METHOD OF ESTABLISHING A BRAIN STATUS INDICATION PARAMETER AND SYSTEM THEREFOR

Abstract

A method of establishing a brain status indication parameter indicative of a brain disorder is disclosed. The method comprising the steps:—determining a brain energy metabolism indicator of at least a part of the brain of a subject,—determining a skull energy metabolism indicator of at least a part of the skull of said subject,—establishing the brain status indication parameter by at least relating said brain energy metabolism indicator to said skull energy metabolism indicator. Also disclosed are a system for establishing such brain status indication parameter, a computer program, and methods for treating a disease.

Claims

1. A method of establishing a brain status indication parameter indicative of a brain disorder, the method comprising the steps: determining a brain energy metabolism indicator of at least a part of the brain of a subject by a brain scanning device, determining a skull energy metabolism indicator of at least a part of the skull of said subject by said brain scanning device, establishing by a computer device the brain status indication parameter by at least relating said brain energy metabolism indicator to said skull energy metabolism indicator.

2. The method according to claim 1, wherein said relating involves calculating a ratio between the brain energy metabolism indicator and the skull energy metabolism indicator, or vice versa.

3-7. (canceled)

8. The method according to claim 1, wherein the brain energy metabolism indicator of the brain of the subject is determined.

9. (canceled)

10. The method according to claim 1, wherein the method comprises establishing a degree of symmetry between at least a part of the right cerebral or cerebellar hemisphere of the brain and a corresponding part of the left cerebral or cerebellar hemisphere of the brain.

11. The method according to claim 1, wherein the degree of symmetry comprises a ratio between at least a part of the right hemisphere of the brain and a corresponding part of the left hemisphere of the brain.

12-15. (canceled)

16. The method according to claim 1, wherein said brain energy metabolism indicator is determined from a brain energy metabolism indicator distribution and wherein said skull energy metabolism indicator is determined from a skull energy metabolism indicator distribution.

17. (canceled)

18. The method according to claim 1, wherein a segmentation on the brain energy metabolism indicator distribution is performed to obtain a brain energy metabolism indicator in one or more parts of the brain.

19-20. (canceled)

21. The method according to claim 1, wherein the method further comprises the step of determining one or more further brain energy metabolism indicators of at least a part of the brain of the subject and of at least a part of the skull of the subject.

22. The method according to claim 1, wherein the method further comprises the step of determining one or more further skull energy metabolism indicators of at least a part of the skull of the subject.

23. The method according to claim 1, wherein the method further comprises a segmentation comprising dividing the brain energy metabolism indicator distribution into a number of substantially regularly shaped three-dimensional zones.

24. (canceled)

25. The method according to claim 23, wherein the method further comprises establishing a synaptic entropy network indication parameter by at least relating said synaptic entropy indicator to a corresponding normalized synaptic entropy indicator being a synaptic entropy indicator a normal population, wherein establishing the brain status indication parameter further comprises integrating the relation between said brain energy metabolism indicator and said skull energy metabolism indicator with said synaptic entropy network indication parameter.

26-27. (canceled)

28. The method according to claim 1, wherein the energy metabolism indicator is determined by a neuroimaging technique selected from the group consisting of a functional Magnetic Resonance Imaging (fMRI) based technique, a Computed Tomography (CT) Scan based technique, a Positron Emission Tomography (PET) based technique, a Magnetoencephalography (MEG) or Electroencephalography (EEG) based technique, a Single-photon emission computed tomography (SPECT) based technique, or an ultrasound-based technique.

29-32. (canceled)

33. The method according to claim 1, wherein the brain status indication parameter gives an indication of a presence of the brain disorder or not.

34. (canceled)

35. The method according to claim 1, wherein the brain status indication parameter gives an indication of a type of the brain disorder.

36. The method according to claim 1, wherein the brain disorder is selected from the group consisting of diaschisis; brain tumor; Mild Cognitive Impairment (MCI); and Alzheimer's disease (AD).

37-41. (canceled)

42. The method according to claim 1, wherein the brain status indication parameter indicative comprises an expression of cerebral function, and the relation between said brain energy metabolism indicator to said skull energy metabolism indicator.

43-45. (canceled)

46. A brain status establishment system for establishing a brain status indication parameter indicative of a brain disorder, the system comprising: a brain scanning device configured to determine a brain energy metabolism indicator of at least a part of the brain of a subject, determine a skull energy metabolism indicator of at least a part of the skull of said subject, a computer device configured to establishing the brain status indication parameter by at least relating said brain energy metabolism indicator to said skull energy metabolism indicator.

47.-48. (canceled)

49. A method of treating a disease comprising performing the method according to claim 1 before administering a drug or performing surgery.

50. A method of treating a disease comprising performing the method according to claim 1 before performing physical exercise.

51. A method of establishing a brain status indication parameter indicative of a brain disorder comprising scanning the brain of a subject with the brain scanning device of the system of claim 46 to determine brain energy metabolism indicator of at least a part of the brain of the subject and to determine a skull energy metabolism indicator of at least a part of the skull of the subject, and relating the brain energy metabolism indicator the skull energy metabolism indicator to establish the brain status indication parameter.

Description

FIGURES

[0103] The invention will now be described with reference to the figures where

[0104] FIG. 1A illustrates a method of establishing a brain status indication parameter according to an embodiment of the invention,

[0105] FIG. 1B illustrates a method of establishing a brain status indication parameter according to an embodiment of the invention,

[0106] FIG. 2 illustrates a brain status establishment system according to an embodiment of the invention,

[0107] FIG. 3 illustrates a segmentation step according to an embodiment of the invention,

[0108] FIGS. 4A-4B illustrate a trans-axial view of an energy metabolism indicator distribution image according to an embodiment of the invention,

[0109] FIGS. 5A-5B illustrate a trans axial view of an energy metabolism indicator distribution image according to an embodiment of the invention,

[0110] FIG. 6A illustrates a coronal view of an energy metabolism indicator distribution image according to an embodiment of the invention, and

[0111] FIG. 6B illustrates a sagittal view of an energy metabolism indicator distribution image according to an embodiment of the invention.

DETAILED DESCRIPTION

[0112] Referring to FIG. 1A, a method of establishing a brain status indication parameter BSI according to an embodiment of the invention is described.

[0113] The brain status indication parameter BSI provides an indicative of a brain disorder, for example as a likelihood of a brain disorder being present, or as a likelihood of at least one or a group of brain disorders being present, or as a likelihood of one or more specific brain disorders being present.

[0114] First, a brain energy metabolism indicator BEM of at least a part of the brain BR of a subject is determined DBI. This may be done by a variety of different techniques, which may provide a more or less direct indication of the energy metabolism.

[0115] Then, a skull energy metabolism indicator SEM of at least a part of the skull SK of a subject is determined DSI. This may typically be done by a similar technique as for the brain energy metabolism indicator BEM. In embodiments, the steps of determining a brain energy metabolism indicator BEM and establishing the brain status indication parameter BSI are executed as a single step in the sense that the brain energy metabolism indicator and the skull energy metabolism indicator are obtained from the same image(s) and subsequently segmented into the brain energy metabolism indicator of at least a part of the brain and the skull energy metabolism indicator of at least a part of the skull. This is illustrated in more detail in FIG. 1B and FIG. 3.

[0116] Then, the brain status indication parameter BSI established. This involves at least relating said brain energy metabolism indicator to said skull energy metabolism indicator. This relation may comprise e.g. comparing or forming a ratio between the brain energy metabolism indicator and the skull energy metabolism indicator. When using the ratio, this may be the ratio between the brain energy metabolism indicator and the skull energy metabolism indicator, or vice versa.

[0117] In some embodiments, this ratio forms part of a single number, or a set of numbers, for example in the sense that it is a factor and/or term in an equation forming basis for calculating the number(s).

