EP4618847A1

EP4618847A1 - Systems and method for tracking progression of tauopathy

Info

Publication number: EP4618847A1
Application number: EP23809204.3A
Authority: EP
Inventors: Ziad Serhal SAAD; Hartmuth Christian Kolb
Original assignee: Janssen Pharmaceutica NV
Current assignee: Janssen Pharmaceutica NV
Priority date: 2022-11-18
Filing date: 2023-11-17
Publication date: 2025-09-24
Also published as: CN120569161A; WO2024105257A1; JP2026500014A

Abstract

A method and Positron Emission Tomography (PET) system for tracking progression of tauopathy in a brain is provided. The method comprises obtaining first PET data and second PET data, which is obtained after the first PET data, of the brain generated with a tau-specific radioactive tracer, aligning the first and second PET data to an image of the brain comprising a plurality of voxels corresponding to regions of interest (ROI) within structures of the brain, generating a first set of tracer uptake data comprising tracer uptake data for each of the voxels and a second set of tracer uptake data for each of the voxels, and determining progression of tauopathy by comparing the baseline tracer uptake data and the follow-up tracer uptake data of each voxel to a progression threshold.

Description

TITLE OF INVENTION

SYSTEMS AND METHOD FOR TRACKING PROGRESSION OF TAUOPATHY

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit and priority of U.S. Provisional Application Serial No. 63/426,502, filed on November 18, 2022, the contents of which are herein incorporated by reference in their entirety.

FIELD OF INVENTION

[0002] The present invention relates to systems and methods of tracking progression of tauopathy in a brain of a subject using Positron Emission Tomography (PET).

BACKGROUND OF INVENTION

[0003] Alzheimer’s Disease (AD) is a degenerative brain disorder that damages and eventually kills neurons. AD is characterized clinically by progressive loss of memory, cognition, reasoning, judgment, and emotional stability that gradually leads to profound mental deterioration and ultimately death. AD is a very common cause of progressive mental failure (dementia) in aged humans. More than 5 million people in the United States are living with AD, and the number is growing with an aging population. Indeed, 10% of people over age 65 have AD, and it is the 5th leading cause of death in this population. Overall, AD is the 6th leading cause of death in the United States (1 in 3 seniors die with AD or another dementia), and it is estimated to cost the US $305 billion in 2020. AD has also been observed in ethnic groups worldwide and presents a major present and future public health problem.

[0004] The brains of individuals with AD exhibit characteristic lesions termed senile (or amyloid) plaques, amyloid angiopathy (amyloid deposits in blood vessels) and neurofibrillary tangles. Large numbers of these lesions, particularly amyloid plaques and neurofibrillary tangles of paired helical filaments, are generally found in several areas of the human brain important for memory and cognitive function in patients with AD.

[0005] Neurofibrillary tangles are primarily composed of aggregates of hyperphosphorylated tau protein. The main physiological function of tau is microtubule polymerization and stabilization. The binding of tau to microtubules takes place by ionic interactions between positive charges in the microtubule binding region of tau and negative charges on the microtubule lattice (Butner and Kirschner, J Cell Biol. 115(3):717-30, 1991). Tau protein contains 85 possible phosphorylation sites, and phosphorylation at many of these sites interferes with the primary function of tau. Tau that is bound to the axonal microtubule lattice is in a hypo-phosphorylation state, while aggregated tau in AD is hyper-phosphorylated, providing unique epitopes that are distinct from the physiologically active pool of tau (Iqbal et al.. Curr Alzheimer Res. 7(8)'. 656-664, 2010).

[0006] The progression of tauopathy in an AD brain follows distinct spreading patterns. A tauopathy transmission and spreading hypothesis has been described based on the Braak stages of tauopathy progression in the human brain and tauopathy spreading after tau aggregate injections in preclinical tau models (Frost c/ al... J Biol Chem. 284:12845-52, 2009; Clavaguera et al.. Nat Cell Biol. 11 :909-13, 2009). It is believed that tauopathy can spread in a prion-like fashion from one brain region to the next. This spreading process would involve an externalization of tau seeds that can be taken up by nearby neurons and induce further tauopathy. Positron emission tomography using tau-specific radiotracers (Tau PET) has been used to detect presence tau neurofibrillary tangles (NFTs) in patients. However, there remains a need for quantitative measurements for measuring spread of tau in patients progressing in tauopathy.

BRIEF SUMMARY OF THE INVENTION

[0007] One exemplary embodiment of the present invention is a method for tracking progression of tauopathy in a brain of a human subject using Positron Emission Tomography (PET). The tauopathy may be selected from the group consisting of familial Alzheimer's disease, sporadic Alzheimer's disease, frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), progressive supranuclear palsy, corticobasal degeneration, Pick's disease, progressive subcortical gliosis, tangle only dementia, diffuse neurofibrillary tangles with calcification, argyrophilic grain dementia, amyotrophic lateral sclerosis parkinsonismdementia complex, Down syndrome, Gerstmann-Straussler-Scheinker disease, Hallervorden- Spatz disease, inclusion body myositis, Creutzfeld- Jakob disease, multiple system atrophy, Niemann-Pick disease type C, prion protein cerebral amyloid angiopathy, subacute sclerosing panencephalitis, myotonic dystrophy, non-Guamanian motor neuron disease with neurofibrillary tangles, postencephalitic parkinsonism, chronic traumatic encephalopathy, and dementia pugulistica (boxing disease). In particular, the tauopathy is Alzheimer’s disease.

[0008] The method comprises obtaining baseline PET data of the brain of the subject generated with a tau-specific radioactive tracer at a baseline time point. Specifically, the tracer may be any tracer that binds to hyperphosphorylated tau, such as, for example, [¹⁸F]MK-6240, [¹⁸F]JNJ-311, [¹⁸F]JNJ-067 [¹⁸F]THK5317, [¹⁸F]THK5351, [¹⁸F]AV1451, [ⁿC]PBB3, [¹⁸F]PM-PBB3, [¹⁸F]RO-948, [¹⁸F]PI-2620, [¹⁸F]GTP1, and [¹⁸F]T808. The method also comprises obtaining first follow-up PET data of the brain of the subject generated with the tracer at a first follow-up time point. The first follow-up time point being after the baseline time point. The method further comprises aligning the baseline PET data and the first followup PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to regions of interest (ROI) within structures of the brain of the subject. The method further comprises generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the baseline PET data aligned to the voxel and a first follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first follow-up PET data aligned to the voxel. The method also further comprises determining progression of tauopathy from the baseline time point to the first follow-up time point in each voxel by comparing the baseline tracer uptake data and the first follow-up tracer uptake data of each voxel to a progression threshold. In some embodiments, the progression threshold corresponds to a threshold intensity distinguishing progression of tauopathy and/or spread of tau in cognitive normal subject from mild cognitive impairment (MCI)subject. For example, the tracer uptake data is expressed as a standardized uptake value (SUV) or a standardized uptake value ratio (SUVR). The progression threshold may be a SUVR from about 1.0 to about 2.5, and a threshold of 1.8 was selected for the MCI cohort considered in Example I.

[0009] In one embodiment, the method may determine progression of tauopathy from the baseline time point to the follow-up time point by comparing the baseline tracer uptake and the first follow-up tracer uptake data to the progression threshold to categorize each voxel as: (a) new, when the baseline tracer uptake data is below the progression threshold and the first follow-up tracer uptake data is above the progression threshold, (b) resolved, when the baseline tracer uptake data is above the progression threshold and the first follow-up tracer uptake data is below the progression threshold, (c) persistent, when the baseline tracer uptake data is above the progression threshold and the first follow-up tracer uptake data is above the progression threshold, or (d) intact, when the baseline tracer data is below the progression threshold and the first follow-up tracer uptake data is below the progression threshold.

[0010] In some embodiments, the method further comprises obtaining second follow-up PET data of the brain of the subject generated with the tracer at a second follow-up time point, aligning the second follow-up PET data to the image of the brain, and generating a second follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the second follow-up PET data aligned to the voxel. The second follow-up time point being after the baseline time point and after the first follow-up time point. The method further comprises determining progression of tauopathy from the first follow-up time point to the second followup time point in each voxel by comparing the first follow-up tracer uptake data and the second follow-up tracer uptake data of each voxel to the progression threshold.

[0011] In one particular example, the method may determine progression of tauopathy from the first follow-up time point to the second follow-up time point by comparing the first follow-up tracer uptake data and the second follow-up tracer uptake data to the progression threshold and to categorize each voxel as: (a) new, when the first follow-up tracer uptake data is below the progression threshold and the second follow-up tracer uptake data is above the progression threshold, (b) resolved, when the first follow-up tracer uptake data is above the progression threshold and the second follow-up tracer uptake data is below the progression threshold, (c) persistent, when the first follow-up tracer uptake data is above the progression threshold and the second follow-up tracer uptake data is above the progression threshold, or (d) intact, when the first follow-up tracer data is below the progression threshold and the second follow-up tracer uptake data is below the progression threshold. The method may further comprise quantifying a measure of spread of tauopathy in the subject based on a volume of voxels categorized as (a) new. In some embodiments, the measure of spread of tauopathy may be based on a combination of the volume of voxels categorized as (a) new and a volume of voxels categorized as at least one of (b) resolved, (c) persistent, and (d) intact. The method may also further comprise determining tauopathy in the subject has progressed when the measure of spread is greater than a threshold amount. Additionally, the method may comprise administering an active agent for treating tauopathy when the method determines tauopathy in the subject has progressed, increasing dosing of an active agent for treating tauopathy administered to the subject when the method determines tauopathy in the subject has progressed. In another embodiment, the method may be used to determine progression of tauopathy in a subject who is administered a pharmaceutically active agent after the baseline time point. The method further comprises quantifying effect of the pharmaceutically active agent in preventing or slowing progression of tauopathy based on the measure of spread.

[0012] In another aspect of the present application, a non-transitory computer-readable storage medium storing a set of instructions which, when executed by a processor, causes the processor to perform the methods described above. [0013] In a further aspect of the present application, a PET system for non-invasively tracking progression of tauopathy. In some embodiments, the tauopathy may be selected from the group consisting of familial Alzheimer's disease, sporadic Alzheimer's disease, frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), progressive supranuclear palsy, corticobasal degeneration, Pick's disease, progressive subcortical gliosis, tangle only dementia, diffuse neurofibrillary tangles with calcification, argyrophilic grain dementia, amyotrophic lateral sclerosis parkinsonism-dementia complex, Down syndrome, Gerstmann-Straussler-Scheinker disease, Hallervorden-Spatz disease, inclusion body myositis, Creutzfeld-Jakob disease, multiple system atrophy, Niemann-Pick disease type C, prion protein cerebral amyloid angiopathy, subacute sclerosing panencephalitis, myotonic dystrophy, nonGuamanian motor neuron disease with neurofibrillary tangles, postencephalitic parkinsonism, chronic traumatic encephalopathy, and dementia pugulistica (boxing disease). In particular, the tauopathy is Alzheimer’s disease.

[0014] The system comprises an imaging device (e.g., an MRI device) configured to obtain an image of a brain of a human subject, the image of the brain comprising a plurality of voxels corresponding to ROIs within structures of the brain of the subject, and a PET imaging device configured to detect radioactive emissions from a tau-specific radioactive tracer administered to the subject and to generate PET data based on intensity of the detected radioactive emissions detected from the subject. Specifically, the tracer may be any tracer that binds to hyperphosphorylated tau, such as, for example, [¹⁸F]MK-6240, [¹⁸F]JNJ-311, [¹⁸F]JNJ-067 [¹⁸F]THK5317, [¹⁸F]THK5351, [¹⁸F]AV1451, [ⁿC]PBB3, [¹⁸F]PM-PBB3, [¹⁸F]RO-948, [¹⁸F]PI-2620, [¹⁸F]GTP1, and [¹⁸F]T808.

[0015] The system also comprises a memory storage device storing the image of the brain obtained by the imaging device, a first PET data of the brain of the subject generated by the imaging device at a first time point, and a second PET data of the brain of the subject generated by the PET imaging device at a second time point after the first time point. The system further comprises a processor and a non-transitory computer readable storage medium including a set of instructions executable by the processor. The set of instructions operable to align the first PET data to the image of the brain, generate a first set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first PET data aligned to the voxel, align the second PET data to the image of the brain, generate a second set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the second PET data aligned to the voxel, and determine progression of tauopathy from the first time point to the second time point in each voxel by comparing the first tracer uptake data and the second tracer uptake data of each voxel to a progression threshold.

[0016] In some embodiments, the set of instructions for determining procession of tauopathy from the first time point to the second time point in each voxel comprise instructions operable to compare the first tracer uptake data and the second tracer uptake data to the progression threshold and to categorize each voxel as: (a) new, when the first tracer uptake data is below the progression threshold and the first second tracer uptake data is above the progression threshold, (b) resolved, when the first tracer uptake data is above the progression threshold and the second tracer uptake data is below the progression threshold, (c) persistent, when the first tracer uptake data is above the progression threshold and the second tracer uptake data is above the progression threshold, or (d) intact, when the first tracer data is below the progression threshold and the second tracer uptake data is below the progression threshold. In some embodiments, the PET system may further comprise a display operably connected to the processor for outputting a progression of images demonstrating spread of tau in the brain of the subject from the first time point to the second time point.

