Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging.

Search for Clinical Markers Could? Transform Alzheimer's Drug Research

In our aging society, diseases in the elderly come more and more into focus. An important issue in research is Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) with their causes, diagnosis, treatment, and disease prediction. We applied the Brain Age Gap Estimation (BrainAGE) method to examine the impact of the Apolipoprotein E (APOE) genotype on structural brain aging, utilizing longitudinal magnetic resonance image (MRI) data of 405 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We tested for differences in neuroanatomical aging between carrier and non-carrier of APOE ε4 within the diagnostic groups and for longitudinal changes in individual brain aging during about three years follow-up. We further examined whether a combination of BrainAGE and APOE status could improve prediction accuracy of conversion to AD in MCI patients. The influence of the APOE status on conversion from MCI to AD was analyzed within all allelic subgroups as well as for ε4 carriers and non-carriers. The BrainAGE scores differed significantly between normal controls, stable MCI (sMCI) and progressive MCI (pMCI) as well as AD patients. Differences in BrainAGE changing rates over time were observed for APOE ε4 carrier status as well as in the pMCI and AD groups. At baseline and during follow-up, BrainAGE scores correlated significantly with neuropsychological test scores in APOE ε4 carriers and non-carriers, especially in pMCI and AD patients. Prediction of conversion was most accurate using the BrainAGE score as compared to neuropsychological test scores, even when the patient’s APOE status was unknown. For assessing the individual risk of coming down with AD as well as predicting conversion from MCI to AD, the BrainAGE method proves to be a useful and accurate tool even if the information of the patient’s APOE status is missing.

Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical β-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r=-0.68) compared to cognitive (r=-0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.

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

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

To quantify and investigate the interactions between multimodal MRI/positron emission tomography (PET) imaging metrics in elderly patients with early Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls. Thirteen early AD, 17 MCI patients, and 14 age-matched healthy aging controls from the Alzheimer's Disease Neuroimaging Initiative database were selected based on availability of data. Default mode network (DMN) functional connectivity and fractional amplitude of low frequency fluctuation (fALFF) were obtained for resting state functional MRI (RS-fMRI). White matter lesion load (WMLL) was quantified from MRI T2-weighted FLAIR images. Amyloid deposition with PET [(18)F]-Florbetapir tracer and metabolism of glucose by means of [(18)F]-fluoro-2-deoxyglucose (FDG) images were quantified using ratio of standard uptake values (rSUV). Whole-brain WMLL and amyloid deposition were significantly higher (P < 0.005) in MCI and AD patients compared with controls. RS-fMRI results showed significantly reduced (corrected P < 0.05) DMN connectivity and altered fALFF activity in both MCI and AD groups. FDG uptake results showed hypometabolism in AD and MCI patients compared with controls. Correlations (P < 0.05) were found between WMLL and amyloid load, FDG uptake and amyloid load, as well as between amyloid load (rSUV) and fALFF. Our quantitative results of four MRI and PET imaging metrics (fALFF/DMN, WMLL, amyloid, and FDG rSUV values) agree with published values. Significant correlations between MRI metrics, including WMLL/functional activity and PET amyloid load suggest the potential of MRI and PET-based biomarkers for early detection of AD.

High Activities of BACE1 in Brains with Mild Cognitive Impairment

18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and 18F-florbetapir PET are approved neuroimaging biomarkers for the Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study aims to compare the efficacy of 18F-FDG and 18F-florbetapir PET at evaluating the cognitive performance of patients with AD, MCI, and normal controls (NC). 63 subjects (36 male/27 female, mean age = 68.3) including 19 AD, 23 MCI, and 21 NC underwent 18F-FDG and 18F-florbetapir PET imaging. A global quantification approach was applied on supra-tentorial, frontal, parieto-occipital, temporal, and cerebellar brain regions by calculating the global SUVmean ratios (GSUVr) as the weighted average of all regional SUVmean. 18F-FDG and 18F-florbetapir GSUVr of each region were subsequently correlated with the Mini-Mental State Examination (MMSE). Subjects were studied in five categories as NC, MCI patients, AD patients, MCI and AD patients grouped together (MCI/AD), and a group including all the subjects (NC/MCI/AD). Both 18F-FDG and 18F-florbetapir could successfully detect subjects with dementia (p < 0.001). Studied in all regions and groups, the correlation analysis of 18F-FDG GSUVr with MMSE scores was significant in more regions and groups compared to that of 18F-florbetapir. We also demonstrated that the correlation of 18F-FDG GSUVr with MMSE is stronger than that of 18F-florbetapir in the supra-tentorial and temporal regions. This study reveals how 18F-FDG-PET global quantification is a superior indicator of cognitive performance in AD and MCI patients compared to 18F-florbetapir PET. Accordingly, we still recommend 18F-FDG-PET over amyloid imaging in the evaluation for AD and MCI.

