Method of evaluating concomitant clinical dementia rating and its future outcome using predicted age difference and program thereof

Abstract

A method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive impairment and predict its future change based on the at least one feature obtained.

Claims

1. A method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain, the method comprising: (1) scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; (2) processing the medical brain image to obtain at least one feature of the image; (3) using a pre-established prediction model to determine a condition of the cognitive impairment and predict its future change based on the at least one feature obtained; and (4) outputting the condition of cognitive impairment and its future change in an output terminal; wherein the pre-established prediction model comprises at least a brain age model.

2. The method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain according to claim 1, wherein: the at least one image is a brain MRI image.

3. The method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain according to claim 1, wherein: the condition of the cognitive impairment is clinically assessed using the clinical dementia rating (CDR).

4. The method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain according to claim 2, wherein: the brain MRI image comprises at least a brain diffusion weighted MRI.

5. The method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain according to claim 1, wherein: the step of processing of the medical brain image comprises at least one step of correcting the artifacts of the medical brain image.

6. The method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain according to claim 1, wherein: the step of processing of the medical brain image further comprises at least one step of spatial normalization process.

7. The method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain according to claim 6, wherein: the step of processing of the medical image further comprises at least one step of feature quantification process.

8. The method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain according to claim 1, wherein: the pre-established prediction model comprises a first determination model for determining the condition of the cognitive impairment; and, a second prediction model for predicting the future change of the condition of the cognitive impairment.

9. A method for evaluating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain, comprising: (1) scanning the individual's brain with a MRI scanning device so as to acquire MRI data of the individual's brain using T1-weighted (T1w) imaging and diffusion tensor imaging (DTI) sequences; (2) processing the acquired T1w data so as to obtain white matter (WM) and grey matter (GM) image segments; (3) registering the WM and GM image segments obtained in the step (2) to the MNI space to obtain the deformation map of the individual's brain; (4) correcting artifacts of the DTI data; (5) obtaining fractional anisotropy (FA) and mean diffusivity (MD) maps for the individual in native space based on the artifact corrected DTI data; (6) extracting FA and MD values of WM region of the individual's brain using the FA and MD maps obtained in the step (5) and the deformation map obtained in the step (3); (7) calculating the WM PAD by inputting the extracted FA and MD of WM to an established dMRI-brain age model; (8) predicting the CDR and CDR change of the individual from the WM PAD using a CDR prediction model and a CDR change prediction model; and (9) outputting the CDR and CDR change in an output terminal.

10. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 9, wherein the step (2) comprises: (i) correction of SI inhomogeneity of T1w data; (ii) segment of tissue probability maps (TPM); (iii) obtaining WM and GM segments.

11. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 9, wherein the step (2) further comprises: inverting T1w contrast to synthesize a pseudo b0 image.

12. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 11, wherein the step (4) comprises: registering the DTI data to pseudo b0 image to correct the artifacts of DTI data.

13. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 9, wherein the step (5) comprises: (i) estimating diffusion tensor at each image pixel of the artifact corrected DTI data; (ii) calculating FA and MD at each pixel using a diffusion tensor indexes derived from the estimated diffusion tensor at each pixel; (iii) generating the FA and MD maps in the native space using the FA and MD at each pixel.

14. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 13, wherein
MD=(λ1+λ2+λ3)/3;
FA=[3(Δλ1.sup.2+Δλ2.sup.2+Δλ3.sup.2)/2(λ1.sup.2+λ2.sup.2+λ3.sup.2)].sup.1/2; wherein λ1, λ2, and λ3 are 1st, 2nd, and 3rd eigenvalues of the diffusion tensor, respectively.

15. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 9, wherein the step (6) comprises: (i) registering the FA and MD maps to the MNI space using the deformation maps in the step (3); and (ii) masking the registered FA and MD maps with WM segments.

16. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 9, wherein: the dMRI-brain age model is built by regressing the chronological age of more than one individual against FA and MD values inside the WM region using a Gaussian process regression method.

17. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 9, wherein: the individual's brain region consists of the white matter region.

18. The method for estimating the clinical dementia rating (CDR) of an individual and CDR's future change from predicted age difference (PAD) based on MRI data of the individual's brain according to claim 9, wherein: the CDR change is the CDR change of the individual in the next 2-3 years since the date of the MRI scanning.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The accompanying drawings illustrate one or more embodiments of the invention and together with the written description, serve to explain the principles of the invention. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like elements of an embodiment.

(2) FIG. 1 shows a flow chart of a process for estimating the CDR from WM PAD;

(3) FIG. 2 shows a flow chart of a process for acquisition of T1w and DTI data;

(4) FIG. 3 shows a flow chart of a process for image processing of T1w;

(5) FIG. 4 shows a flow chart of a process for registration of GM and WM to MNI space;

(6) FIG. 5 shows a flow chart of a process for artifact correction of DTI data;

(7) FIG. 6 shows a flow chart of a process for reconstruction of MD and FA;

(8) FIG. 7 shows a flow chart of a process for extraction of FA and MD;

(9) FIG. 8 shows a flow chart of a process for estimation of WM brain age and PAD;

(10) FIG. 9 shows a flow chart of a process for prediction of the CDR and CDR change;

(11) FIG. 10 shows comparisons between CN1-to-[CN2], [nD1], and [nD2] of Example 2;

(12) FIG. 11 shows comparisons between [D1]-to-CN, [nD1], and [D1]-to-D2 of Example 2;

(13) FIG. 12 shows comparisons between CN1-to-[CN2], D1-to-[CN], and [CN]-to-D1 of Example 2;

(14) FIG. 13 shows spectrum of PAD.

(15) FIG. 14 shows the process for participant's brain being scanned by an MRI scanner.

(16) FIG. 15 shows the process for the anatomical image being converted to the pseudo b.sub.0 image.

(17) FIG. 16 shows the process for extracting the real b.sub.0 image from the dMRI data.

(18) FIG. 17 shows the process the pseudo b.sub.0 image is rigidly registered to the real b.sub.0 image.

(19) FIG. 18 shows the image discrepancy modeling.

(20) FIG. 19 shows the algorithm for estimating β and v.

(21) FIG. 20 shows the results of correcting the artefacts of a participant's dMRI data.

(22) FIG. 21 shows the PAD comparisons between CN1-to-[CN2], [nD1], and [nD2] of Example 3.

(23) FIG. 22 shows the ROC curve analysis to evaluate the performance of differentiating between CDR=0 and CDR >0.

(24) FIG. 23 shows the confusion matrix of the results using the cutoff PAD of 6, and the distribution of the two populations (i.e. CDR=0 and CDR >0) with respect to PAD.

(25) FIG. 24 shows a conceptual data flow diagram illustrating the data flow between different components/means in an exemplary apparatus.

(26) FIG. 25 shows a diagram illustrating an example of a hardware implementation for an apparatus employing a processing system.

DETAILED DESCRIPTION OF THE INVENTION

(27) The invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like reference numerals refer to like elements throughout.

(28) The terms used in this specification generally have their ordinary meanings in the art, within the context of the invention, and in the specific context where each term is used. Certain terms that are used to describe the invention are discussed below, or elsewhere in the specification, to provide additional guidance to the practitioner regarding the description of the invention. For convenience, certain terms may be highlighted, for example using italics and/or quotation marks. The use of highlighting has no influence on the scope and meaning of a term; the scope and meaning of a term is the same, in the same context, whether or not it is highlighted. It will be appreciated that same thing can be said in more than one way. Consequently, alternative language and synonyms may be used for any one or more of the terms discussed herein, nor is any special significance to be placed upon whether or not a term is elaborated or discussed herein. Synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and in no way limits the scope and meaning of the invention or of any exemplified term. Likewise, the invention is not limited to various embodiments given in this specification.

(29) It will be understood that, as used in the description herein and throughout the claims that follow, the meaning of “a”, “an”, and “the” includes plural reference unless the context clearly dictates otherwise. Also, it will be understood that when an element is referred to as being “on” another element, it can be directly on the other element or intervening elements may be present therebetween. In contrast, when an element is referred to as being “directly on” another element, there are no intervening elements present. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

(30) It will be understood that, although the terms first, second, third etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the invention.

(31) Furthermore, relative terms, such as “lower” or “bottom” and “upper” or “top,” may be used herein to describe one element's relationship to another element as illustrated in the Figures. It will be understood that relative terms are intended to encompass different orientations of the device in addition to the orientation depicted in the Figures. For example, if the device in one of the figures is turned over, elements described as being on the “lower” side of other elements would then be oriented on “upper” sides of the other elements. The exemplary term “lower”, can therefore, encompasses both an orientation of “lower” and “upper,” depending of the particular orientation of the figure. Similarly, if the device in one of the figures is turned over, elements described as “below” or “beneath” other elements would then be oriented “above” the other elements. The exemplary terms “below” or “beneath” can, therefore, encompass both an orientation of above and below.

(32) It will be further understood that the terms “comprises” and/or “comprising,” or “includes” and/or “including” or “has” and/or “having”, or “carry” and/or “carrying,” or “contain” and/or “containing,” or “involve” and/or “involving, and the like are to be open-ended, i.e., to mean including but not limited to. When used in this disclosure, they specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and/or groups thereof.