[0017] In some embodiments of the system for non-invasively tracking progression of tauopathy, the progression threshold corresponds to a threshold intensity distinguishing cognitive normal subject from MCI subject. In some embodiments, the progression threshold corresponds to a threshold intensity distinguishing progression of tauopathy and/or spread of tau in cognitive normal subject from MCI subject. For example, the tracer uptake data is expressed as SUV or SUVR. The progression threshold may be a SUVR from about 1.0 to about 2.5, and a threshold of 1.8 was selected for the MCI cohort considered in Example I.

[0018] Another aspect of the present application relates to a method for tracking presence of tau in tau-naive ROI of a brain of a human subject using PET. The method comprises obtaining baseline PET data of the brain of the subject generated with a tau-specific radioactive tracer at a baseline time point; obtaining first follow-up PET data of the brain of the subject generated with the tracer at a first follow-up time point, the first follow-up time point being after the baseline time point; aligning the baseline PET data and the first follow-up PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to ROIs within structures of the brain of the subject; generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the baseline PET data aligned to the voxel and a first follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first follow-up PET data aligned to the voxel; identifying tau-naive ROI, wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold; and determining presence of tau in the tau-naive ROI at the first follow-up time point in each of the one or more voxels of the tau-naive ROI by comparing the first follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the first follow-up tracer uptake data is above the presence threshold. The method may further comprise administering an active agent for treating or preventing tau aggregation when the method determines presence of tau in a tau-naive ROI.

[0019] Yet another aspect of the present application relates to a method of treating or preventing tau aggregation in a subject having tau present in a previously-determined tau- naive ROI of the brain. In some embodiments, the method comprises obtaining baseline PET data of the brain of the subject generated with a tau-specific radioactive tracer at a baseline time point; obtaining first follow-up PET data of the brain of the subject generated with the tracer at a first follow-up time point, the first follow-up time point being after the baseline time point; aligning the baseline PET data and the first follow-up PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to ROIs within structures of the brain of the subject; generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the baseline PET data aligned to the voxel and a first follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first follow-up PET data aligned to the voxel; identifying the tau-naive ROI, wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold; determining presence of tau in the tau-naive ROI at the first follow-up time point in each of the one or more voxels of the tau-naive ROI by comparing the first follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the first follow-up tracer uptake data is above the presence threshold; and administering to the subject a treatment for tau aggregation or for preventing tau aggregation. In other embodiments, the method comprises administering to the subject aa treatment for tau aggregation or for preventing tau aggregation; wherein the subject was determined to have tau present in a previously-determined tau-naive ROI by a method comprising: obtaining baseline PET data of the brain of the subject generated with a tau-specific radioactive tracer at a baseline time point; obtaining first follow-up PET data of the brain of the subject generated with the tracer at a first follow-up time point, the first follow-up time point being after the baseline time point; aligning the baseline PET data and the first follow-up PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to ROIs within structures of the brain of the subject; generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the baseline PET data aligned to the voxel and a first follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first follow-up PET data aligned to the voxel; identifying the tau- naive ROI, wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold; and determining presence of tau in the tau-naive ROI at the first follow-up time point in each of the one or more voxels of the tau-naive ROI by comparing the first follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the first follow-up tracer uptake data is above the presence threshold.

[0020] An aspect of the present invention relates to a method for monitoring a treatment of tau aggregation in a human subject, comprising: obtaining baseline PET data of the brain of the subject receiving treatment, wherein the PET data is generated with a tau-specific radioactive tracer at a baseline time point; obtaining first follow-up PET data of the brain of the subject generated with the tracer at a first follow-up time point, the first follow-up time point being after the baseline time point; aligning the baseline PET data and the first followup PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to ROIs within structures of the brain of the subject; generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the baseline PET data aligned to the voxel and a first follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first follow-up PET data aligned to the voxel; identifying tau-naive ROI, wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold; and determining presence of tau in the tau-naive ROI at the first follow-up time point in each of the one or more voxels of the tau-naive ROI by comparing the first follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the first follow-up tracer uptake data is above the presence threshold. In some embodiments, the method may further comprise adjusting the treatment when the one or more voxels is categorized as tau-present. For instance, the adjustment of treatment may comprise a change in dosing, or if may comprise a change to, or addition of, a new treatment.

[0021] An additional aspect of the present invention relates to a method of identifying an agent capable of preventing tau spreading or tau aggregation in a brain, the method comprising identifying a tau-naive ROI in the brain of a subject; administering the agent to the subject; and determining rate of increase in tau presence in the tau-naive ROI, in which the agent is identified as being capable of preventing tau aggregation when the rate of increase in tau presence in the tau-naive ROI is less than rate of increase in tau presence in a control subject. [0022] These and other aspects of the invention will become apparent to those skilled in the art after a reading of the following detailed description of the invention, including the figures and appended claims.

BRIEF DESCRIPTION OF THE FIGURES

[0023] FIG. 1 shows an exemplary system of the present application for determining, monitoring and/or tracking progression of tauopathy and/or tracking progression of tauopathy in a subject and/or spread of tau in the brain of the subject according to an exemplary embodiment of the present application.

[0024] FIG. 2A shows an exemplary method of the present application for determining, monitoring and/or tracking progression of tauopathy and/or tracking progression of tauopathy in a subject and/or spread of tau in the brain of the subject according to an exemplary embodiment of the present application.

[0025] FIG. 2B shows an exemplary method of the present application for aligning or registering baseline PET data and first follow-up data to data for a three-dimensional image of the physical structures of the brain according to an exemplary embodiment of the present application. [0026] FIG. 3 shows exemplary annualized volume of those voxels identified as a function of progression SUVR thresholds for cognitively normal (CN) and MCI progression subjects of Example I.

[0027] FIG. 4 shows exemplary data for annualized new volume as a fraction of ROI volume in the inferior temporal lobe, medial temporal lobe, and cortex for the CN and MCI subjects of Example I.

[0028] FIG. 5A shows exemplary data for annualized change in SUVR in the inferior temporal lobe of the CN and MCI cohorts of FIG. 4.

[0029] FIG. 5B shows the exemplary data for annualized ‘new’ volume as a fraction of ROI volume in the inferior temporal lobe as provided on the left side of FIG. 4.

[0030] FIG. 6 shows an exemplary method of the present application for determining, monitoring, and/or tracking progression of tauopathy as demonstrated in Example II.

[0031] FIG. 7 illustrates how tau PET SUVR information at baseline led to the identification of site-specific tau naive ROIs and to the identification of epocenter and four function connectivity quartiles, as described in Example II.

[0032] FIG. 8A shows data for SUVR at baseline using Tauvid (T807) and MK6240 tracers in the cortex, quartiles (Q) 1-4, and tau-naive composite ROIs in 4 subject groups: cognitive normal amyloid-beta negative (CN A0-), cognitive normal amyloid-beta positive (CN A0+), mild cognitive impairment amyloid-beta positive (MCI A0+), and Alzheimer’s Disease amyloid-beta positive (AD A0+).

[0033] FIG. 8B shows data for annualized change in SUVR using Tauvid (T807) and MK6240 tracers in the cortex, QI -4, and tau-naive composite ROIs in 4 subject groups: cognitive normal amyloid-beta negative (CN A0-), cognitive normal amyloid-beta positive (CN A0+), mild cognitive impairment amyloid-beta positive (MCI A0+), and Alzheimer’s Disease amyloid-beta positive (AD A0+).

DETAILED DESCRIPTION OF THE INVENTION

[0034] Unless defined otherwise, all technical and scientific terms used herein have the same meaning commonly understood to one of ordinary skill in the art to which this invention pertains. Otherwise, certain terms used herein have the meanings as set in the specification. All patents, published patent applications and publications cited herein are incorporated by reference as if set forth fully herein. It is noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural reference unless the context clearly dictates otherwise.

[0035] Unless otherwise indicated, the term “at least” preceding a series of elements is to be understood to refer to every element in the series. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the invention.

[0036] Throughout this specification and the claims which follow, unless the context requires otherwise, the word “comprise,” and variations such as “comprises” and “comprising,” will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. When used herein the term “comprising” can be substituted with the term “containing” or “including” or sometimes when used herein with the term “having.”

[0037] When used herein “consisting of’ excludes any element, step, or ingredient not specified in the claim element. When used herein, “consisting essentially of’ does not exclude materials or steps that do not materially affect the basic and novel characteristics of the claim. Any of the aforementioned terms of “comprising,” “containing,” “including,” and “having,” whenever used herein in the context of an aspect or embodiment of the application can be replaced with the term “consisting of’ or “consisting essentially of’ to vary scopes of the disclosure.

[0038] As used herein, the conjunctive term “and/or” between multiple recited elements is understood as encompassing both individual and combined options. For instance, where two elements are conjoined by “and/or,” a first option refers to the applicability of the first element without the second. A second option refers to the applicability of the second element without the first. A third option refers to the applicability of the first and second elements together. Any one of these options is understood to fall within the meaning, and therefore satisfy the requirement of the term “and/or” as used herein. Concurrent applicability of more than one of the options is also understood to fall within the meaning, and therefore satisfy the requirement of the term “and/or.”

[0039] Unless otherwise stated, any numerical value, such as a concentration or a concentration range described herein, are to be understood as being modified in all instances by the term “about.” Thus, a numerical value typically includes ± 10% of the recited value. For example, a concentration of 1 mg/mL includes 0.9 mg/mL to 1.1 mg/mL. Likewise, a concentration range of 1 mg/mL to 10 mg/mL includes 0.9 mg/mL to 11 mg/mL. More particularly, the numerical value may include ± 1%, ± 3%, or ± 5% of the recited value. As used herein, the use of a numerical range expressly includes all possible subranges, all individual numerical values within that range, including integers within such ranges and fractions of the values unless the context clearly indicates otherwise.

[0040] The term “subject” or “patient” as used herein refers to an animal, and preferably a mammal. According to particular embodiments, the subject is a mammal including a nonprimate (e.g., a camel, donkey, zebra, cow, pig, horse, goat, sheep, cat, dog, rat, rabbit, guinea pig, marmoset or mouse) or a primate (e.g., a monkey, chimpanzee, or human). In particular embodiments, the subject is a human.

[0041] As used herein, the terms “treat,” “treating,” and “treatment” are all intended to refer to an amelioration or reversal of at least one measurable physical parameter related to a tauopathy which is not necessarily discernible in the subject, but can be discernible in the subject. The terms “treat,” “treating,” and “treatment,” can also refer to causing regression, preventing the progression, or at least slowing down the progression of the disease, disorder, or condition. In a particular embodiment, “treat,” “treating,” and “treatment” refer to an alleviation, prevention of the development or onset, or reduction in the duration of one or more symptoms associated with the tauopathy. In a particular embodiment, “treat,” “treating,” and “treatment” refer to prevention of the recurrence of the disease, disorder, or condition. In a particular embodiment, “treat,” “treating,” and “treatment” refer to an increase in the survival of a subject having the disease, disorder, or condition. In a particular embodiment, “treat,” “treating,” and “treatment” refer to elimination of the disease, disorder, or condition in the subject.

[0042] As used herein a “tauopathy” encompasses any neurodegenerative disease that involves the pathological aggregation of tau within the brain. In addition to familial and sporadic AD, other exemplary tauopathies are frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), progressive supranuclear palsy, corticobasal degeneration, Pick’s disease, progressive subcortical gliosis, tangle only dementia, diffuse neurofibrillary tangles with calcification, argyrophilic grain dementia, amyotrophic lateral sclerosis parkinsonism-dementia complex, Down syndrome, Gerstmann-Straussler-Scheinker disease, Hallervorden-Spatz disease, inclusion body myositis, Creutzfeld-Jakob disease, multiple system atrophy, Niemann-Pick disease type C, prion protein cerebral amyloid angiopathy, subacute sclerosing panencephalitis, myotonic dystrophy, non-Guamanian motor neuron disease with neurofibrillary tangles, postencephalitic parkinsonism, and chronic traumatic encephalopathy, such as dementia pugilistica (boxing disease) (Morris etaL, Neuron, 70:410-26, 2011).

[0043] As used herein, the term “tau” includes proteins comprising mutations, e.g., point mutations, fragments, insertions, deletions and splice variants of full-length wild type tau. The term “tau” also encompasses post-translational modifications of the tau amino acid sequence. Post-translational modifications include, but are not limited to, phosphorylation.

[0044] The present application is directed to a method and system for detecting and/or monitoring progression of tauopathy in a subject. Specifically, the method and system of the present application utilizes imaging obtained from PET over a period of time to determine progression of tauopathy in the patient during that period of time. More particularly, the method and system of the present application analyzes PET images over a period of time to track and quantify spread of tau in the brain of a living patient, and therefore, provides a non- invasive way for monitoring progression of tauopathy in a patient. It is contemplated that the method and system of the present application may be used to monitor progression of tauopathy in healthy subjects, subjects having MCI, subjects having AD, and/or subjects having dementia. Although exemplary embodiments are discussed herein relating to Alzheimer’s disease, it is contemplated that the method and system of the present application may be used to monitor progression of any type of tauopathy.

