Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline

Individuals with prodromal Alzheimer's disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer's disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1-42 levels from the Amsterdam Dementia Cohort and the Alzheimer's Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer's Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer's Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer's disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer's Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer's disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures

Astrocyte biomarkers GFAP and YKL-40 mediate early Alzheimer's disease progression

INTRODUCTION: We studied how biomarkers of reactive astrogliosis mediate the pathogenic cascade in the earliest Alzheimer's disease (AD) stages.// METHODS: We performed path analysis on data from 384 cognitively unimpaired individuals from the ALzheimer and FAmilies (ALFA)+ study using structural equation modeling to quantify the relationships between biomarkers of reactive astrogliosis and the AD pathological cascade.// RESULTS: Cerebrospinal fluid (CSF) amyloid beta (Aβ)42/40 was associated with Aβ aggregation on positron emission tomography (PET) and with CSF p-tau181, which was in turn directly associated with CSF neurofilament light (NfL). Plasma glial fibrillary acidic protein (GFAP) mediated the relationship between CSF Aβ42/40 and Aβ-PET, and CSF YKL-40 partly explained the association between Aβ-PET, p-tau181, and NfL.// DISCUSSION: Our results suggest that reactive astrogliosis, as indicated by different fluid biomarkers, influences the pathogenic cascade during the preclinical stage of AD. While plasma GFAP mediates the early association between soluble and insoluble Aβ, CSF YKL-40 mediates the latter association between Aβ and downstream Aβ-induced tau pathology and tau-induced neuronal injury

Imaging neurodegeneration across the Alzheimer's disease continuum: The contribution of biomarkers to understanding clinical progression

Over the past two decades, the development of biomarkers that can detect Alzheimer’s disease (AD) pathology in living individuals has transformed the field of AD research. The key pathological features of AD, namely amyloid-β (Aβ) plaques and tau neurofibrillary tangles, can be measured in vivo using PET, as well as in the cerebrospinal fluid (CSF), and using blood-based assays. Furthermore, structural MRI can provide estimates of regional neurodegeneration. In this thesis we investigated the structural brain changes of four neurodegeneration measures that occur during the progression of AD: hippocampal volume (HV), cortical thickness, grey matter (GM) networks, and cortical myelin. Furthermore, we studied their relationship with AD pathology markers and clinical progression. In chapter 2 we studied oldest-old (90+) individuals with initially intact cognition, and observed that abnormal amyloid was associated with steeper decline in memory and processing speed performance over 1.5 years. Our findings support the notion that both A pathology and brain atrophy have detrimental effects on cognitive functioning among cognitively normal individuals that are separate from normal ageing. These results suggest that A abnormality is indicative of an neurodegenerative process, that also in the oldest-old with apparent high reserve and maintenance mechanisms lead to cognitive decline. In addition, possibly A independent pathological processes might also be involved in cognitive decline in the oldest-old, as a thinner medial and lateral temporal cortex was related to subsequent decline in memory and language irrespective of A pathology. In chapter 3 we studied a sample of cognitively normal individuals with subjective cognitive decline, and observed modest to moderate correlations and low concordance among different neurodegeneration (N) biomarkers: CSF total-tau, medial temporal lobe atrophy (MTA), HV, serum NfL, serum GFAP. N biomarkers HV, NfL, and GFAP each predicted clinical progression, and had predictive value in addition to Aβ and p-tau. Therefore, we recommend HV, NfL, or GFAP as biomarkers for N. In chapter 4 we demonstrated that higher tau-PET retention is related to greater GM network disruptions in individuals across the AD continuum. More advanced tau-related GM network abnormalities were observed with increasing disease severity. These findings suggest that tau pathology is associated with a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions indicative of a more random network topology across different AD stages. In chapter 5 we observed that T1-w/T2-w ratio values were higher in AD compared to controls, which was contrary to our expectation. These changes tended to be most pronounced in anatomical areas known to be affected in AD such as the interior parietal lobule and precuneus, and were associated with higher levels of the neuronal injury marker tau and worse cognition. Indicating that factors other than demyelination likely influence the T1w/T2w signal in AD. In chapter 6 we demonstrated that GM network measures can aid in identifying individuals with prodromal Alzheimer’s disease who are likely to progress to dementia within the next 2 years. Models combining small-world coefficient, p-tau and hippocampal volume showed the best ability to detect progression. These findings could increase power in Alzheimer’s disease trials by selecting those individuals with abnormal GM network characteristics at high risk for clinical progression within a time frame of 24 months. This thesis investigated the interplay among A deposition, tau aggregation, brain network alterations, and atrophy mechanisms underlying clinical progression. By studying early biomarker changes and their relationship to clinical progression, we are able to gain a better understanding of how these biological processes contribute to AD progression which will be essential to developing early and targeted effective treatments as well as more accurate individual-specific prognoses

