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Frisoni G.B.,LENITEM Laboratory of Epidemiology Neuroimaging and Telemedicine | Prestia A.,LENITEM Laboratory of Epidemiology Neuroimaging and Telemedicine | Geroldi C.,LENITEM Laboratory of Epidemiology Neuroimaging and Telemedicine | Adorni A.,LENITEM Laboratory of Epidemiology Neuroimaging and Telemedicine | And 10 more authors.
International Journal of Geriatric Psychiatry | Year: 2011

Objectives Cognitive impairment is prevalent in older schizophrenia patients but its biological basis is unknown. Neuropathological studies have not revealed Alzheimer disease (AD) lesion burden but in vivo data are lacking. Method We investigated the concentrations of CSF biomarkers of brain amyloidosis (Abeta42) and neurodegeneration (total and p-tau) in a group of older schizophrenia patients and related them to cognitive and MRI measures. Older schizophrenia (n = 11), AD patients (n = 20) and elderly controls (n = 6) underwent cognitive testing, lumbar puncture, and MRI scanning. Abeta42 and total and p-tau concentrations were assayed in the CSF. MRI volumes were assessed using both voxel-based (cortical pattern matching) and region-of-interest analyses. Results CSF tau concentration in older schizophrenia patients was within normal limits (total tau 171 ± 51 pg/ml, p-tau 32 ± 8 pg/ml), while CSF Abeta42 (465 ± 112 pg/ml) levels were significantly lower compared to healthy elders (638 ± 130 pg/ml) but higher than in AD patients (352 ± 76 pg/ml). There was a strong positive relationship between CSF total or p-tau levels and MMSE scores in schizophrenia patients but not in AD, where higher concentrations of total tau were correlated with higher volumes in the occipital cortex (r = 0.63, p = 0.036), while in AD a significant correlation was found between lower Abeta42 concentrations and lower gray matter volume in the cingulate and lateral orbital cortices (r > 0.46, p < 0.05). Conclusions Older schizophrenia patients show a peculiar pattern of CSF Abeta42 and tau concentrations that relates to cognitive and structural markers but is not consistent with neurodegeneration and could be secondary to neurodevelopmental or drug treatment effects. Copyright © 2010 John Wiley & Sons, Ltd.


Dyrba M.,German Center for Neurodegenerative Diseases | Ewers M.,Ludwig Maximilians University of Munich | Wegrzyn M.,German Center for Neurodegenerative Diseases | Kilimann I.,German Center for Neurodegenerative Diseases | And 14 more authors.
PLoS ONE | Year: 2013

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer's disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample. © 2013 Dyrba et al.


PubMed | University of Turku, University of Udine, University of Helsinki, Irccs Centro San Giovanni Of Dio Fbf and Kuopio University Hospital
Type: Journal Article | Journal: Acta neurologica Scandinavica | Year: 2016

We set to investigate the possible role of genes and environment in developing Alzheimers disease (AD) in monozygotic twin pairs discordant for AD.Three pairs of twins discordant for AD, who were enrolled in the Finnish Twin Cohort, were used in the study and compared with 13 controls. Gray matter changes were assessed with magnetic resonance images using voxel-based morphometry with statistical parametric mapping.In the affected twins, the peaks of volume loss were located bilaterally in the temporal (including the hippocampus), the frontal, and the parietal lobes, while in the unaffected siblings, the peaks were located in the frontal gyri and in the parietal lobule. Thus, in the unaffected twins, the pattern of volume loss overlaps with the neocortical but not with the medial temporal areas.These findings suggest that genetic factors more largely control neocortical regions, whereas environmental factors more strongly affect medial temporal regions.


