Andrea Canessa

In vitro activity of a Combretum micranthum extract against herpes simplex virus types 1 and 2

The authors demonstrate in vitro antiviral activity of a methanolic extract of Combretum micranthum leaves against HSV-1 and HSV-2. This activity is present only in the extract dissolved 7 days before the assay, but not in the freshly prepared extract, thus indicating the presence of inactive precursors which undergo spontaneous transformations into active compounds. The alkaline autooxidation of the methanolic extract promotes this rapid transformation. The precursors have been identified as condensed catechinic tannins, which, under alkaline conditions, suffer rapid cleavage, intramolecular rearrangement to catechinic acid and autooxidation. The alkaline autooxidation products of the methanolic extract of C. micranthum and those of the synthetic catechinic acid show similar I.R. and U.V. absorption curves, as well as similar anti-HSV-1 and -HSV-2 activities. EC50s of catechinic acid autooxidation products against HSV-1 and HSV-2 replication were 2 micrograms/ml and 4 micrograms/ml, respectively, when cell cultures were treated with the compound during virus infection.

Recognition of Antigenic Clusters of Candida albicans by T Lymphocytes from Human Immunodeficiency Virus-Infected Persons

The fine specificity of the cellular immune response to Candida albicans (i.e., recognition of different antigenic components) between normal controls and human immunodeficiency virus-infected patients in various stages of disease was compared. C. albicans-specific T cells, enriched by antigen stimulation and interleukin-2 expansion, were challenged with antigenic fractions of different molecular weight obtained by SDS-gel fractionation of C. albicans extracts in the presence of autologous mononuclear cells as antigen-presenting cells. Proliferative responses showed similar patterns of reactivity between controls and category A and B seropositive subjects. Category C patients with concurrent C. albicans infections did not give rise to C. albicans-specific T cell lines, confirming the T cell defect. Patients without clinically evident C. albicans infection had a low but broad reactivity pattern of C. albicans-specific T cells. These results suggest that depletion of C. albicans-specific T cells, independent of their fine specificity, occurs along with disease progression.

A cortical model for binocular vergence control without explicit calculation of disparity

A computational model for the control of horizontal vergence, based on a population of disparity tuned complex cells, is presented. Since the population is able to extract the disparity map only in a limited range, using the map to drive vergence control means to limit its functionality inside this range. The model directly extracts the disparity-vergence response by combining the outputs of the disparity detectors without explicit calculation of the disparity map. The resulting vergence control yields to stable fixation and has small response time to a wide range of disparities. Experimental simulations with synthetic stimuli in depth validated the approach.

A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot

Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors.

A neuromorphic control module for real-time vergence eye movements on the iCub robot head

We implemented a cortical model of vergence eye movements on a humanoid robot head (iCub). The proposed control strategy resorts on a computational substrate of modeled V1 complex cells that provides a distributed representation of binocular disparity information. The model includes a normalization stage that allows for a vergence control independent of the texture of the object and of luminance changes. The disparity information is exploited to provide a signal able to nullify the binocular disparity in a foveal region.

SEEG assistant: a 3DSlicer extension to support epilepsy surgery

BackgroundIn the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicist’s workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors.ResultsIn this paper we present SEEG Assistant, a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEG Assistant consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEG Assistant offers a Graphical User Interface that simplifies the interaction between the user and the tools. SEEG Assistant has been tested on 40 patients segmenting 555 electrodes, and it has been used to identify the neuroanatomical loci and to compute the distance to the nearest cerebral cortex for 9626 contacts. We also performed manual segmentation and compared the results between the proposed tool and gold-standard clinical practice. As a result, the use of SEEG Assistant decreases the post implant processing time by more than 2 orders of magnitude, improves the quality of results and decreases, if not eliminates, errors in post implant processing.ConclusionsThe SEEG Assistant Framework for the first time supports physicists by providing a set of open-source tools for post-implant processing of SEEG data. Furthermore, SEEG Assistant has been integrated into 3D Slicer, a software platform for the analysis and visualization of medical images, overcoming limitations of command-line tools.

Improvement in White Matter Tract Reconstruction with Constrained Spherical Deconvolution and Track Density Mapping in Low Angular Resolution Data: A Pediatric Study and Literature Review

Introduction Diffusion-weighted magnetic resonance imaging (DW-MRI) allows noninvasive investigation of brain structure in vivo. Diffusion tensor imaging (DTI) is a frequently used application of DW-MRI that assumes a single main diffusion direction per voxel, and is therefore not well suited for reconstructing crossing fiber tracts. Among the solutions developed to overcome this problem, constrained spherical deconvolution with probabilistic tractography (CSD-PT) has provided superior quality results in clinical settings on adult subjects; however, it requires particular acquisition parameters and long sequences, which may limit clinical usage in the pediatric age group. The aim of this work was to compare the results of DTI with those of track density imaging (TDI) maps and CSD-PT on data from neonates and children, acquired with low angular resolution and low b-value diffusion sequences commonly used in pediatric clinical MRI examinations. Materials and methods We analyzed DW-MRI studies of 50 children (eight neonates aged 3–28 days, 20 infants aged 1–8 months, and 22 children aged 2–17 years) acquired on a 1.5 T Philips scanner using 34 gradient directions and a b-value of 1,000 s/mm2. Other sequence parameters included 60 axial slices; acquisition matrix, 128 × 128; average scan time, 5:34 min; voxel size, 1.75 mm × 1.75 mm × 2 mm; one b = 0 image. For each subject, we computed principal eigenvector (EV) maps and directionally encoded color TDI maps (DEC-TDI maps) from whole-brain tractograms obtained with CSD-PT; the cerebellar-thalamic, corticopontocerebellar, and corticospinal tracts were reconstructed using both CSD-PT and DTI. Results were compared by two neuroradiologists using a 5-point qualitative score. Results The DEC-TDI maps obtained presented higher anatomical detail than EV maps, as assessed by visual inspection. In all subjects, white matter (WM) tracts were successfully reconstructed using both tractography methodologies. The mean qualitative scores of all tracts obtained with CSD-PT were significantly higher than those obtained with DTI (p-value < 0.05 for all comparisons). Conclusion CSD-PT can be successfully applied to DW-MRI studies acquired at 1.5 T with acquisition parameters adapted for pediatric subjects, thus providing TDI maps with greater anatomical detail. This methodology yields satisfactory results for clinical purposes in the pediatric age group.