GABAA receptor-mediated tonic transmission in sleep-wake cycles

Sleep-wake cycles are an important physiological characteristic of the mammalian rain and essential for wellbeing and cognitive performance. In this review, a novel and comprehensive view on the organisation of sleep in the brain is described. Evidence is presented that sleep is regulated in a local manner and is dependent on prior cortical activity. Moreover, the composition, expression, and role of a specific type of GABA (γ-aminobutyric acid) inhibitory neurotransmission mediated by extrasynaptic δ-GABAa receptors, known as tonic GABAa transmission, is described. Furthermore, this article reviews findings linking the regulation of sleep to this tonic GABAergic conductance that is observed in the thalamo-cortical circuitry relevant to slow wave sleep. This will contribute to our understanding of the basic mechanisms underlying the contribution of GABAergic tonic transmission to the neural basis of sleep-wake regulation, to ultimately develop more efficient clinical interventions to treat sleep disorders

Imaging neurodegeneration across the Alzheimer's disease continuum: The contribution of biomarkers to understanding clinical progression

Over the past two decades, the development of biomarkers that can detect Alzheimer’s disease (AD) pathology in living individuals has transformed the field of AD research. The key pathological features of AD, namely amyloid-β (Aβ) plaques and tau neurofibrillary tangles, can be measured in vivo using PET, as well as in the cerebrospinal fluid (CSF), and using blood-based assays. Furthermore, structural MRI can provide estimates of regional neurodegeneration. In this thesis we investigated the structural brain changes of four neurodegeneration measures that occur during the progression of AD: hippocampal volume (HV), cortical thickness, grey matter (GM) networks, and cortical myelin. Furthermore, we studied their relationship with AD pathology markers and clinical progression. In chapter 2 we studied oldest-old (90+) individuals with initially intact cognition, and observed that abnormal amyloid was associated with steeper decline in memory and processing speed performance over 1.5 years. Our findings support the notion that both A pathology and brain atrophy have detrimental effects on cognitive functioning among cognitively normal individuals that are separate from normal ageing. These results suggest that A abnormality is indicative of an neurodegenerative process, that also in the oldest-old with apparent high reserve and maintenance mechanisms lead to cognitive decline. In addition, possibly A independent pathological processes might also be involved in cognitive decline in the oldest-old, as a thinner medial and lateral temporal cortex was related to subsequent decline in memory and language irrespective of A pathology. In chapter 3 we studied a sample of cognitively normal individuals with subjective cognitive decline, and observed modest to moderate correlations and low concordance among different neurodegeneration (N) biomarkers: CSF total-tau, medial temporal lobe atrophy (MTA), HV, serum NfL, serum GFAP. N biomarkers HV, NfL, and GFAP each predicted clinical progression, and had predictive value in addition to Aβ and p-tau. Therefore, we recommend HV, NfL, or GFAP as biomarkers for N. In chapter 4 we demonstrated that higher tau-PET retention is related to greater GM network disruptions in individuals across the AD continuum. More advanced tau-related GM network abnormalities were observed with increasing disease severity. These findings suggest that tau pathology is associated with a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions indicative of a more random network topology across different AD stages. In chapter 5 we observed that T1-w/T2-w ratio values were higher in AD compared to controls, which was contrary to our expectation. These changes tended to be most pronounced in anatomical areas known to be affected in AD such as the interior parietal lobule and precuneus, and were associated with higher levels of the neuronal injury marker tau and worse cognition. Indicating that factors other than demyelination likely influence the T1w/T2w signal in AD. In chapter 6 we demonstrated that GM network measures can aid in identifying individuals with prodromal Alzheimer’s disease who are likely to progress to dementia within the next 2 years. Models combining small-world coefficient, p-tau and hippocampal volume showed the best ability to detect progression. These findings could increase power in Alzheimer’s disease trials by selecting those individuals with abnormal GM network characteristics at high risk for clinical progression within a time frame of 24 months. This thesis investigated the interplay among A deposition, tau aggregation, brain network alterations, and atrophy mechanisms underlying clinical progression. By studying early biomarker changes and their relationship to clinical progression, we are able to gain a better understanding of how these biological processes contribute to AD progression which will be essential to developing early and targeted effective treatments as well as more accurate individual-specific prognoses

