Functional Magnetic Resonance Imaging of Semantic Memory as a Presymptomatic Biomarker of Alzheimer’s Disease Risk

Extensive research efforts have been directed toward strategies for predicting risk of developing Alzheimer\u27s disease (AD) prior to the appearance of observable symptoms. Existing approaches for early detection of AD vary in terms of their efficacy, invasiveness, and ease of implementation. Several non-invasive magnetic resonance imaging strategies have been developed for predicting decline in cognitively healthy older adults. This review will survey a number of studies, beginning with the development of a famous name discrimination task used to identify neural regions that participate in semantic memory retrieval and to test predictions of several key theories of the role of the hippocampus in memory. This task has revealed medial temporal and neocortical contributions to recent and remote memory retrieval, and it has been used to demonstrate compensatory neural recruitment in older adults, apolipoprotein E ε4 carriers, and amnestic mild cognitive impairment patients. Recently, we have also found that the famous name discrimination task provides predictive value for forecasting episodic memory decline among asymptomatic older adults. Other studies investigating the predictive value of semantic memory tasks will also be presented. We suggest several advantages associated with the use of semantic processing tasks, particularly those based on person identification, in comparison to episodic memory tasks to study AD risk. Future directions for research and potential clinical uses of semantic memory paradigms are also discussed. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease

Applicability of in vivo staging of regional amyloid burden in a cognitively normal cohort with subjective memory complaints: the INSIGHT-preAD study.

BACKGROUND:Current methods of amyloid PET interpretation based on the binary classification of global amyloid signal fail to identify early phases of amyloid deposition. A recent analysis of 18F-florbetapir PET data from the Alzheimer's disease Neuroimaging Initiative cohort suggested a hierarchical four-stage model of regional amyloid deposition that resembles neuropathologic estimates and can be used to stage an individual's amyloid burden in vivo. Here, we evaluated the validity of this in vivo amyloid staging model in an independent cohort of older people with subjective memory complaints (SMC). We further examined its potential association with subtle cognitive impairments in this population at elevated risk for Alzheimer's disease (AD). METHODS:The monocentric INSIGHT-preAD cohort includes 318 cognitively intact older individuals with SMC. All individuals underwent 18F-florbetapir PET scanning and extensive neuropsychological testing. We projected the regional amyloid uptake signal into the previously proposed hierarchical staging model of in vivo amyloid progression. We determined the adherence to this model across all cases and tested the association between increasing in vivo amyloid stage and cognitive performance using ANCOVA models. RESULTS:In total, 156 participants (49%) showed evidence of regional amyloid deposition, and all but 2 of these (99%) adhered to the hierarchical regional pattern implied by the in vivo amyloid progression model. According to a conventional binary classification based on global signal (SUVRCereb = 1.10), individuals in stages III and IV were classified as amyloid-positive (except one in stage III), but 99% of individuals in stage I and even 28% of individuals in stage II were classified as amyloid-negative. Neither in vivo amyloid stage nor conventional binary amyloid status was significantly associated with cognitive performance in this preclinical cohort. CONCLUSIONS:The proposed hierarchical staging scheme of PET-evidenced amyloid deposition generalizes well to data from an independent cohort of older people at elevated risk for AD. Future studies will determine the prognostic value of the staging approach for predicting longitudinal cognitive decline in older individuals at increased risk for AD

Multimodal analysis in normal aging, mild cognitive impairment, and Alzheimer's disease: group differentiation, baseline cognition, and prediction of future cognitive decline

Thesis (Ph.D.)--Boston UniversityAlzheimer's disease (AD) is a progressive neurodegenerative disease with an insidious onset that makes it difficult to distinguish from normal aging. It begins with an impairment of memory that develops into amnestic mild cognitive impairment (aMCI) and later to dementia as deficits become apparent in other cognitive domains. Effective biomarkers that differentiate normal aging, MCI, and AD and predict future cognitive decline are needed. Potential biomarkers have been studied in isolation, but their impact when combined is not understood. The goal of this project is to determine the optimal combination of CSF biomarkers, MRI morphometry, FDG PET metabolism, and neuropsychological test scores to differentiate between normal aging subjects and those with MCI and AD. This study addresses: 1) the optimal normalization region and partial volume correction method to quantify FDG PET analysis, 2) the effects of adjusting MRI-based cortical thickness measures for differences in gray/white matter tissue contrast in normal aging and disease, 3) whether multimodal multivariate stepwise logistic regression models can predict group membership, and 4) whether multimodal multivariate stepwise linear regression models can determine which imaging and CSF biomarker variables best predict future cognitive decline. The results indicate that normalizing FDG PET to the cerebellum along with using a gray matter mask for partial volume correction provides optimal prediction. In contrast, age-associated changes in gray/white matter intensity ratio did not differentiate between the groups and only slightly improved the efficacy of cortical thickness as a biomarker. MRI morphometry of the gray matter and neuropsychological test scores were better able to discriminate between the groups than FDG PET or CSF biomarker concentrations. Combining all modalities significantly improved the index of discrimination, especially at the earliest stages of the disease. MRI gray matter morphometry variables were more highly associated with baseline cognitive function and best predicted future cognitive decline compared to other variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores, and CSF biomarkers provides significantly better discrimination than any modality alone. Hence, the variables important for discriminating between the groups may be candidates for biomarkers in human clinical interventional trials

Motor Timing Intraindividual Variability in Amnestic Mild Cognitive Impairment and Cognitively Intact Elders at Genetic Risk for Alzheimer’s Disease

Introduction: Intraindividual variability (IIV) in motor performance has been shown to predict future cognitive decline. The apolipoprotein E-epsilon 4 (APOE-ε4) allele is also a well-established risk factor for memory decline. Here, we present novel findings examining the influence of the APOE-ε4 allele on the performance of asymptomatic healthy elders in comparison to individuals with amnestic MCI (aMCI) on a fine motor synchronization, paced finger-tapping task (PFTT). Method: Two Alzheimer’s disease (AD) risk groups, individuals with aMCI (n = 24) and cognitively intact APOE-ε4 carriers (n = 41), and a control group consisting of cognitively intact APOE-ε4 noncarriers (n = 65) completed the Rey Auditory Verbal Learning Test and the PFTT, which requires index finger tapping in synchrony with a visual stimulus (interstimulus interval = 333 ms). Results: Motor timing IIV, as reflected by the standard deviation of the intertap interval (ITI), was greater in the aMCI group than in the two groups of cognitively intact elders; in contrast, all three groups had statistically equivalent mean ITI. No significant IIV differences were observed between the asymptomatic APOE-ε4 carriers and noncarriers. Poorer episodic memory performance was associated with greater IIV, particularly in the aMCI group. Conclusions: Results suggest that increased IIV on a fine motor synchronization task is apparent in aMCI. This IIV measure was not sensitive in discriminating older asymptomatic individuals at genetic risk for AD from those without such a genetic risk. In contrast, episodic memory performance, a well-established predictor of cognitive decline in preclinical AD, was able to distinguish between the two cognitively intact groups based on genetic risk

Cognitive-Motor Integration In Normal Aging And Preclinical Alzheimer's Disease: Neural Correlates And Early Detection

