DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-9 and 11-21 are pending for examination in the application filed 09/15/2025. Claims 3, 8, and 14-20 have been amended, claim 10 has been cancelled and claim 21 is new.
Priority
Acknowledgement is made of Applicant’s claim to priority of provisional applications 62/705,329, filing date 06/22/2020, and 63/168,400, filing date 03/31/2021. Acknowledgement is additionally made of the present application as a national stage entry of PCT/IL2021/050762, international filing date: 06/22/2021.
Response to Arguments and Amendments
The objections of claims 3 and 14-20 are withdrawn in light of the amendments.
Applicant's arguments filed 09/15/2025 have been fully considered but they are not persuasive. Applicant argues on pages 10-11 of the Remarks filed 09/15/2025 that Godavarty’s NIR system is incapable of providing the three-dimensional volumetric MRI data required by the claimed invention. As stated in the Non-Final Office Action:
Regarding claim 1, Manza teaches a method of non-invasive diagnosis of a condition in a subject, the method comprising ([pg. 649 para. 4] While a growing body of literature has documented how behavioral measures of Parkinson’s disease correlate with regional changes in striatal structure and activity, few studies have examined how cortico-striatal functional connectivity varies with motor/cognitive deficits, and most existing studies do not examine the different subdivisions of the striatal nuclei, which may have contributed to the variability in findings…To address this gap of research, we took advantage of the large cohort of resting state functional connectivity (rsFC) data from newly diagnosed individuals provided by the Parkinson’s Progression Markers Initiative (PPMI). [pg. 657 para. 4] Here, we demonstrate that functional connectivity of the dorsal caudate (but not other striatal subregions) is particularly associated with Parkinson’s disease-related cognitive deficits, especially in the memory and visuospatial domains):
obtaining, from a medical scanning device, a three-dimensional (3D) scan of the subject, said scan comprising a plurality of voxel values ([pg. 650 para. 1] All MRI and fMRI data were acquired on Siemens Trio Tim 3 Tesla magnets (Siemens Medical Systems, Erlangen, Germany), and imaging parameters were kept constant across all sites. High resolution anatomical images (T1-weighted MP-RAGE) were acquired with repetition time (TR) = 2,300 ms, echo time (TE) = 2.98 ms, flip angle (FA) = 9 degrees, matrix = 240 x 256, field of view (FOV) = 256 mm, voxel size 1 x 1 x 1 mm^3, 176 sagittal slices with slice thickness = 1 mm. Resting state fMRI (rsfMRI) echo-planar images were acquired for 8.5 min (212 volumes) with TR = 2,400 ms, TE = 25 ms, flip angle = 80 degrees, matrix = 68 x 66, FOV = 222 mm, 40 slices (ascending direction) and voxel size 3.3 x 3.3 x 3.3 mm^3);
segmenting the scan, to obtain a segmented region of interest (ROI) of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation);
and analyzing the voxel values along the at least one axis, to diagnose a condition of the subject ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis. [Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease).
Manza does not teach at least one processor and performing a singular value decomposition (SVD) of the ROI, to determine at least one axis of the ROI in the SVD space.
Godavarty, in the same field of endeavor of medical image analysis, teaches at least one processor ([0034] Controller assembly 160 may include processor 162 and source driver 164) and performing a singular value decomposition (SVD) of the ROI, to determine at least one axis of the ROI in the SVD space ([0084] For example, instructions stored in spatio-temporal analysis module 128 may cause processor 104 and/or GPU 106 to calculate singular value decomposition (SVD), principal component analysis (PCA), and/or independent component analyses (ICA), etc., of a selected ROI within an NIR intensity image sample or an entire imaged region of the NIR intensity image sample).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Manza with the teachings of Godavarty to perform singular value decomposition (SVD) of the ROI because ([Godavarty 0085]) by applying a suitable reconstruction or decomposition technique (e.g., SVD, PCA, ICA, etc.) dominant eigenvalue signal components may be extracted from the NIR intensity image sample data based upon the NIR data at a single point in time (sampling point)).
