ComBat-Centiloid: A Calibration-Free Method for Quantifying Centiloid Values in Amyloid PET Imaging

Chapter 19 - The neuroscience of dementia: diagnosis and management methods of amyloid positron emission tomography imaging and its application to the Alzheimer's disease spectrum

ObjectiveThe purpose of this study was to validate the concordance of visual ratings of [18F] flutemetamol amyloid positron emission tomography (PET) images and to investigate the correlation between the agreement of each rater and the Centiloid (CL) scale.MethodsA total of 192 participants, clinically classified as cognitively normal (CN) (n = 59), mild cognitive impairment (MCI) (n = 65), Alzheimer’s disease (AD) (n = 55), or non-AD dementia (n = 13), participated in this study. Three experts conducted visual ratings of the amyloid PET images for all 192 patients, assigning a confidence level to each rating on a three-point scale (certain, probable, or neither). The positive or negative determination of amyloid PET results was made by majority vote. The CL value was calculated using the CapAIBL pipeline.ResultsOverall, 101 images were determined to be positive, and 91 images were negative. Of the 101 positive images, the three raters were in complete agreement for 92 images and in disagreement for 9 images. Of the 91 negative images, the three raters were in complete agreement for 75 images and in disagreement for 16 images. Interrater reliability among the three experts was particularly high, with both Fleiss’ kappa and Conger’s kappa measuring 0.83 (0.76–0.89). The CL values of the unanimous positive group were significantly greater than those of the other groups, whereas the CL values of the unanimous negative group were significantly lower than those of the other groups. Images with rater disagreement had intermediate CLs. In cases with a high confidence level, the positive or negative visual ratings were in almost complete agreement. However, as confidence levels decreased, experts’ visual ratings became more variable. The lower the confidence level was, the greater the number of cases with disagreement in the visual ratings.ConclusionThree experts independently rated 192 amyloid PET images, achieving a high level of interrater agreement. However, in patients with intermediate amyloid accumulation, visual ratings varied. Therefore, determining positive and negative decisions in these patients should be performed with caution.

Amyloid positron emission tomography (PET) imaging with florbetapir 18F (18F-AV-45) allows in vivo assessment of cerebral amyloid load and can be used in the evaluation of progression of Alzheimer's disease (AD) and other dementias associated with b-amyloid. However, cortical amyloid deposition can occur in healthy cases, as well as in patients with AD and quantification of cortical amyloid burden can improve the 18F-AV-45 PET imaging evaluations. The quantification is mostly performed by cortical-to-cerebellum standardized uptake value ratio (SUVr). The aim of our study was to compare two methods for SUVr calculations in amyloid florbetapir 18F PET brain imaging. In amyloid florbetapir 18F PET brain imaging study, we imaged 42 cases with the mean age of 72.6 ± 9.9 (mean ± standard deviation). They were imaged on different PET/computed tomography systems with 369.0 ± 34.2 kBq of 18F florbetapir. Data were reconstructed using the vendor's reconstruction software. Corresponding magnetic resonance imaging (MRI) data were retrieved, and matched PET and MRI data were transferred to a common platform. Two methods were used for the calculation of the ratio of cortical-to-cerebellar signal (SUVr). One method was based on the MIM Software Inc., Version 6.4 software and only uses PET data. The second approach used the PMOD Neuro tool (version 3.5). This approach utilizes PET and corresponding MRI data (preferably T1-weighted) for better brain segmentation. For all the 42 cases, the average SUVr values for MIM and PMOD applications were 1.24 ± 0.26 and 1.22 ± 0.25, respectively, with a mean difference of 0.02 ± 0.15. The repeatability coefficient was 0.15 (12.3% of the mean). The Spearman's rank correlation coefficient was very high, r = 0.96. For amyloid-negative cases, the average SUVr values were lower than all group SUVr average values, 0.96 ± 0.07 and 1.00 ± 0.09, for MIM and PMOD applications, respectively. A mean difference was 0.04 ± 0.12, the repeatability coefficient was 0.12 (12.9% of the mean) and the Spearman's rank correlation coefficient was modest, r = 0.55. For amyloid-positive patients, the average SUVr values were higher than the same all group values, 1.34 ± 0.16 and 1.35 ± 0.20, respectively, with a mean difference of 0.01 ± 0.16. The repeatability coefficient was 0.16 (11.9% of the mean). The Spearman's rank correlation coefficient was high, r = 0.93. Our results indicated that the SUVr values derived using MIM and PMOD Neuro are effectively interchangeable and well correlated. However, PET template-based quantification (MIM approach) is clinically friendlier and easier to use. MRI template-based quantification (PMOD Neuro) better delineates different regions of the brain, can be used with any tracer, and therefore is more suitable for research.

