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Carlo Nicolini - One of the best experts on this subject based on the ideXlab platform.

  • Disrupted modular organization of primary sensory brain areas in schizophrenia.
    NeuroImage. Clinical, 2018
    Co-Authors: Cécile Bordier, Carlo Nicolini, Giulia Forcellini, Angelo Bifone
    Abstract:

    Abstract Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by schizophrenia (SCZ) using functional MRI and other neuroimaging techniques. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the organization of brain connectivity networks. A few studies have shown altered distribution of connectivity within and between functional modules in SCZ patients, an indication of imbalanced functional segregation ad integration. However, no major alterations of modular organization have been reported in patients, and unambiguous identification of the neural substrates affected remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe Resolution Limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This Resolution Limit is likely to have hampered the ability to resolve differences between patients and controls in previous studies. Here, we apply Surprise, a novel Resolution Limit-free approach, to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients and in matched healthy controls. Leveraging these important methodological advances we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings support the hypothesis that cognitive dysfunction in SCZ may involve deficits occurring already at early stages of sensory processing.

  • Disrupted modular organization of primary sensory brain areas in schizophrenics
    2017
    Co-Authors: Cécile Bordier, Carlo Nicolini, Angelo Bifone
    Abstract:

    Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by Schizophrenia (SCZ) using functional MRI and other neuroimaging methods. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the modular organization of brain connectivity networks. A few studies have shown abnormal distribution of connectivity within and between functional modules, an indication of imbalanced functional segregation ad integration in SCZ patients. However, no major alterations in the modular structure of functional connectivity networks in patients have been reported, and unambiguous identification of the neural substrates involved remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe Resolution Limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This Resolution Limit is likely to have hampered the ability to resolve differences between patients and controls in previous cross-sectional studies. Here, we apply a novel, Resolution Limit-free approach to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients, and in matched healthy controls. Leveraging these important methodological advances, we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings provide support to the hypothesis that cognitive dysfunction in SCZ may arise from deficits occurring already at early stages of sensory processing.

  • community detection in weighted brain connectivity networks beyond the Resolution Limit
    NeuroImage, 2017
    Co-Authors: Cécile Bordier, Carlo Nicolini, Angelo Bifone
    Abstract:

    Graph 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.

  • community detection in weighted brain connectivity networks beyond the Resolution Limit
    arXiv: Neurons and Cognition, 2016
    Co-Authors: Cécile Bordier, Carlo Nicolini, Angelo Bifone
    Abstract:

    Graph 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, 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. Finally, we apply our novel approach to functional connectivity networks from resting state fMRI experimenta, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales.

  • modular structure of brain functional networks breaking the Resolution Limit by surprise
    Scientific Reports, 2016
    Co-Authors: Carlo Nicolini, Angelo Bifone
    Abstract:

    The 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.

Angelo Bifone - One of the best experts on this subject based on the ideXlab platform.

  • Disrupted modular organization of primary sensory brain areas in schizophrenia.
    NeuroImage. Clinical, 2018
    Co-Authors: Cécile Bordier, Carlo Nicolini, Giulia Forcellini, Angelo Bifone
    Abstract:

    Abstract Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by schizophrenia (SCZ) using functional MRI and other neuroimaging techniques. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the organization of brain connectivity networks. A few studies have shown altered distribution of connectivity within and between functional modules in SCZ patients, an indication of imbalanced functional segregation ad integration. However, no major alterations of modular organization have been reported in patients, and unambiguous identification of the neural substrates affected remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe Resolution Limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This Resolution Limit is likely to have hampered the ability to resolve differences between patients and controls in previous studies. Here, we apply Surprise, a novel Resolution Limit-free approach, to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients and in matched healthy controls. Leveraging these important methodological advances we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings support the hypothesis that cognitive dysfunction in SCZ may involve deficits occurring already at early stages of sensory processing.

  • Disrupted modular organization of primary sensory brain areas in schizophrenics
    2017
    Co-Authors: Cécile Bordier, Carlo Nicolini, Angelo Bifone
    Abstract:

    Abnormal brain resting-state functional connectivity has been consistently observed in patients affected by Schizophrenia (SCZ) using functional MRI and other neuroimaging methods. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the modular organization of brain connectivity networks. A few studies have shown abnormal distribution of connectivity within and between functional modules, an indication of imbalanced functional segregation ad integration in SCZ patients. However, no major alterations in the modular structure of functional connectivity networks in patients have been reported, and unambiguous identification of the neural substrates involved remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe Resolution Limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This Resolution Limit is likely to have hampered the ability to resolve differences between patients and controls in previous cross-sectional studies. Here, we apply a novel, Resolution Limit-free approach to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients, and in matched healthy controls. Leveraging these important methodological advances, we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings provide support to the hypothesis that cognitive dysfunction in SCZ may arise from deficits occurring already at early stages of sensory processing.

  • community detection in weighted brain connectivity networks beyond the Resolution Limit
    NeuroImage, 2017
    Co-Authors: Cécile Bordier, Carlo Nicolini, Angelo Bifone
    Abstract:

    Graph 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.

