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Table of Contents    
Year : 2019  |  Volume : 67  |  Issue : 5  |  Page : 1310-1317

Biomarker-Based Prediction of Progression to Dementia: F-18 FDG-PET in Amnestic MCI

1 Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
2 Department of Neurology, Cardiothoracic and Neurosciences Centre, All India Institute of Medical Sciences, New Delhi, India
3 Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India

Date of Web Publication19-Nov-2019

Correspondence Address:
Dr. Madhavi Tripathi
Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi - 110 029
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0028-3886.271245

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 » Abstract 

Background: Metabolic patterns on brain F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) can predict the decline in amnestic mild cognitive impairment (aMCI) to Alzheimer's disease dementia (AD) or other dementias.
Objective: This study was undertaken to evaluate the diagnostic accuracy of baseline F-18 FDG-PET in aMCI for predicting conversion to AD or other dementias on follow-up.
Patients and Methods: A total of 87 patients with aMCI were enrolled in the study. Each patient underwent a detailed clinical and neuropsychological examination and FDG-PET at baseline. Each PET scan was visually classified based on predefined dementia patterns. Automated analysis of FDG PET was performed using Cortex ID (GE Healthcare). The mean follow-up duration was 30.4 ± 9.3 months (range: 18–48 months). Diagnosis of dementia at follow-up (obtained using clinical diagnostic criteria) constituted the reference standard, and all the included aMCI patients were divided into two groups: the aMCI converters (MCI-C) and MCI nonconverters (MCI-NC). Diagnostic accuracy of FDG PET was calculated using this reference standard.
Results: There were 23 MCI-C and 64 MCI-NC. Of the 23 MCI-C, 19 were diagnosed as probable AD, 1 as frontotemporal demetia (FTD), and 3 as vascular dementia (VD). Of the 64 MCI-NC, 9 had subjective improvement in cognition, and 55 remained stable. The conversion rate for all types of dementia in our series was 26.4% (23/87) and for Alzheimer's type dementia was 21.8% (19/87). The of PET-based visual interpretation was 91.9%. Sensitivity, specificity, positive predictive value, and negative predictive value for FDG-PET-based prediction of dementia conversion were 86.9% [confidence interval (CI) 66.4%–97.2%)], 93.7% (CI 84.7%–98.2%), 83.3% (CI 65.6%–92.9%), and 95.2% (CI 87.4%–98.9%), respectively. Kappa for agreement between visual and Cortex ID was 0.94 indicating excellent agreement. In the three aMCI patients progressing to VD, no specific abnormality in metabolic pattern was noted; however, there was marked cortical atrophy on computed tomography.
Conclusion: FDG-PET-based visual and cortex ID classification has a good accuracy in predicting progression to dementia including AD in the prodromal aMCI phase. Absence of typical metabolic patterns on FDG-PET can play an important exclusionary role for progression to dementia. Vascular cognitive impairment with cerebral atrophy needs further studies to confirm and uncover potential mechanisms.

Keywords: Alzheimer's disease dementia, F-18 FDG, mild cognitive impairment, positron emission tomography
Key Message: Topographical patterns of hypometabolism in brain FDG-PET are useful to predict the decline of MCI to AD or other neurodegenerative dementias.

How to cite this article:
Tripathi M, Tripathi M, Parida GK, Kumar R, Dwivedi S, Nehra A, Bal C. Biomarker-Based Prediction of Progression to Dementia: F-18 FDG-PET in Amnestic MCI. Neurol India 2019;67:1310-7

How to cite this URL:
Tripathi M, Tripathi M, Parida GK, Kumar R, Dwivedi S, Nehra A, Bal C. Biomarker-Based Prediction of Progression to Dementia: F-18 FDG-PET in Amnestic MCI. Neurol India [serial online] 2019 [cited 2020 Jul 6];67:1310-7. Available from:

Mild cognitive impairment (MCI) represents an intermediary condition between normal cognition and clinically overt dementia.[1],[2] The expected clinical outcomes of amnestic MCI (aMCI) are progression to Alzheimer's disease dementia (AD), progression to dementia other than AD, stable MCI, or improvement in cognition.[2] An early identification of those MCI progressing to AD or other types of dementia is important from the research point of view for early interventions and future prognostication. Studies have shown an average annual progression rate of MCI to AD of 10%–15%.[2],[3],[4] While clinical and neuropsychological methods have limited accuracy in predicting conversion of MCI to dementia, objective changes in positron emission tomography (PET), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) biomarkers may occur before obvious clinical symptoms emerge.[5]

