Denis Peruzzo

Heterogeneity of Cortical Lesions in Multiple Sclerosis: An MRI Perfusion Study

In this study, dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) was used to quantify the cerebral blood flow (CBF), the cerebral blood volume (CBV), and the mean transit time (MTT) and to analyze the changes in cerebral perfusion associated with the cortical lesions in 44 patients with relapsing-remitting multiple sclerosis. The cortical lesions showed a statistically significant reduction in CBF and CBV compared with the normal-appearing gray matter, whereas there were no significant changes in the MTT. The reduced perfusion suggests a reduction of metabolism because of the loss of cortical neurons. A small population of outliers showing an increased CBF and/or CBV has also been detected. The presence of hyperperfused outliers may imply that perfusion could evolve during inflammation. These findings show that perfusion is altered in cortical lesions and that DSC-MRI can be a useful tool to investigate more deeply the evolution of cortical lesions in multiple sclerosis.

Machine Learning Approaches: From Theory to Application in Schizophrenia

In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

Automatic selection of arterial input function on dynamic contrast-enhanced MR images

Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) data analysis requires the knowledge of the arterial input function (AIF) to quantify the cerebral blood flow (CBF), volume (CBV) and the mean transit time (MTT). AIF can be obtained either manually or using automatic algorithms. We present a method to derive the AIF on the middle cerebral artery (MCA). The algorithm draws a region of interest (ROI) where the MCA is located. Then, it uses a recursive cluster analysis on the ROI to select the arterial voxels. The algorithm had been compared on simulated data to literature state of art automatic algorithms and on clinical data to the manual procedure. On in silico data, our method allows to reconstruct the true AIF and it is less affected by partial volume effect bias than the other methods. In clinical data, automatic AIF provides CBF and MTT maps with a greater contrast level compared to manual AIF ones. Therefore, AIF obtained with the proposed method improves the estimate reliability and provides a quantitatively reliable physiological picture.

The impact of schizophrenia on frontal perfusion parameters: a DSC-MRI study.

We performed a dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) analysis to study the role of the demographic/clinical information on perfusion parameters between patients with schizophrenia and normal control subjects. 39 schizophrenia patients and 27 normal controls were studied with a Siemens 1.5T magnet. PWI images were obtained following intravenous injection of paramagnetic contrast agent (gadolinium-DTPA). For each perfusion parameter, i.e. relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), mean transit time (MTT) and time-to-peak (TTP), the best predictor model was computed in left and right frontal cortex following a stepwise strategy. First of all, a linear model, including all the sociodemographic information and clinical variables as predictors was computed. At each step, the least significant predictor was excluded and a new linear model was evaluated until all predictors were excluded. Then, the best predictor model was selected based on the F statistic value and on the p value. The models for the rCBF and the rCBV both in the left and right frontal cortex were estimated independently from each other, and the best models contained the same predictors, i.e. clinical state, age, and length of illness. No significant models were obtained for the MTT and the TTP. This study showed a decrease in rCBF and rCBV frontal cortex values in subject affected by schizophrenia. Future DSC-MRI studies should further investigate the role of cerebral perfusion for the pathophysiology of the disease by recruiting first-episode patients and by considering cerebellar, parietal and temporal regions.

Cerebellar and lobar blood flow in schizophrenia: A perfusion weighted imaging study

It is still not clear whether brain hemodynamics plays a role in the functional and structural alterations in schizophrenia, since prior imaging studies showed conflicting findings. In this study we non-invasively explored cerebral and cerebellar lobe perfusion in the largest population of participants with schizophrenia thus far studied with perfusion-weighted imaging (PWI). Forty-seven participants affected by schizophrenia and 29 normal controls were recruited. PWI images were acquired following the intravenous injection of a paramagnetic contrast agent. Regional cerebral blood volume (CBV), blood flow (rCBF), and mean transit time (MTT) were obtained with the block-Circulant Singular Value Decomposition (cSVD) for frontal, temporal, parietal, occipital, and cerebellar lobes, bilaterally. Perfusion parameters were separately obtained for both gray and white matter in each lobe. Subjects with schizophrenia showed no significant differences in perfusion parameters when compared with controls. Interestingly, inverse correlations between age at onset and occipital, frontal and cerebellar MTT and between length of illness and frontal CBV were found. Preserved cerebral and cerebellar perfusion in our chronic population may in part be due to the effects of antipsychotic treatment which may have normalized blood volume and flow. Hypoperfusion in relation to chronicity, particularly in the frontal lobe, has been observed in accordance with earlier studies using positron emission tomography.

