Patent Application: US-81163304-A

Abstract:
a non - invasive , early stage method to obtain quantitative measures of mild cognitive impairment useful in diagnosing and following degenerative brain disease or closed head injuries by utilizing the image data from individual patient positron emission tomographic scans to construct a cognitive decline index that serves as a diagnostic and screening tool to reveal the onset of mild cognitive impairment and nervous system dysfunction which are sequelae of degenerative brain diseases and closed head injury . the method involves using weighted values of brain region intensities derived from comparing scans of normal subjects to a scan of the patient to calculate a cognitive decline index that is useful as a diagnostic tool for mild cognitive impairment . the weights for the intensity values for each region are derived from the differences of intensity values from regions of the brain of the patient selected by comparing the patient to normal control subjects .

Description:
the data , analysis , calculations and procedures forming the preferred embodiment were produced in studies of four groups of patients : 1 . control subjects from the normal control database , 2 . patients identified retrospectively with early cognitive decline who received negative pet workups , 3 . patients identified retrospectively with cognitive decline and with the pathognomonic changes of ad present on pet imaging , 4 . patients with mci from the pilot study . the data and statistics used as exemplars in the figures and tables are taken from these studies . referring first to fig1 , a database 10 is compiled for normal control subjects . the database of fdg brain scans from healthy control subjects was created to enable the objective examination of a variety of patients who presented for clinical evaluation of cerebral pathology . these subjects are physically examined and screened for neurological and psychiatric illness . entry into the data base requires that subjects have normal or unremarkable mri scans as well as negative cognitive tests on folstein mini mental status examination ( mmse )& gt ; 28 ( folstein et al ., 1975 ). the control subjects &# 39 ; data from fdg pet normal brain scans is validated by comparison with patients with cerebral lesions . the normal control subject database is then used for statistical comparison in the method described as part of the instant invention . referring to fig1 , patients are selected and screened 20 . patients are categorized by the symptoms and history presented at examination . there are four categories : 101 memory complaints , family history of alzheimer &# 39 ; s disease , apoe4 positive or related reasons ; 102 history of substance abuse , concern over possible brain damage or related reasons ; 103 history of head injury , cognitive complaints , headaches , blurry vision or related reasons ; and 104 family history of parkinson &# 39 ; s disease , movement disorder or related reasons . standard clinical evaluation is performed on the presenting patient 110 including physical examination , baseline laboratory tests ; computerized tomography or magnetic resonance imaging and neuro - psychological testing including the mmse as well as the clock - drawing test ( shulman et al ., 1986 , kirby et al , 2001 ) to search for memory and visuo - spatial impairments . these tests were chosen based on established sensitivity demonstrated in the literature ( petersen et al ., 2001 , chen et al ., 2001 ). other causes of cognitive decline are ruled out 115 . these include neoplasm , endocrine imbalance , infection , nutritional deficiency etc . the patient is referred to the pet center for evaluation of cognitive decline index 120 . referring to fig2 a , the cdi is designed to work with fdg pet brain scans obtained from a patient in standard clinical fashion . standard clinical fdg pet scanning procedures were employed . patients are injected with 10 mci of fdg through a peripheral intravenous line . 200 the patient rests quietly in a darkened room with eyes and ears open for one hour for tracer uptake before scanning is begun . control subjects were injected with 5 mci 205 to decrease radioactive exposure , and the scan time for the emission scan is subsequently increased to 20 min . 213 to obtain equivalent counts . scanning is carried out on a ge advance pet scanner in 2d mode with septa in place for all scans as at step 210 . a 10 - minute emission scan is obtained 211 ( 20 min for controls 213 ), as well as a 5 - min transmission scan 211 , 214 . cdi sensitivity changes with variation in processing methods , and is optimized using reconstruction based on ordered subset expectation maximization ( osem ) 215 and segmented transmission scan attenuation correction ( sac ) 220 . in addition to standard clinical processing , a z - axis filtering step is added here as well 225 , to improve attenuation correction in the cerebrum and cerebellum on the base of the brain images are then reformatted for clinical evaluation on the ge advance workstation ( sun ™ ultra 60 ) 230 , and a copy prepared for export to the research workstation for use with spm 235 ( friston et al , 1995a , b ). on the research workstation , scans are converted to analyze 7 . 5 ™ ( analyze direct , lenexa , kans .) format 240 . initial image voxel resolution was 3 . 5 × 3 . 5 × 4 . 5 mm . these images are then further processed and analyzed with spm99 ( spm , friston et al , 1995a ) implemented in matlab ( mathworks , natick ., mass .) 245 . facility in management of the image data is achieved by utilizing the digital imaging and communication in medicine ( dicom ) format . 250 ( see , http :// medical . nema . org ). the ( dicom ) standard was created by the national electrical manufacturers association ( nema ) to aid the distribution and viewing of medical images , such as ct scans , mris , and ultrasound . additionally , the image data may be further conditioned to enable data management on particular work stations . 260 and 265 once the image is loaded into spm , it is spatially transformed based on a brain template into the standardized 3d space developed by the montreal neurological institute ( mni ). 