Biology subjects

Sachdev, P. S.

Publications and source records attributed to Sachdev, P. S..

11 recordsLinked to original sources

bioRxiv2026

Unified Multi-Cohort Harmonisation and Normative Modelling of Neuroimaging Data via Hierarchical GAMLSS

Large-scale neuroimaging studies increasingly pool data across multiple cohorts, scanners, and acquisition protocols, introducing technical between-cohort variation that must be addressed before meaningful biological inference can be drawn. Existing harmonisation methods, particularly ComBat-based approaches, have been widely adopted for this purpose. However, they remain limited by Gaussian assumptions and by their focus on location or location-scale correction. In this study, we propose a unified hierarchical Generalised Additive Models for Location, Scale and Shape (GAMLSS) framework for multi-cohort harmonisation and normative modelling of structural neuroimaging data. The framework models cohort effects directly within all fitted distributional parameters, accommodates any parametric family for which exact inverse mapping is available, and returns harmonised values on the original measurement scale through centile-based quantile mapping. Normative deviation scores are obtained as a direct by-product of the same fitted model, enabling harmonisation and normative inference to be conducted jointly. The method was evaluated in a pooled longitudinal dataset comprising 88,126 observations across 237 structural neuroimaging features from six cohorts spanning childhood to late life: ABCD, IMAGEN, NCANDA, LIFE, UK Biobank, and MAS. Harmonisation performance was compared with ComBat, ComBat-GAM, and ComBat-LS using complementary criteria assessing data retention, residual batch effects, preservation of age-related and sex-related biological signal, and coherence of post-harmonisation lifespan trajectories. GAMLSS achieved near-complete removal of residual cohort effects, retained almost all valid observations post-harmonisation, and showed the strongest overall preservation of biological signal across validation metrics. In particular, it better preserved biologically plausible age trajectories for distributionally complex features such as white matter hypointensity volume, while simultaneously providing harmonised native-scale values and normative deviation scores within a single framework. These findings suggest that hierarchical GAMLSS offers a flexible and practical alternative to existing ComBat-based methods for large-scale neuroimaging harmonisation, particularly for features with non-Gaussian residual distributions and settings where cohort effects extend beyond differences in mean and variance.

neuroscience↗

bioRxiv2025

Associations between volumes of grey matter regions and white matter hyperintensities with cognitive empathy in oldest-old adults

OBJECTIVESTo study the differential associations of regional volumes of grey matter (GM) and white matter hyperintensities (WMH) with behavioural and informant-reported measures of cognitive empathy (CE), especially in brain regions understood to have functional connectivity involvement with CE abilities. METHODSCommunity-dwelling participants from the Sydney Memory and Ageing Study (Sydney MAS) underwent whole brain MRI. Regional cortical GM volumes were derived using FreeSurfer v7.1 for, and WMH volumes were derived using UBO Detector. On follow-up four years later, CE was indexed via the Reading the Mind in the Eyes Test (RMET; a behavioural task) and Interpersonal Reactivity Index - Perspective Taking subscale (IRI-PT; an informant-reported measure). RESULTSIn 129 participants (mean age 87.01 years), Structural equation modelling (SEM) showed associations between insula GM volumes and RMET scores (p=0.001), and between supramarginal gyrus (SMG) GM volumes and IRI-PT scores (p=0.016). Associations remained significant after inclusion of covariates accounting for age, global cognition, affective symptoms, and social networks. DISCUSSIONCE abilities assessed with RMET (a behavioural task) were positively associated with insula GM volumes. CE abilities on the IRI-PT (an informant-reported measure) were positively associated with SMG GM volumes. No associations between WMH volumes and CE measures were found. These findings may inform clinical workflows that use structural MRI investigations looking at social cognitive disturbances in older adults and provide further evidence of the divergent nature of behavioural and self-report measures of CE.

neuroscience↗

bioRxiv2025

Unveiling the evolutionary code of NOTCH3: mammalian bioinformatics sheds light on human pathogenicity.

