Low Frequency Brain Oscillations during the execution and imagination of simple hand movements for Brain-Computer Interface applications.

Low Frequency Brain Oscillations (LFOs) are brief periods of oscillatory activity in delta and lower theta band that appear at motor cortical areas before and around movement onset. It has been shown that LFO power decreases in post-stroke patients and re-emerges with motor functional recovery. To date, LFOs have not yet been explored during the motor execution (ME) and imagination (MI) of simple hand movements, often used in BCI-supported motor rehabilitation protocols post-stroke. This study aims at analyzing the LFOs during the ME and MI of the finger extension task in a sample of 10 healthy subjects and 2 stroke patients in subacute phase. The results showed that LFO power peaks occur in the preparatory phase of both ME and MI tasks on the sensorimotor channels in healthy subjects and their alterations in stroke patients. Clinical Relevance- Results suggest that LFOs could be explored as biomarker of the motor function recovery in rehabilitative protocols based on the movement imagination.

Cortico-Muscular Coupling Allows to Discriminate Different Types of Hand Movements.

Cortico-muscular coupling (CMC) could be used as potential input of a novel hybrid Brain-Computer Interface (hBCI) for motor re-learning after stroke. Here, we aim of addressing the design of a hBCI able to classify different movement tasks taking into account the interplay between the cerebral and residual or recovered muscular activity involved in a given movement. Hence, we compared the performances of four classification methods based on CMC features to evaluate their ability in discriminating finger extension from grasping movements executed by 17 healthy subjects. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performances. Results showed that, regardless of the model, few CMC features (up to 10) allow for a successful classification of two different movements type. Moreover, support vector machine classifier with linear kernel showed the best trade-off between performances and system usability (few electrodes). Thus, these results suggest that a hBCI based on brain-muscular interplay holds the potential to enable more informed neural plasticity and functional motor recovery after stroke. Furthermore, this CMC-based BCI could also allow for a more "natural control" (l.e., that resembling physiological control) of prosthetic devices.

Distinctive physiological muscle synergy patterns define the Box and Block Task execution as revealed by electromyographic features.

Stroke survivors experience muscular pattern alterations of the upper limb that decrease their ability to perform daily-living activities. The Box and Block test (BBT) is widely used to assess the unilateral manual dexterity. Although BBT provides insights into functional performance, it returns limited information about the mechanisms contributing to the impaired movement. This study aims at exploring the BBT by means of muscle synergies analysis during the execution of BBT in a sample of 12 healthy participants with their dominant and non-dominant upper limb. Results revealed that: (i) the BBT can be described by 1 or 2 synergies; the number of synergies (ii) does not differ between dominant and non-dominant sides and (iii) varies considering each phase of the task; (iv) the transfer phase requires more synergies. Clinical Relevance- This preliminary study characterizes muscular synergies during the BBT task in order to establish normative patterns that could assist in understanding the neuromuscular demands and support future evaluations of stroke deficits.

Cocaine-related cervical spinal cord infarction: a case report and review of the literature.

STUDY DESIGN: Case report. OBJECTIVES: To report a clinical case of spinal cord infarction due to cocaine use. SETTING: Spinal Center, IRCCS Fondazione S. Lucia, Rome (Italy). CASE PRESENTATION: Two days after recreational use of cocaine, a 27-year-old Caucasic man was admitted to the emergency department for acute cervical pain, weakness in all four limbs, and urinary retention. A cervical spinal magnetic resonance imaging scan, performed after 2 days, showed a "pencil-like" lesion extending from C4 to T1 metamer, compatible with acute ischemia in the anterior spinal artery territory. Other causes of vascular disorders, as well as inflammatory and infectious disorders were ruled out. At admission in our department, the patient had an incomplete tetraplegia at level C6, an indwelling catheter, and was unable to stand and walk. After 3 months of rehabilitation, he had an AIS score D tetraplegia at level C7, was able to stand and walk using parallel bars, and indwelling catheter was replaced by intermittent catheterization. DISCUSSION AND CONCLUSIONS: The etiology of medullary infarction may remain unexplained in nearly 30-40% of cases. Even if rare, cocaine-induced ischemic myelopathy should be considered and ruled out in the differential diagnosis of any acute nontraumatic myelopathy, especially in young patients.