270 there is a large amount of normal variation in size and shape of the human brain and standardization is necessary to compare patients to the normal controls as well as achieving comparability between patients for the patient database . the standardization system and its resulting coordinate space is known as talairach space , and has been previously described ( talairach and tournoux , 1988 ). coordinates are transformed using mni2tal ( matthew brett &# 39 ; s mni2tal . m can be found at http :// www . mrc - cbu . cam . ac . uk / imaging / mnispace . html ). the method for standardization of image data to this space ( spatial normalization ) involves the use of twelve - parameter linear affine mathematical routines to translate , rotate , scale , and shear the image along the x , y , and z axes ( 4 actions × 3 axes = 12 parameters ). a template brain scan is used as a standard , and the brain scan being normalized is matched as closely as possible to the template in shape , size , and space . this has been well described previously ( friston et al , 1995a ). briefly , the brain image data is moved ( translated ) to the center of the image , twisted to match the orientation of the template ( rotation ), and scaled to best match the size of the template . the fourth step involves shear , such that one plane of the image slides on the next to optimally fit the shape of the object image to that of the target ( template ) image . all four steps are carried out along all three axes in 3d space . a final processing step 280 to optimize across - subject analyses of pet image data is spatially transformed spatial filtering , also known as smoothing . this step increases the signal - to - noise ratio and decreases variance across the pet image data by removing much of the variability between patients due to differences in gyri / sulci patterns . this is necessary to achieve optimal comparability in the patient data base . the optimal smoothing level is generally agreed in the art to be approximately 1 . 5 to 2 times the full - width half - maximum parameter of the scanner , considered to be the spatial resolution . for this research , the ge advance scanner has a resolution of approximately 3 . 5 mm × 3 . 5 mm × 4 . 5 mm . spatial filtering with a gaussian kernel ( the standard method ) also renders the data amenable to analysis via methods incorporating the theory of gaussian fields , which is important for the majority of the statistical routines used by the spm software package . the pet scanner measures the energy from positron emission , and an image of brain function is created for the entire brain . an example is shown in fig3 , with the image in the top panel ( a .) from a 65 year - old normal subject , and ( b .) is an fdg brain image from a 67 year - old patient with probable alzheimer &# 39 ; s dementia . this pattern is pathognomonic of ad . however the gold standard is still brain biopsy , with tissue diagnosis based on amyloid plaques and neurofibrillary tangles . it is notable that often the fdg brain scans from patients with mci appear normal , just like the scan in ( a . ), even to the well - trained nuclear medicine physician &# 39 ; s eye . thus , these scans are read clinically ( subjectively ) as not consistent with mci or ad . radiological evaluation of the mri is even less sensitive , and often patients with moderate ad , like the one in ( b .) still have normal or clinically unremarkable mris . the two brain scans in fig3 a and 3 b are examples of scans that have been spatially normalized . this spatial transformation can be problematic in images that have large lesions or severe atrophy , however in patients with lesions too subtle to detect clinically and / or mild atrophy there is very little error . nevertheless , all brain images were inspected visually post - normalization to assess the quality of the transformation . one notable change from the standard is that all images were interpolated into the template spatial bounding box , instead of the standard bounding box . the template boundaries include the entire brain including the cerebellum , while the standard one cuts out the majority of this important brain component . after the spatial transformation , further residual inter - subject differences ( due mainly to variation in patterns of gyri and sulci ) were minimized by smoothing with a gaussian filter kernel ( 8 mm isotropic ). this also serves the purpose of ensuring the data are normally distributed ; hence gaussian field theory can be applied in the analysis of the images ( see below ). no partial volume correction was carried out for any scans in either group , and there were some scans in both groups with mild generalized atrophy . a previous study examines partial volume correction as a means to correct for the effects of atrophy on the pet metabolic data ( meltzer et al , 1996 ). however , we felt this was unnecessary as there were very few subjects with clinically detectable atrophy , the atrophy present was mild , and there were both mci patients and controls with mild atrophy . further , another report finds that atrophy does not play a major role in pet metabolic data ( ibanez et al , 1998 ). final image voxel size was 2 × 2 × 2 mm . these spatially normalized and smoothed images were then used in the spm analyses to determine regions of significant difference in metabolism between the controls and patients . referring again to fig2 b , the patient data is compared to controls of a similar age range 300 to identify extrema in the increases and decreases in metabolic activity 310 . the group analysis was carried out in spm . spm is a software package designed for the processing and analysis of brain images . the mathematical requirements for the analyses of 3d images involve application of the general linear model and gaussian field theory ( friston et al , 1995b ; worsley et al , 1995 ). the compare - groups statistical model is used in this analysis to produce a spm ( t ) statistical map 320 . this is then converted to the unit normal distribution z score 330 . clusters with significant increased or decreased metabolic activity are then identified 340 . a significance threshold uncorrected for multiple comparisons is used . this is supported by previous reports in the literature which indicated similar patterns of activity ( de leon et al , 2001 ; reiman et al , 2001 ; small et al , 2000 ) and by the analysis showing that many of the brain regions under examination were identified a priori as being likely regions to have decreased metabolism . this analysis enables identification of brain regions for use in creation of the cdi . the locations of significant points of interest are determined from the spm results 350 ( see fig4 and 5 for examples of the features providing these results ). this printout of spm results lists all maxima greater than 8 mm apart . coordinates from the main foci are used in the creation of vois 360 for the calculation of the cdi ( see below ). fig6 and 7 show the maps of significant differences overlaid on canonical brain images , in both cross - sectional ( a ) and 3d rendered ( b ) views . fig6 shows regions of decreased metabolism , and fig7 shows regions of increased metabolism . specific loci from this analysis are used as centers for 3 - d , 1 cm diameter spherical ( vois ) created with the marsbar ™ plug - in ( bret et al , 2002 ) for spm 370 . this size voi is selected because it approximates the spatial resolution of the data post - smoothing . those skilled in the art will recognize , however , that other volumes may be appropriate for use as the volume of interest depending upon such factors as scanner resolution , patient tolerance of radio ligand , refinement of the statistical methods , size of both patient and normal subject database ( the latter for comparison purposes as set out below ), suspected disease / impairment , and other applicable factors . the intensity of each of the voxels