BackgroundThe diagnosis of Alzheimer’s disease (AD) has been greatly improved due to the fundamental role of positron emission tomography (PET) imaging. Also, predicting PET brain imaging alterations using blood‐based biomarkers is of high interest. This way, integrating PET and omics data can provide new insights into AD pathophysiology. Here, we aimed to develop a framework that combines blood transcriptomics with PET data. We hypothesized that integrating omics and PET data will help advance our understanding of AD neurobiology and may reveal relevant new peripheral biomarkers.Method[18F]Fluorodeoxyglucose ([18F]FDG)‐PET imaging and transcriptomics data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Microarray gene expression profiling from blood samples of 99 Cognitively Unimpaired (CU) and 218 Cognitively Impaired (CI) individuals were submitted to differential expression (DE) analysis. Differentially expressed genes (DEGs) were submitted to Gene Ontology (GO) analysis. The GO terms were clustered by semantic similarity using the GOSemSim method for biological processes ontology. All computations analyses were performed in the R statistical environment. Gene clusters obtained in the previous step were selected to undergo integration with [18F]FDG‐PET images using voxel‐wise generalized linear regression (GLR) models adjusted for age, gender, years of education, and APOEε4 (RMINC package).ResultThe GO semantic similarity resulted in 16 GO clusters. The voxel‐wise correlation between [18F]FDG‐PET and GO clusters resulted in t‐statistical maps. Afterwards, only statistically significant correlated voxels (uncorrected t‐value > 2.0) were retained (Fig. 1A). A cluster related to the regulation of protein serine/threonine kinase activity showed a strong correlation with the [18F]FDG‐PET signal in the brain. At the voxel level, this cluster has a positive correlation in the precuneus’ gray matter (95.7% left, 81.1% right) as well in the medial frontal gyrus (79.6% left, 44.66% right) and cingulate region (76.9% left, 56.4% right) (Fig. 1B).ConclusionWe identified several peripheral biological processes associated with [18F]FDG‐PET metabolism in the brain of CU and CI individuals. Furthermore, the prominent enrichment of protein serine/threonine kinase activity‐related genes highlights its potential as novel AD biomarkers and as a proxy of [18F]FDG‐PET metabolism.