Assessment of clinical data of nonlinear stochastic deconvolution versus block-circulant singular value decomposition for quantitative dynamic susceptibility contrast magnetic resonance imaging

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) allows the noninvasive assessment of brain hemodynamics alterations by quantifying, via deconvolution, the cerebral blood flow (CBF) and mean transit time (MTT). Singular value decomposition (SVD) and block-circulant SVD (cSVD) are the most widely adopted deconvolution method, although they bear some limitations, including unphysiological oscillations in the residue function and bias in the presence of delay and dispersion between the tissue and the arterial input function. A nonlinear stochastic regularization (NSR) has been proposed, which performs better than SVD and cSVD on simulated data both in the presence and absence of dispersion. Moreover, NSR allows to quantify the dispersion level. Here, cSVD and NSR are compared for the first time on a group of nine patients with severe atherosclerotic unilateral stenosis of internal carotid artery before and after carotid stenting to investigate the effect of arterial dispersion. According to region of interest-based analysis, NSR characterizes the pathologic tissue more accurately than cSVD, thus improving the quality of the information provided to physicians for diagnosis. In fact, in 7 (78%) of the 9 subjects, CBF and MTT maps provided by NSR allow to correctly identify the pathologic hemisphere to the physician. Moreover, by emphasizing the difference between pathologic and healthy tissues, NSR may be successfully used to monitor the subjects recovery after the treatment and/or surgery. NSR also generates dispersion level and non-dispersed CBF and MTT maps. The dispersion level provides information on CBF and MTT estimates reliability and may also be used as a clinical indicator of pathological tissue state complementary to CBF and MTT, thus increasing the clinical information provided by DSC-MRI analysis.

Mapping brains on grids of features for schizophrenia analysis

This paper exploits the embedding provided by the counting grid model and proposes a framework for the classification and the analysis of brain MRI images. Each brain, encoded by a count of local features, is mapped into a window on a grid of feature distributions. Similar sample are mapped in close proximity on the grid and their commonalities in their feature distributions are reflected in the overlap of windows on the grid. Here we exploited these properties to design a novel kernel and a visualization strategy which we applied to the analysis of schizophrenic patients. Experiments report a clear improvement in classification accuracy as compared with similar methods. Moreover, our visualizations are able to highlight brain clusters and to obtain a visual interpretation of the features related to the disease.

Draft Genome Sequences of Two Neisseria meningitidis Serogroup C Clinical Isolates

Neisseria meningitidis is a human-specific pathogen known for its capability to cause sepsis and meningitis. Here we report the availability of 2 draft genome sequences obtained from patients infected during the same epidemic outbreak. Both bacterial isolates belong to serogroup C, but their genome sequences show local and remarkable differences compared with each other or with the reference genome of strain FAM18.

Estimation of arterial arrival time and cerebral blood flow from QUASAR arterial spin labeling using stable spline.

QUASAR arterial spin labeling (ASL) permits the application of deconvolution approaches for the absolute quantification of cerebral perfusion. Currently, oscillation index regularized singular value decomposition (oSVD) combined with edge‐detection (ED) is the most commonly used method. Its major drawbacks are nonphysiological oscillations in the impulse response function and underestimation of perfusion. The aim of this work is to introduce a novel method to overcome these limitations.

Stable spline deconvolution for dynamic susceptibility contrast MRI

To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI.