Multimodal Discrimination between Normal Aging, Mild Cognitive Impairment and Alzheimer's Disease and Prediction of Cognitive Decline

Detecting Amyloid Positivity Using Morphometric Magnetic Resonance Imaging

Background

Early detection of amyloid-β (Aβ) positivity is essential for an accurate diagnosis and treatment of Alzheimer's disease (AD), but it is currently costly and/or invasive.

Objective

We aimed to classify Aβ positivity (Aβ+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios: one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI).

Methods

Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aβ+, age: 73.3±7.2, 150 male; 246 Aβ-, age: 71.1±7.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best Aβ+ classification model.

Results

In an AD+MCI+CN population scenario, the best-performing model selected 120 features (107 GT features, 12 regional morphometric features and the MMSE total score) and achieved a negative predictive value (NPVadj) of 68.4%, and a balanced accuracy (BAC) of 66.9%. In a AD+MCI scenario, the best model obtained NPVadj of 71.6%, and BAC of 70.7%, using 180 regional morphometric features (98 volumes, 52 areas and 29 thicknesses from temporal, parietal, and frontal brain regions).

Conclusions

Although with currently limited clinical applicability, regional MRI morphometric features have clinical usefulness potential for detecting Aβ status, which may be augmented by a combination with cognitive data when cognitively normal subjects make up a substantial part of the population presenting for diagnosis.

Tau-PET is superior to phospho-tau when predicting cognitive decline in symptomatic AD patients

INTRODUCTION

Biomarkers for the prediction of cognitive decline in patients with amnestic mild cognitive impairment (MCI) and amnestic mild dementia are needed for both clinical practice and clinical trials.

METHODS

We evaluated the ability of tau-PET (positron emission tomography), cortical atrophy on magnetic resonance imaging (MRI), baseline cognition, apolipoprotein E gene (APOE) status, plasma and cerebrospinal fluid (CSF) levels of phosphorylated tau-217, neurofilament light (NfL), and amyloid beta (Aβ)42/40 ratio (individually and in combination) to predict cognitive decline over 2 years in BioFINDER-2 and Alzheimer's Disease Neuroimaging Initiative (ADNI).

RESULTS

Baseline tau-PET and a composite baseline cognitive score were the strongest independent predictors of cognitive decline. Cortical thickness and NfL provided some additional information. Using a predictive algorithm to enrich patient selection in a theoretical clinical trial led to a significantly lower required sample size.

DISCUSSION

Models including baseline tau-PET and cognition consistently provided the best prediction of change in cognitive function over 2 years in patients with amnestic MCI or mild dementia.

Prefrontal Activation During Effortful Processing Differentiates Memory Abilities in Adults with Memory Complaints

Background: Identifying individuals at increased risks for developing Alzheimer’s disease (AD) is crucial for early intervention. Memory complaints are associated with brain abnormalities characteristic of AD in cognitively normal older people. However, the utility of memory complaints for predicting mild cognitive impairment (MCI) or AD onset remains controversial, likely due to the heterogeneous nature of this construct. Objective: We investigated whether prefrontal oxygenation changes measured by functional near-infrared spectroscopy (fNIRS) during an arduous cognitive task, previously shown to be associated with the AD syndrome, could differentiate memory abilities among individuals with memory complaints. Episodic memory performance was adopted as a proxy for MCI/AD risks since it has been shown to predict AD progression across stages. Methods: Thirty-six adults self-reporting memory complaints in the absence of memory impairment completed a verbal list learning test and underwent a digit n-back paradigm with an easy (0-back) and a difficult (2-back) condition. K-means clustering was applied to empirically derive memory complaint subgroups based on fNIRS-based prefrontal oxygenation changes during the effortful 2-back task. Results: Cluster analysis revealed two subgroups characterized by high (n = 12) and low (n = 24) bilateral prefrontal activation during the 2-back but not a 0-back task. The low activation group was significantly less accurate across the n-back task and recalled significantly fewer words on the verbal memory test compared to the high activation group. Conclusion: fNIRS may have the potential to differentiate verbal memory abilities in individuals with self-reported memory complaints.

