The Optimal Setting for Multilayer Modularity Optimization in Multilayer Brain Networks.

Community detection plays a key role in the study of brain networks, as mechanisms of modular integration and segregation are known to characterize the brain functioning. Moreover, brain networks are intrinsically multilayer: they can vary across time, frequency, subjects, conditions, and meaning, according to different definitions of connectivity. Several algorithms for the multilayer community detection were defined to identify communities in time-varying networks. The most used one is based on the optimization of a multilayer formulation of the modularity, in which two parameters linked to the spatial (γ) and temporal (ω) resolution of the uncovered communities can be set. While the effect of different γ-values has been largely explored, which ω-values are most suitable to different purposes and conditions is still an open issue. In this work, we test the algorithm performances under different values of ω by means of ad hoc implemented benchmark graphs that cover a wide range of conditions. Results provide a guide to the choice of the ω-values according to the network features. Finally, we show an application of the algorithm to real functional brain networks estimated from electro-encephalographic (EEG) signals collected at rest with closed and open eyes. The application to real data agrees with the results of the simulation study and confirms the conclusion drawn from it.

EEG source estimation accuracy in presence of simulated cortical lesions.

Methods to reconstruct the neuroelectrical activity in the brain source space can be used to improve the spatial resolution of scalp-recorded EEG and to estimate the locations of electrical sources in the brain. This procedure can improve the investigation of the functional organization of the human brain, exploiting the high temporal resolution of EEG to follow the temporal dynamics of information processing. As for today, the uncertainties about the effects of inhomogeneities due to brain lesions preclude the adoption of EEG functional mapping on patients with lesioned brain. The aim of this work is to quantify the accuracy of a distributed source localization method in recovering extended sources of activated cortex when cortical lesions of different dimensions are introduced in simulated data. For this purpose, EEG source-distributed activity estimated from real data was modified including silent lesion areas. Then, for each simulated lesion, forward and inverse calculations were carried out to localize the produced scalp activity and the reconstructed cortical activity. Finally, the error induced in the reconstruction by the presence of the lesion was computed and analyzed in relation to the number of electrodes and to the size of the simulated lesion. Results returned values of global error in the whole cortex and of error in the non-lesioned area which are strongly dependent on the number of recorded scalp sensors, as they increase when a lower spatial sampling is performed on the scalp (64 versus 32 EEG channels). For increasing spatial sampling frequencies, the accuracy of the source reconstruction improves and even the presence of small lesions induces significantly higher error levels with respect to the lesion-free condition.

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.

Measuring the agreement between brain connectivity networks.

Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing the estimated network with the corresponding ground-truth network; ii) in applications to real data, when it is necessary to compare the structure of a network obtained in a specific subject with a reference (e.g. a baseline condition or normative data). In the simulations, the level of similarity between two networks was manipulated through different factors. We then investigated the effect of such manipulations on the measures of association. Results showed how the three parameters modulated their values according to the level of similarity between the two networks. In particular, the AUC provided the better performances in terms of its capability to synthetize the similarity between two networks, showing high dynamic and sensitivity.

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.

Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery.

OBJECTIVE: It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks. APPROACH: Linear cortical estimation and statistical brain mapping techniques were applied on high-density EEG data acquired from 18 healthy participants performing three different mental imagery tasks. Subjects were divided in two groups, one of BCI trained subjects, according to their previous exposure (at least six months before this study) to motor imagery-based BCI training, and one of subjects who were naive to any BCI paradigms. MAIN RESULTS: Cortical activation maps obtained for trained and naive subjects indicated different spectral and spatial activity patterns in response to the mental imagery tasks. Long-term effects of the previous SMR-based BCI training were observed on the motor cortical spectral activity specific to the BCI trained motor imagery task (simple hand movements) and partially generalized to more complex motor imagery task (playing tennis). Differently, mental imagery with spatial attention and memory content could elicit recognizable cortical spectral activity even in subjects completely naive to (BCI) training. SIGNIFICANCE: The present findings contribute to our understanding of BCI technology usage and might be of relevance in those clinical conditions when training to master a BCI application is challenging or even not possible.

Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment.

In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.

High incidence of post-transplant cytomegalovirus reactivations in myeloma patients undergoing autologous stem cell transplantation after treatment with bortezomib-based regimens: a survey from the Rome transplant network.