Gray matter T1-w/T2-w ratios are higher in Alzheimer's disease

Myelin determines the conduction of neuronal signals along axonal connections in networks of the brain. Loss of myelin integrity in neuronal circuits might result in cognitive decline in Alzheimer's disease (AD). Recently, the ratio of T1-weighted by T2-weighted MRI has been used as a proxy for myelin content in gray matter of the cortex. With this approach, we investigated whether AD dementia patients show lower cortical myelin content (i.e., a lower T1-w/T2-w ratio value). We selected structural T1-w and T2-w MR images of 293 AD patients and 172 participants with normal cognition (NC). T1-w/T2-w ratios were computed for the whole brain and within 90 automated anatomical labeling atlas regions using SPM12, compared between groups and correlated with the neuronal injury marker tau in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE). In contrast to our hypothesis, AD patients showed higher whole brain T1-w/T2-w ratios than NC, and regionally in 31 anatomical areas (p <.0005; d = 0.21 to 0.48), predominantly in the inferior parietal lobule, angular gyrus, anterior cingulate, and precuneus. Regional higher T1-w/T2-w values were associated with higher CSF tau concentrations (p <.0005; r =.16 to.22) and worse MMSE scores (p <.0005; r = −.16 to −.21). These higher T1-w/T2-w values in AD seem to contradict previous pathological findings of demyelination and disconnectivity in AD. Future research should further investigate the biological processes reflected by increases in T1-w/T2-w values

Tau-related grey matter network breakdown across the Alzheimer’s disease continuum

Background: Changes in grey matter covariance networks have been reported in preclinical and clinical stages of Alzheimer’s disease (AD) and have been associated with amyloid-β (Aβ) deposition and cognitive decline. However, the role of tau pathology on grey matter networks remains unclear. Based on previously reported associations between tau pathology, synaptic density and brain structural measures, tau-related connectivity changes across different stages of AD might be expected. We aimed to assess the relationship between tau aggregation and grey matter network alterations across the AD continuum. Methods: We included 533 individuals (178 Aβ-negative cognitively unimpaired (CU) subjects, 105 Aβ-positive CU subjects, 122 Aβ-positive patients with mild cognitive impairment, and 128 patients with AD dementia) from the BioFINDER-2 study. Single-subject grey matter networks were extracted from T1-weighted images and graph theory properties including degree, clustering coefficient, path length, and small world topology were calculated. Associations between tau positron emission tomography (PET) values and global and regional network measures were examined using linear regression models adjusted for age, sex, and total intracranial volume. Finally, we tested whether the association of tau pathology with cognitive performance was mediated by grey matter network disruptions. Results: Across the whole sample, we found that higher tau load in the temporal meta-ROI was associated with significant changes in degree, clustering, path length, and small world values (all p < 0.001), indicative of a less optimal network organisation. Already in CU Aβ-positive individuals associations between tau burden and lower clustering and path length were observed, whereas in advanced disease stages elevated tau pathology was progressively associated with more brain network abnormalities. Moreover, the association between higher tau load and lower cognitive performance was only partly mediated (9.3 to 9.5%) through small world topology. Conclusions: Our data suggest a close relationship between grey matter network disruptions and tau pathology in individuals with abnormal amyloid. This might reflect a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions with tau pathology, indicative of a more random network topology across different AD stages