Godavarty specifically teaches the limitations of at least one processor and performing a singular value decomposition (SVD) of the ROI, to determine at least one axis of the ROI in the SVD space, as described above. For further clarification, Godavarty teaches [0085] By applying a suitable reconstruction or decomposition technique (e.g., SVD, PCA, ICA, etc.) dominant eigenvalue signal components may be extracted from the NIR intensity image sample data based upon the NIR data at a single point in time (sampling point), thus indicating the direction (axis) along which the data in the ROI varies the most. Applicant further argues that it would be impossible to combine Manza and Godavarty. As stated in MPEP § 2145(III), combining the teachings of references does not involve an ability to combine their specific structures. Furthermore, a person of ordinary skill is also a person of ordinary creativity and in many cases will be able to fit teachings of multiple patents together like pieces of a puzzle (See MPEP § 2141.03). Given this creativity the ability to fit teachings of multiple patents together like the pieces of a puzzle, one of ordinary skill in the art would be able to combine the teachings in such a way that it would not render the prior art devices inoperable for their intended purpose, as there are more ways than those proposed by Applicant to combine the references. Thus the 35 USC § 103 rejections of Manza in view of Godavarty are maintained.
Applicant argues on pages 11-12 of the Remarks that new claim 21 is not taught by Manza in view of Godavarty. New claim 21 is a combination of claims 1, 2, 5, and 6. Applicant argues specifically that Manza does not teach any comparison of the quantitative function to a reference quantitative function and that the diagnosis is not based on the comparison. As stated in the Non-Final Office Action:
Manza teaches wherein analyzing the voxel values along the at least one axis comprises: calculating a quantitative function of voxel values along the at least one axis ([pg. 652 para. 6] The fMRI signal time courses were averaged across all voxels for each of the seed regions. We computed the correlation coefficient between the averaged time course of each seed region and the time course of each voxel in the whole brain for each individual. To assess and compare the resting state correlation maps, we converted the r values, which were not normally distributed, to z scores by Fisher’s z transform);
comparing the calculated quantitative function to a reference quantitative function (Supplementary Fig. 2. b) Scatter plots: each data point represents the functional connectivity score for one individual corresponding to the age regression. For the Parkinson’s disease cohort, blue dots correspond to drug naïve individuals (n=23) at the time of the scan, red dots correspond to individuals on Parkinson’s disease medications (n=39) at the time of the scan, and the black line indicates the line of best fit for all 62 subjects. Open circles correspond to the control group individuals (n=36), and the dotted line indicates the line of best fit for all 36 control subjects);
and diagnosing a condition of the subject, based on said comparison ([Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease).
Manza calculates a quantitative function of voxel values along at least one axis through the correlation coefficient. Manza uses the correlation coefficient to compare voxel values in the time series, where the r values were converted to z-scores. Next, as explained in the Non-Final Office Action, Supplementary Fig. 2b shows the difference between z-scores for PD patients (drug naïve and medicated) and control individuals. Thus, the calculated quantitative function is compared to a reference quantitative function, which can be used to diagnose.
Applicant further argues that nowhere in Manza or Godavarty is there a teaching that the quantitative function represents a spatial variation of voxel values along at least one axis through the putamen or caudate. As clearly stated in the Non-Final Office Action:
Manza teaches wherein the segmented ROI comprises a putamen or caudate of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation. [pg. 649 para 4] An additional focus was to examine in greater spatial detail how the strength of these associations vary across subregions of the caudate and putamen. We hypothesized that the rsFC of the caudate nucleus and putamen with the cortex would be respectively modulated by cognitive and motor test scores commonly used to assess individuals with Parkinson’s disease), and wherein the quantitative function represents a spatial variation of voxel values along the at least one axis, through the putamen or the caudate ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis).