Extracellular β-amyloid plaques and intracellular neurofibrillary tau tangles are the primary hallmarks of Alzheimer's disease. β-Amyloid pathology can be directly quantified by positron emission tomography imaging or indirectly by measuring the decrease of cerebrospinal fluid β-amyloid42/β-amyloid40 ratio. Although these two β-amyloid biomarkers may be considered interchangeable, they sometimes show discordance, particularly in early stage of Alzheimer's disease. Individuals with cerebrospinal fluid β-amyloid positive only or β-amyloid positron emission tomography positive only may be at early amyloidosis stage compared to those who are cerebrospinal fluid β-amyloid negative and β-amyloid positron emission tomography negative orcerebrospinal fluid β-amyloid positive and β-amyloid positron emission tomography positive. Besides, β-amyloid pathology may play an initiating role in Alzheimer's disease onset, leading to subsequent tau increases. However, it is still unclear whether individuals with different β-amyloid pathways have distinct spatial patterns of cortical tau tangles in early amyloidosis stage. In this study, we analyzed 238 cognitively unimpaired and 77 mild cognitive impairment individuals with concurrent (interval of acquisition <1 year) 18F-flortaucipir tau positron emission tomography, β-amyloid (18F-florbetapir or 18F-florbetaben) positron emission tomography and cerebrospinal fluid β-amyloid42 and β-amyloid40 and cerebrospinal fluid p-Tau181 and divided them into four different cerebrospinal fluid/positron emission tomography groups based on the abnormal status of cerebrospinal fluid β-amyloid42/β-amyloid40 (cerebrospinal fluid±) and β-amyloid positron emission tomography (±). We determined the cortical regions with significant tau elevations of different cerebrospinal fluid/positron emission tomography groups and investigated the region-wise and voxel-wise associations of tau positron emission tomography images with cerebrospinal fluid β-amyloid42/β-amyloid40, β-amyloid positron emission tomography and cerebrospinal fluid p-Tau/β-amyloid40 in early (cerebrospinal fluid positive/positron emission tomography negative and cerebrospinal fluid negative/positron emission tomography positive) and late (cerebrospinal fluid positive/positron emission tomography positive) amyloidosis stages. By compared to the cerebrospinal fluid negative/positron emission tomography negative individuals (Ref) without evidence of tau increase measured by cerebrospinal fluid or positron emission tomography, cerebrospinal fluid positive/positron emission tomography negative individuals showed higher tau in entorhinal but not in BraakIII/IV and BraakV/VI, whereas cerebrospinal fluid negative/positron emission tomography positive individuals had significant tau elevations in BraakV/VI but not in entorhinal and BraakIII/IV. In contrast, cerebrospinal fluid positive/positron emission tomography positive individuals showed significant tau increases in all the cortical regions than the Ref group. The voxel-wise analyses provided further evidence that lower cerebrospinal fluid β-amyloid42/β-amyloid40 was associated with higher tau in entorhinal, whilst higher β-amyloid positron emission tomography was related to higher tau in BraakV/VI regions in early amyloidosis stage. Both lower cerebrospinal fluid β-amyloid42/β-amyloid40 and higher β-amyloid positron emission tomography were correlated with tau aggregation in all the Braak stages regions in late amyloidosis stage. These findings provide novel insights into the spatial patterns of cortical tau tangles in different amyloidosis stages of Alzheimer's disease, suggesting cerebrospinal fluid β-amyloid and β-amyloid positron emission tomography discordant groups may have distinct characteristics of cortical tau tangles in early amyloidosis stage.

Amyloid positron emission tomography (PET) is an important noninvasive method for detecting amyloid burden in Alzheimer's disease (AD) patients. As amyloid PET images have limited anatomical information, magnetic resonance (MR) imaging is usually acquired to perform reliable spatial normalization needed for large-scale analysis. This work proposed and evaluated the performance of new MR-free spatial normalization methods using a perfusion-like template for amyloid PET imaging. Amyloid PET and MR images were collected in 35 subjects (cohort 1: 8 AD patients and 6 controls; cohort 2: 15 AD patients and 6 controls). Three ligand-related templates (AD, control, mixed group) and a perfusion-like template (pAV-45) from early time frames of amyloid PET images were constructed from cohort 1. The variations of (18)F-AV-45 standardized uptake value ratios (SUVRs) among AD patients, controls, and all subjects were tested with repeated two-way (template × brain region) analysis of variance (ANOVA) in cohort 2. (18)F-AV-45 SUVRs by region of interest analysis and voxelwise analysis between MR-based and MR-free approaches were compared and correlated to clinical and image parameters. Effect size (group mean SUVR difference between AD and control/standard deviation) was also evaluated for each template method. Significantly different (18)F-AV-45 SUVRs between MR-free spatial normalization and MR-based reference images were found among AD patients, controls, and all subjects by the effect of template and brain regions. The highest correlation (r=0.991) of (18)F-AV-45 SUVR to MR-based reference was found in the pAV-45 group. The SUVR percentage difference to MR-based reference showed the least variation and bias (control: -1.31±3.47 %; AD: -0.36±2.50 %) in the pAV-45 group as well. The voxelwise analysis showed the smallest t statistic value in pAV-45 followed by mixed, control, and AD groups when compared to MR-based reference images. Moreover, an overall larger effect size but compatible to that of MR-based reference result was observed in the pAV-45 group as compared to those of the other MR-free template. The novel MR-free template based on the early-phase perfusion images pAV-45 approach for amyloid imaging showed significantly better performance in quantitation accuracy, effect size, and stability when compared with other MR-free PET templates and thus has potential for large-scale clinical applications.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

