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Despite the breakthrough achievements in understanding structural and functional connectivity alterations that underlie autism spectrum disorder (ASD), the exact nature and type of such alterations are not yet clear due to conflicting reports of hyper-connectivity, hypo-connectivity, and –in some cases– combinations of both. In this work, we approach the debate about hyper- vs hypoconnectivity in ASD using a novel network comparison technique designed to capture mesoscopic-scale differential structures. In particular, we build on recent algorithmic advances in the sparsification of functional connectivity matrices, in the extraction of contrast subgraphs, and in the computation of statistically significant maximal frequent itemsets, and develop a method to identify mesoscale structural subgraphs that are maximally dense and different in terms of connectivity levels between the different sets of networks.We apply our method to analyse brain networks of typically developed individuals and ASD patients across different developmental phases and find a set of altered cortical-subcortical circuits between healthy subjects and patients affected by ASD. Specifically, our analysis highlights in ASD patients a significantly larger number of functional connections among regions of the occipital cortex and between the left precuneus and the superior parietal gyrus. At the same time, reduced connectivity characterised the superior frontal gyrus and the temporal lobe regions. More importantly, we can simultaneously detect regions of the brain that show hyper and hypo-connectivity in ASD in children and adolescents, recapitulating within a single framework multiple previous separate observations.

A set of discrete individual points located in an embedding continuum space can be seen as percolating or non-percolating, depending on the radius of the discs/spheres associated with each of them. This problem is relevant in theoretical ecology to analyze, e.g., the spatial percolation of a tree species in a tropical forest or a savanna. Here, we revisit the problem of aggregating random points in continuum systems (from $2$ to $6-$dimensional Euclidean spaces) to analyze the nature of the corresponding percolation transition in spatial point processes. This problem finds a natural description in terms of the canonical ensemble but not in the usual grand-canonical one, customarily employed to describe percolation transitions. This leads us to analyze the question of ensemble equivalence and study whether the resulting canonical continuum percolation transition shares its universal properties with standard percolation transitions, analyzing diverse homogeneous and heterogeneous spatial point processes. We, therefore, provide a powerful tool to characterize and classify a vast class of natural point patterns, revealing their fundamental properties based on percolation phase transitions., Comment: 22 pages, 13 figures

Statistical Physics has proved essential to analyze multi-agent environments. Motivated by the empirical observation of various non-equilibrium features in Barro Colorado and other ecological systems, we analyze a plant-species abundance model, presenting analytical evidence of scale-invariant plant clusters and non-trivial emergent modular correlations. Such first theoretical confirmation of a scale-invariant region, based on percolation processes, reproduces the key features in actual ecological ecosystems and can confer the most stable equilibrium for ecosystems with vast biodiversity., 7 pages, 5 figures, and Supp. Inf

We investigate the application of methodologies for the analysis of complex networks to understand the properties of systems of systems in a cybersecurity context. We are interested to resilience and attribution: the first relates to the behavior of the system in case of faults/attacks, namely to its capacity to recover full or partial functionality after a fault/attack; the second corresponds to the capability to tell faults from attacks, namely to trace the cause of an observed malfunction back to its originating cause(s). We present experiments to witness the effectiveness of our methodology considering a discrete event simulation of a multimodal logistic network featuring 40 nodes distributed across Italy and a daily traffic roughly corresponding to the number of containers shipped through in Italian ports yearly, averaged on a daily basis.

In‐scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in‐scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition‐dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion‐related artifacts between resting‐state and task conditions. Denoising pipelines—including realignment/tissue‐based regression, PCA/ICA‐based methods (aCompCor and ICA‐AROMA, respectively), global signal regression, and censoring of motion‐contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance‐dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance‐dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task‐based functional connectivity data, and more generally for resting‐state data, are discussed., We showed the inefficacy of many denoising strategies in balancing motion‐related artifacts between functional conditions that are differently prone to motion, such as rest and cognitively demanding conditions. While no pipeline was able to completely suppress motion artifacts and simultaneously maximize network identifiability, we found that strategies exploiting an optimized aCompCor yielded the best results. Importantly, we advocate the use of censoring with caution, since we found the method to be cost ineffective, prone to introduce biases and to reduce network identifiability.