PubMed | Vita-Salute San Raffaele University, University of Rostock, Albert Ludwigs University of Freiburg, University Pierre and Marie Curie and 8 more.
Type: Journal Article | Journal: NeuroImage | Year: 2016

The European DTI Study on Dementia (EDSD) is a multicenter framework created to study the diagnostic accuracy and inter-site variability of DTI-derived markers in patients with manifest and prodromal Alzheimers disease (AD). The dynamically growing database presently includes 493 DTI, 512 T1-weighted MRI, and 300 FLAIR scans from patients with AD dementia, patients with Mild Cognitive Impairment (MCI) and matched Healthy Controls, acquired on 13 different scanner platforms. The imaging data is publicly available, along with the subjects demographic and clinical characterization. Detailed neuropsychological information, cerebrospinal fluid information on biomarkers and clinical follow-up diagnoses are included for a subset of subjects. This paper describes the rationale and structure of the EDSD, summarizes the available data, and explains how to gain access to the database. The EDSD is a useful database for researchers seeking to investigate the contribution of DTI to dementia diagnostics.


Prestia A.,Irccs Centro San Giovanni Of Dio Fbf | Boccardi M.,Irccs Centro San Giovanni Of Dio Fbf | Galluzzi S.,Irccs Centro San Giovanni Of Dio Fbf | Cavedo E.,Irccs Centro San Giovanni Of Dio Fbf | And 8 more authors.
Psychiatry Research - Neuroimaging | Year: 2011

Patients with Alzheimer's disease (AD) and schizophrenia display cognitive, behavioural disturbances and morphological abnormalities. Although these latter reflect progressive neurodegeneration in AD, their significance in schizophrenia is still unclear. We explored the patterns of hippocampal and amygdalar atrophy in those patients and their associations with clinical parameters. Structural magnetic resonance imaging was performed in 20 elderly schizophrenia patients, 20 AD and 19 healthy older controls. Hippocampal and amygdalar volumes were obtained by manual segmentation with a standardized protocol and compared among groups. In both schizophrenia and AD patients, left hippocampal and amygdalar volumes were significantly smaller. The hippocampus/amygdala ratio was significantly lower in schizophrenia compared to both AD cases [2.4 bilaterally, 95% C.I. 2.2 to 2.7] and healthy controls bilaterally [2.5, 95% C.I. 2.3 to 2.9 in left and 2.7, 95% C.I. 2.4 to 3.1 in right hemisphere]. In schizophrenia patients, a significant positive correlation was found between age at disease onset and the right hippocampus/amygdala volume ratio (Spearman rho = 0.56). Negative symptoms correlated with higher right/left amygdala volume ratio (Spearman's rho = 0.43). Our data show that unlike AD, the hippocampus/amygdala ratio is abnormally low and correlates with the age at onset in schizophrenia, being a neurodevelopmental signature of the disease. © 2011 Elsevier Ireland Ltd.


Prestia A.,Irccs Centro San Giovanni Of Dio Fbf | Drago V.,Irccs Centro San Giovanni Of Dio Fbf | Rasser P.E.,Schizophrenia Research Institute | Rasser P.E.,University of Newcastle | And 3 more authors.
Journal of Alzheimer's Disease | Year: 2010

Mild cognitive impairment (MCI) is defined by memory impairment with no impact on daily activities. 10 to 15% of MCI convert to Alzheimer's disease (AD) per year. While structural changes in the cortex of AD patients have been extensively investigated, fewer studies analyzed changes in the years preceding conversion. 46 MCI patients and 20 healthy controls underwent structural 1.0T-weighted high-resolution MR scans at baseline and after 1.4 (SD 0.3) years. All subjects were assessed yearly for up to 4 years with a comprehensive neuropsychological battery. Sixteen of the 46 patients converted to AD (cMCI) while 30 remained stable (sMCI). An accurate voxel-based statistical mesh-model technique (cortical pattern matching) with a related region-of-interest analysis based on networks defined from a Brodmann area atlas (BAs) were used to map gray matter changes over time. At baseline, cMCI patients had 10 to 30% less cortical gray matter volume than healthy controls in regions known to be affected by AD pathology (entorhinal, temporoparietal, posterior cingulate, and orbitofrontal cortex, p=0.0001). Over time, cMCI patients lost more gray matter than sMCI in all brain areas but mainly in the olfactory and in the polysynaptic hippocampal network (more than 8% gray matter loss, p<0.024). sMCI patients had 10 to 20% less volume than controls in the posterior cingulate and orbitofrontal cortex (p<0.008) although their progression over time was significantly slower than cMCI. AD patients in the MCI stage show greater gray matter loss in the olfactory and polysynaptic hippocampal network. These findings are in line with neuropathological knowledge. © 2010 - IOS Press and the authors. All rights reserved.


