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Giuliano Geminiani

Publications and source records attributed to Giuliano Geminiani.

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PREDICTABILITY OF AUTISM, SCHIZOPHRENIC AND OBSESSIVE SPECTRA DIAGNOSIS. TOWARD A DAMAGE NETWORK APPROACH

Schizophrenia, obsessive-compulsive and autistic disorders are traditionally considered as three separate psychiatric conditions each with specific symptoms and pattern of brain alterations. This view can be challenged since these three conditions have the same neurobiological origin, stemming from a common root of a unique neurodevelopmental tree.\n\nThe aim of this meta-analytic study was to determine, from a neuroimaging perspective, whether i) white matter and gray matter alterations are specific for the three different spectrum disorders, and the nosographical differentiation of three spectra is supported by different patterns of brain alterations. ii) it might be possible to define new spectra starting from specific brain damage. iii) it is possible to detect a \"brain damage network\" (a connecting link between the damaged areas that relates areas constantly involved in the disorder).\n\nThree main findings emerged from our meta-analysis: O_LIThe three psychiatric spectra do not appear to have their own specific damage.\nC_LIO_LIIt is possible to define two new damage clusters. The first includes substantial parts of the salience network, and the second is more closely linked to the auditory-visual, auditory and visual somatic areas.\nC_LIO_LIIt is possible to define a \"Damage Network\" and to infer a hierarchy of brain substrates in the pattern of propagation of the damage.\nC_LI\n\nThese results suggest the presence of a common pattern of damage in the three pathologies plus a series of variable alterations that, rather than support the sub-division into three spectra, highlight a two-cluster parcellation with an input-output and more cognitive clusters.

Neuroscience

NODE DETECTION USING HIGH-DIMENSIONAL FUZZY PARCELLATION APPLIED TO THE INSULAR CORTEX

Several functional connectivity approaches require the definition of a set of ROIs that act as network nodes. Different methods have been developed to define these nodes and to derive their functional and effective connections, most of which are rather complex. Here we aim to propose a relatively simple \"one-step\" border detection and ROI estimation procedure employing the fuzzy c-mean clustering algorithm.\n\nTo test this procedure and to explore insular connectivity beyond the two/three-region model currently proposed in the literature, we parcellated the insular cortex of a group of twenty healthy right-handed volunteers (10 females) scanned in a resting state condition.\n\nEmploying a high-dimensional functional connectivity-based clustering process, we confirmed the two patterns of connectivity previously described. This method revealed a complex pattern of functional connectivity where the two previously detected insular clusters are subdivided into several other networks, some of which not commonly associated with the insular cortex, such as the default mode network and parts of the dorsal attentional network. Finally, the detection of nodes was reliable as demonstrated by the confirmative analysis performed on a replication group of subjects.

Neuroscience