Laboratory of Epidemiology and Neuroimaging

Brescia, Italy

Laboratory of Epidemiology and Neuroimaging

Brescia, Italy

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Micotti E.,Irccs Instituto Of Ricerche Farmacologiche Mario Negri | Paladini A.,Irccs Instituto Of Ricerche Farmacologiche Mario Negri | Balducci C.,Irccs Instituto Of Ricerche Farmacologiche Mario Negri | Tolomeo D.,Irccs Instituto Of Ricerche Farmacologiche Mario Negri | And 12 more authors.
Neurobiology of Aging | Year: 2015

Alzheimer's disease is experimentally modeled in transgenic (Tg) mice overexpressing mutated forms of the human amyloid precursor protein either alone or combined with mutated presenilins and tau. In the present study, we developed a systematic approach to compare double (TASTPM) and triple (APP/PS2/Tau) Tg mice by serial magnetic resonance imaging and spectroscopy analysis from 4 to 26months of age to define homologous biomarkers between mice and humans. Hippocampal atrophy was found in Tg mice compared with WT. In APP/PS2/Tau the effect was age-dependent, whereas in TASTPM it was detectable from the first investigated time point. Importantly, both mice displayed an age-related entorhinal cortex thinning and robust striatal atrophy, the latter associated with a significant loss of synaptophysin. Hippocampal magnetic resonance spectroscopy revealed lower glutamate levels in both Tg mice and a selective myo-inositol increase in TASTPM. This noninvasive magnetic resonance imaging analysis, revealed common biomarkers between humans and mice, and could, thus, be promoted as a fully translational tool to be adopted in the preclinical investigation of therapeutic approaches. © 2015 Elsevier Inc.


Jacobs H.I.L.,Maastricht University | Visser P.J.,Maastricht University | Visser P.J.,VU University Amsterdam | Van Boxtel M.P.J.,Maastricht University | And 18 more authors.
Neurobiology of Aging | Year: 2012

White matter hyperintensities (WMH) in Mild Cognitive Impairment (MCI) have been associated with impaired executive functioning, although contradictory findings have been reported. The aim of this study was to examine whether WMH location influenced the relation between WMH and executive functioning in MCI participants (55-90 years) in the European multicenter memory-clinic-based DESCRIPA study, who underwent MRI scanning at baseline (N = 337). Linear mixed model analysis was performed to test the association between WMH damage in three networks (frontal-parietal, frontal-subcortical and frontal-parietal-subcortical network) and change in executive functioning over a 3-year period. WMH in the frontal-parietal and in the frontal-parietal-subcortical network were associated with decline in executive functioning. However, the frontal-subcortical network was not associated with change in executive functioning. Our results suggest that parietal WMH are a significant contributor to executive decline in MCI and that investigation of WMH in the cerebral networks supporting cognitive functions provide a new way to differentiate stable from cognitive declining MCI individuals. © 2012 Elsevier Inc.


Clerx L.,Maastricht University | van Rossum I.A.,VU University Amsterdam | Burns L.,Bristol Myers Squibb | Knol D.L.,VU University Amsterdam | And 20 more authors.
Neurobiology of Aging | Year: 2013

Our aim was to compare the predictive accuracy of 4 different medial temporal lobe measurements for Alzheimer's disease (AD) in subjects with mild cognitive impairment (MCI). Manual hippocampal measurement, automated atlas-based hippocampal measurement, a visual rating scale (MTA-score), and lateral ventricle measurement were compared. Predictive accuracy for AD 2 years after baseline was assessed by receiver operating characteristics analyses with area under the curve as outcome. Annual cognitive decline was assessed by slope analyses up to 5 years after baseline. Correlations with biomarkers in cerebrospinal fluid (CSF) were investigated. Subjects with MCI were selected from the Development of Screening Guidelines and Clinical Criteria for Predementia AD (DESCRIPA) multicenter study (. n = 156) and the single-center VU medical center (. n = 172). At follow-up, area under the curve was highest for automated atlas-based hippocampal measurement (0.71) and manual hippocampal measurement (0.71), and lower for MTA-score (0.65) and lateral ventricle (0.60). Slope analysis yielded similar results. Hippocampal measurements correlated with CSF total tau and phosphorylated tau, not with beta-amyloid 1-42. MTA-score and lateral ventricle volume correlated with CSF beta-amyloid 1-42. We can conclude that volumetric hippocampal measurements are the best predictors of AD conversion in subjects with MCI. © 2013 Elsevier Inc.