(33) Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

(34) As used in this disclosure, “around”, “about”, “approximately” or “substantially” shall generally mean within 20 percent, preferably within 10 percent, and more preferably within 5 percent of a given value or range. Numerical quantities given herein are approximate, meaning that the term “around”, “about”, “approximately” or “substantially” can be inferred if not expressly stated.

(35) As used in this disclosure, the phrase “at least one of A, B, and C” should be construed to mean a logical (A or B or C), using a non-exclusive logical OR. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

(36) Embodiments of the invention are illustrated in detail hereinafter with reference to accompanying drawings. The description below is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. The broad teachings of the invention can be implemented in a variety of forms. Therefore, while this invention includes particular examples, the true scope of the invention should not be so limited since other modifications will become apparent upon a study of the drawings, the specification, and the following claims. For purposes of clarity, the same reference numbers will be used in the drawings to identify similar elements. It should be understood that one or more steps within a method may be executed in different order (or concurrently) without altering the principles of the invention.

(37) The description will be made as to the embodiments of the present invention in conjunction with the accompanying drawings in FIGS. 1-20. In accordance with the purposes of this invention, as embodied and broadly described herein, this invention, in one aspect, relates to a method and related apparatus for calculating and predicting the CDR based on the MRI data of an individual's WM PAD.

(38) FIG. 1 shows the overall flowchart of the invention: process of using WM PAD to evaluate the CDR and predict CDR change by a program. The invention starts with the acquisition of MRI data using T1w imaging and diffusion-weighted imaging sequences (step 1). To ensure the same WM component is selected from different brains, spatial registration of each individual's MRI image to a standard template in the MNI space is mandated (step 2 & 3). In one embodiment, steps 2 and/or 3 are spatial normalization process during which the MRI image such as the T1w image is segmented and the segments of certain brain tissues are registered to MNI space. Therefore, a deformation map(s) of the certain brain tissues are obtained during the spatial normalization process. To ensure accurate quantification of diffusion-weighted indexes, i.e. fractional anisotropy (FA) and mean diffusivity (MD), artefacts in the diffusion-weighted MRI data should be corrected, followed by accurate estimation of the FA and MD indexes (step 4 & 5). Having obtained the FA and MD maps in the native space and the deformation matrix between the native space and MNI space, we can transform the FA and MD maps to the MNI space and extract the FA and MD indexes within the WM component (step 6). In one embodiment, steps 5 and/or 6 are feature quantification process, during which FA and MD maps based on the artifact corrected diffusion-weighted MRI data are obtained, and FA and MD indexes of certain brain tissues are extracted. The resulting FA and MD values serve as input to an established dMRI-brain age model to estimate brain age and WM PAD (step 7). In the final step, WM PAD is used to determine the CDR and predict CDR future change using pre-established models in programs (step 8). Detailed description of each step is described as follows.

(39) FIG. 2 shows the process of acquisition of T1w and DTI data by the program.

(40) Step 1.1 is MRI scanning of a person's brain. A person is scanned by a MRI system at 1.5 Tesla or 3 Tesla with a phase-arrayed multi-channel head coil. MRI scanning is contraindicated for participants who are pregnant within 1st trimester, having pacemaker or defibrillator implantation, or vascular clipping or stenting within 6 months.

(41) Step 1.2 is acquisition of T1w. T1w data are acquired using a 3-dimensional magnetization prepared rapid gradient echo sequence. The imaging parameters are detailed as follow: repetition time (TR)=2400 ms, echo time (TE)=3.16 ms, inversion time (TI)=1000 ms, field-of-view (FOV)=256×256×176 mm3, and matric size=256×256×176.

(42) Step 1.3 is acquisition of DTI data. DTI data are acquired using diffusion-weighted 2-dimensional single shot spin-echo echo planar imaging using 24 different magnitudes of diffusion sensitivity (b-values) corresponding to 24 different directions (b-vectors) evenly distributed in the q space.

(43) Step 1.4 is obtaining T1w and DTI data. In this way, one T1w data and one DTI data were obtained for each participant for the processing in step 2 and 4.

(44) FIG. 3 shows the step of image processing of T1w by the program during the spatial normalization process.

(45) Step 2.1 is a person's T1w image. The T1w image is processed using the Segment toolbox in the SPM12 software (Wellcome Trust Centre for Neuroimaging, University College London, London, UK).

(46) Step 2.2 is correction of SI inhomogeneity on T1w. The toolbox corrects signal intensity inhomogeneity.

(47) Step 2.3 is segment tissue probability maps (TPM) using Segment toolbox in SPM12. Having corrected SI inhomogeneity, the toolbox segments the T1w image into various tissue segments, including gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), bone, scalp, and others.

(48) Step 2.4 is obtaining GM and WM segments. In this way, six TPMs were obtained and from which GM and WM segments are selected for the processing in step 3.

(49) Step 2.5 is inverting T1w contrast. In parallel to step 2.3 and 2.4, the contrast of the T1w image, which has been corrected for the signal intensity inhomogeneity, is inverted to synthesize a pseudo b.sub.0 image.

(50) Step 2.6 is obtaining pseudo b.sub.0 image: In this way, we obtain a pseudo b.sub.0 image for each person for the processing in step 4.

(51) FIG. 4 shows the process of registration of GM and WM to MNI space during the spatial normalization process.

(52) Step 3.1 is a person's GM and WM segments. The GM and WM segments are registered to the ICBM152 template which is defined in the MNI space.

(53) Step 3.2 is to register GM and WM segments to ICBM152 template: The registration invokes a process which is a variant of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) (Beg et al., 2005; Miller et al., 2006). Specifically, the initial velocity situated in the ICBM152 space is iteratively estimated by shooting this velocity along the time dimension toward the individual's native space. In the registration, the course is divided into 10 uniform intervals, and an isotropic Gaussian filter with 10 mm full width at half maximum (FWHM) is used to ensure the smoothness of the initial velocity.

(54) Step 3.3 is obtaining a person's deformation maps: In the final stage of convergence, a deformation map which transforms the individual's TPM to match the TPM in the ICBM152 template in obtained. The deformation map is used for the processing in step 6.

(55) FIG. 5 shows the process of artifact correction of DTI data.

(56) Step 4.1 is a person's DTI image. The raw DTI data suffers from various artefacts, including susceptibility-induced distortion, eddy current-induced distortion, head motion, and intensity inhomogeneity, which vary from person to person and should be corrected to ensure accurate registration to the MNI space.

(57) Step 4.2 is to register DTI to pseudo b.sub.0 image. The DTI data are processed using a novel registration-based method which correct these artefacts in an integrated framework. The registration-based method registers a person's raw DTI data to the same person's pseudo b.sub.0 image which serves as the reference image without distortion and intensity inhomogeneity.

(58) Step 4.3 is to reduce artifacts using a series of artefact models. By invoking the artefact models in the integrated framework, the process is able to reduce the artefacts of the DTI data.

(59) Step 4.4 is to obtain artefact-corrected DTI image. In this way, the artefact-corrected DTI image is obtained. When the estimation achieved convergence, the corrected DTI data is also aligned with the T1w image.

(60) FIG. 6 shows the process of reconstruction of MD and FA during the feature quantification process.

(61) Step 5.1 is the artefact-corrected DTI data. The artefact-corrected DTI data is processed to estimate the diffusion tensor and the diffusion tensor indexes derived therein.

(62) Step 5.2 is to estimate diffusion tensor at each image pixel. The diffusion tensor estimation is performed using an approach proposed by (Koay et al., 2007). Briefly, weighted linear least squares method is first conducted, the results are then employed as the initial estimation for the constrained nonlinear least squares method to obtain the diffusion tensors, which are ensured to be positive-definite (i.e., the eigenvalues of the diffusion tensors are all positive).

(63) Step 5.3 is to calculate FA and MD at each pixel. The diffusion tensor indexes, i.e. fractional anisotropy (FA) and mean diffusivity (MD), are derived from the estimated diffusion tensor at each pixel. The MD and FA values are determined using the standard formula: MD=(λ1+λ2+λ3)/3, and FA=[3(Δλ1.sup.2+Δλ2.sup.2+Δλ3.sup.2)/2(λ1.sup.2+λ2.sup.2+λ3.sup.2)].sup.1/2 where λ1, λ2 and λ3 denoted the first, second and third eigenvalues of the diffusion tensor, respectively, and Δλ1, Δλ2 and Δλ3 represented λ1−MD, λ2−MD, and λ3−MD, respectively.

(64) Step 5.4 is to obtain FA and MD maps in the native space: In this way, FA and MD maps for each person in native space were obtained.

(65) FIG. 7 shows the process of extraction of FA and MD during the feature quantification process.

(66) Step 6.1 is a person's FA and MD maps in native space. Having obtained the FA and MD maps in step 5, it is necessary to extract FA and MD values from the standardized WM segment.

(67) Step 6.2 is to register FA and MD maps to MNI space. To do this, the FA and MD maps in the native space are normalized to the MNI space through the deformation map obtained in step 3.

