Patent Publication Number: US-2021186409-A1

Title: Biomarker for early detection of alzheimer disease

Description:
COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent Office patent file or records, but otherwise reserves all copyright rights whatsoever. 
     FIELD OF THE INVENTION 
     The present disclosure relates to a method for predicting Alzheimer&#39;s Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients by providing biomarker. 
     BACKGROUND OF THE INVENTION 
     Cognitive decline is one of the most concerning behavioral symptoms such as AD. Seamless changes in the AD continuum take years if not decades to progress from normal cognition (NC) to MCI, with gradual evolution of clinically probable AD to confirmed AD. Early detection and accurate diagnosis of AD require careful medical assessment, including patient history as well as physical and neurological examinations. 
     The MMSE is a brief cognitive assessment tool commonly used to screen for dementia. Neuroimaging modalities such as MRI that provides biologic evidences which cognitive decline is neurodegenerative as they contain detailed information regarding the subcortical structures, good contrast of the gray matter, and the integrity of the brain tissue. 
     Machine learning techniques have been widely used over the past few years for the analysis of biomedical images, and more particularly to frameworks known as deep learning, which is based on artificial neural networks, which has received increased attention because of its remarkable success in predicting various clinical outcomes of interest. The convolutional neural network (CNN) models are considered to be efficient deep learning techniques for object recognition and classification. 
     However, MRI scans are characterized as complex, unstructured data structures and thus require sophisticated means by which to perform an efficiently quantitative analysis. The most common neurological examination for predicting AD is to monitor the overall volume of Hippocampus from MRI scans. 
     Accordingly, there&#39;s a need to combined the structural information derived from neuroimaging data (i.e., MRI scans) and functional information (i.e., MMSE) derived from screening tools and cognitive assessment methods can result in a better combined metric of predicting AD. 
     SUMMARY 
     According to an aspect of the present invention, a method for predicting AD is provided to create a predictive model of AD by considering detailed structural and anatomic information contained within the MRI images as well as cognitive function assessed using the MMSE. 
     According to an embodiment of the present invention, the method for predicting Alzheimer&#39;s Disease comprising acts providing MRI images containing the anatomical structure of Hippocampus and MMSE data as a training data set; training a processor using the training data set; receiving MRI images and MMSE data as a testing data set from a target; and classifying the test data by the trained processor to include aggregating predictions. 
     The training comprising acts of proceeding an MRI image preprocessing to determine volume of a Hippocampus of each MRI images, and segmenting the Hippocampus into sections; determining surface areas for each section of each Hippocampus in MRI images; determining a Ratio of Principle Curvature (RPC) for each section of each Hippocampus; and selecting candidate parameters as inputs to iteratively train an iterative neural network in the processor, wherein the candidate parameters are selected from the volume of Hippocampus, the surface areas and the PRC of sections of Hippocampus, and scores of MMSE data. 
     According to an aspect of the present invention, a method quantifying the anatomical structure of a Hippocampus is provided, which segments the Hippocampus into sections and quantifies them into biomarkers (factoring features) for predicting Alzheimer&#39;s Disease. 
     According to an embodiment of the present invention, the method for quantifying the anatomical structure of a Hippocampus comprising acts of receiving MRI images of a brain scan; preprocessing the MRI images to identify the Hippocampus for determining volumes of each identified Hippocampus; segmenting each identified Hippocampus into multiple sections; reconstructing the each identified Hippocampus with sections to build a 3D Hippocampus model; smoothing the surface area of the 3D Hippocampus model; calculating a surface area of each sections of each identified Hippocampus; and calculating a maximum curvature and a minimum curvature of each sections of each identified Hippocampus to determine an RPC. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the invention are described in more details hereinafter with reference to the drawings, in which: 