Objective: The interaction between skeletal class and upper airway has been extensively studied. Nevertheless, this relationship has not been clearly elucidated, with the heterogeneity of results suggesting the existence of different patterns for patients' classification, which has been elusive so far, probably due to oversimplified approaches. Hence, a network analysis was applied to test whether different patterns in patients' grouping exist. Settings and sample population: Ninety young adult patients with no obvious signs of respiratory diseases and no previous adeno-tonsillectomy procedures, with thirty patients characterized as Class I (0 4); and 30 as Class III (ANB

Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in forty-four medicated schizophrenic patients and forty healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones., Comment: 37 pages, 8 figures

EBioMedicine 72, 103588 (2021). doi:10.1016/j.ebiom.2021.103588, Published by Elsevier, Amsterdam [u.a.]

Procedures and models of computerized data analysis are becoming researchers' and practitioners' thinking partners by transforming the reasoning underlying biomedicine. Complexity theory, Network analysis and Artificial Intelligence are already approaching this discipline, intending to provide support for patient's diagnosis, prognosis and treatments. At the same time, due to the sparsity, noisiness and time-dependency of medical data, such procedures are raising many unprecedented problems related to the mismatch between the human mind's reasoning and the outputs of computational models. Thanks to these computational, non-anthropocentric models, a patient's clinical situation can be elucidated in the orthodontic discipline, and the growth outcome can be approximated. However, to have confidence in these procedures, orthodontists should be warned of the related benefits and risks. Here we want to present how these innovative approaches can derive better patients' characterization, also offering a different point of view about patient's classification, prognosis and treatment.

The effects of osteopathic manipulative treatment (OMT) on functional brain connectivity in healthy adults is missing in the literature. To make up for this lack, we applied advanced network analysis methods to analyze resting state functional magnetic resonance imaging (fMRI) data, after OMT and Placebo treatment (P) in 30 healthy asymptomatic young participants randomized into OMT and placebo groups (OMTg, Pg). fMRI brain activity measures, performed before (T0), immediately after (T1) and three days after (T2) OMT or P were used for inferring treatment effects on brain circuit functional organization. Repeated measures ANOVA and post-hoc analysis demonstrated that Right Precentral Gyrus (F (2, 32) = 5.995, p &lt, 0.005) was more influential over the information flow immediately after the OMT, while decreased betweenness centrality in Left Caudate (F (2, 32) = 6.496, p &lt, 0.005) was observable three days after. Clustering coefficient showed a distinct time-point and group effect. At T1, reduced neighborhood connectivity was observed after OMT in the Left Amygdala (L-Amyg) (F (2, 32) = 7.269, p &lt, 0.005) and Left Middle Temporal Gyrus (F (2, 32) = 6.452, p &lt, 0.005), whereas at T2 the L-Amyg and Vermis-III (F (2, 32) = 6.772, p &lt, 0.005) increased functional interactions. Data demonstrated functional connectivity re-arrangement after OMT.

Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNA generated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as Akkermansia muciniphila, Rikenellaceae, Bacteroides, Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.