F-18 fluorodeoxyglucose (FDG) is a downstream marker of neuronal injury according to the revised National Institute on Aging–Alzheimer's Association criteria (NIA-AA criteria), and a recent meta-analysis demonstrated positive F-18 FDG PET to be the strongest individual biomarker predictive of incident dementia in MCI.[6] Even though biomarkers of amyloid deposition and neurodegeneration in combination are the best for predicting AD pathology in MCI, the individual biomarker with the best performance was FDG-PET.[7] FDG PET has been included in the NIA-AA research criteria of MCI due to AD.[8],[9] Of note, a recent study on the prognostic value of the NIA-AA,[10] IWG-2, and IWG-1 criteria[11],[12] showed that classification based on NIA-AA criteria for AD dementia diagnosis, in which amyloid PET and FDG-PET biomarkers are considered, reached the highest prediction accuracy in a clinical setting.[13]

Topographical patterns of hypometabolism in brain FDG-PET can potentially predict the decline in MCI to AD or other neurodegenerative dementias. These changes become detectable in individual subjects as a significant deviation from controls 1–2 years before the onset of dementia and are closely related to cognitive impairment.[14] The pattern of AD on PET is characterized by a reduced FDG uptake in the temporoparietal association cortices, including precuneus and posterior cingulate.[15] Neuronal dysfunction in these areas is related to cognitive deficits in nonmemory domains such as language and orientation[16] which holds importance in the stage of transition from a relatively pure memory deficit (as in MCI) to a more extensive cognitive deficit that characterizes dementia. Thus, metabolic patterns on F-18 FDG-PET enable monitoring progression at a prodromal stage, and a preliminary study indicated a substantial increase in study power in clinical trials using regional FDG uptake as an outcome parameter.[17]

We undertook this study to evaluate the diagnostic accuracy of baseline F-18 FDG-PET in aMCI for predicting conversion to AD or other dementia subtypes based on the clinical diagnosis at follow-up. In our setting where we are limited by the availability of amyloid tracers, it was important to evaluate the role of FDG as a biomarker in MCI. The incremental capacity of FDG to predict clinical course in MCI was of practical utility.

 » Patients and Methods Top

This was a prospective study and patients were recruited from the cognitive disorders and memory clinic of the neurology outpatient department. All consecutive patients receiving a clinical diagnosis of aMCI using the revised Petersen criteria[18] by the neurologist were considered for enrollment. Patients were excluded if they had a history of stroke or other neurological conditions such as epilepsy, brain tumor, encephalitis, or subdural hematoma, clinically relevant psychiatric illness, or drug/alcohol abuse.

Each enrolled patient underwent a detailed neuropsychological evaluation which included the Mini Mental State Examination (MMSE/HMSE),[19] Clinical Dementia Rating, Geriatric Depression Scale, Auditory Verbal Learning Test, complex figure test, Trail Making test, and Verbal N Back by a neuropsychologist (ANW). There were 39 single domain and 48 multidomain aMCI.

The study was approved by the institute's ethics committee (IEC/NP-333/2013 RP-10/2013), and both informed and written consent were taken from each patient. Patients were recruited from September 2013 to February 2016, and follow-up was obtained till September 2017 – with a minimum of at least 18 months' duration. Each patient was explained the importance of a follow-up and a reliable contact was obtained in each case.


Each patient underwent a FDG-PET scan within 3 months of initial enrollment. PET/computed tomography (CT) was performed approximately 60 minutes after intravenous injection of 185–222 MBq (5–6 mCi) of F-18 FDG. Imaging was performed on a Biograph mCT (Siemens, Erlangen, Germany). Initial scout of the head was followed by noncontrast CT acquisition (110 mA, 120 kVp) for attenuation correction and anatomical coregistration. This was followed by a single-bed, three-dimensional PET emission scan for 15 minutes. PET images were reconstructed (ultra HD-PET) by iterative reconstruction (5 iterations and 21 subsets).

Image interpretation

The FDG-PET images were interpreted visually on an MMWP workstation (Siemens) by a nuclear medicine physician with experience (10 years) in reading brain FDG-PET images. Visual interpretation was based on decreased FDG uptake (hypometabolism) and classified as consistent with AD when hypometabolism was noted in unilateral or bilateral parietal, including precuneus and posterior cingulate, and temporal cortices. Hypometabolism in any of these territories in isolation or in non-AD territories was classified as unlikely of AD pattern.

The noncontrast head CT of PET/CT was visually assessed by the physician for brain atrophy and ventricular size.

