Biology subjects

the Alzheimer's Disease Neuroimaging Initiative,

Publications and source records attributed to the Alzheimer's Disease Neuroimaging Initiative,.

13 recordsLinked to original sources

bioRxiv2026

Metabolic brain network reorganization precedes clinical conversion in Alzheimer's disease

Structured AbstractO_ST_ABSIntroductionC_ST_ABSBrain glucose hypometabolism is a hallmark of Alzheimers disease (AD), yet conventional [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) analyses have limited sensitivity in preclinical stages. Metabolic brain network approaches may better capture early vulnerability preceding clinical conversion. MethodsCognitively unimpaired individuals (n = 127) from the ADNI cohort with baseline FDG-PET and amyloid (A) and tau (T) status were classified as clinically stable or converters over an average longitudinal follow-up of 5.8 years. Baseline brain FDG uptake patterns were analyzed at the regional, voxel, and network levels across AT profiles. Network density was quantified globally and within functional networks. AD biomarkers and cognitive performance were also examined. ResultsConventional FDG-PET SUVr analyses failed to distinguish cognitively stable individuals from clinical converters at baseline, either at the regional or voxel levels. AT(N) biomarkers and neuropsychological performance likewise did not differ significantly between groups. In contrast, clinical converters exhibited hyperconnected metabolic networks at baseline, including within the default-mode network. These effects were consistent across A-T-, A+T-, and A+T+ groups, with network density higher in clinical converters than in cognitively stable individuals. Conversely, network density among stable individuals declined with AT progression, pointing to divergent network trajectories. DiscussionMetabolic network organization analysis revealed early AD-related vulnerability beyond regional hypometabolism, even before detectable amyloid positivity, and may reflect divergent trajectories of resilience and pathological propagation preceding clinical conversion. By leveraging existing FDG-PET datasets, this framework offers a valuable opportunity to identify individuals at risk of clinical progression at scale.

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↗

bioRxiv2025

Altered brain-ventricle coupling modes over Alzheimer's disease progression detected with fMRI

The temporal coordination between fMRI signals in gray matter and ventricular spaces represents an emerging yet poorly understood dimension of brain physiology with potential relevance for neuroprotection. Here, we analyzed 2177 resting-state fMRI scans from 880 participants spanning the cognitive spectrum from normal aging to dementia, using a unified phase-coupling approach that incorporates fluctuations in both brain tissue and ventricular space. We identify distinct modes of Brain-Ventricle (BraVe) coupling occurring over time, and compare their temporal occupancy across cognitive status groups, neurodegenerative biomarkers and cognitive scores. In the most prevalent BraVe mode, signals across the entire brain evolve in anti-phase with ventricular signals, occurring less frequently with cognitive decline (p < 10-7, g > 0.3). In the remaining BraVe modes, specific cortical regions temporarily align with ventricular fluctuations, shaping canonical resting-state networks at the cortical level. Two BraVe modes overlapping with the Default Mode Network and the Frontoparietal Network, occur less frequently in dementia (p < 10-8, g > 0.35), with the latter correlating significantly with FDG-PET glucose metabolism (r = 0.20, p = 3.65x10-5). Conversely, a mode overlapping with the Limbic Network increases in occupancy with cognitive decline and associates with APOE {varepsilon}4 status (r = 0.11, p = 1.30x10-6). BraVe mode occupancy correlates more strongly and broadly with cognitive function than with disease-specific molecular biomarkers, with between 16 and 27 cognitive and functional variables per mode surviving strict Bonferroni correction, spanning global cognition, episodic memory, executive function, and everyday functioning. These findings reveal that the transient phase shifts underlying resting-state network dynamics are not arbitrary: when cortical subnetworks shift out of phase from the global brain signal, they align with ventricular signals, suggesting that resting-state functional connectivity and brain-ventricle coupling may be two perspectives on the same underlying physical process, one that is directly linked with cognitive function.

neuroscience↗

bioRxiv2024

Network-based amyloid-beta pathology predicts subsequent cognitive decline in cognitively normal older adults

The deposition of amyloid-{beta} (A{beta}) protein in the human brain is a hallmark of Alzheimers disease and is related to cognitive decline. However, the relationship between early A{beta} deposition and future cognitive impairment remains poorly understood, particularly concerning its spatial distribution and network-level effects. Here, we employed a cross-validated machine learning approach and investigated whether integrating subject-specific brain connectome information with A{beta} burden measures improves predictive validity for subsequent cognitive decline. Baseline regional A{beta} pathology measures from positron emission tomography (PET) imaging predicted prospective cognitive decline. Incorporating structural connectome, but not functional connectome, information into the A{beta} measures improved predictive performance. We further identified a neuropathological signature pattern linked to future cognitive decline, which was validated in an independent cohort. These findings advance our understanding of how A{beta} pathology relates to brain networks and highlight the potential of network-based metrics for A{beta}-PET imaging to identify individuals at higher risk of cognitive decline.

neuroscience↗

bioRxiv2024

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry

IntroductionDiffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. MethodsHere, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. ResultsIn two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics sensitivity to dementia. DiscussionWe show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brains neural pathways.

neuroscience↗

bioRxiv2024

Subtypes of brain change in aging and their associations with cognition and Alzheimer's disease biomarkers