[0045] FIG. 1 shows an exemplary system 100 for determining, monitoring and/or tracking progression of tauopathy and/or tracking progression of tauopathy in a subject 102 and/or spread of tau in the brain of the subject 102. The system 100 according to this embodiment comprises an imaging device 110 for obtaining data for an image of the physical structure of at least one region of the central nervous system (CNS) of the subject 102, a PET imaging device 120, and a computing arrangement 130. The imaging device 110 is operably connected to the computing arrangement 130 via a communications network 140 to transmit data for the image of the physical structure of the at least one region of the CNS of the subject to the computing arrangement 130. Similarly, the PET imaging device 120 is operably connected to the computing arrangement 130 via the communications network 140 to transmit PET data of the subject to the computing arrangement 130.

[0046] In an alternative embodiment, the system 100 also includes a separate storage device 150 (e.g., a database) that receives and stores data for the image of the physical structure of at least one region of the central nervous system (CNS) of the subject 102 from the imaging device 120. The storage device 150 may also receive PET data from the PET imaging device 120 and store the PET data of the subject generated by the PET imaging device 120 at different time points across a period of time for determining, monitoring, and/or tracking progression of tauopathy and/or spread of tau in the brain of the subject 102. The storage device 150 may be a part of the computing arrangement 130 or may be separate from the computing arrangement 130 and operably connected to the computing arrangement 130 via the communications network 140 or via a wired or wireless connection between the storage device 150 and the computing arrangement 130. For example, the storage device 150 may be an external database that is operably connected to the imaging device 110 to receive from the imaging device 120 and store data for the image of the physical structure of at least one region of the central nervous system (CNS) of the subject 102. The storage device 150 may also be operably connected to the PET imaging device 120 to receive from the PET imaging device 120 and store PET data of the subject obtained at different time points across the period of time for determining, monitoring, and/or tracking progression of tauopathy and/or spread of tau in the brain of the subject 102. The computing arrangement 130 may be configured to retrieve data for the image of the physical structure of at least one region of the central nervous system (CNS) of the subject 102 and the PET data of the subject obtained at different time points across the period of time that are stored within the storage device 150, and analyze the retrieved data to determine progression of tauopathy in the subject 102.

[0047] The communications network 140 may represent any single or plurality of networks used by the components of the system 100 to communicate with one another. The communications network 140 and all networks that may be included therein may be any type of network for transmitted data therethrough. For example, the communications network 140 may be a local area network (LAN), a wide area network (WAN), a Wi-Fi network, a cellular network, a cloud network, a wired form of these networks, a wireless form of these networks, a combined wired/wireless form of these networks, etc.

[0048] The imaging device 110 may be any suitable device for generating data for a three- dimensional image of at least one region of the central nervous system (CNS) of the subject 102, such as, for example, spinal cord, cerebellum, cerebral cortex, medial temporal lobe, and inferior temporal lobe. In particular, the imaging device 100 may generate data for an image of a portion of the brain, or the entirety of the brain of the subject 102. Preferably, the imaging device 110 generates data for a three-dimensional image of the physical structures of a portion of the brain, or the entirety of the brain of the subject 102. The imaging device 110 may generate data for a three-dimensional image of the physical structures of a portion of the brain, or the entirety of the brain of the subject 102 with or without prior administration of a radioactive tracer to the subject 102. In some embodiments the imaging device 110 may deliver a dose of energy or ionizing radiation to the subject 102 and detect emission of the energy or radiation from the subject 102 to generate data for a three-dimensional image of the physical structures of a portion of the brain, or the entirety of the brain of the subject 102. In one example, the imaging device 110 is a computed tomography (CT) imaging device or a magnetic resonance imaging (MRI) device. Preferably, the imaging device 110 is an MRI device. The imaging device 110 is not a PET imaging device. It is noted that in some embodiments, the imaging device 110 and the PET imaging device 120 may be a single imaging device (e.g., combined PET/MRI scanner) for generating the three-dimensional image (the three- dimensional image being not a PET image) and PET data.

[0049] The PET imaging device 120 may be any suitable device for detecting presence of a radiolabeled tracer in at least one region of the CNS of a subject 102 and generating PET data corresponding to emissions detected from the radiolabeled tracers in the subject 102. Specifically, the PET imaging device 120 generates PET data for a portion of the brain or the entirety of the brain of a subjected administered with a tau-specific tracer. The tau-specific tracer may be any compound, composition, or ingredient suitable for administration to a human subject that binds to tau in brain tissue and provides a detectable radioactive signal for detection by the PET imaging device 120. Specifically, the tau-specific tracer is a PET tracer that binds to hyperphosphorylated tau. Examples of tau-specific tracers include [¹⁸F]MK-6240, [¹⁸F]JNJ- 311, [¹⁸F]JNJ-067 [¹⁸F]THK5317, [¹⁸F]THK5351, [¹⁸F]AV1451, [ⁿC]PBB3, [¹⁸F]PM-PBB3, [¹⁸F]RO-948, [¹⁸F]PI-2620, [¹⁸F]GTP1, [¹⁸F]T807, and [¹⁸F]T808. In one particular embodiment, the tau-specific tracer is [¹⁸F]MK-6240 having a structure of Formula (I):

[0050] The computing arrangement 130 may comprise a processing arrangement 132 that may be e.g., entirely or a part of, or include, but is not limited to, a computer/processor that can include, e.g., one or more microprocessors, and use instructions stored on a computer- accessible medium 134 (e.g., memory storage device). The instructions may direct the computing arrangement 130 to analyze image(s) of the brain of the subject and PET data collected over a period of time to determine progression of tauopathy in the subject 102 during that period of time. The computer-accessible medium 134 may, for example, be a non- transitory computer-accessible medium containing executable instructions therein. A storage arrangement may be provided separately from the computer-accessible medium 134, which may provide the instructions to the processing arrangement 132 to configure the processing arrangement 132 to execute certain exemplary procedures, processes and methods. In some embodiments, the computing arrangement 130 may be connected to an output device 160 to provide visual outputs (e.g., images) to a user. For example, the output device 160 may be a monitor for providing visual outputs (e.g., images to the user). The computing arrangement 130 may also be connected to an input device 162 (e.g., keyboard, mouse, etc.) for receiving input(s) from the user. The computing arrangement 130 may optionally comprise an input/output interface 136 for communicating to the output device 160 and receiving input from the input device 162. In certain embodiments the input device 162 and the output device 160 maybe a single input and output device having both input and output functionality (e.g., touchscreen, smart phone, tablet, etc.).

[0051] FIG. 2A shows an exemplary method 200 for determining, monitoring and/or tracking progression of tauopathy and/or tracking progression of tauopathy in a subject 102 and/or spread of tau in the brain of the subject 102 over a period of time. The period of time starting at a baseline time point and proceed to a follow-up time point, followed by one or more subsequent follow-up time points within the period of time for determining, monitoring and/or tracking progression of tauopathy in the subject. The method 200 provides a way to quantify progression of tauopathy in a subject and/or spread of tau in the brain of the subject at each follow-up time point and thus, allowing for time progressive, qualitative monitoring and/or tracking of tauopathy and/or spread of tau in the brain of the subject across the period of time. [0052] The method 200 of FIG. 2A starts at steps 210 and 220. In step 210, the computing arrangement 130 obtains, either directly from the imaging device 110 or retrieves from the storage device 150 data for a three-dimensional image of the physical structures of a portion of the brain or the entirety of the brain of the subject. In a particular embodiment, the image is an MRI image of a portion of the brain or the entirety of the brain of the subject. The image is obtained at a baseline time point, or within 1 day, within 2 days, within 3 days, within 5 days, or within 7 days of the baseline time point. Typically, the data for the three-dimensional image is obtained as close in time to the baseline time point as practicable, preferably, before the baseline time point. However, the data for the three-dimensional image may be obtained after the baseline time point. In some embodiments, the data for the three-dimensional image is obtained before the baseline time point, for example, within 1 day, within 2 days, within 3 days, within 5 days, within 1 week, within 2 weeks, within 3 weeks, or within 4 weeks before the baseline time point. In other embodiments, the image is obtained after the baseline time point, for example, within 1 day, within 2 days, within 3 days, within 5 days, within 1 week, within 2 weeks, within 3 weeks, within 4 weeks, within 2 months, within 3 months, within 4 months, within 5 months, within 6 months, within 7 months, within 8 months, or within 9 months before the baseline time point. In a specific embodiment, the image is obtained before or after the baseline time point, within 6 months of the baseline time point.

[0053] The three-dimensional image comprises a plurality of voxels corresponding to three-dimensional ROI within the physical structure of the brain of the subject. Specifically, the three-dimensional image may consist of numerous voxels that collectively form the three- dimensional image. In one embodiment, the computing arrangement 130 analyzes the data for the three-dimensional image to segment the three-dimensional image into a plurality of voxels, each voxel corresponding to a three-dimensional ROI within the physical structure of the brain of the subject. In a particular embodiment, the three-dimensional image is an MRI image. The computing arrangement 130 analyzes data for the MRI image and segments the MRI image into a plurality of voxels, each voxel corresponding to a three-dimensional ROI within the physical structure of the brain of the subject. The computing arrangement 130 may executed any suitable set of instructions for segmenting the MRI image into the plurality of voxels, for example, instructions comprising directions to utilize FreeSurfer software package (available from http s : // surfer, nmr . mgh . harvard . edu/) .

[0054] In step 220, the computing arrangement 130 obtains, either directly from the PET imaging device 120 or retrieves from the storage device 150, baseline PET data (PET^BL) of the subject generated by the PET imaging device 120 at the baseline time point for further analysis. In step 230, the computing arrangement 130 obtains, either directly from the PET imaging device 120 or retrieves from the storage device 150, a first follow-up PET data of the subject generated at a first follow-up time point for further analysis in method 200. The first followup time point being after the baseline time point. The first follow-up PET data (PET^FU1) is obtained by the PET imaging device 120 in a similar manner as described above for the baseline PET data in step 220.

[0055] It is noted that the PET data described herein is obtained by administering a tau- specific tracer to the subject and detecting with the PET imaging device 120 intensity of emissions from the tau-specific tracer in the subject at a specified time point. Although PET data discussed herein is collected at specific time points (e.g., at baseline time point, at first follow-up time point, at subsequent follow-up time point), the PET data generated by the PET imaging device 120 at each time point is generated across an operating time frame around the selected time point, during which the PET imaging device 12 detects intensity of emissions from the tau-specific tracer. For example, the baseline PET data may be collected during an operating time frame of approximately 10 mins, 15 mins, 20 mins, 30 mins, 40 mins, 45 mins, 60 mins, 75 mins or 90 mins, during which the PET imaging device 120 scans a portion of the brain or the entirety of the brain of the subject on the day of the baseline time point. The PET data generated may comprise more than one subset (e.g., frame) of data collected across the operating time frame, each subset of data corresponding to a scan of a portion of the brain or the entirety of the brain of the subject covering a window of time within the operating time frame. The plurality of windows of time may be consecutive. In one exemplary embodiment, the PET data may comprise a plurality of subset (e.g., frames) of data collected consecutively for 6 different time windows, each covering a 5 minute window, starting at 90 minutes after administration of the tau-specific tracer for a total operating time frame of 30 minutes.

[0056] In step 232, the computing arrangement 130 aligns or registers the baseline PET data with the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject. The computing arrangement 130 also aligns or registers in step 232 the first follow-up PET data with the data for a three- dimensional image (e.g, MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject. The computing arrangement 130 may align or register the baseline PET data before the first follow-up PET data, may align or register the first follow-up PET data before the baseline PET data, or may align or register the baseline PET and the first follow-up PET data concurrently (e.g, where the process for aligning or registering the baseline PET and the first follow-up PET data may wholly overlap or partly overlap).

[0057] In a particular embodiment, the three-dimensional image (e.g., MRI image) is segmented in step 210 by the computing arrangement 130 into a plurality of voxels, each voxel corresponding to a three-dimensional ROI within the physical structure of the brain of the subject. Specifically, the computing arrangement 130 aligns or registers the baseline PET data with the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject so data corresponding to an intensity of emissions from the tracer recorded from a three-dimensional ROI within the physical structure of the brain of the subject at the baseline time point is aligned with a corresponding voxel of the three-dimensional image. Similarly, the computing arrangement 130 also aligns or registers the first follow-up PET data with the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject so data corresponding to an intensity of emissions from the tracer recorded from a three-dimensional ROI within the physical structure of the brain of the subject at the first follow-up time point is aligned with a corresponding voxel of the three-dimensional image.

[0058] FIG. 2B shows an exemplary method 232 of the present application for aligning or registering baseline PET data and first follow-up data with data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject so data corresponding to an intensity of emissions from the tracer recorded from a three-dimensional ROI within the physical structure of the brain of the subject at the baseline time point is aligned with a corresponding voxel of the three-dimensional image.

[0059] In step 233, the computing arrangement 130 generates spatial midpoint data between the baseline PET data and the first follow-up PET data. The spatial midpoint data provides a spatial alignment midline for PET data collected from two separate time points (e.g., baseline time point and first follow-up time point). For example, the spatial midpoint data may be generated by combining a spatial transform of the baseline PET data with a spatial transform of the first follow-up PET data to generate a midpoint transform of the two spatial transforms as the spatial midpoint data.

