Article

Anatomical MRI and DTI in the Diagnosis of Alzheimer's Disease: A European Multicenter Study.

Department of Psychiatry, University of Rostock, Rostock, Germany DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany.
Journal of Alzheimer's disease: JAD (Impact Factor: 4.17). 01/2012; 31:S33-47. DOI: 10.3233/JAD-2012-112118
Source: PubMed

ABSTRACT Diffusion tensor imaging (DTI) detects microstructural changes of the cerebral white matter in Alzheimer's disease (AD). The use of DTI for the diagnosis of AD in a multicenter setting has not yet been investigated. We used voxel-based analysis of fractional anisotropy, mean diffusivity, and grey matter volumes from multimodal magnetic resonance imaging data of 137 AD patients and 143 healthy elderly controls collected across 9 different scanners. We compared different univariate analysis approaches to model the effect of scanner, including a linear model across all scans with a scanner covariate, a random effects model with scanner as a random variable as well as a voxel-based meta-analysis. We found significant reduction of fractional anisotropy and significant increase of mean diffusivity in core areas of AD pathology including corpus callosum, medial and lateral temporal lobes, as well as fornix, cingulate gyrus, precuneus, and prefrontal lobe white matter. Grey matter atrophy was most pronounced in medial and lateral temporal lobe as well as parietal and prefrontal association cortex. The effects of group were spatially more restricted with random effects modeling of scanner effects compared to simple pooled analysis. All three analysis approaches yielded similar accuracy of group separation in block-wise cross-validated logistic regression. Our results suggest similar effects of center on group separation based on different analysis approaches and confirm a typical pattern of cortical and subcortical microstructural changes in AD using a large multimodal multicenter data set.

1 Bookmark
 · 
84 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Efforts are underway for early-phase trials of candidate treatments for cerebral amyloid angiopathy, an untreatable cause of haemorrhagic stroke and vascular cognitive impairment. A major barrier to these trials is the absence of consensus on measurement of treatment effectiveness. A range of potential outcome markers for cerebral amyloid angiopathy can be measured against the ideal criteria of being clinically meaningful, closely representative of biological progression, efficient for small or short trials, reliably measurable, and cost effective. In practice, outcomes tend either to have high clinical salience but low statistical efficiency, and thus more applicability for late-phase studies, or greater statistical efficiency but more limited clinical meaning. The most statistically efficient markers might be those that are potentially reversible with treatment, although their clinical significance remains unproven. Many of the candidate outcomes for cerebral amyloid angiopathy trials are probably applicable also to other small-vessel brain diseases.
    The Lancet Neurology 02/2014; · 23.92 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The functional organization of the brain in segregated neuronal networks has become a leading paradigm in the study of brain diseases. Diffusion tensor imaging (DTI) allows testing the validity and clinical utility of this paradigm on the structural connectivity level. DTI in Alzheimer's disease (AD) suggests a selective impairment of intracortical projecting fiber tracts underlying the functional disorganization of neuronal networks supporting memory and other cognitive functions. These findings have already been tested for their utility as clinical markers of AD in large multicenter studies. Affective disorders, including major depressive disorder (MDD) and bipolar disorder (BP), show a high comorbidity with AD in geriatric populations and may even have a pathogenetic overlap with AD. DTI studies in MDD and BP are still limited to small-scale monocenter studies, revealing subtle abnormalities in cortico-subcortial networks associated with affect regulation and reward/aversion control. The clinical utility of these findings remains to be further explored. The present paper presents the methodological background of diffusion imaging, including DTI and diffusion spectrum imaging, and discusses key findings in AD and affective disorders. The results of our review strongly point toward the necessity of large-scale multicenter multimodal transnosological networks to study the structural and functional basis of neuronal disconnection underlying different neuropsychiatric diseases.
    European Archives of Psychiatry and Clinical Neuroscience 03/2014; · 2.75 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.
    NeuroImage 03/2014; · 6.25 Impact Factor