[0118] Turning to FIG. 1B, a method of establishing a brain status indication parameter BSI according to an embodiment of the invention is described.

[0119] First, in a measuring step MES, one or more images of an energy metabolism indicator is recorded. The one or more images are then segmented in a segmentation step SEG. First, the brain part(s) of the image(s) are separated to form basis for determining DBI the brain energy metabolism indicator BEM. Then, the skull part(s) of the image(s) are separated to form basis for determining DSI the skull energy metabolism indicator SEM.

[0120] It is noted that the segmentation step SEG may divide the brain into smaller segments, e.g. right and left hemisphere, cerebrum and cerebellum, or right and left hemispheres of both the cerebrum and cerebellum. Smaller segments may also be applied.

[0121] In FIG. 1B, the brain energy metabolism indicator determining step DBI is shown before the skull energy metabolism indicator determining step DSI. However, in other embodiments, they may e.g. be performed in the opposite order, partly overlapping or concurrently executed.

[0122] Then, a step of demining further parameter(s) DFP is executed according to FIG. 1B. In this step, one or more further parameters may be determined, e.g. from one or more distributions of energy metabolisms of the brain and/or skull forming basis for the brain energy metabolism indicator determination step DBI and/or the skull energy metabolism indicator determination step DSI. These one or more further parameter(s) may include parameters indicative of symmetry aspects of the brain or part thereof, of the cerebral function etc. In some embodiments, this step may be omitted.

[0123] Then, a diagnosis establishing step EDI follows. This step comprises at least relating said brain energy metabolism indicator BEM to said skull energy metabolism indicator SEM.

[0124] In embodiments comprising a step of demining further parameter(s) DFP, the diagnosis establishing step EDI may further comprise calculations based also on such one or more further parameter(s).

[0125] When the method also is directed to treatment of any brain disorder(s) resulting from the diagnosis establishing step EDI, the method comprises a treatment step TRT.

[0126] This step may comprise administration of an effective amount of one or more active pharmaceutical ingredients (i.e. one or more drugs) and/or performing surgery.

[0127] In some embodiments, the treatment step TRT may comprise performing physical exercises.

[0128] Turning to FIG. 2, a brain status establishment system BSS for establishing a brain status indication parameter BSI indicative of a brain disorder. The system (BSS) comprises a brain scanning device BSD and a computer device CD.

[0129] The brain scanning device BSD is configured to determine a brain energy metabolism indicator BEM of at least a part of the brain BR of a subject SUB, and to determine a skull energy metabolism indicator SEM of at least a part of the skull (SK) of said subject SUB. In FIG. 2, the brain scanning device BSD is illustrated as a positron emission tomography (PET) scanner but may be any other scanner capable of measuring energy metabolism or an indicator thereof in the brain and skull.

[0130] The computer device CD is configured to establish the brain status indication parameter BSI by at least relating said brain energy metabolism indicator BEM to said skull energy metabolism indicator SEM. When further parameter(s) are determined, as described in relation to FIG. 1B, such further parameter(s) may form part of the basis for the establishing of the brain status indication parameter BSI by the computer device CD.

[0131] Referring now to FIG. 3, the segmentation step SEG is illustrated according to an embodiment of the invention. First, one or more image(s) of an energy metabolism indicator is recorded, as e.g. illustrated in FIG. 2. As can be seen, the image(s), shown to the upper left, covers both the brain and the skull. Then, the image(s) is segmented, i.e. broken down into at least a skull part and a brain part. In FIG. 3, the brain part is further segmented into a left hemisphere of cerebrum LCE, a right hemisphere of cerebrum RCE, a left hemisphere of cerebellum LCB, and a right hemisphere of cerebellum RCB.

[0132] In some further embodiments, the brain and/or the skull is further segmented, e.g. into rather small parts, such as a plurality of square fields. By utilizing computerized segmentation, such fields may be rather small, e.g. giving a resolution of tens or hundreds of fields for each direction in the image. It is noted that such deep segmentation is especially advantageous when advanced computerized processing is available, e.g. using machine learning-based methods, such as deep learning-based methods.

