662 research outputs found
Total and phosphorylated tau proteins: Evaluation as core biomarker candidates in frontotemporal dementia
No full textAn ever increasing number of patients with neurodegenerative disorders calls for the evaluation of potential diagnostic markers that allow an early diagnosis and an early initiation of specific therapy. Clinical diagnosis of Alzheimer's disease (AD), the most common neurodegenerative disorder, reaches 80-90% accuracy upon autopsy in specialized clinical centers. Diagnosis of AD in early clinical or preclinical stages is far less accurate, as is the differential diagnosis between AD and other primary dementias, such as frontotemporal dementia (FTD). Microtubule-associated tau protein is abnormally phosphorylated in AD and aggregates as paired helical filaments in neurofibrillary tangles. Recently, immunoassays have been developed detecting tau phosphorylated at specific epitopes in cerebrospinal fluid (CSF). Four years of clinical research consistently demonstrate that CSF phosphorylated tau (p-tau) is highly increased in AD compared to healthy controls and may differentiate AD from its most relevant differential diagnoses. Tau phosphorylated at threonine 231 (p-tau(231)) shows excellent differentiation between AD and FTD, whereas serine 181 (p-tau(181)) enhances accurate differentiation between AD and dementia with Lewy bodies. Moreover, p-tau(231) levels decline with disease progression, correlating with cognitive performance at baseline. Total tau (t-tau) is regarded as a general marker of neurodegeneration for evaluation in future population-based studies. p-tau(231) and p-tau(181) yield excellent discrimination between AD and non-AD dementias including FTD, exceeding the differential diagnostic and prognostic accuracy of t-tau. Therefore, p-tau is a core biological marker candidate for future evaluation in large national and international multicenter networks. Copyright (C) 2004 S. Karger AG, Basel
Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease
No full textHippocampus volumetry currently is the best-established imaging biomarker for AD. However, the effect of multicenter acquisition on measurements of hippocampus volume needs to be explicitly considered when it is applied in large clinical trials, for example by using mixed-effects models to take the clustering of data within centers into account. The marker needs further validation in respect of the underlying neurobiological substrate and potential confounds such as vascular disease, inflammation, hydrocephalus, and alcoholism, and with regard to clinical outcomes such as cognition but also to demographic and socioeconomic outcomes such as mortality and institutionalization. The use of hippocampus volumetry for risk stratification of predementia study samples will further increase with the availability of automated measurement approaches. An important step in this respect will be the development of a standard hippocampus tracing protocol that harmonizes the large range of presently available manual protocols. In the near future, regionally differentiated automated methods will become available together with an appropriate statistical model, such as multivariate analysis of deformation fields, or techniques such as cortical-thickness measurements that yield a meaningful metrics for the detection of treatment effects. More advanced imaging protocols, including DTI, DSI, and functional MRI, are presently being used in monocenter and first multicenter studies. In the future these techniques will be relevant for the risk stratification in phase IIa type studies (small proof-of-concept trials). By contrast, the application of the broader established structural imaging biomarkers, such as hippocampus volume, for risk stratification and as surrogate end point is already today part of many clinical trial protocols. However, clinical care will also be affected by these new technologies. Radiologic expert centers already offer “dementia screening” for well-off middle-aged people who undergo an MRI scan with subsequent automated, typically VBM-based analysis, and determination of z-score deviation from a matched control cohort. Next-generation scanner software will likely include radiologic expert systems for automated segmentation, deformation-based morphometry, and multivariate analysis of anatomic MRI scans for the detection of a typical AD pattern. As these developments will start to change medical practice, first for selected subject groups that can afford this type of screening but later eventually also for other cohorts, clinicians must become aware of the potentials and limitations of these technologies. It is decidedly unclear to date how a middle-aged cognitively intact subject with a seemingly AD-positive MRI scan should be clinically advised. There is no evidence for individual risk prediction and even less for specific treatments. Thus, the development of preclinical diagnostic imaging poses not only technical but also ethical problems that must be critically discussed on the basis of profound knowledge. From a neurobiological point of view, the main determinants of cognitive impairment in AD are the density of synapses and neurons in distributed cortical and subcortical networks. MRI-based measures of regional gray matter volume and associated multivariate analysis techniques of regional interactions of gray matter densities provide insight into the onset and temporal dynamics of cortical atrophy as a close proxy for regional neuronal loss and a basis of functional impairment in specific neuronal networks. From the clinical point of view, clinicians must bear in mind that patients do not suffer from hippocampus atrophy or disconnection but from memory impairment, and that dementia screening in asymptomatic subjects should not be used outside of clinical studies
Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data
No full textDiffusion 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
Feasibility of a standard cognitive assessment in European academic memory clinics
No full textIntroduction: Standardized cognitive assessment would enhance diagnostic reliability across memory clinics. An expert consensus adapted the Uniform Dataset (UDS)-3 for European centers, the clinician's UDS (cUDS). This study assessed its implementation acceptability and feasibility. Methods: We developed a survey investigating barriers, facilitators, and willingness to implement the cUDS. With a mixed-methods design, we analyzed data from academic memory clinics. Results: Seventy-eight percent of responding clinicians were experienced neuropsychologists/psychologists and 22% were medical specialists coming from 18 European countries. Sixty-five percent clinicians were willing to implement cUDS. General barriers related to implementation (43%) and clinical-methodological domains (21%). Favorable clinicians reported finances (15%) and digitalization (9%) as facilitating, but unavailability of local norms (23%) as hindering. Unfavorable clinicians reported logistical (23%) and time issues (18%). Discussion: Despite challenges, data showed moderate clinicians' acceptability and requirements to improve feasibility. Nonetheless, these results come from academic clinicians. The next steps will require feasibility evaluation in non-academic contexts
Atrophy and structural covariance of the cholinergic basal forebrain in primary progressive aphasia
No full textAbstractPrimary progressive aphasia (PPA) is characterized by profound destruction of cortical language areas. Anatomical studies suggest an involvement of cholinergic basal forebrain (BF) in PPA syndromes, particularly in the area of the nucleus subputaminalis (NSP). Here we aimed to determine the pattern of atrophy and structural covariance as a proxy of structural connectivity of BF nuclei in PPA variants. We studied 62 prospectively recruited cases with the clinical diagnosis of PPA and 31 healthy older control participants from the cohort study of the German consortium for frontotemporal lobar degeneration (FTLD). We determined cortical and BF atrophy based on high-resolution magnetic resonance imaging (MRI) scans. Patterns of structural covariance of BF with cortical regions were determined using voxel-based partial least square analysis. We found significant atrophy of total BF and BF subregions in PPA patients compared with controls [F(1, 82) = 20.2, p < .001]. Atrophy was most pronounced in the NSP and the posterior BF, and most severe in the semantic variant and the nonfluent variant of PPA. Structural covariance analysis in healthy controls revealed associations of the BF nuclei, particularly the NSP, with left hemispheric predominant prefrontal, lateral temporal, and parietal cortical areas, including Broca's speech area (p < .001, permutation test). In contrast, the PPA patients showed preserved structural covariance of the BF nuclei mostly with right but not with left hemispheric cortical areas (p < .001, permutation test). Our findings agree with the neuroanatomically proposed involvement of the cholinergic BF, particularly the NSP, in PPA syndromes. We found a shift from a structural covariance of the BF with left hemispheric cortical areas in healthy aging towards right hemispheric cortical areas in PPA, possibly reflecting a consequence of the profound and early destruction of cortical language areas in PPA
Regional distribution of white matter hyperintensities in vascular dementia, Alzheimer's disease and healthy aging
No full textBackground: White matter hyperintensities (WMH) on MRI scans indicate lesions of the subcortical fiber system. The regional distribution of WMH may be related to their pathophysiology and clinical effect in vascular dementia (VaD), Alzheimer's disease (AD) and healthy aging. Methods: Regional WMH volumes were measured in MRI scans of 20 VaD patients, 25 AD patients and 22 healthy elderly subjects using FLAIR sequences and surface reconstructions from a three-dimensional MRI sequence. Results: The intraclass correlation coefficient for interrater reliability of WMH volume measurements ranged between 0.99 in the frontal and 0.72 in the occipital lobe. For each cerebral lobe, the WMH index, i.e. WMH volume divided by lobar volume, was highest in VaD and lowest in healthy controls. Within each group, the WMH index was higher in frontal and parietal lobes than in occipital and temporal lobes. Total WMH index and WMH indices in the frontal lobe correlated significantly with the MMSE score in VaD. Category fluency correlated with the frontal lobe WMH index in AD, while drawing performance correlated with parietal and temporal lobe WMH indices in VaD. Conclusions: A similar regional distribution of WMH between the three groups suggests a common (vascular) pathogenic factor leading to WMH in patients and controls. Our findings underscore the potential of regional WMH volumetry to determine correlations between subcortical pathology and cognitive impairment. Copyright (C) 2004 S. Karger AG, Basel
Regional gray matter atrophy in frontotemporal dementia evaluated by voxel-based MRI morphometry
No full textRessourcensicherheit strategischer Metalle: Entwicklung von Strategien zur Erhöhung der Ressourceneffizienz von Lithium
No full textVerfügbarkeit knapper metallischer Rohstoffe und innovative Möglichkeiten zu ihrer Substitution
No full text- …