The FDG images of each case were further analyzed using Cortex ID (GE Healthcare, Waukesha, WI, USA) on ADW 4.7 (GE Healthcare) workstation. The Cortex ID application involves generation of brain maps which are compared with a commercially available comprehensive database of normal brain scans and normalized globally (or to the pons/cerebellum). Automated voxel-by-voxel Z-scores were generated for the cortical region of interest which included parietal, frontal, lateral temporal, mesial temporal, precuneus, cingulate (anterior and posterior), and occipital cortices. Z-score = (mean subject − mean database)/standard deviation database. The voxel-based color-coded statistical analysis of average Z-scores displayed the magnitude of metabolic change for each region. A Z-score threshold of 2, corresponding to a P value of 0.05 (two-tailed), was applied for demarcation of significant abnormalities, negative Z-scores indicating hypometabolism. Prediction of outcome was done at a minimum follow-up of 18 months.

On follow-up, of minimum 18 months and maximum 48 months – the MCI subjects either met the criteria for a diagnosis of dementia or still fulfilled the clinical criteria for MCI or reverted to normal cognition. The clinical evaluation/diagnosis was made by a neurologist (MT) along with neuropsychological evaluation (AN). The diagnosis at the final follow-up was taken as the reference standard to make a final diagnosis of AD/other forms of dementia. The FDG-PET report was not taken into consideration while making the diagnosis. The diagnosis of AD, VD (NINDS-AIREN), and behavioral variant FTD was made using appropriate diagnostic criteria.[10],[20],[21]

Statistical analysis

Chi-square test and Mann–Whitney U-test were used to compare demographic and clinical characteristics between MCI-C and MCI-NC. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy with 95% confidence interval (CI) were calculated for FDG-PET using the clinical diagnosis as reference standard. Kappa statistic was used to look for agreement between visual and Cortex ID on FDG-PET. Correlation between follow-up time and percentage of conversion was evaluated using Pearson's correlation coefficient. XLSTAT statistical software was used for statistical analysis.

 » Results Top

A total of 87 patients with aMCI were included in the analysis [Table 1], the mean age was 66.6 ± 9.1 years (range 48–82 years), male: female ratio was 2.1:1 (59:28), and MMSE was 26.8 ± 1.8 (range 24–29).
Table 1: Clinical characteristics of participant group

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All included MCI patients were divided into two groups [Figure 1] – study flow diagram]: the MCI converters (MCI-C) and MCI nonconverters (MCI-NC) based on the neurologists and neuropsychological evaluation at the end of the follow-up. The mean follow-up duration was 30.4 ± 9.3 months (range: 18–48 months) with one death due to unrelated cause. There were 23 MCI-C and 64 MCI-NC. Of the 23 MCI-C, 19 were diagnosed as probable AD, 1 as FTD, and 3 as VD. Of the 64 MCI-NC, 9 had subjective improvement in cognition, and 55 remained stable.
Figure 1: Flow chart of study

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The conversion rate for all types of dementia in our series was 26.4% (23/87) and for Alzheimer's type dementia was 21.8% (19/87). As expected, there was a significant difference in the MMSE between converters and nonconverters. The number of female in the MCI-C group was higher than males.

F-18 FDG-PET-based classification: Visual analysis

There were 23 patients with unilateral or bilateral parietotemporal, including posterior cingulate and precuneus hypometabolism which was taken as a pattern suggestive of AD [Figure 2]. This included one patient with dilated ventricles on CT.
Figure 2: F-18 FDG plain PET images of a case of aMCI progressing to AD (a) reveal hypometabolism in left parietal, frontal, and temporal cortices including posterior cingulate. Cortex ID (b) shows significant hypometabolism in the left parietal, temporal, and frontal regions (color maps displayed on the left-hand side)

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One patient had frontal (right more than left) and anterior temporal hypometabolism and was interpreted as FTD [Figure 3].
Figure 3: F-18 FDG plain PET images of a case of aMCI progressing to FTD (a) reveal hypometabolism in both frontal (R > L) and temporal cortices. Cortex ID (b) shows significant hypometabolism in both the frontal and temporal lobes (R > L) (color maps displayed on the left-hand side)

Click here to view

In all, 28 patients (32%) had hypometabolism in unilateral parietal,[3] temporal,[6] frontal,[1] occipital,[1] or bilateral parietal,[1] temporal,[13] parieto-occipital,[1] and insular[2] cortices/lobes. This included two patients diagnosed as VD on subsequent follow-up, one had bilateral parietal and one had prefrontal hypometabolism (along with cortical atrophy on CT). The most common non-pattern type of hypometabolism was bilateral followed by unilateral temporal hypometabolism. Two patients with temporal hypometabolism and gliotic lesions in the temporal lobes had a history of trauma in the past.