(68) Step 6.3 is to mask the registered FA and MD maps with WM segment. Having normalized to the MNI space, the FA and MD values in WM are masked by the standardized WM segment in the ICBM152 template.

(69) Step 6.4 is to obtain FA and MD values of WM. In the final step, the values of FA and MD of WM for each person were obtained. The results are used for the processing in step 7.

(70) FIG. 8 shows the process for estimation of WM brain age and PAD.

(71) Step 7.1 is a person's FA and MD in WM: A persons' FA and MD are used as the input to an established dMRI-brain age model which was trained and validated in advance using the DTI data acquired from the same MRI scanner.

(72) Step 7.2 is inputting the FA and MD values to an established dMRI-brain age model to estimate brain age. To build the dMRI-brain age model, a Gaussian process regression (GPR) method was used to regress the chronological age of the participants against the FA and MD values inside the WM region. The output of the model is a predicted age, called dMRI-brain age, estimated on the basis of the person's FA and MD values and the GRP model. The performance of the model has been tested and quantified in terms of the mean absolute error (MAE) and the Pearson's correlation coefficient (r) between the dMRI-brain age and chronological age.

(73) Step 7.3 is calculating the WM PAD from the estimated brain age and chronological age of the person. Having obtained the dMRI-brain age, predicted age difference of white matter (WM PAD) is calculated by the formula: dMRI-brain age—chronological age.

(74) Step 7.4 is obtaining WM PAD. In this way, WM PAD was obtained for estimating the CDR and CDR change.

(75) FIG. 9 shows the process for determining of the CDR and prediction of CDR future change.

(76) Step 8.1 is a person's WM PAD. A person's WM PAD is used as input of an established prediction model to determine his/her CDR and/or predict CDR future change.

(77) Step 8.2 is to determine the CDR from the established model relating WM PAD to the CDR. To build the prediction model of the CDR from WM PAD, a linear regression model is applied to a dataset from a group of people whose WM PAD and concomitant CDR are known in advance. In one embodiment, the dataset from the group of people whose WM PAD and concomitant CDR are known in advance is used to establish a brain age model.

(78) Step 8.3 is to obtain determined CDR. Using the established regression model of the CDR and/or brain age model, the CDR of a person is determined from the same person's WM PAD.

(79) Step 8.4 is to predict the CDR future change from the established model relating WM PAD to CDR future change. To build the prediction model of the CDR future change from WM PAD, a linear regression model is applied to a dataset of a group of people whose WM PAD and the records of the CDR 2 to 3 years after WM PAD measurement are known. In one embodiment, such a model is a brain age model. The CDR change is defined as the difference between the CDR which is taken at the same time as WM PAD measurement and the CDR taken 2 to 3 years later.

(80) Step 8.5 is to obtain predicted CDR change. Using the established regression model of the CDR change, the CDR change of a person is predicted from the same person's WM PAD.

(81) In one embodiment, a pre-established prediction model includes both the dMRI-brain age model being used in calculating the WM PAD from the extracted FA and

(82) MD value in step 7, as well as the models relating WM PAD to the CDR and CDR change in step 8.

(83) In one embodiment, the medical brain images of a number of patients are processed to extract at least one feature of each patient. Given a set of training data, this pre-established prediction model is constructed by associating the features with the conditions of cognitive impairment using machine-learning methods. For independent data, the model can be used to predict the conditions of cognitive impairment by inputting the brain image features.

(84) FIG. 24 is a conceptual data flow diagram illustrating the data flow between different components/means in an exemplary apparatus 2400. In one embodiment, one or more image acquiring device (e.g. MRI scanner(s)) collect patient's brain image data and transmits the data to a reception component 2402 for being further processing. The reception component 2402 then transmits the image data to an image process component 2403. It should be noted that in other embodiment, the image acquiring device(s), may directly transmit the image data to the image process component 2403, without using the reception component 2402.

(85) The image process component 2403 processes the steps 2.1-2.6 to obtain pseudo b.sub.0 image and GM and WM segments information. It then transmits the GM and WM segments information to a registration component 2406 for step 3.1-3.3 to obtain the deformation map. In parallel, the pseudo b.sub.0 image information and DTI data were transmitted to an artefact correction component 2405 for step 4.1-4.4, during which the artefact-corrected DTI image is obtained.

(86) In one embodiment, a prediction component 2407 then receives the deformation map from the registration component 2406, and artefact-corrected DTI data from the artefact correction component 2405. The prediction component 2407 may then proceed steps 5-8. In another embodiment, the steps 6.1-6.4 may take place at the registration component 2406 for obtaining FA and MD values of WM.

(87) FIG. 25 is a diagram 2500 illustrating an example of a hardware implementation for a processing system 2400. The processing system 2500 may be implemented with a bus architecture, represented generally by a bus 2503. The bus 2503 may include any number of interconnecting buses and bridges depending on the specific application of the processing system 2500 and the overall design constraints. The bus 2503 links together various circuits including one or more processors and/or hardware components, represented by one or more processors 2501, the image process component 2403, the artefact correction component 2405, the registration component 2406, the prediction component 2407, and a computer-readable medium/memory 2502. The bus 2503 may also link various other circuits such as timing sources, peripherals, voltage regulators, and power management circuits, etc.

(88) The processing system 2500 may be coupled to an image acquiring device 2401, which may be a MRI scanner or other clinical image acquiring device.

(89) The processing system 2500 includes one or more processors 2501 coupled to a computer-readable medium/memory 2502. The one or more processors 2501 are responsible for general processing, including the execution of software stored on the computer-readable medium/memory 2502. The software, when executed by the one or more processors 2501, causes the processing system 2500 to perform the various functions described supra for any particular apparatus. The computer-readable medium/memory 2502 may also be used for storing data that is manipulated by the one or more processors 2501 when executing software. The processing system 2500 further includes at least one of the image process component 2403, the artefact correction component 2405, the registration component 2406, the prediction component 2407. The components may be software components running in the one or more processors 2501, resident/stored in the computer readable medium/memory 2502, one or more hardware components coupled to the one or more processors 2501, or some combination thereof.

(90) It is understood that the specific order or hierarchy of blocks in the processes/flowcharts disclosed is an illustration of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of blocks in the processes/flowcharts may be rearranged. Further, some blocks may be combined or omitted. The accompanying method claims present elements of the various blocks in a sample order, and are not meant to be limited to the specific order or hierarchy presented.

Example 1

(91) This artifact correction steps 4.1-4.3 and FIG. 5 is presented in a more detailed manner in FIG. 14-20.

(92) In particular, the participant's brain is scanned by a MRI scanner. The dMRI data and the anatomical image (T1w or T2w) are acquired. (FIG. 14.) The anatomical image is converted to the pseudo b.sub.0 image, which has similar image contrast to the real b.sub.0 image. (FIG. 15) The real b.sub.0 image is extracted from the dMRI data. (FIG. 16) The pseudo b.sub.0 image is rigidly registered to the real b.sub.0 image. This registered image is denoted as f.sub.0:Ω.fwdarw..sup.←f.sub.0:Ω.fwdarw..sup.← and the real b.sub.0 b.sub.0 image is f.sub.1:Ω.fwdarw..sup.←f.sub.1:Ω.fwdarw..sup.←. Here Ω⊂.sup.3Ω⊂.sup.3 denotes the image domain. The coordinate of f.sub.0f.sub.0 is r∈Ωr∈Ω. The PE direction of f.sub.1f.sub.1 is p∈.sup.2p∈.sup.2. (FIG. 17)

(93) It is assumed that the bias field is multiplicative and model it with e.sup.β, where β:Ω.fwdarw.. In this way, would be similar to f.sub.1. The smoothness of the β field is achieved via differential operator L.sub.β, with the norm of β defined as ∥β∥.sup.2=L.sub.ββ|β.sub.2 According to FIG. 18, ψ:Ω.fwdarw. is used to denote the field map (in unit of Hz). The relation between the distortion map and the field map is
φ(r)=r+k.Math.ψ(r).Math.p,∀r∈Ω,  (1)
where k=τV, τ is the effective echo-spacing time and Vis the filed of view if the DW image in the PE direction. Let v=kψ be the initial velocity. The smoothness of v is achieved via differential operator L.sub.v, with the norm of v defined as ∥v∥2=L.sub.vv|v2.

(94) The distortion corrected image is |Dφ|f.sub.1∘φ, where ∘ denotes function composition. (FIG. 18)

(95) FIG. 19 shows the process to estimate β and v. The cost function to be minimized is E.sub.1+E.sub.d+E.sub.β+E.sub.v, where

(96) $\begin{array}{cc}{E}_d=\frac{1}{2}{\sigma }_d{\int }_{r\in \Omega }{.Math.D\phi .Math.}^{-1}{\left(e^{\beta }{f}_0-.Math.D\phi .Math.f_1\circ \phi \right)}^{2}dr& \left(2\right)\end{array}$$E_{\beta }=\frac{1}{2}{\sigma }_{\beta }{.Math.L_{\beta }\beta .Math.}^2$$E_v=\frac{1}{2}{\sigma }_v{.Math.L_{v}v.Math.}^2.$

(97) We use the sum of squared differences (SSD) to quantify the data-matching between the real and pseudo b.sub.0 images (E.sub.d), with the regularizations penalizing non-smooth bias field (E.sub.β) and non-smooth initial velocity (E.sub.v). Parameters σ.sub.d, σ.sub.β, and σ.sub.v are the weightings for the data-matching and the regularization terms, respectively.