[0133] Referring now to FIGS. 4A-4B, 5A-5B, and 6A-6B, an energy metabolism indicator distribution image is illustrated according to an embodiment of the invention. The images shown in FIGS. 4A-4B, 5A-5B, and 6A-6B, is recorded by a Positron Emission Tomography (PET) based technique with fludeoxyglucose (FDG) as a tracer. It is noted that other energy metabolism indicator recording techniques are also usable within the scope of the invention. FIGS. 4A-B, 5A-B, and 6A-B are images of the same subject having been diagnosed with brain cancer. The location of the brain tumor is more easily seen in FIG. 4A, in the upper center part of the image, corresponding to a location in the left part of the cerebrum.

[0134] FIGS. 4A and 4B show areas segmented in the cerebrum and the skull, respectively, where the energy metabolism indicator distribution exceeds a certain threshold. FIGS. 4A and 4B are identical, except that FIG. 4B shows segmentation only for the left hemisphere whereas FIG. 4A shows this for both left and right hemispheres. Also, as it can be seen from FIG. 4A in particular, the corresponding emphasized areas are partitioned into left and right hemispheres, both for the skull and the cerebrum.

[0135] FIGS. 5A and 5B show views somewhat similar to FIGS. 4A and 4B, but in a trans-axial plane through the cerebellum, thus showing segmented areas for the cerebellum and the skull in FIG. 5B, but only for the skull in FIG. 5A.

[0136] FIG. 6A shows a coronal view of the cerebrum and the skull, with only the left hemisphere being segmented.

[0137] FIG. 6B shows a sagittal view of the skull and the cerebrum and the cerebellum, with segmented regions.

[0138] These images illustrate the complexity of performing a subjective analysis based on a perceived normality or abnormality, even when comparing with another image representing a healthy subject. In contrast, the present invention provides an objective, reliable and reproducible output.

EXAMPLES

[0139] FDG-PET images of 47 subjects (37 patients and 10 control subjects) were obtained. From these images, total energy metabolism values were calculated. These values are given in tables 1-2.

TABLE-US-00001 TABLE 1 Total energy metabolism values after segmentation. Subject No. Diagnosis Sk_Wh Sk_L Sk_R Br_Wh Ce_Wh  1 NL 290 141 149 2307 2085  2 NL 384 195 189 4256 3920  3 NL 383 192 190 3085 2809  4 NL 190 88 102 2250 2065  5 NL 326 164 162 3515 3197  6 NL 378 181 197 6083 5665  7 NL 417 207 210 4493 4075  8 NL 396 205 191 4570 4085  9 NL 347 177 171 3860 3497 10 NL 338 170 168 4614 4248 11 AD 222 114 108 2496 2282 12 AD 196 99 97 3345 3062 13 AD 195 95 100 3411 3092 14 AD 179 86 92 3369 3040 15 AD 148 75 73 2068 1877 16 AD 251 130 121 4495 4106 17 AD 239 117 122 3019 2742 18 AD 184 91 92 2372 2149 19 AD 230 113 117 3479 3171 20 AD 318 159 159 3836 3480 21 AD 186 95 91 3644 3334 22 AD 171 86 86 2529 2298 23 AD 282 139 143 3274 2952 24 AD 296 150 146 1983 1763 25 AD 260 132 129 3328 3006 26 MCI 189 92 97 2752 2481 27 MCI 226 119 107 4375 4033 28 MCI 259 130 129 4623 4210 29 MCI 299 152 147 2012 1797 30 MCI 353 173 180 4464 4104 31 MCI 227 111 116 3846 3523 32 MCI 212 112 100 3194 2969 33 MCI 220 112 109 3505 3187 34 Glioma 197 101 96 2595 2358 35 Glioma 259 130 128 2436 2247 36 Glioma 268 131 137 3079 2823 37 Glioma 366 181 185 4091 3763 38 Glioma 218 109 109 1853 1721 39 Glioma 508 240 268 2878 2585 40 Glioma 204 103 101 2254 2020 41 Glioma 165 86 79 1981 1840 42 Glioma 276 130 146 2844 2549 43 Glioma 306 155 151 1993 1818 44 Glioma 316 160 156 2681 2413 45 Glioma 570 295 275 3952 3536 46 Glioma 484 229 255 3013 2745 47 Glioma 306 156 149 2522 2282 Table 1. Total energy metabolism for the brain or skull part in question. Sk_Wh denotes whole skull, Sk_L denotes left hemisphere of skull, Sk_R detotes right hemisphere of skull, Br_Wh denotes whole brain, Ce_Wh denotes whole Cerebrum. NL signifies a control subject. AD signifies a subject diagnosed with Alzheimer's Disease. MCI signifies Mild Cognitive Impairment.

