Biomedical subjects

Bruno Alfano

Publications and source records attributed to Bruno Alfano.

6 recordsLinked to original sources

PubMed2006

Neuropsychological assessment, quantitative MRI and ApoE gene polymorphisms in a series of MS patients treated with IFN beta-1b.

Few trials issued the effect of disease-modifying medications on cognitive functions in multiple sclerosis. We designed an open-label longitudinal study to evaluate, during 2 years, cognitive performance and its relationship with MRI data and ApoE polymorphism findings in a group of relapsing-remitting (RR) multiple sclerosis (MS) Interferon (IFN) beta-1b-treated patients (median age 30 years, median disease duration 3.4 years). Complete neuropsychological battery was grouped into attention, information learning/memory, language and visuo-spatial functions. Fifty-two patients (33 females) were enrolled in the study. Six patients (11.5%) dropped out, mainly due to side effects. At baseline neuropsychological evaluation, we found 54% normal, 42% mildly impaired and 4% moderately impaired patients. At 2 years follow-up, cognitive status was stable in 65%, improved in 33% and worsened in 2% of patients. No significant relations were found between global cognitive outcome vs. EDSS change, clinical disease activity, MRI data or ApoE gene polymorphisms over the 2 years follow-up. EDSS and MRI fractional volumes were found to correlate with the performance at single tests. Twenty-one patients (45.6%) showed active MRI scans throughout the study, without any worsening at the corresponding neuropsychological examination. This ongoing trial suggests a possible beneficial effect of IFN beta-1b treatment on cognitive functions in RRMS patients. Extension of follow-up and further data analyses are needed to confirm and clarify these findings.

Adolescent↗

PubMed2004

Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction.

UNLABELLED: We present software for integrated analysis of brain PET studies and coregistered segmented MRI that couples a module for automated placement of regions of interest (ROI) with 4 alternative methods for partial-volume-effect correction (PVEc). The accuracy and precision of these methods have been measured using 4 simulated (18)F-FDG PET studies with increasing degrees of atrophy. METHODS: The software allows the application of a set of labels, defined a priori in the Talairach space, to segmented and coregistered MRI. Resulting ROIs are then transferred onto the PET study, and corresponding values are corrected according to the 4 PVEc techniques under investigation, providing corresponding corrected values. To evaluate the PVEc techniques, the software was applied to 4 simulated (18)F-FDG PET studies, introducing increasingly larger experimental errors, including errors in coregistration (0- to 6-pixel misregistration), segmentation (-13.7% to 14.1% gray matter [GM] volume change) and resolution estimate errors (-16.9% to 26.8% full-width-at-half-maximum mismatch). RESULTS: Even in the absence of segmentation and coregistration errors, uncorrected PET values showed -37.6% GM underestimation and 91.7% WM overestimation. Voxel-based correction only for the loss of GM activity as a result of spill-out onto extraparenchymal tissues left a residual underestimation of GM values (-21.2%). Application of the method that took into account both spill-in and spill-out effects between any possible pair of ROIs (R-PVEc) and of the voxel-based method that corrects also for the WM activity derived from R-PVEC (mMG-PVEc) provided an accuracy above 96%. The coefficient of variation of the GM ROIs, a measure of the imprecision of the GM concentration estimates, was 8.5% for uncorrected PET data and decreased with PVEc, reaching 6.0% for mMG-PVEc. Coregistration errors appeared to be the major determinant of the imprecision. CONCLUSION: Coupling of automated ROI placement and PVEc provides a tool for integrated analysis of brain PET/MRI data, which allows a recovery of true GM ROI values, with a high degree of accuracy when R-PVEc or mMG-PVEc is used. Among the 4 tested PVEc methods, R-PVEc showed the greatest accuracy and is suitable when corrected images are not specifically needed. Otherwise, if corrected images are desired, the mMG-PVEc method appears the most adequate, showing a similar accuracy.

Brain↗

PubMed2003

Segmentation of magnetic resonance brain images through discriminant analysis.

Segmentation (tissue classification) of medical images obtained from a magnetic resonance (MR) system is a primary step in most applications of medical image post-processing. This paper describes nonparametric discriminant analysis methods to segment multispectral MR images of the brain. Starting from routinely available spin-lattice relaxation time, spin-spin relaxation time, and proton density weighted images (T1w, T2w, PDw), the proposed family of statistical methods is based on: (i) a transform of the images into components that are statistically independent from each other; (ii) a nonparametric estimate of probability density functions of each tissue starting from a training set; (iii) a classic Bayes 0-1 classification rule. Experiments based on a computer built brain phantom (brainweb) and on eight real patient data sets are shown. A comparison with parametric discriminant analysis is also reported. The capability of nonparametric discriminant analysis in improving brain tissue classification of parametric methods is demonstrated. Finally, an assessment of the role of multispectrality in classifying brain tissues is discussed.

Brain↗

PubMed2003

Brain tissue volume changes in relapsing-remitting multiple sclerosis: correlation with lesion load.

