may provide a useful tool to evaluate the influence or the side-effects of the treatment from patient to patient. The analysis was carried out through a systematic analysis of the fractal component of the subthalamic local field potentials (STN-LFP) that may reveal, with respect to the classical power spectrum analysis, novel important information about the dynamic modulation caused by the drug intake. Indeed, so far, much of what is known about that is related to the presence of a spectral peak in the beta frequency band then attenuated after the levodopa administration. The nonlinear power-law exponent goes beyond this feature, exploring differences that reflect the fractal (scale-free) behavior of the PD brain dynamics. Here, in order to demonstrate that the presence or absence of the peak has no effect on the computation of the power-law exponent, we used simulated LFP recordings. After that, we performed the fractal analysis in shorts epochs of STN LFPs recordings ( N=24 patients, 12 females and 12 males) before and after Levodopa administration. We found no differences in the nonlinear power-law exponent for simulated data, reinforcing the idea that the parameter was not influenced by the attenuation of the hallmark peak for PD patients. As regard real LFP time series, we found that pharmacological treatment for PD differently altered LFP power of non-oscillatory activity, as well as changed the level of fractal exponent in specific frequency bands. Particularly we observed an increase of the fractal exponent in condition of post-levodopa with significant differences related to the response to levodopa in Parkinson's disease. Clinical Relevance- This study points out a potentially novel non-oscillatory biomarker which could reflect intrinsic properties of complex biological systems thus constituting a potential target parameter for novel and alternative neuroprosthetic applications.

Action Observation Therapy Before Sleep Hours: An EEG Study.

Action Observation Therapy (AOT) is a rehabilitation method which aims at stimulating motor memory by means of the repetitive observation of motor tasks presented through video-clips. Since sleep seems to have a positive effect on learning processes, it is reasonable to hypothesize that the delivery of AOT immediately before sleep hours could enhance the effects of motor training. The objective of the present work was to test the effect of AOT delivered before the sleep hours in terms of improvements in manual dexterity and changes in cortical activity through Electroencephalography (EEG) on healthy subjects. Specifically, EEG traces acquired on a treatment and on a control group before and after three weeks of training during the execution of a Nine Hole Peg Test were analyzed. The spectral analysis of brain signals showed an increased activation of the motor cortex on a subgroup of the treatment subjects. Moreover, a significantly higher involvement of frontal areas was observed in the treatment group.

Selecting a pre-processing pipeline for the analysis of EEG event-related rhythms modulation.

When deciding how to pre-process EEG data, researchers need to make a choice at each single step of the procedure among different possibilities, equally valid. Therefore, in this work, we illustrate how these decisions may affect the quality of the final cleaned data in an Action Observation/Motor Imagery protocol, using quantitative indices. In particular, we showed the effect of segmenting or not the data in epochs around the stimulus presentation time on the independent component analysis (ICA) used for artifact removal. For ICA analysis, we tested two algorithms (SOBI and Extended Infomax). Finally, three re-reference approaches (Common averaged reference-CAR, robust-CAR and reference electrode standardization technique - REST) were also applied and their effects compared. Results showed that the segmenting method has a prominent effect on the cleaning procedure and consequently on final EEG data quality. Extended Infomax is confirmed as the method of choice for the identification of the artifactual components and, finally, CAR and the REST re-referencing techniques led to similar good results.

An intra-operative feature-based classification of microelectrode recordings to support the subthalamic nucleus functional identification during deep brain stimulation surgery.

Objective. The subthalamic nucleus (STN) is the most selected target for the placement of the Deep Brain Stimulation (DBS) electrode to treat Parkinson's disease. Its identification is a delicate and challenging task which is based on the interpretation of the STN functional activity acquired through microelectrode recordings (MERs). Aim of this work is to explore the potentiality of a set of 25 features to build a classification model for the discrimination of MER signals belonging to the STN.Approach.We explored the use of different sets of spike-dependent and spike-independent features in combination with an ensemble trees classification algorithm on a dataset composed of 13 patients receiving bilateral DBS. We compared results from six subsets of features and two dataset conditions (with and without standardization) using performance metrics on a leave-one-patient-out validation schema.Main results.We obtained statistically better results (i.e. higher accuracyp-value = 0.003) on the RAW dataset than on the standardized one, where the selection of seven features using a minimum redundancy maximum relevance algorithm provided a mean accuracy of 94.1%, comparable with the use of the full set of features. In the same conditions, the spike-dependent features provided the lowest accuracy (86.8%), while a power density-based index was shown to be a good indicator of STN activity (92.3%).Significance.Results suggest that a small and simple set of features can be used for an efficient classification of MERs to implement an intraoperative support for clinical decision during DBS surgery.

A novel spatiotemporal tool for the automatic classification of fMRI noise based on Independent Component Analysis.

In this study, a semi-automatic, easy-to-use classification method for the identification and removal of fMRI noise is proposed and tested. The method relies on subject-level spatial independent component analysis (ICA) of fMRI data. Starting from a reference set of labeled independent components (ICs), novel ICs are classified as physiological/artefactual by combining a spatial correlation (SC) analysis with the reference ICs and relative power spectral (PS) analysis. Here, ICs from a task-based fMRI dataset were used as reference. SC and SP thresholds were set using a test dataset (5 subjects, same fMRI protocol) based on Receiving Operating Characteristic curves. The tool performance and versatility were measured on a resting-state fMRI dataset (5 subjects). Our results show that the method can automatically identify noise-related ICs with accuracy, specificity and sensitivity higher than 80% across different fMRI protocols. These findings also suggest that the reference set provided in the present study might be used to mark ICs coming from independent taskrelated or resting-state fMRI datasets.Clinical relevance- The new method will be included in a userfriendly, open-source tool for removal of noisy contributions from fMRI datasets to be used in clinical and research practices.

Characterization of Microelectrode Recordings for the Subthalamic Nucleus identification in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, when the pharmacological approach has no more effect. DBS efficacy strongly depends on the accurate localization of the STN and the adequate positioning of the stimulation electrode during DBS stereotactic surgery. During this procedure, the analysis of microelectrode recordings (MER) is fundamental to assess the correct localization. Therefore, in this work, we explore different signal feature types for the characterization of the MER signals associated to STN from NON-STN structures. We extracted a set of spike-dependent (action potential domain) and spike-independent features in the time and frequency domain to evaluate their usefulness in distinguishing the STN from other structures. We discuss the results from a physiological and methodological point of view, showing the superiority of features having a direct electrophysiological interpretation.Clinical Relevance- The identification of a simple, clinically interpretable, and powerful set of features for the STN localization would support the clinical positioning of the DBS electrode, improving the treatment outcome.

Optimization of the Cortical Traveling Wave Analysis framework for feasibility in Stereo-Electroencephalography.

The study of brain waves propagation is of interest to understand the neural involvement in both physiological and pathological events, such as interictal epileptic spikes (IES). The possibility to track the trajectory of IESs could be useful to better characterize the role of the involved structures in the epileptic network, adding valuable information to the epileptic focus localization. Methods for the cortical traveling wave analysis (CTWA) have been proposed to trace the preferred propagation path of sleep slow waves, using scalp high-density EEG and reconstructing the trajectories both in the sensors and in the sources space. In this work, we propose a feasibility study of the application of these concepts to Stereo-EEG (SEEG) data for the analysis of IES. Through simulations, we selected the best performing Electrical Source Imaging inverse solution for our purpose and illustrate the CTWA procedure. We further show an exemplary application on real data and discuss advantages and pitfalls of the application of CTWA in SEEG.

On arousal from sleep: time-frequency analysis.

Time-frequency analysis of the heart rate variability during arousal from sleep, with and without EMG activation, coming from five obese healthy subjects was performed. Additionally, a comparative analysis of three time-frequency distributions, smooth pseudo Wigner-Ville (SPWVD), Choi-Williams (CWD) and Born-Jordan distribution (BJD) is presented in this study. SPWVD showed higher capacity for eliminating the cross terms independently of the signal. After applying Hilbert transformation to real signals BJD and CWD lost some important mathematic properties as marginals, on the contrary PSWVD remains unchanged. BJD showed results comparable with CWD. During arousal episodes, analogous energy distribution and spectral indexes were obtained by the three time-frequency representations. Arousals with chin activity presented stronger changes in RR intervals and LF (related to sympathetic activity) component, being statistically different with respect to arousal without chin activity, only around the period of maximum change in beta activity on the EEG. These results suggest a more evident stress for the heart when an arousal is related to external muscular activity.

Spatiotemporal dynamics of single-letter reading: a combined ERP-FMRI study.

