The Effect of Clinical Characteristics and Subtypes on Amyloid Positivity in Patients with Amnestic Mild Cognitive Impairment

Moderation of Amyloid-β Deposition on the Effect of Cholinesterase Inhibitors on Cognition in Mild Cognitive Impairment

Background

Clinical trial findings on cholinesterase inhibitors (ChEIs) for mild cognitive impairment (MCI) are inconclusive, offering limited support for their MCI treatment. Given that nearly half of amnestic MCI cases lack cerebral amyloid-β (Aβ) deposition, a hallmark of Alzheimer's disease; this Aβ heterogeneity may explain inconsistent results.

Objective

This study aimed to assess whether Aβ deposition moderates ChEI effects on amnestic MCI cognition.

Methods

We examined 118 individuals with amnestic MCI (ages 55-90) in a longitudinal cohort study. Baseline and 2-year follow-up assessments included clinical evaluations, neuropsychological testing, and multimodal neuroimaging. Generalized linear models were primarily analyzed to test amyloid positivity's moderation of ChEI effects on cognitive change over 2 years. Cognitive outcomes included Mini-Mental Status Examination score, the total score of the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological battery, and Clinical Dementia Rating-sum of boxes.

Results

The analysis found no significant ChEI use x amyloid positivity interaction for all cognitive outcomes. ChEI use, irrespective of Aβ status, was associated with more cognitive decline over the 2-year period.

Conclusions

Aβ pathology does not appear to moderate ChEI effects on cognitive decline in MCI.

A graph theoretic approach to neurodegeneration: five data-driven neuropsychological subtypes in mild cognitive impairment

Mild cognitive Impairment (MCI) is notoriously heterogenous in terms of clinical presentation, neuroimaging correlates, and subsequent progression. Predicting who will progress to dementia, which type of dementia, and over what timeframe is challenging. Previous work has attempted to identify MCI subtypes using neuropsychological measures in an effort to address this challenge; however, there is no consensus on approach, which may account for some of the variability. Using a hierarchical community detection approach, we examined cognitive subtypes within an MCI sample (from the Alzheimer's Disease Neuroimaging Initiative [ADNI] study). We then examined whether these subtypes were related to biomarkers (e.g., cortical volumes, fluorodeoxyglucose (FDG)-positron emission tomography (PET) hypometabolism) or clinical progression. We identified five communities (i.e., cognitive subtypes) within the MCI sample: 1) predominantly memory impairment, 2) predominantly language impairment, 3) cognitively normal, 4) multidomain, with notable executive dysfunction, 5) multidomain, with notable processing speed impairment. Community membership was significantly associated with 1) cortical volume in the hippocampus, entorhinal cortex, and fusiform cortex; 2) FDG PET hypometabolism in the posterior cingulate, angular gyrus, and inferior/middle temporal gyrus; and 3) conversion to dementia at follow up. Overall, community detection as an approach appears a viable method for identifying unique cognitive subtypes in a neurodegenerative sample that were linked to several meaningful biomarkers and modestly with progression at one year follow up.

Machine Learning Model to Predict Diagnosis of Mild Cognitive Impairment by Using Radiomic and Amyloid Brain PET

PURPOSE

The study aimed to develop a deep learning model for predicting amnestic mild cognitive impairment (aMCI) diagnosis using radiomic features and amyloid brain PET.

PATIENTS AND METHODS

Subjects (n = 328) from the Alzheimer's Disease Neuroimaging Initiative database and the EudraCT 2015-001184-39 trial (159 males, 169 females), with a mean age of 72 ± 7.4 years, underwent PET/CT with 18 F-florbetaben. The study cohort consisted of normal controls (n = 149) and subjects with aMCI (n = 179). Thirteen gray-level run-length matrix radiomic features and amyloid loads were extracted from 27 cortical brain areas. The least absolute shrinkage and selection operator regression was used to select features with the highest predictive value. A feed-forward neural multilayer network was trained, validated, and tested on 70%, 15%, and 15% of the sample, respectively. Accuracy, precision, F1-score, and area under the curve were used to assess model performance. SUV performance in predicting the diagnosis of aMCI was also assessed and compared with that obtained from the machine learning model.

RESULTS

The machine learning model achieved an area under the receiver operating characteristic curve of 90% (95% confidence interval, 89.4-90.4) on the test set, with 80% and 78% for accuracy and F1-score, respectively. The deep learning model outperformed SUV performance (area under the curve, 71%; 95% confidence interval, 69.7-71.4; 57% accuracy, 48% F1-score).

CONCLUSIONS

Using radiomic and amyloid PET load, the machine learning model identified MCI subjects with 84% specificity at 81% sensitivity. These findings show that a deep learning algorithm based on radiomic data and amyloid load obtained from brain PET images improves the prediction of MCI diagnosis compared with SUV alone.

Decreased Cerebrovascular Reactivity in Mild Cognitive Impairment Phenotypes

BACKGROUND

Cerebrovascular health plays an important role in cognitive health in older adults. Cerebrovascular reactivity (CVR), a measure of cerebrovascular health, changes in both normal and pathological aging, and is increasingly being conceptualized as contributory to cognitive decline. Interrogation of this process will yield new insights into cerebrovascular correlates of cognition and neurodegeneration.

OBJECTIVE

The current study examines CVR using advanced MRI in prodromal dementia states (amnestic and non-amnestic mild cognitive impairment phenotypes; aMCI and naMCI, respectively) and older adult controls.

