1,354,362 research outputs found
Community detection in weighted brain connectivity networks beyond the resolution limit
No full textAbstractGraph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or "communities", that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules (Nicolini and Bifone, 2016), in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymptotical Surprise with leading community detection methods currently in use and show its superior sensitivity in the detection of small modules even in the presence of noise and intersubject variability such as those observed in fMRI data. We apply our novel approach to functional connectivity networks from resting state fMRI experiments, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales. Finally, we discuss the implications of these findings for the identification of connector hubs, the brain regions responsible for the integration of the different network elements, showing that the improved resolution afforded by Asymptotical Surprise leads to a different classification compared to current methods
NMR applications for the diagnosis of female pathologies
No full textNuclear magnetic resonance (NMR) imaging (MRI) and spectroscopy (MRS) are powerful techniques that can be used to study the in-vivo human body and diseases. MRS allows for the quantification of metabolites and a chemical analysis of the analyzed sample, whereas MRI is a multi-parametric technique that provides detailed cross-sectional images of the body’s internal structures using non-ionizing radiation. In particular, Diffusion-Weighted imaging (DWI) allows the evaluation of the diffusion properties of water in biological tissues without requiring contrast agents and with excellent resolution dependent on diffusion length. These techniques applied in the brain studies have been revolutionizing the neuroimaging field, leading to important information related to the microstructure and function of the brain. To extract this information, the development of mathematical models has been the topic of greatest interest over the last 20 years. Indeed, in DWI the NMR signal is the Fourier transform of the motion propagator, which is easily calculated for free water, but it is more complex in biological tissues leading to the development of models such as the Intravoxel incoherent motion (IVIM) model and the Kurtosis representation. All these models were first applied and optimized for neurological application; hence the body application should be carried out considering these limitations (related to the structural and physiological differences between cerebral and ex cranial tissues) and adapting the models to the specific analyzed tissues to avoid the evaluation of parameters without physical sense. Sometimes, clinical NMR images are affected by high level of noise which imply the wrong evaluation of the NMR parameters. Hence, it is necessary to know the signal to noise ratio (SNR) of each image and perform a denoising if the SNR is too low.
In this thesis, a biophysical model with two perfusion compartments was adapted for placental tissues analysis to study the diffusion and perfusion properties of water in the placenta which is characterized by three main compartments: the fastest perfusion compartment due to the fetal villous-trees, a slower perfusion compartment related to the trophoblastic activity and a diffusion compartment linked to the maternal blood in the intra-villous space. The model was applied to evaluate the characteristics of the normal placental tissues and to find any differences between normal placentas and those affected by two specific pathologies: the fetal growth restriction disease (FGR) and the placental accretism. The application of this new model allowed the identification of new biomarkers non-obtainable applying the more known and used IVIM model, which nevertheless is able to highlight important perfusion features. Indeed, the IVIM model was used to study the differences between normal placentas and those belonging to women with SARS-CoV-2 infection, pinpointing substantial differences in the values of the diffusion coeffcient due to a damage to the microstructure of the tissue.
DWI also has important applications in the oncology field. In fact, it has been widely used to study the complexity of tumor tissues in the brain. In this work, DWI was adopted to study the cervical cancer and the endometrial cancer in particular. Generally, the diagnosis of these kinds of pathologies is performed by the histology which is an invasive technique and it is limited by the size of the taken tissue. In this thesis, tissue complexity and tumor grade were evaluated using the kurtosis representation combined with a clusterization based on the tissue differences in the same region of interest obtaining a higher variance of the kurtosis parameters in tumor tissues than in the healthy endometrium. Since the kurtosis representation is sensitive to image noise, a denoising was performed before applying the model and the non-dependence of the parameter to the noise was verified.
MRS was used to pinpoint early biomarkers in bone marrow and muscles to study the osteoporosis and osteoarthritis diseases, highlighting interesting differences in the fat lipid content and in the level of unsaturated fatty-acids in both bones and muscles, corroborating the hypothesis that these pathologies involve the whole musculoskeletal system.
Although the promising results, new perspectives should be considered for the diffusion model’s selection. Indeed, the dynamics underlying the studied system is crucial for the model selection since the application of the wrong model would bring the evaluation of physically no-sense parameters. In this work a preliminary experimental study was conducted on PEG to evaluate its true dynamics and then choose the right diffusion model applying a particular “recipe” conceived and developed in the NMR laboratory of the CNR-ISC & Sapienza where I carried out my PhD work. These last results underline the importance of the knowledge of the tissue diffusion properties a priori to perform more precise and faithful quantification of diffusion parameters indispensable for more precise and early clinical diagnostic
Functional connectivity in the rat brain: a complex network approach.