The incidence of cytomegalovirus (CMV) reactivations in patients with multiple myeloma (MM) receiving autologous stem cell transplantation (ASCT) is relatively low. However, the recent increased use of novel agents, such as bortezomib and/or immunomodulators, before transplant, has led to an increasing incidence of Herpesviridae family virus infections. The aim of the study was to establish the incidence of post-engraftment symptomatic CMV reactivations in MM patients receiving ASCT, and to compare this incidence with that of patients treated with novel agents or with conventional chemotherapy before transplant. The study was a survey of 80 consecutive patients who underwent ASCT after treatment with novel agents (Group A). These patients were compared with a cohort of 89 patients treated with VAD regimen (vincristine, doxorubicin, and dexamethasone) before ASCT (Group B). Overall, 7 patients (4.1%) received an antiviral treatment for a symptomatic CMV reactivation and 1 died. The incidence of CMV reactivations was significantly higher in Group A than in Group B (7.5% vs. 1.1%; P = 0.048). When compared with Group B, the CMV reactivations observed in Group A were significantly more frequent in patients who received bortezomib, whether or not associated with immunomodulators (9.4% vs. 1.1%; P = 0.019), but not in those treated with immunomodulators only (3.7% vs. 1.1%; P = 0.396). These results suggest that MM patients treated with bortezomib-based regimens are at higher risk of developing a symptomatic CMV reactivation after ASCT.

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.

Investigating statistical differences in connectivity patterns properties at single subject level: a new resampling approach.

Methods based on the multivariate autoregressive (MVAR) approach are commonly used for effective connectivity estimation as they allow to include all available sources into a unique model. To ensure high levels of accuracy for high model dimensions, all the observations are used to provide a unique estimation of the model, and thus of the network and its properties. The unavailability of a distribution of connectivity values for a single experimental condition prevents to perform statistical comparisons between different conditions at a single subject level. This is a major limitation, especially when dealing with the heterogeneity of clinical conditions presented by patients. In the present paper we proposed a novel approach to the construction of a distribution of connectivity in a single subject case. The proposed approach is based on small perturbations of the networks properties and allows to assess significant changes in brain connectivity indexes derived from graph theory. Its feasibility and applicability were investigated by means of a simulation study and an application to real EEG data.

The effect of normalization of Partial Directed Coherence on the statistical assessment of connectivity patterns: a simulation study.

Partial Directed Coherence (PDC) is a spectral multivariate estimator for effective connectivity, relying on the concept of Granger causality. Even if its original definition derived directly from information theory, two modifies were introduced in order to provide better physiological interpretations of the estimated networks: i) normalization of the estimator according to rows, ii) squared transformation. In the present paper we investigated the effect of PDC normalization on the performances achieved by applying the statistical validation process on investigated connectivity patterns under different conditions of Signal to Noise ratio (SNR) and amount of data available for the analysis. Results of the statistical analysis revealed an effect of PDC normalization only on the percentages of type I and type II errors occurred by using Shuffling procedure for the assessment of connectivity patterns. No effects of the PDC formulation resulted on the performances achieved during the validation process executed instead by means of Asymptotic Statistic approach. Moreover, the percentages of both false positives and false negatives committed by Asymptotic Statistic are always lower than those achieved by Shuffling procedure for each type of normalization.

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%.

A new statistical approach for the extraction of adjacency matrix from effective connectivity networks.

Graph theory is a powerful mathematical tool recently introduced in neuroscience field for quantitatively describing the main properties of investigated connectivity networks. Despite the technical advancements provided in the last few years, further investigations are needed for overcoming actual limitations in the field. In fact, the absence of a common procedure currently applied for the extraction of the adjacency matrix from a connectivity pattern has been leading to low consistency and reliability of ghaph indexes among the investigated population. In this paper we proposed a new approach for adjacency matrix extraction based on a statistical threshold as valid alternative to empirical approaches, extensively used in Neuroscience field (i.e. fixing the edge density). In particular we performed a simulation study for investigating the effects of the two different extraction approaches on the topological properties of the investigated networks. In particular, the comparison was performed on two different datasets, one composed by uncorrelated random signals (null-model) and the other one by signals acquired on a mannequin head used as a phantom (EEG null-model). The results highlighted the importance to use a statistical threshold for the adjacency matrix extraction in order to describe the real existing topological properties of the investigated networks. The use of an empirical threshold led to an erroneous definition of small-world properties for the considered connectivity patterns.

miRNA let-7c promotes granulocytic differentiation in acute myeloid leukemia.

MicroRNAs (miRNAs), small non-coding RNAs that regulate gene expression post-transcriptionally, are involved in many complex cellular processes. Several miRNAs are differentially expressed in hematopoietic tissues and play important roles in normal differentiation, but, when aberrantly regulated, contribute to the abnormal proliferation and differentiation of leukemic cells. Recently, we reported that a small subset of miRNAs is differentially expressed in acute promyelocytic leukemia (APL) blasts and is modulated by treatment with all-trans-retinoic acid (ATRA). In particular, PML/RARα-positive blasts from APL patients display lower levels of miRNA let-7c, a member of the let-7 family, than normal promyelocytes and its expression increases after ATRA treatment. In this study, we investigated the effects of let-7c in acute myeloid leukemia (AML) cells. We found that ectopic expression of let-7c promotes granulocytic differentiation of AML cell lines and primary blasts. Moreover, we identified PBX2, a well-known homeodomain protein whose aberrant expression enhances HoxA9-dependent leukemogenesis, as a novel let-7c target that may contribute to the AML phenotype. Together, these studies raise the possibility that perturbation of the let-7c-PBX2 pathway may have a therapeutic value in AML.