[0060] In one exemplary embodiment, the computing arrangement 130 generates spatial midpoint data by first generating a baseline spatial average of the plurality of subsets of the baseline PET data and a first follow-up spatial average of the plurality of subsets of the first follow-up PET data. Next, the computing arrangement 130 registers each of the plurality of subsets of the baseline PET data to the baseline spatial average to generate a baseline average frame data, and also registers each of the plurality of subsets of the first follow-up spatial average of the plurality of subsets of the first follow-up PET data to the first follow-up spatial average to generate first follow-up average frame data. The baseline PET data is then registered to the baseline average frame data to generate a spatial transform of the baseline PET data. Similarly, the first follow-up PET data is registered to the first follow-up average frame data to generate a spatial transform of the first follow-up PET data. The spatial transform of the baseline PET data and the spatial transformed of the first follow-up PET data are combined to generate a midpoint transform as the spatial midpoint data.

[0061] In step 234, the computing arrangement 130 aligns or registers the baseline PET data to the spatial midpoint data to generate a modified baseline PET data (rPET^BL). The computing arrangement 130 also aligns or registers the first follow-up PET data to the spatial midpoint data to generate a modified first follow-up PET data (rPET^FU1).

[0062] In step 235, the computing arrangement 130 aligns or registers the three- dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject to the spatial midpoint data. In a particular embodiment, the three-dimensional image is an MRI image that is segmented in step 210 by the computing arrangement 130 into a plurality of voxels, each voxel corresponding to a three-dimensional ROI within the physical structure of the brain of the subject. For example, in step 235, the computing arrangement 130 aligns or registers MRI image to the spatial midpoint data. The computing arrangement 130 may align the MRI image to the spatial midpoint data using any suitable methods, for example, using at least one of the following alignment methodologies (e.g., cost functions): Normalized Mutual Information, Correlation Ratio Symmetrized with multiplication, Correlation Ratio symmetrized with addition, Hellinger metric, Least squares, and/or Local Pearson Coefficient (Absolute). In one example, the MRI image is aligned to the spatial midpoint data using a Hellinger metric cost function. Alignment of the MRI image to the spatial midpoint data and therefore, correlates rPET^BL and rPET^FU1 to the data for a three- dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject.

[0063] In step 236, a quality control step may optionally be included to confirm that good alignment between rPET^BL and the MRI image has been achieved. Step 236 may also optionally include a quality control step to confirm that good alignment between rPET^FU1 and the MRI image has been achieved. Although the embodiment of the quality control step provided below is described in reference to rPET^BL, the process described below may be applied in the same manner for quality control of alignment between rPET^FU1 and the MRI image.

[0064] In one embodiment, the quality control step includes the computing arrangement 130 instructing an output device 160 to output images for review by a user, and obtaining inputs from the user, via the input device 162, in response. The inputs and outputs may be provided in any suitable form for interacting with a user, such as, for example, via a graphical user interface (GUI) for display on the output device 160. For example, the computing arrangement 130 may instruct the output device 160 to provide images that rapidly toggle between the MRI image and the rPET^BL to visually confirm that the MRI image and the rPET^BL are in alignment. The computing arrangement 130 may instruct the output device 160 to rapidly toggle between the MRI image and the rPET^BL continuously. Alternatively, the computing arrangement 130 may instruct the output device 160 to toggle between the MRI image and the rPET^BL upon receiving manual inputs from the user for triggering toggling from the MRI image to the rPET^BL, or vice versa. Toggling, and particularly, rapid toggling between the MRI image and the rPET^BL allows for small alignment deviations to visually stand out to the eyes of the user, and therefore, allowing the user to visually identify any misalignments between the MRI image and the rPET^BL.

[0065] Alternatively, the quality control step may include the computing arrangement 130 analyzing data for the MRI image to generate three-dimensional edges of the MRI image. The computing arrangement 130 then directs the output device 160 to display a composite image overlaying the three-dimensional edges of the MRI image to the baseline PET data. The overlayed image allows for alignment deviations to visually stand out to the eyes of the user, and therefore, allowing the user to visually identify any misalignments between the MRI image and the rPET^BL.

[0066] Subsequently, the computing arrangement 130 may direct the output arrangement to display an output requesting input from the user indicating whether any misalignments have been visually observed, and/or whether alignment between the MRI image and the rPET^BL is poor. If the user provides an input indicating that misalignments between the MRI image and the rPET^BL have been visually observed and/or that alignment between the MRI image and the rPET^BL is poor, then the computing arrangement 130 may apply further adjustments to the alignment of the MRI image and the rPET^BL. Such further adjustments may include coarse rotations and/or masking of neck regions. Additionally or in the alternative, an alternate alignment methodology (e.g., alternate cost function) from a previously applied alignment methodology may be used to re-analyze and align the rPET^BLto the MRI image. For example, if alignment was initially determined using a least-squares cost function and the user provides input indicating that alignment between the MRI image and the rPET^BL is poor, then further adjustments to alignment of the MRI image and the rPET^BL may include re-analyzing and aligning the MRI image and the rPET^BL using a Normalized Mutual Information or a Correlation Ratio cost function. After such further adjustments, the computing arrangement 130 may direct the output device 160 to repeat the display of images for visual inspect by the user (e.g., toggling or display of an overlay image of the three-dimensional edges of the MRI image to the baseline PET data), display a further output requesting input from the user indicating whether any misalignments have been visually observed and/or whether alignment between the MRI image and the rPET^BL remains poor, and receive further input from the user indicating whether any misalignments have been visually observed and/or whether alignment between the MRI image and the rPET^BLremains poor, all as described above. Such adjustments and visual observations may be repeated until the user provides input via the input device 162 indicating that no further misalignments are visually observed and/or that alignment between the MRI image and the rPET^BL is acceptable. If the user provides input indicating that no further misalignments are visually observed and/or that alignment between the MRI image and the rPET^BL is acceptable, the method 200 proceeds to step 224 using the further adjusted alignment between the MRI image and the rPET^BL.

[0067] If after repeated adjustments to the alignment of the MRI image and the rPET^BL, the user continues to provide input indicating that misalignments have been visually observed or alignment between the MRI image and the rPET^BL remains poor, then the computing arrangement 130 modifies the rPET^BL using at least one pre-processing step, and the computing arrangement 130 then aligns or registers the modified rPET^BLto the MRI image in the manner described above. Alternatively, the computing arrangement 130 modifies the MRI image using at least one processing step, and then the computing arrangement 130 aligns or registers the rPET^BL to the modified MRI image in the manner described above. Further, the computing arrangement 130 may modify both the MRI image and the rPET^BL using pre-processing steps, and then the computing arrangement 130 aligns or modifies the modified MRI image to the modified rPET^BL in the manner described above. The pre-processing step(s) may comprise a number of different adjustments to the MRI image and/or the rPET^BL that bring the modified MRI image and/or the modified rPET^BL closer into alignment with each other before the computing arrangement 130 re-analyzes to align or register the two in the manner described above. Examples of pre-processing steps may include, but are not limited to: applying initial rotations to the MRI image to bring it to coarse alignment with rPET^BL, axial cropping of the MRI image and the rPET^BL below the cerebellum, and intensity normalization of the rPET^BL with square root operation.

[0068] Once the baseline PET data and the first follow-up PET data are aligned to the three- dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain, the method 200 proceeds to step 237. In step 237, the computing arrangement 130 analyzes the baseline PET data corresponding to each voxel to quantify tracer uptake based on an intensity of emission recorded in the portion of the baseline PET data aligned to the voxel to generate a baseline set of tracer uptake data. The computing arrangement 130 also analyzes the first follow-up PET data corresponding to each voxel to quantify tracer uptake based on an intensity of emission recorded in the portion of the first follow-up PET data aligned to the voxel to generate a first follow-up set of tracer uptake data. [0069] For example, upon aligning or registering rPET^BL and rPET^FU1 with the MRI image, the computing arrangement 130 may segment the aligned MRI image or modified MRI image into rROIs, each corresponding to an ROI within the physical structure of the brain of the subject. For each rROI. The computing arrangement 130 analyzes a corresponding portion of the rPET^BL or modified rPET^BL to quantify PET tracer uptake. The PET tracer uptake may be quantified by any suitable quantitative or semi -quantitative measure (e.g., ratios). For example, the PET tracer update may be quantified as SUV of the tau-specific tracer, in the rROI at the baseline time point. In addition, PET tracer uptake, e.g., SUV, of the tau-specific tracer may be quantified for a reference region, for example, cerebellar gray matter. The reference region may be used to determine the SUVR of the tau-specific tracer in each rROI. The SUVR of the rROI being a ratio of the SUV of the rROI to the SUV of the reference region (e.g., cerebellar gray matter). Other suitable measures for PET tracer uptake may include distribution volume ratio (DVR) and/or binding potential (BP). Similarly, the computing arrangement 130 analyzes a corresponding portion of the rPET^FU1 or modified rPET^FU1 to quantify PET tracer uptake, for example, SUV, SUVR, DVR and/or BP of the tau-specific tracer, in the rROI at the first followup time point.

[0070] In one exemplary embodiment, steps 232, 233, 234, 235, 236 and/or 237 may be carried out by the computing arrangement 130 by executing a set of instructions that utilize software modules from the AFNI software package, including 3dcalc and 3dAllineate (available at https://afni.nimh.nih.gov/about_afni) for performing volume-based operations. In addition, the quality control step described above may be semi-automated with scripted commands that direct a GUI, e.g., AFNI GUI, thereby minimizing the slow and repetitive process of GUI setup with each set of new data.

[0071] In step 238, the computing arrangement 130 determines progression of tauopathy from the baseline time point to a follow-up time point in each voxel by comparing the baseline tracer uptake data and the follow-up tracer uptake data of each voxel to a progression threshold. The progression threshold is a threshold value of tau-tracer uptake (e.g., SUV, SUVR, DVR or BP) that results in a maximal estimate of tau spread that contrasts with estimates of the spread in healthy subjects, as demonstrated for example in FIG. 3 and discussed further below. [0072] The progression threshold may be empirically determined prior to execution of method 200. For example, tau-tracer uptake for a cohort of cognitively impaired subjects (e.g., subjects having MCI, Alzheimer’s disease and/or dementia) and/or amyloid positive subjects and a cohort of cognitively normal and/or amyloid negative subjects may be analyzed to determine a suitable progression threshold for distinguishing between these two different cohorts of subjects. More particularly, the progression threshold is selected to result in an elevated measure of spread (e.g., volume of voxels being identified as above the progression threshold) in the cohort of MCI and/or amyloid positive subjects and a lower measure of spread in the cognitively normal and/or amyloid negative cohort identified as below the progression threshold. For tracer uptake expressed as a SUVR, progression thresholds may be considered at SUVR from about 1.0 to about 2.5. In one example, the progression threshold is a SUVR threshold at or about 1.0 to 2.5, 1.2 to 2.3, 1.5 to 2.0, 1.7 to 1.9, or 1.8. Specifically, the SUVR threshold is 1.8. A threshold of 1.8 was selected for the MCI cohort considered in Example I. [0073] In one example, the computing arrangement 130 may analyze the baseline PET data and the follow-up PET data for each voxel and identify those voxels having tau-tracer uptake greater than the progression threshold as high uptake regions. The computing arrangement 130 may quantify the volume of voxels that are identified as high uptake regions and thereby providing a quantitative metric for measuring spread of tau in the brain of the subject. An increase in the volume of voxels that are identified as high uptake regions from the baseline time point to the follow-up time point may indicate further progression of tauopathy in the subject from the baseline time point to the follow-up time point.

[0074] In another embodiment of step 238, the computing arrangement 130 compares the baseline tracer uptake data and the follow-up tracer uptake data of each voxel to the progression threshold and categorizes each voxel into one of the following four categories:

(a) new, when the baseline tracer uptake data is below the progression threshold and the follow-up tracer uptake data is above the progression threshold,

(b) resolved, when the baseline tracer uptake data is above the progression threshold and the follow-up tracer uptake data is below the progression threshold,

(c) persistent, when the baseline tracer uptake data is above the progression threshold and the follow-up tracer uptake data is above the progression threshold, or

(d) intact, when the baseline tracer data is below the progression threshold and the follow-up tracer uptake data is below the progression threshold.

[0075] The category of (a) new identifies those voxels where there is cortical spread of between from the baseline time point to the follow-up time point. The category (b) resolved reflect those voxels of uptake estimation noise. The category (c) persistent reflect voxels containing tau at the baseline time point and remain above progression threshold at the followup time point. The category (d) intact reflects voxels that remain below progression threshold at baseline and follow-up time points.

[0076] The computing arrangement 130 may quantify a measure of spread of tauopathy in the subject based on the volume of voxels that are identified as (a) new either solely, or in combination with volumes of voxels that are identified as (b) resolved, (c) persistent and/or (d) intact, from the baseline time point to the follow-up time point. In one example, the measure of spread is based on the volume of voxels that are identified as (a) new and (c) persistent. Additionally, for each of the above four categories, additional quantitative measurements for progression of tauopathy, such as total volume of voxels in each category or SUVR statistical values (e.g., estimates of SUVR, mean SUVR, changes in SUVR, etc.) for each of the four categories may also be generated by the computing arrangement 130 as further quantitative measurements of progression of tauopathy in the subject. Estimates of SUVR and change in SUVR may be obtained for each of categories, which may provide for a more complete capturing of progression of tauopathy and/or spread of tau in the brain of the subject.