Translation, Adaptation and Validation of the Five-Word Test (Test Delle 5 Parole, T5P) in an Italian Sample: A Rapid Screening for the Assessment of Memory Impairment

Discriminating between Alzheimer’s disease (AD), mild cognitive impairment (MCI) and functional memory disorders (FMD) can sometimes be difficult because amnesic syndrome can be confused with memory dysfunction due to aging and/or depression. This work aims to translate, adapt and validate the five-word test (FWT) in an Italian sample (Test delle 5 Parole, T5P). FWT is a screening memory task specifically built to discriminate the hippocampal memory deficit—typical of AD and amnesic MCI—from those related to functional disorders. We involved a large number of subjects (n = 264): 60 with AD, 80 with MCI, 46 with FMD, and 78 healthy controls (CTRL). Two traditional screening tests and the T5P were administered to all participants. Results showed an excellent convergent validity of the T5P (p < 0.05). Moreover, the T5P appeared as an effective test able to discriminate between the four conditions, except for FMD and CTRL. In particular, a total score of ≤9 allowed for accurately discriminating subjects with AD + MCI compared to participants with FMD + CTRL with high sensitivity (83.87%) and specificity (76.43%). Overall, the T5P is a simple, rapid and sensible test of the hippocampal memory that is recommended in clinical practice to screen persons with subjective/probable amnesic difficulties and to discriminate between patients with AD or MCI and CTRL.

Alzheimer resemblance atrophy index, BrainAGE, and normal pressure hydrocephalus score in the prediction of subtle cognitive decline: added value compared to existing MR imaging markers

OBJECTIVES

Established visual brain MRI markers for dementia include hippocampal atrophy (mesio-temporal atrophy MTA), white matter lesions (Fazekas score), and number of cerebral microbleeds (CMBs). We assessed whether novel quantitative, artificial intelligence (AI)-based volumetric scores provide additional value in predicting subsequent cognitive decline in elderly controls.

METHODS

A prospective study including 80 individuals (46 females, mean age 73.4 ± 3.5 years). 3T MR imaging was performed at baseline. Extensive neuropsychological assessment was performed at baseline and at 4.5-year follow-up. AI-based volumetric scores were derived from 3DT1: Alzheimer Disease Resemblance Atrophy Index (AD-RAI), Brain Age Gap Estimate (BrainAGE), and normal pressure hydrocephalus (NPH) index. Analyses included regression models between cognitive scores and imaging markers.

RESULTS

AD-RAI score at baseline was associated with Corsi (visuospatial memory) decline (10.6% of cognitive variability in multiple regression models). After inclusion of MTA, CMB, and Fazekas scores simultaneously, the AD-RAI score remained as the sole valid predictor of the cognitive outcome explaining 16.7% of its variability. Its percentage reached 21.4% when amyloid positivity was considered an additional explanatory factor. BrainAGE score was associated with Trail Making B (executive functions) decrease (8.5% of cognitive variability). Among the conventional MRI markers, only the Fazekas score at baseline was positively related to the cognitive outcome (8.7% of cognitive variability). The addition of the BrainAGE score as an independent variable significantly increased the percentage of cognitive variability explained by the regression model (from 8.7 to 14%). The addition of amyloid positivity led to a further increase in this percentage reaching 21.8%.

CONCLUSIONS

The AI-based AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.

KEY POINTS

• AD-RAI score at baseline was associated with Corsi score (visuospatial memory) decline. • BrainAGE score was associated with Trail Making B (executive functions) decrease. • AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.

Determinants of Cognitive Trajectories in Normal Aging: A Longitudinal PET-MRI Study in a Community-based Cohort

BACKGROUND

The determinants of the progressive decrement of cognition in normal aging are still a matter of debate. Alzheimer disease (AD)-signature markers and vascular lesions, but also psychological variables such as personality factors, are thought to have an impact on the longitudinal trajectories of neuropsychological performances in healthy elderly individuals.

OBJECTIVE

The current research aimed to identify the main determinants associated with cognitive trajectories in normal aging.