The putamen and caudate seed regions along the anterior-posterior axis are shown in Figure 2. The correlation coefficient was computed between the averaged time course of each seed region and the time course of each voxel in the whole brain. See below for the entire 35 USC § 103 rejection of new claim 21.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier, as explained in MPEP §2181, subsection I (note that the list of generic placeholders below is not exhaustive, and other generic placeholders may invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph):
A. The Claim Limitation Uses the Term “Means” or “Step” or a Generic Placeholder (A Term That Is Simply A Substitute for “Means”)
With respect to the first prong of this analysis, a claim element that does not include the term “means” or “step” triggers a rebuttable presumption that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply. When the claim limitation does not use the term “means,” examiners should determine whether the presumption that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, paragraph 6 does not apply is overcome. The presumption may be overcome if the claim limitation uses a generic placeholder (a term that is simply a substitute for the term “means”). The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f) or pre- AIA 35 U.S.C. 112, paragraph 6: “mechanism for,” “module for,” “device for,” “unit for,” “component for,” “element for,” “member for,” “apparatus for,” “machine for,” or “system for.” Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Massachusetts Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media,161 F.3d at 704, 48 USPQ2d at 1886–87; Mas- Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir.1998). This list is not exhaustive, and other generic placeholders may invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, paragraph 6.
Such claim limitations are:
In claim 12:
A system for non-invasive diagnosis of a condition in a subject ([0048] system 100 may be implemented as a software module, a hardware module, or any combination thereof. For example, system 100 may be, or may include a computing device such as element 1 of Fig. 1, and may be adapted to execute one or more modules of executable code (e.g., element 5 of Fig. 1) to perform non-invasive diagnosis of a condition in a subject)
a medical scanning device ([005] the medical scanning device may be a magnetic resonance imaging (MRI) scanning device)
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-7, 12-16, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Manza (P. Manza, S. Zhang, C.-S. R. Li, H.-C. Leung, Resting-state functional connectivity of the striatum in early-stage Parkinson’s disease: Cognitive decline and motor symptomatology. Hum. Brain Mapp. 37, 648–662 (2016)) in view of Godavarty (US20150190061A1).
Regarding claim 1, Manza teaches a method of non-invasive diagnosis of a condition in a subject, the method comprising ([pg. 649 para. 4] While a growing body of literature has documented how behavioral measures of Parkinson’s disease correlate with regional changes in striatal structure and activity, few studies have examined how cortico-striatal functional connectivity varies with motor/cognitive deficits, and most existing studies do not examine the different subdivisions of the striatal nuclei, which may have contributed to the variability in findings…To address this gap of research, we took advantage of the large cohort of resting state functional connectivity (rsFC) data from newly diagnosed individuals provided by the Parkinson’s Progression Markers Initiative (PPMI). [pg. 657 para. 4] Here, we demonstrate that functional connectivity of the dorsal caudate (but not other striatal subregions) is particularly associated with Parkinson’s disease-related cognitive deficits, especially in the memory and visuospatial domains):
obtaining, from a medical scanning device, a three-dimensional (3D) scan of the subject, said scan comprising a plurality of voxel values ([pg. 650 para. 1] All MRI and fMRI data were acquired on Siemens Trio Tim 3 Tesla magnets (Siemens Medical Systems, Erlangen, Germany), and imaging parameters were kept constant across all sites. High resolution anatomical images (T1-weighted MP-RAGE) were acquired with repetition time (TR) = 2,300 ms, echo time (TE) = 2.98 ms, flip angle (FA) = 9 degrees, matrix = 240 x 256, field of view (FOV) = 256 mm, voxel size 1 x 1 x 1 mm^3, 176 sagittal slices with slice thickness = 1 mm. Resting state fMRI (rsfMRI) echo-planar images were acquired for 8.5 min (212 volumes) with TR = 2,400 ms, TE = 25 ms, flip angle = 80 degrees, matrix = 68 x 66, FOV = 222 mm, 40 slices (ascending direction) and voxel size 3.3 x 3.3 x 3.3 mm^3);
segmenting the scan, to obtain a segmented region of interest (ROI) of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation);
and analyzing the voxel values along the at least one axis, to diagnose a condition of the subject ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis. [Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease).