Enrichment through biomarkers in clinical trials of Alzheimer's drugs in patients with mild cognitive impairment

Tau pathology mediated the plasma biomarkers and cognitive function in patients with mild cognitive impairment

It has been suggested that mild cognitive impairment (MCI) patients deteriorate faster than the healthy elderly population and have an increased risk of developing dementia. Certain blood molecular biomarkers have been identified as prognostic markers in Alzheimer's disease (AD). The present study was aimed to assess the status of the platelet amyloid precursor protein (APP) metabolism in MCI and AD subjects and establish to what extent any variation could have a prognostic value suggestive of predictive AD in MCI patients. Thirty-four subjects diagnosed with MCI and 45 subjects with AD were compared to 28 healthy elderly individuals for assessing for protein levels of APP, β-APP cleaving enzyme 1 (BACE1), presenilin 1 (PS1) and a disintegrin and metalloproteinase-10 (ADAM-10) by western blot, and for the enzyme activities of BACE1 and γ-secretase by using specific fluorogenic substrates, in samples of platelets. A similar pattern in the healthy elderly and MCI patients was found for BACE1 and PS1 levels. A reduction of APP levels in MCI and AD patients compared with healthy elderly individuals was found. Augmented levels of ADAM-10 in both MCI and AD were displayed in comparison with age-matched control subjects. The ratio ADAM-10/BACE1 was higher for the MCI group versus AD group. Whereas BACE1 and PS1 levels were only increased in AD regarding to controls, BACE1 and γ-secretase activities augmented significantly in both MCI and AD groups. Finally, differences and similarities between MCI and AD patients were observed in several markers of platelet APP processing. Larger sample sets from diverse populations need to be analyzed to define a signature for the presence of MCI or AD pathology and to early detect AD at the MCI stage.

Successively predicting whether mild cognitive impairment patients will progress to Alzheimer's disease is of significant clinical relevance. This ability may provide information that can be leveraged by emerging intervention approaches and thus mitigate some of the negative effects of the disease. Neuroimaging biomarkers have gained some attention in recent years and may be useful in predicting the conversion of mild cognitive impairment to Alzheimer's disease. We implemented a novel multi-modal approach that allowed us to evaluate the potential of different imaging modalities, both alone and in different degrees of combinations, in predicting the conversion to Alzheimer's disease of mild cognitive impairment patients. We applied this approach to the imaging data from the Alzheimer's Disease Neuroimaging Initiative that is a multi-modal imaging dataset comprised of MRI, Fluorodeoxyglucose PET, Florbetapir PET and diffusion tensor imaging. We included a total of 480 mild cognitive impairment patients that were split into two groups: converted and stable. Imaging data were segmented into atlas-based regions of interest, from which relevant features were extracted for the different imaging modalities and used to construct machine-learning models to classify mild cognitive impairment patients into converted or stable, using each of the different imaging modalities independently. The models were then combined, using a simple weight fusion ensemble strategy, to evaluate the complementarity of different imaging modalities and their contribution to the prediction accuracy of the models. The single-modality findings revealed that the model, utilizing features extracted from Florbetapir PET, demonstrated the highest performance with a balanced accuracy of 83.51%. Concerning multi-modality models, not all combinations enhanced mild cognitive impairment conversion prediction. Notably, the combination of MRI with Fluorodeoxyglucose PET emerged as the most promising, exhibiting an overall improvement in predictive capabilities, achieving a balanced accuracy of 78.43%. This indicates synergy and complementarity between the two imaging modalities in predicting mild cognitive impairment conversion. These findings suggest that β-amyloid accumulation provides robust predictive capabilities, while the combination of multiple imaging modalities has the potential to surpass certain single-modality approaches. Exploring modality-specific biomarkers, we identified the brainstem as a sensitive biomarker for both MRI and Fluorodeoxyglucose PET modalities, implicating its involvement in early Alzheimer's pathology. Notably, the corpus callosum and adjacent cortical regions emerged as potential biomarkers, warranting further study into their role in the early stages of Alzheimer's disease.

