Advances in Alzheimer's Disease

Abstract

Alzheimer's disease (AD) remains one of the greatest challenges medicine has faced, causing tremendous burden on patients, caregivers, and health care systems given the lack of effective and/or disease-modifying treatments. The recent years, however, have seen unprecedent developments both with regard to how the disease can reliably be detected already at early potentially pre-clinical disease stages using biomarkers, and with regard to several positive clinical trial read-outs from treatment studies evaluating the effects of potentially disease-modifying treatments for AD.

Established biomarker modalities, such as positron emission tomography (PET) using ligands sensitive to disease-specific aggregates and processes or the assessment of disease-specific proteins in cerebrospinal fluid (CSF), have been further developed and validated for both early disease detection and staging, as well as for stratification of trial recruitment and monitoring of treatment effects. Unfortunately, these modalities are mostly limited to specialized care or research/trial settings.

Recent developments in assays and platforms for the analysis of disease-specific blood-based biomarkers as well as in digitalized cognitive tests that can be administered remotely offer the potential of scalable, cost-efficient, and easily accessible diagnostic and prognostic means for AD also in less specialized settings in a foreseeable future.

Until now, associative studies exploring the relationship between relevant biomarkers and clinical and neuropathological measures have established a high level of diagnostic and prognostic performance for an array of the above-mentioned biomarkers, but mechanistic underpinnings of these associations are oftentimes still poorly understood.

In this issue of Brain Connectivity, you will find articles covering highly relevant topics such as how measures of brain connectivity relate to clinical disease progression (Malotaux et al.), how current symptomatic treatment with cholinesterase inhibitors (ChEIs) affects hippocampal connectivity (Rizzi et al.), how functional brain connectivity associates with cognition in the early pre-clinical stage of AD (Fountain-Zaragoza et al.), a review on alterations of static and dynamic functional connectivity (FC) in AD and other neuropsychiatric diseases (Matsui and Yamashita), and studies using advanced correlative biochemical imaging based on immunofluorescence microscopy and mass spectrometry imaging (MSI) to elucidate the properties underlying heterogeneity of beta-amyloid (Aβ)-plaque pathology.

Cholinesterase Inhibitors Response Might Be Related to Right Hippocampal Functional Connectivity in Mild Alzheimer's Disease (https://doi.org/10.1089/brain.2022.0026)

The response to ChEIs treatment is variable in patients with AD. Patients and physicians would benefit if these drugs could be targeted at those most likely to respond in a clinical setting. Therefore, Liara Rizzi and Marcio Luiz Figueredo Balthazar along with their colleagues aimed to evaluate the ability of CSF AD biomarkers, hippocampal volumes, and default mode network (DMN) FC to predict clinical response to ChEIs treatment in mild AD.

They followed up mild AD participants using ChEIs at therapeutic doses. All subjects underwent clinical evaluation, neuropsychological assessment, MRI examination, and CSF biomarkers quantification at the first assessment. The Mini-Mental Status Examination (MMSE) was used to measure the global cognitive status before and after the follow-up. Participants were considered “Responders” if the MMSE remained stable or improved the score between evaluations and “Non-Responders” those who have worsened the MMSE score. They performed univariate and multivariate logistic regressions to predict the clinical response from each biomarker.

Thirty-six percent of patients were classified as “Responders” to ChEIs treatment after the follow-up. The multivariate model with measures of right hippocampus, adjusted for gender and interval between assessments, was significant (odds ratio: 1.09 confidence interval [95% CI 1.00–1.19], ρ = 0.0392). This model achieved an accuracy of 60%. They conclude that the FC of right hippocampus might be an early imaging biomarker to predict clinical response to ChEIs drugs in mild AD.

Functional Network Alterations Associated with Cognition in Pre-Clinical Alzheimer's Disease (https://doi.org/10.1089/brain.2022.0032)

Accumulation of cerebral Aβ is a risk factor for cognitive decline and defining feature of AD. Aβ is implicated in brain network disruption, but the extent to which these changes correspond with observable cognitive deficits in pre-clinical AD has not been tested. Stephanie Fountain-Zaragoza and Andreana Benitez along with their colleagues utilized individual-specific functional parcellations to sensitively evaluate the relationship between network connectivity and cognition in adults with and without Aβ deposition.

The authors evaluated cognitively unimpaired adults of ages 45–85 years completed amyloid PET, resting-state-functional MRI (fMRI), and neuropsychological tests of episodic memory and executive function. Participants in the upper tertile of maximal SUV ratio were considered Aβ+, whereas others were Aβ−. Individualized functional network parcellations were generated from resting-state fMRI data. They examined the effects of group, network, and group-by-network interactions on memory and executive function.

They observed several interactions such that within the Aβ+ group, preserved network integrity (i.e., greater connectivity within specific networks) was associated with better cognition, whereas network desegregation (i.e., greater connectivity between relative to within networks) was associated with worse cognition. This dissociation was most apparent for cognitive networks (frontoparietal, dorsal and ventral attention, limbic, and default mode), with connectivity relating to executive function in the Aβ+ group specifically.