Manza does not teach at least one processor and performing a singular value decomposition (SVD) of the ROI, to determine at least one axis of the ROI in the SVD space.
Godavarty, in the same field of endeavor of medical image analysis, teaches at least one processor ([0034] Controller assembly 160 may include processor 162 and source driver 164) and performing a singular value decomposition (SVD) of the ROI, to determine at least one axis of the ROI in the SVD space ([0084] For example, instructions stored in spatio-temporal analysis module 128 may cause processor 104 and/or GPU 106 to calculate singular value decomposition (SVD), principal component analysis (PCA), and/or independent component analyses (ICA), etc., of a selected ROI within an NIR intensity image sample or an entire imaged region of the NIR intensity image sample).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Manza with the teachings of Godavarty to perform singular value decomposition (SVD) of the ROI because ([Godavarty 0085]) by applying a suitable reconstruction or decomposition technique (e.g., SVD, PCA, ICA, etc.) dominant eigenvalue signal components may be extracted from the NIR intensity image sample data based upon the NIR data at a single point in time (sampling point)).
Regarding claim 2, Manza and Godavarty teach the method of claim 1. Manza further teaches wherein analyzing the voxel values along the at least one axis comprises: calculating a quantitative function of voxel values along the at least one axis ([pg. 652 para. 6] The fMRI signal time courses were averaged across all voxels for each of the seed regions. We computed the correlation coefficient between the averaged time course of each seed region and the time course of each voxel in the whole brain for each individual. To assess and compare the resting state correlation maps, we converted the r values, which were not normally distributed, to z scores by Fisher’s z transform);
comparing the calculated quantitative function to a reference quantitative function (Supplementary Fig. 2. b) Scatter plots: each data point represents the functional connectivity score for one individual corresponding to the age regression. For the Parkinson’s disease cohort, blue dots correspond to drug naïve individuals (n=23) at the time of the scan, red dots correspond to individuals on Parkinson’s disease medications (n=39) at the time of the scan, and the black line indicates the line of best fit for all 62 subjects. Open circles correspond to the control group individuals (n=36), and the dotted line indicates the line of best fit for all 36 control subjects);
and diagnosing a condition of the subject, based on said comparison ([Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease).
Regarding claim 5, Manza and Godavarty teach the method of claim 2. Manza further teaches wherein the segmented ROI comprises a putamen of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation. [pg. 649 para 4] An additional focus was to examine in greater spatial detail how the strength of these associations vary across subregions of the caudate and putamen. We hypothesized that the rsFC of the caudate nucleus and putamen with the cortex would be respectively modulated by cognitive and motor test scores commonly used to assess individuals with Parkinson’s disease), and wherein the quantitative function represents a spatial variation of voxel values along the at least one axis, through the putamen ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis).
Regarding claim 6, Manza and Godavarty teach the method of claim 2. Manza further teaches wherein the segmented ROI comprises a caudate of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation. [pg. 649 para 4] An additional focus was to examine in greater spatial detail how the strength of these associations vary across subregions of the caudate and putamen. We hypothesized that the rsFC of the caudate nucleus and putamen with the cortex would be respectively modulated by cognitive and motor test scores commonly used to assess individuals with Parkinson’s disease), and wherein the quantitative function represents a spatial variation of voxel values along the at least one axis, through the caudate ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis).
