Dataset Information

Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.

ABSTRACT: Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

SUBMITTER: Salvatore C

PROVIDER: S-EPMC4555016 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.

Salvatore Christian C Cerasa Antonio A Battista Petronilla P Gilardi Maria C MC Quattrone Aldo A Castiglioni Isabella I

Frontiers in neuroscience 20150901

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the differe ...[more]

PMID: 26388719

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Project description:Early detection is critical for effective management of Alzheimer's disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb efficiency in automated feature learning with the use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown to be beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL, i.e., the CNN-EL approach, to identify subjects with MCI or AD using MRI: i.e., classification between (1) AD and healthy cognition (HC), (2) MCIc (MCI patients who will convert to AD) and HC, and (3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified fivefold cross-validation method for 10 times. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice sets, transformed into the standard Montreal Neurological Institute (MNI) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. HC, MCIc vs. HC, and MCIc vs. MCInc, comparable to previous reports and a 3D deep learning approach (3D-SENet) based on a more state-of-the-art and popular Squeeze-and-Excitation Networks model using channel attention mechanism. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. Further research is needed to examine the clinical implication of the finding, capability of the advocated CNN-EL approach to help understand and evaluate an individual subject's disease status, symptom burden and progress, and the generalizability of the advocated CNN-EL approach to locate the most discriminable brain regions in the detection of other brain disorders such as schizophrenia, autism, and severe depression, in a data-driven way.

Project description:PurposeThis study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.MethodsBrain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.ResultsAfter feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.ConclusionThe combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

Project description:A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations depend upon the expected benefits of a treatment (effect size), the accuracy of measurement of the primary outcome, and the level of power specified by the investigators. In this study, we show that radiomic models, which leverage complex brain MRI patterns and machine learning, can be utilized in clinical trials with protocols that incorporate baseline MR imaging to significantly increase statistical power to detect treatment effects. Akin to the historical control paradigm, we propose to utilize a radiomic prediction model to generate a pseudo-control sample for each individual in the trial of interest. Because the variability of expected outcome across patients can mask our ability to detect treatment effects, we can increase the power to detect a treatment effect in a clinical trial by reducing that variability through using radiomic predictors as surrogates. We illustrate this method with simulations based on data from two cohorts in different neurologic diseases, Alzheimer's disease and glioblastoma multiforme. We present sample size requirements across a range of effect sizes using conventional analysis and models that include a radiomic predictor. For our Alzheimer's disease cohort, at an effect size of 0.35, total sample size requirements for 80% power declined from 246 to 212 for the endpoint cognitive decline. For our glioblastoma multiforme cohort, at an effect size of 1.65 with the endpoint survival time, total sample size requirements declined from 128 to 74. This methodology can decrease the required sample sizes by as much as 50%, depending on the strength of the radiomic predictor. The power of this method grows with increased accuracy of radiomic prediction, and furthermore, this method is most helpful when treatment effect sizes are small. Neuroimaging biomarkers are a powerful and increasingly common suite of tools that are, in many cases, highly predictive of disease outcomes. Here, we explore the possibility of using MRI-based radiomic biomarkers for the purpose of improving statistical power in clinical trials in the contexts of brain cancer and prodromal Alzheimer's disease. These methods can be applied to a broad range of neurologic diseases using a broad range of predictors of outcome to make clinical trials more efficient.

Project description:BackgroundUsed as contrast agents for brain magnetic resonance imaging (MRI), markers for beta-amyloid deposits might allow early diagnosis of Alzheimer's disease (AD). We evaluated the cost-effectiveness of such a diagnostic test, MRI+CLP (contrastophore-linker-pharmacophore), should it become clinically available.Methodology/principal findingsWe compared the cost-effectiveness of MRI+CLP to that of standard diagnosis using currently available cognition tests and of standard MRI, and investigated the impact of a hypothetical treatment efficient in early AD. The primary analysis was based on the current French context for 70-year-old patients with Mild Cognitive Impairment (MCI). In alternative "screen and treat" scenarios, we analyzed the consequences of systematic screenings of over-60 individuals (either population-wide or restricted to the ApoE4 genotype population). We used a Markov model of AD progression; model parameters, as well as incurred costs and quality-of-life weights in France were taken from the literature. We performed univariate and probabilistic multivariate sensitivity analyses. The base-case preferred strategy was the standard MRI diagnosis strategy. In the primary analysis however, MRI+CLP could become the preferred strategy under a wide array of scenarios involving lower cost and/or higher sensitivity or specificity. By contrast, in the "screen and treat" analyses, the probability of MRI+CLP becoming the preferred strategy remained lower than 5%.Conclusions/significanceIt is thought that anti-beta-amyloid compounds might halt the development of dementia in early stage patients. This study suggests that, even should such treatments become available, systematically screening the over-60 population for AD would only become cost-effective with highly specific tests able to diagnose early stages of the disease. However, offering a new diagnostic test based on beta-amyloid markers to elderly patients with MCI might prove cost-effective.

Project description:Alzheimer's disease (AD) has an insidious onset and lacks clear early diagnostic markers, and by the time overt dementia symptoms appear, the disease is already in the mid-to-late stages. The search for early diagnostic markers of AD may open a critical window for Alzheimer's treatment and facilitate early intervention to slow the progression of AD. In this study, we aimed to explore the imaging markers for early diagnosis of AD through the combined application of structural magnetic resonance imaging (sMRI), resting-state functional magnetic resonance imaging (rs-fMRI), and 1H-magnetic resonance spectroscopy (1H-MRS) multimodal magnetic resonance imaging (MRI) techniques at the animal experimental level, with the aim to provide a certain reference for early clinical diagnosis of AD. First, sMRI scans were performed on 4-month-old amyloid beta precursor protein/presenilin 1 (APP/PS1) transgenic AD model mice and wild type mice of the same litter using a 7.0 T animal MRI scanner to analyze the differential brain regions with structural changes in the gray matter of the brain by voxel-based morphometry (VBM). Next, rs-fMRI scans were performed to analyze the differential brain regions between groups for local spontaneous brain activity and functional connectivity (FC) between brain regions. Finally, 1H-MRS scans were performed to quantify and analyze intergroup differences in the relative concentrations of different metabolites within regions of interest (cortex and hippocampus). Compared with wild type mice, the volume of the left hippocampus, and right olfactory bulb of APP/PS1 transgenic AD model mice were reduced, the functional activity of the bilateral hippocampus, right piriform cortex and right caudate putamen was reduced, the functional network connectivity of the hippocampus was impaired, and the relative content of N-acetylaspartate (NAA)in the hippocampus was decreased. In addition, this study found that imaging changes in olfactory-related brain regions were closely associated with AD diagnosis, and these findings may provide some reference for the early diagnosis of AD.

Project description:BackgroundMultiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.MethodsSystematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms.ResultsOf the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.ConclusionsML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.

Dataset Information

Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.

Publications

Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.

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