Hall A.,University of Eastern Finland | Mattila J.,VTT Technical Research Center of Finland | Koikkalainen J.,VTT Technical Research Center of Finland | Lotjonen J.,VTT Technical Research Center of Finland | And 13 more authors.
Current Alzheimer Research | Year: 2015

We evaluated the performance of the Disease State Index (DSI) method when predicting progression to Alzheimer’s disease (AD) in patients with subjective cognitive impairment (SCI), amnestic or non-amnestic mild cognitive impairment (aMCI, naMCI). The DSI model measures patients’ similarity to diagnosed cases based on available data, such as cognitive tests, the APOE genotype, CSF biomarkers and MRI. We applied the DSI model to data from the DESCRIPA cohort, where non-demented patients (N=775) with different subtypes of cognitive impairment were followed for 1 to 5 years. Classification accuracies for the subgroups were calculated with the DSI using leave-one-out crossvalidation. The DSI’s classification accuracy in predicting progression to AD was 0.75 (AUC=0.83) in the total population, 0.70 (AUC=0.77) for aMCI and 0.71 (AUC=0.76) for naMCI. For a subset of approximately half of the patients with high or low DSI values, accuracy reached 0.86 (all), 0.78 (aMCI), and 0.85 (naMCI). For patients with MRI or CSF biomarker data available, theywere 0.78 (all), 0.76 (aMCI) and 0.76 (naMCI), while for clear cases the accuracies rose to 0.90 (all), 0.83 (aMCI) and 0.91 (naMCI). The results show that the DSI model can distinguish between clear and ambiguous cases, assess the severity of the disease and also provide information on the effectiveness of different biomarkers. While a specific test or biomarker may confound analysis for an individual patient, combining several different types of tests and biomarkers could be able to reveal the trajectory of the disease and improve the prediction of AD progression. © 2015 Bentham Science Publishers.


Redolfi A.,Laboratory of Epidemiology and Neuroimaging | Manset D.,Gnubila France | Barkhof F.,VU University Amsterdam | Wahlund L.-O.,Karolinska Institutet | And 5 more authors.
PLoS ONE | Year: 2015

Background and Purpose: The measurement of cortical shrinkage is a candidate marker of disease progression in Alzheimer's. This study evaluated the performance of two pipelines: Civet-CLASP (v1.1.9) and Freesurfer (v5.3.0). Methods: Images from 185 ADNI1 cases (69 elderly controls (CTR), 37 stable MCI (sMCI), 27 progressive MCI (pMCI), and 52 Alzheimer (AD) patients) scanned at baseline, month 12, and month 24 were processed using the two pipelines and two interconnected e-infrastructures: neuGRID (https://neugrid4you.eu) and VIP (http://vip.creatis.insa-lyon.fr). The vertex-by-vertex cross-algorithm comparison was made possible applying the 3D gradient vector flow (GVF) and closest point search (CPS) techniques. Results: The cortical thickness measured with Freesurfer was systematically lower by one third if compared to Civet 's. Cross-sectionally, Freesurfer's effect size was significantly different in the posterior division of the temporal fusiform cortex. Both pipelines were weakly or mildly correlated with the Mini Mental State Examination score (MMSE) and the hippocampal volumetry. Civet differed significantly from Freesurfer in large frontal, parietal, temporal and occipital regions (p<0.05). In a discriminant analysis with cortical ROIs having effect size larger than 0.8, both pipelines gave no significant differences in area under the curve (AUC). Longitudinally, effect sizes were not significantly different in any of the 28 ROIs tested. Both pipelines weakly correlated with MMSE decay, showing no significant differences. Freesurfer mildly correlated with hippocampal thinning rate and differed in the supramarginal gyrus, temporal gyrus, and in the lateral occipital cortex compared to Civet (p<0.05). In a discriminant analysis with ROIs having effect size larger than 0.6, both pipelines yielded no significant differences in the AUC. Conclusions: Civet appears slightly more sensitive to the typical AD atrophic pattern at the MCI stage, but both pipelines can accurately characterize the topography of cortical thinning at the dementia stage. © 2015 Redolfi et al.


