Sossio Cirillo

Professor of Neuroradiology

Name Sossio
Surname Cirillo
Institution Università degli Studi della Campania Luigi Vanvitelli
Address CTO Viale dei Colli Aminei 21, Naples, Italy
Sossio Cirillo


  • Small animal imaging facility: new perspectives for the radiologist.

    Publication Date: 01/02/2009 on La Radiologia medica
    by Grassi R, Cavaliere C, Cozzolino S, Mansi L, Cirillo S, Tedeschi G, Franchi R, Russo P, Cornacchia S, Rotondo A
    DOI: 10.1007/s11547-008-0352-8

    In recent years, new technologies have become available for imaging small animals. The use of animal models in basic and preclinical sciences, for example, offers the possibility of testing diagnostic markers and drugs, which is becoming crucial in the success and timeliness of research and is allowing a more efficient approach in defining study objectives and providing many advantages for both clinical research and the pharmaceutical industry. The use of these instruments offers data that are more predictive of the distribution and efficacy of a compound. The mouse, in particular, has become a key animal model system for studying human disease. It offers the possibility of manipulating its genome and producing accurate models for many human disorders, thus resulting in significant progress in understanding pathologenic mechanisms. In neurobiology, the possibility of simulating neurodegenerative diseases has enabled the development and validation of new treatment strategies based on gene therapy or cell grafting. Noninvasive imaging in small living animal models has gained increasing importance in preclinical research, itself becoming an independent specialty. The aim of this article is to review the characteristics of these systems and illustrate their main applications.

  • Alzheimer's disease and other dementing conditions.

    Publication Date: 01/10/2008 on Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
    by Tedeschi G, Cirillo M, Tessitore A, Cirillo S
    DOI: 10.1007/s10072-008-1003-5

    Dementia is a common and growing problem, with 20% of those over 80 years of age suffering from this disorder. The prospect of more effective treatments has caused an increasing demand for a more accurate and earlier diagnosis of different dementia syndromes. Neuroimaging techniques may have an important role in the clinical evaluation of dementia for early diagnosis, differential diagnosis and may help in the prediction of conversion to dementia in individuals at a higher risk of developing the disorder. Moreover, new MRI techniques might not only further broaden our understanding of the pathophysiology but also accelerate treatment discovery. This review will focus on the use of conventional and non-conventional MRI techniques to investigate dementias.

  • Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI.

    Publication Date: 01/09/2008 on Magnetic resonance imaging
    by Esposito F, Aragri A, Pesaresi I, Cirillo S, Tedeschi G, Marciano E, Goebel R, Di Salle F
    DOI: 10.1016/j.mri.2008.01.045

    Resting-state functional magnetic resonance imaging (RS-fMRI) is a technique used to investigate the spontaneous correlations of blood-oxygen-level-dependent signals across different regions of the brain. Using functional connectivity tools, it is possible to investigate a specific RS-fMRI network, referred to as "default-mode" (DM) network, that involves cortical regions deactivated in fMRI experiments with cognitive tasks. Previous works have reported a significant effect of aging on DM regions activity. Independent component analysis (ICA) is often used for generating spatially distributed DM functional connectivity patterns from RS-fMRI data without the need for a reference region. This aspect and the relatively easy setup of an RS-fMRI experiment even in clinical trials have boosted the combined use of RS-fMRI and ICA-based DM analysis for noninvasive research of brain disorders. In this work, we considered different strategies for combining ICA results from individual-level and population-level analyses and used them to evaluate and predict the effect of aging on the DM component. Using RS-fMRI data from 20 normal subjects and a previously developed group-level ICA methodology, we generated group DM maps and showed that the overall ICA-DM connectivity is negatively correlated with age. A negative correlation of the ICA voxel weights with age existed in all DM regions at a variable degree. As an alternative approach, we generated a distributed DM spatial template and evaluated the correlation of each individual DM component fit to this template with age. Using a "leave-one-out" procedure, we discuss the importance of removing the bias from the DM template-generation process.

  • Problem in diagnostic radiology.

    Publication Date: 01/03/2008 on Clinical anatomy (New York, N.Y.)
    by Conforti R, Taglialatela G, Dinacci D, Scuotto A, Tedeschi G, Cirillo S
    DOI: 10.1002/ca.20582

    The case of a 58-year-old woman is described. She presented with what initially seemed to be a transient ischaemic attack. Clinical imaging, however,revealed an intracranial lipoma of the cisterna magna associated with a defect of the occipital bone and spina bifida occulta of the atlas.

  • Predictors of remission of hyperprolactinaemia after long-term withdrawal of cabergoline therapy.

    Publication Date: 01/09/2007 on Clinical endocrinology
    by Colao A, Di Sarno A, Guerra E, Pivonello R, Cappabianca P, Caranci F, Elefante A, Cavallo LM, Briganti F, Cirillo S, Lombardi G
    DOI: 10.1111/j.1365-2265.2007.02905.x

    Remission rates of 76, 69.5 and 64.3% have been reported in patients with nontumoural hyperprolactinaemia (NTH), microprolactinoma and macroprolactinoma, respectively, 2-5 years after cabergoline (CAB) withdrawal.

  • Trigeminal perineural spread of head and neck tumors.