TABLE-US-00002 TABLE 2 Total energy metabolism values after segmentation. Subject No. Diagnosis Ce_L Ce_R Cb_Wh Cb_L Cb_R  1 NL 1030 1055 222 107 115  2 NL 1966 1954 335 157 179  3 NL 1443 1366 276 139 137  4 NL 901 1164 185 90 95  5 NL 1635 1562 319 182 137  6 NL 2809 2856 418 219 199  7 NL 2013 2062 419 211 208  8 NL 2139 1947 484 230 254  9 NL 1792 1706 363 180 183 10 NL 2155 2093 366 190 176 11 AD 1106 1176 214 124 90 12 AD 1490 1572 283 138 145 13 AD 1471 1622 318 159 160 14 AD 1730 1310 329 155 174 15 AD 786 1091 191 128 62 16 AD 1913 2193 388 211 177 17 AD 1477 1265 277 160 117 18 AD 943 1206 224 124 100 19 AD 1775 1397 308 168 140 20 AD 1538 1942 356 201 156 21 AD 1668 1665 311 177 134 22 AD 1322 976 231 128 103 23 AD 1337 1615 323 163 160 24 AD 902 861 220 109 111 25 AD 1351 1655 321 184 137 26 MCI 1324 1157 271 138 133 27 MCI 2025 2007 342 166 176 28 MCI 2095 2115 413 217 196 29 MCI 919 879 214 97 118 30 MCI 2034 2069 360 194 166 31 MCI 1754 1770 323 152 171 32 MCI 1439 1530 225 119 105 33 MCI 1631 1556 319 202 116 34 Glioma 1100 1258 237 114 124 35 Glioma 1225 1023 189 96 93 36 Glioma 1328 1495 256 123 133 37 Glioma 1524 2239 328 200 128 38 Glioma 667 1054 132 84 48 39 Glioma 1130 1454 293 152 141 40 Glioma 675 1344 234 154 80 41 Glioma 1166 674 141 54 87 42 Glioma 1525 1025 294 81 213 43 Glioma 950 868 175 110 66 44 Glioma 1066 1347 268 88 181 45 Glioma 1375 2161 416 290 126 46 Glioma 1015 1731 268 180 87 47 Glioma 1158 1124 240 115 125 Table 2. Total energy metabolism for the brain or skull part in question. Ce_L denotes left hemisphere of Cerebrum, Ce_R denotes right hemisphere of Cerebrum, Cb_Wh denotes whole Cerebellum, Cb_L denotes left hemisphere of Cerebellum, Cb_R denotes right hemisphere of Cerebellum. NL. Signifies a control subject. AD signifies a subject diagnosed with Alzheimer's Disease. MCI signifies Mild Cognitive Impairment.

[0140] These values may possibly be corrected using comparison of energy metabolism in the left and right hemispheres of the skull.

[0141] Further values are calculated based on the measured energy metabolism values or corrected values obtained therefrom. The following equations have been written and standardized so that the results, in normal controls, equal to one.