within the spherical voi is read and the average is obtained 380 . raw data uncorrected for global intensity differences are used , since a ratio created from these data intrinsically corrects for differences across subjects . in the studies used to reduce the preferred embodiment to practice , mean image intensity values were collected from 13 regions for each subject . these regions are composed of areas that showed either increased metabolism ( cerebellum , pons , sensorimotor ) or decreased metabolism ( temporal lobe , hippocampus , parietal lobe , frontal lobe , posterior cingulate ). two steps for determination of weights for each voi are used . the first set of weights for each voi are based on the frequency of abnormality of the voi data from all the study patients as compared to all controls , with higher weights applied for increasing frequency 390 . the cdi calculated with these weights is designated cdi 1 . these weights are then used as a baseline for calculation of a second cdi ( cdi 2 ) involving iterative optimization of each weight to maximally separate the patient from the controls according to observer criteria 395 . for both cdi 1 and cdi 2 steps , the global mean of the weighted control group voi ratio is normalized to a value of 1 , and this normalization factor then universally applied . the resulting , mean , weighted , normalized voi ratio forms the cdi . once cdi values were obtained for each patient in the exemplar study , the normal distribution of the data was established with a kolmogorov - smirnov statistic . groups were analyzed for significant differences with analysis of variance , and the confidence level was determined for each group . two - tailed t - tests were applied to establish the significant differences between the groups of subjects . the analysis performed in the exemplar study determined weights for each voi . with the method completed and validated , the weights thus produced are used for each new patient that presents . the marsbar ™ spm toolbox is a plug - in type program for spm , and is used to create the 3d , one cm diameter , spherical volumes of interest ( vois ) used to sample data ( brett et al , 2002 ). it produces a mean intensity value for the volume elements ( voxels ) present within the volume of interest . the voxels are cubes 2 mm on a side after spatial normalization and the spherical vois represent a bounded volume containing the voxel . a voi is thus not a perfect sphere . see fig8 , step 395 , for an example of the location and representative size of an exemplar voi . the cdi is derived from 13 vois located at specific coordinates . these include specific locations in the parietal cortex , medial and lateral temporal areas , frontal cortex , posterior cingulate cortex , as well as the sensorimotor cortex , cerebellum , and pons . in the performed study there were initially more vois from the majority of the maxima presented in the tables from fig4 and 5 , however it was discovered that several of the regions were not necessary . this was discovered by iteratively examining the cdi results with fewer and fewer vois . the use of 13 vois proved optimal , although 11 worked well too . the sensitivity , as judged by the separation of the patients from the control cdis , dropped somewhat if fewer regions were used , and did not appreciably increase if more regions were used . their exact locations in 3d space are given in table 1 : weighting factors and coordinates of regions used for development of the cdi . cdi 1 weights were derived as described in the test from examination of the frequency of abnormalities in the patient group , while cdi 2 weights were derived arbitrarily to optimize the separation between the two groups . this is why the image must be spatially normalized , to ensure that the vois are sampled at exact coordinates determined by the spm analysis to be sensitive to the metabolic changes of mci , in the same spot in every subject . mean image intensity data was sampled for all 13 vois in all subjects . data for each voi and patient is displayed in matlab and saved to a text file for further processing and analysis . uncorrected ( raw ) image intensity data was sampled , because any global intensity correction or scaling at this point is unnecessary . this is because a ratio of some vois to others from the same image will be obtained , and this automatically and intrinsically corrects for global inter - subject intensity differences . these vois formed the raw data for the creation of the cdi . the voi ratio without weights has good performance in separating patients from controls . this ratio is determined by obtaining the mean , { overscore ( x )}, of the four vois from regions with increased metabolism ( defined here as x 1 through x 4 ), the mean , { overscore ( y )}, of the nine vois from regions with decreased metabolism ( y 1 through y 9 ) and dividing the mean of increases by the mean of decreases , { overscore ( x )}/{ overscore ( y )}. the results of this calculation for the above referenced study are shown in table 2 : however , the preferred embodiment is a more sensitive indicator and discriminator . the preferred embodiment is obtained by determining and applying weighting factors to each voi . multiple mechanisms were evaluated for determining and assessing appropriate weights . in the initial method , weights for each voi were based on frequency of abnormality of that voi across all patients and all controls , with higher weights applied for increasing frequency . the cdi calculated with these weights is designated cdi 1 . for the frequency analysis , the un - weighted voi values are used to generate two voi ratio datasets . the first dataset is composed of nine voi ratios formed by dividing the mean of the four increases , { overscore ( x )}, by each of the decreases ; namely , r 1 ={ overscore ( x )}/ y 1 for 1 ≦ i ≦ 9 . the second dataset is composed of 4 voi ratios formed by dividing each of the increases by the mean of the decreases ; namely r j = x j /{ overscore ( y )} for 1 ≦ j ≦ 4 . in the exemplar study , each of the 13 voi ratios for each patient was compared to the controls to assess the degree of overlap in the patient vs . controls there was in the data ranges . separate weights were calculated for the numerator and denominator vois . for example , for the vois from the posterior cingulate at [− 4 , − 70 , 30 ], the voi range for all patients was 0 . 869 - 1 . 895 , and the control range was 1 . 988 - 2 . 