(98) For notational convenience, we define some assistant functions. The function b=vec(A) converts the 3D field A:Ω.fwdarw. to the vector b∈.sup.n.sup.xyz.sup.×1 by concatenating the field column by column, where n.sub.xyz is the total number of voxels. The reverse operation is achieved by A=ivec(b). In addition, the function B=diag(b) converts the b vector to the diagonal matrix B.

(99) The Gauss-Newton approach is used to update the values of β. Let
g.sub.β=|Dφ|.sup.−1e.sup.βf.sub.0(e.sup.βf.sub.0−|Dφ|f.sub.1∘φ)
H.sub.β=2|Dφ|.sup.−1(e.sup.βf.sub.0).sup.2,  (3)
and b.sub.β=vec(β), g.sub.β=vec(g.sub.β), and H.sub.β=diag(vec(H.sub.β). The descent deviation is computed via
δb.sub.β=(σ.sub.dH.sub.β+L.sub.β).sup.−1(σ.sub.dg.sub.β+L.sub.βb.sub.β),  (4)
or in the 3D field form δβ=ivec(δb.sub.β), then the β field is updated by
β.sup.(iter+1)=B.sup.(iter)−γ.sub.β.Math.δβ.sup.(iter),  (5)
Where 0<γ.sub.β≤1 is a scaling factor controlling the descent size. The differential operator L.sub.β is implemented through
L.sub.ββ|β.sub.2=λ.sub.β,1∥∇β(r)∥.sup.2+λ.sub.β,2∥∇.sub.2β(r)∥.sub.2dr,  (6)
where the first term in the integral is the membrane energy and the second is the bending energy, λ.sub.β,1 and λ.sub.β,2 control their strength of regularization, respectively. Practically, this differential operator is expressed in the matrix form L.sub.β∈.sup.n.sup.xyz.sup.×n.sup.xyz.

(100) Same as the bias field, Gauss-Newton approach is used to update the initial velocity field v. Let
g.sub.v=e.sup.βf.sub.0∇p(|Dφ|.sup.−1(e.sup.βf.sub.0−|Dφ|f.sub.1∘φ))
H.sub.v=|Dφ|.sup.2∇.sub.p(|Dφ|.sup.−1e.sup.βf.sub.0).Math.∇.sub.p.sup.T(|Dφ|.sup.−1e.sup.βf.sub.0).  (7)
Here ∇.sub.p is the gradient along the p direction. Also let b.sub.v=vec(v), g.sub.v=vec(g.sub.v), H.sub.v=diag(vec(H.sub.v)), the descent deviation is computed via
δb.sub.v=(σ.sub.dH.sub.v+L.sub.v).sup.−1(o.sub.dg.sub.v+σ.sub.vL.sub.vb.sub.v),  (8)
or in the 3D field form δv=ivec(δb.sub.v), then the v field is updated by
v.sup.(iert+1)=v.sup.(iter)−γ.sub.v.Math.δv.sup.(iter),  (9)
where 0<γ.sub.v≤1 controls the descent size. The differential operator L.sub.v is defined with the same form as L.sub.β, where the strength of membrane energy and that of bending energy are controlled by Δ.sub.v,1 and λ.sub.v,2, respectively. This differential operator is encoded in the matrix L.sub.v∈.sup.n.sup.xyz.sup.×n.sup.xyz.

(101) Compare to existing method, the current registration method is dedicated for the susceptibility-induced distortions, which is a 1D deformation along the PE direction. While the first derivative (g.sub.v) has been shown previously, the Hessian (H.sub.v) which is an approximation to the second derivative is our invention. Therefore, this method estimates the field map using a Gauss-Newton optimization scheme, which is more efficient as compared to existing methods.

(102) Moreover, a term called “bias field” is introduced to model the contrast discrepancy between the real b.sub.0 image and the pseudo b.sub.0 image. Existing anatomical image-base methods do not explicitly consider the contrast discrepancy in the estimation procedures.

(103) FIG. 20 shows an example of using the proposed method. As it can be observed, the raw b.sub.0 image (FIG. 20(a)) suffers from large distortions around the frontal regions, with reference to the pseudo b.sub.0 image (FIG. 20(e)). If the artefact models do not take the intensity biases into consideration, the method falls into local minima, rendering the distortions not adequately corrected, such as those regions indicated by the green arrows in the b.sub.0 image (FIG. 20(b)) and the fractional anisotropy (FA) map (FIG. 20(c)). On the other hand, using the bias field (FIG. 20(i)) to model the intensity biases can result in a proper registration, hence the b.sub.0 image (FIG. 20(f)) and the FA map (FIG. 20(g)) aligned to the pseudo b.sub.0 image very well. The field map estimated using the model with intensity biases (FIG. 20(h)) is much smoother than that without (FIG. 20(d)). An explanation to the results is the locally large image discrepancy between the real and pseudo b.sub.0 images. Recall that two primary sources contribute to the image discrepancy: spatial discrepancy (by susceptibility-induced distortions) and the contrast discrepancy (by intensity biases). If the algorithm disregards the contribution of the intensity biases, the algorithm would attempt to reduce the cost function (i.e., SSD) by spatially aligning the registered images. It is well known that registration, particularly non-linear, is an ill-posed problem. In this manner, registering regions with large intensity biases is easy to result in wrong point-by-point correspondence and wrong field map.

(104) The results reveal that (1) the estimated field map could adequately correct the susceptibility-induced distortions, and (2) modeling the intensity biases could facilitate the correction for the susceptibility-induced distortions.

Example 2

(105) Method

(106) 1. Subjects

(107) The participants in OASIS-3 were enrolled by different projects related to Knight ADRC. The participants were (1) generally healthy and cognitively normal (CDR=0) individuals with or without a family history of AD, and (2) generally healthy individuals with CDR=0.5, 1 or 2. The exclusion criteria included medical conditions that precluded longitudinal participation, e.g. end-stage renal disease requiring dialysis or contraindications for the MRI study, e.g. pacemaker implantation. All participants gave informed consent following procedures approved by the Institutional Review Board of Washington University School of Medicine.

(108) 2. Data Selection

(109) Participants in OASIS-3 undertook one to multiple sessions of brain MRI scanning on three 3T MRI scanners (Siemens, Erlangen, Germany) including two scanner models, i.e., Biograph and TIM Trio. To reduce scanner-wise variability, MRI data acquired from the same scanner model (TIM Trio) using the same acquisition scheme for T1w imaging and diffusion tensor imaging (DTI) were selected for the analysis. Since the analysis required acceptable image quality of T1w and DTI data, participants whose images presented severe image artefact such as motion blurring on T1w or failed artefact correction on DTI were excluded. Consequently, a total of 529 participants in 575 MRI scanning sessions were enrolled for subsequent analysis.

(110) 3. MRI Data Acquisition

(111) As presented above in FIG. 2 and related description, T1w data were acquired using the 3-dimensional magnetization prepared rapid gradient echo sequence. The imaging parameters were detailed as follow: repetition time (TR)=2400 ms, echo time (TE)=3.16 ms, inversion time (TI)=1000 ms, field-of-view (FOV)=256×256×176 mm3, and matric size=256×256×176. DTI data were acquired using diffusion-weighted 2-dimensional single shot spin-echo echo planar imaging using 24 different magnitudes of diffusion sensitivity (b-values) corresponding to 24 different directions (b-vectors), repeated twice. The b-values and b-vectors are listed in the Supplementary File 1. The imaging parameters were described as follow: TR=14500 ms, TE=112 ms, FOV=224×224 mm.sup.2, matric size=112×112, slice thickness=2 mm. The two DTI acquisitions were concatenated to form a single DTI dataset, and so there were one T1w data and one DTI data in each MR session for each participant.

(112) 4. Image Processing

(113) All MRI data were processed following the procedures described below. It entailed tissue segmentation, artefact correction, diffusion tensor estimation, image registration and diffusion index extraction.

(114) 4.1 As presented in FIG. 3 and related description, for brain segmentation on T1w image, the T1w image was processed using the Segment toolbox in the SPM12 software (Wellcome Trust Centre for Neuroimaging, University College London, London, UK). The toolbox corrected signal intensity inhomogeneity and segmented the T1w image into various tissue components, including gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), bone, scalp, and others. The outputs of the Segment toolbox were six TPMs, each represented a brain tissue component, and the T1w image with intensity inhomogeneity being corrected. The corrected T1w image were used in the subsequent artefact correction on DTI data, and the TPMs of GM and WM were used for spatial normalization of the images to the MNI space.

