Biology subjects

Ossenkoppele, R.

Publications and source records attributed to Ossenkoppele, R..

14 recordsLinked to original sources

bioRxiv2026

The modulatory role of early-life experiences and sex on cognitive resilience in APP/PS1 mice over time

BACKGROUND: Cognitive resilience to Alzheimer's disease (AD) can be modelled in amyloid-expressing rodents through early handling (EH). This study investigated how EH and sex affect cognition and pathology over time. METHODS: We assessed cognition (Open field, Novel object recognition test, Barnes maze) in 6- and 12-month-old APP/PS1 and WT mice (N=180, EH/Ctrl X M/F), performed biochemical and histological analyses of several brain areas to assess effects on amyloid-beta, glial cells, and other markers of pathology, and investigated how behavior correlates with pathology. RESULTS: EH improved non-spatial memory at 6 months, consistent with a reduction of hippocampal microglia density, and normalized locomotor activity at 12 months (both p<0.05). Females demonstrated similar (6 & 12 months) or better (12 months) cognition, despite greater pathology across timepoints. Correlations implicated microglial recruitment in EH-related effects, supported by morphological analysis of prefrontal regions (p<0.05). CONCLUSION: Our findings highlight the critical role of early postnatal experiences and sex in shaping resilience in the context of AD pathology.

animal behavior and cognition↗

bioRxiv2026

Dynamical Aβ-Tau-Neurodegeneration Model Predicts Alzheimer's Disease Mechanisms and Biomarker Progression

Alzheimers disease is characterised by the pathological interaction of two proteins, amyloid-beta (A{beta}) and tau, which collectively drive neurodegeneration and cognitive decline. The progression of A{beta}, tau, and neurodegeneration biomarkers is captured by the ATN framework, which is a powerful tool for disease classification. However, since the ATN framework is mainly descriptive, it cannot quantify or predict relationships between biomarkers over time. We address this limitation by introducing a dynamical ATN (dATN) model that mechanistically simulates the spatiotemporal progression of A{beta}, tau, and neurodegeneration. The dATN model integrates mechanisms of prion-like protein aggregation of A{beta} and tau, network-based tau propagation, A{beta}-driven catalysis of tau progression, and tau-driven neurodegeneration. We calibrated the model using multimodal longitudinal imaging data from both the ADNI and BioFINDER-2 cohorts and show that it accurately fits longitudinal regional A{beta}, tau, and neurodegeneration data. Using the dATN model, we show that A{beta}-induced effects predict Braak-like cortical tau progression, that the spatial colocalisation of A{beta} and tau is a crucial biomarker of disease acceleration, and that tau-driven atrophy strongly correlates with observed neurodegeneration. Furthermore, by integrating the disease progression model with pharmacokinetic-pharmacodynamic simulations, we present a powerful tool that facilitates regional evaluation of therapeutic strategies targeting A{beta}, identification of critical intervention windows, and prediction of heterogeneous treatment effects across brain regions. This framework unifies mechanistic understanding with clinical imaging biomarkers, offering a quantitative approach for forecasting disease progression, testing mechanistic hypotheses, and optimising personalised treatment strategies in AD. One Sentence SummaryColocalisation of A{beta} and tau predicts regional tau progression and optimal A{beta}-targeted intervention windows, while tau predicts neurodegeneration.

neuroscience↗

bioRxiv2025

Patient-specific functional brain architecture explains cortical patterns of tau PET in Alzheimer's disease

The spatial distribution of tau pathology, a key correlate of neurodegeneration and cognitive decline in Alzheimer's disease (AD), varies markedly across individuals. While tau is thought to spread along brain networks, the role of inter-individual variability in accounting for these patterns remains underexplored. Using resting-state fMRI and tau-PET from 805 BioFINDER participants across the AD continuum, with replication in ADNI (n=361) and A4 (n=336), we studied whether subject-specific functional connectivity (FC) profiles enhance the characterization of tau deposition patterns. A hybrid approach integrating individual and group-average FC explained individual tau-PET topographies better than either FC representation alone, particularly in symptomatic individuals and at finer spatial resolutions. Hybrid FC also better captured individual tau topographies than canonical tau-PET maps derived from cohort-level data. These effects were specific to tau and not similarly observed for {beta}-amyloid, and the explanatory advantage of FC-based models increased with spatial granularity. Furthermore, baseline hybrid FC explained follow-up tau-PET topography better than template FC, suggesting that individualized baseline connectivity contains information about future tau-PET progression. The main FC model-comparison findings replicated in ADNI and A4. Collectively, these findings show that individual functional brain architecture is associated with heterogeneity in tau-PET topography. While not establishing a causal propagation mechanism, our findings are consistent with network-spread models. This work advances the methodological characterization of tau-PET heterogeneity in AD and highlights functional connectivity as a potentially informative marker of individual tau-PET trajectories.