[0077] In step 240, the computing arrangement 130 obtains, either directly from the PET imaging device 120 or retrieves from the storage device 150, a subsequent follow-up PET data (PET^FU) of the subject generated at a subsequent follow-up time point for further analysis in method 200. The subsequent follow-up time point being after the baseline time. The subsequent follow-up PET data is obtained by the PET imaging device 120 in a similar manner as described above for the baseline PET data in step 220 and the first follow-up PET data in step 230.

[0078] Step 242 is similar to step 232 discussed above. In step 242, the computing arrangement 130 aligns or registers the subsequent follow-up PET data with the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject. Specifically, the computing arrangement 130 aligns or registers the subsequent follow-up PET data with the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject so data corresponding to an intensity of emissions from the tracer recorded from a three-dimensional ROI within the physical structure of the brain of the subject at the subsequent follow-up time point is aligned with a corresponding voxel of the three-dimensional image. In one exemplary embodiment, the computing arrangement 130 aligns or registers the subsequent PET data to the spatial midpoint data to generate a modified subsequent follow-up PET data (rPET^FU). As discussed above in step 235, the spatial midpoint data is aligned to the spatial midpoint data and therefore correlates the rPET^FU to the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject.

[0079] Similar to step 237, in step 247, the computing arrangement 130 analyzes the subsequent follow-up PET data corresponding to each voxel to quantify tracer uptake based on an intensity of emission recorded in the portion of the subsequent follow-up PET data aligned to the voxel to generate a baseline set of tracer uptake data. For example, upon aligning or registering rPET^FU with the MRI image, the computing arrangement 130 may segment the aligned MRI image or modified MRI image into rROIs, each corresponding to an ROI within the physical structure of the brain of the subject. For each rROI, the computing arrangement 130 analyzes a corresponding portion of the rPET” or modified rPET^FU to quantify PET tracer uptake. The PET tracer uptake may be quantified by any suitable quantitative or semi- quantitative (e.g., ratio) measure. For example, the PET tracer update may be quantified as SUV of the tau-specific tracer, in the rROI at the subsequent follow-up time point. In addition, PET tracer uptake, e.g. , SUV, of the tau-specific tracer may be quantified for a reference region, for example, cerebellar gray matter. The reference region may be used to determine the SUVR of the tau-specific tracer in each rROI. The SUVR of the rROI being a ratio of the SUV of the rROI to the SUV of the reference region (e.g., cerebellar gray matter). Other suitable measures for PET tracer uptake may include distribution volume ratio (DVR) and/or binding potential (BP).

[0080] Similar to step 238, in step 248, the computing arrangement 130 determines progression of tauopathy from a previous follow-up time point to the subsequent follow-up time point in each voxel by comparing the previously follow-up tracer uptake data and the subsequent follow-up tracer uptake data of each voxel to the progression threshold in the same manner as discussed above in step 238. It is contemplated that steps 240 to 248 may be iterated for as many subsequent follow-up time points as desired for monitoring progression of tauopathy in a subject. In the first iteration, the previous follow-up time point is the first followup time point of step 230. In subsequent iterations, the previous follow-up time point is the follow-up time immediately prior to the subsequent follow-up time point. For example, steps 240 to 248 may be iterated at a regular frequency having a consistent interval (e.g., weekly, every 2 weeks, every 3 weeks, every 4 weeks, every 6 weeks, every 8 weeks, etc.) until the end of the period of time for determining, monitoring, and/or tracking progression of tauopathy and/or spread of tau in the brain of the subject.

[0081] In one example, the computing arrangement 130 may analyze the previous followup PET data and the subsequent follow-up PET data for each voxel and identify those voxels having tracer uptake greater than the progression threshold as high uptake regions, and quantify the volume of voxels that are identified as high uptake regions. Similar to step 248, an increase in the volume of voxels that are identified as high uptake regions from the previous follow-up time point to the subsequent follow-up time point may indicate further progression of tauopathy in the subject from the previous follow-up time point to the subsequent follow-up time point.

[0082] In another embodiment of step 248, the computing arrangement 130 compares the previous follow-up tracer uptake data and the subsequent follow-up tracer uptake data of each voxel to the progression threshold and categories each voxel into one of the following four categories:

(a) new, when the previous follow-up tracer uptake data is below the progression threshold and the subsequent follow-up tracer uptake data is above the progression threshold,

(b) resolved, when the previous follow-up tracer uptake data is above the progression threshold and the subsequent follow-up tracer uptake data is below the progression threshold,

(c) persistent, when the previous follow-up tracer uptake data is above the progression threshold and the subsequent follow-up tracer uptake data is above the progression threshold, or

(d) intact, when the previous follow-up tracer data is below the progression threshold and the subsequent follow-up tracer uptake data is below the progression threshold.

[0083] The category of (a) new identifies those voxels where there is cortical spread of between from the previous follow-up time point to the subsequent follow-up time point. The category (b) resolved reflect those voxels of uptake estimation noise. The category (c) persistent reflect voxels containing tau at the previous follow-up time point and remain above progression threshold at the subsequent follow-up time point. The category (d) intact reflect voxels that remain below progression threshold at the previous follow-up time point and subsequent follow-up time point.

[0084] Similar to step 238, in step 248, the computing arrangement 130 may quantify a measure of spread of tauopathy in the subject based on the volume of voxels that are identified as (a) new either solely, or in combination with volumes of voxels that are identified as (b) resolved, (c) persistent and/or (d) intact, from the previous follow-up time point to the subsequent follow-up time point. In one example, the measure of spread is based on the volume of voxels that are identified as (a) new and (c) persistent. Additionally, for each of the above four categories additional quantitative measurements for progression of tauopathy, such as total volume of voxels in each category or SUVR statistical values (e.g., means SUVR) for each of the four categories may also be generated by the computing arrangement 130 as further quantitative measurements of progression of tauopathy in the subject from the previous followup time point to the subsequent follow-up time point.

[0085] In subjects that have tauopathy, in particular, Alzheimer’s Disease, abnormal phosphorylation of tau is prevalent and believed to precede and/or trigger aggregation of tau into fibrils, also known as paired helical filaments. The major constituent of paired helical filaments is hyperphosphorylated tau. The methods of the present application can be used to track the progression of tauopathy (e.g., Alzheimer’s disease) in at least one region of the CNS of the subject, such as, for example, cerebral cortex, medial temporal lobe, and/or inferior temporal lobe. Specifically, the methods of the present application track progression of spread of tau in at least one region of the CNS of the subject, such as, for example, cerebral cortex, medial temporal lobe, and/or inferior temporal lobe.

[0086] In both steps 238 and 248, the volume of voxels categorized as (a) new and (c) persistent may be compared to a threshold amount to determine whether tauopathy in the subject has progressed. If the volume of voxels categorized as (a) new and (c) persistent is greater than the threshold amount, the computing arrangement 130 determines tauopathy as having progressed in the subject.

[0087] In another aspect of the present application, a method of preventing, reducing and/or slowing of the progression of neurodegeneration in a subject is provided. The subject may be any human in need of preventing, reducing and/or slowing of the progression of neurodegeneration. In a further aspect of the present application, a method of preventing, reducing and/or slowing the accumulation of hyperphosphorylated tau in a subject provided. The subject may be any human in need of preventing, reducing and/or slowing the accumulation of hyperphosphorylated tau in the brain. In particular, the subject is a subject having tauopathy, specifically, Alzheimer’s Disease. In particular, the subject is a subject having tauopathy, specifically, Alzheimer’s Disease. In some embodiments, the subject may be a human have MCI or a human that is pre-symptomatic for tauopathy, in particular, Alzheimer’s disease. In other embodiments, the subject may be a human having a mutation associated with tauopathy, in particular, Alzheimer’s disease.

[0088] In one embodiment, if the computing arrangement 130 determines tauopathy as having progressed in the subject, the subject may be administered an active agent for treating cognitive decline or tauopathy, such as, for example, a therapeutic agent for treating Alzheimer’s disease. In some examples, the computing arrangement 130 may direct the active agent to be provided to the subject automatically. In other examples, the computing arrangement 130 may direct the output device 160 to generate an output indicating to the user that administration of the active agent is recommended. In another embodiment, if the computing arrangement 130 determines tauopathy as having progressed in the subject, a treatment regimen with an active agent for treating cognitive decline or tauopathy, such as, for example, a therapeutic agent for treating Alzheimer’s disease, may be initiated. In some examples, the computing arrangement 130 may direct the active agent to be provided to the subject automatically according to the treatment regimen. In other examples, the computing arrangement 130 may direct the output device 160 to generate an output indicating to the user that a treatment regimen with the active agent is recommended. Active agents for treating tauopathy may include anti-tau antibodies, anti-p217+tau antibodies, small interfering RNA (siRNA) against human tau, siRNA against p217+tau, cholinesterase inhibitors, N-m ethyl D- aspartate (NMD A) antagonist, etc.

[0089] In another embodiment, the subject may already be undergoing treatment with an active agent for treating cognitive decline or tauopathy, such as, for example, a therapeutic agent fortreating Alzheimer’s disease. If the computing arrangement 130 determines tauopathy as having progressed in the subject undergoing treatment with an active agent for treating cognitive decline or tauopathy, the computing arrangement 130 may direct dosing of the active agent to be increased. In some examples, the computing arrangement 130 may direct dosing of the active agent to be increased automatically. In other examples, the computing arrangement 130 may direct the output device 160 to generate an output indicating to the user that increased dosing of the active agent is recommended.

[0090] Additionally, the systems and methods described herein can be used for various diagnostic and/or monitoring purposes, e.g., for diagnosing and/or monitoring Alzheimer’s disease or other tauopathies, in a subject, monitoring the effectiveness of a treatment, identifying a subject suitable for an anti-tau treatment, pre-screening subjects for plasma or CSF assays for further detection of Alzheimer’s disease, other tauopathies, identification of subjects for enrollment in clinical trials relating to Alzheimer’s disease or other tauopathies, etc.

[0091] In one example, the method 200 may be used to quantify an effect of a pharmaceutically active agent administered to the subject after the baseline time point. The effect of the pharmaceutically active agent may be determined based on the measure of spread through follow-up time point(s). A steady decline in the measure of spread over repeated iterations of method 200 indicates that the pharmaceutically active agent may be effective in treating, ameliorating and/or slowing progression of tauopathy in the subject. In another embodiment, the effect of the pharmaceutically active agents may be determined based on the measure of spread through follow-up time point(s). A pharmaceutically active agent that is effective in treating, ameliorating and/or slowing progression of tauopathy in the subject may slow spread of tau in the brain of the subject. Therefore, efficacy of the pharmaceutically active agent may be quantified in the measure of spread as compared to subjects treated with placebo. It is believed that clinical trial endpoint based on quantifying the measure of spread would show a larger effect size than clinical trial approaches that evaluate change in SUVR over a set of pre-determined ROIs. [0092] An aspect of the present application relates to a method for tracking presence of tau in tau-naive ROI of a brain of a human subject using PET. Another aspect of the present application relates to a method of treating or preventing tau aggregation in a subject having tau present in a previously-determined tau-naive ROI of the brain. Yet another aspect of the present invention relates to a method for monitoring a treatment of tau aggregation in a human subject. These methods comprises many of the steps set forth in the method 200 of FIG. 2A. For example, the methods may involve a step 210 of obtaining either directly from an imaging device, or retrieving from a storage device, data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject, at baseline time point, or close to the baseline time point. As described above, the three-dimensional image may comprise a plurality of voxels corresponding to three- dimensional ROIs within the physical structure of the brain of the subject.

[0093] The method may also comprise a step 220 of obtaining, either directly from the PET imaging device, or retrieving from the storage device, PET^BL of the subject generated by the PET imaging device at the baseline time point for further analysis; and a step 230 of obtaining either directly from the PET imaging device, or retrieving from the storage device, a follow-up PET data of the subject generated at a follow-up time point for further analysis. The follow-up time point is after the baseline time point. The follow-up PET data (PET^FU) is obtained by the PET imaging device in a similar manner as described above for the baseline PET data in step 220. Notably, the follow-up time point is not necessarily the first instance after the baseline time point in which PET data of the subject is obtained or retrieved. The PET data may be obtained by administering a tau-specific tracer to the subject and detecting with the PET imaging device intensity of emissions from the tau-specific tracer in the subject at a specified time point, as described above.

[0094] The methods may also comprise a step 232 of aligning or registering the baseline PET data with the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject. The computing arrangement also aligns or registers in step 232 the first follow-up PET data with the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject. The computing arrangement may align or register the baseline PET data before the first follow-up PET data, may align or register the first follow-up PET data before the baseline PET data, or may align or register the baseline PET and the first follow-up PET data concurrently (e.g., where the process for aligning or registering the baseline PET and the first follow-up PET data may wholly overlap or partly overlap). In some embodiments, the three-dimensional image (e.g., MRI image) is segmented in step 210 by the computing arrangement into a plurality of voxels, each voxel corresponding to a three-dimensional ROI within the physical structure of the brain of the subject. The computing arrangement may align or register the baseline PET data and the follow PET data with the data for a three-dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject so data corresponding to an intensity of emissions from the tracer recorded from a three-dimensional ROI within the physical structure of the brain of the subject at the baseline time point and follow-up time point, respectively , is aligned with a corresponding voxel of the three- dimensional image, as described above.