Marizzoni M.,Laboratory of Epidemiology and Neuroimaging | Forloni G.,Mario Negri Institute for Pharmacological Research | Frisoni G.B.,Laboratory of Epidemiology and Neuroimaging | Frisoni G.B.,University of Geneva
Drug Discovery Today: Therapeutic Strategies | Year: 2013

The pathological process of Alzheimer's disease (AD) starts years before the appearance of clinical symptoms. The understanding of those mechanisms at the basis of such long phase will permit the development of new drugs to counter neurodegeneration before irreversible neuronal losses occur. Ideally, the development of such drugs should be based on the markers of disease progression homologous in humans and animals. The perfect experimental model recapitulating the main pathological characteristics of AD has yet to be engineered, but available models address a number of pathological AD features allowing to translate human markers to mice. The present paper is an overview of the neuroimaging markers used to map AD-like pathology and its progression in vivo in mice models of amyloidosis. Mice models are widely used to test AD candidate drugs and to predict their effects in human. Therefore, the crucial key is the identification of AD progression imaging markers homologous to those validated in early AD patients. © 2013 Elsevier Ltd. All rights reserved.


Caroli A.,Laboratory of Epidemiology and Neuroimaging | Caroli A.,Mario Negri Institute for Pharmacological Research | Prestia A.,Laboratory of Epidemiology and Neuroimaging | Chen K.,Banner Alzheimers Institute | And 9 more authors.
Journal of Nuclear Medicine | Year: 2012

In the recently revised diagnostic criteria for Alzheimer disease (AD), the National Institute on Aging and Alzheimer Association suggested that confidence in diagnosing dementia due to AD and mild cognitive impairment (MCI) due to AD could be improved by the use of certain biomarkers, such as 18F-FDG PET evidence of hypometabolism in AD-affected brain regions. Three groups have developed automated data analysis techniques to characterize the AD-related pattern of hypometabolism in a single measurement. In this study, we sought to directly compare the ability of these three 18F-FDG PET data analysis techniques - the PMOD Alzheimer discrimination analysis tool, the hypometabolic convergence index, and a set of meta-analytically derived regions of interest reflecting AD hypometabolism pattern (metaROI) - to distinguish moderate or mild AD dementia patients and MCI patients who subsequently converted to AD dementia from cognitively normal older adults. Methods: One hundred sixty-six 18F-FDG PET patients from the AD Neuroimaging Initiative, 308 from the Network for Efficiency and Standardization of Dementia Diagnosis, and 176 from the European Alzheimer Disease Consortium PET study were categorized, with masking of group classification, as AD, MCI, or healthy control. For each AD-related 18F-FDG PET index, receiver-operating-characteristic curves were used to characterize and compare subject group classifications. Results: The 3 techniques were roughly comparable in their ability to distinguish each of the clinical groups from cognitively normal older adults with high sensitivity and specificity. Accuracy of classification (in terms of area under the curve) in each clinical group varied more as a function of dataset than by technique. All techniques were differentially sensitive to disease severity, with the classification accuracy for MCI due to AD to moderate AD varying from 0.800 to 0.949 (PMOD Alzheimer tool), from 0.774 to 0.967 (metaROI), and from 0.801 to 0.983 (hypometabolic convergence index). Conclusion: The 3 tested techniques have the potential to help detect AD in research and clinical settings. Additional efforts are needed to clarify their ability to address particular scientific and clinical questions. Their incremental diagnostic value over other imaging and biologic markers makes them easier to implement by other groups for these purposes. COPYRIGHT © 2012 by the Society of Nuclear Medicine, Inc.