neuroscience↗

bioRxiv2025

Age and Alzheimer's disease affect functional connectivity along separate axes of functional brain organization

Aging and Alzheimers disease (AD) are accompanied by alterations to large-scale communication patterns in the brain, which can be tracked in vivo using functional connectivity (FC). The location, direction and relevance of these changes remain widely debated, though they are rarely studied in the context of whole-cortex communication dynamics. In two independent cohorts (BioFINDER-2, N=973; ADNI, N=129), we show that FC changes associated with aging and AD are strongly aligned with separate fundamental axes of hierarchical brain communication. Early accumulation of AD pathology and subsequent cognitive decline are both linked to functional change along the sensory-association axis. Meanwhile, age-related functional changes occur along the representation-executive axis consistently throughout the adult lifespan. These findings together suggest AD and aging both alter major but orthogonal functional pathways in the brain. More broadly, our findings position whole-brain connectivity dynamics as a unifying framework for interpreting functional changes across the adult lifespan.

neuroscience↗

bioRxiv2025

Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer's disease

In Alzheimers disease (AD), tau pathology accumulates gradually throughout the brain, with clinical decline reflecting tau progression. A comprehensive understanding of, first, whether tau propagation is predominantly governed by connectome-based diffusion, regional vulnerability, or an interplay of both, and second, which types of brain connectivity or regional factors best explain tau propagation, remains crucial for advancing our understanding of AD progression. Here, we apply multi-scale, biologically informed disease progression simulations to human data, to disentangle the influence of local mechanisms on global tau progression patterns in AD. We find that whether tau reaches a brain region (presence) and how much tau accumulates there (load) are governed by different mechanisms. Tau presence patterns are highly consistent across the population, and can be largely explained through synaptic spread through white-matter networks and excitatory-inhibitory dynamics. Meanwhile tau load differs across people, and is driven by a combination of synaptic spread and intrinsic or extrinsic regional properties, including regional {beta}-amyloid load, MAPT gene expression and regional blood flow. Finally, while distinct tau patterns in the population could each be explained by established AD mechanisms, our models highlight a role of distinct brain networks (parietal networks in MTL-sparing AD tau subtype) and neurotransmitter systems (cholinergic system in posterior subtype). Together, this work suggests that network dynamics likely determine the sequence of regional tau progression, while individual-specific tissue-vulnerability factors influence regional tau load.

neuroscience↗

bioRxiv2025

Hemispheric Asymmetry of Tau Pathology is Related to Asymmetric Amyloid Deposition in Alzheimer's Disease

The distribution of tau pathology in Alzheimers disease (AD) shows remarkable inter-individual heterogeneity, including hemispheric asymmetry. However, the factors driving this asymmetry remain poorly understood. We explored whether tau asymmetry is linked to i) reduced inter-hemispheric brain connectivity (potentially restricting tau spread), or ii) asymmetry in amyloid-beta (A{beta}) distribution (indicating greater hemisphere-specific vulnerability to AD pathology). 452 participants from the Swedish BioFINDER-2 cohort with evidence of both A{beta} pathology (CSF A{beta}42/40 or neocortical A{beta}-PET) and tau pathology (temporal tau-PET), were categorised as left asymmetric (n=102), symmetric (n=306), or right asymmetric (n=44) based on temporal lobe tau-PET uptake distribution. Edge-wise inter-hemispheric functional (RSfMRI; n=318) and structural connectivity (dMRI; n=352) patterns were examined but no differences in inter-hemispheric functional or structural connectivity were found between groups. However, a strong association was observed between tau and A{beta} laterality patterns based on PET uptake (n=233; {beta}=0.632, p<0.001), which was replicated in three independent cohorts (n=234; {beta}=0.535, p<0.001). In a longitudinal A{beta}-positive sample, baseline A{beta} asymmetry predicted the progression of tau laterality over time (n=289; {beta}=0.025, p=0.028). These findings suggest that tau asymmetry is not associated with a weaker inter-hemispheric connectivity but might reflect hemispheric differences in vulnerability to A{beta} pathology, underscoring the role of regional vulnerability in determining the distribution of AD pathology.