         FIG. 1  is a flowchart illustrating a method for predicting Alzheimer&#39;s Disease (AD) in accordance with an embodiment of the present invention; 
         FIG. 2  is an exemplary diagram illustrating the observing factors in the AD progression; 
         FIG. 3A  is a flowchart illustrating the acts of S 111  with corresponding MRI images for each of steps in accordance with an embodiment of the present invention; 
         FIG. 3B  is an exemplary diagram illustrating the sections of the Hippocampus; 
         FIG. 4A  is an exemplary diagram of a 3D Hippocampus model in accordance with an embodiment of the present invention; 
         FIG. 4B  is a flowchart illustrating the step of S 112  in accordance with an embodiment of the present invention; and 
         FIG. 5  is an exemplary diagram of an iterative neural network in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, For simplicity and illustrative purposes, the present disclosure is described by referring mainly to an example thereof. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be readily apparent however, that the present disclosure may be practiced without limitation to these specific details. In other instances, some methods and structures have not been described in detail so as not to unnecessarily obscure the present disclosure. As used herein, the terms “a” and “an” are intended to denote at least one of particular elements, the term “includes/comprises” means includes but not limited to, the term “including/comprising” means including but not limited to, and the term “based on” means based at least in part on. 
     With reference to  FIGS. 1 and 2 ,  FIG. 1  is a flowchart illustrating a method for predicting AD in accordance with an embodiment of the present invention; and  FIG. 2  is an exemplary diagram illustrating the observing factors in the AD progression. As shown in  FIG. 2 , the Hippocampus volume is considered the most important and crucial factor to distinguish individuals with AD, especially from the ones who have MCI is crucial within the realm of early detection of AD. 
     The objective of the present invention is to create a predictive computing model of AD by considering detailed structural and anatomic information contained within the MRI images as well as cognitive function assessed using the MMSE. 
     Accordingly, as shown in  FIG. 1 , in an embodiment, A method for predicting Alzheimer&#39;s Disease comprising acts of S 100  providing MRI images containing the anatomical structure of Hippocampus and MMSE data as a training data set, S 110  training a processor using the training data set, S 120  receiving MRI images and MMSE data as a testing data set from a target, and S 130  classifying the test data by the trained processor to include aggregating predictions. 
     The acts of S 110  training of the processor using the training data set is comprising acts of S 111  proceeding an MRI image preprocessing to determine volume of a Hippocampus of each MRI images, and segmenting the Hippocampus into sections, S 112  determining surface areas for each section of each Hippocampus in MRI images, S 113  determining a Ratio of RPC for each section of each Hippocampus, and S 114  selecting candidate parameters as inputs to iteratively train an iterative neural network in the processor. the candidate parameters are selected from the volume of Hippocampus, the surface areas and PRC of sections of Hippocampus, and scores of the MMSE data. 
     With further reference to  FIGS. 3A and 3B ,  FIG. 3A  is a flowchart illustrating the acts of S 111  with corresponding MRI images for each of steps in accordance with an embodiment of the present invention; and  FIG. 3B  is an exemplary diagram illustrating the sections of the Hippocampus. As shown in  FIG. 3A , in this embodiment, the MRI image preprocessing S 111  comprising acts of S 1110  an intensity normalization, S 1111  linear stereotaxic registration, S 1112  creating linear mask, S 1113  linear classification and S 1114  linear segmentations. 
     In the present disclosure, the acts of S 1110  to S 1114  for preprocessing and segmenting the MRI images is proceed by a computer using FreeSurfer. FreeSurfer is a software for the analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies. It is developed by the Laboratory for Computational Neuroimaging at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital. For structural MRI image, FreeSurfer provides a cortical and subcortical full processing pipeline describing as following: 
     1. Intensity correction, noise filtering, artifact correction, skull stripping and gray-white matter segmentation;
         The surface-based stream       

