Tiziana Franchin

Gray matter decrease distribution in the early stages of Anorexia Nervosa restrictive type in adolescents.

Few studies have used Voxel-Based Morphometry (VBM) to examine brain structure in Anorexia Nervosa patients. The purpose of the present study was to investigate a sample of Anorexia Nervosa restrictive type (AN-r) adolescent patients in the early stages of the illness, using VBM in order to characterize morphometric gray matter (GM) changes. Participants were 16 AN-r female patients (with no other psychiatric disorders) whose AN-r had been in progress for less than 12 months and 16 age-matched healthy female subjects. High-resolution T1-weighted magnetic resonance images were preprocessed according to the optimized VBM method, and statistically analyzed. The analyses revealed a significant global GM decrease in the AN-r patients; furthermore, a significant region-specific decrease in GM volume was found bilaterally in the middle cingulate cortex, the precuneus, and the inferior and superior parietal lobules. The significant early GM decrease in the aforementioned regions in AN-r adolescent patients suggests that there might be a region-specific GM vulnerability that could play a role in the pathophysiology of the disease. Given that these regions are also involved in the manipulation of mental images and the mental representation of the self, this might explain the presence of a distorted body image in these patients.

Diagnosis of Noonan syndrome and related disorders using target next generation sequencing

BackgroundNoonan syndrome is an autosomal dominant developmental disorder with a high phenotypic variability, which shares clinical features with other rare conditions, including LEOPARD syndrome, cardiofaciocutaneous syndrome, Noonan-like syndrome with loose anagen hair, and Costello syndrome. This group of related disorders, so-called RASopathies, is caused by germline mutations in distinct genes encoding for components of the RAS-MAPK signalling pathway. Due to high number of genes associated with these disorders, standard diagnostic testing requires expensive and time consuming approaches using Sanger sequencing. In this study we show how targeted Next Generation Sequencing (NGS) technique can enable accurate, faster and cost-effective diagnosis of RASopathies.MethodsIn this study we used a validation set of 10 patients (6 positive controls previously characterized by Sanger-sequencing and 4 negative controls) to assess the analytical sensitivity and specificity of the targeted NGS. As second step, a training set of 80 enrolled patients with a clinical suspect of RASopathies has been tested. Targeted NGS has been successfully applied over 92% of the regions of interest, including exons for the following genes: PTPN11, SOS1, RAF1, BRAF, HRAS, KRAS, NRAS, SHOC, MAP2K1, MAP2K2, CBL.ResultsAll expected variants in patients belonging to the validation set have been identified by targeted NGS providing a detection rate of 100%. Furthermore, all the newly detected mutations in patients from the training set have been confirmed by Sanger sequencing. Absence of any false negative event has been excluded by testing some of the negative patients, randomly selected, with Sanger sequencing.ConclusionHere we show how molecular testing of RASopathies by targeted NGS could allow an early and accurate diagnosis for all enrolled patients, enabling a prompt diagnosis especially for those patients with mild, non-specific or atypical features, in whom the detection of the causative mutation usually requires prolonged diagnostic timings when using standard routine. This approach strongly improved genetic counselling and clinical management.

Analysis of Outliers Effects in Voxel-Based Morphometry by means of Virtual Phantoms

Voxel-Based Morphometry (VBM) is a technique for analyzing inter-group neuroanatomic differences, frequently used to study a number of neurologic diseases. An important limit to the diffusion of VBM is related to the length of the recruiting process, due to the need of homogeneous and statistically significant samples. Therefore, quite often the need to search for statistically significant differences between small samples arises. The opportunity of analyzing small samples with VBM should be carefully considered. In each sample there could be one or more subjects with atypical local anatomic characteristics (outliers) that are not identifiable a priori, thus leading to erroneous inferences about the specific structural features correlated to the studied pathology. It follows the need to evaluate the limits within which including a certain number of outliers can be accepted when relying on small samples. The robustness of VBM performed within SPM2 with respect to the inclusion of outliers was studied by implementing in MatLab sets of virtual phantoms with predetermined characteristics. A matrix of voxels cubes was superimposed on the preprocessed gray matter image of each scanned subject. Each cube has a uniform gray level, while its intensity distribution within each group is Gaussian with controlled group mean and variance. Variance and mean difference were chosen according to data from two ongoing clinical studies. The model implemented shows that applying VBM to small samples could lead to erroneous inferences. The minimum number of subjects should be evaluated according to specific variance and group mean difference values of the experimental samples. The implementation of virtual phantoms enabled the quantitative evaluation of outliers effects when performing VBM on small samples. This approach provides the clinicians with a support tool for evaluating the reliability of the results yielded by VBM analyses and for planning the recruiting process.

