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Barkhof, F.

Publications and source records attributed to Barkhof, F..

At least 19 recordsLinked to original sources

Normative Modeling of Molecular-Enriched Functional Connectivity for Detecting Deviations from Healthy Brain Aging

Inter-individual variability in adult brain aging can obscure early pathological alterations and is only partly represented by population-average or scalar brain-aging measures. We combined Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) with normative modeling (NM) to derive spatially resolved, molecularly informed deviation scores for dopamine transporter (DAT)-, norepinephrine transporter (NET)-, and serotonin transporter (SERT)-enriched resting-state functional connectivity (FC). We first evaluated whether these normative models could be transferred to independently processed external data through local calibration and then explored whether the resulting deviation profiles differed with cerebral amyloid burden in cognitively unimpaired older adults. Hierarchical Bayesian regression (HBR) models with a SHASHb likelihood were estimated separately for 204 cortical molecular-enriched FC features in 4,152 healthy adults (18.0--89.8 years) from seven publicly available neuroimaging datasets and evaluated in a held-out healthy test set. Pretrained models were locally adapted to three AMYPAD-PNHS acquisition batches using cognitively unimpaired, amyloid-negative participants (global Clinical Dementia Rating [CDR] = 0; Centiloid [CL] [lt] 10). The independent primary comparison contrasted participants with intermediate amyloid burden (10 [≤] CL [lt] 30; n = 111) and amyloid-positive participants (CL [≥] 30; n = 66); secondary analyses tested linear associations with continuous CL within participants with CL [≥] 10. Of 204 normative reference models, 199 (97.5%) met the predefined diagnostic criteria; median held-out explained variance (EXPV) was 0.174. Of 612 feature-by-batch transfers, 541 (88.4%) met the strict transfer-diagnostic criteria. In the primary false discovery rate (FDR)-controlled regional analysis, DAT-enriched right caudal middle frontal cortex showed lower locally standardized deviation scores in the intermediate-amyloid-burden group than in the amyloid-positive group (adjusted difference = -0.582, q = 0.016). DAT extreme-deviation burden was also greater in the intermediate-amyloid-burden group (difference = 0.032, q = 0.044). The right frontal effect was reproduced with model-native scores across the three acquisition batches (pooled standardized effect = -0.698, q = 0.010). No NET- or SERT-enriched regional difference between these two groups survived FDR correction, and no regional or subject-level linear association with continuous CL values survived FDR correction in the CL [≥] 10 group. Normative modeling can provide spatially resolved reference distributions of molecular-enriched FC that are deployable in independently processed external data when local adaptation, calibration, and feature-level transfer diagnostics are incorporated. The AMYPAD-PNHS application identified modest, spatially selective, and molecular-system-specific categorical differences during a cognitively unimpaired stage of amyloid accumulation, while continuous analyses within CL [≥] 10 did not support a linear association. These findings support a methodological basis for distributed application of molecular-enriched normative models and motivate independent and longitudinal evaluation of their biological relevance.

bioengineering↗

Multi-cohort, multi-sequence harmonisation for cerebrovascular brain age

IntroductionHigher brain-predicted age gaps (BAG), based on anatomical brain scans, have been associated with cognitive decline among elderly participants. Adding a cerebrovascular component, in the form of arterial spin labelling (ASL) perfusion MRI, can improve the BAG predictions and potentially increase sensitivity to cardiovascular health, a contributor to brain ageing and cognitive decline. ASL acquisition differences are likely to influence brain age estimations, and data harmonisation becomes indispensable for multi-cohort brain age studies including ASL. In this multi-cohort, multi-sequence study, we investigate harmonisation methods to improve the generalisability of cerebrovascular brain age. MethodsA multi-study dataset of 2608 participants was used, comprising structural T1-weighted (T1w), FLAIR, and ASL 3T MRI data. The single scanner training dataset consisted of 806 healthy participants, age 50{+/-}17, 18-95 years. The testing datasets comprised four cohorts (n=1802, age 67{+/-}8, 37-90 years). Image features included grey and white matter (GM/WM) volumes (T1w), WM hyperintensity volumes and counts (FLAIR), and ASL cerebral blood flow (CBF) and its spatial coefficient of variation (sCoV). Feature harmonisation was performed using NeuroComBat, CovBat, NeuroHarmonize, OPNested ComBat, AutoComBat, and RELIEF. ASL-only and T1w+FLAIR+ASL brain age models were trained using ExtraTrees. Model performance was assessed through the mean absolute error (MAE) and mean BAG. ResultsASL feature differences between cohorts decreased after harmonisation for all methods (p<0.05), mostly for RELIEF. Negative associations between age and GM CBF (b=-0.37, R2=0.13, unharmonised) increased after harmonisation for all methods (b<-0.42, R2>0.12) but weakened for RELIEF (b=-0.28, R2=0.14). In the ASL-only model, MAE improved for all harmonisation methods from 11.1{+/-}7.5 years to less than 8.8{+/-}6.2 years (p<0.001), while BAGs changed from 0.6{+/-}13.4 years to less than -1.03{+/-}7.92 years (p<0.001). For T1w+FLAIR+ASL, MAE (5.9{+/-}4.6 years, unharmonised) increased for all harmonisation methods non-significantly to above 6.0{+/-}4.9 years (p>0.42) and significantly for RELIEF (6.4{+/-}5.2 years, p=0.02), while BAGs non-significantly differed from -1.6{+/-}7.3 years to between -1.3{+/-}4.7 and -2.0{+/-}8.0 years (p>0.82). In general, the ASL-specific parameter harmonisation method AutoComBat performed nominally best. DiscussionHarmonisation of ASL features improves feature consistency between studies and also improves brain age estimations when only ASL features are used. ASL-specific parameter harmonisation methods perform nominally better than basic mean and scale adjustment or latent-factor approaches, suggesting that ASL acquisition parameters should be considered when harmonising ASL data. Although multi-modal brain age estimations were improved less by ASL-only harmonisation, possibly due to weaker associations between age and ASL features compared to T1w features feature importance, studies investigating pathological ASL-feature distributions might still benefit from harmonisation. These findings advocate for ASL-parameter specific harmonisation to explore associations between cardiovascular risk factors, brain ageing, and cognitive decline using multi-cohort ASL and cerebrovascular brain age studies.

neuroscience↗

Incorporating Multi-threshold Polygenic Risk in Hippocampal-based Normative Models Improves Cognitive Decline Prediction

Predicting cognitive decline using MRI measures of brain volume is challenging. Normative models capture the evolution of biomarkers over time in a healthy group, quantifying an individuals deviation from that norm. Here, we improve the precision of normative models for hippocampal volume, a key cognitive biomarker, by integrating multi-threshold polygenic scores (PGS) that capture genetic predisposition to its variability. We built Gaussian Process Regression (GPR) models on genetic, imaging, and demographic data from 23,997 UK Biobank (UKBB) participants, validating on 3,000 out-of-sample participants from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the European Prevention of Alzheimers Disease (EPAD) cohorts. The genetically-informed models improved associations significantly across five experimental designs and 13 key neurocognitive measures, including Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Alzheimers Disease Assessment Scale (ADAS), and importantly, improving prediction of future cognitive decline. These findings underscore the value of integrating multi-threshold PGS with neuroimaging-based predictive models for improving prognostication in neurodegenerative diseases.

bioinformatics↗

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↗

MRI-based computational model generation for cerebral perfusion simulations in health and ischaemic stroke

Cerebral perfusion models were found to be promising research tools to predict the impact of acute ischaemic stroke and related treatments on cerebral blood flow (CBF) linked to patients functional outcome. To provide insights relevant to clinical trials, perfusion simulations need to become suitable for group-level investigations, but computational studies to date have been limited to a few patient-specific cases. This study set out to overcome issues related to automated parameter inference, that restrict the sample size of perfusion simulations, by integrating neuroimaging data. Seventy-five brain models were generated using measurements from a cohort of 75 healthy elderly individuals to model resting-state CBF distributions. Computational perfusion model geometries were adjusted using healthy reference subjects T1-weighted MRI. Haemodynamic model parameters were determined from CBF measurements corresponding to arterial spin labelling perfusion MRI. Thereafter, perfusion simulations were conducted for 150 acute ischaemic stroke cases by simulating an occlusion and cessation of blood flow in the left and right middle cerebral arteries. The anatomical (geometrical) fitness of the brain models was evaluated by comparing the simulated grey and white matter (GM and WM) volumes to measurements in healthy reference subjects. Statistically significant, strong positive correlations were found in both cases (GM: Pearsons r 0.74, P-value< 0.001; WM: Pearsons r 0.84, P-value< 0.001). Haemodynamic parameter tuning was verified by comparing total volumetric blood flow rate to the brain in reference subjects and simulations resulting in Pearsons r 0.89, and P-value< 0.001. In acute ischaemic stroke cases, the simulated infarct volume using a perfusion-based proxy was 197{+/-}25 ml. Computational results showed excellent agreement with anatomical and haemodynamic literature data corresponding to T1-weighted, T2-weighted, and phase-contrast MRI measurements both in healthy scenarios and in acute ischaemic stroke cases. Simulation results represented solely worst-case stroke scenarios with large infarcts because compensatory mechanisms, e.g. collaterals, were neglected. The established computational brain model generation framework provides a foundation for population-level cerebral perfusion simulations and for in silico clinical stroke trials which could assist in medical device and drug development.