     2. Reconstruction of cortical surface models (gray-white boundary surface); 
     3. Nonlinear registration of the cortical surface of an individual with a stereotaxic atlas (MNI305 atlas);
         The volume-based stream       

     4. Labeling of cortical regions and subcortical regions; 
     5. Statistical analysis of group morphometry differences; and 
     6. Subfields of Hippocampus segmentation. 
     Therefore, the volume and the sections of Hippocampus is able to obtained by using the MRI image through FreeSurfer. As shown in  FIG. 3B , the sections of Hippocampus at least comprise alveus, parasubiculum, presubiculum, subiculum, CA1, CA2/3, CA4, GC-DG, HATA, fimbria, molecular layer, Hippocampus fissure and Hippocampus tail. 
     With reference to  FIGS. 4A and 4B ,  FIG. 4A  is an exemplary diagram of a 3 Dimensions (3D) Hippocampus model in accordance with an embodiment of the present invention; and  FIG. 4B  is a flowchart illustrating the step of S 112  in accordance with an embodiment of the present invention. In act of S 112 , the Hippocampus is built in a 3D Hippocampus model with each identical section. In this embodiment, the 3D Hippocampus model is built by Marching cubes. The “marching cubes” described here is the technique directing to a patent, U.S. Pat. No. 4,710,876 “System And Method For The Display Of Surface Structures Contained Within The Interior Region Of A Solid Body” by Harvey E. Cline and William E. Lorensen, issued on Dec. 1, 1987. 
     As shown in  FIG. 4A , a block of volumetric data is displayed, it determines interfaces between adjacent data values indicating a change in the measured value, and then models the surfaces with triangular elements having a vector normal to the surface at each of the vertices of the triangle. 
     Laplacian smoothing is then applied to the 3D Hippocampus model which is configured for improving the quality of the triangulation while remaining faithful to the original surface geometry. The Hippocampus surface and each surface areas of the sections are obtained after the surface smoothing. Therefore, as shown in  FIG. 4B , the act of  112  further comprises acts of S 120  reconstructing the each identified Hippocampus with sections to build a 3D Hippocampus model, S 1121  smoothing the surface area of the 3D Hippocampus model; and S 122  calculating the surface areas of each sections of Hippocampus. 
     In act of S 113 , a curvature analysis to the 3D Hippocampus model is proceeded to determine an average of a Ratio of Principle Curvature (RPC) for each section of each hippocampus. During the calculation of curvature, maximum curvatures and minimum curvatures of each sections are calculated, wherein the average of RPC is 
     
       
         
           
             
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     With reference to  FIG. 5 ,  FIG. 5  is an exemplary diagram of an iterative neural network in accordance with an embodiment of the present invention. As describe in above, the volume, average of curvature for each section, and surface of the Hippocampus are obtained. These values are considered to be candidate parameters as inputs for the iterative neural network. As shown in  FIG. 5 , there&#39;re multiple input nodes, at least one output node and multiple layers of neuron nodes between the input and the output. 
     According to inventor&#39;s experiments, in an prefer embodiment, the values of Hippocampus volume, Subiculum surface area, CA1 surface area, CA3 surface area, and the average RPCs of Subiculum, CA1 and CA3 are selected to be the candidate parameters. In contrast, unlikely conventional studies only relied on the volume of the Hippocampus, the present invention uses curvatures to quantify the Hippocampus as features. 
     Besides neurological examinations, in order to enhance the accuracy of prediction, especially for predicting AD from MCI patients, the present invention further uses physical/cognitive function assessed using MMSE data to train the iterative neural network. the parameters both in MRI images and MMSE data can be used in a single machine learning model, or separately into two individual models. As a person skilled in the art will realize that for separating models, the results can be combined later using the majority voting. 
     The MMSE is a brief cognitive assessment tool commonly used to screen for dementia. The MMSE is composed of 11 major items forming a 30-point questionnaire with five different domains of cognition analyses. The five domains are (1) Orientation, contributing a maximum of 10 points, (2) Memory/Recall, contributing a maximum of 6 points, (3) Attention and calculation, as a measure of working memory, contributing a maximum of 5 points, (4) Language, contributing a maximum of 8 points, and (5) Design copying, contributing a maximum of 1 point. The 11 items are temporal orientation (5 points), spatial orientation (5 points), immediate memory (3 points), attention/concentration (5 points), delayed recall (3 points), naming (2 points), verbal repetition (1 points), verbal comprehension (3 points), writing (1 points), reading a sentence (1 points), and constructional praxis (1 points). In general, the MMSE was administered and scored by a medical doctor certified in internal-medicine with extensive dementia experience. 
     Accordingly, the scores of MMSE data are used as candidate parameters for additional inputs. Based on inventor&#39;s experiment, orientation, attention, recall and language are the most effective features for accuracy. 
     Below three tables shows the accuracy results of predicting AD with different inputs in iterative neural networks. In this embodiment, a basic Multilayer Perceptron (MLP) architecture is used with numerous of hidden layers. Table I uses only neuropsychological data (i.e., the MMSE score) as parameters. The accuracy is between 67.78% to 72.22%. Table II uses only neuroimaging features (i.e., the volume, surface area and RPC of the Hippocampus) as parameters. The accuracy is between 67.16% to 72.65%. Table III is combination of the neuropsychological data and neuroimaging feature data. The accuracy is between 75.12% to 75.86%. 
     