This work investigates the neural correlates of single-letter reading by combining event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI), thus exploiting their complementary spatiotemporal resolutions. Three externally-paced reading tasks were administered with an event-related design: passive observation of letters and symbols and active reading aloud of letters. ERP and fMRI data were separately recorded from 8 healthy adults during the same experimental conditions. Due to the presence of artifacts in the EEG signals, two subjects were discarded from further analysis. Independent Component Analysis was applied to ERPs, after dimensionality reduction by Principal Component Analysis: some independent components were clearly related to specific reading functions and the associated current density distributions in the brain were estimated with Low Resolution Electromagnetic Tomography Analysis method (LORETA). The impulse hemodynamic response function was modeled as a linear combination of linear B-spline functions and fMRI statistical analysis was performed by multiple linear regression. fMRI and LORETA maps were superimposed in order to identify the overlapping activations and the activated regions specifically revealed by each modality. The results showed the existence of neuronal networks functionally specific for letter processing and for explicit verbal-motor articulation, including the temporo-parietal and frontal regions. Overlap between fMRI and LORETA results was observed in the inferior temporal-middle occipital gyrus, suggesting that this area has a crucial and multifunctional role for linguistic and reading processes, likely because its spatial location and strong interconnection with the main visual and auditory sensory systems may have favored its specialization in grapheme-phoneme matching.

The putative role of neuronal network synchronization as a potential biomarker for bipolar disorder: A review of EEG studies.

Impaired intra-hemispheric and inter-hemispheric communication play a major role in the pathophysiology and cognitive disturbances of bipolar disorder (BD). Brain connectivity in BD has been largely investigated using magnetic resonance imaging (MRI) techniques, which have found alterations in prefronto-limbic coupling. In contrast, evidence for functional neural circuitry abnormalities in BD is less consistent. Indeed, just a few studies employing the electroencephalographic (EEG) technique, enabling the exploration of oscillatory brain dynamics, addressed this issue. Therefore, in the present review we summarize the results from EEG studies examining connectivity in patients with BD, to further clarify the putative role of neuronal network synchronization as a potential biomarker of this disabling mental illness.

Functional Clustering approach for the analysis of Stereo-EEG activity patterns in correspondence of epileptic seizures.

In this study, a functional clustering approach is proposed and tested for the identification of brain functional networks emerging during sleep-related seizures. Stereo-EEG signals recorded in patients with Type II Focal Cortical Dysplasia (FCD type II), were analyzed. This novel approach is able to identify the network configuration changes in pre-ictal and early ictal periods, by grouping Stereo-EEG signals on the basis of the Cluster Index, after wavelet multiscale decomposition. Results showed that the proposed method is able to detect clusters of interacting leads, mainly overlapped on the Epileptogenic Zone (EZ) identified by a clinical expert, with distinctive configurations related to analyzed frequency ranges. This suggested the presence of coupling activities between the elements of the epileptic system at different frequency scales.

Healthy and tumoral tissue resistivity in wild-type and sparc-/- animal models.

Despite the technological improvement of radiologic, endoscopic and nuclear imaging, the accuracy of diagnostic procedures for tumors can be limited whenever a mass-forming lesion is identified. This is true also because bioptical sampling cannot be properly guided into the lesions so as to puncture neoplastic tissue and to avoid necrotic areas. Under these circumstances, invasive and expensive procedures are still required to obtain diagnosis which is mandatory to plan the most appropriate therapeutic strategy. In order to test if electrical impedance spectroscopy may be helpful in providing further evidence for cancer detection, resistivity measurements were taken on 22 mice, 11 wild-type and 11 sparc-/- (knock out for the protein SPARC: secreted protein acidic and rich in cysteine), bearing mammary carcinomas, by placing a needle-probe into tumor, peritumoral and contralateral healthy fat areas. Tumor resistivity was significantly lower than both peritumoral fat and contralateral fat tissues. Resistivity in sparc-/- mice was lower than wild-type animals. A significant frequency dependence of resistivity was present in tissues analyzed. We conclude that accurate measurements of resistivity may allow to discriminate between tissues with different pathological and/or structural characteristics. Therefore, resistivity measurements could be considered for in vivo detection and differential diagnosis of tumor masses.

Assessment of heart rate variability changes during dipyridamole infusion and dipyridamole-induced myocardial ischemia: a time variant spectral approach.