[0095] The method may also comprise a step 233 of generating a spatial midpoint data between the baseline PET data and the follow-up PET data as described above; and a step 234 of aligning or registering the baseline PET data to the spatial midpoint data to generate a rPET^BL, and s step of aligning or registering the follow-up PET data to the spatial midpoint data to generate a rPET^FU.

[0096] The methods may further comprise a step 235 of aligning or registering the three- dimensional image (e.g., MRI image) of the physical structures of a portion of the brain or the entirety of the brain of the subject to the spatial midpoint data. In a particular embodiment, the three-dimensional image is an MRI image that is segmented in step 210 into a plurality of voxels, each voxel corresponding to a three-dimensional ROI within the physical structure of the brain of the subject, as described above. Optionally, the methods may include a quality control step 236 as described above.

[0097] In addition, the methods may comprise a step 237 of analyzing the baseline PET data corresponding to each voxel to quantify tracer uptake based on an intensity of emission recorded in the portion of the baseline PET data aligned to the voxel to generate a baseline set of tracer uptake data, and analyzing the follow-up PET data corresponding to each voxel to quantify tracer uptake based on an intensity of emission recorded in the portion of the first follow-up PET data aligned to the voxel to generate a first follow-up set of tracer uptake data, as described above. The PET tracer uptake may be quantified by any suitable quantitative or semi -quantitative measure (e.g., ratios), as described above.

[0098] The methods may also comprise a step of identifying tau-naive ROI, wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold. The methods may further comprise determining presence of tau in the tau-naive ROI at the follow-up time point in each of the one or more voxels of the tau- naive ROI by comparing the first follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the follow-up tracer uptake data is above the presence threshold.

[0099] In some embodiments, the methods may comprise one or more of the other steps shown in FIG. 2B and/or 2C, and described above.

[00100] The presence threshold is a threshold value of tau-tracer uptake (e.g., SUV, SUVR, DVR or BP) that indicates the presence of tau versus the absence of tau. The threshold may be empirically determined prior to execution of the methods. For example, tau-tracer uptake for a cohort of cognitively normal subjects (e.g., subjects having no signs or evidence of any cognitive impairment, Alzheimer’s disease, and/or dementia) and/or amyloid negative subjects may be analyzed to determine a suitable presence threshold. The presence threshold may be set as, for instance, ± 0.25 SD, or ± 0.5 SD, or ± 0.75 SD, ± 1 SD, ± 1.25 SD, ± 1.5 SD, ± 1.75 SD, or ± 2 SD, of tau-tracer uptake in these subjects, wherein tau-tracer uptake may be expressed as SUV, SUVR, DVR, or BP.

[00101] More particularly, the progression threshold is selected to result in an elevated measure of spread (e.g., volume of voxels being identified as above the progression threshold) in the cohort of MCI and/or amyloid positive subjects and a lower measure of spread in the cognitively normal and/or amyloid negative cohort identified as below the progression threshold. For tracer uptake expressed as a SUVR, progression thresholds may be considered at SUVR from about 1.0 to about 2.5. In one example, the progression threshold is a SUVR threshold at or about 1.0 to 2.5, 1.2 to 2.3, 1.5 to 2.0, 1.7 to 1.9, or 1.8. Specifically, the SUVR threshold is 1.8. A threshold of 1.8 was selected for the MCI cohort considered in Example I.

[00102] The methods may further comprise administering an active agent for treating tauopathy if it is determined that there is a presence of tau in the tau-naive ROI.

[00103] In some embodiments, the methods may further comprise obtaining multiple sets of follow-up PET data of the brain of the subject generated with the tracer at more than one follow-up time point. Alignments can be made of the baseline PET data and each of the multiple sets of follow-up PET data with data for an image of the brain. Follow-up sets of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the multiple sets of followup PET data aligned to the voxel can be generated. The presence of tau in the tau-naive ROI at each of the follow-up time points in each of the one or more voxels of the tau-naive ROI can be determined by comparing the follow-up sets of tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold. The more than one follow up time points are after the baseline time points, for example, 1 day, 2 days, 3 days, 5 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, or greater, any time in-between, after the baseline time point. Each of the one or more follow up time points may be at regular intervals of time from each other (e.g., regular intervals of 1 week, 2 weeks, 3 weeks, 4 weeks, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 1 year, 2 years, 3 years, 4 years, 5 years, or greater, or any interval in-between), or at irregular intervals of time from each other.

[00104] Another aspect of the present application relates to a method of treating or preventing tau aggregation in a subject having tau present in a previously-determined tau-naive ROI of the brain, comprising administering to the subject an active agent for treating or preventing tau aggregation. The subject can be determined to have tau present in a previously- determined tau-naive ROI by the methods described above.

[00105] The treatment for tau aggregation or to prevent tau aggregation may be any type of procedure or pharmaceutically active agent for treating or slowing progression of tau aggregation, tauopathy or amyloidogenic disease. For example, the treatment may be a pharmaceutical composition comprising an active agent having potential to treat or slow progression of tau aggregation or tauopathy, such as, anti -tau antibodies, anti-p217+tau antibodies, small interfering RNA (siRNA) against human tau, siRNA against p217+tau, etc. In another example, the treatment may be a pharmaceutical composition comprising an active agent having potential to treat or slow progression of the amyloidogenic disease, such as, anti-amyloid antibodies, beta secretase inhibitors, gamma secretase inhibitors, small interfering RNA (siRNA) against human P-amyloid, etc. In a particular example, the treatment under clinical trial is an active agent having potential to treat or slow progression of Alzheimer’s disease.

[00106] In methods for monitoring a treatment of tau aggregation in a human subject of the present application, the methods may further comprise adjusting the treatment when the one or more voxels is categorized as tau-present. The adjustment may be a change in dosing, or a change in the type of treatment, or the addition of removal of a treatment. [00107] In some embodiments, the subject has normal cognition and is amyloid A0+. In other embodiments, the subject has mild cognitive impairment and is A0+. In yet other embodiments, the subject has Alzheimer’s Disease, such as early Alzheimer’s Disease, prodromal Alzheimer’s Disease, mild Alzherimer’s Disease, or advanced Alzheimer’s Disease. [00108] An additional aspect of the present invention relates to a method of identifying an agent capable of modifying, slowing, stopping, or preventing any one or more of: tau seeding, tau spreading, formation of neurofibrillary tangles, and tau aggregation in the brain of a subject. The method comprises identifying a tau-naive ROI in the brain of the subject, which may involve one or more of the steps described above; for example, obtaining baseline PET data of the brain of the subject generated with a tau-specific radioactive tracer at a baseline time point, aligning the baseline PET data with data for an image of the brain in which the image of the brain comprises a plurality of voxels corresponding to ROIs within structures of the brain of the subject, generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the PET data aligned to the voxel, and identifying the tau-naive ROI that corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold.

[00109] The method of identifying an agent capable of modifying or preventing any one or more of: tau seeding, tau spreading, formation of neurofibrillary tangles, and tau aggregation in the brain of a subject also comprises administering the agent to the subject. The agent may be any active agent for treating or slowing progression of tau spread or tau aggregation, tauopathy or amyloidogenic disease, including, but not limited to, an active agent having potential to treat or slow progression of tau aggregation or tauopathy, such as, anti-tau antibodies, anti-p217+tau antibodies, siRNA against human tau, siRNA against p217+tau; etc. or an active agent having potential to treat or slow progression of the amyloidogenic disease, such as, anti-amyloid antibodies, beta secretase inhibitors, gamma secretase inhibitors, siRNA against human P-amyloid, etc.

[00110] The method of identifying an agent capable of modifying or preventing any one or more of: tau seeding, tau spreading, formation of neurofibrillary tangles, and tau aggregation in the brain of a subject further comprises determining the change or rate of change, e.g., increase or rate of increase, in tau presence in the tau-naive ROI, which may involve one of more of the steps described above. For example, determining rate of increase in tau presence in the tau-naive ROI may comprise obtaining follow-up PET data of the brain of the subject generated with the tracer at a follow-up time point, the follow-up time point being after the administration of the agent; aligning the follow-up PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to ROIs within structures of the brain of the subject; generating follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the follow-up PET data aligned to the voxel; comparing the follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the follow-up tracer uptake data is above the presence threshold; and calculating a rate of increase in tau presence in the tau-naive ROI. The agent is identified as being capable of preventing tau aggregation when the rate of increase in tau presence in the tau-naive ROI is less than rate of increase in tau presence in a control subject.

[00111] In some embodiments, the rate of increase in tau presence may be calculated based on the change in the number of voxels categorized as tau present over time, i.e., between the follow up time point and the baseline time point. In some embodiments, follow-up PET data of the brain of the subject generated with the tracer may be obtained at more than one followup time point, and therefore the rate of increase in tau presence may be calculated based on the change in the number of voxels categorized as tau present between an earlier follow up time point and a later follow up time point.

[00112] In some embodiments, the subject receiving the agent has a condition or disease associated with the presence of tau, for example, early stage Alzheimer’s Disease (AD), early stage of another tauopathy, etc. In some embodiments, the subject exhibit MCI.

[00113] The control subject is a subject with same or similar disease state or condition (e.g., has early stage of Alzheimer’s Disease (AD) or other early stage tauopathy) as the subject receiving the agent, but is administered a placebo. The change or rate of change, e.g., increase or rate of increase, of tau presence in the control subject may be determined using steps as described above, for example, obtaining baseline PET data of the control subject’s brain, aligning the baseline PET data with data for an image of the brain, generating a baseline set of tracer uptake data, etc. In some embodiments, the change, rate of change, increase or rate of increase of tau presence in the control subject is determined in a tau-naive ROI of the control subject; in other embodiments, the change, rate of change, increase or rate of increase of tau presence in the control subject is not determined in a tau-naive ROI of the control subject. It is to be understood that “control subject” may comprise more than one individual, such that, for example, the “rate of increase of tau presence in the control subject” is calculated based on rates of increase of tau presence in multiple individuals who received a placebo (e.g., the “rate of increase of tau presence in the control subject” is a mean, mode, median, etc., of rates of increase of tau presence in multiple individuals).

EXAMPLE

[00114] The following example is to further illustrate the nature of the invention. It should be understood that the following example does not limit the invention and that the scope of the invention is to be determined by the appended claims.

Example I

[00115] An exemplary embodiment of the method 200 of FIG. 2A described above is provided in Example I. The exemplary method of Example I utilizes MRI images to provide the three-dimensional image of at least one region of the CNS of the subject. The method of Example I includes two aspects: (1) quantifying update in each of baseline (BL) and follow-up (FU) PET scans, and (2) quantifying tau spread in the subject. In Example I, the tau-specific tracer for generating PET data is [¹⁸F]MK-6240.

Quantifying Uptake in Each of BL and FU Scans

[00116] In the first aspect of the method of Example, tracer uptake for each of the BL and FU PET scans is quantified for each ROI. This first aspect of the method of Example I includes modules for segmenting the MRI image, registering PET scans from each visit (baseline and follow-up visits) to the MRI image, extracting intensity of emissions detected for each voxel, and quantifying tau tracer update (e.g., generating SUVR) for each voxel.

[00117] First, a T1 weighted MRI volume is used to segment the brain into ROI using the FreeSurfer software package (available from https://surfer.nmr.mgh.harvard.edu/). The T1 volume is typically acquired closest in time to the baseline (BL) PET scan, however the use of an MRI scan from the Follow Up (FU) visit is acceptable if a BL MRI scan is not available.

[00118] The TI weighted MRI volume is then used to register PET scans from each visit (baseline and follow-up visits) to the MRI image. In particular, volumes of BL and FU PET scans are registered (aligned) across time and visits using rigid-body affine transformations that describe 6 movement parameters: 3 shifts and 3 rotations. For a set of BL and FU scans, the process (steps 1 to 7) is as follows. In step 1, average each PET scan over time frames. A PET scan typically has multiple frames, i.e., volumes, collected consecutively. For example, a PET scan can have 6 frames, each covering a 5 minute window, starting a 90 minutes post tracer injection for a total scanning duration of 30 minutes. In step 2, register each PET scan time frame to the scan’s average time frame. In step 3, register average frames from BL and FU scans. In step 4, combine spatial transforms from steps 2 and 3 and compute the midpoint transform. In step 5, create new registered PET scan volumes (rPET^BL, rPET^FU) by transforming BL and FU scans by their respective midline transforms. Alignment to a midpoint treats BL and FU scans equally, thereby minimizing interpolation smoothing bias. Registration in the first iteration of Example I is carried out using a least-squares cost function. In step 6, a quality control of the alignment is conducted visually to ensure that BL and FU scans are in alignment. This is done using a visual process whereby the display is interactively toggled between scans which makes small alignment deviations stand out to the eye. If the alignment is found to be of poor quality, coarse rotations and masking of neck regions may be applied. Additionally, alternate registration cost functions such as normalized mutual information or correlation ratio may be used instead of least squares. The process of registration over time and visits is repeated from step 1 until the registration outcome is satisfactory. In step 7, average time frames of registered BL PET scan (Avg. rPET^BL) is used to register the T1 volume with the PET data.

