Biology subjects

Nicolini, C.

Publications and source records attributed to Nicolini, C..

2 recordsLinked to original sources

bioRxiv2019

Scale-resolved analysis of brain functional connectivity networks with spectral entropy

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between an empirical functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure.\n\nWe apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks.

neuroscience↗

bioRxiv2017

Disrupted modular organization of primary sensory brain areas in schizophrenics

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.

neuroscience↗