No systematic data on hospitalized SARS-COV-2 patients from Western countries are available. We report onset, course, correlations with comorbidities, and diagnostic accuracy of nasopharyngeal swab in 539 individuals suspected to carry SARS-COV-2 admitted to the hospital of Crema, Italy. All individuals underwent clinical and laboratory exams, SARS-COV-2 reverse transcriptase-polymerase chain reaction on nasopharyngeal swab, and chest X-ray and/or computed tomography (CT). Data on onset, course, comorbidities, number of drugs including angiotensin converting enzyme (ACE) inhibitors and angiotensin-II-receptor antagonists (sartans), follow-up swab, pharmacological treatments, non-invasive respiratory support, ICU admission, and deaths were recorded. Among 411 SARS-COV-2 patients (66.6% males) median age was 70.5 years (range 1-99). Chest CT was performed in 317 (77.2%) and showed interstitial pneumonia in 304 (96%). Fatality rate was 17.5% (74% males), with 6.6% in 60-69 years old, 21.1% in 70-79 years old, 38.8% in 80-89 years old, and 83.3% above 90 years. No death occurred below 60 years. Non-invasive respiratory support rate was 27.2% and ICU admission 6.8%. Older age, cough and dyspnea at onset, hypertension, cardiovascular diseases, diabetes, renal insufficiency, >7 drugs intake and positive X-ray, low lymphocyte count, high C-reactive protein, aspartate aminotransferase and lactate dehydrogenase values, and low PO2 partial pressure with high lactate at arterial blood gas analysis at admission were significantly associated with death. Use of ACE inhibitors or sartans was not associated with outcomes. Comorbidity network analysis revealed homogenous distribution of deceased and 60-80 aged SARS-COV-2 patients across diseases. Among 128 swab negative patients at admission (63.3% males) median age was 67.7 years (range 1-98). Chest CT was performed in 87 (68%) and showed interstitial pneumonia in 76 (87.3%). Follow-up swab turned positive in 13 of 32 patients. Using chest CT at admission as gold standard on the entire study population of 539 patients, nasopharyngeal swab had 80% sensitivity. SARS-CoV-2 caused high mortality among patients older than 60 years and correlated with pre- existing multiorgan impairment. ACE inhibitors and sartans did not influence patients’ outcome.

“Sense of agency” (SoA), the feeling of control for events caused by one's own actions, is deceived by visuomotor incongruence. Sensorimotor networks are implicated in SoA, however little evidence exists on brain functionality during agency processing. Concurrently, it has been suggested that the brain's intrinsic resting‐state (rs) activity has a preliminary influence on processing of agency cues. Here, we investigated the relation between performance in an agency attribution task and functional interactions among brain regions as derived by network analysis of rs functional magnetic resonance imaging. The action‐effect delay was adaptively increased (range 90–1,620 ms) and behavioral measures correlated to indices of cognitive processes and appraised self‐concepts. They were then regressed on local metrics of rs brain functional connectivity as to isolate the core areas enabling self‐agency. Across subjects, the time window for self‐agency was 90–625 ms, while the action‐effect integration was impacted by self‐evaluated personality traits. Neurally, the brain intrinsic organization sustaining consistency in self‐agency attribution was characterized by high connectiveness in the secondary visual cortex, and regional segregation in the primary somatosensory area. Decreased connectiveness in the secondary visual area, regional segregation in the superior parietal lobule, and information control within a primary visual cortex‐frontal eye fields network sustained self‐agency over long‐delayed effects. We thus demonstrate that self‐agency is grounded on the intrinsic mode of brain function designed to organize information for visuomotor integration. Our observation is relevant for current models of psychopathology in clinical conditions in which both rs activity and sense of agency are altered., We investigated the relation between agency and functional interactions among brain regions using network analysis. Self‐agency is grounded on the intrinsic mode of brain function designed to organize information for visuomotor integration. This is relevant for current models of psychopathology in clinical conditions in which both brain functional activity and sense of agency are altered.

Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNAgenerated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as Akkermansia muciniphila, Rikenellaceae, Bacteroides, Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.

