Patent Description:
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'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.

<NPL>) proposes to combine MRI data with a neuropsychological test, mini-mental state examination (MMSE), as input to a multi-dimensional space for the classification of Alzheimer's disease (AD) and it's prodromal stages-mild cognitive impairment (MCI) including amnestic MCI (aMCI) and nonamnestic MCI (naMCI).

<NPL>) proposed a novel classification system to distinguish among elderly subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC), based on 3D magnetic resonance imaging (MRI) scanning.

<NPL>) provide and evaluate an open-source software solution for automatically measuring hippocampal volume and hippocampal surface roughness based on T1-weighted MRI, which allows for discriminating between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC) using only one scan.

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 the present invention, the method for providing biomarker for early detection of Alzheimer's Disease comprises an act of training a processor using magnetic resonance imaging (MRI) images containing the anatomical structure of Hippocampus and Mini-Mental State Examination (MMSE) data as a training data set.

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; building a <NUM> Dimensions (3D) Hippocampus model and 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 comprise 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 disclosure, 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's Disease.

According to an embodiment of the present disclosure, 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.

Embodiments of the invention are described in more details hereinafter with reference to the drawings, in which:.

For simplicity and illustrative purposes, the present disclosure is described by referring mainly to an example thereof. 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 <FIG> and <FIG>, <FIG> is a flowchart illustrating a method for predicting AD in accordance with an embodiment of the present invention; and <FIG> is an exemplary diagram illustrating the observing factors in the AD progression. As shown in <FIG>, 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>, a method of providing biomarker for early detection of Alzheimer's disease comprises the act of S110 training a processor using the magnetic resonance imaging (MRI) images containing the anatomical structure of Hippocampus and Mini-Mental State Examination (MMSE) data as a training data set. The act of S110 will be described in the next paragraph. In an embodiment, before the act of S110 (training a processor), an act of S100 providing MRI images containing the anatomical structure of Hippocampus and MMSE data as a training data set is performed. After the act of S110 (training a processor), the acts of S <NUM> receiving MRI images and MMSE data as a testing data set from a target, and S130 classifying the test data by the trained processor to include aggregating predictions can be performed.

The acts of S110 training of the processor using the training data set is comprising acts of S111 proceeding an MRI image preprocessing to determine volume of a Hippocampus of each MRI images, and segmenting the Hippocampus into sections, S112 building a <NUM> Dimensions (3D) Hippocampus model and determining surface areas for each section of each Hippocampus in MRI images, S113 determining a Ratio of RPC for each section of each Hippocampus, and S114 selecting candidate parameters as inputs to iteratively train an iterative neural network in the processor. The candidate parameters comprise the volume of Hippocampus, the surface areas and PRC of sections of Hippocampus, and scores of the MMSE data.

With further reference to <FIG> and <FIG>, <FIG> is a flowchart illustrating the acts of S111 with corresponding MRI images for each of steps in accordance with an embodiment of the present invention; and <FIG> is an exemplary diagram illustrating the sections of the Hippocampus. As shown in <FIG>, in this embodiment, the MRI image preprocessing S111 comprising acts of S1110 an intensity normalization, S1111 linear stereotaxic registration, S1112 creating linear mask, S1113 linear classification and S1114 linear segmentations.

In the present disclosure, the acts of S1110 to S1114 for preprocessing and segmenting the MRI images is proceeding 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:.

Therefore, the volume and the sections of Hippocampus is able to obtained by using the MRI image through FreeSurfer. As shown in <FIG>, the sections of Hippocampus at least comprise alveus, parasubiculum, presubiculum, subiculum, CA1, CA2/<NUM>, CA4, GC-DG, HATA, fimbria, molecular layer, Hippocampus fissure and Hippocampus tail.

With reference to <FIG> and <FIG>, <FIG> is an exemplary diagram of a <NUM> Dimensions (3D) Hippocampus model in accordance with an embodiment of the present invention; and <FIG> is a flowchart illustrating the step of S112 in accordance with an embodiment of the present invention. In act of S112, 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, <CIT>.

As shown in <FIG>, 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>, the act of <NUM> further comprises acts of S1120 reconstructing the each identified Hippocampus with sections to build a 3D Hippocampus model, S1121 smoothing the surface area of the 3D Hippocampus model; and S1122 calculating the surface areas of each sections of Hippocampus.

In act of S113, 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 <MAT>.

With reference to <FIG> 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>, there're multiple input nodes, at least one output node and multiple layers of neuron nodes between the input and the output.

According to inventor's experiments, and in the context of the claimed invention, 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 among the candidate parameters. In contrast, unlike conventional studies which 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 <NUM> major items forming a <NUM>-point questionnaire with five different domains of cognition analyses. The five domains are (<NUM>) Orientation, contributing a maximum of <NUM> points, (<NUM>) Memory/Recall, contributing a maximum of <NUM> points, (<NUM>) Attention and calculation, as a measure of working memory, contributing a maximum of <NUM> points, (<NUM>) Language, contributing a maximum of <NUM> points, and (<NUM>) Design copying, contributing a maximum of <NUM> point. The <NUM> items are temporal orientation (<NUM> points), spatial orientation (<NUM> points), immediate memory (<NUM> points), attention/concentration (<NUM> points), delayed recall (<NUM> points), naming (<NUM> points), verbal repetition (<NUM> points), verbal comprehension (<NUM> points), writing (<NUM> points), reading a sentence (<NUM> points), and constructional praxis (<NUM> 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'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 <NUM>% to <NUM>%. Table II uses only neuroimaging features (i.e., the volume, surface area and RPC of the Hippocampus) as parameters. The accuracy is between <NUM>% to <NUM>%. Table III is combination of the neuropsychological data and neuroimaging feature data. The accuracy is between <NUM>% to <NUM>%.

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.

Claim 1:
A method of providing biomarker for early detection of Alzheimer's Disease, comprising:
training a processor using magnetic resonance imaging (MRI) images containing the anatomical structure of Hippocampus and Mini-Mental State Examination (MMSE) data as a training data set (S110), and 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 (S111), wherein the segmentations of the Hippocampus are alveus, parasubiculum, presubiculum, subiculum, CA1, CA2/<NUM>, CA4, GC-DG, HATA, fimbria, molecular layer, Hippocampus fissure and Hippocampus tail;
building a <NUM> Dimensions (3D) Hippocampus model and determining surface areas for each section of each Hippocampus in MRI images (S112);
determining a Ratio of Principal Curvature (RPC) for each section of each Hippocampus (S113); and
selecting candidate parameters as inputs to iteratively train an iterative neural network in the processor, wherein the candidate parameters comprise the volume of Hippocampus, the surface areas and the RPC of sections of Hippocampus, and scores of MMSE data (S114).