neuroscience↗

bioRxiv2024

Multiscale Quantification of Hemispheric Asymmetry in Cortical Maps Using Geometric Eigenmodes

Hemispheric asymmetry is a universal property of brain organization with wide implications into brain function and structure, and diseases. This study presents a laterality index for characterizing hemispheric asymmetries that underlie cortical maps using geometric eigenmodes derived from human cortical surfaces.We develop a generalized design to quantify asymmetries across various cortical spatial scales. While the design is individual-specific, we implement normalization steps to enable unbiased comparisons across individuals. As a proof of concept, we validated the method on cortical maps of 545 subjects across two datasets, using fMRI maps of healthy individuals and tau-PET maps of patients across the Alzheimers disease continuum. Our results reveal that cortical regions in different canonical functional networks have connectivity patterns that entail different degrees of hemispheric asymmetry. Moreover, aggregates of the pathological tau protein manifest subtle asymmetries at varying spatial scales along the disease continuum.

neuroscience↗

bioRxiv2023

Disease progression modelling reveals heterogeneity in trajectories of Lewy-type alpha-synuclein pathology

Lewy body (LB) disorders, characterized by the aggregation of misfolded -synuclein proteins, exhibit notable clinical heterogeneity. This may be due to variations in accumulation patterns of LB neuropathology. By applying data-driven disease progression modelling to regional neuropathological LB density scores from 814 brain donors, we describe three inferred trajectories of LB pathology that were characterized by differing clinicopathological presentation and longitudinal antemortem clinical progression. Most donors (81.9%) showed earliest pathology in the olfactory bulb, followed by accumulation in either limbic (60.8%) or brainstem (21.1%) regions. The remaining donors (18.1%) exhibited the first abnormalities in brainstem regions. Early limbic pathology was associated with Alzheimers disease-associated characteristics. Meanwhile, brainstem-first pathology was associated with progressive motor impairment and substantial LB pathology outside of the brain. Our data provides evidence for heterogeneity in the temporal spread of LB pathology, possibly explaining some of the clinical disparities observed in LBDs.

pathology↗

bioRxiv2021

Independent information from PET, CSF and plasma biomarkers of tau pathology in Alzheimer's disease

PET, CSF and plasma biomarkers of tau pathology may be differentially associated with Alzheimers disease (AD) related demographic, cognitive, genetic and neuroimaging markers. We examined 771 participants with normal cognition, mild cognitive impairment or dementia from BioFINDER-2 (n=400) and ADNI (n=371). All had tau-PET ([18F]RO948 in BioFINDER-2, [18F]flortaucipir in ADNI) and CSF p-tau181 biomarkers available. Plasma p-tau181 and plasma/CSF p-tau217 were available in BioFINDER-2 only. Concordance between PET, CSF and plasma tau biomarkers ranged between 66% and 95%. Across the whole group, ridge regression models showed that increased CSF and plasma p-tau181 and p-tau217 levels were independently of tau PET associated with higher age, and APOE{varepsilon}4-carriership and A{beta}-positivity, while increased tau-PET signal in the temporal cortex was associated with worse cognitive performance and reduced cortical thickness. We conclude that biofluid and neuroimaging markers of tau pathology convey partly independent information, with CSF and plasma p-tau181 and p-tau217 levels being more tightly linked with early markers of AD (especially A{beta} pathology), while tau-PET shows the strongest associations with cognitive and neurodegenerative markers of disease progression.