Sequential combined treatment with allopurinol and benznidazole in the chronic phase of Trypanosoma cruzi infection: a pilot study.

OBJECTIVES: Even though the use of combined drugs has been proved to be effective in other chronic infections, assessment of combined treatment of antiparasitic drugs in human Chagas' disease has not been performed. Herein, a pilot study was conducted to evaluate the tolerance and side effects of a sequential combined treatment of two antiparasitic drugs, allopurinol and benznidazole, in the chronic phase of Trypanosoma cruzi infection. PATIENTS AND METHODS: Changes in total and T. cruzi-specific T and B cells were monitored during a median follow-up of 36 months. Allopurinol was administered for 3 months (600 mg/day) followed by 30 days of benznidazole (5 mg/kg/day) in 11 T. cruzi-infected subjects. RESULTS: The combined sequential treatment of allopurinol and benznidazole was well tolerated. The levels of T. cruzi-specific antibodies significantly decreased after sequential combined treatment, as determined by conventional serology and by a multiplex assay using recombinant proteins. The frequency of T. cruzi-specific interferon-γ-producing T cells significantly increased after allopurinol treatment and decreased to background levels following benznidazole administration in a substantial proportion of subjects evaluated. The levels of total naive (CD45RA + CCR7 + CD62L+) CD4 + and CD8 + T cells were restored after allopurinol administration and maintained after completion of the combined drug protocol, along with a decrease in T cell activation in total peripheral CD4 + and CD8 + T cells. CONCLUSIONS: This pilot study shows that the combination of allopurinol and benznidazole induces significant modifications in T and B cell responses indicative of a reduction in parasite burden, and sustains the feasibility of administration of two antiparasitic drugs in the chronic phase of Chagas' disease.

Seronegative conversion after incomplete benznidazole treatment in chronic Chagas disease.

In 12-18% of adult patients, treatment with benznidazole for chronic Chagas disease has to be discontinued because of side-effects. We identified and analysed a cohort of 81 adult patients with three positive tests for Trypanosoma cruzi infection and serological monitoring following incomplete treatment with benznidazole for a median of 10 days. Twenty percent of these patients (16/81) met the criteria of cure, showing that the optimal schedule of benznidazole administration remains to be determined.

Hepatitis C virus infection prevalence and liver dysfunction in a cohort of B-cell non-Hodgkin's lymphoma patients treated with immunochemotherapy.

Several studies have reported a higher prevalence of hepatitis C virus (HCV) infection in patients with B-cell non-Hodgkin's lymphoma (NHL) than in the general population. Treatment for NHL includes the use of chemotherapeutic agents such as cytotoxic drugs, corticosteroids, and rituximab, which can be immunosuppressive and hepatotoxic. While reactivation of hepatitis B virus (HBV) when undergoing immunosuppressive therapy for haematological malignancies is a well-documented complication, data on HCV reactivation or liver function impairment after chemotherapy for NHL are controversial. From January 2006 to December 2009, 207 consecutive NHL patients treated with chemotherapy without rituximab (CHOP) or with rituximab (R-CHOP) were observed; screening for HCV infection and baseline liver function tests were performed in all patients. The prevalence of HCV infection was 9.2%. This prevalence is higher than that observed in the general population in Italy (3%). Among the HCV-infected subjects, the incidence of hepatitis flares was 26.3% vs 2.1% among the HCV-uninfected individuals. Although less frequent and less severe than in HBV-infected subjects, liver dysfunction can occur as a consequence of rituximab-containing regimens in HCV-infected patients with NHL. In the cases considered in this study, no patient treated with chemotherapy without rituximab developed hepatitis flares. The frequency and the severity of this complication vary in different reports. Therefore, we recommend the assessment of liver function and the screening of all patients with NHL for HCV infection before starting chemotherapy; we also recommend monitoring of liver function tests and HCV-RNA serum levels during treatment.

Describing relevant indices from the resting state electrophysiological networks.

The "Default Mode Network" concept was defined, in fMRI field, as a consistent pattern, involving some regions of the brain, which is active during resting state activity and deactivates during attention demanding or goal-directed tasks. Several fMRI studies described its features also correlating the deactivations with the attentive load required for the task execution. Despite the efforts in EEG field, aiming at correlating the spectral features of EEG signals with DMN, an electrophysiological correlate of the DMN hasn't yet been found. In this study we used advanced techniques for functional connectivity estimation for describing the neuroelectrical properties of DMN. We analyzed the connectivity patterns elicited during the rest condition by 55 healthy subjects by means of Partial Directed Coherence. We extracted some graph indexes in order to describe the properties of the resting network in terms of local and global efficiencies, symmetries and influences between different regions of the scalp. Results highlighted the presence of a consistent network, elicited by more than 70% of analyzed population, involving mainly frontal and parietal regions. The properties of the resting network are uniform among the population and could be used for the construction of a normative database for the identification of pathological conditions.