OBJECTIVES: We sought to evaluate changes in RR interval variability during dipyridamole infusion and dipyridamole-induced myocardial ischemia. BACKGROUND: Myocardial ischemia and the autonomic nervous system can be mutually interdependent. Spectral analysis of RR interval variability is a useful tool in assessing autonomic tone. METHODS: We used a time variant autoregressive spectral estimation algorithm that could extract spectral variables even in the presence of nonstationary signals. Two groups were considered: group A (patients with ischemia, n = 15) with effort or mixed angina, angiographically assessed coronary artery disease and positive exercise and dipyridamole echocardiographic test results, and group B (control subjects, n = 10) with normal exercise and dipyridamole echocardiographic test results. We investigated the following variables: RR interval mean and variance, low frequency (LF) and high frequency (HF) power in normalized units, LF ratio (LF/LFbasal power), HF ratio (HF/HFbasal power) and LF/HF ratio. For each test epoch, we calculated for group A and group B the mean value +/- SE of all indexes considered. Differences due to an effect either of group (ischemic vs. control) or of time (including both drug and ischemia effects) were analyzed by using analysis of variance for repeated measurements. RESULTS: Dipyridamole injection was characterized by a reduction of all spectral components in negative test. The LF ratio was the only variable able to discriminate patients with ischemia from control subjects (p < 0.05), whereas a time effect was evident for both mean RR interval and high frequency power in normalized units (p < 0.05). The LF ratio decreased in group B from 1 +/- 0.00 (basal) to 0.31 +/- 0.22 (peak), and increased in group A from 1 +/- 0.00 to 15.41 +/- 6.59, respectively. Results of an unpaired t test comparing the peak values of the two groups were also statistically significant (p < 0.01). CONCLUSIONS: Our data show that time variant analysis of heart rate variability evidences an increase in the low frequency ratio that allows differentiation of positive from negative test results, suggesting that the electrocardiogram may contain ischemia information unrelated to ST-T variations, even if their enhancement requires a more complex data processing procedure.

Kidney transplantation from donors aged 65 years or more as single or dual grafts.

AIM: The organ shortage and aging donor population force transplant centers to accept donors previously considered unusable for kidney transplantation. We report the experience of two Italian transplant centers with single (SKTx) and dual (DKTx) kidney transplantation from donors aged 65 years or more. METHODS: The study population comprised 75 SKTx (mean donor age 70.5 years) and 28 DKTx (mean donor age 75.0 years). Kidneys from donors with a calculated admission creatinine clearance <50 mL/min, a Karpinski's score on kidney biopsy between 5 and 7, or both were allocated to DKTx. Grafts with better function or lower biopsy scores were employed for SKTx. RESULTS: Delayed graft function occurred in 45.3% of SKTx and in 39.3% of DKTx. After a mean follow-up period of 30.0 +/- 19.5 months, the acute rejection rate was 24.0% in SKTx and 7.1% in DKTx. Mean serum creatinine was 1.8 +/- 0.9 and 1.8 +/- 1.3 mg/dL in SKTx, and 1.8 +/- 1.6 mg/dL and 1.3 +/- 0.2 mg/dL in DKTx at 1 and 5 years, respectively. Patient survival was 93.3% and 91.2% in SKTx, and 92.9% and 92.9% in DKTx at 1 and 5 years, respectively. Graft survival was 92.0% and 88.3% in SKTx, and 89.3% and 89.3% in DKTx at the same time intervals. Keeping preservation time below 16 hours and avoiding calcineurin inhibitors were both associated with improved graft survival and function. CONCLUSION: Careful donor selection, short preservation time, and tailored immunosuppression allow safe and efficient use of elderly donor kidneys.

Clusters of mu rhythm from EEG data: A comparative study between 61 and 19 channel datasets.

The EEG mu rhythm is a sensorimotor oscillation which is desynchronized by voluntary movement execution. Independent Component Analysis (ICA) allows the decomposition of recorded scalp EEG data into temporally, functionally, and spatially independent source signals. Clustering techniques applied to independent sources resolved with ICA have been proven to be successful in the identification of clusters of sensorimotor mu rhythm across different subjects. The present work deals with the issue regarding the minimum number of data channels that is recommended to find reliable clusters of mu rhythm. Left and right mu clusters were identified from high-density EEG recordings (61 channels) belonging to a publicly available EEG database. A second dataset was created by selecting a small subset of the same high-density EEG recordings. Specifically, only the 19 channels belonging to the standard 10-20 International System were used for the identification of left and right mu clusters. Quantitative parameters computed from mu clusters obtained from both the 61-channel and the 19-channel datasets were statistically compared. The obtained results suggest that clusters of mu rhythm in sensorimotor areas can be reliably found from a lower number of EEG channels compared to high-density electrodes configuration.

Can home-monitoring of sleep predict depressive episodes in bipolar patients?

The aim of this study is the evaluation of the autonomic regulations during depressive stages in bipolar patients in order to test new quantitative and objective measures to detect such events. A sensorized T-shirt was used to record ECG signal and body movements during the night, from which HRV data and sleep macrostructure were estimated and analyzed. 9 out of 20 features extracted resulted to be significant (p<;0.05) in discriminating among depressive and non-depressive states. Such features are representation of HRV dynamics in both linear and non-linear domain and parameters linked to sleep modulations.