The authors conclude that using an innovative approach to constructing individual-specified resting-state functional connectomes, they were able to detect differences in brain–cognition associations in pre-clinical AD. Their findings provide novel insight into specific functional network alterations occurring in the presence of Aβ that relate to cognitive function in asymptomatic individuals.

Default-Mode Network Connectivity Changes During the Progression Towards Alzheimer's Dementia: A Longitudinal Functional MRI Study (https://doi.org/10.1089/brain.2022.0008)

Brain function changes with AD progression. Evaluating those changes longitudinally is important to understand the complex relationships between brain pathologies and cognition. Vincent Malotaux and Bernard Hanseeuw along with their colleagues aimed (1) to identify longitudinal changes in FC in patients with mild cognitive impairment (MCI) characterized for Aβ status and (2) to relate these functional changes to clinical progression.

They followed up 44 patients with MCI using serial fMRI for 1.2 years (three sessions) and cognitive testing for 3.1 years (five sessions). Intra and internetwork connectivities were computed to assess changes in brain connectivity using a network atlas adapted for late adulthood. Sixteen low Aβ clinically normal older adults underwent a single fMRI session for group comparisons at baseline. Linear mixed-effects models with random intercept and slope were used to predict changes in connectivity based on Aβ status and progression to dementia.

At baseline, intra and internetwork resting-state fMRI connectivities did not differ by baseline clinical diagnosis, Aβ status, or clinical progression to dementia. At the final imaging session, progressive MCI had significantly higher connectivity compared with stable MCI, specifically within the DMN. Longitudinally, progressive MCI had increasing intra-DMN connectivity over time compared with stable MCI, and the rate of changes in connectivity was significantly associated with the rate of cognitive decline.

This study demonstrates that intra-DMN connectivity increases in MCI patients progressing toward dementia, suggesting aberrant synchronization in the symptomatic stages of AD.

Correlative Chemical Imaging Identifies Amyloid Peptide Signatures of Neuritic Plaques and Dystrophy in Human Sporadic Alzheimer's Disease (https://doi.org/10.1089/brain.2022.0047)

AD is the most common neurodegenerative disease. The predominantly sporadic form of AD (sAD) is age related, but the underlying pathogenic mechanisms remain not fully understood. Current efforts to combat the disease focus on the main pathological hallmarks, in particular Aβ plaque pathology. According to the amyloid cascade hypothesis, Aβ is the critical early initiator of AD pathogenesis. Plaque pathology is very heterogeneous, where a subset of plaques, neuritic plaques, are considered most neurotoxic rendering their in-depth characterization essential to understand Aβ pathogenicity.

To delineate the chemical traits specific to neuritic plaque types, Srinivas Koutarapu and Jörg Hanrieder along with their colleagues investigated senile Aβ pathology in postmortem human sporadic AD brain using advanced correlative biochemical imaging based on immunofluorescence microscopy and MSI.

Immunostaining-guided MSI identified distinct Aβ signatures of neuritic plaques characterized by increased Aβ1-42(ox) and Aβ2-42. Moreover, correlation with a marker of dystrophy (reticulon 3, RTN3) identified key Aβ species that both delineate neuritic plaques and display association with neuritic dystrophy. Together these correlative imaging data shed light on the complex biochemical architecture of neuritic plaques and associated dystrophic neurites. These in turn are obvious targets for disease-modifying treatment strategies, as well as novel biomarkers of Aβ pathogenicity.

Static and Dynamic Functional Connectivity Alterations in Alzheimer's Disease and Neuropsychiatric Diseases (https://doi.org/10.1089/brain.2022.0044)

To date, numerous studies have documented various alterations in resting brain activity in AD and other neuropsychiatric diseases. In particular, disease-related alterations of FC in the resting-state networks (RSNs) have been documented. Altered FC in RSNs is useful not only for interpreting the phenotype of diseases but also for diagnosing the diseases. More recently, several studies proposed the dynamics of resting brain activity as a useful marker for detecting altered RSNs related to AD and other diseases.

In contrast to previous studies, which focused on FC calculated using an entire fMRI scan (static FC), these newer studies focused on temporal dynamics of FC within the scan (dynamic FC) to provide more sensitive measures to characterize RSNs. However, despite the increasing popularity of dynamic functional connectivity (dFC), several studies cautioned that the results obtained in commonly used analyses for dFC require careful interpretation.

In this minireview, Teppei Matsui and Ken-ichiro Yamashita review recent studies exploring alterations of static and dynamic FC in AD and other neuropsychiatric diseases. They then discuss how to utilize and interpret dFC for studying resting brain activity in diseases.

I thank all the authors for their time and effort in contributing to this special edition of Brain Connectivity, and thank you the journal audience for their time and effort and their interest in Alzheimer's research to advance the field for better treatment.