Dataset Information

Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of A? species.

ABSTRACT: Positron emission tomography (PET) neuroimaging with the Pittsburgh Compound_B (PiB) is widely used to assess amyloid plaque burden. Standard quantification approaches normalize PiB-PET by mean cerebellar gray matter uptake. Previous studies suggested similar pons and white-matter uptake in Alzheimer's disease (AD) and healthy controls (HC), but lack exhaustive comparison of normalization across the three regions, with data-driven diagnostic classification. We aimed to compare the impact of distinct reference regions in normalization, measured by data-driven statistical analysis, and correlation with cerebrospinal fluid (CSF) amyloid ? (A?) species concentrations. 243 individuals with clinical diagnosis of AD, HC, mild cognitive impairment (MCI) and other dementias, from the Biomarkers for Alzheimer's/Parkinson's Disease (BIOMARKAPD) initiative were included. PiB-PET images and CSF concentrations of A?₃₈, A?₄₀ and A?₄₂ were submitted to classification using support vector machines. Voxel-wise group differences and correlations between normalized PiB-PET images and CSF A? concentrations were calculated. Normalization by cerebellar gray matter and pons yielded identical classification accuracy of AD (accuracy-96%, sensitivity-96%, specificity-95%), and significantly higher than A? concentrations (best accuracy 91%). Normalization by the white-matter showed decreased extent of statistically significant multivoxel patterns and was the only method not outperforming CSF biomarkers, suggesting statistical inferiority. A?₃₈ and A?₄₀ correlated negatively with PiB-PET images normalized by the white-matter, corroborating previous observations of correlations with non-AD-specific subcortical changes in white-matter. In general, when using the pons as reference region, higher voxel-wise group differences and stronger correlation with A?₄₂, the A?₄₂/A?₄₀ or A?₄₂/A?₃₈ ratios were found compared to normalization based on cerebellar gray matter.

SUBMITTER: Oliveira F

PROVIDER: S-EPMC6120605 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of Aβ species.

Oliveira Francisco F Leuzy Antoine A Castelhano João J Chiotis Konstantinos K Hasselbalch Steen Gregers SG Rinne Juha J Mendonça Alexandre A Otto Markus M Lleó Alberto A Santana Isabel I Johansson Jarkko J Anderl-Straub Sarah S Arnim Christine C Beer Ambros A Blesa Rafael R Fortea Juan J Sanna-Kaisa Herukka H Portelius Erik E Pannee Josef J Zetterberg Henrik H Blennow Kaj K Moreira Ana P AP Abrunhosa Antero A Nordberg Agneta A Castelo-Branco Miguel M

NeuroImage. Clinical 20180819

Positron emission tomography (PET) neuroimaging with the Pittsburgh Compound_B (PiB) is widely used to assess amyloid plaque burden. Standard quantification approaches normalize PiB-PET by mean cerebellar gray matter uptake. Previous studies suggested similar pons and white-matter uptake in Alzheimer's disease (AD) and healthy controls (HC), but lack exhaustive comparison of normalization across the three regions, with data-driven diagnostic classification. We aimed to compare the impact of dist ...[more]

PMID: 30186764

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Project description:The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N?=?34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (18F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global 18F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVrCT, SUVrMRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVrCT and SUVrMRI global uptake scores showed negligible differences (mean?±?sd 0.01?±?0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1?mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis.