705 . for the patients , 29 / 32 vois were outside the range for the normal group . the voi with the lowest number falling outside the normal range was at [− 24 , − 12 , − 28 ] ( left medial temporal ), with 17 / 32 . this was set to one by subtracting 16 , which was also subtracted from all the weights for other vois in the denominator , thus leaving a range for individual weight values , designated as w i , from 1 for left temporal to 13 for posterior cingulate . a similar process was carried out for weights for vois in the numerator , resulting in individual weights , designated v j , for both components of the ratio , shown in tables 2 and 3 . once the weights were generated and applied , the weighted vois were used to calculate the cdi for the study patients . the weighted voi ratio was then normalized , such that the grand mean of the voi ratios from the control group was set to one , and the resulting correction factor was then applied to all weighted voi ratios . the mean , weighted , normalized voi ratio constitutes the cdi 1 400 . cdi = c x + [ ∑ j = 1 n ⁢ v j ⁢ x j / n ] / [ ∑ i = 1 m ⁢ w i ⁢ y i / m ] v j denotes the j th weight for the j th increased intensity value ; y i denotes the i th decreased intensity value ; and w i denotes the i th weight for the i th decreased intensity value . c x is the correction factor used to normalize the dataset . the weights have been established with n = 4 and m = 9 . once established , this set of weights is used for each new patient presenting for scanning and diagnosis . the set of steps for calculating cdi 1 are : ( 1 ) import voi data into spreadsheet 391 ; ( 2 ) determine intensity range overlap for each voi ratio 392 ; ( 3 ) create weights for each intensity extreme 393 ; ( 4 ) create weighted voi ratio 394 ; and ( 5 ) scale and normalize ratio 395 . demographic and screening information are presented in table 3 for the subject groups used in the referenced exemplar study . it was not possible in the study to determine the mmse scores of all patients identified retrospectively . the group of older controls was used for the spm analysis , but all controls were included in the cdi results for comparative purposes . for the mci patients from the pilot study , the mean mmse was 25 . 3 ± 2 , and the cdt was 3 . 3 ± 0 . 8 . for the older subset of controls used in the spm analysis , the mean mmse was 29 . 3 ± 0 . 8 , and the mean cdt was 3 . 8 ± 0 . 4 . the results of the spm group analysis of the patients with mci vs . controls are shown in fig4 and 5 . the retrospective and prospective mci scan datasets were pooled for this evaluation . patients were compared to a subset of controls matched for age . fig3 shows the regions of decreased cerebral metabolism that were present in the group analysis . these included many regions characteristic of that seen in previous studies of mci and ad , including the basal nucleus region , posterior cingulate , bilateral parietal , several left temporal and hippocampus regions , and left frontal regions were found . fig4 shows the regions of increased metabolism in patients with mci . this includes regions in bilateral motor areas , cerebellum , pons , and a right parietal area that is more medial and superior to the regions found with decreased metabolism . fig5 and 6 show the maps of significant differences overlaid on canonical brain images , in both cross - sectional ( a .) and 3d rendered ( b .) views . fig5 shows regions of decreased metabolism , and fig6 shows regions of increased metabolism . fig8 shows an example of one voi ( posterior cingulate ). data was sampled for vois from all 13 regions to calculate the cdi as described above . a comparison of the grouped cdi 1 values was carried out , and the results are shown in table 4 . as it was unclear whether these data were normally distributed , a kolmogorov - smirnov test was carried out and indicated a normal distribution of the data ( table 5 ): all patient groups compared to controls were highly significant , but there was no difference between patients with early cognitive decline identified retrospectively and those obtained from the pilot mci study . the normal range for this study was 0 . 949 to 1 . 042 , ( 95 % ci 0 . 990 - 1 . 010 ). this critical data range is the embodiment of the normal standard range to which all patient cdi values are compared . the cdi 1 was successful in discriminating 100 % of the mci patients in both the retrospectively and prospectively identified groups ( range 1 . 051 - 1 . 222 , 95 % ci 1 . 088 - 1 . 136 ), as well as all of probable ad patients ( range 1 . 076 - 1 . 414 , 95 % ci 1 . 172 - 1 . 276 ). the excellent separation of patients with mci from controls is shown in fig9 . this graph also shows the results for the ad group for comparison . the lack of a relationship of the cdi to age is shown in fig1 . there is no correlation with age . additional modifications leading to the improvement of the cdi have been investigated using results of the exemplar study . in this effort , the initial weights were used as a baseline for calculation of a second cdi ( cdi 2 ) involving iterative optimization of each weight to maximally separate the study patients from the controls in neural - network fashion . a dynamic table was created where the results of a change of a given weight upon the separation of the groups could be assessed in real - time . weights were iteratively adjusted with the goal to maximize the separation between the control and mci populations while minimizing within - group variance . using this arbitrary method , the weights resulting in optimal separation between the two populations were determined , and are shown in tables 8 and 9 . results from the group analysis using cdi 2 are shown in table 8 , and are presented graphically in fig1 and 12 . statistical significance is presented in table 9 . while this method of cdi creation does result in the best discrimination of mci patients from older controls , it did not discriminate patients with ad as well as cdi 1 . the spm analysis as discussed above is valuable , but has some significant drawbacks . determination of significance is somewhat arbitrary . the most conservative significance level is non - a - priori , based upon the intensity at the single - voxel level , and typically requires an spm ( z ) statistic in the 4 . 5 to 5 range to be determined truly significant . the least conservative significance level is based on an a priori hypothesis about activity in a given region , examines spatial extent or a combination of extent and peak height more so than intensity , and can be as low as a z score of perhaps 2 . 