METHODS

We performed a 4.5-year longitudinal study in 90 older community-dwellers coupling two neuropsychological assessments, medial temporal atrophy (MTA), number of cerebral microbleeds (CMB), and white matter hyperintensities (WMH) at inclusion, visual rating of amyloid and FDG PET at follow-up, and APOE genotyping. Personality factors were assessed at baseline using the NEO-PIR. Univariate and backward stepwise regression models were built to explore the association between the continuous cognitive score (CCS) and both imaging and personality variables.

RESULTS

The number of strictly lobar CMB at baseline (4 or more) was related to a significant increase in the risk of cognitive decrement. In multivariable models, amyloid positivity was associated with a 1.73 unit decrease of the CCS at follow-up. MTA, WMH and abnormal FDG PET were not related to the cognitive outcome. Among personality factors, only higher agreeableness was related to better preservation of neuropsychological performances.

CONCLUSION

CMB and amyloid positivity are the only imaging determinants of cognitive trajectories in this highly selected series of healthy controls. Among personality factors, higher agreeableness confers a modest but significant protection against the decline of cognitive performances.

Microbleeds and Medial Temporal Atrophy Determine Cognitive Trajectories in Normal Aging: A Longitudinal PET-MRI Study

BACKGROUND

The cognitive trajectories in normal aging may be affected by medial temporal atrophy (MTA) and amyloid burden, as well as vascular pathologies such as cortical microbleeds (CMB) and white matter hyperintensities (WMH).

OBJECTIVE

We addressed here the role of imaging markers in their prediction in a real-world situation.

METHODS

We performed a 4.5-year longitudinal study in 90 older community-dwellers coupling two neuropsychological assessments, MTA estimated with the Schelten's scale, number of CMB, and WMH evaluated with the Fazekas score at inclusion and follow-up, visual rating of amyloid PET and glucose hypometabolism at follow-up, and APOE genotyping. Regression models were built to explore the association between the continuous cognitive score (CCS) and imaging parameters.

RESULTS

The number of strictly lobar CMB at baseline (4 or more) was related to a 5.5-fold increase of the risk of cognitive decrement. This association persisted in multivariable models explaining 10.6% of the CCS decrease variance. MTA, and Fazekas score at baseline and amyloid positivity or abnormal FDG PET, were not related to the cognitive outcome. The increase of right MTA at follow-up was the only correlate of CCS decrease both in univariate and multivariable models explaining 9.2% of its variance.

CONCLUSION

The present data show that the accumulation of more than four CMB is associated with significant cognitive decrement over time in highly educated elderly persons. They also reveal that the progressive deterioration of cognitive performance within the age-adjusted norms is also related to the increase of visually assessed MTA.

Novel characterization of the relationship between verbal list-learning outcomes and hippocampal subfields in healthy adults

The relationship between hippocampal subfield volumetry and verbal list‐learning test outcomes have mostly been studied in clinical and elderly populations, and remain controversial. For the first time, we characterized a relationship between verbal list‐learning test outcomes and hippocampal subfield volumetry on two large separate datasets of 447 and 1,442 healthy young and middle‐aged adults, and explored the processes that could explain this relationship. We observed a replicable positive linear correlation between verbal list‐learning test free recall scores and CA1 volume, specific to verbal list learning as demonstrated by the hippocampal subfield volumetry independence from verbal intelligence. Learning meaningless items was also positively correlated with CA1 volume, pointing to the role of the test design rather than word meaning. Accordingly, we found that association‐based mnemonics mediated the relationship between verbal list‐learning test outcomes and CA1 volume. This mediation suggests that integrating items into associative representations during verbal list‐learning tests explains CA1 volume variations: this new explanation is consistent with the associative functions of the human CA1.

Prediction of Subtle Cognitive Decline in Normal Aging: Added Value of Quantitative MRI and PET Imaging