Use of an FDG-PET derived hypometabolic convergence index enrichment strategy to reduce sample sizes in Alzheimer's disease clinical trials: findings from the Alzheimer's Disease Neuroimaging Initiative (ADNI)

Recent developments of tau Positron Emission Tomography (PET) allows assessment of regional neurofibrillary tangles (NFTs) deposition in human brain. Among the tau PET molecular probes, 18F-AV1451 is characterized by high selectivity for pathologic tau aggregates over amyloid plaques, limited non-specific binding in white and gray matter, and confined off-target binding. The objectives of the study are (1) to quantitatively characterize regional brain tau deposition measured by 18F-AV1451 PET in cognitively normal older adults (CN), mild cognitive impairment (MCI), and AD participants; (2) to evaluate the correlations between cerebrospinal fluid (CSF) biomarkers or Mini-Mental State Examination (MMSE) and 18F-AV1451 PET standardized uptake value ratio (SUVR); and (3) to evaluate the partial volume effects on 18F-AV1451 brain uptake.Methods: The study included total 115 participants (CN = 49, MCI = 58, and AD = 8) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed 18F-AV1451 PET images, structural MRIs, and demographic and clinical assessments were downloaded from the ADNI database. A reblurred Van Cittertiteration method was used for voxelwise partial volume correction (PVC) on PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. The parametric images of 18F-AV1451 SUVR relative to cerebellum were calculated. The ROI SUVR measurements from PVC and non-PVC SUVR images were compared. The correlation between ROI 18F-AV1451 SUVR and the measurements of MMSE, CSF total tau (t-tau), and phosphorylated tau (p-tau) were also assessed.Results: 18F-AV1451 prominently specific binding was found in the amygdala, entorhinal cortex, parahippocampus, fusiform, posterior cingulate, temporal, parietal, and frontal brain regions. Most regional SUVRs showed significantly higher uptake of 18F-AV1451 in AD than MCI and CN participants. SUVRs of small regions like amygdala, entorhinal cortex and parahippocampus were statistically improved by PVC in all groups (p < 0.01). Although there was an increasing tendency of 18F-AV-1451 SUVRs in MCI group compared with CN group, no significant difference of 18F-AV1451 deposition was found between CN and MCI brains with or without PVC (p > 0.05). Declined MMSE score was observed with increasing 18F-AV1451 binding in amygdala, entorhinal cortex, parahippocampus, and fusiform. CSF p-tau was positively correlated with 18F-AV1451 deposition. PVC improved the results of 18F-AV-1451 tau deposition and correlation studies in small brain regions.Conclusion: The typical deposition of 18F-AV1451 tau PET imaging in AD brain was found in amygdala, entorhinal cortex, fusiform and parahippocampus, and these regions were strongly associated with cognitive impairment and CSF biomarkers. Although more deposition was observed in MCI group, the 18F-AV-1451 PET imaging could not differentiate the MCI patients from CN population. More tau deposition related to decreased MMSE score and increased level of CSF p-tau, especially in ROIs of amygdala, entorhinal cortex and parahippocampus. PVC did improve the results of tau deposition and correlation studies in small brain regions and suggest to be routinely used in 18F-AV1451 tau PET quantification.

In this article, we describe an image analysis strategy with improved power for tracking longitudinal amyloid-β (Aβ) PET changes and evaluating Aβ-modifying treatments. Our aims were to compare the power of template-based cerebellar, pontine, and cerebral white matter reference regions to track 24-mo florbetapir standardized uptake value (SUV) ratio (SUVR) changes; to relate those changes to 24-mo clinical declines; and to evaluate Aβ-modifying treatments in Aβ-positive (Aβ+) and Aβ-negative (Aβ-) patients with probable Alzheimer dementia (pAD), in patients with mild cognitive impairment (MCI), in cognitively normal controls (NCs), and in cognitively normal apolipoprotein E4 (APOE4) carriers and noncarriers. We used baseline and follow-up (∼24 mo) florbetapir PET scans from 332 Aβ+ and Aβ- subjects participating in the multicenter Alzheimer's Disease Neuroimaging Initiative. Each of the proposed analyses included 31 pAD patients, 187 MCI patients, and 114 NCs. Cerebral-to-white matter, cerebellar, and pontine SUVRs were characterized in terms of their longitudinal variability; their power to track longitudinal fibrillar Aβ increases in Aβ+ and Aβ- subgroups and cognitively normal APOE4 carriers and noncarriers; the sample sizes needed to detect attenuated accumulation of or clearance of fibrillar Aβ accumulation in randomized clinical trials; and their ability to relate 24-mo fibrillar Aβ increases to clinical declines. As predicted, cerebral-to-white matter SUVR changes were significantly less variable and had significantly greater power to detect 24-mo fibrillar Aβ increases and evaluate Aβ-modifying treatment effects in Aβ+ pAD, MCI, and NC subjects and cognitively normal APOE4 carriers. They were also distinguished by the ability to detect significant associations between 24-mo Aβ increases and clinical declines. A cerebral white matter reference region may improve the power to track longitudinal fibrillar Aβ increases, to characterize their relationship to longitudinal clinical declines, and to evaluate Aβ-modifying treatments in randomized clinical trials.