Flumazenil therapy for a gabapentin-induced coma: a case report.

BACKGROUND: Spasticity and neuropathic pain are common in patients after spinal cord injury and negatively affect patients' quality of life. Gabapentin and baclofen are frequently used to treat these conditions. We present a flumazenil-reversed gabapentin-induced coma case, which, to our knowledge, is the second one described in scientific literature. CASE PRESENTATION: A 70-year-old Caucasian man was admitted to our neurorehabilitation ward following a fall with cervical trauma that resulted in immediate tetraplegia. During his stay, he suffered from lower limb pain, both neuropathic and due to severe spasticity. Gradual baclofen and gabapentin administration was prescribed, with reduction in both pain and spasticity. One morning, the patient was found unresponsive, with a Glasgow Coma Score of 3. Head computerized tomography, electrocardiogram, electroencephalogram, vital signs, blood tests, breathing, and blood oxygenation were normal. Renal and liver failure were ruled out. Intravenous 0.25 mg of flumazenil (Anexate) was administered, resulting in complete neurocognitive recovery with a Glasgow Coma Score of 15. DISCUSSION AND CONCLUSIONS: This case report highlights the importance of the individual response to certain pharmacological agents and suggests that further studies need to be conducted both on flumazenil and gabapentin pharmacodynamics to better understand their molecular-receptor activity, and on possible multiple flumazenil mechanisms of action, beyond its classical strict benzodiazepine antagonist action.

Brain connectivity networks at the basis of human attention components: An EEG study.

The Attention Network Task (ANT) was developed to disentangle the three components of attention identified in the Posner's theoretical model (alerting, orienting and executive control) and to measure the corresponding behavioral efficiency. Several fMRI studies have already provided evidences on the anatomical separability and interdependency of these three networks, and EEG studies have also unveiled the associated brain rhythms. What is still missing is a characterization of the brain circuits subtending the attentional components in terms of directed relationships between the brain areas and their frequency content. Here, we want to exploit the high temporal resolution of the EEG, improving its spatial resolution by means of advanced source localization methods, and to integrate the resulting information by a directed connectivity analysis. The results showed in the present study demonstrate the possibility to associate a specific directed brain circuit to each attention component and to identify synthetic indices able to selectively describe their neurophysiological, spatial and spectral properties.

Community detection: Comparison among clustering algorithms and application to EEG-based brain networks.

Community structure is a feature of complex networks that can be crucial for the understanding of their internal organization. This is particularly true for brain networks, as the brain functioning is thought to be based on a modular organization. In the last decades, many clustering algorithms were developed with the aim to identify communities in networks of different nature. However, there is still no agreement about which one is the most reliable, and to test and compare these algorithms under a variety of conditions would be beneficial to potential users. In this study, we performed a comparative analysis between six different clustering algorithms, analyzing their performances on a ground-truth consisting of simulated networks with properties spanning a wide range of conditions. Results show the effect of factors like the noise level, the number of clusters, the network dimension and density on the performances of the algorithms and provide some guidelines about the use of the more appropriate algorithm according to the different conditions. The best performances under a wide range of conditions were obtained by Louvain and Leicht & Newman algorithms, while Ronhovde and Infomap proved to be more appropriate in very noisy conditions. Finally, as a proof of concept, we applied the algorithms under exam to brain functional connectivity networks obtained from EEG signals recorded during a sustained movement of the right hand, obtaining a clustering of scalp electrodes which agrees with the results of the simulation study conducted.

Transcranial cerebellar direct current stimulation: Effects on brain resting state oscillatory and network activity.

Transcranial cerebellar direct current stimulation (tcDCS) can offer new insights into the cerebellar function and disorders, by modulating noninvasively the activity of cerebellar networks. Taking into account the functional interplay between the cerebellum and the cerebral cortex, we addressed the effects of unilateral tcDCS (active electrode positioned over the right cerebellar hemisphere) on the electroencephalographic (EEG) oscillatory activity and on the cortical network organization at resting state. Effects on spectral (de)synchronizations and functional connectivity after anodal and cathodal stimulation were assessed with respect to a sham condition. A lateralized synchronization over the sensorimotor area in gamma band, as well as an increase of the network segregation in sensory-motor rhythms and a higher communication between hemispheres in gamma band, were detected after anodal stimulation. The same measures after cathodal tcDCS returned responses similar to the sham condition.