[00119] All volume operations in Example I are carried out using NSBiom customized shell and R scripts that call software modules from the AFNI software package, including 3dcalc and 3dAllineate (available at https://afni.nimh.nih.gov/about_afni) to carry out volume-based operations. The quality control process of step 6 is semi-automated with scripted commands that drive AFNI’s graphical interface, thereby minimizing the slow and repetitive process of GUI setup with each set of new data.

[00120] The T1 MRI volume is registered to match the midpoint-aligned average time frame of the BL PET scan (rPET^BL) using the following iterative process. Align T1 to average rPET^BL any one or more of the following cost functions: Normalized Mutual Information, Correlation Ratio Symmetrized with multiplication, Correlation Ratio symmetrized with addition, Hellinger metric, Least squares, Local Pearson Coefficient (Absolute). Conduct quality control of the alignment using a visual process that overlays 3D edges of the aligned T1 volume with the reference PET volume or allows for a rapid toggle between the T1 and PET volume. If a good alignment is achieved, the transform corresponding to the best outcome is selected and used for aligning MRI to PET. If none of the cost functions resulted in an acceptable alignment, pre-processing steps are applied to the volumes before repeating at alignment step 1. Examples of pre-processing steps include: applying initial rotations to the MRI volume to bring it in coarse alignment with the reference PET volume; axial cropping below the cerebellum; and intensity normalization of the reference PET scan with square root operation. Once an optimal alignment is identified, PET registered versions of the T1 volume (rTl) and segmented ROIs (rROIs) are created.

[00121] Using rROIs, rPET^BL, and rPET^FU scans, intensity of emissions detected for each voxel corresponding to each ROI is determined. Specifically, tracer uptake (SUV) in each ROI, including the reference region ROI, is determined. The reference region used in Example I for longitudinal analysis corresponds to the cerebellar gray matter. PET tracer uptake in each ROI relative to the reference region (SUVR) is computed regionally by dividing regional SUV by the SUV in the reference region and averaging across all time frames. Similarly, voxelwise SUVR maps are created by dividing voxelwise SUV by the SUV in the reference region and averaging across all time frames.

Procedure for Quantifying Tau Spread

[00122] Using voxelwise SUVR values generated above, tau spread is quantified as follows. First, high uptake regions are identified in each of BL and FU scans. A high uptake region is defined as a collection of voxels where SUVR exceeds a pre-determined progression SUVR threshold as detailed further below. High uptake regions are determined for each of BL and FU scans. The voxels may be partitioned into 4 separate classes based on tracer update at BL and FU:

1. New: Elevated tau voxels at Follow up Only

2. Resolved: Elevated tau voxels at Baseline Only

3. Persistent: Elevated tau voxels at Baseline and Follow up

4. Intact: Voxels that do not show elevated uptake at either Follow up or Baseline For each partition class, descriptive parameters such as volume and SUVR statistics from BL and FU scans are generated.

[00123] The progression SUVR threshold for determining high uptake areas is selected to distinguish between cognitive normal, amyloid negative subjects and MCI, amyloid positive subjects. First, the volume for new areas is calculated as a function of progression SUVR thresholds for a control cohort consisting of cognitively normal, amyloid negative subjects, and for a cohort of MCI, amyloid positive subjects. The threshold is selected to result in elevated average new volume in the target cohort and low new volume in the control cohort. In the Example I, an SUVR threshold of 1.8 is used.

[00124] FIG. 3 shows a function of progression SUVR thresholds vs. annualized volume of those voxels identified as new in the inferior temporal lobe for a cognitively normal (CN) cohort (n=32) and a function of progression SUVR thresholds for a control cohort and a cohort of MCI (n=9), amyloid positive subjects. Both of these functions sweep across thresholds and estimate new volumes in CN and MCI cohorts. A progression SUVR threshold of 1.8 is selected based on effect size in MCI cohort and good contrast relative to CN cohort.

[00125] FIG. 4 shows exemplary data for annualized ‘new’ volume as a fraction of ROI volume in the inferior temporal lobe, medial temporal lobe, and cortex for the CN and MCI cohorts. As can be seen in FIG. 4, in each of the inferior temporal lobe, medial temporal lobe, and cortex the percentage of annualized “new” volume as a fraction of the total ROI volume is greater in MCI subjects as compared to CN subjects. Therefore, FIG. 4 shows that quantifying those voxels that are partitioned to the category of “new” can provide a quantitative measurement for spread of tau and/or progression of tauopathy.

[00126] FIG. 5A shows exemplary data for annualized change in SUVR in the inferior temporal lobe of the CN and MCI cohorts. FIG. 5B shows the exemplary data for annualized ‘new’ volume as a fraction of ROI volume in the inferior temporal lobe as provided on the left side of FIG. 4. As can be seen in Figs. 5a and 5b, the annualized ‘new’ volume as a fraction of ROI volume (effect size =.0.73) demonstrates a greater effect size than change in SUVR volume (effect size =.1.38) for the same cohort of MCI subjects.

Example II

[00127] A demonstration of longitudinal tau PET analysis involving tau-naive regions of the brain as described above is provided in Example II. The objective of Example II was to implement and test performance of a pipeline for analyzing longitudinal tau PET for multiple tracers and across disease stages in anatomical and subject-specific ROIs.

[00128] The pipeline is summarized in FIG. 6. It involved a T1 -weighted MRI in addition to tau PET scans. T1 -weighted MRI and PET scan were obtained at baseline and PET scans were performed in follow-up visits. For minimizing smoothing bias, longitudinal tau PET scans were aligned to a spatial midpoint and their average was used as a reference for aligning MRI scans and derived ROI. The pipeline produced output using multiple cost functions, and a quality control (QC) process was used to select the best alignment or modify parameters when needed. The pipeline utilized FreeSurfer (Fischl etal., Neuron. 33:341-55, 2002), AFNI (Cox, Comput Biomed Res. 29: 162-73, 1996; Saad & Reynolds, Neuroimage 62: 768-73, 2012), and ANTS (Yushkevich et al., NeuroImage 31 :1116-28, 2006) software for segmentation, registration, and quantification. Tau PET standardized uptake value ratio (SUVR) was estimated using cerebellar gray as a reference region with the option of uniformizing the smoothness across longitudinal PET scans. [00129] As depicted in FIG. 7, SUVR estimates were obtained over: (i) anatomically defined regions; (ii) individualized ROIs based on functional connectedness defined by quartiles 1-4 (Q1-Q4) from more proximal to distal to the NFT epicenter at baseline (Franzmeier et cd.. Set. Adv. 6: eabdl327, 2020); and (iii) subject-specific tau-naive composite ROIs, which consisted of brain regions within ± 1 SD of the SUVR in a tracer-matched control cohort of cognitively normal (CN) amyloid negative (A0-) participants. The tau-naive composite ROI allowed for the assessment of treatment impact in regions where NFT had yet to form. In total, data was analyzed from 765 ADNI (Petersen et cd.. Neurology 74: 201-09, 2010) participants (298 with follow up), for which the PET scans used the Tauvid® (T807) tracer; and 794 subjects from the AIBL study and the Cerveau consortium (194 with follow up), for which the PET scans used the MK6240 tracer.

[00130] FIGS. 8A and 8B show results from cortex, Q1-Q4, and tau-naive composite ROI in the following 4 groups: CN AP-, CN Ap+, MCI Ap+, AD Ap+. Tau PET signal was progressively and significantly (p<0.05) higher across the 4 groups in cortex and QI ROIs, and similar trends were observed for Q2, Q3, and Q4 with monotonic decrease from QI to Q4 (FIG. 8A) For cortex, QI, and tau naive ROIs, SUVR change increased (p<0.05) across the first three groups but not between MCI and AD (FIG. 8B).

[00131] These results show that cross-sectional SUVR and annualized SUVR changes were consistent across cohorts and tracers, supporting efforts to derive a universal scale and pooling data for establishing tau PET surrogacy. The tau naive ROI results suggest that treatment effects can be detected in regions without NFT evidence at BL. Tau naive ROI may be optimal when assessing the effects of treatment that aim to halt the spread of tangles to unaffected brain areas

The invention described and claimed herein is not to be limited in scope by the specific embodiments herein disclosed since these embodiments are intended as illustrations of several aspects of this invention. Any equivalent embodiments are intended to be within the scope of this invention. Indeed, various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims. All publications cited herein are incorporated by reference in their entirety.

Claims

CLAIMS What is claimed is:

1. A method for tracking progression of tauopathy in a brain of a human subject using Positron Emission Tomography (PET), comprising:

(i) obtaining baseline PET data of the brain of the subject generated with a tau- specific radioactive tracer at a baseline time point;

(ii) obtaining first follow-up PET data of the brain of the subject generated with the tracer at a first follow-up time point, the first follow-up time point being after the baseline time point;

(iii) aligning the baseline PET data and the first follow-up PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to regions of interest (ROI) within structures of the brain of the subject;

(iv) generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the baseline PET data aligned to the voxel and a first follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first follow-up PET data aligned to the voxel; and

(v) determining progression of tauopathy from the baseline time point to the first follow-up time point in each voxel by comparing the baseline tracer uptake data and the first follow-up tracer uptake data of each voxel to a progression threshold.

2. The method of claim 1, wherein step (v) comprises comparing the baseline tracer uptake and the first follow-up tracer uptake data to the progression threshold to categorize each voxel as:

(a) new, when the baseline tracer uptake data is below the progression threshold and the first follow-up tracer uptake data is above the progression threshold,

(b) resolved, when the baseline tracer uptake data is above the progression threshold and the first follow-up tracer uptake data is below the progression threshold,

(c) persistent, when the baseline tracer uptake data is above the progression threshold and the first follow-up tracer uptake data is above the progression threshold, or (d) intact, when the baseline tracer data is below the progression threshold and the first follow-up tracer uptake data is below the progression threshold.

3. The method of claim 1, further comprising:

(vi) obtaining second follow-up PET data of the brain of the subject generated with the tracer at a second follow-up time point, the second follow-up time point being after the baseline time point and after the first follow-up time point;

(vii) aligning the second follow-up PET data to the image of the brain;

(viii) generating a second follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the second follow-up PET data aligned to the voxel; and

(ix) determining progression of tauopathy from the first follow-up time point to the second follow-up time point in each voxel by comparing the first follow-up tracer uptake data and the second follow-up tracer uptake data of each voxel to the progression threshold.

4. The method of claim 3, wherein step (ix) comprises comparing the first follow-up tracer uptake data and the second follow-up tracer uptake data to the progression threshold and to categorize each voxel as:

(a) new, when the first follow-up tracer uptake data is below the progression threshold and the second follow-up tracer uptake data is above the progression threshold,

(b) resolved, when the first follow-up tracer uptake data is above the progression threshold and the second follow-up tracer uptake data is below the progression threshold,

(c) persistent, when the first follow-up tracer uptake data is above the progression threshold and the second follow-up tracer uptake data is above the progression threshold, or

(d) intact, when the first follow-up tracer data is below the progression threshold and the second follow-up tracer uptake data is below the progression threshold.

5. The method of claim 4, further comprising:

(x) quantifying a measure of spread of tauopathy in the subject based on a volume of voxels categorized as (a) new in step (ix).

6. The method of claim 4, wherein the measure of spread of tauopathy is further based on a volume of voxels categorized as at least one of (b) resolved, (c) persistent, and (d) intact.

7. The method of claim 5 or claim 6, further comprising determining tauopathy in the subject has progressed when the measure of spread is greater than a threshold amount.

8. The method of claim 7, further comprising: administering an active agent for treating tauopathy when the method determines tauopathy in the subject has progressed.

9. The method of claim 7, further comprising: increasing dosing of an active agent for treating tauopathy administered to the subject when the method determines tauopathy in the subject has progressed.

10. The method of claim 7, wherein the subject is administered a pharmaceutically active agent after the baseline time point, the method further comprising: quantifying effect of the pharmaceutically active agent in preventing or slowing progression of tauopathy based on the measure of spread.

11. The method of any one of claims 1-10, wherein the image of the brain is a magnetic resonance image (MRI) of the brain of the subject obtained at the baseline time point.

12. The method of any one of claims 1-10, wherein the tracer binds to hyperphosphorylated tau.

13. The method of claim 12, wherein the tracer is selected from the group consisting of: [¹⁸F]MK-6240, [¹⁸F]JNJ-311, [¹⁸F]JNJ-067 [¹⁸F]THK5317, [¹⁸F]THK5351, [¹⁸F]AV1451, [^{1 ]}C]PBB3, [¹⁸F]PM-PBB3, [¹⁸F]RO-948, [¹⁸F]PI-2620, [¹⁸F]GTP1, and [¹⁸F]T808.

14. The method of any one of claims 1-13, wherein the progression threshold corresponds to a threshold intensity distinguishing progression of tauopathy in cognitive normal subject from MCI subject.

15. The method of any one of claims 1-14, wherein the tracer uptake data is a standardized uptake value (SUV) of the tracer in each voxel.