[00002]$\mathrm{Cerebral}\mathrm{Function}\left(\mathrm{CF}\right)=K_{CF}\frac{C{e}_{Wh}}{B{r}_{Wh}}$$\mathrm{Cerebral}\mathrm{Symmetry}I\left(\mathrm{CeSI}\right)=K_{\mathrm{CeSI}}\ln \frac{.Math.{\mathrm{Ce}}_L-C{e}_R.Math.}{C{e}_{Wh}}$$\mathrm{Cerebellar}\mathrm{Symmetry}I\left(\mathrm{CbSI}\right)=K_{\mathrm{CbSI}}\ln \frac{.Math.{\mathrm{Cb}}_L-C{b}_R.Math.}{C{b}_{Wh}}$$\mathrm{Cerebral}\mathrm{Symmetry}\mathrm{II}\left(\mathrm{CeSII}\right)=K_{\mathrm{CeSII}}\frac{\mathrm{Min}\left(C{e}_L,{\mathrm{Ce}}_R\right)}{\mathrm{Max}\left(C{e}_L,{\mathrm{Ce}}_R\right)}$$\mathrm{Cerebellar}\mathrm{Symmetry}\mathrm{II}\left(\mathrm{CbSII}\right)=K_{\mathrm{CbSII}}\frac{\mathrm{Min}\left({\mathrm{Cb}}_L,{\mathrm{Cb}}_R\right)}{\mathrm{Max}\left({\mathrm{Cb}}_L,{\mathrm{Cb}}_R\right)}$$\mathrm{Skull}-\mathrm{cerebellar}\mathrm{ratio}\left(\mathrm{SVI}\right)=K_{\mathrm{SVI}}\frac{S{k}_{Wh}}{C{b}_{Wh}}$$\mathrm{Skull}-\mathrm{cerebral}\mathrm{ratio}\left(\mathrm{SVII}\right)=K_{\mathrm{SVII}}\frac{S{k}_{Wh}}{C{e}_{Wh}}$

[0142] Here, Min(X, Y) is the minimum value of X and Y, and Max(X, Y) is the maximum value of X and Y.

[0143] Constants (K) in each equation are as follows:

K.sub.CeSI=K.sub.CbSI=−0.33

K.sub.CeSII=1.08

K.sub.CbSII=K.sub.SVI=1.11

K.sub.CF=1.12

K.sub.SVII=10

These above constants are set to give unity values (i.e. values of 1) for healthy control subjects.

[0144] The above defined values (CF, CeS I, CbS I, CeS II, CbS II, SV I, SV II) are calculated to see if a value below or above 1 was obtained. Simplified values TCF, TCeS I, TCbS I, TCeS II, TCbS II, TSV I, and TSV II were then obtained as 1 (binary true) when the corresponding equation gave a result above 1, and 0 (binary false) when the corresponding equation gave a result not above 1.

[0145] Then, a brain function score was defined as follows:

BFS=4CF+4Max(CeSI,CbSI)+4Max(CeSII,SVI)+Max(CbSII,SVII)−3

[0146] The obtained values are listed in table 3.

TABLE-US-00003 TABLE 3 Brain function score (BFS) for subjects 1-47 are listed. No. Diagnosis BFS 1 NL 10 2 NL 9 3 NL 10 4 NL 10 5 NL 10 6 NL 9 7 NL 10 8 NL 9 9 NL 10 10 NL 10 11 AD 5 12 AD 6 13 AD 6 14 AD 6 15 AD 1 16 AD 1 17 AD 1 18 AD 1 19 AD 1 20 AD 1 21 AD 5 22 AD 1 23 AD 6 24 AD 6 25 AD 1 26 MCI 6 27 MCI 5 28 MCI 10 29 MCI 10 30 MCI 9 31 MCI 10 32 MCI 10 33 MCI 9 34 Glioma 1 35 Glioma 10 36 Glioma 10 37 Glioma 5 38 Glioma 6 39 Glioma 6 40 Glioma 2 41 Glioma 5 42 Glioma 6 43 Glioma 10 44 Glioma 6 45 Glioma 6 46 Glioma 6 47 Glioma 10

[0147] Theoretically, the above function may give values between −3 and 10; however, in a living brain, all the numbers in Table 3 can hardly equal to zero. For example, a clinically impaired brain should the value of about 1, whereas a clinically healthy brain gets the maximum values. Minus numbers are left for brain death, comatose state, severe encephalopathies, or very severely impaired brain conditions.