5 . it was noted that a characteristic pattern of decreased metabolism could emerge if the significance level was set to low levels . this “ trend ” in characteristic patterns was what was seen in several patients with mci , where there were no obvious clinically defined lesions . because of the problematic statistical significance question involved , and because it can be difficult to interpret a pattern of activity , especially in the light of low thresholds , it was necessary to create a way of objective examination of the pet brain image data that is more definitive and usable on the single patient level . because analysis of rois removes the major problem of multiple comparisons and conservative bonferroni adjustments , this method was the main focus of further research beyond spm and led to the methodology discussed in the preferred embodiment . using rois to examine both semi - quantitative and absolute brain image data was once the major methodology in use ( and is still quite common ), as the voxel - by - voxel approach incorporated in spm is a relatively novel method . most studies of semi - quantitative image data intensity normalized the data by dividing a given roi value by that obtained from the pons ( e . g ., de santi et al , 2001 ), the cerebellum ( e . g ., cappa et al , 2001 ), some other “ standard ” ( presumptively unaffected ) region , such as the sensorimotor cortex ( e . g ., arnaiz et al , 2001 ), or by an estimate of the global value ( de leon et al , 2001 ). it is interesting to note that in the research presented here , the cerebellum , pons , and sensorimotor area were all found to have increased activity in the spm analysis when patients with mci were compared to control subjects . all these regions , as outlined above , have been used as reference regions in studies of ad , in the belief that they are preserved and thus represent normal rates of metabolism . alternatively , it is possible to look at the relationship between two regions on opposite sides of the brain by creating a ratio , as in the “ asymmetry index ” ( russel et al , 1997 ). one of the major problems of roi analysis of functional imaging data ( pre - spm ) is that definition of the shape , size , and location of the roi is often subjective and arbitrary . there are many variations on this theme extant in the literature . in essence , using rois arbitrarily predefine a hypothetical lesion . for example , if an roi 2 cm in size is arbitrarily placed in the temporal lobe to interrogate for a region of hypometabolism , and is positioned over a portion of a 1 cm lesion , then the ( averaged ) intensity value from the roi will have increased variance due to being the mean of voxels from outside the lesion that are averaged together with voxels from within the lesion . the end result may be an roi value that lacks significance . the solution to this problem is to place rois in positions where there is known pathology , e . g ., based on the results of an spm analysis . rois have been previously derived from spm regional maxima ( buchel and friston , 1997 ). thus , the use of rois in the instant invention , has evolved past the earlier usage . the employment of rois in the instant invention escapes the major problem inherent in previous of spm analyses ( multiple comparisons ), and also escapes the main problem historically associated with roi analysis of having an arbitrary location in relation to the suspected pathology . the cdi was derived by examining the regions found to be significant , or trending towards significance in the spm analysis . all regions used for the cdi were derived from the spm analysis . while it may be possible to obtain an equally valid cdi with more or fewer regions , arriving at the 9 regions of decreased metabolism and 4 regions of increased metabolism was essentially arbitrary . these were the major regions that separated out of the spm analysis of older controls vs . mci patients . the number of regions used was derived selecting spm - defined regions of maximal difference , and determining which of these regions had the highest degree of separation between the two groups ( frequency analysis ). the cdi of the instant invention is unique because it is constructed using weights based upon the frequency of intensity abnormalities found in the 13 regions . whereas most ratios are between one region under examination and another region used as a standard , the ratio in the cdi is derived from the mean of four of the weighted rois divided by the mean of the other nine weighted rois . all rois are being examined experimentally ; there are none that are arbitrarily chosen as the “ standard ” region or regions . this use of rois has not been taught previously in the art . thus , forming a mean for the numerator and denominator is novel , derivation of the weights is novel , and using rois from increases for the numerator and rois from decreases for the denominator is novel . forming a mean , weighted , normalized ratio is thus a unique approach in the detection of mci . while several of the regions derived from the spm analysis are consistent with those reported in the literature as being involved in mci / ad pathophysiology ( e . g ., nbm , medial temporal , posterior cingulate , superior parietal ) several of them , especially regions of increased activity , have not previously been reported , and are thus essentially novel to this method . moreover , while there have been anecdotal reports of sensorimotor cortex preservation in ad ( arnaiz et al , 2001 ), no one has previously reported increased activity in this region from an spm analysis , related to mci . it has become apparent from many studies appearing in the functional imaging literature that the cerebellum often plays a major role in cognitive as well as it &# 39 ; s more well known motor functions ( parsons et al , 1997 ; rapoport et al , 2000 ). the neo - cerebellum has undergone striking parallel evolution with the neo - cortex , particularly in combination with the major frontal lobe expansion unique to humans . while the exact function of this expanded cerebellum remains to be established , the sheer size and magnitude of the corticopontocerebellar connections give a clue to its likely involvement in cognitive processes ( leiner et al 1986 , 1989 ). it has been implicated in language processing ( leiner et al , 1991 ) and there is anatomical evidence supporting a role for the cerebellum in cognition ( middleton and strick 1994 ; schmahmann and pandya , 1995 ). many functional neuroimaging studies demonstrate cerebellar involvement in cognitive processes . in cognitive activation studies that include the cerebellum , it is common to find increased activity in the cerebellum . one of the pioneer studies on the role of cerebellum in cognition indicated involvement of certain cerebellar regions in processing of sensory information rather than fine motor control ( gao et al , 1996 ). the cerebellum receives cortical afferents via the pontine relay nuclei . these afferents have recently been discovered to come from more widespread areas of the cortex than was originally thought , and seem to be reciprocal ( schmahmann and pandya , 1997 ). thus , a network exists to support involvement in cognition . cerebellar lesions have been linked to a cognitive affective syndrome ( schmahmann and sherman , 1998 ). this report of twenty patients with disease confined to the cerebellum found striking cognitive and behavioral deficits including difficulties with verbal fluency , working memory , visuospatial organization , personality changes and blunting of affect , in addition to other changes . cerebellar changes have also been found before in dementia . a recent pet study found decreased cerebellar metabolism in patients with severe alzheimer &# 39 ; s , however they also found significant declines in glucose metabolism throughout the cerebrum ( ishii et al , 1997 ). the magnitude of the metabolic changes seen was least in the cerebellum , and greatest in the parietal cortex . the cerebellar changes found were only significant in patients with severe alzheimer &# 