METHODS

CVR was assessed in 41 subjects (20 controls, 11 aMCI, 10 naMCI) using multiband multi-echo breath-holding task functional magnetic resonance imaging. Imaging data were preprocessed and analyzed using AFNI. All participants also completed a battery of neuropsychological tests. T-tests and ANOVA/ANCOVA analyses were conducted to compare controls to MCI groups on CVR and cognitive metrics. Partial correlation analyses between CVR derived from regions-of-interest (ROIs) and different cognitive functions were conducted.

RESULTS

CVR was found to be significantly lower in aMCI and naMCI patients compared to controls. naMCI showed intermediate patterns between aMCI and controls (though aMCI and naMCI groups did not significantly differ). CVR of ROIs were positively correlated with neuropsychological measures of processing speed, executive functioning, and memory.

CONCLUSION

The findings highlight regional CVR differences in MCI phenotypes compared to controls, where aMCI may have lower CVR than naMCI. Our results suggest possible cerebrovascular abnormalities associated with MCI phenotypes.

Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment

The presence of amyloid-β (Aβ) deposition is considered important in patients with amnestic mild cognitive impairment (aMCI), since they can progress to Alzheimer’s disease dementia. Amyloid positron emission tomography (PET) has been used for detecting Aβ deposition, but its high cost is a significant barrier for clinical usage. Therefore, we aimed to develop a new predictive scale for amyloid PET positivity using easily accessible tools. Overall, 161 aMCI patients were recruited from six memory clinics and underwent neuropsychological tests, brain magnetic resonance imaging (MRI), apolipoprotein E (APOE) genotype testing, and amyloid PET. Among the potential predictors, verbal and visual memory tests, medial temporal lobe atrophy, APOE genotype, and age showed significant differences between the Aβ-positive and Aβ-negative groups and were combined to make a model for predicting amyloid PET positivity with the area under the curve (AUC) of 0.856. Based on the best model, we developed the new predictive scale comprising integers, which had an optimal cutoff score ≥ 3. The new predictive scale was validated in another cohort of 98 participants and showed a good performance with AUC of 0.835. This new predictive scale with accessible variables may be useful for predicting Aβ positivity in aMCI patients in clinical practice.

Enhanced Association of Tau Pathology and Cognitive Impairment in Mild Cognitive Impairment Subjects with Behavior Symptoms

BACKGROUND

Mild cognitive impairment (MCI) individuals with neuropsychiatric symptoms (NPS) are more likely to develop dementia.

OBJECTIVE

We sought to understand the relationship between neuroimaging markers such as tau pathology and cognitive symptoms both with and without the presence of NPS during the prodromal period of Alzheimer's disease.

METHODS

A total of 151 MCI subjects with tau positron emission tomographic (PET) scanning with 18F AV-1451, amyloid-β (Aβ) PET scanning with florbetapir or florbetaben, magnetic resonance imaging, and cognitive and behavioral evaluations were selected from the Alzheimer's Disease Neuroimaging Initiative. A 4-group division approach was proposed using amyloid (A-/A+) and behavior (B-/B+) status: A-B-, A-B+, A+B-, and A+B+. Pearson's correlation test was conducted for each group to examine the association between tau deposition and cognitive performance.

RESULTS

No statistically significant association between tau deposition and cognitive impairment was found for subjects without behavior symptoms in either the A-B-or A+B-groups after correction for false discovery rate. In contrast, tau deposition was found to be significantly associated with cognitive impairment in entorhinal cortex and temporal pole for the A-B+ group and nearly the whole cerebrum for the A+B+ group.

CONCLUSION

Enhanced associations between tauopathy and cognitive impairment are present in MCI subjects with behavior symptoms, which is more prominent in the presence of elevated amyloid pathology. MCI individuals with NPS may thus be at greater risk for further cognitive decline with the increase of tau deposition in comparison to those without NPS.

Amyloid PET in Dementia Syndromes: A Chinese Multicenter Study

Cerebral β-amyloid deposits and regional glucose metabolism assessed by PET are used to distinguish between Alzheimer disease (AD) and other dementia syndromes. In the present multicenter study, we estimated the prevalence of β-amyloid deposits on PET imaging in a wide variety of dementia syndromes and mild cognitive impairment (MCI) within a memory clinic population. Methods: Of the 1,193 consecutive patients with cognitive impairment (CI) who received 1 ¹¹C-PIB PET or ¹⁸F-AV45 PET or both ¹¹C-PIB PET and ¹⁸F-AV45 PET, 960 were diagnosed with AD, 36 with frontotemporal dementia (FTD), 5 with dementia with Lewy bodies, 144 with MCI, 29 with vascular dementia, 4 with corticobasal syndrome, and 15 with unclassifiable dementia. Baseline clinical diagnoses were independently established without access to PET imaging results. Apolipoprotein E (ApoE) genotype analysis was performed on CI patients and 231 sex- and age-matched controls. Results: Of the 1,193 CI patients, 860 (72.1%) were amyloid-positive. The prevalence of amyloid positivity in AD and MCI patients was 86.8% (833/960) and 9.7% (14/144), respectively. In FTD patients, the prevalence of β-amyloid deposits was 5.6% (2/36). In the 4 corticobasal syndrome patients, 2 were amyloid-positive. Three of the 5 patients with dementia with Lewy bodies showed amyloid positivity, as did 6 of the 29 vascular dementia (20.7%) patients. The ApoEε4 allele frequency was significantly increased in amyloid-positive CI patients (30.5%) as compared with other amyloid-negative CI patients (14%) or controls (7.3%). Conclusion: Amyloid imaging may potentially be the most helpful parameter for differential diagnosis in dementia, particularly to distinguish between AD and FTD. Amyloid PET can be used in conjunction with the ApoEε4 allele genetic risk test for amyloid deposits.