The remaining 35 patients did not reveal regional cortical or subcortical hypometabolism. This included one patient of VD who had marked cerebral atrophy on CT, and FDG uptake was commensurate with atrophy.

F-18 FDG-PET-based classification: Voxel-based Cortex ID analysis

Each FDG-PET scan was further analyzed using Cortex ID on a ADW 4.7 workstation (GE). Z-scores were arranged in ascending order and significant hypometabolism was interpreted for scores <−2. Significant hypometabolism (Z-score <−2) involving unilateral or bilateral parietal, temporal, precuneus, and posterior cingulate cortices was taken as the pattern for AD. Significant hypometabolism (Z-score <−2) in unilateral or bilateral frontal and temporal lobes was characteristic of FTD. The mean Z-scores of MCI-C and MCI-NC groups for the AD-related cortical regions are shown in [Table 2] along with P values showing a significant difference between the two groups in all regions excluding the left mesial temporal region. A total of 22 patients had Z-scores indicating hypometabolism in AD-related regions [Figure 1] and 1 had Z-score indicative of hypometabolism in frontotemporal cortices consistent with FTD [Figure 3]. Each of the regions was evaluated individually for its diagnostic capacity to predict AD. The inferior parietal was found to be the most sensitive followed by posterior cingulate and precuneus for AD prediction. The temporal cortices, especially medial temporal, were the least specific for AD.
Table 2: Regional Z-scores in MCI-C and MCI-NC on Cortex ID

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Seventeen of the 19 patients with temporal hypometabolism on visual evaluation demonstrated significant hypometabolism on Cortex ID which involved only the mesial temporal cortices in two patients, lateral temporal cortices in seven patients, and both mesial and lateral temporal cortices in eight patients. The mean Z-scores for mesial temporal hypometabolism were −3.1 ± 1.7 (right) and −4.0 ± 2.3 (left) and for lateral temporal were 2.7 ± 1.4 (right) and −2.9 ± 1.0 (left). It was interesting to note that left mesial temporal hypometabolism did not reach statistical significance between the MCI-C and NC subgroups. None of these patients showed progression till the last follow-up.

Kappa for agreement between visual and Cortex ID was 0.94 indicating excellent agreement. FDG-PET patterns suggestive of dementia were then compared to the clinical diagnosis at follow-up. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for FDG-PET-based prediction of dementia conversion were 86.9% (95% CI 66.4%–97.2%), 93.7% (95% CI 84.7%–98.2%), 83.3% (95% CI 65.6%–92.9%), 95.2% (95% CI 87.4%–98.9%), and 91.9% (95% CI 84.1%–96.7%), respectively.

The clinical characteristics of the group with FDG pattern suggestive of dementia were compared with those not showing any specific pattern of hypometabolism. A significant difference was noted in only MMSE scores. Similarly, when the group of clinical converters on FDG was compared with converters not identified on FDG, a significant difference was noted only for MMSE score. There was a strong positive correlation between follow-up time and percentage of conversion (r2 = 0.98).

Four MCI subjects with FDG-PET hypometabolism patterns specific for AD showed a stable cognitive status on follow-up. All patients with temporal lobe dysfunction (TLD) and areas of regional hypometabolism remained cognitively stable at follow-up. Three of the MCI cases, two with regional hypometabolism and one with no hypometabolism on FDG-PET, were diagnosed as VD on follow-up. Common to all three was severe cerebral atrophy which was appreciated on the CT probably suggestive of underlying cerebrovascular pathology.[22]

 » Discussion Top

The critical issue in MCI is the possibility of progression to AD or other dementia subtypes. FDG-PET has been included as a supporting biomarker for AD diagnosis,[10] and in MCI for early detection of AD.[8] In this study, we aimed to assess the performance of FDG-PET in the prediction of conversion to dementia in a cohort of aMCI subjects. In a limited resource setting like ours where amyloid tracers are unavailable, FDG-PET has great practical utility not only for predicting progression but also in predicting clinical course. The conversion rate for all types of dementia in our cohort of aMCI subjects was 26.4%, and for AD it was 21.8% with a strong positive correlation between follow-up time and percentage of conversion. This is in line with the conversion rates (which ranged from 22% to 50%) reported by Smailagic et al.[23] in their review with a positive correlation between follow-up time and percentage of conversion. The normal conversion rate of MCI to AD reported is between 8% and 16%.[24]

We considered a combination of temporoparietal, precuneus, and posterior cingulate hypometabolism – unilateral or bilateral – a must for the pattern predictive of conversion to AD. Both visual- and voxel-based Cortex ID were used for defining this pattern. Bilateral temporoparietal hypometabolism is the standard FDG-PET finding which is also highly correlated with the pathological diagnosis of AD;[25] posterior cingulate cortex represents the brain area in which hypometabolism occurs in the earliest disease stage.[26] Hypometabolism or hypoperfusion in the inferior parietal lobule has been the most reliable functional indicator of progression from aMCI to AD, while changes in the posterior cingulate cortex/inferior precuneus are most likely nonspecific as they were observed in nonconverters when compared with control subjects.[27] Although our longitudinal follow-up was limited, we feel a combination of precuneus, posteior cingulated, and temporoparietal hypometabolism at least unilateral was the most reliable indicator for a pattern suggesting progression to AD. Visual and Cortex ID evaluation revealed this pattern of hypometabolism in all converters. Cortex ID analysis showed statistically significant difference in all these regions in converters versus nonconverters [Table 2]. Analysis of individual regions for diagnostic capacity on Cortex ID revealed the inferior parietal region to be the most sensitive region for prediction of progression to AD and the mesial temporal region to be the least specific. Four of the patients with a metabolic pattern suggestive of AD did not show progression to AD on clinical evaluation and a longer follow-up will probably be required to define clinical course.

Temporal lobe hypometabolism was identified in 19 patients. TLD, more precisely mesial TLD (mTLD), has been one of the patterns of hypometabolism identified by other study groups also.[28],[29],[30] This has also been taken as a marker of limbic predominant Alzheimer's.[28] None of the patients with temporal lobe hypometabolism in our series (or in the ones reported with mTLD) showed progression of cognitive decline. A slow rate of progression has been documented in limbic predominat Alzheimer's.[28] Patients with hypometabolism in single or dual territories also did not show progression to dementia till the end of follow-up. Further biomarker studies and a longer follow-up would be essential to predict the significance of this hypometabolism in terms of eventual conversion to dementia.

One case of FTD behavioral variant was correctly identified on FDG-PET at the aMCI stage, based on a frontotemporal pattern of hypometabolism. FDG-PET patterns of hypometabolism can effectively identify both AD and FTD and these patterns have been included as supportive feature in the clinical/research diagnostic criteria of many dementing neurodegenerative disorders.[10],[21],[31],[32],[33],[34] Thus, FDG-PET is a crucial biomarker for the classification scheme in dementia and can predict clinical course of MCI patients with a caveat that three cases of VD who had marked cortical atrophy as the only feature were not effectively classified at baseline. CT was a useful adjunct especially for disproportionate cortical atrophy in these cases. All three patients were females, with a younger mean age (56.6 years) and higher MMSE[27] in comparison to the remaining converters. Cortical changes are now considered a clinically relevant component of subcortical vascular disease.[35],[36],[37] Brain atrophy has been shown to be one of the strongest predictors of cognitive impairment in patients with pure vascular disease, and there is increasing evidence to suggest that the effects of subcortical ischemic lesions on cognitive functioning are mediated by the ensuing loss of cortical gray matter.[38]

An important aspect of FDG-PET in aMCI is its exclusionary role. A normal scan has been shown to be a reliable indicator of nonprogression.[39] A negative FDG-PET pattern suggests the need for reconsidering a diagnosis of neurodegenerative disease.[40] In our series, 35 aMCI patients did not demonstrate hypometabolism on FDG-PET and 34 of them did not show progression. FDG-PET had a high specificity (93.7%) and negative predictive value (95.2%) in aMCI and can be extremely useful to rule out progression. This utility was also seen in the study by Caminiti et al. wherein normal CSF measures were seen in only half of stable or reverter MCI cases, while FDG-PET was probably a more accurate predictor of nonconversion.[41]

The most common way to evaluate hypometabolism is visual reading in routine practice, but it may not be accurate enough[42],[43] particularly in the early stages of the disease (MCI) when expert readers are not available on site. For this reason, automated software such as Cortex ID has been applied to analyze patient scans and increase physicians' interpretative confidence. The use of validated semi-quantitative methods and standard operating procedures for reading PET scans in dementia has been strongly recommended.[44],[45] We had excellent agreement between visual and Cortex ID–based interpretation of FDG PET scans (Kappa = 0.94). Caminiti et al.[40] have reported high accuracy of FDG-PET assessment based on the optimized features of the statistical parametric mapping (SPM) single-subject procedure. Specificity and sensitivity values for both early and differential diagnosis of dementia significantly increase in clinical settings when semi-quantitative approaches are used.[46]

The predictive power of FDG-PET in MCI conversion to AD in various studies has varied widely from 25% to 100% sensitivity and 15% to 100% specificity.[47],[48],[49],[50],[51],[52],[53],[54] Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for FDG PET based prediction of dementia conversion in our study were 86.9% (95% CI 66.4%–97.2%), 93.7% (95% CI 84.7%–98.2%), 83.3%(95% CI 65.6%–92.9%), 95.2% (95% CI 87.4%–98.9%), and 91.9% (95% CI 84.1%–96.7%), respectively. The use of a clinical diagnosis as standard at follow-up is associated with the inherent limitation in terms of its diagnostic accuracies. The duration of follow-up also becomes critical and should be sufficiently long to capture the natural course of conversion.