39 ; s . another recent examination of patients with olivopontocerebellar atrophy found that this group had deficits in tasks requiring intact frontal and parietal cortices . they postulated that the cerebellum was involved in modulation of these cortical areas , and thus the atrophy had resulted in the cognitive changes seen ( arroyo - anllo and botez - marquard , 1998 ). a case report of a patient with a cyclic cognitive - affective syndrome examined cerebral perfusion using spect ( patterson , 2001 ). this patient with atypical symptoms of dementia shows increased flow in the cerebellum , which may represent increased activity of the purkinje cell &# 39 ; s inhibitory output , or increased activity of cells upstream to the purkinje neurons . the increase may have been compensatory , secondary to deficits in other interconnected areas such as the posterior parietal lobe . in the exemplar study , relative increases in metabolism are reported in the pons , cerebellum , and motor area of patients with mci . there are previously reported findings of increased cerebellar metabolism in patients with ad ( patterson et al , 2002 ). there have been numerous works that report the use of the cerebellum as a reference or control region for normalizing semi - quantitative pet or spect data . indeed , there has been debate on this topic , and at least one previous study has been done to validate the use of the cerebellum as a reference region in ad ( pickut et al , 1999 ). others have found either no change ( pickut et al 1999 , soonawala et al , 2002 ), or decreased cerebellar metabolism in ad ( ishii et al , 1997 ). one further study found that pontine metabolism was most preserved in patients with ad compared to controls ( minoshima et al , 1995b ). the use of a ratio of cerebellar to brain activity is not a novel methodology , in fact it is a standard means of “ normalizing ” semi - quantitative data ( see above section on rois ). as our work here is semi - quantitative , it is possible that the increases found in the pons , cerebellum , and motor strip are the result of global declines in the mci population , sparing these regions . it is also possible that the metabolic decline found in some regions ( posterior cingulate , parietal , etc ) have resulted in compensatory activity in other nodes in a network of brain regions . there have been numerous reports in the literature documenting the involvement of the cerebellum in cognition ( see rapoport et al , 2000 for review ), so it is not necessarily safe to presume that the cerebellum is uninvolved in ad or other disorders involving cognition . we postulate here that our results may not simply be intensity normalization due to global changes , but compensatory increased activity . further study on this question using absolute glucose metabolic rate and / or structural equation modeling to examine nodal interactions is certainly warranted , but whether or not the actual metabolism in these regions is increased or normal , they still serve as optimal regions for the calculation of the cdi . roi data based on spm results has been used to examine functional connectivity between the cerebellum and other regions in a study of acute psychosis and response to antipsychotic medication . functional connectivity analysis is simply looking at the correlation coefficient between two regions ( e . g ., the cerebellum and the left dorsolateral prefrontal cortex ) in a population , and comparing that value to one obtained in another population . this data has not been published as it contains some admittedly serious confounds . however , the underlying method of using spm maxima to define rois , and then using the roi data to look at the relationship between two or more brain regions is still valid . the concept of using a region of activity as a locus for an roi is extant in the literature , and has been used previously ( buchel et al , 1997 ). this study used the locations of regional maxima from an spm analysis as seed points for rois , and this data was then entered into a structural equation model ( sem ) analysis . the sem and similar methodologies are more advanced than simple functional connectivity - type correlation analyses , and are called “ effective connectivity ” analyses . this method is important to review as it bears some similarities to my method . sem involves the use of a set of mean intensity values derived from rois typically taken from specific regions . these regions can be defined by spm maxima , as in the cited study . the underlying concept is based upon defined anatomical connections , and thus interprets a relationship between two regions as composed of either a direct connection , an indirect connection , or ( more commonly ) a combination of the two . this is important as the addition of greater than three “ nodes ” in this network increases the complexity of the calculation by a least an order of magnitude , and this complexity increases in a geometric fashion for each node added . one similarity to my method is that weights are assigned to a given “ path ” between two nodes . these weights are used to calculate a path coefficient that represents the strength or activity of the connection between the two nodes . this type of analysis is useful to examine the relationship between several regions , and how it may change with different cognitive activities . the cdi samples mean roi intensity data from multiple regions , and those values are entered into a formula to calculate the cdi . while one component of the formula involves weights , this is not to examine the relationship between regions . while there may be a relationship between certain regions sampled for the cdi , this is not implied , intrinsic , or necessary for the cdi to be valid and functional . as discussed before , three previous reports indicate that it is possible to detect brain metabolic changes using pet across groups of premorbid patients , who have not yet developed mci , before subjective symptoms or neuropsychological impairment occurs ( de leon et al , 2001 ; reiman et al , 2001 ; small et al , 2000 ). all three use group analysis to detect changes in groups of subjects . the spm results used in the instant invention are highly consistent with these previous reports . one study examined brain scans in a post - hoc measures , separating them based upon whether they develop ad later in life . the other two were also longitudinal studies . none of the three present a methodology that enables evaluation and production of a measure that is usable in a single patient . de leon and others followed 48 healthy elderly individuals , and scanned them at baseline and again after 3 years . some of the subjects showed evidence of cognitive decline . by grouping these patients post - hoc , and looking at their first scans as a function of whether they developed cognitive decline at 3 years , they found decreased metabolism in the entorhinal cortex , as well as increased frequency of apoe4 + genotype . the reiman study followed normal subjects who either had or didn &# 39 ; t have