Ly M.,William S Middleton Memorial Veterans Hospital | Ly M.,University of Wisconsin - Madison | Canu E.,Irccs Centro San Giovanni Of Dio Fbf | Xu G.,William S Middleton Memorial Veterans Hospital | And 16 more authors.
Human Brain Mapping | Year: 2014

Objectives: Although age-related brain changes are becoming better understood, midlife patterns of change are still in need of characterization, and longitudinal studies are lacking. The aim of this study was to determine if baseline fractional anisotropy (FA), obtained from diffusion tensor imaging (DTI) predicts volume change over a 4-year interval. Experimental design: Forty-four cognitively healthy middle-age adults underwent baseline DTI and longitudinal T1-weighted magnetic resonance imaging. Tensor-based morphometry methods were used to evaluate volume change over time. FA values were extracted from regions of interest that included the cingulum, entorhinal white matter, and the genu and splenium of the corpus callosum. Baseline FA was used as a predictor variable, whereas gray and white matter atrophy rates as indexed by Tensor-based morphometry were the dependent variables. Principal observations: Over a 4-year period, participants showed significant contraction of white matter, especially in frontal, temporal, and cerebellar regions (P < 0.05, corrected for multiple comparisons). Baseline FA in entorhinal white matter, genu, and splenium was associated with longitudinal rates of atrophy in regions that included the superior longitudinal fasciculus, anterior corona radiata, temporal stem, and white matter of the inferior temporal gyrus (P < 0.001, uncorrected for multiple comparisons). Conclusions: Brain change with aging is characterized by extensive shrinkage of white matter. Baseline white matter microstructure as indexed by DTI was associated with some of the observed regional volume loss. The findings suggest that both white matter volume loss and microstructural alterations should be considered more prominently in models of aging and neurodegenerative diseases. © 2013 Wiley Periodicals, Inc.


Canu E.,Irccs Centro San Giovanni Of Dio Fbf | McLaren D.G.,William S Middleton Memorial Veterans Hospital | McLaren D.G.,University of Wisconsin - Madison | Fitzgerald M.E.,William S Middleton Memorial Veterans Hospital | And 11 more authors.
Journal of Alzheimer's Disease | Year: 2010

Although it is established that Alzheimer's disease (AD) leads to cerebral macrostructural atrophy, microstructural diffusion changes have also been observed, but it is not yet known whether these changes offer unique information about the disease pathology. Thus, a multi-modal imaging study was conducted to determine the independent contribution of each modality in moderate to severe AD. Seventeen patients with moderate-severe AD and 13 healthy volunteers underwent diffusion-weighted and T1-weighted MR scanning. Images were processed to obtain measures of macrostructural atrophy (gray and white matter volumes) and microstructural damage (fractional anisotropy and mean diffusivity). Microstructural diffusion changes independent of macrostructural loss were investigated using an ANCOVA where macrostructural maps were used as voxel-wise covariates. The reverse ANCOVA model was also assessed, where macrostructural loss was the dependent variable and microstructural diffusion tensor imaging maps were the imaging covariates. Diffusion differences between patients and controls were observed after controlling for volumetric differences in medial temporal, retrosplenial regions, anterior commissure, corona radiata, internal capsule, thalamus, corticopontine tracts, cerebral peduncle, striatum, and precentral gyrus. Independent volumetric differences were observed in the entorhinal cortex, inferior temporal lobe, posterior cingulate cortex, splenium and cerebellum. While it is well known that AD is associated with pronounced volumetric change, this study suggests that measures of microstructure provide unique information not obtainable with volumetric mapping in regions known to be pivotal in AD and in those thought to be spared. As such this work provides great understanding of the topography of pathological changes in AD that can be captured with imaging. © 2010 - IOS Press and the authors. All rights reserved.