Advantages of a next generation sequencing targeted approach for the molecular diagnosis of retinoblastoma

BackgroundRetinoblastoma (RB) is the most common malignant childhood tumor of the eye and results from inactivation of both alleles of the RB1 gene. Nowadays RB genetic diagnosis requires classical chromosome investigations, Multiplex Ligation-dependent Probe Amplification analysis (MLPA) and Sanger sequencing. Nevertheless, these techniques show some limitations. We report our experience on a cohort of RB patients using a combined approach of Next-Generation Sequencing (NGS) and RB1 custom array-Comparative Genomic Hybridization (aCGH).MethodsA total of 65 patients with retinoblastoma were studied: 29 cases of bilateral RB and 36 cases of unilateral RB. All patients were previously tested with conventional cytogenetics and MLPA techniques. Fifty-three samples were then analysed using NGS. Eleven cases were analysed by RB1 custom aCGH. One last case was studied only by classic cytogenetics. Finally, it has been tested, in a lab sensitivity assay, the capability of NGS to detect artificial mosaicism series in previously recognized samples prepared at 3 different mosaicism frequencies: 10, 5, 1 %.ResultsOf the 29 cases of bilateral RB, 28 resulted positive (96.5 %) to the genetic investigation: 22 point mutations and 6 genomic rearrangements (four intragenic and two macrodeletion). A novel germline intragenic duplication, from exon18 to exon 23, was identified in a proband with bilateral RB. Of the 36 available cases of unilateral RB, 8 patients resulted positive (22 %) to the genetic investigation: 3 patients showed point mutations while 5 carried large deletion. Finally, we successfully validated, in a lab sensitivity assay, the capability of NGS to accurately measure level of artificial mosaicism down to 1 %.ConclusionsNGS and RB1-custom aCGH have demonstrated to be an effective combined approach in order to optimize the overall diagnostic procedures of RB. Custom aCGH is able to accurately detect genomic rearrangements allowing the characterization of their extension. NGS is extremely accurate in detecting single nucleotide variants, relatively simple to perform, cost savings and efficient and has confirmed a high sensitivity and accuracy in identifying low levels of artificial mosaicisms.

Adopting European Network for Health Technology Assessments (EunetHTA) core model for diagnostic technologies for improving the accuracy and appropriateness of blood gas analyzers’ assessment

Abstract Background: Point-of-care testing (POCT) is a successful methodology for meeting clinical expectations of rapid and accurate results. Scientific literature has moreover highlighted and confirmed the necessity of individuating the best technological solution, in accordance with clinical requirements and contextualized to the whole health organization, where it will be implemented. Health Technology Assessment (HTA) can assist in reaching an appropriate and contextualized decision on a health technology. The aim of this study is to adapt a HTA core model for improving the evaluation of a POCT technology: blood gas analyzers. Methods: The European Network for Health Technology Assessment (EUnetHTA) core model for diagnostic technologies was applied for evaluating globally marketed blood gas analyzers. Evaluation elements were defined according to available literature and validated using the Delphi method. Results: A HTA model of 71 issues, subdivided into 26 topics and 10 domains, was obtained by interviewing 11 healthcare experts over two rounds of Delphi questionnaires. Ten context parameters were identified in order to define the initial scenario from which the technology assessment was to begin. Conclusions: The model presented offers a systematic and objective structure for the evaluation of blood gas analyzers, which may play a guidance role for healthcare operators approaching the evaluation of such technologies thus improving, in a contextualized fashion, the appropriateness of purchasing.

Independent Component Analysis of EEG-fMRI data for studying epilepsy and epileptic seizures

Here we present a method for classifying fMRI independent components (ICs) by using an optimized algorithm for the individuation of noisy signals from sources of interest. The method was applied to estimate brain activations from combined EEG-fMRI data for the exploration of epilepsy. Spatial ICA was performed using the above-mentioned optimized algorithm and other three popular algorithms. ICs were sorted considering the value: of the coefficients of determination R2, obtained from the multiple regression analysis with morphometric maps of cerebral matter; of the kurtosis, which features the signal energy. The validation of the method was performed comparing the brain activations obtained with those resulted using the General Linear Model (GLM). The ICA-derived activations in different datasets comprised subareas of the GLM-revealed activations, even if the volume and the shape of activated areas do not correspond exactly. The method proposed also detects additional negative regions implicated in a default mode of brain activity, and not clearly identified by GLM. Compared with a traditional GLM approach, the ICA one provides a flexible way to analyze fMRI data that reduces the assumptions placed upon the hemodynamic response of the brain and the temporal constrains.