[0148] Table 3 shows BFS values for each patient. One may easily distinguish the difference between disease groups. Results show the status of the brain with only one number which makes it easy to understand how good the patient's condition is.

[0149] A more advanced approach is obtained by using the following equation:

ψ.sub.n=(K.sub.i.sub.nCeF).sup.λi.sup.n(K.sub.ii.sub.n20.sup.CeSI).sup.λii.sup.n(K.sub.iii.sub.n20.sup.CbSI).sup.λiii.sup.n(K.sub.iv.sub.nCeSII).sup.λiv.sup.n(K.sub.v.sub.nCbSII).sup.λv.sup.n(K.sub.vi.sub.nSVI).sup.λvi.sup.n(K.sub.vii.sub.nSVII).sup.λvii.sup.n

[0150] For the purpose of this example, the above equation is executed as ψ.sub.1-ψ.sub.6 and ψ.sub.ext, using the constants defined in table 4.

TABLE-US-00004 TABLE 4 Constants Constants ψ.sub.1 ψ.sub.2 ψ.sub.3 ψ.sub.4 ψ.sub.5 ψ.sub.6 ψ.sub.ext Coefficients K.sub.i 1 1 1 1 1 1 1 K.sub.ii 2 2 2 2 2 2 2 K.sub.iii 2 2 2 2 2 2 2 K.sub.iv 0.96 0.96 0.96 0.96 0.96 0.96 0.96 K.sub.v 1 1 1 1 1 1 1 K.sub.vi 0.9 0.9 0.9 0.9 0.9 0.9 0.9 K.sub.vii 1 1 1 1 1 1 1 Expo λ.sub.i 3500 3500 −800 −800 0 −5000 0 λ.sub.ii 10 5 0 26 0 20 190 λ.sub.iii 0 8 40 −25 0 −350 −500 λ.sub.iv 1500 1500 1500 1500 0 0 0 λ.sub.v 100 100 200 200 4000 −200 0 λ.sub.vi −150 200 0 0 15000 1400 0 λ.sub.vii −100 200 −600 −10 15000 1000 0 Table 4. Constants including coefficients and exponents for use in calculation of ψ.sub.n.

[0151] It is noted that the above constants listed in table 4, i.e. K.sub.i.sub.n.sub.-vii.sub.n, and λ.sub.i.sub.n.sub.-vii.sub.n, are constants in n.sup.th equation. A set of equations may be written in this form with altered constants differentiating various diseases.

[0152] Using this, ψ.sub.1-ψ.sub.6 and Ω.sub.ext are calculated. Typically, either very high numbers above 100000 or numbers very close to zero are obtained. For the purpose of the present example, a threshold value of 100000 is used to signify “large numbers”, whereas numbers below 10000 signify a “small number”. Intermediate numbers signify uncertainty with respect to the result, whereas extreme numbers (very high, very low) signify a higher degree of certainty.

[0153] Finally, the below list of questions was used to determine an indication of the diagnosis: [0154] 1) Is ψ.sub.6 a large number? If yes, the brain status indication parameter indicates glioma. [0155] 2) If question 1) is no, is ψ.sub.5 a large number, and is ψ.sub.ext a small number? If yes, the brain status indication parameter indicates glioma. [0156] 3) If question 2) is no, are all of ψ.sub.1, ψ.sub.2, and ψ.sub.3 large numbers? If yes, the brain status indication parameter indicates normal condition. [0157] 4) If question 3) is no, is ψ.sub.4 a small number and ψ.sub.5 a large number? If yes, the brain status indication parameter indicates glioma. [0158] 5) If question 4) is no, is at least five of ψ.sub.1, ψ.sub.2, ψ.sub.3, ψ.sub.4, ψ.sub.5, ψ.sub.6, and ψ.sub.ext large numbers? If yes, the brain status indication parameter indicates glioma. [0159] 6) If question 5) is no, is ψ.sub.ext a large number, and is BFS at least 8? If yes, the brain status indication parameter indicates MCI. [0160] 7) If question 6) is no, the brain status indication parameter indicates Alzheimer's disease.

