Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

Abstract

Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

Alterations in Dynamic Functional Connectivity in Individuals With Subjective Cognitive Decline

“…In contrast to the remarkable dynamic FC alterations, we did not find differences in either global or local topological parameters by graph theory approaches. Previous studies have shown topological alterations in SCD subjects (Chen et al, 2020 ; Xu et al, 2020 ), and we speculated that the discrepancies may be attributable to the different diagnostic criteria for SCD, the variations in demographics of the cohorts, and methodological aspects (Wang et al, 2020 ). These studies also revealed no group differences in the static analysis of global and local efficiency between AD dementia patients and NCs (Peraza et al, 2015 ; Schumacher et al, 2019 ).…”

Microstructural and Cerebral Blood Flow Abnormalities in Subjective Cognitive Decline Plus: Diffusional Kurtosis Imaging and Three-Dimensional Arterial Spin Labeling Study

“…Incorporating examinations of these objective biomarkers associated with AD pathology may be more important for detecting patients with SCD plus. Finally, multimodal imaging techniques and more advanced post-processed methods (Zhang et al, 2016 ; Jiskoot et al, 2019 ) would yield a more comprehensive understanding for elucidating the pathophysiologic mechanisms underlying SCD plus (Parker et al, 2020 ; Wang et al, 2020 ).…”

Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease

“…These facts drive interest in more sophisticated neuroimaging, such as positron emission tomography-based studies, which are able to image the amyloid and tau proteins (4), and connectomicbased approaches, leveraging imaging studies such as functional magnetic resonance imaging (fMRI) and diffusion tractography imaging (DTI) (5). A growing number of researchers work on the development of personalized, reproducible, non-invasive, and neuroscientifically interpretable biomarkers for early diagnosis or prediction of AD even on the subjective cognition decline (SCD) stage (6)(7)(8), yet most of which is focused on the consistent abnormal connection within the multimodal imaging as the combination with DTI and fMRI (9,10). Given the subtle and often diffuse nature of dementing disorders, machine learningbased approaches provide the most realistic method for complex imaging datasets (11,12).…”

“…Most such efforts utilize a method for modeling features of either DTI and/or fMRI studies, which allow a model to differentiate between some combination of healthy controls, patients with mild cognitive impairment, and those with AD. While early identification of patients who will progress to clinical AD would provide a clinically critical patient cohort who are the best candidates for disease-modifying therapies (8), models that provide a yes vs. no answer ignore the possibility of heterogeneity of phenotypes, have limited ability to provide insight into stages of the disease, and lack the ability to provide actionable information about where in the brain a specific patient's disease is located and what specifically is happening. Treatments such as repetitive transcranial magnetic stimulation (rTMS) provide a safe and potentially useful tool that may palliate symptoms in patients even if not disease-modifying, but for which it is unclear what the appropriate target is (22).…”