Mechanisms of anesthetic drug-induced sedation and unconsciousness are still incompletely understood. Functional neuroimaging modalities provide a window to study brain function changes during anesthesia allowing us to explore the sequence of neuro-physiological changes associated with anesthesia. Cerebral perfusion change under an assumption of intact neurovascular coupling is an indicator of change in large-scale neural activity. In this experiment, we have investigated resting state cerebral blood flow (CBF) changes in the human brain during mild sedation, with propofol. Arterial spin labeling (ASL) provides a non-invasive, reliable, and robust means of measuring cerebral blood flow (CBF) and can therefore be used to investigate central drug effects. Mild propofol sedation-related CBF changes were studied at rest (n = 15), in a 3 T MR scanner using a PICORE-QUIPSS II ASL technique. CBF was reduced in bilateral paracingulate cortex, premotor cortex, Broca’s areas, right superior frontal gyrus and also the thalamus. This cerebral perfusion study demonstrates that propofol induces suppression of key cortical (frontal lobe) and subcortical (thalamus) regions during mild sedation.

It has long been assumed that the language function is hierarchically organized into specific cortical areas. Here, for the first time, we present direct evidence that the spinal cord takes part in language processing. In a randomized-double blind design, sixteen aphasics underwent a language treatment combined with transcutaneous spinal direct current stimulation (tsDCS). During the treatment, each subject received tsDCS (20 min, 2 mA) over the thoracic vertebrae (IX-X vertebrae) in two different conditions: (1) anodal, and (2) sham while performing a verb naming task. Each experimental condition was run in five consecutive daily sessions over two weeks. Before and after each condition, all patients underwent a resting state functional magnetic resonance imaging (rs-fMRI). After anodal tsDCS, significant functional connectivity changes were found in a cerebellar-cortical network recruiting regions such as the left cerebellum, the right parietal and premotor cortex known to be also involved in action-related verb processing. Indeed, this increase of connectivity significantly correlated with the greatest amount of improvement found in verb naming. In line with our experimental data, we also found a greater improvement after anodal tsDCS also on untreated items of the language test but only on tasks which required the use of verbs, such as verb naming and picture description. No significant changes were found in noun naming. Thus, this evidence emphasizes, for the first time, that the neural response due to tsDCS combined with language treatment changes during the course of recovery by enhancing activity into cortical regions which influence verb processing.

Sequencing-based studies have identified novel risk genes for rare, severe epilepsies and revealed a role of rare deleterious variation in common epilepsies. To identify the shared and distinct ultra-rare genetic risk factors for rare and common epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,364 controls of European ancestry. We focused on three phenotypic groups; the rare but severe developmental and epileptic encephalopathies (DEE), and the commoner phenotypes of genetic generalized epilepsy (GGE) and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy, with the strongest enrichment seen in DEE and the least in NAFE. Moreover, we found that inhibitory GABAA receptor genes were enriched for missense variants across all three classes of epilepsy, while no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEE and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the top associations, including CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study confirms a convergence in the genetics of common and rare epilepsies associated with ultra-rare coding variation and highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology in the largest epilepsy WES study to date.

Recent studies have shown that the systematic and repetitive observation of actions belonging to the experiential human motor repertoire without verbal facilitation enhances the recovery of verbs in non fluent aphasia. However, it is still an open question whether this approach extends its efficacy also on discourse productivity by improving the retrieval of other linguistic units (i.e. nouns, sentences, content words). Moreover, nothing is known regarding the neural substrates which support the language recovery process due to action observation treatment.In the present study, ten non fluent aphasics were presented with two videoclips (real everyday life context vs. familiar pantomimed context), each video for six consecutive weeks (Monday to Friday, weekend off). During the treatment, they were asked to observe each video and to describe it without verbal facilitation from the therapist. In all patients, language measures were collected before and at the end of treatment. Before and after each treatment condition (real vs. pantomimed context), each subject underwent a rest- ing state fMRI. After the treatment, significant changes in functional connectivity were found in right sensory-motor net- works which were accompanied by a significant improvement for the different linguistic units in the real context condition. On the contrary, the language recovery obtained in the pantomimed context did not match any functional modifica- tion. The evidence for a recruitment of the sensory-motor cortices during the observation of actions embedded in real context suggests to potentially enhance language recovery in non fluent aphasia through a simulation process related to the sensory-motor properties of actions.

Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may be not the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation. The human brain is organized in functional networks, characterized by long range functional connections between brain areas. This network behavior is modulated by the execution of tasks. In our work, we show that modulations associated to a task are massive and widespread, but changes are scale invariant and the overall topology of the networks is well preserved under stimulation, confirming that the functional networks are intrinsic features of the human brain function. We found also that the amplitude of the massive change we observed is heavily influenced by the degree of connectivity at rest, indicating that the magnitude of connectivity change is not an independent metric for the assessment of functional network dynamics.

Introduction Magnetic resonance imaging (MRI) is a sensitive, noninvasive and widely available technique for studying Parkinson's disease (PD) from both research and clinical perspective. Several issues may physically impede execution of MRI. Moreover, the severity of motor or non-motor symptoms of PD might reduce compliance to MRI. Here we investigated predictors affecting compliance to MRI in PD patients. Methods Two-hundred-thirty-six PD patients underwent clinical, neuropsychological and neuropsychiatric investigations. Accordingly to their ability/inability to perform MRI scan, they were divided into 3 groups. Forty-two patients had physical incompatibility to MRI (PI); 51 patients refused to undergo scan during the MRI evaluation session (RR); 143 patients accepted to undergo and successfully completed MRI (SP). Multivariate/Univariate Analyses of Variance, followed by Bonferroni's post-hoc comparisons, were used to assess differences among groups. To identify predictors of compliance to MRI scan in the whole PD sample (SP vs. RR + PI) we carried out a logistic regression analysis. Results PI subjects were significantly older, had higher UPRDRS-III score, received lower daily dopamine agonist doses, and displayed worse cognitive performances than SP. RR subjects had significantly higher anxiety severity than SP. Lower daily dopamine agonist equivalents and higher anxiety scores were the significant whole predictors of not compliance to MRI in the logistic regression analysis. Conclusions These results show that demographic, neuropsychological and neuropsychiatric features may limit compliance to MRI in PD, and provide valuable aid for setting and interpreting research and clinical MRI studies in PD.

In the past few years, cognitive enhancing drugs (CEDs) have gained growing interest and the focus of investigations aimed at exploring their use to potentiate the cognitive performances of healthy individuals. Most of this exploratory CED-related research has been performed on young adults. However, CEDs may also help to maintain optimal brain functioning or compensate for subtle and or subclinical deficits associated with brain aging or early-stage dementia. In this study, we assessed effects on resting state brain activity in a group of healthy elderly subjects undergoing acute administration of modafinil, a wakefulness-promoting agent. To that aim, participants (n = 24) were investigated with resting state functional Magnetic Resonance Imaging (rs-fMRI) before and after the administration of a single dose (100 mg) of modafinil. Effects were compared to age and size-matched placebo group. Rs-fMRI effects were assessed, employing a graph-based approach and Eigenvector Centrality (EC) analysis, by taking in account topological changes occurring in functional brain networks. The main finding of the study is that modafinil promotes enhanced centrality, a measure of the importance of nodes within functional networks, of the bilateral primary visual (V1) cortex. EC analysis also revealed that modafinil-treated subjects show increased functional connectivity between the V1 and specific cerebellar (Crus I, Crus II, VIIIa lobule) and frontal (right inferior frontal sulcus and left middle frontal gyrus) regions. Present findings provide functional data supporting the hypothesis that modafinil can modulate the cortico-cerebellar connectivity of the aging brain.

The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming a highly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions.

