Posterior Cingulate Cortex Hypometabolism in Non-Amnestic Variants of Alzheimer’s Disease

Abstract

Background:

Hypometabolism of the posterior cingulate cortex (PCC) is an important diagnostic feature of late-onset, amnestic Alzheimer’s disease (AD) measured with ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET). However, it is unclear whether PCC hypometabolism has diagnostic value in young-onset, non-amnestic variants of AD, which exhibit less pathology in the hippocampus and default mode network.

Objective:

Evaluate the prevalence and diagnostic value of PCC hypometabolism in non-amnestic variants of AD.

Methods:

We retrospectively identified 60 patients with young-onset, atypical dementia who have undergone a detailed clinical evaluation, FDG-PET, and an amyloid biomarker (amyloid-PET or cerebrospinal fluid analysis). We quantitatively analyzed regional hypometabolism in 70 regions of interest (ROI) using the MIMneuro® software.

Results:

Based on a cut-off of z-score < –1.5 for significant PCC hypometabolism, the prevalence of PCC hypometabolism in non-amnestic variants of AD was 65% compared to 28% in clinical variants of frontotemporal dementia (FTD). The ROI with the maximal hypometabolism was the dominant middle temporal gyrus in the language variant of AD (mean z score –2.28), middle occipital gyrus in PCA (–3.24), middle temporal gyrus in frontal AD (–2.70), and angular gyrus in corticobasal syndrome due to AD (–2.31). The PCC was not among the 10 most discriminant regions between non-amnestic variants of AD versus clinical variants of FTD.

Conclusion:

We conclude that PCC hypometabolism is not a discriminant feature to distinguish non-amnestic variants of AD from clinical variants of FTD—and should be interpreted with caution in patients with young-onset, non-amnestic dementia.

Keywords

Alzheimer’s disease FDG-PET non-amnestic variants of AD primary progressive aphasia posterior cingulate cortex

INTRODUCTION

While Alzheimer’s disease (AD) is best known as a late-onset progressive memory disorder, researchers and clinicians have long described young-onset variants with focal language, visuospatial or behavioral impairment, and relative sparing of memory [1 –6]. These ‘atypical’, non-amnestic AD syndromes often present at a younger age [7 –9], have a different genetic profile [10], and exhibit distinct atrophy patterns [11 –14], yet they all exhibit the defining pathological hallmarks of AD: neurofibrillary tau tangles and amyloid-β plaques [4, 12]. Although memory dysfunction has long been a mandatory criterion for a diagnosis of AD, the updated 2011 NIA-AA criteria for AD recognized the existence of three non-amnestic presentations of AD: the language variant, the visuospatial variant, and the executive variant [15].

In academic dementia centers, where the youngest and most complex dementia patients are seen, clinicians often face diagnostic dilemmas between non-amnestic AD syndromes and disorders along the frontotemporal dementia (FTD) spectrum [1, 16]. About 40% of patients with prominent and progressive language impairment—a condition called ‘primary progressive aphasia’ (PPA) [17]—have underlying AD [18], and it is sometimes hard to identify which PPA patients have AD or FTD on a clinical basis [18 –20]. Similarly, while patients with progressive behavioral impairment were traditionally thought to have behavioral variant FTD (bvFTD), researchers have shown that some of them in fact have the ‘behavioral/dysexecutive’ variant of AD (bdAD) [13 , 22]. Finally, patients with a posterior presentation (corticobasal syndrome [CBS] or posterior cortical atrophy [PCA] [23]) have variable prevalence of AD versus FTD pathology and this can be challenging to label on a clinical basis [22, 24, 25].

To solve these difficult diagnostic dilemmas, clinicians have to rely on more sophisticated diagnostic tools, including cerebrospinal fluid (CSF) analysis, amyloid-β positron emission tomography (PET), brain ¹⁸F-fluorodeoxyglucose PET (FDG-PET), magnetic resonance imaging (MRI), and neuropsychological testing. FDG PET is a powerful tool for the evaluation of dementias with atypical presentations. It is used in tertiary-care memory clinics around the world to differentiate AD from other conditions like FTD when basic clinical evaluation does not allow reaching a definite diagnosis [26 –31]. FDG-PET allows in vivo evaluation of the regional cerebral glucose metabolism, which is a proxy of synaptic function and density [32 –35]. Patients with amnestic AD have predominant hypometabolism in temporoparietal regions, including the precuneus and posterior cingulate cortex, and in the frontal cortex [31 , 37]. Posterior cingulate cortex (PCC) hypometabolism was shown to be highly sensitive and specific for AD, even in the earliest stages, and therefore constitute the core of its FDG-PET diagnosis [29 , 36–38]. The PCC is a core component of the default mode network (DMN), whose disruption is central to the pathophysiology of AD [39 –41].