Regarding claim 7, Manza and Godavarty teach the method of claim 1. Manza further teaches wherein analyzing the voxel values along the at least one axis further comprises: calculating a quantitative correlation function, representing correlation between (a) quantitative values of one or more voxels along the at least one axis of the ROI and (b) quantitative values of one or more voxels located in another region of the subject's brain ([pg. 652 para. 6] The fMRI signal time courses were averaged across all voxels for each of the seed regions. We computed the correlation coefficient between the averaged time course of each seed region and the time course of each voxel in the whole brain for each individual. To assess and compare the resting state correlation maps, we converted the r values, which were not normally distributed, to z scores by Fisher’s z transform);
comparing the calculated correlation function to a reference correlation function (Supplementary Fig. 2. b) Scatter plots: each data point represents the functional connectivity score for one individual corresponding to the age regression. For the Parkinson’s disease cohort, blue dots correspond to drug naïve individuals (n=23) at the time of the scan, red dots correspond to individuals on Parkinson’s disease medications (n=39) at the time of the scan, and the black line indicates the line of best fit for all 62 subjects. Open circles correspond to the control group individuals (n=36), and the dotted line indicates the line of best fit for all 36 control subjects);
and diagnosing a condition of the subject, based on said comparison ([Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease).
Regarding claim 12, Manza teaches obtain, from a medical scanning device (Siemens Trio Tim 3 Tesla magnets), a three-dimensional (3D) scan of the subject (MRI), said scan comprising a plurality of voxel values ([pg. 650 para. 1] All MRI and fMRI data were acquired on Siemens Trio Tim 3 Tesla magnets (Siemens Medical Systems, Erlangen, Germany), and imaging parameters were kept constant across all sites. High resolution anatomical images (T1-weighted MP-RAGE) were acquired with repetition time (TR) = 2,300 ms, echo time (TE) = 2.98 ms, flip angle (FA) = 9 degrees, matrix = 240 x 256, field of view (FOV) = 256 mm, voxel size 1 x 1 x 1 mm^3, 176 sagittal slices with slice thickness = 1 mm. Resting state fMRI (rsfMRI) echo-planar images were acquired for 8.5 min (212 volumes) with TR = 2,400 ms, TE = 25 ms, flip angle = 80 degrees, matrix = 68 x 66, FOV = 222 mm, 40 slices (ascending direction) and voxel size 3.3 x 3.3 x 3.3 mm^3);
segment the scan, to obtain a segmented region of interest (ROI) of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation);
and analyze the voxel values along the at least one axis, to diagnose a condition of the subject ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis. [Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease).
Manza does not teach a system for non-invasive diagnosis of a condition in a subject, the system comprising: a non-transitory memory device, wherein modules of instruction code are stored, and a processor associated with the memory device, and configured to execute the modules of instruction code, whereupon execution of said modules of instruction code, the processor is configured to: perform a singular value decomposition (SVD) of the ROI, to determine at least one axis of the ROI in the SVD space.
Godavarty, in the same field of endeavor of medical image analysis, teaches a system for non-invasive diagnosis of a condition in a subject ([0008] A near-infrared optical imaging system and method that can be used for hemodynamic imaging, pulse monitoring, and mapping of spatio-temporal features is disclosed. In one embodiment, the system comprises a hand-held optical scanner. The optical scanner provides for a portable, non-invasive imaging system that can monitor the hemodynamic changes within an imaged tissue sample. [0026] FIG. 1 illustrates a block diagram of an exemplary system 100 in accordance with an exemplary aspect of the present disclosure. System 100 may include a control module 102, a probe assembly 150, and a source assembly 180. [0044] In some embodiments, control module 102 may be implemented as any suitable computing device separate from probe assembly 150, such as a laptop computer, a dedicated imaging control system, a personal computer, a tablet computer, etc.), the system comprising: a non-transitory memory device (memory 114), wherein modules of instruction code are stored, and a processor (processor 104) associated with the memory device, and configured to execute the modules of instruction code ([0050] In accordance with various embodiments, memory 114 is a computer-readable non-transitory storage device that may include any combination of volatile (e.g., a random access memory (RAM), or a non-volatile memory (e.g., battery-backed RAM, FLASH, etc.). Memory 114 may be configured to store instructions executable on processor 104 and/or GPU 106. These instructions may include machine-readable instructions that, when executed by CPU 102 and/or GPU 106, cause CPU 102 and/or GPU 106 to perform various acts), whereupon execution of said modules of instruction code, the processor is configured to:
perform a singular value decomposition (SVD) of the ROI, to determine at least one axis of the ROI in the SVD space ([0084] For example, instructions stored in spatio-temporal analysis module 128 may cause processor 104 and/or GPU 106 to calculate singular value decomposition (SVD), principal component analysis (PCA), and/or independent component analyses (ICA), etc., of a selected ROI within an NIR intensity image sample or an entire imaged region of the NIR intensity image sample).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the system of Manza with the teachings of Godavarty to perform singular value decomposition (SVD) of the ROI because ([Godavarty 0085]) by applying a suitable reconstruction or decomposition technique (e.g., SVD, PCA, ICA, etc.) dominant eigenvalue signal components may be extracted from the NIR intensity image sample data based upon the NIR data at a single point in time (sampling point)).