(115) 4.2 Artefact correction on DTI data: As presented in FIG. 5 and related description, the raw DTI data suffered from various artefacts, including susceptibility-induced distortion, eddy current-induced distortion, head motion, and intensity inhomogeneity. The DTI data was processed using a novel registration-based process which corrected these artefacts in an integrated framework. Briefly, this process registered the raw DTI data to a pseudo b.sub.0 image which was synthesized by inverting the contrast of the T1w image, intensity inhomogeneity of which had been corrected in the step 4.1. In this manner, the pseudo b.sub.0 image served as the reference image without distortion and intensity inhomogeneity. By incorporating the artefact models in an integrated framework, the process was able to reduce the artefacts of the DTI data. Meanwhile, the corrected DTI data was readily aligned with the space of T1w image when the estimation achieved convergence.

(116) 4.3 Diffusion tensor estimation: According to FIG. 6, the artefact-corrected DTI data was processed to estimate the diffusion tensor using the approach proposed by (Koay et al., 2007). Briefly, the weighted linear least squares method was first conducted, the results were then employed as the initial estimation for the constrained nonlinear least squares method to obtain the diffusion tensors, which were ensured to be positive-definite (i.e., the eigenvalues of the diffusion tensors were all positive). The associated fractional anisotropy (FA) and mean diffusivity (MD) were derived from the estimated diffusion tensor at each pixel. The MD and FA values were determined using the standard formula: MD=(λ1+λ2+λ3)/3, and FA=[3(Δλ1.sup.2+Δλ2.sup.2+Δλ3.sup.2)/2(λ1.sup.2+λ2.sup.2+λ3.sup.2)].sup.1/2, where λ1, λ2 and λ3 denoted the first, second and third eigenvalues of the diffusion tensor, respectively, and Δ1.sup.1, Δ1.sup.2 and Δ1.sup.3 represented λ1-MD, λ2-MD, and λ3-MD, respectively.

(117) 4.4 Spatial normalization to the MNI space: The TPMs of GM and WM, segmented from the T1w image, were registered to the ICBM152 template (defined in the MNI space) using a variant of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) algorithm (Hsu et al., 2012, Hsu et al., 2015, Beg et al., 2005; Miller et al., 2006). Specifically, the initial velocity situated in the ICBM152 space was iteratively estimated by shooting this velocity along the time dimension toward the individual's native space. On convergence, the associated deformation map could transform the individual's TPMs to match the ICBM152 TPM template. In the registration, the course was divided into 10 uniform intervals, and an isotropic Gaussian filter with 10 mm full width at half maximum (FWHM) was used to ensure the smoothness of the initial velocity.

(118) 4.5 Diffusion index extraction: The FA and MD maps in the native space were normalized to the MNI space through the deformation map derived from the LDDMM registration. The FA and MD values in the WM pixels were extracted using the mask created from the WM TPM of the ICBM152 template. Since FA and MD were DTI indices representing white matter microstructural property, these values were used as the features in modeling the dMRI-brain age.

(119) All the image processing procedures were conducted using in-house programs in MATLAB (The MathWorks, Inc., Natick, Mass., USA) except the brain segmentation on T1w image for which SPM12 was used.

(120) 5. Grouping According to CDR Track Records

(121) The participants enrolled in the study were grouped into 9 groups according to the track records of their CDR values. The 9 groups were named as follow: [CN]-Modeling, [CN1]-to-CN2, CN1-to-[CN2], [CN]-to-D1, D1-to-[CN], [D1]-to-CN, [nD1], [D1]-to-D2, and [nD2]. All groups were mutually exclusive except [CN1]-to-CN2 and CN1-to-[CN2] which recruited the same participants, undertaking the first and second MRI scanning, respectively. The characteristics of the 9 groups were described below. There were 475 people included in the analysis after grouping, 258 participants in the CN-Modeling group and 217 participants in the rest of the groups.

(122) 5.1 [CN]-Modeling

(123) This group comprised participants who were cognitively normal (CDR=0, CN for short) and stable. The CDR was 0 in all clinical records and had at least one such record within 1 year before or after the MRI scanning. The data in this group served as the training data to build the dMRI-brain age model. [CN] denoted the cognitive status of CN around the time of MR scanning.

(124) 5.2 [CN1]-to-CN2

(125) This group comprised 46 participants who were cognitively normal and stable. The screening criteria were the same as the [CN]-Modeling group. The data in this group served as the testing data of the dMRI-brain age modeling. [CN1] denoted that the participants were CN around the time of the first MR scanning.

(126) 5.3 CN1-to-[CN2]

(127) Participants in this group (N=46) were the same participants as those in [CN1]-to-CN2. As described above, the MRI scanning date in this group was later than that in [CN1]-to-CN2 with the inter-scan interval of 2.91±0.67 years. [CN2] denoted that the participants were CN around the time of the second MR scanning.

(128) 5.4 [CN]-to-D1

(129) Participants in this group (N=34) were cognitively normal within the interval of ±180 days from the date of MRI scanning but converted to very mild symptomatic AD (CDR=0.5, D1 for short) after the interval.

(130) 5.5 D1-to-[CN]

(131) Participants in this group (N=25) were cognitively normal within the interval of ±180 days from the MRI scanning date but were in D1 before the interval.

(132) 5.6 [D1]-to-CN

(133) Participants in this group (N=26) were in D1 within the interval of ±180 days from the MRI scanning date but turned to CN after the interval. [D1] denoted the cognitive status of D1 around the time of MRI scanning.

(134) 5.7 [nD1]

(135) Participants in this group (N=34) were in D1 within the interval of ±180 days from the MRI scanning date. After the interval, 19 participants had at least one clinical record showing that they stayed in the D1 stage, while rest of the participants (N=15) did not have any record of the CDR available. Therefore, nominal D1 (nD1 for short) was named for this group. [nD1] denoted that the participants were in nominal D1 around the time of MRI scanning.

(136) 5.8 [D1]-to-D2

(137) Participants in this group (N=28) were in D1 within the interval of ±180 days from the MRI scanning date but converted to mild symptomatic AD (CDR=1, D2 for short) after the interval.

(138) 5.9 [nD2]

(139) Participants in this group (N=24) were in D2 within the interval of ±180 days from the MRI scanning date. After the interval, 13 participants converted to moderate symptomatic AD (CDR=2, D3 for short), 6 participants had at least one clinical record showing that they stayed unchanged, and 5 participants did not have any CDR record. Therefore, nominal D2 (nD2 for short) was named for this group. [nD2] denoted that the participants were in nominal D2 around the time of MRI scanning.

(140) Table 1 lists the demographics of the people in the analysis and Table 2 summarizes the statistical results of the pairwise comparison among the seven groups.

(141) Table 1. Demographics

(142) TABLE-US-00001 Elapsed CDR Time Sample Gender Age (sum of Education APOE PAD Group Category (years) size Female Male (years) MMSE box) (years) e4‡ (years) [CN]-Modeling Training 258 57.8% 42.2% 68.54 ± 29.24 ± 0.01 ± 16.22 ± 31.9% Data  8.75  1.17 0.06  2.47 [CN1]-to-CN2 Testing 2.91 ±  46 65.2% 34.8% 66.19 ± 29.17 ± 0.00 ± 16.13 ± 34.8% −0.86 ± Data 0.67†  8.13  0.93 0.00  2.60  5.48 CN1-to-[CN2] Statistical 2.91 ±  46 65.2% 34.8% 69.10 ± 29.09 ± 0.00 ± 16.13 ± 34.8% −0.75 ± Analysis 0.67†  7.86  1.15 0.00  2.60  5.53 [CN]-to-Dl Statistical 2.49 ±  34 50.0% 50.0% 75.51 ± 28.85 ± 0.04 ± 15.41 ± 38.2%  3.10 ± Analysis 1.17  6.45  1.42 0.14  2.70  7.72 D1-to-[CN] Statistical 3.06 ±  25 60.0% 40.0% 75.75 ± 29.08 ± 0.00 ± 16.08 ± 36.0%  2.90 ± Analysis 1.87  8.27  1.04 0.00  2.80  7.19 [D1]-to-CN Statistical 1.44 ±  26 38.5% 61.5% 74.87 ± 28.35 ± 0.94 ± 15.27 ± 42.3%  2.99 ± Analysis 0.58  7.29  1.57 0.59  2.44  7.16 [nDl] Statistical  34 50.0% 50.0% 76.23 ± 26.65 ± 1.91 ± 14.91 ± 52.9%  5.48 ± Analysis  7.21  2.91 0.97  3.39  6.68 [D1]-to-D2 Statistical 2.18 ±  28 32.1% 67.9% 75.45 ± 25.71 ± 2.45 ± 15.29 ± 57.1%  9.21 ± Analysis 1.06  6.20  2.45 1.07  3.09  6.14 [nD2] Statistical  24 33.3% 66.7% 76.40 ± 21.13 ± 5.46 ± 13.71 ± 54.2% 12.33 ± Analysis 10.31  3.89 1.11  2.93 10.89 MMSE: Mini-Mental State Examination; CDR: Clinical Dementia Rating; CN: CDR = 0 (Cognitively Normal); D1: CDR = 0.5; D2: CDR = 1; PAD: Predicted Age Difference. †The elapsed time here represents the inter-scan interval between [CN1]-to-CN2 and CN1-to-[CN2] ‡APOE e4 means the percentage in the group population that poses either one or two e4 alleles.