  • community detection in weighted brain connectivity networks beyond the Resolution Limit
    arXiv: Neurons and Cognition, 2016
    Co-Authors: Cécile Bordier, Carlo Nicolini, Angelo Bifone
    Abstract:

    Graph 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, 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. Finally, we apply our novel approach to functional connectivity networks from resting state fMRI experimenta, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales.

  • modular structure of brain functional networks breaking the Resolution Limit by surprise
    Scientific Reports, 2016
    Co-Authors: Carlo Nicolini, Angelo Bifone
    Abstract:

    The 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.

Remy Boyer - One of the best experts on this subject based on the ideXlab platform.

  • On the Angular Resolution Limit Uncertainty Under Compound Gaussian Noise
    Signal Processing, 2019
    Co-Authors: Maria Greco, Remy Boyer
    Abstract:

    The Angular Resolution Limit (ARL) is a fundamental statistical metric to quantify our ability to resolve two closely-spaced narrowband far-field complex sources. This statistical quantity, is defined as the minimal angular deviation between the two sources to be separated for a prefixed detection-based performance. In this work, we assume that the sources of interest are corrupted by a compound-Gaussian noise. In the standard literature, denoting with δ the true distance between the two sources, the derivation of the ARL is based on the statistical distribution of the Generalized Likelihood Ratio Test (GLRT) for a binary test where there is only one source under the null hypothesis (i.e., δ = 0) and two sources under the alternative hypothesis δ = 0. In literature, the true angular distance (TAD) is generally considered as an unknown deterministic parameter, then a maximum likelihood-based estimation of δ is exploited in the GLRT. In this paper, breaking away from existing contributions, we suppose that the TAD is a random variable, Gaussian distributed, meaning that δ ∼ N (δ0, σ 2 δ). The TAD uncertainty can have many causes as for instance moving sources or/and platform, antenna calibration error, etc. In this work, a generic and flexible (but common) statistical model of the uncertain knowledge of the TAD is preferred instead of a too much specified error model. The degree of randomness (or uncertainty) is quantified by the ratio ξ = δ 2 0 /σ 2 δ. The standard framework of the GLRT is no longer feasible for our problem formulation. To cope with the compound Gaussian noise modeling and the random model of the TAD, a powerful upper bound from information/geometry theory is exploited in this work. More precisely, a new expected Chernoff Upper Bound (CUB) on the minimal error probability is introduced. Based on the analysis of this upper bound, we show that the expected-CUB is highly dependent on the degree of uncertainty, ξ. As a by-product, the optimal s-value in the Chernoff divergence for which the expected-CUB is the tightest upper bound is analytically studied and the role of the mean value δ0 in the ARL context is analyzed.

  • Statistical Resolution Limit for source localization with clutter interference in a MIMO radar context
    IEEE Transactions on Signal Processing, 2012
    Co-Authors: Mohammed Nabil El Korso, Remy Boyer, Alexandre Renaux, Sylvie Marcos
    Abstract:

    During the last decade, multiple-input multiple-ouput (MIMO) radar has received an increasing interest. One can find several estimation schemes in the literature related to the direction of arrivals and/or direction of departures, but their ultimate performance in terms of the statistical Resolution Limit (SRL) have not been fully investigated. In this correspondence, we fill this lack. Particulary, we derive the SRL to resolve two closely spaced targets in clutter interference using a MIMO radar with widely separated antennas. Toward this end, we use a hypothesis test formulation based on the generalized likelihood ratio test (GLRT). Furthermore, we investigate the link between the SRL and the minimum signal-to-noise ratio (SNR) required to resolve two closely spaced targets for a given probability of false alarm and for a given probability of detection. Finally, theoretical and numerical analysis of the SRL are given for several scenarios (with/without clutter interference, known/unknown parameters of interest and known/unknown noise variance).

  • statistical Resolution Limit for the multidimensional harmonic retrieval model hypothesis test and cramer rao bound approaches
    EURASIP Journal on Advances in Signal Processing, 2011
    Co-Authors: Mohammed Nabil El Korso, Remy Boyer, Alexandre Renaux, Sylvie Marcos
    Abstract:

    The statistical Resolution Limit (SRL), which is defined as the minimal separation between parameters to allow a correct resolvability, is an important statistical tool to quantify the ultimate performance for parametric estimation problems. In this article, we generalize the concept of the SRL to the multidimensional SRL (MSRL) applied to the multidimensional harmonic retrieval model. In this article, we derive the SRL for the so-called multidimensional harmonic retrieval model using a generalization of the previously introduced SRL concepts that we call multidimensional SRL (MSRL). We first derive the MSRL using an hypothesis test approach. This statistical test is shown to be asymptotically an uniformly most powerful test which is the strongest optimality statement that one could expect to obtain. Second, we link the proposed asymptotic MSRL based on the hypothesis test approach to a new extension of the SRL based on the Cramer-Rao Bound approach. Thus, a closed-form expression of the asymptotic MSRL is given and analyzed in the framework of the multidimensional harmonic retrieval model. Particularly, it is proved that the optimal MSRL is obtained for equi-powered sources and/or an equi-distributed number of sensors on each multi-way array.

