Stefan J. Teipel

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration

Summary Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).

Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives

Advances in therapeutic strategies for Alzheimers disease that lead to even small delays in onset and progression of the condition would significantly reduce the global burden of the disease. To effectively test compounds for Alzheimers disease and bring therapy to individuals as early as possible there is an urgent need for collaboration between academic institutions, industry and regulatory organizations for the establishment of standards and networks for the identification and qualification of biological marker candidates. Biomarkers are needed to monitor drug safety, to identify individuals who are most likely to respond to specific treatments, to stratify presymptomatic patients and to quantify the benefits of treatments. Biomarkers that achieve these characteristics should enable objective business decisions in portfolio management and facilitate regulatory approval of new therapies.

Core candidate neurochemical and imaging biomarkers of Alzheimer's disease*

In the earliest clinical stages of Alzheimers disease (AD) when symptoms are mild, clinical diagnosis can be difficult. AD pathology most likely precedes symptoms. Biomarkers can serve as early diagnostic indicators or as markers of preclinical pathologic change. Candidate biomarkers derived from structural and functional neuroimaging and those measured in cerebrospinal fluid (CSF) and plasma show the greatest promise. Unbiased exploratory approaches, eg, proteomics or cortical thickness analysis, could yield novel biomarkers. The objective of this article was to review recent progress in selected imaging and neurochemical biomarkers for early diagnosis, classification, progression, and prediction of AD.

Intravenous immunoglobulins containing antibodies against β-amyloid for the treatment of Alzheimer’s disease

Objective: Active or passive immunisation can mitigate plaque pathology in murine models of Alzheimer’s disease (AD). Recently, it has been shown that antibodies against β-amyloid (Aβ) are present in human immunoglobulin preparations (IVIgG), which specifically recognise and inhibit the neurotoxic effects of Aβ. This study reports the results from a pilot study using IVIgG in patients with AD. Methods: Five patients with AD were enrolled and received monthly IVIgG over a 6 month period. Efficacy assessment included total Aβ/Aβ1–42 measured in the CSF/serum as well as effects on cognition (ADAS-cog; CERAD) at baseline and at 6 months following IVIgG. Results: Following IVIgG, total Aβ levels in the CSF decreased by 30.1% (17.3–43.5%) compared to baseline (p<0.05). Total Aβ increased in the serum by 233% (p<0.05). No significant change was found in Aβ1–42 levels in the CSF/serum. Using ADAS-cog, an improvement of 3.7±2.9 points was detected. Scores in the MMSE were essentially unchanged (improved in four patients, stable in one patient) following IVIgG compared to baseline. Conclusion: Although the sample size of this pilot study is too small to draw a clear conclusion, the results of this pilot study provide evidence for a more detailed investigation of IVIgG for the treatment of AD.

Value of CSF β-amyloid1-42 and tau as predictors of Alzheimer's disease in patients with mild cognitive impairment

Subjects with mild cognitive impairment (MCI) are at a high risk of developing clinical Alzheimers disease (AD). We asked to what extent the core biomarker candidates cerebro-spinal fluid (CSF) β-amyloid1–42 (Aβ1–42) and CSF tau protein concentrations predict conversion from MCI to AD. We studied 52 patients with MCI, 93 AD patients, and 10 healthy controls (HC). The MCI group was composed of 29 patients who had converted to AD during follow-up, and of 23 patients who showed no cognitive decline. CSF Aβ1–42 and tau protein levels were assessed at baseline in all subjects, using enzyme-linked immunosorbent assays. For assessment of sensitivity and specificity, we used independently established reference values for CSF Aβ1–42 and CSF tau. The levels of CSF tau were increased, whereas levels of Aβ1–42 were decreased in MCI subjects. Aβ1–42 predicted AD in converted MCI with a sensitivity of 59% and a specificity of 100% compared to HC. Tau yielded a greater sensitivity of 83% and a specificity of 90%. In a multiple Cox regression analysis within the MCI group, low baseline levels of Aβ1–42, but not other predictor variables (tau protein, gender, age, apolipoprotein E ɛ4 carrier status, Mini Mental Status Examination score, observation time, antidementia therapy), correlated with conversion status (P<0.05). Our findings support the notion that CSF tau and Aβ1–42 may be useful biomarkers in the early identification of AD in MCI subjects.

CSF tau protein phosphorylated at threonine 231 correlates with cognitive decline in MCI subjects

Abstract—In this longitudinal study of 77 patients with mild cognitive impairment (MCI), the authors analyzed whether levels of tau protein phosphorylated at threonine 231 (p-tau231) in CSF correlate with progression of cognitive decline. High CSF p-tau231 levels at baseline, but not total tau protein levels, correlated with cognitive decline and conversion from MCI to AD. Independently, old age and APOE-&egr;4 carrier status were predictive as well. Our data indicate that an increased p-tau231 level is a potential risk factor for cognitive decline in patients with MCI.

Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls.

We investigated the potential of fully automated measurements of cortical thickness to reproduce the clinical diagnosis in Alzheimers Disease (AD) using 19 patients and 17 healthy controls. Thickness maps were analyzed using three different discriminant techniques to separate patients from controls. All analyses were performed using leave-one-out cross-validation to avoid overtraining of the discriminants. The results show regionally variant patterns of discrimination ability, with over 90% accuracy obtained in the medial temporal lobes and other limbic structures. Multivariate discriminant analysis produced 100% accuracy with six different combinations, all involving the parahippocampal gyrus. We therefore propose automated measurements of cortical thickness as a tool to improve the clinical diagnosis of probable AD, as well as a research method to gain unique insight into the etiology of cortical pathology in the disease.

Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment.

Automated deformation-based analysis of MRI scans can be used to detect specific pattern of brain atrophy in Alzheimers disease (AD), but it still lacks an established model to derive the individual risk of AD in at-risk subjects, such as patients with mild cognitive impairment (MCI). We applied principal component analysis to deformation maps derived from MRI scans of 32 AD patients, 18 elderly healthy controls and 24 MCI patients. Principal component scores were used to discriminate between AD patients and controls and between MCI converters and MCI non-converters. We found a significant regional pattern of atrophy (p<0.001) in medial temporal lobes, neocortical association areas, thalamus and basal ganglia and corresponding widening of cerebrospinal fluid (CSF) spaces (p<0.001) in AD patients compared to controls. Accuracy was 81% for CSF- and 83% for brain-based deformation maps to separate AD patients from controls. Nine out of 24 MCI patients converted to AD during clinical follow-up. Discrimination between MCI converters and non-converters reached 80% accuracy based on CSF maps and 73% accuracy based on brain maps. In a logistic regression model, principal component scores based on CSF maps predicted clinical outcome in MCI patients even after controlling for age, gender, MMSE score and time of follow-up. Our findings indicate that multivariate network analysis of deformation maps detects typical features of AD pathology and provides a powerful tool to predict conversion into AD in non-demented at risk patients.

Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimers disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

Multivariate network analysis of fiber tract integrity in Alzheimer’s disease

Axonal and dendritic integrity is affected early in Alzheimers disease (AD). Studies using region of interest or voxel-based analysis of diffusion tensor imaging data found significant decline of fractional anisotropy, a marker of fiber tract integrity, in selected white matter areas. We applied a multivariate network analysis based on principal component analysis to fractional anisotropy maps derived from diffusion-weighted scans from 15 AD patients, and 14 elderly healthy controls. Fractional anisotropy maps were obtained from an EPI diffusion sequence using parallel imaging to reduce distortion artifacts. We used high-dimensional image warping to control for partial volume effects due to white matter atrophy in AD. We found a significant regional pattern of fiber changes (p < 0.01) indicating that the integrity of intracortical projecting fiber tracts (including corpus callosum, cingulum and fornix, and frontal, temporal and occipital lobe white matter areas) was reduced, whereas extracortical projecting fiber tracts, including the pyramidal and extrapyramidal systems and somatosensory projections, were relatively preserved in AD. Effects of a univariate analysis were almost entirely contained within the multivariate effect. Our findings illustrate the use of a multivariate approach to fractional anisotropy data that takes advantage of the highly organized structure of anisotropy maps, and is independent of multiple comparison correction and partial volume effects. In agreement with post-mortem evidence, our study demonstrates dissociation between intracortical and extracortical projecting fiber systems in AD in the living human brain.