No full textFunctional connectivity analyses of fMRI data can provide a wealth of information on the brain functional organization and have been widely applied to the study of the human brain. More recently, these methods have been extended to preclinical species, thus providing a powerful translational tool. Here, we review methods and findings of functional connectivity studies in the rat. More specifically, we focus on correlation analysis of pharmacological MRI (phMRI) responses, an approach that has enabled mapping the patterns of connectivity underlying major neurotransmitter systems in vivo. We also review the use of novel statistical approaches based on a network representation of the functional connectivity and their application to the study of the rat brain functional architecture
Modular structure of brain functional networks: breaking the resolution limit by Surprise
No full textThe modular organization of brain networks has been widely investigated using graph theoretical approaches. Recently, it has been demonstrated that graph partitioning methods based on the maximization of global fitness functions, like Newman's Modularity, suffer from a resolution limit, as they fail to detect modules that are smaller than a scale determined by the size of the entire network. Here we explore the effects of this limitation on the study of brain connectivity networks. We demonstrate that the resolution limit prevents detection of important details of the brain modular structure, thus hampering the ability to appreciate differences between networks and to assess the topological roles of nodes. We show that Surprise, a recently proposed fitness function based on probability theory, does not suffer from these limitations. Surprise maximization in brain co-activation and functional connectivity resting state networks reveals the presence of a rich structure of heterogeneously distributed modules, and differences in networks' partitions that are undetectable by resolution-limited methods. Moreover, Surprise leads to a more accurate identification of the network's connector hubs, the elements that integrate the brain modules into a cohesive structure
Community structure in networks of functional connectivity: resolving functional organization in the rat brain with pharmacological MRI
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Segmented Echo Planar Imaging Improves Detection of Subcortical Functional Connectivity Networks in the Rat Brain
No full textSusceptibility artifacts in the vicinity of aural and nasal cavities result in significant signal drop-out and image distortion in echo planar imaging of the rat brain. These effects may limit the study of resting state functional connectivity in deep brain regions. Here, we explore the use of segmented EPI for resting state fMRI studies in the rat, and assess the relative merits of this method compared to single shot EPI. Sequences were evaluated in terms of signal-to-noise ratio, geometric distortions, data driven detection of resting state networks and group level correlations of time series. Multishot imaging provided improved SNR, temporal SNR and reduced geometric distortion in deep areas, while maintaining acceptable overall image quality in cortical regions. Resting state networks identified by independent component analysis were consistent across methods, but multishot EPI provided a more robust and accurate delineation of connectivity patterns involving deep regions typically affected by susceptibility artifacts. Importantly, segmented EPI showed reduced between-subject variability and stronger statistical significance of pairwise correlations at group level over the whole brain and in particular in subcortical regions. Multishot EPI may represent a valid alternative to snapshot methods in functional connectivity studies, particularly for the investigation of subcortical regions and deep gray matter nuclei
A Neural Switch for Active and Passive Fear -erratum
No full text(Neuron 67, 656–666; August 26, 2010)
In this article, the author list misspelled Aldo Giovannelli's last name as “Giovanelli.” The spelling is correct as shown above, and the authors regret this error
Translational approach to develop novel medications on alcohol addiction: focus on neuropeptides.
No full textResearch on alcohol and drug dependence has shown that the development of addiction depends on a complex interplay of psychological factors, genetic or epigenetic predisposing factors, and neurobiological adaptations induced by drug consumption. A greater understanding of the mechanisms leading to alcohol abuse will allow researchers to identify genetic variation that corresponds to a specific biological vulnerability to addiction, thus defining robust endophenotypes that might help deconstruct these complex syndromes into more tractable components. To this end, it is critical to develop a translational framework that links alterations at the molecular level, to changes in neuronal function, and ultimately to changes at the behavioral and clinical levels. Translational phenotypes can be identified by the combination of animal and human studies designed to elucidate the neurofunctional, anatomical and pharmacological mechanisms underlying the etiology of alcohol addiction. The present article offers an overview of medication development in alcoholism with a focus on the critical aspect of translational research. Moreover, significant examples of promising targets from neuropeptidergic systems, namely nociceptin/orphanin FQ and neuropeptide S are given
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