  • Performance bounds and angular Resolution Limit for the moving colocated MIMO radar
    IEEE Transactions on Signal Processing, 2011
    Co-Authors: Remy Boyer
    Abstract:

    To identify a target, the moving noncoherent colocated multiple-input multiple-output (MIMO) radar system takes advantage of multiple antennas in transmission and reception which are close in space. In this paper, we study the estimation performance and the Resolution Limit for this scheme in which each array geometry is described by the sample-variance of the sensor distribution. So, our analysis encompasses any sensor distributions, including varying intersensors distances or/and lacunar (missing sensors) configuration. As in the space-time MIMO model considered here the radar is moving, the target Doppler frequency cannot be assumed invariant to the target position/angle. The first part of this paper derives and analyzes closed form (nonmatrix) expressions of the deterministic Cramér-Rao lower bound (CRB) for the direction and the velocity of a moving target contaminated by a structured noise (clutter echoes) and a background noise, including the cases of the clutter-free environment and the high signal-to-noise ratio (SNR) regime. The analysis of the proposed expressions of the CRB allows to better understand the characterization of the target. In particular, we prove the coupling between the direction parameter and the velocity of the target is linear with the radar velocity. In the second part, we focus our study on the analytical (closed form) derivation and the analysis of the angular Resolution Limit (ARL). Based on the Resolution of an equation involving the CRB, the ARL can be interpreted as the minimal separation to resolve two closely spaced targets. Consequently, the ARL is a key quantity to evaluate the performance of a radar system. We show that the ARL is in fact quasi-invariant to the movement of the MIMO radar.

  • statistical Resolution Limit of the uniform linear cocentered orthogonal loop and dipole array
    IEEE Transactions on Signal Processing, 2011
    Co-Authors: M El N Korso, Remy Boyer, Alexandre Renaux, Sylvie Marcos
    Abstract:

    Among the family of polarization sensitive arrays, we can find the so-called cocentered orthogonal loop and dipole uniform linear array (COLD-ULA). The COLD-ULA exhibits some interesting properties, e.g., the insensibility of the polarization vector with respect to the source localization in the plan of the array. In this correspondence, we derive the statistical Resolution Limit (SRL) characterizing the minimal separation, in terms of direction-of-arrivals, to resolve two closely spaced known polarized sources impinging on a COLD-ULA. Toward this end, nonmatrix closed form expressions of the deterministic Cramer-Rao bound (CRB) are derived and thus, the SRL is deduced. A comparison between the SRL of the COLD-ULA and the classical ULA are given. Particularly, it is shown that, in the case of orthogonal known signal sources, the SRL of the COLD-ULA is equal to the SRL of the ULA, meaning that it is not a function of polarization parameters. Furthermore, due to the derived SRL, it is shown that, under some general conditions, the SRL of the COLD-ULA is smaller than the one of the ULA.

Katharina Gaus - One of the best experts on this subject based on the ideXlab platform.

  • the lipid raft hypothesis revisited new insights on raft composition and function from super Resolution fluorescence microscopy
    BioEssays, 2012
    Co-Authors: Dylan M Owen, Astrid Magenau, David Williamson, Katharina Gaus
    Abstract:

    Recently developed super-Resolution microscopy techniques are changing our understanding of lipid rafts and membrane organisation in general. The lipid raft hypothesis postulates that cholesterol can drive the formation of ordered domains within the plasma membrane of cells, which may serve as platforms for cell signalling and membrane trafficking. There is now a wealth of evidence for these domains. However, their study has hitherto been hampered by the Resolution Limit of optical microscopy, making the definition of their properties problematic and contentious. New microscopy techniques circumvent the Resolution Limit and, for the first time, allow the fluorescence imaging of structures on length scales below 200 nm. This review describes such techniques, particularly as applied to the study of membrane organisation, synthesising newly emerging facets of lipid raft biology into a state-of-the art model. Editor's suggested further reading in BioEssays: Super-Resolution imaging prompts re-thinking of cell biology mechanisms Abstract and Quantitative analysis of photoactivated localization microscopy (PALM) datasets using pair-correlation analysis Abstract

  • the lipid raft hypothesis revisited new insights on raft composition and function from super Resolution fluorescence microscopy
    BioEssays, 2012
    Co-Authors: Dylan M Owen, Astrid Magenau, David Williamson, Katharina Gaus
    Abstract:

    Recently developed super-Resolution microscopy techniques are changing our understanding of lipid rafts and membrane organisation in general. The lipid raft hypothesis postulates that cholesterol can drive the formation of ordered domains within the plasma membrane of cells, which may serve as platforms for cell signalling and membrane trafficking. There is now a wealth of evidence for these domains. However, their study has hitherto been hampered by the Resolution Limit of optical microscopy, making the definition of their properties problematic and contentious. New microscopy techniques circumvent the Resolution Limit and, for the first time, allow the fluorescence imaging of structures on length scales below 200 nm. This review describes such techniques, particularly as applied to the study of membrane organisation, synthesising newly emerging facets of lipid raft biology into a state-of-the art model.

Stefan W. Hell - One of the best experts on this subject based on the ideXlab platform.

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