bioengineering↗

Cortical distribution of neurofilaments associates with pathological hallmarks and MRI measures of atrophy and diffusivity in Parkinson's disease

Increased neurofilament levels in biofluids, commonly used as a proxy for axonal degeneration in the brain, have been found in Parkinsons disease (PD), PD with dementia (PDD) and dementia with Lewy bodies (DLB), and correlate with MRI biomarkers. The aim of the current study was to unravel the regional distribution of cortical neurofilaments and their association with pathology load and MRI measures of atrophy and diffusivity in the post-mortem brain. Using a within-subject post-mortem MRI-pathology approach, we included 9 PD, 12 PDD/DLB and 18 age-matched control donors. Cortical thickness and mean diffusivity (MD) metrics were extracted respectively from 3DT1 and DTI at 3T in-situ MRI. After autopsy, pSer129 alpha-synuclein (pSer129-Syn), p-tau, and amyloid-beta, together with neurofilament light-chain (NfL) and phosphorylated neurofilament medium- and heavy-chain (p-NfM/H) immunoreactivity were quantified in 7 cortical regions, and studied in detail with confocal-laser scanning microscopy. The correlations between MRI and pathological measures were studied using linear mixed models. Compared to controls, p-NfM/H immunoreactivity was increased in all cortical regions in PD and PDD/DLB, whereas NfL immunoreactivity was mainly increased in the parahippocampal and entorhinal cortex in PDD/DLB. NfL-positive neurons showed degenerative morphological features and axonal fragmentation. Increased p-NfM/H correlated with p-tau load, and NfL correlated with pSer129-Syn but more strongly with p-tau load in PDD/DLB. Lastly, neurofilament immunoreactivity correlated with cortical thinning in PD and with increased cortical MD in PDD/DLB. Taken together, increased neurofilament immunoreactivity suggests underlying axonal injury and neurofilament accumulation in morphologically altered neurons with increasing pathological burden. Importantly, we demonstrate that such neurofilament markers at least partly explain MRI measures that are associated with the neurodegenerative process.

neuroscience↗

The European Prevention of Alzheimer's Dementia (EPAD) MRI Dataset and Processing Workflow

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimers Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features -- i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features -- Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) -- were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

neuroscience↗

Amyloid-beta, p-tau, and reactive microglia load are correlates of MRI cortical atrophy in Alzheimer's disease

INTRODUCTIONThe aim of this study was to identify the histopathological correlates of MRI cortical atrophy in (a)typical Alzheimers disease (AD) donors. METHODS19 AD and 10 control donors underwent post-mortem in-situ 3T-3DT1-MRI, from which cortical thickness was calculated. Upon subsequent autopsy, 21 cortical brain regions were selected and immunostained for amyloid-beta, phosphorylated-tau, and reactive microglia. MRI-pathology associations were assessed using linear mixed models. Post-mortem MRI was compared to ante-mortem MRI when available. RESULTSHigher amyloid-beta load weakly correlated with a higher cortical thickness globally. Phosphorylated-tau strongly correlated with cortical atrophy in temporo-frontal regions. Reactive microglia load strongly correlated with cortical atrophy in the parietal region. Post-mortem scans showed high concordance with ante-mortem scans acquired <1 year before death. DISCUSSIONDistinct histopathological markers differently correlate with cortical atrophy, highlighting their different roles in the neurodegenerative process. This study contributes in understanding the pathological underpinnings of MRI atrophy patterns.