Regarding claim 13, Manza and Godavarty teach the system of claim 12. Manza further teaches wherein the processor is configured to analyze the voxel values along the at least one axis by: calculating a quantitative function of voxel values along the at least one axis ([pg. 652 para. 6] The fMRI signal time courses were averaged across all voxels for each of the seed regions. We computed the correlation coefficient between the averaged time course of each seed region and the time course of each voxel in the whole brain for each individual. To assess and compare the resting state correlation maps, we converted the r values, which were not normally distributed, to z scores by Fisher’s z transform);
comparing the calculated quantitative function to a reference quantitative function (Supplementary Fig. 2. b) Scatter plots: each data point represents the functional connectivity score for one individual corresponding to the age regression. For the Parkinson’s disease cohort, blue dots correspond to drug naïve individuals (n=23) at the time of the scan, red dots correspond to individuals on Parkinson’s disease medications (n=39) at the time of the scan, and the black line indicates the line of best fit for all 62 subjects. Open circles correspond to the control group individuals (n=36), and the dotted line indicates the line of best fit for all 36 control subjects);
and diagnosing or predicting a condition of the subject, based on said comparison ([Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease).
Regarding claim 14, Manza and Godavarty teach the system of claim 13. Manza further teaches wherein the segmented ROI comprises a putamen of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation. [pg. 649 para 4] An additional focus was to examine in greater spatial detail how the strength of these associations vary across subregions of the caudate and putamen. We hypothesized that the rsFC of the caudate nucleus and putamen with the cortex would be respectively modulated by cognitive and motor test scores commonly used to assess individuals with Parkinson’s disease), and wherein the quantitative function represents a spatial variation of voxel values along the at least one axis, through the putamen ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis).
Regarding claim 15, Manza and Godavarty teach the system of claim 13. Manza further teaches wherein the segmented ROI comprises a caudate of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation. [pg. 649 para 4] An additional focus was to examine in greater spatial detail how the strength of these associations vary across subregions of the caudate and putamen. We hypothesized that the rsFC of the caudate nucleus and putamen with the cortex would be respectively modulated by cognitive and motor test scores commonly used to assess individuals with Parkinson’s disease), and wherein the quantitative function represents a spatial variation of voxel values along the at least one axis, through the caudate ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis).
Regarding claim 16, Manza and Godavarty teach the system of claim 12. Manza further teaches wherein the processor is configured to analyze the voxel values along the at least one axis by: calculating a quantitative correlation function, representing correlation between (a) quantitative values of one or more voxels along the at least one axis of the ROI and (b) quantitative values of one or more voxels located in another region of the subject's brain ([pg. 652 para. 6] The fMRI signal time courses were averaged across all voxels for each of the seed regions. We computed the correlation coefficient between the averaged time course of each seed region and the time course of each voxel in the whole brain for each individual. To assess and compare the resting state correlation maps, we converted the r values, which were not normally distributed, to z scores by Fisher’s z transform);
comparing the calculated correlation function to a reference correlation function (Supplementary Fig. 2. b) Scatter plots: each data point represents the functional connectivity score for one individual corresponding to the age regression. For the Parkinson’s disease cohort, blue dots correspond to drug naïve individuals (n=23) at the time of the scan, red dots correspond to individuals on Parkinson’s disease medications (n=39) at the time of the scan, and the black line indicates the line of best fit for all 62 subjects. Open circles correspond to the control group individuals (n=36), and the dotted line indicates the line of best fit for all 36 control subjects);
and diagnosing a condition of the subject, based on said comparison ([Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease).