Identifying the dynamics of actin and tubulin polymerization in iPSCs and in iPSC-derived neurons

The development of the nervous system requires cytoskeleton-mediated processes coordinating self-renewal, migration, and differentiation of neurons. It is not surprising that many neurodevelopmental problems and neurodegenerative disorders are caused by deficiencies in cytoskeleton-related genes. For this reason, we focus on the cytoskeletal dynamics in proliferating iPSCs and in iPSC-derived neurons to better characterize the underpinnings of cytoskeletal organization looking at actin and tubulin repolymerization studies using the cell permeable probes SiR-Actin and SiR-Tubulin. During neurogenesis, each neuron extends an axon in a complex and changing environment to reach its final target. The dynamic behavior of the growth cone and its capacity to respond to multiple spatial information allows it to find its correct target. We decided to characterize various parameters of the actin filaments and microtubules. Our results suggest that a rapid re-organization of the cytoskeleton occurs 45 minutes after treatments with de-polymerizing agents in iPSCs and 60 minutes in iPSC-derived neurons in both actin filaments and microtubules. The quantitative data confirm that the actin filaments have a primary role in the re-organization of the cytoskeleton soon after de-polymerization, while microtubules have a major function following cytoskeletal stabilization. In conclusion, we investigate the possibility that de-polymerization of the actin filaments may have an impact on microtubules organization and that de-polymerization of the microtubules may affect the stability of the actin filaments. Our results suggest that a reciprocal influence of the actin filaments occurs over the microtubules and vice versa in both in iPSCs and iPSC-derived neurons.

EEG-fMRI Multimodal Integration for Epilepsy Research

Epileptologists have now at their disposal a variety of tools for investigating human brain functions. Among the technologies of non-invasive functional imaging that have flowered in the last years, two techniques became particularly popular: the electroencephalogram (EEG) which records electrical voltages from the electrodes placed on the scalp and functional magnetic resonance imaging (fMRI) which records magnetization changes due to variations in blood oxygenation. Each of these methods have its own advantages and disadvantages and no single method is best suited for all experimental and clinical conditions. EEG is a long-established tool for the non-invasive brain investigation characterized by the high temporal resolution (measured in milliseconds) but very low spatial resolution (measured in square centimetres). In contrast, fMRI provides good spatial resolution (measured in square millimetres) but relatively poor temporal resolution (measured in seconds). Combining EEG and fMRI provides integration of information that results in an enhanced view of the phenomena of interest. This fusion of information is particularly useful in the context of the study of the epileptic disorders. The EEG was used in the study of epilepsy since it was discovered and it remains nowadays the gold-standard for the diagnosis of epilepsy, the classification of the seizures types and the localization of the generators of the epileptic activity. The EEG measurements recorded on the scalp is visually inspected by the neurophysiologists in order to detect any epileptic pattern such as spikes, spike-wave bursts, seizures, etc. and to diagnose the epilepsy. From a spatial point of view only a topographic localization of the generators of ictal and interictal activity is possible. Because of poor spatial resolution of the EEG technique, in many cases it means just a lateralization of the generators and not their precise localization. fMRI, first demonstrated in 1990, is a technique that, through the blood oxygen level dependent (BOLD) effect, allows the localization of brain areas in which there is a variation of the level of neuronal activity during an experimental condition compared to a control condition. fMRI is mostly used in the study of sensory, motor and cognitive functions, in which the experimental condition differs from the control condition in a way that is controlled by the experimenter. In the context of epilepsy or spontaneous physiological changes in brain state, one can consider the control condition at the time when the EEG is at baseline and the experimental condition to occur in presence of endogenous electrophysiological phenomena such as an epileptic discharge or a sleep spindle. To define

A robust independent component analysis algorithm for removing ballistocardiogram artifacts from EEG and fMRI recordings

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings provide complementary advantages with regard to the temporal and spatial resolution of brain activity. This methodology still now suffers from several artifacts, such as the gradient, the ballistocardiogram (BCG) and electrooculogram (EOG). A number of procedures have been developed in recent years for removing BCG artifacts and the usefulness of Independent Component Analysis (ICA) in this purpose was largely discussed and demonstrated. The aim of this study is to propose a more efficient and robust independent component analysis algorithm (RobustICA) for removing BCG and EOG artifacts. The algorithm has been validated on EEG datasets acquired inside the static magnetic field of a 1,5 T RM scanner and its performances were compared with those of other already applied processing methods (Optimal Basis Set, FastICA).