Structural brain changes underly cognitive changes in older age and contribute to inter-individual variability in cognition. Here, we assessed how changes in cortical thickness, surface area, and subcortical volume, are related to cognitive change in cognitively unimpaired older adults using structural magnetic resonance imaging (MRI) data-driven clustering. Specifically, we tested (1) which brain structural changes over time predict cognitive change in older age (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimers disease (AD) biomarkers phosphorylated tau (p-tau) and amyloid-{beta} (A{beta}42), and (3) the degree of overlap between clusters derived from different structural features. In total 1899 cognitively healthy older adults (50 - 93 years) were followed up to 16 years with neuropsychological and structural MRI assessments, a subsample of which (n = 612) had CSF p-tau and A{beta}42 measurements. We applied Monte-Carlo Reference-based Consensus clustering to identify subgroups of older adults based on structural brain change patterns over time. Four clusters for each brain feature were identified, representing the degree of longitudinal brain decline. Each brain feature provided a unique contribution to brain aging as clusters were largely independent across modalities. Cognitive change and baseline cognition were best predicted by cortical area change, whereas higher levels of p-tau and A{beta}42 were associated with changes in subcortical volume. These results provide insights into the link between changes in brain morphology and cognition, which may translate to a better understanding of different aging trajectories.

neuroscience↗

bioRxiv2024

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

neuroscience↗

bioRxiv2022

DeepParcellation: a novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians

Accurate parcellation of cortical regions is crucial for distinguishing morphometric changes in aged brains, particularly in degenerative brain diseases. Normal aging and neurodegeneration precipitate brain structural changes, leading to distinct tissue contrast and shape in people aged > 60 years. Manual parcellation by trained radiologists can yield a highly accurate outline of the brain; however, analyzing large datasets is laborious and expensive. Alternatively, newly-developed computational models can quickly and accurately conduct brain parcellation, although thus far only for the brains of Caucasian individuals. DeepParcellation, our novel deep learning model for 3D magnetic resonance imaging (MRI) parcellation, was trained on 5,035 brains of older East Asians (Gwangju Alzheimers & Related Dementia) and 2,535 brains of Caucasians. We trained full 3D models for N-way individual regions of interest using memory reduction techniques. Our method showed the highest similarity and robust reliability among age-ethnicity groups, especially when parcellating the brains of older East Asians.

neuroscience↗

bioRxiv2022

Penalized Decomposition Using Residuals (PeDecURe) for Nuisance Variable Adjustment in Multivariate Pattern Analysis

SO_SCPLOWUMMARYC_SCPLOWIn neuroimaging studies, multivariate methods provide a framework for studying associations between complex patterns distributed throughout the brain and neurological, psychiatric, and behavioral phenotypes. However, mitigating the influence of nuisance variables, such as confounders, remains a critical challenge in multivariate pattern analysis (MVPA). In studies of Alzheimers Disease, for example, imbalance in disease rates across age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly adjusted for, nuisance variables can obscure interpretations and preclude the generalizability of findings from neuroimaging studies. Motivated by this critical issue, in this work we examine the impact of nuisance variables on features extracted from image decomposition methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new MVPA method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between residualized imaging features and a variable of interest (e.g., Alzheimers diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between residualized imaging features and those variables. Using features estimated using PeDecURes first direction of variation, we train an accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower partial correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by MVPA in the context of neuroimaging, it is broadly applicable to datasets where the dimensionality or complexity of the covariance structure calls for novel methods to handle sources of nuisance variation.

neuroscience↗

bioRxiv2022

A snapshot of brain and cognition in healthy mid-life and older adults

Deficits in memory are seen as a canonical sign of ageing and a prodrome to dementia in older adults. However, the nature of cognitive and brain changes across a wider aperture of adulthood is not well known. We quantify the relationship between cognitive function and brain morphology from mid-life to older adulthood, and the influence of age, sex, amyloid and genetic risk for dementia. We analyzed three observational cohorts (PISA, AIBL, ADNI) with cognitive, genetic and neuroimaging measures comprising a total of 1570 healthy mid-life and older adults (mean age 72, range 49-90 years, 1330 males) and 1365 age- and sex-matched adults with mild cognitive impairment or Alzheimers disease. Among healthy adults, we find robust modes of co-variation between regional sulcal width and multidomain cognitive function that change from mid-life to the older age range. The most prominent cortical changes in mid-life are predominantly associated with changes in executive functions, whereas they are most strongly associated with poorer memory function in older age. These cognitive changes are accompanied by an age-dependent pattern of sulcal widening. Amyloid exerts a weak, but significant, influence on cognition, but not on sulcal width. The APOE {varepsilon}4 allele also exerts a weak influence on cognition, but only significantly in the (larger and older) AIBL cohort. These findings provide new insights into brain and cognition in mid-life and older adults, suggesting that cognitive screening in mid-life cohorts should encompass executive functions as well as memory.

neuroscience↗

bioRxiv2018

Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimers disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS, performing better than the classifiers trained and tested on each of these datasets independently. All the code of the framework and the experiments is publicly available.

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↗

bioRxiv2017

Multivariate genotypic analyses that identify specific genotypes to characterize disease and control groups in ADNI

INTRODUCTIONGenetic contributions to Alzheimers Disease (AD) are likely polygenic and not necessarily explained by uniformly applied linear and additive effects. In order to better understand the genetics of AD, we require statistical techniques to address both polygenic and possible non-additive effects.\n\nMETHODSWe used partial least squares-correspondence analysis (PLS-CA)--a method designed to detect multivariate genotypic effects. We used ADNI-1 (N = 756) as a discovery sample with two forms of PLS-CA: diagnosis-based and ApoE-based. We used ADNI-2 (N= 791) as a validation sample with a diagnosis-based PLS-CA.\n\nRESULTSWith PLS-CA we identified some expected genotypic effects (e.g., APOE/TOMM40, and APP) and a number of new effects that include, for examples, risk-associated genotypes in RBFOX1 and GPC6 and control-associated genotypes in PTPN14 and CPNE5.\n\nDISCUSSIONThrough the use of PLS-CA, we were able to detect complex (multivariate, genotypic) genetic contributions to AD, which included many non-additive and non-linear risk and possibly protective effects.

genomics↗