Autonomous learning of disparity-vergence behavior through distributed coding and population reward

A robotic system implementation that exhibits autonomous learning capabilities of effective control for vergence eye movements is presented. The system, directly relying on a distributed (i.e. neural) representation of binocular disparity, shows a large tolerance to the inaccuracies of real stereo heads and to the changeable environment. The proposed approach combines early binocular vision mechanisms with basic learning processes, such as synaptic plasticity and reward modulation. The computational substrate consists of a network of modeled V1 complex cells that act as oriented binocular disparity detectors. The resulting population response, besides implicit binocular depth cues about the environment, also provides a global signal (i.e. the overall activity of the population itself) to describe the state of the system and thus its deviation from the desired vergence position. The proposed network, by taking into account the modification of its internal state as a consequence of the action performed, evolves following a differential Hebbian rule. The overall activity of the population is exploited to derive an intrinsic signal that drives the weights update. Exploiting this signal implies a maximization of the population activity itself, thus providing an highly effective reward for the developing of a stable and accurate vergence behavior. The role of the different orientations in the learning process is evaluated separately against the whole population, evidencing that the interplay among the differently oriented channels allows a faster learning capability and a more accurate control. The efficacy of the proposed intrinsic reward signal is thus comparatively assessed against the ground-truth signal (the actual disparity) providing equivalent results, and thus validating the approach. Trained in a simulated environment, the proposed network, is able to cope with vergent geometry and thus to learn effective vergence movements for static and moving visual targets. Experimental tests with real robot stereo pairs demonstrate the capability of the architecture not just to directly learn from the environment, but to adapt the control to the stimulus characteristics. We implemented a cortical model for the vergence control based on a population of disparity detectors.The model is able to autonomously learn its behavior by means of an internal parameter.The speed of convergence and the precision of the control precision were evaluated on different disparity ranges and learning signals.The informative content of the different orientation channels was assessed.The learning capabilities on real robot stereo pairs demonstrate an adaptation to the stimulus characteristics.

How a population-based representation of binocular visual signal can intrinsically mediate autonomous learning of vergence control

Abstract Designing an active visual system, able to autonomously learn its behavior, implies to make the learning controller independent of an external signal (e.g. the error between the actual and the desired vergence angle) or of perceptual decisions about dispar–ity (e.g. from the response of a previously trained network). The proposed approach is based on a direct use of a computational substrate of modeled V1 complex cells that provide a distributed representation of binocular disparity information. The design strategies of the cortical-like architecture, including uniform coverage in feature space and divisive normalization mechanisms, allow the global energy of the population to effectively mediate the learning process towards the proper motor control. Since the learning controller is based on an intrinsic representation of the visual signal, it comes to overlap and coincide with the system that is learning the behaviour, thus closing at an inner cycle the perception-action loop necessary for learning. Experi–mental tests proved that the control architecture is both able to learn an effective vergence behavior, and to exploit it to fixate static and moving visual targets.

Striatal Dopaminergic Innervation Regulates Subthalamic Beta-Oscillations and Cortical-Subcortical Coupling during Movements: Preliminary Evidence in Subjects with Parkinson’s Disease

Activation of the basal ganglia has been shown during the preparation and execution of movement. However, the functional interaction of cortical and subcortical brain areas during movement and the relative contribution of dopaminergic striatal innervation remains unclear. We recorded local field potential (LFP) activity from the subthalamic nucleus (STN) and high-density electroencephalography (EEG) signals in four patients with Parkinson’s disease (PD) off dopaminergic medication during a multi-joint motor task performed with their dominant and non-dominant hand. Recordings were performed by means of a fully-implantable deep brain stimulation (DBS) device at 4 months after surgery. Three patients also performed a single-photon computed tomography (SPECT) with [123I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane (FP-CIT) to assess striatal dopaminergic innervation. Unilateral movement execution led to event-related desynchronization (ERD) followed by a rebound after movement termination event-related synchronization (ERS) of oscillatory beta activity in the STN and primary sensorimotor cortex of both hemispheres. Dopamine deficiency directly influenced movement-related beta-modulation, with greater beta-suppression in the most dopamine-depleted hemisphere for both ipsi- and contralateral hand movements. Cortical-subcortical, but not interhemispheric subcortical coherencies were modulated by movement and influenced by striatal dopaminergic innervation, being stronger in the most dopamine-depleted hemisphere. The data are consistent with a role of dopamine in shielding subcortical structures from an excessive cortical entrapment and cross-hemispheric coupling, thus allowing fine-tuning of movement.