16. The method of any one of claims 1-14, wherein the tracer uptake data is a standardized uptake value ratio (SUVR) of the tracer in each voxel to a reference value.

17. The method of any one of claims 1-14, wherein the tracer uptake data is a distribution volume ratio (DVR) of the tracer in each voxel.

18. The method of any one of claims 1-14, wherein the tracer uptake data is a binding potential (BP) of the tracer in each voxel.

19. The method of claim 18, wherein the progression threshold is a SUVR from about 1.0 to about 2.5.

20. The method of claim 19, wherein the progression threshold is about 1.8.

21. The method of any one of claims 1-20, wherein the tauopathy is selected from the group consisting of familial Alzheimer's disease, sporadic Alzheimer's disease, frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), progressive supranuclear palsy, corticobasal degeneration, Pick's disease, progressive subcortical gliosis, tangle only dementia, diffuse neurofibrillary tangles with calcification, argyrophilic grain dementia, amyotrophic lateral sclerosis parkinsonism-dementia complex, Down syndrome, Gerstmann-Straussler-Scheinker disease, Hallervorden-Spatz disease, inclusion body myositis, Creutzfeld-Jakob disease, multiple system atrophy, Niemann-Pick disease type C, prion protein cerebral amyloid angiopathy, subacute sclerosing panencephalitis, myotonic dystrophy, non-Guamanian motor neuron disease with neurofibrillary tangles, postencephalitic parkinsonism, chronic traumatic encephalopathy, and dementia pugulistica (boxing disease).

22. The method of claim 21, wherein the tauopathy is Alzheimer's disease.

23. A non-transitory computer-readable storage medium storing a set of instructions which, when executed by a processor, causes the processor to perform the steps of any one of claims 1-22.

24. A Positron Emission Tomography (PET) system for non-invasively tracking progression of tauopathy, comprising: an imaging device configured to obtain an image of a brain of a human subject, the image of the brain comprising a plurality of voxels corresponding to regions of interest (RO I) within structures of the brain of the subject; a PET imaging device configured to detect radioactive emissions from a tau-specific radioactive tracer administered to the subject and to generate PET data based on intensity of the detected radioactive emissions detected from the subject; a memory storage device storing the image of the brain obtained by the imaging device, a first PET data of the brain of the subject generated by the imaging device at a first time point, and a second PET data of the brain of the subject generated by the PET imaging device at a second time point after the first time point; and a processor and a non-transitory computer readable storage medium including a set of instructions executable by the processor, the set of instructions operable to align the first PET data to the image of the brain, generate a first set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first PET data aligned to the voxel, align the second PET data to the image of the brain, generate a second set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the second PET data aligned to the voxel, and determine progression of tauopathy from the first time point to the second time point in each voxel by comparing the first tracer uptake data and the second tracer uptake data of each voxel to a progression threshold.

25. The PET system of claim 24, wherein the set of instructions for determining progression of tauopathy from the first time point to the second time point in each voxel comprise instructions operable to compare the first tracer uptake data and the second tracer uptake data to the progression threshold and to categorize each voxel as: (a) new, when the first tracer uptake data is below the progression threshold and the first second tracer uptake data is above the progression threshold,

(b) resolved, when the first tracer uptake data is above the progression threshold and the second tracer uptake data is below the progression threshold,

(c) persistent, when the first tracer uptake data is above the progression threshold and the second tracer uptake data is above the progression threshold, or

(d) intact, when the first tracer data is below the progression threshold and the second tracer uptake data is below the progression threshold.

26. The PET system of claim 25, wherein the set of instructions further comprise instructions operable to quantify a measure of spread of tauopathy in the subject based on a volume of voxels categorized as (a) new.

27. The PET system of claim 26, wherein the set of instructions further comprises instructions operable to quantify a measure of spread of tauopathy in the subject based on the volume of voxels categorized as (a) new and a volume of voxels categorized as at least one of (b) resolved, (c) persistent, and (d) intact.

28. The PET system of claims 24 or 25, further comprising a display operably connected to the processor for outputting a progression of images demonstrating spread of tau in the brain of the subject from the first time point to the second time point.

29. The PET system of claim 28, wherein the image device is a magnetic resonance image (MRI) device configured to obtain an MRI image of the brain of the subject.

30. The PET system of any one of claims 24-29, wherein the tracer binds to hyperphosphorylated tau.

31. The PET system of any one of claims 24-30, wherein the tracer is selected from the group consisting of [¹⁸F]MK-6240, [¹⁸F]JNJ-311 , [¹⁸F]JNJ-067 [¹⁸F]THK5317, [¹⁸F]THK5351, [¹⁸F]AV1451, [^{1 !}C]PBB3, [¹⁸F]PM-PBB3, [¹⁸F]RO-948, [¹⁸F]PI-2620, [¹⁸F]GTP1, and [¹⁸F]T808.

32. The PET system of any one of claims 24-31, wherein the progression threshold corresponds to threshold intensity distinguishing progression of tauopathy in cognitive normal subject from MCI subjects.

33. The PET system of any one of claims 24-32, wherein the tracer uptake take data is a standardized uptake value ratio (SUVR) of the tracer in each voxel to a reference value.

34. The PET system of claim 33, where in the progression threshold is a SUVR from about 1.5 to about 2.0.

35. The PET system of claim 34, wherein the progression threshold is about 1.8.

36. The PET system of any one of claims 24-35, wherein the tauopathy is selected from the group consisting of familial Alzheimer's disease, sporadic Alzheimer's disease, frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), progressive supranuclear palsy, corticobasal degeneration, Pick's disease, progressive subcortical gliosis, tangle only dementia, diffuse neurofibrillary tangles with calcification, argyrophilic grain dementia, amyotrophic lateral sclerosis parkinsonism-dementia complex, Down syndrome, Gerstmann-Straussler-Scheinker disease, Hallervorden-Spatz disease, inclusion body myositis, Creutzfeld-Jakob disease, multiple system atrophy, Niemann-Pick disease type C, prion protein cerebral amyloid angiopathy, subacute sclerosing panencephalitis, myotonic dystrophy, non-Guamanian motor neuron disease with neurofibrillary tangles, postencephalitic parkinsonism, chronic traumatic encephalopathy, and dementia pugulistica (boxing disease).

37. The PET system of claim 36, wherein the tauopathy is Alzheimer's disease.

38. A method for tracking presence of tau in tau-naive region of interest (ROI) of a brain of a human subject using Positron Emission Tomography (PET), comprising:

(i) obtaining baseline PET data of the brain of the subject generated with a tau- specific radioactive tracer at a baseline time point; (ii) obtaining first follow-up PET data of the brain of the subject generated with the tracer at a first follow-up time point, the first follow-up time point being after the baseline time point;

(iii) aligning the baseline PET data and the first follow-up PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to ROIs within structures of the brain of the subject;

(iv) generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the baseline PET data aligned to the voxel and a first follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the first follow-up PET data aligned to the voxel;

(v) identifying tau-naive ROI, wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold;

(vi) determining presence of tau in the tau-naive ROI at the first follow-up time point in each of the one or more voxels of the tau-naive ROI by comparing the first follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the first followup tracer uptake data is above the presence threshold.

39. The method of claim 38, further comprising:

(vii) obtaining second follow-up PET data of the brain of the subject generated with the tracer at a second follow-up time point, the second follow-up time point being after the baseline time point and after the first follow-up time point;

(viii) aligning the second follow-up PET data to the image of the brain;

(ix) generating a second follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the second follow-up PET data aligned to the voxel; and

(x) determining presence of tau in the tau-naive ROI at the second follow-up time point in each of the one or more voxels of the tau-naive ROI by comparing the second follow-up tracer uptake data of each of the one or more voxels of the tau-naive ROI to the presence threshold, wherein the voxel is categorized as tau-present when the second followup tracer uptake data is above the presence threshold.

40. The method of claim 38 or 39, further comprising:

(xi) quantifying a measure of extent of tau presence in the tau-naive ROI based on a volume of voxels categorized as tau-present in step (vi) or (x).

41. The method of claim 38 or 39, further comprising: administering an active agent for treating or preventing tau aggregation when the method determines presence of tau in a tau-naive ROI.

42. A method of treating or preventing tau aggregation in a subject having tau present in a previously-determined tau-naive region of interest (ROI) of the brain, comprising

(i) obtaining baseline Positron Emission Tomography (PET) data of the brain of the subject generated with a tau-specific radioactive tracer at a baseline time point;

(v) identifying the tau-naive ROI, wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold;

(vi) determining presence of tau in the tau-naive ROI at the first follow-up time point in each of the one or more voxels of the tau-naive ROI by comparing the first follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the first followup tracer uptake data is above the presence threshold; and (vii) administering to the subject a treatment for tau aggregation or for preventing tau aggregation.

43. A method of treating or preventing tau aggregation in a subject having tau present in a previously-determined tau-naive region of interest (ROI) of the brain, comprising administering to the subject a treatment for tau aggregation or for preventing tau aggregation; wherein the subject was determined to have tau present in a previously-determined tau-naive ROI by a method comprising:

(v) identifying the tau-naive ROI, wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold; and

44. A method for monitoring a treatment of tau aggregation in a human subject, comprising: (i) obtaining baseline Positron Emission Tomography (PET) data of the brain of the subject receiving treatment, wherein the PET data is generated with a tau-specific radioactive tracer at a baseline time point;

(v) identifying tau-naive region of interest (ROI), wherein the tau-naive ROI corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold; and

45. The method of claim 44, further comprising adjusting the treatment when the one or more voxels is categorized as tau-present.

46. The method of claim 45, wherein the adjustment of the treatment comprises a change in dosing.

47. The method of claim 45, wherein the adjustment of the treatment comprises a change to or addition of a new treatment.

48. A method of identifying an agent capable of preventing tau spreading or tau aggregation in a brain, comprising

(i) identifying a tau-naive region of interest (ROI) in the brain of a subject;

(ii) administering the agent to the subject; and

(iii) determining rate of increase in tau presence in the tau-naive ROI, wherein the agent is identified as being capable of preventing tau spreading or tau aggregation when the rate of increase in tau presence in the tau-naive ROI is less than rate of increase in tau presence in a control subject.

49. The method of claim 48, wherein identifying the tau-naive ROI in the brain of the subject in (i) comprises obtaining baseline Positron Emission Tomography (PET) data of the brain of the subject generated with a tau-specific radioactive tracer at a baseline time point; aligning the baseline PET data with data for an image of the brain in which the image of the brain comprises a plurality of voxels corresponding to ROIs within structures of the brain of the subject; generating a baseline set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the PET data aligned to the voxel; and identifying the tau-naive ROI that corresponds to one or more voxels wherein the intensity of emissions from the tracer recorded in the portion of the baseline PET data aligned to the one or more voxels is below a presence threshold.

50. The method of claim 48 or 49, wherein determining rate of increase in tau presence in the tau-naive ROI in (iii) comprises obtaining follow-up PET data of the brain of the subject generated with the tracer at a follow-up time point, the follow-up time point being after the administration of the agent; aligning the follow-up PET data with data for an image of the brain, the image of the brain comprising a plurality of voxels corresponding to ROIs within structures of the brain of the subject; generating follow-up set of tracer uptake data comprising tracer uptake data for each of the plurality of voxels based on an intensity of emissions from the tracer recorded in a portion of the follow-up PET data aligned to the voxel; comparing the follow-up tracer uptake data of each of the one of more voxels of the tau-naive ROI to the presence threshold, wherein the one or more voxels is categorized as tau-present when the follow-up tracer uptake data is above the presence threshold; and calculating a rate of increase in tau presence in the tau-naive ROI.

51. The method of any one of claims 48-50, wherein the subject administered the agent has a condition or disease associated with the presence of tau.

52. The method of claim 51, wherein the control subject has the same condition or disease as the subject administered the agent, and the control subject is administered a placebo.

53. The method of any one of claims 38-47 and 49-52, wherein the image of the brain is a magnetic resonance image (MRI) of the brain of the subject obtained at the baseline time point.

54. The method of any one of claims 38-47 and 49-53, wherein the tracer binds to hyperphosphorylated tau.

55. The method of claim 54, wherein the tracer is selected from the group consisting of: [¹⁸F]MK-6240, [¹⁸F]JNJ-311, [¹⁸F]JNJ-067 [¹⁸F]THK5317, [¹⁸F]THK5351, [¹⁸F]AV1451, [ⁿC]PBB3, [¹⁸F]PM-PBB3, [¹⁸F]RO-948, [¹⁸F]PI-2620, [¹⁸F]GTP1, and [¹⁸F]T808.

56. The method of any one of claims 38-47 and 49-55, wherein the presence threshold corresponds to a threshold intensity distinguishing presence of tau from background noise.

57. The method of any one of claims 38-47 and 49-56, wherein the tracer uptake data is a standardized uptake value (SUV) of the tracer in each voxel.

58. The method of any one of claims 38-47 and 49-56, wherein the tracer uptake data is a standardized uptake value ratio (SUVR) of the tracer in each voxel to a reference value.

59. The method of any one of claims 38-47 and 49-56, wherein the tracer uptake data is a distribution volume ratio (DVR) of the tracer in each voxel.

60. The method of any one of claims 38-47 and 49-56, wherein the tracer uptake data is a binding potential (BP) of the tracer in each voxel.