       
         
           
               
               
             
               
                 TABLE I 
               
             
            
               
                   
               
               
                 Input feature 
                   
               
               
                 Basic MLP architecture 
                 Neuropsychological data 
               
            
           
           
               
               
               
               
            
               
                   
                 Neuropsy- 
                 Number 
                 2 hidden layers with 4 neurons 
               
            
           
           
               
               
               
               
               
               
            
               
                 Selection 
                 chological 
                 of 
                 Accu- 
                 Sensi- 
                 Speci- 
               
               
                 method 
                 test 
                 Features 
                 racy 
                 tivity 
                 ficity 
               
               
                   
               
               
                 Univariate 
                 MMSE 
                 4 
                 72.22% 
                 70.37% 
                 74.07% 
               
               
                 selection 
               
               
                 Feature 
                 MMSE 
                 2 
                 67.78% 
                 57.04% 
                 78.52% 
               
               
                 importance 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
             
               
                 TABLE II 
               
             
            
               
                   
               
               
                 Input feature 
                   
               
               
                 Basic MLP architecture 
                 Neuroimaging features 
               
            
           
           
               
               
               
            
               
                   
                 Number 
                 2 hidden layers with 12 neurons 
               
            
           
           
               
               
               
               
               
               
            
               
                 Selection 
                   
                 of 
                 Accu- 
                 Sensi- 
                 Speci- 
               
               
                 method 
                 Criteria 
                 features 
                 racy 
                 tivity 
                 ficity 
               
               
                   
               
               
                 Univariate 
                 p-value &lt; 0.01 
                 11 
                 67.16% 
                 70.00% 
                 64.32% 
               
               
                 selection 
               
               
                 Univariate 
                 p-value &lt; 0.05 
                 19 
                 71.17% 
                 69.38% 
                 72.96% 
               
               
                 selection 
               
               
                 Univariate 
                 p-value &lt; 0.1 
                 25 
                 69.26% 
                 70.99% 
                 67.53% 
               
               
                 selection 
               
               
                 Feature 
                 Random forest 
                 10 
                 67.59% 
                 68.02% 
                 67.16% 
               
               
                 importance 
                 Top 10 
               
               
                 Feature 
                 Random forest 
                 15 
                 71.91% 
                 69.75% 
                 74.07% 
               
               
                 importance 
                 Top 15 
               
               
                 Feature 
                 Random forest 
                 20 
                 72.65% 
                 71.48% 
                 73.83% 
               
               
                 importance 
                 Top 20 
               
               
                 Feature 
                 Random forest 
                 25 
                 71.54% 
                 69.01% 
                 74.07% 
               
               
                 importance 
                 Top 25 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
             
               
                 TABLE III 
               
             
            
               
                   
               
               
                 Input feature 
                   
               
               
                 Basic MLP architecture 
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Neuropsy- 
                   
                 Neuroimaging + neuropsy- 
               
               
                   
                 chological 
                   
                 chological data 
               
               
                   
                 test 
                 Number 
                 2 hidden layers with 15 neurons 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 (Univariate 
                 of 
                 Accu- 
                 Sensi- 
                 Speci- 
               
               
                 Criteria 
                 selection) 
                 features 
                 racy 
                 tivity 
                 ficity 
               
               
                   
               
            
           
           
               
            
               
                 Univariate selection method 
               
            
           
           
               
               
               
               
               
               
            
               
                 p-value &lt; 0.05 
                 MMSE 
                 23 
                 75.86% 
                 74.07% 
                 77.65% 
               
               
                 p-value &lt; 0.1 
                 MMSE 
                 29 
                 73.95% 
                 72.10% 
                 75.80% 
               
            
           
           
               
            
               
                 Feature importance method (Random forest classifier) 
               
            
           
           
               
               
               
               
               
               
            
               
                 Random forest 
                 MMSE 
                 19 
                 75.12% 
                 76.05% 
                 74.20% 
               
               
                 Top 15 
               
               
                 Random forest 
                 MMSE 
                 24 
                 75.56% 
                 74.94% 
                 76.17% 
               
               
                 Top 20 
               
               
                   
               
            
           
         
       
     
     Accordingly, the present invention combines the structural information derived from functional information and neuroimaging data that is derived from quantifying the anatomical structure of a Hippocampus which achieves better accuracy of predicting AD. 
     The foregoing description of the present invention has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations will be apparent to the practitioner skilled in the art. 
     The embodiments were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling others skilled in the art to understand the invention for various embodiments and with various modifications that are suited to the particular use contemplated.