neuroscience↗

bioRxiv2020

Differential brain atrophy and neurogenetic profiles in cognitively-defined Alzheimer's disease subgroups

Elucidating mechanisms underlying the clinical heterogeneity observed among individuals with Alzheimers disease (AD) is key to facilitate personalized treatments. We categorized 679 individuals with AD into subgroups based on a relative impairment in one cognitive domain (i.e. AD-Memory, AD-Executive-Functioning, AD-Language and AD-Visuospatial-Functioning). We compared atrophy patterns derived from MRI and identified patterns that closely matched the respective cognitive profiles, i.e. medial temporal lobe atrophy in AD-Memory, fronto-parietal in AD-Executive-Functioning, asymmetric left-temporal in AD-Language, and posterior in AD-Visuospatial-Functioning. We then determined spatial correlations between subgroup-specific atrophy and a transcriptomic atlas of gene expression, which revealed both shared (e.g. mitochondrial respiration and synaptic function/plasticity) and subgroup-specific (e.g. cell-cycle for AD-Memory, protein metabolism in AD-Language, and modification of gene expression in AD-Visuospatial-Functioning) biological pathways associated with each subgroups atrophy patterns. We conclude that cognitive heterogeneity in AD is related to neuroanatomical differences, and specific biological pathways may be involved in their emergence.

neuroscience↗

bioRxiv2019

Identifying a task-invariant cognitive reserve network using task potency

Cognitive reserve (CR) is thought to protect against the consequence of age- or disease-related structural brain changes across multiple cognitive domains. The neural basis of CR may therefore comprise a functional network that is actively involved in many different cognitive processes. To investigate the existence of such a \"task-invariant\" CR network, we measured functional connectivity in a cognitively normal sample between 20-80 years old (N=265), both at rest and during the performance of 11 separate tasks that aim to capture four latent cognitive abilities (i.e. vocabulary, episodic memory, processing speed, and fluid reasoning). For each individual, we determined the change in functional connectivity from the resting state to each task state, which is referred to as \"task potency\" (Chauvin et al., 2017; Chauvin et al., 2018). Task potency was calculated for each pair among 264 nodes (Power et al., 2011) and then summarized across tasks reflecting the same cognitive ability. Subsequently, we established the correlation between task potency and premorbid IQ or education (i.e. CR factors). We identified a set of 57 pairs in which task potency showed significant correlations with IQ, but not education, across all four cognitive abilities. These pairs were included in a principal component analysis, from which we extracted the first component to obtain a latent variable reflecting task potency in this task-invariant CR network. This task potency variable moderated the relationship between cortical thickness and episodic memory performance ({beta}=-.64, p=.01), and showed a direct effect on fluid reasoning ({beta}=.08, p<.01) after adjusting for the effects of cortical thickness. Our identification of this task-invariant network contributes to a better understanding of the mechanism underlying CR, which may facilitate the development of CR-enhancing treatments. Our work also offers a useful alternative operational measure of CR future studies.

neuroscience↗

bioRxiv2019

Data-driven detection of latent atrophy factors related to phenotypical variants of posterior cortical atrophy