AMYLOID-β IS ASSOCIATED WITH THINNER CORTEX IN COGNITIVELY NORMAL OLDEST-OLD INDIVIDUALS

Background: Previous autopsy studies have demonstrated a high prevalence of widespread Alzheimer's disease (AD) pathology in individuals without dementia at high ages (>85y), which has raised doubts on the relationship of amyloid-β (Aβ) with dementia. In younger elderly (60-80y) without cognitive impairment, Aβ accumulation has been associated with subtle cognitive changes and cortical thinning. However, it remains unclear whether Aβ accumulation in oldest-old individuals with normal cognition is associated with changes in brain structure and cognitive functioning. We studied this using data from the EMIF-AD 90+ study. Methods: We selected 61 cognitively normal (CN) individuals from the EMIF-AD 90+ study, with available amyloid positron emission tomography (PET), and structural magnetic resonance imaging (MRI). Visual rating of the amyloid PET scan was used to classify the subjects as CN Aβ- (n=34) or CN Aβ+ (n=27). Cognitive performance on the Logical Delayed Memory test and MMSE were compared between groups. Subcortical volumes and cortical thickness measures in each ROI of FreeSurfer's Desikan-Killany atlas were calculated from 3T 3D-T1 images, and compared between groups using general linear models with Bonferroni correction and adjusted for age, and sex. Results: The individuals had a mean age of 93 years (range 88-102y), and 44% were rated as Aβ+. CN Aβ+ individuals had lower, but non-significant, memory scores and worse MMSE scores compared to the Aβ- group (Table1). Moreover, compared to the Aβ- group, CN individuals with Aβ+ showed thinner parahippocampal, rostral anterior cingulate, medial orbitofrontal, fusiform, superior temporal cortex, and smaller amygdala volume (pBonferron

Amyloid-β, cortical thickness, and subsequent cognitive decline in cognitively normal oldest-old

Objective: To investigate the relationship between amyloid-β (Aβ) deposition and markers of brain structure on cognitive decline in oldest-old individuals with initial normal cognition. Methods: We studied cognitive functioning in four domains at baseline and change over time in fifty-seven cognitively intact individuals from the EMIF-AD 90+ study. Predictors were Aβ status determined by [18F]-flutemetamol PET (normal = Aβ − vs. abnormal = Aβ+), cortical thickness in 34 regions and hippocampal volume. Mediation analyses were performed to test whether effects of Aβ on cognitive decline were mediated by atrophy of specific anatomical brain areas. Results: Subjects had a mean age of 92.7 ± 2.9 years, of whom 19 (33%) were Aβ+. Compared to Aβ−, Aβ+ individuals showed steeper decline on memory (β ± SE = −0.26 ± 0.09), and processing speed (β ± SE = −0.18 ± 0.08) performance over 1.5 years (P < 0.05). Furthermore, medial and lateral temporal lobe atrophy was associated with steeper decline in memory and language across individuals. Mediation analyses revealed that part of the memory decline observed in Aβ+ individuals was mediated through parahippocampal atrophy. Interpretation: These results show that Aβ abnormality even in the oldest old with initially normal cognition is not part of normal aging, but is associated with a decline in cognitive functioning. Other pathologies may also contribute to decline in the oldest old as cortical thickness predicted cognitive decline similarly in individuals with and without Aβ pathology