(143) TABLE-US-00002 TABLE 2 Adjusted p-value of pairwise comparison (two sample t-test; adjusted for multiple comparisons using the Benjamini-Hochberg method) Comparison PAD of Group 1 PAD of Group 2 Original Adjusted Significant # Name of Group 1 (years) Name of Group 2 (years) p-value p-value (FDR of 0.05) 1 CN1-to-[CN2] −0.75 ± 5.53   [CN]-to-D1 3.10 ± 7.72 0.0112 0.0213 Yes 2 CN1-to-[CN2] −0.75 ± 5.53   D1-to-[CN] 2.90 ± 7.19 0.0197 0.0318 Yes 3 CN1-to-[CN2] −0.75 ± 5.53   [D1]-to-CN 2.99 ± 7.16 0.0158 0.0276 Yes 4 CN1-to-[CN2] −0.75 ± 5.53   [nD1] 5.48 ± 6.68 0.0000 0.0001 Yes 5 CN1-to-[CN2] −0.75 ± 5.53   [D1]-to-D2 9.21 ± 6.14 0.0000 0.0000 Yes 6 CN1-to-[CN2] −0.75 ± 5.53   [nD2] 12.33 ± 10.89 0.0000 0.0000 Yes 7 [CN]-to-D1 3.10 ± 7.72 D1-to-[CN] 2.90 ± 7.19 0.9219 1.0000 No 8 [CN]-to-D1 3.10 ± 7.72 [D1]-to-CN 2.99 ± 7.16 0.9555 1.0000 No 9 [CN]-to-D1 3.10 ± 7.72 [nD1] 5.48 ± 6.68 0.1774 0.2192 No 10 [CN]-to-D1 3.10 ± 7.72 [D1]-to-D2 9.21 ± 6.14 0.0012 0.0028 Yes 11 [CN]-to-D1 3.10 ± 7.72 [nD2] 12.33 ± 10.89 0.0004 0.0020 Yes 12 D1-to-[CN] 2.90 ± 7.19 [D1]-to-CN 2.99 ± 7.16 0.9663 0.9663 No 13 D1-to-[CN] 2.90 ± 7.19 [nD] 5.48 ± 6.68 0.1610 0.2255 No 14 D1-to-[CN] 2.90 ± 7.19 [D1]-to-D2 9.21 ± 6.14 0.0012 0.0035 Yes 15 D1-to-[CN] 2.90 ± 7.19 [nD2] 12.33 ± 10.89 0.0008 0.0028 Yes 16 [D1]-to-CN 2.99 ± 7.16 [nD1] 5.48 ± 6.68 0.1696 0.2227 No 17 [D1]-to-CN 2.99 ± 7.16 [D1]-to-D2 9.21 ± 6.14 0.0012 0.0031 Yes 18 [D1]-to-CN 2.99 ± 7.16 [nD2] 12.33 ± 10.89 0.0007 0.0030 Yes 19 [nD1] 5.48 ± 6.68 [D1]-to-D2 9.21 ± 6.14 0.0271 0.0406 Yes 20 [nD1] 5.48 ± 6.68 [nD2] 12.33 ± 10.89 0.0044 0.0093 Yes 21 [D1]-to-D2 9.21 ± 6.14 [nD2] 12.33 ± 10.89 0.2006 0.2340 No CDR: Clinical Dementia Rating; CN: CDR = 0 (Cognitively Normal); D1: CDR = 0.5; D2: CDR = 1; PAD: Predicted Age Difference; FDR: False Discovery Rate.

(144) 6. dMRI-Brain Age

(145) Data in the CN-Modeling group were used to train the dMRI-brain age model. The Gaussian process regression method was used to regress the chronological age of the participants against the FA and MD values inside the WM region. After the training process, this model was applied to each MRI data in other groups to estimate the dMRI-brain age. The predicted age difference (PAD) was calculated by subtracting the chronological age from the dMRI-brain age. The performance of the model was tested in the CN1 group and quantified by the mean absolute error (MAE) and the Pearson's correlation coefficient (r) between the dMRI-brain age and chronological age.

(146) 7. Statistical Inference

(147) PAD values were compared between any two of the seven groups, namely CN1-to-[CN2], [CN]-to-D1, D1-to-[CN], [D1]-to-CN, [nD1], [D1]-to-D2, and [nD2]; amounting to 21 pairwise comparisons. For each pair of groups, two sample t-test was conducted. The Benjamini-Hochberg procedure with a false discovery rate (FDR) of 0.05 was used to determine if a test was considered statistically significant.

(148) Results

(149) i. Comparison Between Different CDR Levels

(150) FIG. 10 shows the groups with different CDR levels, i.e., CN1-to-[CN2], [nD1], and [nD2]. The mean and standard deviation of PAD values of CN1-to-[CN2], [nD1], and [nD2] were −0.75±5.53, 5.48±6.68, and 12.33±10.89 years, respectively. All comparisons on the three pairs of groups showed statistically significant differences (Table 2, #4, #6, #20). The results indicated that the higher the CDR level, the higher the PAD values.

(151) ii. Comparison within Participants with Baseline CDR of 0.5

(152) FIG. 11 shows the participants who had the CDR of 0.5 at the time of MRI scanning who reverted to CN ([D1]-to-CN), stayed relatively stable ([nD1]), or converted to D2 ([D1]-to-D2) in 2 to 3 years (FIG. 11). The mean and standard deviation of PAD values of [D1]-to-CN, [nD1], and [D1]-to-D2 were 2.99±7.16, 5.48±6.68, 9.21±6.14 years, respectively. The PAD value in [D1]-to-D2 was significantly higher than that in [D1]-to-CN (adjusted p=0.0031) and that in [nD1] (adjusted p=0.0406) (Table 2, #17, #19). The PAD value in [D1]-to-CN was lower than that in [nD1], but the comparison between the two groups did not reach statistical significance (adjusted p=0.2227) (Table 2, #16).

(153) iii. Comparison within Participants with Baseline CDR of 0

(154) FIG. 12 shows the participants who were in CN at the time of MRI scanning but presented relatively stable (CN1-to-[CN2]) or unstable states ([CN]-to-D1 and D1-to-[CN]) in 1 to 2 years. The mean and standard deviation of PAD values of CN1-to-[CN2], [CN]-to-D1, and D1-to-[CN] were −0.75±5.53, 3.10±7.72, and 2.90±7.19 years, respectively. Both groups of [CN]-to-D1 (adjusted p=0.0213) and D1-to-[CN] (adjusted p=0.0318) had PAD values significantly higher than the CN1-to-[CN2] group (Table 2, #1, #2). There was no significant difference between the [CN]-to-D1 and D1-to-[CN] groups (adjusted p=1.0000) (Table 2, #7).

(155) iv. Spectrum of PAD

(156) FIG. 13 shows the seven groups sorted by their mean PAD values, and we can observe five sub-groups as follow. The first sub-group comprised the participants with relatively stable normal cognition (CN1-to-[CN2]), and the mean PAD of which was approximately 0 years. The second sub-group included those exhibiting transitions between CN and D1 (i.e. [CN]-to-D1, D1-to-[CN], and [D1]-to-CN), and the mean PADs were approximately 3 years. The third, fourth and fifth sub-groups were the participants with [nD1], [D1]-to-D2, and [nD2], with the mean PAD of 5.48, 9.21 and 12.33 years, respectively.

(157) In conclusion, for older people with the CDR of 0, 0.5 and 1, DTI-derived PAD corresponds to the CDR scores, and PAD differs between people with relatively stable CDR and those with the CDR changed to higher scores in a couple of years. The results produced by the present invention suggest that PAD is essential in grading the dementia severity and predicting the change of severity in a few years. The capability of PAD can solve the current limitations of the CDR which is subjected to inaccurate information obtained from unreliable informants and inability to predict CDR change in the coming years. Therefore, the solution in the present invention can be a surrogate marker of the concomitant CDR and, furthermore, a prediction marker of the CDR change 2 to 3 years from the baseline.

(158) Association of PAD with low to moderate severity of dementia

(159) In the present invention, it is demonstrated that PAD derived from white matter microstructural property, as indicated by FA and MD of DTI, was associated with the CDR of 0, 0.5 and 1 (FIG. 10).

(160) PAD in Patients with the Baseline CDR of 0.5 is Associated with the CDR Change in 1 to 2 Years

(161) In patients with the CDR of 0.5, they presented different outcomes in approximately 1 to 2 years (FIG. 11). The present invention demonstrated that PADs among these patients were significantly different. Patients whose CDR changed from 0.5 to 1 in approximately 2 years had mean PAD of 9.21 years, significantly higher than patients with constant CDR of 0.5 (mean PAD=5.48 years, adjusted p=0.0406) or those with the baseline CDR of 0.5 but turned to 0 in approximately 1.5 years (mean PAD=2.99 years, adjusted p=0.0031). The results have clinical implications. Currently exercise and cognitive training are the interventions recommended for patients with MCI (Petersen et al., 2018), whose CDR is mostly 0.5. However, no information is available to predict each patient's cognitive decline, and therefore, the intervention cannot be further tailored to each individual patient according to the decline risk. Findings in the present invention imply that PAD may be useful to stratify patients with MCI into high risk group (CDR changed from 0.5 to 1) and low risk group (constant CDR of 0.5 or CDR changed from 0.5 to 0). Appropriate intervention can then be designed for different risk groups.