Posterior cortical atrophy is a clinical-radiological syndrome characterized by visual processing deficits and atrophy in posterior parts of the brain, most often caused by Alzheimers disease pathology. Recent consensus criteria describe four distinct phenotypical variants of posterior cortical atrophy defined by clinical and radiological features; i) object perception/occipitotemporal (ventral), ii) space perception/temporoparietal (dorsal), iii) non-visual/dominant parietal and iv) primary visual (caudal). We employed a data-driven approach to identify atrophy factors related to these proposed variants in a multi-center cohort of 119 individuals with posterior cortical atrophy (age: 64 SD 7, 38% male, MMSE: 21 SD 5, 71% amyloid-{beta} positive, 29% amyloid-{beta} status unknown). A Bayesian modelling framework based on latent Dirichlet allocation was used to compute four latent atrophy factors in accordance with the four proposed variants. The model uses standardized gray matter density images as input (adjusted for age, sex, intracranial volume, field strength and whole-brain gray matter volume) and provides voxelwise probabilistic maps for all atrophy factors, allowing every individual to express each factor to a degree without a priori classification. The model revealed four distinct yet partially overlapping atrophy factors; right-dorsal, right-ventral, left-ventral, and limbic. Individual participant profiles revealed that the vast majority of participants expressed multiple factors, rather than predominantly expressing a single factor. To assess the relationship between atrophy factors and cognition, neuropsychological test scores covering four posterior cortical atrophy-specific cognitive domains were assessed (object perception, space perception, non-visual parietal functions and primary visual processing) and we used general linear models to examine the association between atrophy factor expression and cognition. We found that object perception and primary visual processing were associated with atrophy that predominantly reflects the right-ventral factor. Furthermore, space perception was associated with atrophy that predominantly represents the right-ventral and right-dorsal factors. Similar to the atrophy factors, most participants had mixed clinical profiles with impairments across multiple domains. However, when selecting four participants with an isolated impairment, we observed atrophy patterns and factor expressions that were largely in accordance with the hypothesized variants. Taken together, our results indicate that variants of posterior cortical atrophy exist but these constitute phenotypical extremes and most individuals fall along a broad clinical-radiological spectrum, indicating that classification into four mutually exclusive variants is unlikely to be clinically useful.

neuroscience↗

bioRxiv2019

PET and CSF amyloid-β status are differently predicted by patient features: Information from discordant cases

BackgroundAmyloid-{beta} PET and CSF A{beta}42 yield discordant results in 10-20% of patients, possibly providing unique information. Although the predictive power of demographic, clinical, genetic and imaging features for amyloid-positivity has previously been investigated, it is unknown whether these features differentially predict amyloid-{beta} status based on PET or CSF, or whether this differs by disease stage.\n\nMethodsWe included 768 patients (subjective cognitive decline (SCD, n=194), mild cognitive impairment (MCI, n=127), dementia (AD and non-AD, n=447) with amyloid-{beta} PET and CSF A{beta}42 measurement within one year. 97(13%) patients had discordant PET/CSF amyloid-{beta} status. We performed parallel random forest models predicting separately PET and CSF status using 17 patient features (demographics, APOE4 positivity, CSF (p)tau, cognitive performance, and MRI visual ratings) in the total patient group and stratified by syndrome diagnosis. Thereafter, we selected features with the highest variable importance measure (VIM) as input for logistic regression models, where amyloid status on either PET or CSF was predicted by (i) the selected patient feature, and (ii) the patient feature adjusted for the status of the other amyloid modality.\n\nResultsAPOE4, CSF tau and p-tau had highest VIM for PET and CSF in all groups. In the amyloid-adjusted logistic regression models, p-tau was a significant predictor for PET-amyloid in SCD (OR=1.02[1.01-1.04], pFDR=0.03), MCI (OR=1.05[1.02-1.07], pFDR<0.01) and dementia (OR=1.04[1.03-1.05], pFDR<0.001), but not for CSF-amyloid. APOE4 (OR=3.07[1.33-7.07], punc<0.01) was associated with CSF-amyloid in SCD, while it was only predictive for PET-amyloid in MCI (OR=9.44[2.93,30.39], pFDR<0.01). Worse MMSE scores (OR=1.21[1.03-1.41], punc=0.02) were associated to CSF-amyloid status in SCD, whereas worse memory (OR=1.17[1.05-1.31], pFDR=0.02) only predicted PET positivity in dementia.\n\nConclusionAmyloid status based on either PET or CSF was predicted by different patient features and this varied by disease stage, suggesting that PET-CSF discordance yields unique information. The stronger associations of both APOE4 carriership and worse memory z-scores with CSF-amyloid in SCD suggests that CSF-amyloid is more sensitive early in the disease course. The higher predictive value of CSF p-tau for a positive PET scan suggests that PET is more specific to AD pathology. These findings can influence the choice between amyloid biomarkers in future studies or trials.

neuroscience↗

bioRxiv2018

Data-driven approaches improve Tau-PET biomarkers in Alzheimer’s disease

Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published \"pathology-driven\" ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimers disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [18F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [18F]AV1451 scans. We performed linear models comparing [18F]AV1451 signal across all 40 ROIs to several tests of global cognition, adjusting for age, sex and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [18F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.

neuroscience↗