Studies who have explored the diagnostic value of PCC hypometabolism have been conducted on the typical amnestic form of AD, and not the non-amnestic variants [30 , 43]. Since non-amnestic AD variants have a distinct genetic background and atrophy patterns, we do not know the diagnostic value of PCC hypometabolism in these syndromes. The two greatest predictors of PCC hypometabolism in typical AD—hippocampal pathology and the ɛ4 allele of apolipoprotein E (APOE) [44 –46]—are less prevalent in non-amnestic than amnestic AD. Indeed, there is a growing body of evidence suggesting that PCC hypometabolism is strongly linked to hippocampal pathology, by amyloid-facilitated transneuronal spread of tau pathology through the cingulum bundle [47]; and from cingulum pathology itself, disconnecting the PCC from its hippocampal input [46 , 48–51]. Patients with temporal lobectomy for epilepsy have post-operative PCC hypometabolism that corresponds to the deficits observed in AD [52]. Since non-amnestic variants of AD generally spare the hippocampus, with early and most severe pathology in neocortical areas [11, 12], they may lack the PCC hypometabolism observed in amnestic AD. Furthermore, while the APOE ɛ4 allele has long been associated with PCC hypometabolism, even in cognitively normal young adults [53 –55], non-amnestic AD have a significantly lower prevalence of APOE ɛ4 than amnestic AD [10 , 56], adding to our doubts regarding the diagnostic value of PCC hypometabolism in these clinical variants. A recent study suggested that PCC hypometabolism was less specific to AD than previously thought: using different data-driven approaches, Scheltens et al. found that 27 to 42% of patients with bvFTD (clinical diagnosis, amyloid-negative at CSF analysis) had PCC hypometabolism [57]. In this study, we sought to investigate the diagnostic value of PCC hypometabolism in a heterogeneous population of tertiary-care memory clinic atypical dementia syndromes.

METHODS

Patient selection

Patients were selected retrospectively from the Clinique Interdisciplinaire de Mémoire (CIME) du CHU de Québec (Québec, Canada) and the Hospital Clinico San Carlos (Madrid, Spain). Both centers are tertiary-care memory clinics with access to neuropsychological evaluation, FDG-PET and amyloid biomarkers (CSF analysis, amyloid PET). Compared to population-based dementia studies, tertiary-care memory clinic patients have a generally earlier symptom onset, higher level of education and atypical, non-amnestic cognitive profiles [25, 58]. In this study, we searched our clinical databases for patients who had undergone both an FDG-PET and an amyloid biomarker in their clinical work-up. These patients were, by definition, complex/atypical dementia cases. For each patient, we retrieved the working diagnosis, age at the time of FDG-PET, sex, education level, Mini-Mental State Examination (MMSE) score, and amyloid status. Diagnostic categories included the different atypical variants of AD (early-onset amnestic, corticobasal syndrome, visuospatial [posterior cortical atrophy; PCA], language [logopenic variant primary progressive aphasia; lvPPA], behavioral/dysexecutive variants of AD) and FTD (behavioural variant, semantic variant PPA [svPPA] and non-fluent PPA [nfvPPA]) [15 , 59]. In Quebec, amyloid status was determined using CSF analysis (amyloid-β 1–42 >680 pg/mL) or visual read of ¹⁸F-NAV4694 amyloid-PET [25]; while in Madrid it was determined by visual read of ¹⁸F-florbetapir amyloid-PET [60].

FDG-PET

All FDG PET scans were acquired between 2014 and 2018 under standardized FDG-PET acquisition protocols [58]. Patients fasted for at least 4 h before administration of 18F-FDG. The serum glucose level was measured for all patients. The ¹⁸F-FDG brain PET was obtained either with a dual-head Philips Vertex MCDAC coincidence camera (Philips Healthcare, Best, Netherlands), Siemens Biograph 6 (Siemens Healthcare, Erlangen, Germany) or GE Discovery VCT-16 (GE Healthcare, Cleveland, OH, USA) PET/CT scanners. In a dimly lit room, between 111 and 370 MBq (3–10 mCi) ¹⁸F-FDG were injected via a venous catheter. Acquisition was started after 30–60 min (gamma camera: 64×64×16 matrix, 64 steps, 25 seconds/steps; PET/CT: 3D mode, 10 or 15 min). Measured attenuation and scatter corrections were applied to the iterative reconstruction method. For image analysis, dementias were classified visually according to generally accepted criteria [36] by readers who were experienced in FDG brain PET data. For that purpose, the readers evaluated 1) transaxial PET slices of the FDG images, and 2) three-dimensional z-score surface projections as obtained by the 3D-SSP/NEUROSTAT software [61]. As this is a retrospective effort, readers were not blind to the clinician’s diagnostic hypotheses. FDG-PET were performed and visually rated on a clinical basis. Visual read of FDG-PET was hence performed before knowledge of the amyloid status. For quantitative analysis, FDG-PET images from both centers were analyzed using the MIMneuro® software (MIM, Cleveland, OH, USA). FDG-PET images were co-registered to brain CT for anatomic segmentation. Then, FDG-PET was normalized to whole-brain activity and compared against a database of cognitively healthy to determine presence of hypometabolism based on standardized uptake value (SUV). The MIM normal database consists of brain FDG-PET scans from 43 subjects with mean age 63±10 years and age range of 41 to 80 years. All volunteers in the normal control group had MMSE scores of 25 or higher and Memory Impairment Screen (MIS) scores of 6 or higher. Regional SUV was converted to z-scores (SUV ratios [SUVR]) for defining FDG metabolism in 70 regions of interest (ROIs). To validate our results, we performed an automated quantitative analysis of PCC hypometabolism measured with FDG-PET using the Hermes BRASS™ software (Hermes Medical Solutions, Stockholm, Sweden) [62, 63].

Statistical analyses

MIMneuro^® software provides SUVR in 70 pre-defined ROIs by comparing them to 43 cognitively healthy controls. We calculated mean z-score for each ROI in different diagnostic categories. We present the three ROIs with the most severe mean hypometabolism for each variant of AD. We calculated a discrepancy between mean SUVR of each ROI in patients with non-amnestic AD variants versus clinical variants of FTD. We present the ten ROIs with the highest metabolic discrepancy between non-amnestic variants of AD versus clinical variants of FTD. We calculated the correlation coefficient (R²) between PCC metabolic z-score calculated using MIMneuro^® software versus the Hermes BRASS™ software. We calculated the SUVR cut-off of PCC hypometabolism presenting the better correlation with visual reads in our cohort. We conducted sensitivity analyses by comparing the demographic data and the mean SUVR of clinically relevant ROI of each clinical syndrome in the Quebec cohort versus the Madrid cohort. All statistical analyses were performed in SAS 9.3 (SAS Institute Inc). The significance level was set at p < 0.05.

RESULTS

Patient population

A total of 60 patients were included in this study, 19 from Madrid Hospital Clinico San Carlos and 41 from CIME in Quebec City. Mean age was 63 years old at FDG-PET on average), mean education was 13 years and mean MMSE was 24/30. 48/60 patients had positive amyloid biomarkers; 19 were diagnosed with lvPPA, 9 with PCA, 8 with behavioral/dysexecutive AD, 4 with CBS-AD, and 8 with early-onset amnestic AD. Among the 12 patients with negative amyloid biomarkers, 9 were diagnosed with bvFTD, 2 with nfvPPA and 1 with svPPA. There were no significant differences in age or MMSE score between clinical variants of AD versus FTD (see Table 1).

Table 1

Patients’ characteristics

	n	Age	MMSE
Clinical variants of AD
lvPPA	19	65.7	22.0
PCA	9	58.0	25.0
Behavioral/dysexecutive AD	8	61.6	20.7
CBS-AD	4	61.8	26.7
Early-onset amnestic AD	8	60.5	23.9
Clinical variants of FTD
bvFTD	9	62.7	24.0
nfvPPA	2	64	28.5
svPPA	1	69	25
Total	60	62.6	23.9

AD, Alzheimer’s disease; bvFTD, behavioral variant of frontotemporal dementia; CBS, corticobasal syndrome; lvPPA, logopenic variant of primary progressive aphasia; PCA, posterior cortical atrophy.

Validation of the quantitative measurement method for PCC hypometabolism

The correlation coefficient (R²) of PCC SUVR calculated using MIMneuro^® software versus the Hermes BRASS™ software was 0.60. We determined that the PCC cut-off SUVR presenting the best correlation with visual reads of PCC hypometabolism was Z = –1.5. Using this cut-off, there was an 86% correspondence between Hermes and MIM softwares, and a 75% concordance between visual reads and quantitative measurement using the MIMneuro^® software.

