Currently, over five million Americans suffer with Alzheimer’s disease (AD). In the absence of a cure, this number could increase to 13.8 million by 2050. A critical goal of biomedical research is to establish indicators of AD during the preclinical stage (i.e. biomarkers) allowing for early diagnosis and intervention. Numerous advances have been made in developing biomarkers for AD using neuroimaging approaches. These approaches offer tremendous versatility in terms of targeting distinct age-related and pathophysiological mechanisms such as structural decline (e.g. volumetry, cortical thinning), functional decline (e.g. fMRI activity, network correlations), connectivity decline (e.g. diffusion anisotropy), and pathological aggregates (e.g. amyloid and tau PET). In this review, we survey the state of the literature on neuroimaging approaches to developing novel biomarkers for the amnestic form of AD, with an emphasis on combining approaches into multimodal biomarkers. We also discuss emerging methods including imaging epigenetics, neuroinflammation, and synaptic integrity using PET tracers. Finally, we review the complementary information that neuroimaging biomarkers provide, which highlights the potential utility of composite biomarkers as suitable outcome measures for proof-of-concept clinical trials with experimental therapeutics.
Introduction Loss of entorhinal cortex (EC) layer II neurons represents the earliest Alzheimer's disease (AD) lesion in the brain. Research suggests differing functional roles between two EC subregions, the anterolateral EC (aLEC) and the posteromedial EC (pMEC). Methods We use joint label fusion to obtain aLEC and pMEC cortical thickness measurements from serial magnetic resonance imaging scans of 775 ADNI‐1 participants (219 healthy; 380 mild cognitive impairment; 176 AD) and use linear mixed‐effects models to analyze longitudinal associations among cortical thickness, disease status, and cognitive measures. Results Group status is reliably predicted by aLEC thickness, which also exhibits greater associations with cognitive outcomes than does pMEC thickness. Change in aLEC thickness is also associated with cerebrospinal fluid amyloid and tau levels. Discussion Thinning of aLEC is a sensitive structural biomarker that changes over short durations in the course of AD and tracks disease severity—it is a strong candidate biomarker for detection of early AD.
Introduction: Loss of entorhinal cortex (EC) layer II neurons represents the earliest AD lesion in the brain. Research suggests differing functional roles between two EC subregions, the anterolateral EC (aLEC) and the posteromedial EC (pMEC). Methods: We use joint label fusion to obtain aLEC and pMEC cortical thickness measurements from serial MRI scans of 775 ADNI-1 participants (219 healthy; 380 MCI; 176 AD) and use linear mixed-effects models to analyze longitudinal associations between cortical thickness, disease status and cognitive measures. Results: Group status is reliability predicted by aLEC thickness, which also exhibits greater associations with cognitive outcomes than does pMEC thickness. Change in aLEC thickness is also associated with CSF amyloid and tau levels. Discussion: Thinning of aLEC is a sensitive structural biomarker that changes over short durations in the course of AD and tracks disease severity; it is a strong candidate biomarker for detection of early AD.
Introduction:We tested whether Alzheimer's disease (AD) pathology predicts memory deficits in non-demented older adults through its effects on medial temporal lobe (MTL) subregional volume. Methods: Thirty-two, non-demented older adults with cerebrospinal fluid (CSF) (amyloid-beta [Aβ] 42 /Aβ 40 , phosphorylated tau [p-tau] 181 , total tau [t-tau]), positron emission tomography (PET; 18F-florbetapir), high-resolution structural magnetic resonance imaging (MRI), and neuropsychological assessment were analyzed. We examined relationships between biomarkers and a highly granular measure of memory consolidation, retroactive interference (RI). Results: Biomarkers of AD pathology were related to RI. Dentate gyrus (DG) and CA3 volume were uniquely associated with RI, whereas CA1 and BA35 volume were related to both RI and overall memory recall. AD pathology was associated with reduced BA35, CA1, and subiculum volume. DG volume and Aβ were independently associated with RI, whereas CA1 volume mediated the relationship between AD pathology and RI. Discussion: Integrity of distinct hippocampal subfields demonstrate differential relationships with pathology and memory function, indicating specificity in vulnerability and contribution to different memory processes.
Background Decline in episodic memory is a hallmark feature of Alzheimer’s disease (AD). Episodic memories can be operationalized as detailed conjunctions of items and their spatiotemporal context. Our past work has uncovered deficits in pattern separation of similar object memories in nondemented older adults. However, separation of similar spatial locations was only slightly impaired in this population. We hypothesized that these deficits may be more readily observable in individuals with subclinical memory impairment (assessed by verbal list recall). We also sought to determine the neural mechanisms by which spatial pattern separation deficits may manifest in older adults, focusing on the posterior parietal network which is involved in spatial processing and implicated in AD. Methods Two cohorts were included in this analysis. The first is a group of older adults with subclinical memory impairment (aged impaired – AI, age range: 61‐91; mean MMSE: 28.3 ± 1.50; mean RAVLT delayed recall: 9.66 ± 4.05). The second is a group of non‐demented older adults without memory impairment (aged unimpaired – AU; age range: 61‐86; mean MMSE: 28.1 ± 1.59; RAVLT delayed recall: 11.4 ± 3.01). Participants underwent whole brain high‐resolution resting state functional magnetic resonance imaging (1.5 x 1.5 x 2 mm resolution). They were also administered a battery of neuropsychological tests including tests of object and spatial pattern separation. We used performance on the RAVLT as the outcome predicted by performance on the discrimination tests. Specifically, we used delayed recall (DR), retroactive interference (RI), and percent forgetting (PF). Results Behaviorally, spatial, but not item, pattern separation performance predicted RAVLT DR, RI and PF scores in the AI group, but not the AU group. Functional connectivity analyses revealed that the connectivity of regions involved in spatial processing was significantly related to spatial pattern separation performance in the AI group, but not the AU group. Conclusion These results validate the neurobiological basis of spatial pattern separation and suggest that such tasks can be used to detect early stages of AD, which has important implications for future diagnostics and treatment approaches.
The incidence of dementia is rapidly increasing. Identifying risk factors for dementia may help improve risk assessment, increase awareness for risk reduction, and identify potential targets for interventions. We use a life-course multi-disciplinary modeling framework to examine leading predictors of incident dementia (ID). We use the Health and Retirement Study (HRS) to measure 57 exposures across 7 different domains: (1) demographic, (2) adverse childhood socioeconomic and psychosocial, (3) adverse adulthood experiences, (4) adult socioeconomic status, (5) health behaviors, (6) social connections, and (7) adult psychological conditions. Our outcome is ID (over 8-years) operationalized using Langa-Weir classification for adults aged 65+ years who meet criteria for cognitively normal at the baseline when all exposures are measured (Nf 1,622 in testing set and Nf1,460 in validation set). We compare standard methods (Logistic regression) with machine learning (ML) approaches (Lasso, Random Forest) in identifying highly predictive exposures across the risk domains of interest. Standard methods identified lower education, childhood financial duress, and pessimism as among the leading factors associated with ID. Psychological factors explained the highest variance for ID, followed by adult socioeconomic and adverse childhood factors. However, ML techniques differed in their identification of (1) predictors and (2) factors predictive importance. The findings emphasize the importance of upstream risk factors and the long-reach of childhood experiences on cognitive health. The ML approaches highlight the importance of life-course multi-disciplinary frameworks for improving dementia risk assessment. Further investigations are needed to identify how complex interactions of life-course risk factors can be addressed through interventions.
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