The objectives of the studies included in this dissertation were to characterize how the ability to integrate cognition into action is disrupted by both normal and pathological aging, to evaluate the effectiveness of kinematic measures in discriminating between individuals who are and are not at increased Alzheimer’s disease (AD) risk, and to examine the structural and functional neural correlates of cognitive-motor impairment in individuals at increased AD risk. The underlying hypothesis, based on previous research, is that measuring visuomotor integration under conditions that place demands on visual-spatial and cognitive-motor processing may provide an effective behavioural means for the early detection of brain alterations associated with AD risk. To this end, the first study involved testing participants both with and without AD risk factors on visuomotor tasks using a dual-touchscreen tablet. Comparisons between high AD risk participants and both young and old healthy control groups revealed significant performance disruptions in at-risk participants in the most cognitively demanding task. Furthermore, a stepwise discriminant analysis was able to distinguish between high and low AD risk participants with a classification accuracy of 86.4%. Based on the prediction that the impairments observed in high AD risk participants reflect disruption to the intricate reciprocal communication between hippocampal, parietal, and frontal brain regions required to successfully prepare and update complex reaching movements, the second and third studies were designed to examine the underlying structural and functional connectivity associated with cognitive-motor performance. Young adult and both low AD risk and high AD risk older adult participants underwent anatomical, diffusion-weighted, and resting-state functional connectivity scans. These data revealed significant age-related declines in white matter integrity that were more pronounced in the high AD risk group. Decreased functional connectivity in the default mode network (DMN) was also found in high AD risk participants. Furthermore, measures of white matter integrity and resting-state functional connectivity with DMN seed-regions were significantly correlated with task performance. These data support our hypothesis that disease-related disruptions in visuomotor control are associated with identifiable brain alterations, and thus behavioural assessments incorporating both cognition and action together may be useful in identifying individuals at increased AD risk

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Differentiating between healthy control participants and those with mild cognitive impairment using volumetric MRI data

OBJECTIVE: To determine whether volumetric measures of the hippocampus or entorhinal cortex in combination with other cortical measures can differentiate between cognitively normal individuals and participants with amnestic mild cognitive impairment (MCI). METHODS: T1-weighted magnetic resonance imaging (MRI) data acquired from 46 cognitively normal participants and 50 participants with amnestic MCI as part of the Boston University Alzheimer's Disease Center research registry and the Alzheimer's Disease Neuroimaging Initiative were used in this cross-sectional study. Cortical and subcortical volumes, including hippocampal subfield volumes, were automatically generated from each participant’s structural MRI data using FreeSurfer v6.0. Nominal logistic regression models containing these variables were used to evaluate their ability to identify participants with MCI. RESULTS: A model containing 11 regions of interest (insula, superior parietal cortex, rostral middle frontal cortex, middle temporal cortex, pars opercularis, paracentral lobule, whole hippocampus, subiculum, superior temporal cortex, precentral cortex and caudal anterior cingulate cortex) fit the data best (R2 = 0.7710, whole model test chi square = 102.4794, p < 0.0001). CONCLUSIONS: Volumetric measures acquired from MRI were able to correctly identify most healthy control subjects and those with amnestic MCI using measures of selected medial temporal lobe structures in combination with those from other cortical areas yielding an overall classification of 95.83% for this dataset. These findings support the notion that while clinical features of amnestic MCI may reflect medial temporal atrophy, differences that can be used to distinguish between these two populations are present elsewhere in the brain. This finding further affirming that atrophy can be identified before clinical features are expressed. Additional studies are needed to assess how well other imaging modalities, such as resting state functional connectivity, diffusion imaging, and amyloid and tau position emission tomography (PET), perform in classifying participants who are cognitively normal versus those who are amnestic MCI

Egocentric versus Allocentric Spatial Memory in Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease

Background: Diagnosis of behavioural variant frontotemporal dementia (bvFTD) can be challenging, in particular when patients present with significant memory problems, which can increase the chance of a misdiagnosis of Alzheimer’s disease (AD). Growing evidence suggests spatial orientation is a reliable cognitive marker able to differentiate these two clinical syndromes. Objective: Assess the integrity of egocentric and allocentric heading orientation and memory in bvFTD and AD, and their clinical implications. Method: A cohort of 22 patient with dementia (11 bvFTD; 11 AD) and 14 healthy controls were assessed on the virtual supermarket task of spatial orientation and a battery of standardized neuropsychological measures of visual and verbal memory performance. Results: Judgements of egocentric and allocentric heading direction were differentially impaired in bvFTD and AD, with AD performing significantly worse on egocentric heading judgements than bvFTD. Both patient cohorts, however, showed similar degree of impaired allocentric spatial representation, and associated hippocampal pathology. Conclusions: The findings suggest egocentric heading judgements offer a more sensitive discriminant of bvFTD and AD than allocentric map-based measures of spatial memory