Quantitative imaging processing tools have been proposed to improve clinic-radiological correlations but their added value at the initial stages of cognitive decline is still a matter of debate. We performed a longitudinal study in 90 community-dwelling elders with three neuropsychological assessments during a 4.5 year follow-up period, and visual assessment of medial temporal atrophy (MTA), white matter hyperintensities, cortical microbleeds (CMB) as well as amyloid positivity, and presence of abnormal FDG-PET patterns. Quantitative imaging data concerned ROI analysis of MRI volume, amyloid burden, and FDG-PET metabolism in several AD-signature areas. Multiple regression models, likelihood-ratio tests, and areas under the receiver operating characteristic curve (AUC) were used to compare quantitative imaging markers to visual inspection. The presence of more or equal to four CMB at inclusion and slight atrophy of the right MTL at follow-up were the only parameters to be independently related to the worst cognitive score explaining 6% of its variance. This percentage increased to 24.5% when the ROI-defined volume loss in the posterior cingulate cortex, baseline hippocampus volume, and MTL metabolism were also considered. When binary classification of cognition was made, the area under the ROC curve increased from 0.69 for the qualitative to 0.79 for the mixed imaging model. Our data reveal that the inclusion of quantitative imaging data significantly increases the prediction of cognitive changes in elderly controls compared to the single consideration of visual inspection.

Evaluating the association between brain atrophy, hypometabolism, and cognitive decline in Alzheimer's disease: a PET/MRI study

Glucose metabolism reduction and brain volume losses are widely reported in Alzheimer’s disease (AD). Considering that neuroimaging changes in the hippocampus and default mode network (DMN) are promising important candidate biomarkers and have been included in the research criteria for the diagnosis of AD, it is hypothesized that atrophy and metabolic changes of the abovementioned regions could be evaluated concurrently to fully explore the neural mechanisms underlying cognitive impairment in AD. Twenty-three AD patients and Twenty-four age-, sex- and education level-matched normal controls underwent a clinical interview, a detailed neuropsychological assessment and a simultaneous 18F-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET)/high-resolution T1-weighted magnetic resonance imaging (MRI) scan on a hybrid GE SIGNA PET/MR scanner. Brain volume and glucose metabolism were examined in patients and controls to reveal group differences. Multiple linear regression models were employed to explore the relationship between multiple imaging features and cognitive performance in AD. The AD group had significantly reduced volume in the hippocampus and DMN regions (P < 0.001) relative to that of normal controls determined by using ROI analysis. Compared to normal controls, significantly decreased metabolism in the DMN (P < 0.001) was also found in AD patients, which still survived after controlling for gray matter atrophy (P < 0.001). These findings from ROI analysis were further confirmed by whole-brain confirmatory analysis (P < 0.001, FWE-corrected). Finally, multiple linear regression results showed that impairment of multiple cognitive tasks was significantly correlated with the combination of DMN hypometabolism and atrophy in the hippocampus and DMN regions. This study demonstrated that combining functional and structural features can better explain the cognitive decline of AD patients than unimodal FDG or brain volume changes alone. These findings may have important implications for understanding the neural mechanisms of cognitive decline in AD.

Narrative impairment, white matter damage and CSF biomarkers in the Alzheimer's disease spectrum

BACKGROUND

Narrative discourse (ND) refers to one's ability to verbally reproduce a sequence of temporally and logically-linked events. Impairments in ND may occur in subjects with Amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD), but correlates across this function, neuroimaging and cerebrospinal fluid (CSF) AD biomarkers remain understudied.

OBJECTIVES

We sought to measure correlates among ND, Diffusion Tensor Imaging (DTI) indexes and AD CSF biomarkers in patients within the AD spectrum.

RESULTS

Groups differed in narrative production (NProd) and comprehension. aMCI and AD presented poorer inference abilities than controls. AD subjects were more impaired than controls and aMCI regarding WB (p<0.01). ROIs DTI assessment distinguished the three groups. Mean Diffusivity (MD) in the uncinate, bilateral parahippocampal cingulate and left inferior occipitofrontal fasciculi negatively correlated with NProd. Changes in specific tracts correlated with T-tau/Aβ1-42 ratio in CSF.

CONCLUSIONS

AD and aMCI patients presented more ND impairments than controls. Those findings were associated with changes in ventral language-associated and in the inferior parahippocampal pathways. The latest were correlated with biomarkers' levels in the CSF.

METHODS

AD (N=14), aMCI (N=31) and Control (N=39) groups were compared for whole brain (WB) and regions of interest (ROI) DTI parameters, ND and AD CSF biomarkers.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer's Disease by Means of Textures Analysis on Magnetic Resonance Images

The current criteria for diagnosing Alzheimer’s disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant (p < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD.