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Antonio Giorgio

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6 papers

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YNICL Journal 2019 Journal Article

Fractal dimension of cerebral white matter: A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Leonardo Pantoni
Chiara Marzi
Anna Poggesi
Antonio Giorgio
Nicola De Stefano
Mario Mascalchi
Domenico Inzitari
Emilia Salvadori

Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T1-weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD. In our study, we included 64 patients (mean age ± standard deviation, 74. 6 ± 6. 9, education 7. 9 ± 4. 2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age ± standard deviation, 72. 3 ± 4. 4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error. For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores (p-value =. 039), Symbol Digit Modalities Test scores (p-value =. 039), and Trail Making Test Part A scores (p-value =. 025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature. In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging.

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YNICL Journal 2019 Journal Article

Relevance of brain lesion location for cognition in vascular mild cognitive impairment

Antonio Giorgio
Ilaria Di Donato
Alessandro De Leucio
Jian Zhang
Emilia Salvadori
Anna Poggesi
Stefano Diciotti
Mirco Cosottini

BACKGROUND: Vascular mild cognitive impairment (VMCI) is a potentially transitional state between normal aging and vascular dementia. The presence of macroscopic white matter lesions (WML) of moderate or severe extension on brain MRI is the hallmark of the VMCI. OBJECTIVE: To assess the clinical relevance of the frequency of WML in patients with VMCI independently of total lesion volume (LV). METHODS: In this multicenter study, we included 110 patients with VMCI (age: 74.3 ± 6.6 years; sex: 60 women). Cognitive assessment was performed with the VMCI-Tuscany Neuropsychological Battery, which allowed to identify four VMCI groups: amnestic single (n = 9) and multi-domain (n = 76), non-amnestic single- (n = 10) and multi-domain (n = 15). Distribution and frequency of WML on MRI FLAIR images were evaluated with lesion probability map (LPM). Voxelwise statistics was performed with nonparametric permutation tests, controlling for age, sex, slice thickness, center, magnetic field strength, total LV and head size (p < .01, family-wise error-corrected for multiple comparisons across space). RESULTS: LPM of the WML had a fairly symmetric and widespread distribution across brain. A higher frequency of WML along association tracts of the WM such as inferior longitudinal fascicle, inferior fronto-occipital fascicle and superior longitudinal fascicle, was correlated with worst cognitive scores at the Trail Making Test Part A and Copy of the Rey-Osterrieth Complex Figure. The non-amnestic groups showed a higher frequency of WML in the anterior cingulum and superior longitudinal fascicle close to the frontal gyrus. CONCLUSIONS: Our study showed that in patients with VMCI, independently of total LV, the higher frequency of lesions along association tracts of the WM, which mediate intrahemispheric long-range connectivity, is related with psychomotor speed and constructional praxis. Moreover, a prevalence of lesions in the frontal WM seems to characterize VMCI patients with involvement of non-amnestic domains.

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YNICL Journal 2019 Journal Article

SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis

Viktor Wottschel
Declan T. Chard
Christian Enzinger
Massimo Filippi
Jette L. Frederiksen
Claudio Gasperini
Antonio Giorgio
Maria A. Rocca

Machine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.

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YNICL Journal 2016 Journal Article

Relationship of white and gray matter abnormalities to clinical and genetic features in myotonic dystrophy type 1

Stefano Zanigni
Stefania Evangelisti
Maria Pia Giannoccaro
Federico Oppi
Roberto Poda
Antonio Giorgio
Claudia Testa
David Neil Manners

BACKGROUND: Myotonic dystrophy type 1 (DM1) represents a multisystemic disorder in which diffuse brain white and gray matter alterations related to clinical and genetic features have been described. We aimed to evaluate in the brain of adult patients with DM1 (i) white and gray matter differences, including cortical-subcortical gray matter volume and cortical thickness and (ii) their correlation with clinical disability, global neuropsychological performance and triplet expansion. METHODS: We included 24 adult genetically-confirmed DM1 patients (14 males; age: 38.5 ± 11.8 years) and 25 age- and sex-matched healthy controls (14 males; age: 38.5 ± 11.3 years) who underwent an identical brain MR protocol including high-resolution 3D T1-weighted, axial T2 FLAIR and DTI sequences. All patients underwent an extensive clinical and neuropsychological evaluation. Voxel-wise analyses of white matter, performed by using Tract Based Spatial Statistics, and of gray matter, with Voxel-based Morphometry and Cortical Thickness, were carried out in order to test for differences between patients with DM1 and healthy controls (p < 0.05, corrected). The correlation between MRI measures and clinical-genetic features was also assessed. RESULTS: Patients with DM1 showed widespread abnormalities of all DTI parameters in the white matter, which were associated with reduced gray matter volume in all brain lobes and thinning in parieto-temporo-occipital cortices, albeit with less extensive cortical alterations when congenital cases were removed from the analyses. White matter alterations correlated with clinical disability, global cognitive performance and triplet expansions. CONCLUSION: In patients with DM1, the combined smaller overall gray matter volume and white matter alterations seem to be the main morpho-structural substrates of CNS involvement in this condition. The correlation of white matter differences with both clinical and genetic findings lends support to this notion.

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YNIMG Journal 2015 Journal Article

A human post-mortem brain model for the standardization of multi-centre MRI studies

Amgad Droby
Carsten Lukas
Anne Schänzer
Isabella Spiwoks-Becker
Antonio Giorgio
Ralf Gold
Nicola De Stefano
Harald Kugel

Multi-centre MRI studies of the brain are essential for enrolling large and diverse patient cohorts, as required for the investigation of heterogeneous neurological and psychiatric diseases. However, the multi-site comparison of standard MRI data sets that are weighted with respect to tissue parameters such as the relaxation times (T1, T2) and proton density (PD) may be problematic, as signal intensities and image contrasts depend on site-specific details such as the sequences used, imaging parameters, and sensitivity profiles of the radiofrequency (RF) coils. Water or gel phantoms are frequently used for long-term and/or inter-site quality assessment. However, these phantoms hardly mimic the structure, shape, size or tissue distribution of the human brain. The goals of this study were: (1) to validate the long-term stability of a human post-mortem brain phantom, performing quantitative mapping of T1, T2, and PD, and the magnetization transfer ratio (MTR) over a period of 18months; (2) to acquire and analyse data for this phantom and the brain of a healthy control (HC) in a multi-centre study for MRI protocol standardization in four centres, while conducting a voxel-wise as well as whole brain grey (GM) and white matter (WM) tissue volume comparison. MTR, T2, and the quotient of PD in WM and GM were stable in the post-mortem brain with no significant changes. T1 was found to decrease from 267/236ms (GM/WM) to 234/216ms between 5 and 17weeks post embedment, stabilizing during an 18-month period following the first scan at about 215/190ms. The volumetric measures, based on T1-weighted MP-RAGE images obtained at all participating centres, revealed inter- and intra-centre variations in the evaluated GM and WM volumes that displayed similar trends in both the post-mortem brain as well as the HC. At a confidence level of 95%, brain regions such as the brainstem, deep GM structures as well as boundaries between GM and WM tissues were found to be less reproducible than other brain regions in all participating centres. The results demonstrate that a post-mortem brain phantom may be used as a reliable tool for multi-centre MR studies.

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YNIMG Journal 2010 Journal Article

Age-related changes in grey and white matter structure throughout adulthood

Antonio Giorgio
Luca Santelli
Valentina Tomassini
Rose Bosnell
Steve Smith
Nicola De Stefano
Heidi Johansen-Berg

Normal ageing is associated with gradual brain atrophy. Determining spatial and temporal patterns of change can help shed light on underlying mechanisms. Neuroimaging provides various measures of brain structure that can be used to assess such age-related change but studies to date have typically considered single imaging measures. Although there is consensus on the notion that brain structure deteriorates with age, evidence on the precise time course and spatial distribution of changes is mixed. We assessed grey matter (GM) and white matter (WM) structure in a group of 66 adults aged between 23 and 81. Multimodal imaging measures included voxel-based morphometry (VBM)-style analysis of GM and WM volume and diffusion tensor imaging (DTI) metrics of WM microstructure. We found widespread reductions in GM volume from middle age onwards but earlier reductions in GM were detected in frontal cortex. Widespread age-related deterioration in WM microstructure was detected from young adulthood onwards. WM decline was detected earlier and more sensitively using DTI-based measures of microstructure than using markers of WM volume derived from conventional T1-weighted imaging.

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