neuroscience↗

Tau-first subtype of Alzheimer's disease consistently identified across in vivo and post mortem studies

Alzheimers disease (AD) is marked by the spread of misfolded amyloid-{beta} and tau proteins throughout the brain. While it is commonly believed that amyloid-{beta} abnormality drives the cascade of AD pathogenesis, several in vivo and post mortem studies indicate that in some subjects localized tau-based neurofibrillary tangles precede amyloid-{beta} pathology. This suggests that there may be multiple distinct subtypes of protein aggregation pathways within AD, with potentially different demographic, cognitive and comorbidity profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post mortem immunohistochemistry and in vivo positron emission tomography (PET) and cerebrospinal fluid (CSF) based measures of protein pathologies in two large observational cohorts. We consistently identified both amyloid-first and tau-first AD subtypes, where tau-first subjects had higher levels of soluble TREM2 compared to amyloid-first subjects. Our work provides insight into AD progression that may be valuable for interventional trials targeting amyloid-{beta} and tau.

neuroscience↗

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↗

TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer's disease biomarker levels

BackgroundNeurofilament light (NF-L), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are utilized as biomarkers for Alzheimers disease (AD), to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. Here we performed genome-wide association study (GWAS) analyses using all three biomarkers as outcome. MethodsDNA and cerebrospinal fluid (CSF) samples originated from the European Medical Information Framework AD Multimodal Biomarker Discovery (EMIF-AD MBD) study. Overlapping genotype/phenotype data were available for n=671 (NF-L), 677 (YKL-40), and 672 (Ng) individuals. GWAS analyses applied linear regression models adjusting for relevant covariates. FindingsWe identify novel genome-wide significant associations with markers in TMEM106B and CSF levels of NF-L. Additional novel signals were observed with DNA variants in CPOX and CSF levels of YKL-40. Lastly, we confirmed previous work suggesting that YKL-40 levels are regulated by cis protein quantitative trait loci (pQTL) in CHI3L1. InterpretationOur study provides important new insights into the genetic architecture underlying inter-individual variation in all three tested AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.

genomics↗

DNA methylation classification in diffuse glioma shows little spatial heterogeneity after adjusting for tumor purity

Intratumoral heterogeneity is a hallmark of diffuse gliomas. We used neuronavigation to acquire 133 image-guided and spatially-separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma, which we characterized using DNA methylation arrays. Samples were obtained from regions with and without imaging abnormalities. Methylation profiles were analyzed to devise a three-dimensional reconstruction of genetic and epigenetic heterogeneity. Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and largely explains apparent epigenetic spatial heterogeneity. Indeed, we observed that DNA methylation subtypes are highly conserved in space after adjusting for tumor purity. Genome-wide heterogeneity analysis showed equal or increased heterogeneity among normal tissue when compared to tumor. These findings were validated in a separate cohort of 61 multi-sector tumor and 64 normal samples. Our findings underscore the infiltrative nature of diffuse gliomas and suggest that heterogeneity in DNA methylation is innate to somatic cells and not a characteristic feature of this tumor type.

cancer biology↗

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. The toolbox adheres to previously defined international standards for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts to increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

neuroscience↗

Genome-wide association study of Alzheimer’s disease CSF biomarkers in the EMIF-AD Multimodal Biomarker Discovery dataset

Alzheimers disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Susceptibility to AD is considerably determined by genetic factors which hitherto were primarily identified using case-control designs. Elucidating the genetic architecture of additional AD-related phenotypic traits, ideally those linked to the underlying disease process, holds great promise in gaining deeper insights into the genetic basis of AD and in developing better clinical prediction models. To this end, we generated genome-wide single-nucleotide polymorphism (SNP) genotyping data in 931 participants of the European Medical Information Framework Alzheimers Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample to search for novel genetic determinants of AD biomarker variability. Specifically, we performed genome-wide association study (GWAS) analyses on 16 traits, including 14 measures of amyloid-beta (A{beta}) and tau-protein species in the cerebrospinal fluid (CSF). In addition to confirming the well-established effects of apolipoprotein E (APOE) on diagnostic outcome and phenotypes related to A{beta}42, we detected novel potential signals in the zinc finger homeobox 3 (ZFHX3) for CSF-A{beta}38 and CSF-A{beta}40 levels, and confirmed the previously described sex-specific association between SNPs in geminin coiled-coil domain containing (GMNC) and CSF-tau. Utilizing the results from independent case-control AD GWAS to construct polygenic risk scores (PRS) revealed that AD risk variants only explain a small fraction of CSF biomarker variability. In conclusion, our study represents a detailed first account of GWAS analyses on CSF-A{beta} and -tau related traits in the EMIF-AD MBD dataset. In subsequent work, we will utilize the genomics data generated here in GWAS of other AD-relevant clinical outcomes ascertained in this unique dataset.