Interfacing brain with computer to improve communication and rehabilitation after brain damage.

Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.

Pharmacological targeting of miR-155 via the NEDD8-activating enzyme inhibitor MLN4924 (Pevonedistat) in FLT3-ITD acute myeloid leukemia.

High levels of microRNA-155 (miR-155) are associated with poor outcome in acute myeloid leukemia (AML). In AML, miR-155 is regulated by NF-κB, the activity of which is, in part, controlled by the NEDD8-dependent ubiquitin ligases. We demonstrate that MLN4924, an inhibitor of NEDD8-activating enzyme presently being evaluated in clinical trials, decreases binding of NF-κB to the miR-155 promoter and downregulates miR-155 in AML cells. This results in the upregulation of the miR-155 targets SHIP1, an inhibitor of the PI3K/Akt pathway, and PU.1, a transcription factor important for myeloid differentiation, leading to monocytic differentiation and apoptosis. Consistent with these results, overexpression of miR-155 diminishes MLN4924-induced antileukemic effects. In vivo, MLN4924 reduces miR-155 expression and prolongs the survival of mice engrafted with leukemic cells. Our study demonstrates the potential of miR-155 as a novel therapeutic target in AML via pharmacologic interference with NF-κB-dependent regulatory mechanisms. We show the targeting of this oncogenic microRNA with MLN4924, a compound presently being evaluated in clinical trials in AML. As high miR-155 levels have been consistently associated with aggressive clinical phenotypes, our work opens new avenues for microRNA-targeting therapeutic approaches to leukemia and cancer patients.

Effect of inter-trials variability on the estimation of cortical connectivity by Partial Directed Coherence.

Partial Directed Coherence (PDC) is a powerful estimator of effective connectivity. In neuroscience it is used in different applications with the aim to investigate the communication between brain regions during the execution of different motor or cognitive tasks. When multiple trials are available, PDC can be computed over multiple realizations, provided that the assumption of stationarity across trials is verified. This allows to improve the amount of data, which is an important constraint for the estimation accuracy. However, the stationarity of the data across trials is not always guaranteed, especially when dealing with patients. In this study we investigated how the inter-trials variability of an EEG dataset affects the PDC accuracy. Effects of density variations and of changes of connectivity values across trials were first investigated with a simulation study and then tested on real EEG data collected from two post-stroke patients during a motor imagery task and characterized by different inter-trials variability. Results showed the effect of different factors on the PDC accuracy and the robustness of such estimator in a range of conditions met in practical applications.

Human HMGA2 protein overexpressed in mice induces precursor T-cell lymphoblastic leukemia.

T-cell acute lymphoblastic leukemia (T-ALL) is a neoplasia of thymocytes characterized by the rapid accumulation of the precursors of T lymphocytes. HMGA2 (high-mobility group AT-hook 2) gene expression is extremely low in normal adult tissues, but it is overexpressed in many tumors. To identify the biological function of HMGA2, we generated transgenic mice carrying the human HMGA2 gene under control of the VH promoter/Eµ enhancer. Approximately 90% of Eµ-HMGA2 transgenic mice became visibly sick between 4 and 8 months due to the onset and progression of a T-ALL-like disease. Characteristic features included severe alopecia (30% of mice); enlarged lymph nodes and spleen; and profound immunological abnormalities (altered cytokine levels, hypoimmunoglobulinemia) leading to reduced immune responsiveness. Immunophenotyping showed accumulation of CD5+CD4+, CD5+CD8+ or CD5+CD8+CD4+ T-cell populations in the spleens and bone marrow of sick animals. These findings show that HMGA2-driven leukemia in mice closely resembles spontaneous human T-ALL, indicating that HMGA2 transgenic mice should serve as an important model for investigating basic mechanisms and potential new therapies of relevance to human T-ALL.

A new descriptor of neuroelectrical activity during BCI-assisted motor imagery-based training in stroke patients.

In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training session which is independent from the settings adopted for the online control and which is able to describe the properties of neuroelectrical activations across sessions. Results suggest that such index can be adopted to sort the trails within a session according to the adherence to the task.

Individual cortical connectivity changes after stroke: a resampling approach to enable statistical assessment at single-subject level.

One of the main limitations commonly encountered when dealing with the estimation of brain connectivity is the difficulty to perform a statistical assessment of significant changes in brain networks at a single-subject level. This is mainly due to the lack of information about the distribution of the connectivity estimators at different conditions. While group analysis is commonly adopted to perform a statistical comparison between conditions, it may impose major limitations when dealing with the heterogeneity expressed by a given clinical condition in patients. This holds true particularly for stroke when seeking for quantitative measurements of the efficacy of any rehabilitative intervention promoting recovery of function. The need is then evident of an assessment which may account for individual pathological network configuration associated with different level of patients' response to treatment; such network configuration is highly related to the effect that a given brain lesion has on neural networks. In this study we propose a resampling-based approach to the assessment of statistically significant changes in cortical connectivity networks at a single subject level. First, we provide the results of a simulation study testing the performances of the proposed approach under different conditions. Then, to show the sensitivity of the method, we describe its application to electroencephalographic (EEG) data recorded from two post-stroke patients who showed different clinical recovery after a rehabilitative intervention.

Aged-related changes in brain activity classification with respect to age by means of graph indexes.

Recent studies have investigated changes in the human brain network organization during the normal aging. A reduction of the connectivity between brain areas was demonstrated by combining neuroimaging technologies and graph theory. Clustering, characteristic path length and small-worldness are key topological measures and they are widely used in literature. In this paper we propose a new methodology that combine advanced techniques of effective connectivity estimation, graph theoretical approach and classification by SVM method. EEG signals recording during rest condition from 20 young subjects and 20 mid-aged adults were studied. Partial Directed Coherence was computed by means of General Linear Kalman Filter and graph indexes were extracted from estimated patterns. At last small-worldness was used as feature for the SVM classifier. Results show that topological differences of brain networks exist between young and mid-aged adults: small-worldness is significantly different between the two populations and it can be used to classify the subjects with respect to age with an accuracy of 69%.

Tumor suppressor WWOX binds to ΔNp63α and sensitizes cancer cells to chemotherapy.

The WWOX tumor suppressor is a WW domain-containing protein. Its function in the cell has been shown to be mediated, in part, by interacting with its partners through its first WW (WW1) domain. Here, we demonstrated that WWOX via WW1 domain interacts with p53 homolog, ΔNp63α. This protein-protein interaction stabilizes ΔNp63α, through antagonizing function of the E3 ubiquitin ligase ITCH, inhibits nuclear translocation of ΔNp63α into the nucleus and suppresses ΔNp63α transactivation function. Additionally, we found that this functional crosstalk reverses cancer cells resistance to cisplatin, mediated by ΔNp63α, and consequently renders these cells more sensitive to undergo apoptosis. These findings suggest a functional crosstalk between WWOX and ΔNp63α in tumorigenesis.

EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb.

Brain-Computer Interfaces (BCIs) process brain activity in real time, and mediate non-muscular interaction between and individual and the environment. The subserving algorithms can be used to provide a quantitative measurement of physiological or pathological cognitive processes - such as Motor Imagery (MI) - and feed it back the user. In this paper we propose the clinical application of a BCI-based rehabilitation device, to promote motor recovery after stroke. The BCI-based device and the therapy exploiting its use follow the same principles that drive classical neuromotor rehabilitation, and (i) provides the physical therapist with a monitoring instrument, to assess the patient's participation in the rehabilitative cognitive exercise; (ii) assists the patient in the practice of MI. The device was installed in the ward of a rehabilitation hospital and a group of 29 patients were involved in its testing. Among them, eight have already undergone a one-month training with the device, as an add-on to the regular therapy. An improved system, which includes analysis of Electromyographic (EMG) patterns and Functional Electrical Stimulation (FES) of the arm muscles, is also under clinical evaluation. We found that the rehabilitation exercise based on BCI-mediated neurofeedback mechanisms enables a better engagement of motor areas with respect to motor imagery alone and thus it can promote neuroplasticity in brain regions affected by a cerebrovascular accident. Preliminary results also suggest that the functional outcome of motor rehabilitation may be improved by the use of the proposed device.

Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness.

The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.