Alzheimer's disease pathology:pathways between central norepinephrine activity, memory, and neuropsychiatric symptoms

The locus coeruleus (LC) supplies norepinephrine to the brain, is one of the first sites of tau deposition in Alzheimer's disease (AD) and modulates a variety of behaviors and cognitive functions. Transgenic mouse models showed that norepinephrine dysregulation after LC lesions exacerbates inflammatory responses, blood-brain barrier leakage (BBB), and cognitive deficits. Here, we investigated relationships between central norepinephrine metabolism, tau and beta-amyloid (Aβ), inflammation, BBB-dysfunction, neuropsychiatric problems, and memory in-vivo in a memory clinic population (total n = 111, 60 subjective cognitive decline, 36 mild cognitively impaired, and 19 AD dementia). Cerebrospinal fluid (CSF) and blood samples were collected and analyzed for 3-methoxy-4-hydroxyphenylethyleneglycol (MHPG), CSF/plasma albumin ratio (Q-alb), Aβ, phosphorylated tau, and interleukins. The verbal word learning task and the neuropsychiatric inventory assessed memory functioning and neuropsychiatric symptoms. Structural equation models tested the relationships between all fluid markers, cognition and behavior, corrected for age, education, sex, and clinical dementia rating score. Our results showed that neuropsychiatric symptoms show strong links to both MHPG and p-tau, whereas memory deficits are linked to MHPG via a combination of p-tau and inflammation-driven amyloidosis (30-35% indirect effect contribution). These results suggest that the LC-norepinephrine may be pivotal to understand links between AD pathology and behavioral and cognitive deficits in AD

Sparse Asymmetry in Locus Coeruleus Pathology in Alzheimer's Disease.

peer reviewed Tau accumulation in and neurodegeneration of locus coeruleus (LC) neurons is observed in Alzheimer's disease (AD). We investigated whether tangle and neuronal density in the rostral and caudal LC is characterized by an asymmetric pattern in 77 autopsy cases of the Rush Memory and Aging Project. We found left-right equivalence for tangle density across individuals with and without AD pathology. However, neuronal density, particularly in the caudal-rostral axis of the LC, is asymmetric among individuals with AD pathology. Asymmetry in LC neuronal density may signal advanced disease progression and should be considered in AD neuroimaging studies of LC neurodegeneration

Multiscale structural mapping of Alzheimer's disease neurodegeneration

The recently described biological framework of Alzheimer’s disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the ‘amyloid/tau/neurodegeneration’ (A-T-N) status. The ‘neurodegenerative’ component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the ‘cortical signature of AD’ provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as ‘multi-scale structural mapping’; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment

Multiscale structural mapping of Alzheimer's disease neurodegeneration

The recently described biological framework of Alzheimer's disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the 'amyloid/tau/neurodegeneration' (A-T-N) status. The 'neurodegenerative' component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the 'cortical signature of AD' provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as 'multi-scale structural mapping'; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment

Alzheimer's Disease Biomarkers Have Distinct Associations with Specific Hippocampal Subfield Volumes

Measures of amyloid-β (Aβ) and phosphorylated tau (p-tau) concentrations in cerebrospinal fluid are extensively used for diagnostic and research purposes in Alzheimer's disease (AD) as correlates of cortical thinning and cognitive outcomes. The present study investigated the relationship of Aβ and p-tau with hippocampal subfield volumes Cornu Ammonis (CA) 1-4, dentate gyrus (DG), and subiculum. Subfields were segmented from T1-weighted images from the ADNI-population using FreeSurfer v6. Linear and polynomial regression models revealed distinct associations of Aβ and p-tau with subfield volumes. Aβ had a quadratic relationship with all hippocampal subfield volumes and the inflection point was higher than the validated cut-off for Aβ. For p-tau the relationships were linear, except for CA3, in which it was quadratic. For the CA1 and CA3, these quadratic relationships with Aβ were only observed when p-tau was low. Amyloid and p-tau contributed equally to the explained variance in CA4 and DG volume. Subicular volume was best explained by Aβ alone. These biomarker relationships with hippocampal subfield volumes seem to mirror the hippocampal-specific topography of Aβ and tau reported in neuropathological staging models. In addition, using continuous values of Aβ reveals positive patterns with imaging markers for individuals around the positivity threshold that would be masked when using dichotomized biomarker groups, which can be important for early detection and accurate inclusion of potential participants at risk for AD in clinical trials.</p

Inter-network connectivity and amyloid-beta linked to cognitive decline in preclinical Alzheimer’s disease: a longitudinal cohort study

Abstract Background Amyloid-beta (Aβ) has a dose-response relationship with cognition in healthy adults. Additionally, the levels of functional connectivity within and between brain networks have been associated with cognitive performance in healthy adults. Aiming to explore potential synergistic effects, we investigated the relationship of inter-network functional connectivity, Aβ burden, and memory decline among healthy individuals and individuals with preclinical, prodromal, or clinical Alzheimer’s disease. Methods In this longitudinal cohort study (ADNI2), participants (55–88 years) were followed for a maximum of 5 years. We included cognitively healthy participants and patients with mild cognitive impairment (with or without elevated Aβ) or Alzheimer’s disease. Associations between memory decline, Aβ burden, and connectivity between networks across the groups were investigated using linear and curvilinear mixed-effects models. Results We found a synergistic relationships between inter-network functional connectivity and Aβ burden on memory decline. Dose-response relationships between Aβ and memory decline varied as a function of directionality of inter-network connectivity across groups. When inter-network correlations were negative, the curvilinear mixed-effects models revealed that higher Aβ burden was associated with greater memory decline in cognitively normal participants, but when inter-network correlations were positive, there was no association between the magnitude of Aβ burden and memory decline. Opposite patterns were observed in patients with mild cognitive impairment. Combining negative inter-network correlations with Aβ burden can reduce the required sample size by 88% for clinical trials aiming to slow down memory decline. Conclusions The direction of inter-network connectivity provides additional information about Aβ burden on the rate of expected memory decline, especially in the preclinical phase. These results may be valuable for optimizing patient selection and decreasing study times to assess efficacy in clinical trials