The aim of this study was to simultaneously measure in vivo volumes of gray matter (GM), normal white matter (WM), abnormal white matter (aWM), and cerebro-spinal fluid (CSF), and to assess their relationship in 50 patients with relapsing-remitting multiple sclerosis (RR-MS) (age range, 21-59; mean EDSS, 2.5; mean disease duration, 9.9 years), using an unsupervised multiparametric segmentation procedure applied to brain MR studies. Tissue volumes were normalized to total intracranial volume providing corresponding fractional volumes (fGM, faWM, fWM, and fCSF), subsequently corrected for aWM-related segmentation inaccuracies and adjusted to mean patients' age according to age-related changes measured in 54 normal volunteers (NV) (age range 16-70). In MS patients aWM was 23.8 +/- 29.8 ml (range 0.4-138.8). A significant decrease in fGM was present in MS patients as compared to NV (49.5 +/- 3.2% vs 53.3 +/- 2.1%; P < 0.0001), with a corresponding increase in fCSF (13.0 +/- 3.8% vs 9.1 +/- 2.4%; P < 0.0001). No difference could be detected between the two groups for fWM (37.5 +/- 2.6% vs 37.6 +/- 2.2%). faWM correlated inversely with fGM (R = -0.434, P < 0.001 at regression analysis), and directly with fCSF (R = 0.473, P < 0.001), but not with fWM. There was a significant correlation between disease duration and EDSS, while no relationship was found between EDSS or disease duration and fractional volumes. Brain atrophy in RR-MS is mainly related to GM loss, which correlates with faWM. Both measures do not appear to significantly affect EDSS, which correlates to disease duration.

Adult↗

PubMed2002

Voxel-based comparison of rCBF SPET images in frontotemporal dementia and Alzheimer's disease highlights the involvement of different cortical networks.

Characteristic patterns of regional cerebral blood flow (rCBF) reduction, as detected by technetium-99m hexamethylpropylene amine oxime ((99m)Tc-HMPAO) single-photon emission tomography (SPET), may help clinicians in differentiating patients with frontotemporal dementia (FTD) from those with Alzheimer's disease (AD). However, in some cases these patients may share common rCBF abnormalities and the visual analysis and/or the region of interest (ROI) approach may not sensitively detect more localised focal changes that could be more specific for each pathology. Recently, automated voxel-by-voxel statistical analysis of perfusion brain maps has been applied to SPET images. This method has the advantage of including the rCBF information for the whole brain for statistical analysis without any a priori hypothesis regarding the regions possibly involved. This could result in a better characterisation of rCBF differences in brain regions while also reducing the operator's subjectivity and the time required for data analysis. The purpose of this study was to apply such a technique to highlight the specific brain areas showing a relative functional involvement in FTD and AD. Thus, we compared the relative rCBF patterns obtained in eight FTD patients with those obtained in 21 AD patients using (99m)Tc-HMPAO SPET and statistical parametric mapping (SPM). When FTD patients were compared with AD patients, relatively lower rCBF was observed in right medial frontal cortex (BA 8, 9, 10), right anterior cingulate cortex (BA 32), right temporal cortex (BA 21/22), right orbitofrontal cortex (BA 11) and ventrolateral prefrontal cortex (BA 47); in BA 47 the reduction was evident bilaterally but was more marked on the right side. On the other hand, when AD patients were compared with FTD patients, a significant relative rCBF decrease was found in the bilateral superior parietal cortex (BA 7); this decrease was more extensive on the left side, where it also included the inferior parietal (BA 40), superior occipital (BA 19) and temporo-occipital regions (BA 39, 19). The results of this study confirm the preferential involvement of the frontotemporal regions in FTD patients and of the temporoparietal regions in AD patients. Furthermore, they highlight the networks that are more specifically impaired in these disorders and that could be implicated in the emotional-behavioural and cognitive disturbances that characterise FTD and AD respectively.

Aged↗

PubMed2002

Stereotaxy-based regional brain volumetry applied to segmented MRI: validation and results in deficit and nondeficit schizophrenia.

A method for postprocessing of segmented routine brain MRI studies providing automated definition of major structures (frontal, parietal, occipital, and temporal lobes; cerebellar hemispheres; and lateral ventricles) according to the Talairach atlas is presented. The method was applied to MRI studies from 25 normal subjects (NV), 14 patients with deficit schizophrenia (DS), and 14 with nondeficit schizophrenia (NDS), to evaluate their gray matter and CSF regional volumes. The two patient groups did not differ in mean age at illness onset, duration of illness, severity of psychotic symptoms, or disorganization; DS had more severe avolition and worse social functioning than NDS. For validation purposes, brain structures were manually outlined on original MR images in 10 studies, thus obtaining reference measures. Manual and automated measures were repeated 1 month apart to measure reproducibilities of both methods. The automated method required less than 1 min/operator per study vs more than 30 min for manual assessment. Mean absolute difference per structure between the two techniques was 4.8 ml. Overall reproducibility did not significantly differ between the two methods. In subjects with schizophrenia, a significant decrease in GM and increase in CSF were found. GM loss was confined to frontal and temporal lobes. Lateral ventricles were significantly larger bilaterally in NDS compared to NV and only on the right in NDS compared to DS. The finding of greater structural brain abnormalities in NDS adds to the evidence that deficit schizophrenia does not represent just the more severe end of the schizophrenia continuum.

Adolescent↗