(162) PAD in Patients with Baseline CDR of 0 is Associated with the CDR Change in 2 to 3 Years

(163) The present invention also showed that participants with constant CDR of 0 had significantly different PAD from those with baseline CDR of 0 but turned to 0.5 in approximately 2.5 year (mean PAD: −0.75 years vs. 3.10 years, adjusted p=0.0213; FIG. 12). The results indicate that there is a variation of PAD even in cognitively normal people. Multiple brain age studies on cognitively normal people have demonstrated that PAD is associated with lifestyle factors, such as physical exercise, alcohol and tobacco consumption and interpersonal relationship, and cardiovascular risks, such as blood pressure, diabetes and body mass index (Cole, 2020; Hatton et al., 2018; Kolbeinsson et al., 2020; Ning et al., 2020; Ronan et al., 2016; Steffener et al., 2016). These studies suggest that, to some extent, the variation of PAD arises from health-related risk factors of each individual, the more the risk factors, the higher the PAD. Moreover, many epidemiological studies have shown that poor health-related risk factors have higher risks of afflicting dementia later in life (Akbaraly et al., 2019; Cations et al., 2016; Fayosse et al., 2020; Kivipelto et al., 2006; Mukadam et al., 2019; WHO, 2019). To sum, previous studies reported the association between PAD and lifestyle risk factors, and the associations between lifestyle factors and dementia. The present study further suggests that for the cognitively healthy people above 60 years old (approximately mean−SD=69.10-7.86 years in Table 1, CN1-to-[CN2]), those with PAD higher than 10 years (approximately mean+SD=3.10+7.72 years in Table 2, [CN]-to-D1) might be more likely to become cognitively impaired (CDR=0.5) in 3 years than those with PAD lower than −6 years (approximately mean—SD=−0.75-5.53 years in Table 2, CN1-to-[CN2]).

(164) Correspondence of PAD with Stable and Meta-Stable CDR States

(165) The present invention found that some of the groups in the study population exhibited relatively stable CDR scores in 2 to 3 years, i.e., CN1-to-[CN2], [nD1] and [nD2], while some groups presented relatively rapid transitions of the CDR, i.e., [CN]-to-D1, D1-to-[CN], [D1]-to-CN, and [D1]-to-D2 (Table 2). Notably, when labeling each group with PAD, a continuum of PAD revealed across the relatively stable and meta-stable groups (FIG. 13). PAD was smallest in the cognitively normal group (CN, −0.75 years), followed by 3 meta-stable groups ([CN]-to-D1, D1-to-[CN], and [D1]-to-CN, presenting 3.10, 2.90, and 2.99 years, respectively), the relatively stable group of CDR=0.5 ([nD1], 5.48 years), another meta-stable group ([D1]-to-D2, 9.21 years), and the largest in the relatively stable group of CDR=1 or beyond ([nD2], 12.33 years). The spectrum of PAD corresponding to the stable and meta-stable states of the CDR provides a new perspective to the CDR in elderly people. Given a CDR score of 0, 0.5, or 1 of an individual, we could use PAD to predict the probability of staying cognitively normal (CDR=0) or transitioning to a different CDR stage (CDR=0.5 or 1) in a couple of years.

Example 3

(166) Another example of the current invention provides proof of concept to support the proposed intended use of the device. The computer based program reads the specified brain image data of an individual as inputs and estimates the person's PAD, and according to the predefined cutoff value of PAD, the program determines whether the person is likely/or unlikely to be cognitively normal if he/she were assessed by the CDR.

(167) A retrospective study on a cohort of a databank OASIS-3 was performed with the current invention, which evaluated the performance of PAD in identifying patients who were likely/or unlikely to be cognitively normal (CDR=0) by comparing the results with patient's CDR scores.

(168) To obtain positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, odds ratio (OR), and accuracy of distinguishing between CDR >0 and CDR=0 at different cutoff values of PAD.

(169) Data Selection

(170) Same to Example 3, participants in OASIS-3 of Example 3 undertook one to multiple sessions of brain MRI scanning on three 3T MRI scanners (Siemens, Erlangen, Germany) including two scanner models, i.e., Biograph and TIM Trio. To reduce scanner-wise variability, MRI data acquired from the same scanner model (TIM Trio) using the same acquisition scheme for T1-weighted (T1w) imaging and diffusion tensor imaging (DTI) were selected for the analysis. Since the analysis required acceptable image quality of T1w and DTI data, participants whose images presented severe image artefact such as motion blurring on T1w or failed artefact correction on DTI were excluded. A total of 529 participants in 575 MRI scanning sessions were enrolled for subsequent analysis met the selection criteria.

(171) Grouping According to the CDR

(172) The participants enrolled in the study were grouped into 5 groups according to the track records of their CDR values. The 5 groups were named as follow: [CN]-Modeling, [CN1]-to-CN2, CN1-to-[CN2], [nD1], and [nD2]. All groups were mutually exclusive except [CN1]-to-CN2 and CN1-to-[CN2] which recruited the same participants, undertaking the first and second MRI scanning, respectively. The characteristics of the 5 groups were described below.

(173) i) [CN]-Modeling: This group comprised 258 participants who were cognitively normal (CDR=0, CN for short) and stable. The CDR was 0 in all clinical records and had at least one such record within 1 year before or after the MRI scanning. The data in this group served as the training data to build the dMRI-brain age model. [CN] denotes the cognitive status of CN around the time of MR scanning.

(174) ii) [CN1]-to-CN2: This group comprised 46 participants who were cognitively normal and stable. The screening criteria were the same as the [CN]-Modeling group. The data in this group served as the testing data of the dMRI-brain age modeling. [CN1] denotes that the participants were CN around the time of the first MR scanning.

(175) iii) CN1-to-[CN2]: Participants in this group (N=46) were the same participants as those in [CN1]-to-CN2. As described above, the MRI scanning date in this group was later than that in [CN1]-to-CN2 with the inter-scan interval of 2.91±0.67 years. [CN2] denotes that the participants were CN around the time of the second MR scanning.

(176) iv) [nD1]: Participants in this group (N=34) were in D1 within the interval of ±180 days from the MRI scanning date. After the interval, 19 participants had at least one clinical record showing that they stayed in the D1 stage, while rest of the participants (N=15) did not have any record of the CDR available. Therefore, nominal D1 (nD1 for short) was named for this group. [nD1] denotes that the participants were in nominal D1 around the time of MRI scanning.

(177) v) [nD2]: Participants in this group (N=24) were in D2 within the interval of ±180 days from the MRI scanning date. After the interval, 13 participants converted to moderate symptomatic AD (CDR=2, D3 for short), 6 participants had at least one clinical record showing that they stayed unchanged, and 5 participants did not have any CDR record. Therefore, nominal D2 (nD2 for short) was named for this group. [nD2] denotes that the participants were in nominal D2 around the time of MRI scanning.

(178) Analysis and Statistics

(179) Brain age modeling: Data in the [CN]-Modeling group were used to train the dMRI-brain age model. The Gaussian Process Regression method was used to regress the chronological age of the participants against the DTI indices of the brain. After the training process, the model was applied to each MRI data in other groups to estimate the dMRI-brain age. The predicted age difference (PAD) was calculated by subtracting the chronological age from the dMRI-brain age. The performance of the model was tested in the [CN1]-to-CN2 group and quantified by the mean absolute error (MAE) and the Pearson's correlation coefficient (r) between the dMRI-brain age and chronological age.

(180) Group comparison: PAD values were compared between any two of the three groups, namely CN1-to-[CN2], [nD1], and [nD2], amounting to 3 pairwise comparisons. For each pair of groups, two sample t-test was conducted. The Benjamin-Hochberg procedure with a false discovery rate (FDR) of 0.05 was used to determine if a test was considered statistically significant.

(181) Performance of the intended use: To evaluate the performance of PAD in ruling out cognitively normal condition, we pooled the [nD1] and [nD2] groups to form a new group (named [nD1]+[nD2]) to represent the group with CDR >0 which is to be ruled out by our intended use. The receiver operating characteristic (ROC) curve analysis was performed to evaluate the accuracy of PAD in differentiating between CDR=0 and CDR >0. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, and odds ratio (OR) of differentiating between CDR >0 and CDR=0 was evaluated at different cutoff values of PAD.

(182) Results

(183) Demographics: Table 3 summarizes the demographics of the 5 groups including [CN]-Modeling (training data), [CN1]-to-CN2 (testing data) and 3 comparison groups, namely CN1-to-[CN2], [nD1], and [nD2]. Qualitatively, we can see a tendency of the increase in the CDR and PAD and the decrease in the MMSE from CN1-to-[CN2], [nD1] to [nD2].