[0161] Evaluating the above described method, the following results were obtained:

[0162] First, it was evaluated how accurate the above described method was when differentiating diseased from healthy, the results are indicated in table 5.

TABLE-US-00005 TABLE 5 Here, “Test +” indicates a positive result for a brain condition from the brain status indication parameter, “Test −” indicates a negative result, “Disease +” indicates subject having a brain disorder, and “Disease −” indicates subject within brain disorder. Disease + Disease − Totals Test + 36 0 36 Test − 1 10 11 Totals 37 10 47

[0163] As can be seen from table 5, only 1 out of 47 was wrongly indicated to have a disease by the brain status indication parameter.

[0164] When differentiating Alzheimer's disease (AD) from MCI, the results are indicated in table 6.

TABLE-US-00006 TABLE 6 “AD” indicates subjects with AD. “MCI” indicates subjects with MCI. AD MCI Totals Predicted AD 15 2 17 Predicted MCI 0 5 5 Totals 15 7 22

[0165] From table 6, the following was concluded:

Sensitivity=100%

Specificity=71.4%

[0166] Positive predictive values (PPV)=88.2%
Negative predictive values (NPV)=100%

Accuracy=90.9%

[0167] When differentiating AD from Glioma, the results are indicated in table 7.

TABLE-US-00007 TABLE 7 AD Glioma Totals Predicted AD 15 1 16 Predicted Glioma 0 13 13 Totals 15 14 29

[0168] From table 7, the following was concluded:

Sensitivity=100%

Specificity=92.8%

PPV=93.7%

NPV=100%

Accuracy=96.5%

[0169] When differentiating Glioma from MCI, the results are indicated in table 8.

TABLE-US-00008 TABLE 8 Glioma MCI Totals Predicted Glioma 13 0 13 Predicted MCI 0 5 5 Totals 13 5 18

[0170] From table 8, the following was concluded:

Sensitivity=100%

Specificity=100%

PPV=100%

NPV=100%

Accuracy=100%

[0171] A total summation of results with respect to accuracy are indicated in table 9.

TABLE-US-00009 TABLE 9 Disease NL AD MCI Glioma Totals Predicted NL 10 0 1 0 11 AD 0 15 2 1 18 MCI 0 0 5 0 5 Glioma 0 0 0 13 13 Totals 10 15 8 14 47 “NL” indicates a control subject without a brain disorder.

[0172] As can be seen from table 9, the overall accuracy obtained was 91.48%.

[0173] Therefore, it was concluded that the presently described the brain status indication parameter according to the present invention results in a very accurate indication of brain disorders, both with respect to the presence of a brain disorder and with respect to the specific brain disorder.

FIGURE REFERENCES

[0174] BR. Brain [0175] SK. Skull [0176] LCE. Left hemisphere of cerebrum [0177] RCE. Right hemisphere of cerebrum [0178] LCB. Left hemisphere of cerebellum [0179] RCB. Right hemisphere of cerebellum [0180] BSI. Brain status indication parameter [0181] BEM. Brain energy metabolism indicator [0182] SEM. Skull energy metabolism indicator [0183] SEG. Segmentation step [0184] DBI. Determining brain energy metabolism indicator step [0185] DSI. Determining skull energy metabolism indicator step [0186] EBI. Establish brain status indication parameter [0187] MES. Measuring step [0188] DFP. Demining further parameter(s) step [0189] EDI. Establish diagnosis step [0190] TRT. Treatment step [0191] BSD. Brain scanning device [0192] SUB. Subject [0193] CD. Computer device [0194] BSS. Brain status establishment system