The correct interpretation of clinical positron emission tomography (PET) data depends largely on the physical limits of the PET scanner. The partial volume effect (PVE) is related to the size of the studied object compared to the spatial resolution. It represents one of the most important limiting factors in quantitative data analysis. This effect is increased in the case of atrophy, as in patients with Alzheimer disease (AD), and it influences measurement of the metabolic reduction generally seen in cerebral degeneration. In this case, interpretation can be biased, because cortical activity will be underestimated due to the atrophy. In general, anatomical images of AD patients have shown diffuse atrophy, while PET studies have found widespread hypometabolism affecting the parietal and temporal lobes. Although hypometabolic areas usually correspond to atrophic regions, they also occur without such changes. Thus, the aim is to differentiate authentic hypometabolism (decrease of glucose consumption per unit volume of gray matter) from that due to PVE from atrophy (cell loss). Consequently, we are using a method for three-dimensional (3D) correction of human PET data with 3D magnetic resonance imaging (MRI). We measured atrophy and metabolism by using both T1-weighted MR images and high and medium resolution PET scans. We injected 12 patients and controls with [18F]fluorodeoxyglucose for glucose consumption measurements. Atrophy was estimated in the following way. We isolated the cerebral structures, using a segmentation technique on the MRI scans, into gray matter (GM), white matter, and cerebrospinal fluid. We superimposed the PET images onto the MR images to obtain anatomo-functional correlations. We degraded the segmented MR images to the resolution of the PET images by a convolution process to create a PET image correction map. We corrected the metabolic PET data for the PVE. We studied the cerebral metabolic rate of glucose in the GM where metabolic variation is the most relevant to AD. By dealing with problems relating to the sensitivity to the segmentation and to the PET-MRI coregistration, computation of MRI convolution processes provided the degree of PVE on a pixel-by-pixel basis, allowing correction of hypometabolisms contained in GM PET values. Global cortical metabolism increased after correction for PVE by, on average, 29 and 24% for tomographs acquired with medium (TTV03 LETI) and high (ECAT 953B CTI/Siemens) resolution, respectively, whereas the cortical metabolism increased by 75 and 65% for the respective tomographs in AD patients. The difference of metabolism between scans after correction for PVE was less than before correction, decreasing from 31 to 17%. This difference was most marked in the frontal and temporal lobes. Fusion imaging allowed correction for PVE in metabolic data using 3D MRI and determination of whether a change in the apparent radiotracer concentration in PET data reflected an alteration in GM volume, a change in radiotracer concentration per unit volume of GM, or both.

A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β-amyloid (Aβ) and tau PET image data across multiple scanners and tracers. We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B-PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre-processing, refacing, magnetic resonance imaging (MRI)/PET co-registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. Measurements from 10,105 cross-sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. The B-PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community.

Current theory suggests that β-amyloid accumulation may be an early step in the cascade that leads to cognitive impairment in Alzheimer's disease. β-Amyloid targeted positron emission tomography (PET) imaging potentially provides a direct, relatively noninvasive estimate of brain β-amyloid burden. This has recently been supported by demonstration that amyloid plaque binding on PET was strongly correlated with brain β-amyloid burden at autopsy. Additionally, there is growing consensus that PET imaging can identify subjects with elevated β-amyloid burden, even at early stages of disease. Finally, preliminary evidence suggests that abnormal β-amyloid accumulation, as evidenced by PET imaging, has implications for both present nd future cognitive performance. Although large longitudinal studies like the ongoing ADNI trial will be required for definitive evaluation, present data suggest that PET amyloid imaging has the potential to promote earlier and more specific diagnosis of dementia.

