Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
YNICL Journal 2020 Journal Article
F]FDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD.
YNICL Journal 2019 Journal Article
BACKGROUND: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. METHODS: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). RESULTS: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. CONCLUSION: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.
YNICL Journal 2017 Journal Article
Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.
YNICL Journal 2016 Journal Article
Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.
YNIMG Journal 2011 Journal Article
Assessment of temporal lobe atrophy from magnetic resonance images is a part of clinical guidelines for the diagnosis of prodromal Alzheimer's disease. As hippocampus is known to be among the first areas affected by the disease, fast and robust definition of hippocampus volume would be of great importance in the clinical decision making. We propose a method for computing automatically the volume of hippocampus using a modified multi-atlas segmentation framework, including an improved initialization of the framework and the correction of partial volume effect. The method produced a high similarity index, 0. 87, and correlation coefficient, 0. 94, with semi-automatically generated segmentations. When comparing hippocampus volumes extracted from 1. 5T and 3T images, the absolute value of the difference was low: 3. 2% of the volume. The correct classification rate for Alzheimer's disease and cognitively normal cases was about 80% while the accuracy 65% was obtained for classifying stable and progressive mild cognitive impairment cases. The method was evaluated in three cohorts consisting altogether about 1000 cases, the main emphasis being in the analysis of the ADNI cohort. The computation time of the method is about 2 minutes on a standard laptop computer. The results show a clear potential for applying the method in clinical practice.
YNIMG Journal 2011 Journal Article
In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and compared to the conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N =772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86. 0% for the classification of control and AD subjects, and 72. 1% for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM.
YNIMG Journal 2010 Journal Article
We propose a new method of measuring atrophy of brain structures by simultaneously segmenting longitudinal magnetic resonance (MR) images. In this approach a 4D graph is used to represent the longitudinal data: edges are weighted based on spatial and intensity priors and connect spatially and temporally neighboring voxels represented by vertices in the graph. Solving the min-cut/max-flow problem on this graph yields the segmentation for all timepoints in a single step. By segmenting all timepoints simultaneously, a consistent and atrophy-sensitive segmentation is obtained. The application to hippocampal atrophy measurement in 568 image pairs (Baseline and Month 12 follow-up) as well as 362 image triplets (Baseline, Month 12, and Month 24) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) confirms previous findings for atrophy in Alzheimer's disease (AD) and healthy aging. Highly significant correlations between hippocampal atrophy and clinical variables (Mini Mental State Examination, MMSE and Clinical Dementia Rating, CDR) were found and atrophy rates differ significantly according to subjects' ApoE genotype. Based on one year atrophy rates, a correct classification rate of 82% between AD and control subjects is achieved. Subjects that converted from Mild Cognitive Impairment (MCI) to AD after the period for which atrophy was measured (i. e. , after the first 12months) and subjects for whom conversion is yet to be identified were discriminated with a rate of 64%, a promising result with a view to clinical application. Power analysis shows that 67 and 206 subjects are needed for the AD and MCI groups respectively to detect a 25% change in volume loss with 80% power and 5% significance.
YNIMG Journal 2007 Journal Article
Human striatum is involved in the regulation of movement, reinforcement, learning, reward, cognitive functioning, and addiction. Previous classical volumetric MRI studies have implicated age-, disease- and medication-related changes in striatal structures. Yet, no studies to date have addressed the effects of these factors on the shape variability and local structural alterations in the striatum. The local alterations may provide meaningful additional information in the context of functional neuroanatomy and brain connectivity. We developed image analysis methodology for the measurement of the volume and local shape variability of the human striatum. The method was applied in a group of 43 healthy controls to study the effects of age and gender on striatal shape variability. In the volume analysis, the volume of the striatum was normalized using the volume of the whole brain. In the local shape analysis, the deviations from a mean surface were studied for each surface point using high-dimensional mapping. Also, discriminant functions were constructed from a statistical shape model. The accuracy and reproducibility of the methods used were evaluated. The results confirmed that the volume of the striatum decreases as a function of age. However, the volume decrease was not uniform and age-related shape differences were observed in several subregions of the human striatum whereas no local gender differences were seen. Examination of the variability of striatal shape in the healthy population will pave the way for applying this method in clinical settings. This method will be particularly useful for investigating neuropsychiatric disorders that are associated with subtle morphological alterations of the brain, such as schizophrenia.