Automatic volumetry on MR brain images can support diagnostic decision making.

Background: Diagnostic decisions in clinical imaging currently rely almost exclusively on visual image interpretation. This can lead to uncertainty, for example in dementia disease, where some of the changes resemble those of normal ageing. We hypothesized that extracting volumetric data from patients MR brain images, relating them to reference data and presenting the results as a colour overlay on the grey scale data would aid diagnostic readers in classifying dementia disease versus normal ageing. Methods: A proof-of-concept forced-choice reader study was designed using MR brain images from 36 subjects. Images were segmented into 43 regions using an automatic atlas registration-based label propagation procedure. Seven subjects had clinically probable AD, the remaining 29 of a similar age range were used as controls. Seven of the control subject data sets were selected at random to be presented along with the seven AD datasets to two readers, who were blinded to all clinical and demographic information except age and gender. Readers were asked to review the grey scale MR images and to record their choice of diagnosis (AD or non-AD) along with their confidence in this decision. Afterwards, readers were given the option to switch on a false-colour overlay representing the relative size of the segmented structures. Colorization was based on the size rank of the test subject when compared with a reference group consisting of the 22 control subjects who were not used as review subjects. The readers were then asked to record whether and how the additional information had an impact on their diagnostic confidence. Results: The size rank colour overlays were useful in 18 of 28 diagnoses, as determined by their impact on readers diagnostic confidence. A not useful result was found in 6 of 28 cases. The impact of the additional information on diagnostic confidence was significant (p < 0.02). Conclusion: Volumetric anatomical information extracted from brain images using automatic segmentation and presented as colour overlays can support diagnostic decision making. © 2008 Heckemann et al; licensee BioMed Central Ltd.Published versio

5-HTTLPR genotype influences amygdala volume

Functional imaging studies in healthy individuals revealed an association between 5-HTTLPR genotype and neuronal activity in the amygdala. The aim of this study was firstly to investigate a possible overall impact of the 5-HTTLPR on amygdala volume in patients with bipolar disorder and healthy individuals and secondly to test a diagnosis specific influence of the 5-HTTLPR on amygdala volume. We performed a region of interest analysis of amygdala volume in 37 patients with bipolar I disorder and 37 healthy control subjects. The 5-HTTLPR genotype of each proband was determined and the subjects were separated according to 5-HTTLPR genotype and for statistical analyses the groups SS and SL were combined and compared with the group LL. This study shows that carriers of the short allele (SL or SS) of the 5-HTTLPR polymorphism exhibit a relatively increased volume of the right amygdala compared to homozygous L-allele carriers irrespective of diagnosis status. However, further analyses with the factors genotype and diagnosis were not able to reproduce this result. The present findings are consistent with the view that the 5-HTTLPR polymorphism might modulate neuronal size or number in the amygdala. It would be worthwhile investigating the relationship between serotonin transporter function and amygdala function and volume in further studies

Diagnosis of Alzheimer's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera

After decades of Alzheimer's disease (AD) research, the development of a definitive diagnostic test for this disease has remained elusive. The discovery of blood-borne biomarkers yielding an accurate and relatively non-invasive test has been a primary goal. Using human protein microarrays to characterize the differential expression of serum autoantibodies in AD and non-demented control (NDC) groups, we identified potential diagnostic biomarkers for AD. The differential significance of each biomarker was evaluated, resulting in the selection of only 10 autoantibody biomarkers that can effectively differentiate AD sera from NDC sera with a sensitivity of 96.0% and specificity of 92.5%. AD sera were also distinguishable from sera obtained from patients with Parkinson's disease and breast cancer with accuracies of 86% and 92%, respectively. Results demonstrate that serum autoantibodies can be used effectively as highly-specific and accurate biomarkers to diagnose AD throughout the course of the disease

Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology

BACKGROUND: Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer’s Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS: It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS: Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS: All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET)

Abnormal function of potassium channels in platelets of patients with Alzheimer's disease.

BACKGROUND: Reports of abnormalities of potassium-channel function in various cultured cells of Alzheimer's disease patients led us to attempt to characterise the pharmacological characteristics of the abnormal channel. METHODS: We studied platelets from 14 patients with Alzheimer-type dementia and 14 non-demented controls matched for age and sex. The effects of specific inhibitors of K+ channels on the efflux of rubidium-86 ions, a radioactive analogue of K+, from the platelets were measured. FINDINGS: Normal platelets contain three types of K+ channel, sensitive to the inhibitory actions of apamin (small-conductance calcium-dependent potassium channels), charybdotoxin (of less specificity, but probably intermediate-conductance calcium-dependent K+ channels), and alpha-dendrotoxin (voltage-sensitive K+ channels). However, 8Rb+ efflux from the platelets of patients with Alzheimer-type dementia was not inhibited by either apamin or charybdotoxin. By contrast, inhibition by alpha-dendrotoxin did occur. INTERPRETATION: Our results suggest that calcium-dependent K+ channels in platelets are selectively impaired in Alzheimer's disease. A similar abnormality in neurons could contribute to the pathophysiology of the disorder