the apoe4 phenotype , and scanned twice with a two - year interval . they found that subjects who were apoe4 + had decreased metabolism in regions of the temporal lobe , posterior cingulate , prefrontal cortex , basal forebrain , parahippocampus , and thalamus , in regions similar to those found in the present study . the last study by small and others was similar . they followed 61 subjects , 54 of whom were aware of mild memory loss , but who were “ normal ” as determined by cognitive tests . in this population , apoe4 + genotype was associated with initial decreased metabolism in the posterior cingulate , inferior parietal and lateral temporal areas . these metabolic changes predicted cognitive decline . these studies show that pet scanning using fdg is the most sensitive measure known for the detection of this disorder . previous data indicates that using pet to diagnose early ad was cost - effective and no more expensive than other methods , and resulted in improved accuracy ( silverman et al , 2002 ). if our data holds up in further study , then pet will become even more accurate in the diagnosis of mci . there have been previous reports that have attempted to discriminate patients with early cognitive changes or ad from normal subjects by using various methods of objective analysis . a study using a diagnostic index based on parietal lobe z - scores was able to detect 97 % of ad patients ( minoshima et al , 1995a ). this same group extended this technique to patients with isolated memory impairment , but were able to detect only 50 % ( berent et al 1999 ). another report that used multiple regression and discriminant analysis correctly identified 87 % of patients with mild to moderate ad and controls ( azari et al , 1993 ). another study that used logistic regression identified 95 % of ad patients using a combined regressor of fdg metabolic data ( from an arbitrarily defined roi in the left temporoparietal area ), along with performance on a “ block design ” cognitive test ( arnaiz et al 2001 ). a spect study using singular value decomposition and discriminant function analyses was able to detect about 60 % of patients with early ad / mci ( johnson et al , 1998 ). the findings from all of these studies can be distinguished from the findings of the exemplar study presented here and the methodology of the instant invention by the variability in mathematical approaches , sensitivity , and most important and unique in the instant invention of the use of spm - derived regional maxima as loci for the vois . this method eliminates the confounding condition of arbitrarily defining the lesion site , as well as bypasses the confounding requisite bonferroni correction for multiple comparisons in spm . our method of using a cdi based on multiple vois allows for variance across the presentation , while methods that examine only one region ( e . g ., the parietal lobe ) do not . the method of the preferred embodiment of the instant invention also examines 13 major nodes that we believe are most affected by the processes of ad , based on the a priori knowledge gleaned from the spm analysis . the data presented here for the cdi 2 made use of an iterative optimization technique for voi weighting that bears some similarity to neural - network classification . there have been two previous studies that used a neural - network method ( kippenham , et al , 1992 , 1994 ) to classify patients with ad from controls . these two reports used neural - network models based on 67 rois drawn in all the major regions of the brain . the 1992 study reported that , for patients with possible ad ( mmse of 19 ± 8 ), the area under the relative operating curve ( roc ) was 0 . 81 , similar to that for the clinical evaluation . this was somewhat higher for probable ad ( mmse 15 ± 7 ) with a roc area of 0 . 85 , and improved even more in the 1994 study by using a scanner with higher resolution ( roc area 0 . 95 ). one important difference between the kippenham studies and the data presented here is that we used a priori knowledge of where the pathological regions were ( determined with spm ) to sample voi data . in the instant invention , the spm methodology is utilized in a unique way , to determine the exart location of regionally significant change for a 3d spherical voi . by doing this , it is possible to bypass the major drawback of using roi analysis , which is that the roi is typically drawn arbitrarily . even when drawn based upon anatomically defined areas , there still is no assurance that an area so defined will respond homogenously and thus provide a homogenous response . the use of vois in the instant invention is determined in this manner , along with the weights used to calculate the cdi are likely responsible for the very high sensitivity present in our data . pet is a technology that can make use of a variety of radioligands , and is not limited to fdg . ad has been studied with radioligands that bind to cholinergic receptors ( e . g ., shinotoh et al , 2003 ) as well as to neurofibrillary plaques ( shoghi - jadid et al , 2002 ). these techniques are quite different than the results presented here as they make use of completely different radioligands and do not examine brain metabolism . the cdi as described in the preferred embodiment is a marker that can be used to predict ad . as stated above , the marker / method described in u . s . pat . nos . 5 , 873 , 823 and 5 , 632 , 276 may be applied to the detection and diagnoses of multiple disease states , including both pd and ad . to apply the teachings of the instant patent to identify and predict the onset of more severe symptoms in other conditions such as pd , change would be made to the methodology of the instant invention to use different voi &# 39 ; s . this is due to the fact that the spm analysis in the instant patent provides loci that are specific for mci , and thus the loci sampled as used in the cdi are specific for mci and ad . for diagnoses of pd , an spm analysis of patients with pd compared to control would have to be completed . with this requirement established , the following examples illustrate extensions of the cdi to additional clinical presentations . pd is a disorder of the brain which affects the dopaminergic neurons of the brainstem first and foremost . by the time that patients first notice a movement disorder or feel the first clinically noticeable signs and symptoms , 50 % of the dopamine neurons have been destroyed . previous reports have shown that there are metabolic changes present in the cerebral cortex as well as subcortical structures in early pd . one specific report by eidelberg , as reported above , and others used fdg - pet and scaled subprofile modeling to make predictions about disease states in pd . the description of the methodology in both the eidelberg patent and the manuscript lacks clarity . the methodology of the instant patent may be applied to gather data in the same fashion with early pd as was done with patients with mci . baseline fdg pet scans can be obtained for a group of patients presenting with these symptoms