The recent Cochrane review[23] concluded that FDG-PET as a single test lacks accuracy to identify those people with MCI who would develop AD or other forms of dementia. Across 14 studies included in this meta-analysis, the large variation in sensitivity (25%–100%) and specificity values (29%–100%), a low median specificity (82%), and the estimated sensitivity of 76% led to this conclusion. The European Association of Nuclear Medicine voiced their opinion against this review and suggested that the variability observed in different FDG-PET studies should not be attributed to the method which is standardized. Technical issues such as lower sensitivity and spatial resolution of PET scanners, robust study designs, small control groups for statistical comparison, and a lack of adequate biomarker quantification[55] are probably the factors which result in low accuracy obtained in the prediction of MCI progression to dementia.

This study has some limitations. First, a longer clinical-neuropsychological follow-up is certainly needed in nonconverter subjects with FDG-PET indicative of underlying neurodegeneration. Another limitation is the lack of biomarker correlations and postmortem neuropathological confirmation of the final diagnosis. More robust techniques such as SPM may be needed to be implemented for patterns of global hypometabolism in the presence of atrophy.

 » Conclusion Top

Our results support the use of FDG-PET visual and Cortex ID classification in predicting progression to AD in the prodromal MCI phase and in the exclusion of progression. Vascular cognitive impairment with cortical atrophy on CT needs to be recognized as one of the manifestations of subcortical vascular impairment and needs to be kept in mind while evaluating aMCI patients for prediction of progression.


The authors wish to acknowledge Ramchandra B. Pokale, Chief Artist, Centre for Community Medicine, AIIMS, New Delhi, for his help.

Financial support and sponsorship

This study was supported by a grant from the Department of Science and Technology-Cognitive Science Research Initiative (SR/CSI/324/2012).

Conflicts of interest

There are no conflicts of interest.

 » References Top

Morris JC, Price JL. Pathologic correlates of non demented aging, mild cognitive impairment, and early-stage Alzheimer's disease. J Mol Neurosci 2001;17:101-18.  Back to cited text no. 1
Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE, Ivnik RJ, et al. Mild cognitive impairment: Ten years later. Arch Neurol 2009;66:1447-55.  Back to cited text no. 2
Bruscoli M, Lovestone S. Is MCI really just early dementia? A systematic review of conversion studies. Int Psychogeriatr 2004;16:129-40.  Back to cited text no. 3
Mattsson N, Zetterberg H, Hansson O, Andreasen N, Parnetti L, Jonsson M, et al. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 2009;302:385-93.  Back to cited text no. 4
Beckett LA, Harvey DJ, Gamst A, Donohue M, Kornak J, Zhang H, et al. The Alzheimer's disease neuroimaging initiative: Annual change in biomarkers and clinical outcomes. Alzheimers Dement 2010;6:257-64.  Back to cited text no. 5
Frisoni GB, Bocchetta M, Chételat G, Rabinovici GD, de Leon MJ, Kaye J, et al. Imaging markers for Alzheimer disease: Which vs how. Neurology 2013;81:487-500.  Back to cited text no. 6
Prestia A, Caroli A, Wade SK, van der Flier WM, Ossenkoppele R, Van BerckelB, et al. Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimers Dement 2015;11:1191-201.  Back to cited text no. 7
Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:270-9.  Back to cited text no. 8
Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:280-92.  Back to cited text no. 9
McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Jr, Kawas CH, et al. The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:263-9.  Back to cited text no. 10
Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer's disease: Revising the NINCDS-ADRDA criteria. Lancet Neurol 2007;6:734-46.  Back to cited text no. 11
Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer's disease: The IWG-2 criteria. Lancet Neurol 2014;13:614-29.  Back to cited text no. 12
Vos SJB, Verhey F, Frölich L, Kornhuber J, Wiltfang J, Maier W, et al. Prevalence and prognosis of Alzheimer's disease at the mild cognitive impairment stage. Brain 2015;138:1327-38.  Back to cited text no. 13
Herholz K. Cerebral glucose metabolism in preclinical and prodromal Alzheimers disease. Expert Rev Neurother 2010;10:1667-73.  Back to cited text no. 14
Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE. Metabolic reduction in the posterior cingulate cortex in very early Alzheimers disease. Ann Neurol 1997;42:85-94.  Back to cited text no. 15
Haxby JV, Grady CL, Koss E, Horwitz B, Heston L, Schapiro M,et al. Longitudinal study of cerebral metabolic asymmetries and associated neuropsychological patterns in early dementia of the Alzheimers type. Arch Neurol 1990;47:753-60.  Back to cited text no. 16
Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: A potential outcome measure in Alzheimers disease treatment studies. Am J Psychiatry 2002;159:738-45.  Back to cited text no. 17
Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004;256:183-94.  Back to cited text no. 18
Ganguli M, Ratcliff G. A Hindi version of the MMSE: The development ofa cognitive screening instrument for alargely illiterate rural elderly population in India. Int J Geriatr Psychiatr 1995;10:367-77.  Back to cited text no. 19
Román GC, Tatemichi TK, Erkinjuntti T, Cummings J, Masdeu J, Garcia JH, et al. Vascular dementia diagnostic criteria for research studies: Report of the NINDS-AIREN International Workshop. Neurology 1993;43:250-60.  Back to cited text no. 20
Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 2011;134:2456-77.  Back to cited text no. 21
Heiss WD, Herholz K, Böcher-Schwarz HG, Pawlik G, Wienhard K, Steinbrich W, et al. PET, CT, and MR imaging in cerebrovascular disease. J Comput Assist Tomogr 1986;10:903-11.  Back to cited text no. 22
Smailagic N, Vacante M, Hyde C, Martin S, Ukoumunne O, Sachpekidis C.18F-FDG PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev 2015;1:CD010632.  Back to cited text no. 23
Mitchell AJ, Shiri-Feshki M. Rate of progression of mild cognitive impairment to dementia–meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand 2009;119:252-65.  Back to cited text no. 24
Hoffman JM, Welsh-Bohmer KA, Hanson M, Crain B, Hulette C, Earl N, et al. FDG PET imaging in patients with pathologically verified dementia. J Nucl Med 2000;41:1920-8.  Back to cited text no. 25
Lucignani G, Frost JF. Neurochemical imaging with emission tomography: Clinical applications. In: Baert AL, Sartor K, Schiepers C, editors. Diagnostic Nuclear Medicine. Medical Radiology. Berlin, Heidelberg: Springer; 2006. p. 7-37.  Back to cited text no. 26
Schroeter ML, Stein T, Maslowski N, Neumann J. Neural correlates of Alzheimer's disease and mild cognitive impairment: A systematic and quantitative meta-analysis involving 1351 patients. Neuro Image 2009;47:1196-206.  Back to cited text no. 27
MarraC, Villa G, Quaranta D, Valenza A, Vita MG, Gainotti G. Probable Alzheimer's disease patients presenting as “focal temporal lobe dysfunction” show a slow rate of cognitive decline. J Int Neuropsychol Soc 2012;18:144-50.  Back to cited text no. 28
Whitwell JL, Dickson DW, Murray ME, Weigand SD, Tosakulwong N, Senjem ML, et al. Neuroimaging correlates of pathologically defined subtypes of Alzheimer's disease: A case-control study. Lancet Neurol 2012;11:868-77.  Back to cited text no. 29
