Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0220723 (PCA)
 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The scaled subprofile model (SSM)(1-4) is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data(2,5,6). Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors(7,8). Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects(5,6). Cross-validation within the derivation set can be performed using bootstrap resampling techniques(9). Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets(10). Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation(11). These standardized values can in turn be used to assist in differential diagnosis(12,13) and to assess disease progression and treatment effects at the network level(7,14-16). We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
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PMID:Identification of disease-related spatial covariance patterns using neuroimaging data. 2385 55

Purkinje cell cytoplasmic antibody type 1 (PCA-1), or anti-Yo, is the most frequently detected autoantibody in paraneoplastic cerebellar degeneration (PCD). The vast majority of cases of anti-Yo PCD, however, occur in females over 60 years old and are associated with gynecologic tumors. Only 10 cases have been reported in males, and only 2 were associated with cancer of the lung. Here we describe the youngest known case of PCA-1 positive PCD in a male, whose lung tumor was undetectable even on FDG-PET.
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PMID:Anti-yo associated paraneoplastic cerebellar degeneration in a man with large cell cancer of the lung. 2416 48

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.
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PMID:Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features. 2591 50

Tau protein aggregations are a hallmark of pathology in the amyloid-associated Alzheimer's disease and some forms of non-amyloid-associated fronto-temporal lobar degeneration (FTLD). In recent years, several tracers for in-vivo tau imaging are under evaluation. This study investigates the ability of Flortaucipir PET to not only assess tau-positivity but in addition also to differentiate between amyloid-positive and -negative forms of neurodegeneration based on different Flortaucipir PET signatures. Methods: Flortaucipir PET data of 35 patients with amyloid-positive, 19 patients with amyloid-negative forms of neurodegeneration and 17 healthy controls were included in a data-driven scaled subprofile modelling/principal component analysis (SSM/PCA) identifying spatial covariance patterns. SSM/PCA component pattern expression strengths (PES) were tested for their ability to predict amyloid status in a receiver operating characteristic analysis and validated with a leave-one-out approach. Results: PES predicted amyloid status with a sensitivity of 0.94 and a specificity of 0.83. A support vector machine classification based on PES in two different SSM/PCA components yielded a prediction accuracy of 98%. Anatomically, prediction performance was driven by parietooccipital grey matter in amyloid-positive patients vs. predominant white matter binding in amyloid-negative neurodegeneration. Conclusion: SSM/PCA derived binding patterns of Flortaucipir differentiate between amyloid positive and negative neurodegenerative diseases with high accuracy. Flortaucipir PET alone may convey additional information equivalent to an amyloid PET. Together with a perfusion-weighted early-phase acquisition (FDG-PET equivalent), a single scan potentially contains comprehensive information on amyloid (A), tau (T) and neurodegeneration (N) status as required by recent biomarker classification algorithms (A/T/N).
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PMID:One stop shop: Flortaucipir PET differentiates amyloid positive and negative forms of neurodegenerative diseases. 3262 Jul 4