Prevalence of PCC hypometabolism in non-amnestic variants of AD

Based on a cut-off SUVR of < –1.5 for significant PCC hypometabolism, the prevalence of PCC hypometabolism in non-amnestic variants of AD was 31/48 (65%) compared to 4/12 (28%) in clinical variants of FTD.

Most hypometabolic regions in non-amnestic variants of AD

We calculated mean SUVR in 70 pre-defined ROIs using the MIMneuro^® software. The regions with the earliest and most severe hypometabolism in the amnestic variant of AD was the PCC (–2.03), followed by the angular gyrus (–1.56) and precuneus (–1.46). The PCC was not the ROI with the most hypometabolism in any of the non-amnestic variants of AD. The ROIs with the most hypometabolism revealed being the dominant middle temporal gyrus in lvPPA (–2.28), middle occipital gyrus in PCA (–3.24), middle temporal gyrus in frontal AD (–2.70) and angular gyrus in CBS-AD (–2.31) (See Table 2).

Table 2

Most hypometabolic regions of interest at FDG-PET of distinct clinical variants of AD

Variant	Most hypometabolic regions of interest
	1	2	3
Early-onset amnestic AD	Posterior cingulated (–2.03)	Angular gyrus (–1.56)	Precuneus (–1.46)
lvPPA	Middle temporal gyrus (–2.28)^*	Amygdala (–2.12)^*	Posterior cingulate (–2.06)^*
PCA	Middle occipital gyrus (–3.24)	Inferior occipital gyrus (–3.10)	Precuneus (–2.92)
Behavioral/Dysexecutive AD	Middle temporal gyrus (–2.70)	Posterior cingulate (–2.20)	Angular gyrus (–1.97)
CBS-AD	Angular gyrus (–2.31)	Middle temporal gyrus (–1.55)	Inferior frontal gyrus (–1.5)

Data represents mean of FDG SUVR in MIM software regions-of-interest (ROIs). We present data for the 3 ROIs with the most hypometabolism (lower mean z-score) over 70 total ROIs. ^*For language variants of AD, which are known to be markedly asymmetrical, we present data for the most affected hemisphere (3 right-sided, 16 left-sided). AD, Alzheimer’s disease; bvFTD, behavioral variant of frontotemporal dementia; CBS, corticobasal syndrome; lvPPA, logopenic variant of primary progressive aphasia; PCA, posterior cortical atrophy.

Most discriminant ROIs in non-amnestic variants of AD

We calculated the difference between mean SUVR of non-amnestic variants of AD versus clinical variants of FTD for each of the 70 MIMneuro^® ROIs. The PCC was not among the 10 most discriminant regions for non-amnestic variants of AD (mean z-score –1.86 in non-amnestic AD versus –0.87 for clinical variants of FTD). The regions with the highest discrepancy between these groups were in fact the angular gyrus (z-score –1.87 versus 0.19), middle temporal gyrus (–1.73 versus –0.03), inferior temporal gyrus (–1.12 versus 0.48), parietal lobe (–0.88 versus 0.62) and precuneus (–1.37 versus 0.11) (See Table 3).

Table 3

FDG-PET regions of interest showing the highest metabolic discrepancy between non-amnestic variants of AD and clinical variants of FTD

Rank	Region of interest (ROI)	Clinical variants of FTD	Non-amnestic variants of AD	Discrepancy
		(Mean SUVR)	(Mean SUVR)
1	Angular Gyrus	0.19	–1.87	2.06
2	Middle Temporal Gyrus	–0.03	–1.73	1.70
3	Inferior Temporal Gyrus	0.48	–1.12	1.61
4	Parietal Lobe	0.62	–0.88	1.49
5	Precuneus	0.11	–1.37	1.48
6	Inferior Occipital Gyrus	0.57	–0.89	1.46
7	Superior Parietal Lobule	0.66	–0.79	1.45
8	Lateral Temporal Lobe	–0.07	–1.52	1.45
9	Temporal Lobe	–0.23	–1.44	1.21
10	Fusiform Gyrus	0.21	–0.93	1.14
14	Posterior cingulate cortex	–0.87	–1.86	0.99

Data represents mean of FDG SUVR in MIM software regions-of-interest (ROIs). We present data for the 10 ROIs (over 70 total ROIs) showing the greater discrepancy between non-amnestic AD variants and clinical variants of FTD. The PCC was not part of these 10 regions. For language variants of AD, which are known to be markedly asymmetrical, we present data for the most affected hemisphere (3 right-sided, 16 left-sided).