Several studies have already shown that transcranial direct current stimulation (tDCS) is a useful tool for enhancing recovery in aphasia. However, no reports to date have investigated functional connectivity changes on cortical activity because of tDCS language treatment. Here, nine aphasic persons with articulatory disorders underwent an intensive language therapy in two different conditions: bilateral anodic stimulation over the left Broca's area and cathodic contralesional stimulation over the right homologue of Broca's area and a sham condition. The language treatment lasted 3 weeks (Monday to Friday, 15 sessions). In all patients, language measures were collected before (T0) and at the end of treatment (T15). Before and after each treatment condition (real vs. sham), each participant underwent a resting-state fMRI study. Results showed that, after real stimulation, patients exhibited the greatest recovery not only in terms of better accuracy in articulating the treated stimuli but also for untreated items on different tasks of the language test. Moreover, although after the sham condition connectivity changes were confined to the right brain hemisphere, real stimulation yielded to stronger functional connectivity increase in the left hemisphere. In conclusion, our data provide converging evidence from behavioral and functional imaging data that bilateral tDCS determines functional connectivity changes within the lesioned hemisphere, enhancing the language recovery process in stroke patients.

Dipolar couplings between macroscopically distant spins in solution nuclear magnetic resonance (NMR) are treated. We propose a novel technique to calculate the intermolecular multiple-quantum coherences (iMQCs) based on a boson operators approach. The choice of an annihilation and creation operator basis set allows us to produce a general expression of the NMR signal depending on local inhomogeneities of magnetic field. The general expression we derive fits into the well known background of iMQCs signal descriptions [J. Jeener, J. Chem. Phys. 112 (2000) 5091] and turns into the conventional quantum-mechanical Warren formulation [S. Lee, W. Richter, S. Vathyam, W.S. Warren, J. Chem. Phys. 105 (1996) 874] by means of appropriate approximations.

The thermodynamic behaviour of mixed cationic–zwitterionic lipid mixtures dioleoyltrimethylammoniumpropane–dipalmitoylphosphatidylcholine (DOTAP–DPPC) at different mole fractions have been investigated by means of surface pressure measurements (Langmuir isotherms). Excess Gibbs energies of mixing have been calculated as a function of layer composition and surface pressure. Deviation from ideality evidences different molecular organisation of the two lipids, yielding the formation of cationic lipid domains in a zwitterionic lipid matrix. The stability of the mixed monolayer in the presence of both a simple electrolyte solution (NaCl) and a polyelectrolyte solution (NaPAA of different molecular weights) in the same charge ratio interval has been investigated and the influence of the polymer conformation briefly discussed.

The radiowave dielectric behavior of sodium polyacrylate (NaPAA) aqueous solutions, in an extended concentration range and at various degrees of polymerization, was analyzed in the light of the scaling approach for polyelectrolyte solutions recently proposed by Dobrynin et al. [Macromolecules 1995, 28, 1859]. In the frequency range from 1 MHz to 1 GHz, the observed dielectric relaxation is attributed to the fluctuation of loosely bound counterions over a characteristic correlation length which depends on the polymer concentration. Assuming that free counterions fluctuate on a length scale that is proportional to the correlation length ξ0 of the semidilute solution, we derived power laws for the dielectric strength Δe and the relaxation frequency ν0. In agreement with our recently published data on conductivity, the dielectric data indicate that the effective charge on the NaPAA chains increases as concentration is raised, until the correlation length reaches the size of the electrostatic blob. By comparin...

Two different dielectric models of biological cell suspensions have been used to describe the dielectric and conductive properties of the membrane of erythrocyte cells. The passive electrical properties of the cell membrane (the membrane permittivity e ′ and the membrane conductivity σ ) have been evaluated on the basis of the Maxwell–Wagner–Hanai and Looyenga mixture equations and the results have been compared in order to discuss the validity of the two models employed. Based on a simultaneous fit of the permittivity e ′ and the electrical conductivity σ , the Looyenga equation furnishes a better description of the dielectric behavior of an erythrocyte cell suspension over an extended frequency range, in comparison to the Maxwell–Wagner–Hanai equation.