or complaints , and compared to a group of age matched controls using spm . the spm statistical data can be used to determine the location ( specific coordinates ) of regions of significant change . these regions can be sampled with a 5 mm radius voi using marsbar ™. estimates of the frequency of abnormality of each region can be calculated across the patient sample , and used to generate weights for each region . the mean of the weighted vois from regions where significant increased metabolism is found can be divided by that from regions with decreased metabolism . the grand mean of this ratio in the control subjects can be adjusted to 1 , and the resulting adjustment factor used to normalize all ratios to this standard . this value can be called the “ parkinson &# 39 ; s disease index ,” or pdi . chi is an altogether too common affliction . patients who have suffered from concussive illnesses often have very little if any objective evidence on an mri or ct scan to indicate that a traumatic injury has occurred . however , neuropsychological tests and behavioral measures often do find sometimes subtle changes . the purpose of using a pet index in chi is to provide a definitive and objective measure that can guide treatment and prognosis . the cdi methodology may be further adapted for use in the objective analysis of functional brain data ( fdg - pet scans ) from patients with chi . this would involve the use of vois from regions that vary on a per - patient basis , as potential metabolic lesions would vary from patient to patient , depending on the location of the traumatic insult and the degree of coup / contre - coup type injury . baseline fdg pet scans can be obtained for a given patient , and each individual patient can be compared to a group of controls using spm . the spm statistical data can be used to determine the location ( specific coordinates ) of regions of significant change . these regions can be sampled with a 5 mm radius voi using marsbar ™. the mean of the vois from regions where significant increased metabolism was found can be divided by that from regions with decreased metabolism . the grand mean of this ratio in the control subjects can be adjusted to 1 , and the resulting adjustment factor used to normalize all ratios to this standard . abuse of dangerous and illicit substances is often found to be associated with pathophysiology in the orbitofrontal cortex and associated limbic and paralimbic regions . while the specific regions may vary with the substance being abused , the rationale remains the same . the purpose of using a pet index in patients who have abused drugs is potentially manifold : to investigate the risk of addiction in certain populations , to study the effect that acute substance abuse or dependency has on cerebral metabolism , and to evaluate populations for lesions who are abstinent but who have abused or were dependent in the past . baseline fdg pet scans would be obtained for a group of patients with a history of drug use , and compared to a group of age matched controls using spm . the spm statistical data can be used to determine the location ( specific coordinates ) of regions of significant change . these regions can be sampled with a 5 mm radius voi using marsbar ™. estimates of the frequency of abnormality of each region can be calculated across the patient sample , and used to generate weights for each region . the mean of the weighted vois from regions where significant increased metabolism is found can be divided by that from regions with decreased metabolism . the grand mean of this ratio in the control subjects can be adjusted to 1 , and the resulting adjustment factor used to normalize all ratios to this standard . lewy body dementia , pick &# 39 ; s dementia , huntington &# 39 ; s disease three other less common dementing diseases are lewy body dementia , pick &# 39 ; s dementia , and huntington &# 39 ; s disease . these diseases have characteristic metabolic lesion patterns on the pet scan , and thus it is quite feasible to propose cognitive decline indices that are specific for the exact dementia type . the purpose of using a specific pet index for these types of dementia would be several fold : to detect the dementing process as early as possible , to discriminate which type of dementing process it is , and to facilitate the early treatment of these disease processes . baseline fdg pet scans can be obtained for a group of patients presenting with these symptoms or complaints , and compared to a group of age matched controls using spm . the spm statistical data can be used to determine the location ( specific coordinates ) of regions of significant change . these regions can be sampled with a 5 mm radius voi using marsbar ™. estimates of the frequency of abnormality of each region can be calculated across the patient sample , and used to generate weights for each region . the mean of the weighted vois from regions where significant increased metabolism was found can be divided by that from regions with decreased metabolism . the grand mean of this ratio in the control subjects can be adjusted to 1 , and the resulting adjustment factor used to normalize all ratios to this standard . in developing the cdi for any of these conditions , a patient &# 39 ; s cdi is compared to established normal ranges of values . the presence of normality or abnormality is determined from the cdi value 500 . if the cdi reading is negative , the patient is advised and educated about the clinical course of potential illnesses and told the signs to watch for . the potential benefits of preventative measures including anti - oxidants , mental exercises , beneficial diet and adequate rest are discussed 510 . if the cdi reading is positive , the patient is educated about the meaning of the positive reading , and informed about the projected clinical course of the illness . the benefits of medication , and the potential benefit of ameliorative measures such as anti - oxidants , mental exercises , beneficial diet and adequate rest are discussed . 520 . in either case , results are given to the referring physician and the patient is scheduled for re - evaluation 530 . patient data is stored in the comprehensive patient database 540 . the above descriptions of the exemplary embodiments of methods for the determination of clinical conditions and for the quantitative description of metabolically correlated brain function are for illustrative purposes . those skilled in the art who have the benefit of this disclosure will recognize that certain changes can be made to the component parts of the method of the present invention without changing the manner in which those parts function to achieve their intended result . the instant invention may also be practiced in the absence of any element not specifically disclosed . all such changes , and others which will be clear to those skilled in the art from this description of the preferred embodiments of the invention , are intended to fall within the scope of the following , non - 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