    Publication Date: 28/02/2007 on The neuroradiology journal
    by Bartiromo F, Cirillo L, Caranci F, Elefante A, D'Amico A, Tortora F, Brunetti A, Cirillo S
    DOI: 10.1177/197140090702000119

    Perineural tumor spread (PNS) of head and neck malignancies is a well-known form of metastatic disease in which a lesion can migrate away from the primary site along the endoneurium or perineurium. MR imaging is considered the primary method for evaluating patients with symptoms related to the trigeminal nerve in most clinical settings. Both CT and MR imaging can detect perineural spread, but MRI is the modality of choice because of its capability to detect direct signs (nerve enlargement and enhancement) and indirect signs (neuropathic muscular atrophy, obliteration of fat planes). In addition, MRI is more sensitive because of its superior soft-tissue contrast, its multiplanar capability and decreased artifacts from dental hardware. Fat suppression images after contrast injection are mandatory to better detect nerve enhancement. CT is useful in detecting foraminal enlargement or more destructive bone patterns. Nerve function can be perserved until later in the course of the disease: patients with perineural spread demonstrated at radiologic or pathologic examination may have normal or nonspecific nerve function at clinical examination (patients are misdiagnosed with Bell's palsy or trigeminal neuralgia). Hence MRI assessment of perineural tumor location and extension is important.

  • Giant intracranial chordoma: neuroradiological and radiotherapeutic aspects.

    Publication Date: 31/01/2007 on The neuroradiology journal
    by Conforti R, Taglialatela G, Scuotto A, D'agostino V, Cirillo M, Cirillo L, Barone A, Giordano A, Parlato C, Cirillo S

    We describe a rare case of giant intracranial chordoma, emphasizing the patient's long survival and his excellent response to radiotherapy that led to a progressive regression of neurological symptomatology up to disappearance, in the absence of cerebral white matter damages.

  • Non-Inferential Multi-Subject Study of Functional Connectivity during Visual Stimulation.

    Publication Date: 31/01/2007 on The neuroradiology journal
    by Esposito F, Cirillo M, Aragri A, Caranci F, Cirillo L, Di Salle F, Cirillo S

    Independent component analysis (ICA) is a powerful technique for the multivariate, non-inferential, data-driven analysis of functional magnetic resonance imaging (fMRI) data-sets. The non-inferential nature of ICA makes this a suitable technique for the study of complex mental states whose temporal evolution would be difficult to describe analytically in terms of classical statistical regressors. Taking advantage of this feature, ICA can extract a number of functional connectivity patterns regardless of the task executed by the subject. The technique is so powerful that functional connectivity patterns can be derived even when the subject is just resting in the scanner, opening the opportunity for functional investigation of the human mind at its basal "default" state, which has been proposed to be altered in several brain disorders. However, one major drawback of ICA consists in the difficulty of managing its results, which are not represented by a single functional image as in inferential studies. This produces the need for a classification of ICA results and exacerbates the difficulty of obtaining group "averaged" functional connectivity patterns, while preserving the interpretation of individual differences. Addressing the subject-level variability in the very same framework of "grouping" appears to be a favourable approach towards the clinical evaluation and application of ICA-based methodologies. Here we present a novel strategy for group-level ICA analyses, namely the self-organizing group-level ICA (sog-ICA), which is used on visual activation fMRI data from a block-design experiment repeated on six subjects. We propose the sog-ICA as a multi-subject analysis tool for grouping ICA data while assessing the similarity and variability of the fMRI results of individual subject decompositions.

  • Independent component model of the default-mode brain function: Assessing the impact of active thinking.

    Publication Date: 16/10/2006 on Brain research bulletin
    by Esposito F, Bertolino A, Scarabino T, Latorre V, Blasi G, Popolizio T, Tedeschi G, Cirillo S, Goebel R, Di Salle F
    DOI: 10.1016/j.brainresbull.2006.06.012

    The "default-mode" network is an ensemble of cortical regions, which are typically deactivated during demanding cognitive tasks in functional magnetic resonance imaging (fMRI) studies. Using functional connectivity, this network can be conceptualized and studied as a "stand-alone" function or system. Regardless of the task, independent component analysis (ICA) produces a picture of the "default-mode" function even when the subject is performing a simple sensori-motor task or just resting in the scanner. This has boosted the use of default-mode fMRI for non-invasive research in brain disorders. Here, we studied the effect of cognitive load modulation of fMRI responses on the ICA-based pictures of the default-mode function. In a standard graded working memory study based on the n-back task, we used group-level ICA to explore the variability of the default-mode network related to the engagement in the task, in 10 healthy volunteers. The analysis of the default-mode components highlighted similarities and differences in the layout under three different cognitive loads. We found a load-related general increase of deactivation in the cortical network. Nonetheless, a variable recruitment of the cingulate regions was evident, with greater extension of the anterior and lesser extension of the posterior clusters when switching from lower to higher working memory loads. A co-activation of the hippocampus was only found under no working memory load. As a generalization of our results, the variability of the default-mode pattern may link the default-mode system as a whole to cognition and may more directly support use of the ICA model for evaluating cognitive decline in brain disorders.

  • How does spatial extent of fMRI datasets affect independent component analysis decomposition?

    Publication Date: 01/09/2006 on Human brain mapping
    by Aragri A, Scarabino T, Seifritz E, Comani S, Cirillo S, Tedeschi G, Esposito F, Di Salle F
    DOI: 10.1002/hbm.20215

    Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.