Sensitivity analyses

Since our dataset combines data from two dementia centers, we performed sensitivity analyses by comparing the demographic data and the mean SUVR of clinically relevant ROI of each clinical syndrome in the Quebec cohort versus the Madrid cohort. There was no statistically significant difference between demographic data and mean SUVR in any clinical cohort between the two cohorts.

DISCUSSION

In this study, we aimed to investigate the diagnostic value of PCC hypometabolism on FDG-PET in non-amnestic variants of AD. PCC hypometabolism is a well-known feature of typical amnestic AD and is established as an important discriminant feature or signature to distinguish AD from non-AD dementias. However, many features of non-amnestic variants of AD (relative sparing of the hippocampus [11, 12], cortical-predominant atrophy distribution [14], lower prevalence of APOE ɛ4 allele[10]) suggest that PCC hypometabolism may have a lower diagnostic value in these variants, especially since their main differential diagnosis (clinical variants of FTD) can exhibit variable degrees of PCC hypometabolism as well [57]. The main finding of the study is that PCC hypometabolism is not a salient feature of non-amnestic variants of AD—at least in their early stage—and does not have, in isolation, much discriminative value against FTD variants. Our study highlights that hypometabolism of the angular gyrus and middle/inferior temporal gyri are more discriminant than hypometabolism of the PCC to distinguish non-amnestic variants of AD from clinical variants of FTD.

Our findings are consistent with results from a recent study by Scheltens et al., which classified up to 40% of patients with bvFTD as exhibiting PCC hypometabolism both at visual read and using a data-driven approach [57]. Similarly, in previous studies examining patterns of regional hypometabolism in non-amnestic variants of AD, regions with the highest hypometabolism was to be the left temporo-parietal region in lvPPA, right parieto-occipital region in PCA, and the inferior and superior frontal regions bilaterally in early-onset AD [64, 65]. These studies suggested that non-amnestic variants of AD have more pathology in networks outside the DMN than in the DMN; however, these same studies showed some degree of pathology in the DMN, suggesting that the DMN might represent a common hub between these non-amnestic variants [13 , 65]. A subsequent study using connectivity analyses of functional MRI data found reduced connectivity in early-onset and non-amnestic AD patients in executive-control, language and higher visual networks compared with controls; with preserved or even increased connectivity in DMN sub-networks [66]. We conclude that prediction of underlying pathology in patients with young-onset, non-amnestic dementia should rely on the identification of syndrome-specific patterns of regional abnormalities (highlighted in Table 2) and anterior to posterior gradient [29], and not solely on the presence or absence of PCC hypometabolism.

Our study has limitations. First, non-amnestic variants of AD are uncommon, hence the sample size is small. Second, quantitative measurements of PCC hypometabolism can be influenced by factors such as local atrophy, ventriculomegaly and encephalomalacia from stroke. Nevertheless, quantitative measurements of PCC hypometabolism were consistent between two commonly used statistical sotfwares (MIMneuro^® and Hermes BRASS) and visual reads from trained nuclear medicine specialists. Furthermore, quantitative measurements allow for a data-driven interpretation of regional hypometabolism. Third, our cohort combines data from two dementia centers, using different scanners and slightly different protocols, which might induce bias in the results. However, sensitivity analyses did not detect any significant difference between patients included from Quebec compared to those included from Madrid; in each clinical syndrome, patients had similar age, disease stage (MMSE), and FDG-PET profile (similar mean SUVR in clinically relevant ROI). Fourth, we retrospectively selected patients who had undergone both an FDG-PET and an amyloid biomarker (CSF analysis or amyloid PET), which creates a selection bias. Indeed, patients with a clear clinical phenotype and regional FDG-PET hypometabolism do not systematically undergo amyloid biomarkers, hence included patients are those with the most complex, indeterminate or mixed phenotypes. Patients with typical, late-onset amnestic AD do not routinely undergo amyloid biomarkers for diagnostic purposes, therefore our study does not include a control group of typical amnestic AD. On the other hand, our study provides helpful guidance for the interpretation of FDG-PET in these difficult cases. Fifth, FDG-PET are often performed early in the disease as a diagnostic procedure. It is likely that PCC hypometabolism would be more prevalent in later stages of non-amnestic variants of AD, as disease spreads though the DMN [13, 64].

We conclude that PCC hypometabolism is not a discriminant feature to distinguish non-amnestic variants of AD from clinical variants of FTD and should be interpreted with caution in patients with young-onset, non-amnestic dementia.

Footnotes

ACKNOWLEDGMENTS

Authors wish to thank the Canadian Institutes of Health Research (CIHR) for the Vanier Graduate Scholarship.

Authors’ disclosures available online ().

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