Cover K.S.,VU University Amsterdam | van Schijndel R.A.,VU University Amsterdam | van Dijk B.W.,VU University Amsterdam | Redolfi A.,Laboratory of Epidemiology and Neuroimaging | And 4 more authors.
Psychiatry Research - Neuroimaging | Year: 2011

SienaX and Siena are widely used and fully automated algorithms for measuring whole brain volume and volume change in cross-sectional and longitudinal MRI studies and are particularly useful in studies of brain atrophy. The reproducibility of the algorithms was assessed using the 3D T1 weighted MP-RAGE scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The back-to-back (BTB) MP-RAGE scans in the ADNI data set makes it a valuable benchmark against which to assess the performance of algorithms of measuring atrophy in the human brain with MRI scans. A total of 671 subjects were included for SienaX and 385 subjects for Siena. The annual percentage brain volume change (PBVC) rates were -0.65 ± 0.82%/year for the healthy controls, -1.15 ± 1.21%/year for mild cognitively impairment (MCI) and -1.84 ± 1.33%/year for AD, in line with previous findings. The median of the absolute value of the reproducibility of SienaX's normalized brain volume (NBV) was 0.96% while the 90th percentile was 5.11%. The reproducibility of Siena's PBVC had a median of 0.35% and a 90th percentile of 1.37%. While the median reproducibility for SienaX's NBV was in line with the values previously reported in the literature, the median reproducibility of Siena's PBVC was about twice that reported. Also, the 90th percentiles for both SienaX and Siena were about twice the size that would be expected for a Gaussian distribution. Because of the natural variation of the disease among patients over a year, a perfectly reproducible whole brain atrophy algorithm would reduce the estimated group size needed to detect a specified treatment effect by only 30% to 40% as compared to Siena's. © 2011 Elsevier Ireland Ltd.


Redolfi A.,Laboratory of Epidemiology and Neuroimaging | Bosco P.,Laboratory of Epidemiology and Neuroimaging | Manset D.,Gnubila France | Frisoni G.B.,Laboratory of Epidemiology and Neuroimaging
Functional Neurology | Year: 2013

The brain of a patient with Alzheimer's disease (AD) undergoes changes starting many years before the development of the first clinical symptoms. The recent availability of large prospective datasets makes it possible to create sophisticated brain models of healthy subjects and patients with AD, showing pathophysiological changes occurring over time. However, these models are still inadequate; representations are mainly single-scale and they do not account for the complexity and interdependence of brain changes. Brain changes in AD patients occur at different levels and for different reasons: at the molecular level, changes are due to amyloid deposition; at cellular level, to loss of neuron synapses, and at tissue level, to connectivity disruption. All cause extensive atrophy of the whole brain organ. Initiatives aiming to model the whole human brain have been launched in Europe and the US with the goal of reducing the burden of brain diseases. In this work, we describe a new approach to earlier diagnosis based on a multimodal and multiscale brain concept, built upon existing and well-characterized single modalities. © CIC Edizioni Internazionali.


Cover K.S.,VU University Amsterdam | van Schijndel R.A.,VU University Amsterdam | Popescu V.,VU University Amsterdam | Van Dijk B.W.,VU University Amsterdam | And 5 more authors.
Psychiatry Research - Neuroimaging | Year: 2014

The back-to-back (BTB) acquisition of MP-RAGE MRI scans of the Alzheimer's Disease Neuroimaging Initiative (ADNI1) provides an excellent data set with which to check the reproducibility of brain atrophy measures. As part of ADNI1, 131 subjects received BTB MP-RAGEs at multiple time points and two field strengths of 3. T and 1.5. T. As a result, high quality data from 200 subject-visit-pairs was available to compare the reproducibility of brain atrophies measured with FSL/SIENA over 12 to 18 month intervals at both 3. T and 1.5. T. Although several publications have reported on the differing performance of brain atrophy measures at 3. T and 1.5. T, no formal comparison of reproducibility has been published to date. Another goal was to check whether tuning SIENA options, including -B, -S, -R and the fractional intensity threshold (f) had a significant impact on the reproducibility. The BTB reproducibility for SIENA was quantified by the 50th percentile of the absolute value of the difference in the percentage brain volume change (PBVC) for the BTB MP-RAGES. At both 3. T and 1.5. T the SIENA option combination of "-B f=0.2", which is different from the default values of f=0.5, yielded the best reproducibility as measured by the 50th percentile yielding 0.28 (0.23-0.39)% and 0.26 (0.20-0.32)%. These results demonstrated that in general 3. T had no advantage over 1.5. T for the whole brain atrophy measure - at least for SIENA. While 3. T MRI is superior to 1.5. T for many types of measurements, and thus worth the additional cost, brain atrophy measurement does not seem to be one of them. © 2014 Elsevier Ireland Ltd.

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