(184) TABLE-US-00003 TABLE 3 Demographics Elapsed Education APOE Time Sample Gender Age MMSE (sum of e4‡ Group Category (years) Size Female Male (years) CDR box) (years) PAD (years) [CN]-Modeling Training 258 57.80% 42.20% 68.54 ± 29.24 ± 0.01 ± 16.22 ± 31.90% Data  8.75 1.17 0.06 2.47 [CN1]-to-CN2 Testing 2.91 ± 46 65.20% 34.80% 66.19 ± 29.17 ± 0.00 ± 16.13 ± 34.80%  −0.86 ± Data 0.67†  8.13 0.93 0.00 2.60 5.48 CN1-to-[CN2] Statistical 2.91 ± 46 65.20% 34.80% 69.10 ± 29.09 ± 0.00 ± 16.13 ± 34.80%  −0.75 ± Analysis 0.67†  7.86 1.15 0.00 2.60 5.53 [nD1] Statistical 34 50.00% 50.00% 76.23 ± 26.65 ± 1.91 ± 14.91 ± 52.90%    5.48 ± Analysis  7.21 2.91 0.97 3.39 6.68 [nD2] Statistical 24 33.30% 66.70% 76.40 ± 21.13 ± 5.46 ± 13.71 ± 54.20%   12.33 ± Analysis 10.31 3.89 1.11 2.93 10.89 MMSE: Mini-Mental State Examination; CDR: Clinical Dementia Rating; CN: CDR = 0 (Cognitively Normal); D1: CDR = 0.5; D2: CDR = 1; PAD: Predicted Age Difference, †: The elapsed time here represents the inter-scan interval between [CN1]-to-CN2 and CN1-to-[CN2]. ‡: APOE e4 means the percentage in the group population that poses either one or two e4 alleles.

(185) Performance of brain age prediction: The model performance tested in the [CN1]-to-CN2 group (testing data) gave MAE=4.35±3.39 years and r=0.77 (p-value =3.64×10.sup.−10).

(186) Group comparison: FIG. 21 shows the groups with different CDR levels, i.e., CN1-to-[CN2], [nD1], and [nD2]. The mean and standard deviation of PAD values of CN1-to-[CN2], [nD1], and [nD2] were −0.75±5.53, 5.48±6.68, and 12.33±10.89 years, respectively. All comparisons on the three pairs of groups showed statistically significant differences. The results indicated that the higher the CDR level, the higher the PAD values.

(187) ROC curve analysis: FIG. 22 shows the results of the ROC curve analysis. Area under the curve (AUC) was 0.82. Table 4 lists the performance of differentiating between CDR=0 and CDR >0 at different cutoff values of PAD. The best cutoff value of PAD was found to be 3 years. Using PAD=3 as the cutoff value, the performance of differentiating between CDR=0 and CDR >0 showed sensitivity=0.79, specificity=0.74, PPV=0.79, NPV=0.74, OR=10.86, and accuracy=0.77.

(188) TABLE-US-00004 TABLE 4 The performance of differentiating between CDR = 0 and CDR > 0 at different cutoff values of PAD. PAD TP FP FN TN OR ACC PPV NPV Sens. Spec. AUC 0 49 20 9 26 7.08 0.72 0.71 0.74 0.84 0.57 0.82 1 48 18 10 28 7.47 0.73 0.73 0.74 0.83 0.61 2 47 14 11 32 9.77 0.76 0.77 0.74 0.81 0.70 3 46 12 12 34 19.86 0.77 0.79 0.74 0.79 0.74 4 39 7 19 39 11.44 0.75 0.85 0.67 0.67 0.85 5 35 6 23 40 10.14 0.72 0.85 0.63 0.60 0.87 6 33 4 25 42 13.86 0.72 0.89 0.63 0.57 0.91 7 30 4 28 42 11.25 0.69 0.88 0.60 0.52 0.91 8 27 3 31 43 12.48 0.67 0.90 0.58 0.47 0.93 9 26 3 32 43 11.65 0.66 0.90 0.57 0.45 0.93 10 25 3 33 43 10.86 0.65 0.89 0.57 0.43 0.93 Abbreviation: PAD = predicted age difference, TP = true positive, FP = false positive, FN = false negative, TN = true negative, OR = odds ratio, ACC = accuracy, PPV = positive predictive value, NPV = negative predicitive value, Sens. = sensitivity, Spec. = specificity, AUC = area under the curve.

Conclusion

(189) The current invention shows that the best cutoff value of PAD for differentiating between CDR=0 and CDR >0 is 3 years, resulting in sensitivity=0.79, specificity=0.74, PPV=0.79, NPV=0.74, OR=10.86, and accuracy=0.77. However, the intended use is to rule out CDR=0 in patients who are suspected to have dementia. With this intended use, the performance of PPV, specificity and OR, rather than sensitivity and accuracy, become the end points of primary interest. In this case, PAD=6 years appears to be the best cutoff value, giving PPV=0.89, specificity=0.91, and OR=13.86. FIG. 23 shows the confusion matrix of the results using the cutoff PAD of 6, and the distribution of the two populations (i.e. CDR=0 and CDR >0) with respect to PAD. The results mean that for those whose PAD>=6 years, 89% of them are not cognitively normal (i.e. CDR >0), and for those who are cognitively normal (i.e. CDR=0), 91% of them have PAD<6 years. The OR of 13.86, defined as (TP×TN)/(FP×FN), means the ratio of the true outcome (TP and TN) with respect to the false outcome (FP and FN) is more than 10 folds. On the other hand, using PAD=6 years as the cutoff value, sensitivity=0.57 and NPV=0.63. The results mean that for those who are not cognitively normal (i.e. CDR >0), 57% of them have PAD>=6 years, and for those who have PAD<6 years, 63% are cognitively normal (i.e. CDR=0). In sum, using PAD=6 years as the cutoff value has satisfactory performance to identify patients who are very unlikely to be cognitively normal, but it performs poorly in identifying those who are cognitively normal. Therefore, we conclude that the satisfactory performance of PPV and OR supports the intended use of our proposed device.

(190) Importantly, it should be noted that the present invention is pioneering in the field of CDR estimation and prediction with respect to at least three aspects. First, the brain age model being built by the present invention is based on the white matter microstructural metrics, named dMRI-brain age and the resulting WM PAD. Second, what WM PAD does is to predict a person's concomitant measures of the CDR. Third, in addition, WM PAD also predicts the same person's CDR change 2 to 3 years from the time when dMRI-brain age is measured.

(191) In contrast to the present invention, the existing method for estimating the CDR either built the brain age model on the basis of morphometric features of gray matter obtained from T1M MRI, named GM brain age and the resulting GM PAD, and found associations between GM PAD and concomitant measures of the CDR (Beheshti et al., 2018); or calculated FW content from diffusion MRI and found that FW was associated with concomitant measures of the CDR and future status of the CDR (Maillard et al., 2019).

(192) Other researches and inventions in the field scrutinize the associations between MRI metrics and the whole array of cognitive performance measured by validated tools. In those results, it is reported that a particular MRI metric which is associated with a particular cognitive measure. However, the present invention establishes the unique correspondence between WM PAD and concomitant CDR, and the correspondence between WM PAD and CDR change 2 to 3 years later from the baseline. Such specific correspondence between dMRI-brain age and the CDR is not reported before and so it is not obvious to contemporary inventors.

(193) WM PAD has the advantages over prior solutions due to (1) better sensitivity to the CDR than GM brain age, and (2) less susceptibility to the cerebrospinal fluid (CSF) partial volume effect than the FW metric.

(194) First, WM PAD is more sensitive than GM brain age in distinguishing CDR scores. In the paper of Beheshti et al., they only showed the correlation between GM brain age and the CDR scores (from 0, 0.5 to 1). They did not show the results of comparison between brain age and the CDR, probably due to null results owing to marked overlaps between CDR scores (FIG. 7b in Beheshti's paper).

(195) On the other hand, dMRI-brain age maybe less susceptible to the CSF partial volume effect induced by brain atrophy than is FW metric. Hypothetically, FW content is mainly attributed by the CSF partial volume effect in white matter neighboring the ventricles or cerebral sulci. The FW content would be likely to increase as the brain becomes atrophic with age. In other words, FW content is confounded by brain atrophy. By contrast, WM PAD is derived from FA and MD in the whole brain white matter. Although FA and MD values are likely to be altered in the white matter pixels adjacent to the ventricles and cerebral sulci, the effect is negligible because the number of pixels with the partial volume effect is substantially small as compared with the whole brain white matter pixels.

(196) The foregoing description of the exemplary embodiments of the invention has been presented only for the purposes of illustration and description and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching.

(197) While there has been shown several and alternate embodiments of the present invention, it is to be understood that certain changes can be made as would be known to one skilled in the art without departing from the underlying scope of the invention as is discussed and set forth above and below including claims and drawings. Furthermore, the embodiments described above and claims set forth below are only intended to illustrate the principles of the present invention and are not intended to limit the scope of the invention to the disclosed elements.

(198) References cited in the instant application, which may include patents, patent applications and various publications, are cited and discussed in the description of this invention. The citation and/or discussion of such references is provided merely to clarify the description of the present invention and is not an admission that any such reference is “prior art” to the invention described herein. All references cited and discussed in the description of this invention, are incorporated herein by reference in their entireties and to the same extent as if each reference was individually incorporated by reference.