genomics↗

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↗

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↗

Automated quantification of small vessel disease brain changes on MRI predicts cognitive and functional decline

Background and purposeCerebral small vessel disease (SVD) is characterized by a wide range of focal and global brain changes. We used automated MRI segmentation to quantify multiple types of SVD brain changes and examined their individual and combined predictive value on cognitive and functional abilities.\n\nMethodsMRI scans of 560 subjects of the Leukoaraiosis and Disability Study (LADIS) were analyzed using automated atlas- and convolutional neural network-based segmentation methods yielding volumetric measures of white matter hyperintensities (WMH), lacunes, cortical infarcts, enlarged perivascular spaces and regional brain atrophy. The subjects were followed up with annual neuropsychological examinations for 3 years and evaluation of instrumental activities of daily living for 7 years.\n\nResultsThe strongest predictors of cognitive performance and functional outcome over time were total volumes of WMH, grey matter (GM) and hippocampi (p<0.001 for global cognitive function, processing speed, executive functions and memory; and p<0.001 for poor functional outcome). Volumes of lacunes, cortical infarcts and EVPS were significantly associated with part of the outcome measures, but their contribution was weaker. In a multivariable linear mixed model, volumes of WMH, lacunes, GM and hippocampi remained as independent predictors of cognitive impairment. A combined measure of these markers based on z-scores strongly predicted cognitive and functional outcomes (p<0.001) even above the contribution of the individual brain changes.\n\nConclusionsGlobal burden of SVD-related brain changes as quantified by automated image segmentation is a powerful predictor of long-term cognitive decline and functional disability. A combined measure of WMH, lacunar, GM and hippocampal volumes could be used as an imaging identification model of vascular cognitive impairment.

neuroscience↗

Accelerated brain ageing and disability in multiple sclerosis

BackgroundBrain atrophy occurs in both normal ageing and in multiple sclerosis (MS), but it occurs at a faster rate in MS, where it is the major driver of disability progression. Here, we employed a neuroimaging biomarker of structural brain ageing to explore how MS influences the brain ageing process.\n\nMethodsIn a longitudinal, multi-centre sample of 3,565 MRI scans in 1,204 MS/clinically isolated syndrome (CIS) patients and 150 healthy controls (HCs) (mean follow-up time: patients 3*41 years, HCs 1*97 years) we measured brain-predicted age using T1-weighted MRI. Brain-predicted age difference (brain-PAD) was calculated as the difference between the brain-predicted age and chronological age. Positive brain-PAD indicates a brain appears older than its chronological age. We compared brain-PAD between MS/CIS patients and HCs, and between disease subtypes. In patients, the relationship between brain-PAD and Expanded Disability Status Scale (EDSS) at study entry and over time was explored.\n\nFindingsAdjusted for age, sex, intracranial volume, cohort and scanner effects MS/CIS patients had markedly older-appearing brains than HCs (mean brain-PAD 11*8 years [95% CI 9*1--14*5] versus -0*01 [-3*0--3*0], p<0*0001). All MS subtypes had greater brain-PAD scores than HCs, with the oldest-appearing brains in secondary-progressive MS (mean brain-PAD 18*0 years [15*4--20*5], p<0*05). At baseline, higher brain-PAD was associated with a higher EDSS, longer time since diagnosis and a younger age at diagnosis. Brain-PAD at study entry significantly predicted time-to-EDSS progression (hazard ratio 1*02 [1*01--1*03], p<0*0001): for every 5 years of additional brain-PAD, the risk of progression increased by 14*2%.\n\nInterpretationMS increases brain ageing across all MS subtypes. An older-appearing brain at baseline was associated with more rapid disability progression, suggesting brain-age could be an individualised prognostic biomarker from a single, cross-sectional assessment.\n\nFundingUK MS Society; National Institute for Health Research University College London Hospitals Biomedical Research Centre.

neuroscience↗