Prestia A.,Irccs Centro San Giovanni Of Dio Fbf
Future Neurology | Year: 2011

Evaluation of: Horesh Y, Katsel P, Haroutunian V, Domany E: Gene expression signature is shared by patients with Alzheimers disease and schizophrenia at the superior temporal gyrus. Eur. J. Neurol. DOI: 10.1111/j.1468-1331.2010. 03166.x (2010) (Epub ahead of print). This study tried to detect any molecular substrate that might be shared by brain disorders in general, comparing gene expression profiles across multiple brain areas determined by post-mortem samples from 83 patients affected by well-characterized diseases of the brain with marked differences in neuropathology, etiology and symptoms, such as Alzheimers disease (AD; n = 55) and schizophrenia (n = 28). Brodmann area 22, namely the superior temporal gyrus, had a greater number of abnormally expressed genes in both diseases; moreover, genes that differentiated AD and schizophrenia patients from normal elders (n = 22) were principally downregulated and more present in Brodmann area 8, part of the superior frontal cortex. The findings point to a specific molecular background shared by AD and schizophrenia, suggesting that impairment of the autophagy pathway regulation system could be one of the common bases of the two diseases; however, further studies are necessary, taking into account exposure to medications, sex hormone influences and with a significant expanded sample size in order to strengthen the evidence in support of the notion that at least part of the molecular background of AD and schizophrenia is shared by these two diseases. © 2011 Future Medicine Ltd.


Prestia A.,Irccs Centro San Giovanni Of Dio Fbf | Caroli A.,Irccs Centro San Giovanni Of Dio Fbf | Caroli A.,Mario Negri Institute for Pharmacological Research | Van Der Flier W.M.,VU University Amsterdam | And 13 more authors.
Neurology | Year: 2013

Objectives: The current model of Alzheimer disease (AD) stipulates that brain amyloidosis biomarkers turn abnormal earliest, followed by cortical hypometabolism, and finally brain atrophy ones. The aim of this study is to provide clinical evidence of the model in patients with mild cognitive impairment (MCI). Methods: A total of 73 patients with MCI from 3 European memory clinics were included. Brain amyloidosis was assessed by CSF Aβ42 concentration, cortical metabolism by an index of temporoparietal hypometabolism on FDG-PET, and brain atrophy by automated hippocampal volume. Patients were divided into groups based on biomarker positivity: 1) Aβ42- FDG-PET- Hippo-, 2) Aβ42+ FDG-PET- Hippo-, 3) Aβ42 + FDG-PET + Hippo-, 4) Aβ42 + FDG-PET+ Hippo+, and 5) any other combination not in line with the model. Measures of validity were prevalence of group 5, increasing incidence of progression to dementia with increasing biological severity, and decreasing conversion time. Results: When patients with MCI underwent clinical follow-up, 29 progressed to dementia, while 44 remained stable. A total of 26% of patients were in group 5. Incident dementia was increasing with greater biological severity in groups 1 to 5 from 4% to 27%, 64%, and 100% (p for trend < 0.0001), and occurred increasingly earlier (p for trend = 0.024). Conclusions: The core biomarker pattern is in line with the current pathophysiologic model of AD. Fully normal and fully abnormal pattern is associated with exceptional and universal development of dementia. Cases not in line might be due to atypical neurobiology or inaccurate thresholds for biomarker (ab)normality. © 2013 American Academy of Neurology.

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