Regarding claim 21, Manza teaches a method of non-invasive diagnosis of a condition in a subject, the method comprising ([pg. 649 para. 4] While a growing body of literature has documented how behavioral measures of Parkinson’s disease correlate with regional changes in striatal structure and activity, few studies have examined how cortico-striatal functional connectivity varies with motor/cognitive deficits, and most existing studies do not examine the different subdivisions of the striatal nuclei, which may have contributed to the variability in findings…To address this gap of research, we took advantage of the large cohort of resting state functional connectivity (rsFC) data from newly diagnosed individuals provided by the Parkinson’s Progression Markers Initiative (PPMI). [pg. 657 para. 4] Here, we demonstrate that functional connectivity of the dorsal caudate (but not other striatal subregions) is particularly associated with Parkinson’s disease-related cognitive deficits, especially in the memory and visuospatial domains):
obtaining, from a medical scanning device, a three-dimensional (3D) scan of the subject, said scan comprising a plurality of voxel values ([pg. 650 para. 1] All MRI and fMRI data were acquired on Siemens Trio Tim 3 Tesla magnets (Siemens Medical Systems, Erlangen, Germany), and imaging parameters were kept constant across all sites. High resolution anatomical images (T1-weighted MP-RAGE) were acquired with repetition time (TR) = 2,300 ms, echo time (TE) = 2.98 ms, flip angle (FA) = 9 degrees, matrix = 240 x 256, field of view (FOV) = 256 mm, voxel size 1 x 1 x 1 mm^3, 176 sagittal slices with slice thickness = 1 mm. Resting state fMRI (rsfMRI) echo-planar images were acquired for 8.5 min (212 volumes) with TR = 2,400 ms, TE = 25 ms, flip angle = 80 degrees, matrix = 68 x 66, FOV = 222 mm, 40 slices (ascending direction) and voxel size 3.3 x 3.3 x 3.3 mm^3);
segmenting the scan, to obtain a segmented region of interest (ROI) of the subject ([pg. 651 para. 2] Each individual structural image was then segmented and normalized to an Montreal Neurological Institute (MNI) gray matter template with affine registration followed by nonlinear transformation);
and analyzing the voxel values along the at least one axis, to diagnose a condition of the subject ([pg. 652 para. 5] To examine the functional connectivity patterns of the striatum in a more refined manner, we also created small spherical seed regions that were equally distributed along the anterior-posterior axis of each striatal mask. The larger seed regions from the main analysis were used for statistical power; the primary purpose of these additional smaller seeds was to describe in more detail the findings from the main analysis. This is potentially useful because of the large body of data suggesting that DA loss and Parkinson’s disease-related pathology progresses along a posterior-anterior gradient in the striatum, and because multiple striatal functional circuits have been found along a gradient. Table III. Center coordinates for striatal regions of interest for spatially refined analysis. [Supplementary Material para. 1] Visual inspection shows that these results resemble findings from previous studies in healthy adults and in individuals with Parkinson’s disease);
wherein analyzing the voxel values along the at least one axis comprises: calculating a quantitative function of voxel values along the at least one axis ([pg. 652 para. 6] The fMRI signal time courses were averaged across all voxels for each of the seed regions. We computed the correlation coefficient between the averaged time course of each seed region and the time course of each voxel in the whole brain for each individual. To assess and compare the resting state correlation maps, we converted the r values, which were not normally distributed, to z scores by Fisher’s z transform);
comparing the calculated quantitative function to a reference quantitative function (Supplementary Fig. 2. b) Scatter plots: each data point represents the functional connectivity score for one individual corresponding to the age regression. For the Parkinson’s disease cohort, b