The co-occurrence of both Alzheimer disease (AD) pathology and vascular brain injury (VBI) is very common, especially amongst the oldest of old. In neuropathologic studies, the prevalence of AD, VBI, and mixed AD/VBI lesions ranks ahead of Lewy bodies and hippocampal sclerosis. In the modern era of structural magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) imaging, this review examines 1) the prevalence of mixed AD and VBI pathology, 2) the significance of these pathologies for cognitive impairment (AD and vascular cognitive impairment (VCI)), and 3) the diagnosis and treatment of mixed AD/VCI. Although epidemiologic studies report that vascular risk factors for arteriosclerosis increase the risk of incident AD, both autopsy and amyloid PET studies indicate that AD and VBI contribute additively, but independently, to the risk of dementia. The literature confirms the malignancy of AD and highlights the adverse effects of microinfarcts on cognitive function. For the clinical diagnosis of mixed AD/VCI, the presence of AD can be recognized by neuropsychological profile, structural imaging, cerebrospinal fluid biomarkers, and glucose PET and amyloid PET imaging. The diagnosis of VBI, however, still hinges predominantly on the structural MRI findings. Severe amnesia and atrophy of the hippocampus are characteristic of early AD, whereas the cognitive profile for VCI is highly variable and dependent on size and location of VBI. The cognitive profile of mixed AD/VBI is dominated by AD. With the notable exception of microinfarcts (which elude in vivo detection), infarcts, hemorrhages, and white matter hyperintensities on structural MRI currently represent the best markers for the presence VBI. Better markers that reflect the health and reactivity of intracerebral blood vessels are needed. For prevention and treatment, the type of underlying cerebrovascular disease (for example, arteriosclerosis or cerebral amyloid angiopathy) should be considered. It is likely that reduction of vascular risk factors for arteriosclerosis can significantly reduce vascular contributions to mixed dementia.

Positron Emission Tomography—the Promise of Metabolic Imaging

Amyloid positron emission tomography (PET) imaging is being investigated as a screening tool to identify amyloid-positive patients as an enrichment strategy for Alzheimer disease (AD) clinical trial enrollment. In a multicenter, phase 1b trial, patients meeting clinical criteria for prodromal or mild AD underwent florbetapir PET scanning at screening. PET, magnetic resonance imaging, and coregistered PET/magnetic resonance imaging scans were reviewed by 2 independent readers and binary visual readings tabulated. Semiquantitative values of cortical to whole cerebellar standard uptake value ratios were computed (threshold 1.10). Of 278 patients with an evaluable PET scan, 170 (61%) and 185 (67%) were amyloid-positive by visual reading and quantitative analysis, respectively; 39% were excluded from the study due to an amyloid-negative scan based on visual readings. More ApoE ε4 carriers than noncarriers were amyloid-positive (80% vs. 43%). Comparison of visual readings with quantitative results identified 21 discordant cases (92% agreement). Interreader and intrareader agreements from visual readings were 98% and 100%, respectively. Amyloid PET imaging is an effective and feasible screening tool for enrollment of amyloid-positive patients with early stages of AD into clinical trials.

Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

BackgroundAnalyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β‐Amyloid (Aβ) deposition in Alzheimer’s patients requires a lot of time and effort from physicians and also the variation of each interpreter may differ. For this reason, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to predict the Aβ‐positive and Aβ‐negative status from brain amyloid PET images.MethodA total of 154 cognitively impaired subjects were used in this study. The 18F‐florbetaben (18F‐FBB) PET was administered on all participants and the criteria for differentiating Aβ‐positive and Aβ‐negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 52 augmented 18F‐FBB PET images per subject from two classes: Aβ‐positive and Aβ‐negative states that were determined based on the BAPL scores.ResultThe binary prediction of the model average performance matrices was evaluated after 40 epochs of 5 trial‐based on test datasets. The model accuracy for predicting Aβ‐positivity and Aβ‐negativity was 82.00±0.03 in the test dataset. The sensitivity and specificity were 97.00±0.02 and 97.00±0.02 with an area under the curve (AUC) of 90.00±0.03.ConclusionBased on this study, the designed CNN model has the potential to be used clinically for screening amyloid PET images.