Cerami C, Della Rosa PA, Magnani G, Santangelo R, Marcone A, Cappa SF, et al. Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia. Neuro Image Clin2015;7:187-94.  Back to cited text no. 30
Armstrong MJ, Litvan I, Lang AE, Bak TH, Bhatia KP, Borroni B,et al. Criteria for the diagnosis of corticobasal degeneration. Neurology 2013;80, 496-503.  Back to cited text no. 31
Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, et al. Classification of primary progressive aphasia and its variants. Neurology 2011;76:1006-14.  Back to cited text no. 32
Litvan I, Mangone CA, McKee A, Verny M, Parsa A, Jellinger K, et al. Natural history of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome) and clinical predictors of survival: A clinic opathological study. J Neurol Neurosurg Psychiatry 1996;60:615-20.  Back to cited text no. 33
McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor JP, Weintraub D, et al. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 2017;89:88-100.  Back to cited text no. 34
Duering M, Righart R, Wollenweber FA, Zietemann V, Gesierich B, Dichgans M. Acute infarcts cause focal thinning in remote cortex via degeneration of connecting fiber tracts. Neurology 2015;84:1685-92.  Back to cited text no. 35
Smith EE, Schneider JA, Wardlaw JM, Greenberg SM. Cerebral micro infarcts: The invisible lesions. Lancet Neurol 2012;11:272-82.  Back to cited text no. 36
Peres R, De Guio F, Chabriat H, Jouvent E. Alterations of the cerebral cortex in sporadic small vessel disease: A systematic review of in vivo MRI data. J Cereb Blood Flow Metab 2016;36:681-95.  Back to cited text no. 37
Tuladhar AM, Reid AT, Shumskaya E, de Laat KF, van Norden AG, van Dijk EJ,et al. Relationship between white matter hyperintensities, cortical thickness, and cognition. Stroke 2015;46:425-32.  Back to cited text no. 38
Bohnen NI, Djang DSW, Herholz K, Anzai Y, Minoshima S. Effectiveness and safety of 18F-FDG PET in the evaluation of dementia: A review of the recent literature. JNucl Med 2012;53:59-71.  Back to cited text no. 39
Lo RY, Hubbard AE, Shaw LM, Trojanowski JQ, Petersen RC, Aisen PS, et al. Longitudinal change of biomarkers in cognitive decline. ArchNeurol 2011;68:1257-66.  Back to cited text no. 40
Caminiti SP, Ballarini T, Sala A, Cerami C, Presotto L, Santangelo R, et al. FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. NeuroImage Clin 2018;18:167-77.  Back to cited text no. 41
Foster NL, Heidebrink JL, Clark CM, Jagust WJ, Arnold SE, Barbas NR, et al. FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease. Brain 2007;130:2616-35.  Back to cited text no. 42
Patterson JC, Lilien DL, Takalkar A, Pinkston JB. Early detection of brain pathology suggestive of early AD using objective evaluation of FDG-PET scans. Int J Alzheimer's Dis 2010;2011:946590.  Back to cited text no. 43
Frisoni GB, Perani D, Bastianello S, Bernardi G, Porteri C, Boccardi M, et al. Biomarkers for the diagnosis of Alzheimer's disease in clinical practice: An Italian intersocietal roadmap. Neurobiol Aging 2017;52:119-31.  Back to cited text no. 44
Mattson MP, Longo VD, Harvie M. Impact of intermittent fasting on health and disease processes. Ageing Res Rev 2017;39:46-58.  Back to cited text no. 45
Perani D, Schillaci O, Padovani A, Nobili FM, Iaccarino L, Rosa D, et al. Erratum to “A survey of FDG-and amyloid-PET imaging indementia and gradeanalysis.” Biomed Res Int 2014;2014:785039.  Back to cited text no. 46
Anchisi D, Borroni B, Franceschi M, Kerrouche N, Kalbe E, Beuthien-Beumann B, et al. Heterogeneity of brain glucose metabolism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch Neurol 2005;62:1728-33.  Back to cited text no. 47
Arbizu J, Prieto E, Martínez-Lage P, Martí-Climent JM, García-Granero M, Lamet I, et al. Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer's disease dementia. Eur J Nucl Med Mol Imaging 2013;40:1394-405.  Back to cited text no. 48
Chen X, Zhou Y, Wang R, Cao H, Reid S, Gao R, et al. Potential clinical value of multiparametric PET in the prediction of Alzheimer's disease progression. PLoS One 2016;11:e0154406.  Back to cited text no. 49
Chételat G, Desgranges B, de la Sayette V, Viadre F, Eustache F, Baron JC. Mild cognitive impairment: Can FDG-PET predict who is to rapidly convert to Alzheimers Dis 2003;60:1374-7.  Back to cited text no. 50
Landau SM, Harvey D, Madison CM, Reiman EM, Foster NL, Aisen PS, et al. Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 2010;75:230-8.  Back to cited text no. 51
Ossenkoppele R, Prins ND, Pijnenburg YA, Lemstra AW, van der Flier WM, Adriaanse SF,et al. Impact of molecular imaging on the diagnostic process in a memory clinic. Alzheimers Dement 2013;9:414-21.  Back to cited text no. 52
Perani D, Della Rosa PA, Cerami C, GallivanoneF, Fallanca F, Vanoli EG, et al. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. Neuro Image Clin 2014;6:445-54.  Back to cited text no. 53
Perani D, Cerami C, Caminiti SP, Santangelo R, Coppi E, Ferrari L, et al. Cross-validation of biomarkers for the early differential diagnosis and prognosis of dementia in a clinical setting. Eur J Nucl Med Mol Imaging 2016;43:499-508.  Back to cited text no. 54
Morbelli S, Garibotto V, Van De Giessen E, Arbizu J, Chételat G, Drezgza A,et al. A Cochrane review on brain [18F] FDG PET in dementia: Limitations and future perspectives. Eur J Nucl Med Mol Imaging 2015;42:1487-91.  Back to cited text no. 55


  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2]


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