Project description:The relationships between clinical phenotype, β-amyloid (Aβ) deposition and neurodegeneration in Alzheimer's disease (AD) are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of Aβ deposition measured by ((11)C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA-AA) who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27), a language-predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9). All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA), a method that enables joint analyses of multimodal imaging data. The relationships between PET components and clinical group were examined using a Receiver Operator Characteristic approach, including age, gender, education and apolipoprotein E ε4 allele carrier status as covariates. Results of the first set of analyses independently examining the relationship between components from each modality and clinical group showed three significant components for FDG: a left inferior frontal and temporoparietal component associated with AD-language (area under the curve [AUC] 0.82, p = 0.011), and two components associated with AD-visuospatial (bilateral occipito-parieto-temporal [AUC 0.85, p = 0.009] and right posterior cingulate cortex [PCC]/precuneus and right lateral parietal [AUC 0.69, p = 0.045]). The AD-memory associated component included predominantly bilateral inferior frontal, cuneus and inferior temporal, and right inferior parietal hypometabolism but did not reach significance (AUC 0.65, p = 0.062). None of the PiB components correlated with clinical group. Joint analysis of PiB and FDG with pICA revealed a correlated component pair, in which increased frontal and decreased PCC/precuneus PiB correlated with decreased FDG in the frontal, occipital and temporal regions (partial r = 0.75, p < 0.0001). Using multivariate data analysis, this study reinforced the notion that clinical phenotype in AD is tightly linked to patterns of glucose hypometabolism but not amyloid deposition. These findings are strikingly similar to those of univariate paradigms and provide additional support in favor of specific involvement of the language network, higher-order visual network, and default mode network in clinical variants of AD. The inverse relationship between Aβ deposition and glucose metabolism in partially overlapping brain regions suggests that Aβ may exert both local and remote effects on brain metabolism. Applying multivariate approaches such as pICA to multimodal imaging data is a promising approach for unraveling the complex relationships between different elements of AD pathophysiology.

Project description:UNLABELLED:The primary goal of this study was to assess the suitability of (11)C-Pittsburgh compound B ((11)C-PiB) blood-brain barrier delivery (K1) and relative delivery (R1) parameters as surrogate indices of cerebral blood flow (CBF), with a secondary goal of directly examining the extent to which simplified uptake measures of (11)C-PiB retention (amyloid-? load) may be influenced by CBF, in a cohort of controls and patients with mild cognitive impairment (MCI) and Alzheimer disease (AD). METHODS:Nineteen participants (6 controls, 5 AD, 8 MCI) underwent MR imaging, (15)O-water PET, and (11)C-PiB PET in a single session. Fourteen regions of interest (including cerebellar reference region) were defined on MR imaging and applied to dynamic coregistered PET to generate time-activity curves. Multiple analysis approaches provided regional (15)O-water and (11)C-PiB measures of delivery and (11)C-PiB retention that included compartmental modeling distribution volume ratio (DVR), arterial- and reference-based Logan DVR, simplified reference tissue modeling 2 (SRTM2) DVR, and standardized uptake value ratios. Spearman correlation was performed among delivery measures (i.e., (15)O-water K1 and (11)C-PiB K1, relative K1 normalized to cerebellum [Rel-K1-Water and Rel-K1-PiB], and (11)C-PiB SRTM2-R1) and between delivery measures and (11)C-PiB retention, using the Bonferroni method for multiple-comparison correction. RESULTS:Primary analysis showed positive correlations (? ?0.2-0.5) between (15)O-water K1 and (11)C-PiB K1 that did not survive Bonferroni adjustment. Significant positive correlations were found between Rel-K1-Water and Rel-K1-PiB and between Rel-K1-Water and (11)C-PiB SRTM2-R1 (? ?0.5-0.8, P < 0.0036) across primary cortical regions. Secondary analysis showed few significant correlations between (11)C-PiB retention and relative (11)C-PiB delivery measures (but not (15)O-water delivery measures) in primary cortical areas that arose only after accounting for cerebrospinal fluid dilution. CONCLUSION:(11)C-PiB SRTM2-R1 is highly correlated with regional relative CBF, as measured by (15)O-water K1 normalized to cerebellum, and cross-sectional (11)C-PiB retention did not strongly depend on CBF across primary cortical regions. These results provide further support for potential dual-imaging assessments of regional brain status (i.e., amyloid-? load and relative CBF) through dynamic (11)C-PiB imaging.

Dataset Information

Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of A? species.

Publications

Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of Aβ species.

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