NOTCH3 is a highly conserved transmembrane receptor implicated in CADASIL, a hereditary small vessel disease driven by mutations in its extracellular EGF-like repeats. The mechanism by which these mutations cause pathology remains unclear. We present the first large-scale comparative bioinformatic analysis of NOTCH3 across 113 mammalian species, uncovering three novel insights: i) a remarkable evolutionary conservation of all 204 cysteines, with the only exception being eight naturally occurring cysteine mutations in jaguar (EGFr13-15); ii) a unique deletion in Brandts bat regulatory region, which may expose it to proteases, potentially altering signaling; iii) a rare human NOTCH3-X1 isoform, absent in most mammals but shared with select primates, a bat, and elephants, involving a cysteine-depleting deletion spanning EGFr20-22. These features provide novel evolutionary insights into human pathogenicity and suggest testable targets for in vivo experiments. Our study highlights the potential of comparative bioinformatics to identify previously hidden functional elements in disease-associated mammalian proteins. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/678708v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@5b54cforg.highwire.dtl.DTLVardef@1d0725org.highwire.dtl.DTLVardef@1c0417corg.highwire.dtl.DTLVardef@10b4b5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

An Enhanced Conditional Variational Autoencoder-Based Normative Model for Neuroimaging Analysis

Normative modelling in neuroimaging provides a powerful framework for quantifying individual deviations from expected brain measures as a function of relevant covariates. While most established approaches have focused on analysing distinct variables in isolation, there is a growing need for deep learning-based methods capable of handling multiple response variables simultaneously. Conditional variational autoencoders (cVAEs) have previously been applied in this context; however, existing inference methods still face challenges in providing reliable probabilistic predictions. In this study, we introduce an enhanced cVAE-based framework for normative modelling of neuroimaging data. Our key contribution is the development of a novel inference method that integrates latent space sampling and bootstrapping. This method directly leverages the generative nature of cVAEs to estimate the conditional distributions of brain measures. We demonstrate the effectiveness of this approach using white matter hyperintensity (WMH) volumes as a test case for developing, testing, and validating the model. Our dataset includes 8,551 normotensive and 18,180 hypertensive participants from the UK Biobank. We evaluated our method against three well-established and popular normative modelling techniques, including Generalised Additive Models for Location, Scale, and Shape (GAMLSS), Multivariate Fractional Polynomial Regression (MFPR), and Hierarchical Bayesian Regression (HBR). Our results indicate that the proposed cVAE-based framework achieves comparable performance across various metrics, while capturing individual deviations that correlate with the severity of hypertension. The model is particularly well-suited for high-dimensional, non-linear data, making it a robust tool for assessing individual deviations in brain structure. This enhanced normative modelling architecture paves the way for more nuanced and reliable assessments of brain health, with potential implications for personalised medicine and early detection of neurological disorders.

neuroscience↗

bioRxiv2024

Identification of Circular RNAs associated with Ageing of the Dorsolateral Prefrontal Cortex across the Adult Lifespan

BackgroundCircular RNAs (circRNAs) are emerging as crucial regulators of biological processes and have been implicated in age-related diseases. Few studies have explored age-related circRNA expression in the human brain across the adult lifespan. This study aims to identify age-related differentially expressed circRNAs in human post-mortem dorsolateral prefrontal cortex (DLPFC) samples, a region critically involved in cognition that exhibits early signs of age-related changes. MethodsTotal RNA sequencing was conducted on a discovery cohort of 67 postmortem DLPFC samples, from individuals with no neurological disease diagnosis at the time of death (35-103 years). CircRNAs were identified using CIRCexplorer2, with 11,907 circRNAs available for analyses. Linear regression was used to analyse the relationships between circRNA expression and age at death. Replication of the results was performed in an independent neurologically healthy dataset from the CommonMind Consortium (n=321, age at death: 35-91 years). Co-expression network analysis was performed to identify modules of highly co-expressed circRNAs associated with age. Potential microRNA and RNA-binding protein target sites were predicted. ResultsIn the discovery dataset, 37 circRNAs were age-associated (FDR <0.05). Seven out of the 37 were successfully replicated. The host genes of replicated age-associated circRNAs are implicated in synapse regulation. Co-expression analysis revealed two circRNA modules significantly correlated with age. We identified 484 microRNA and 99 RNA-binding protein target sites on the replicated circRNAs. ConclusionSeven age-associated circRNAs were identified as important candidates for involvement in post-transcriptional regulatory networks in the DLPFC. Future studies should aim to elucidate their functional roles in brain ageing.

genetics↗

bioRxiv2024

Alzheimer's disease induced neurons bearing PSEN1 mutations exhibit reduced excitability

Alzheimers disease (AD) is a devastating neurodegenerative condition that affects memory and cognition, characterized by neuronal loss and currently lacking a cure. Mutations in PSEN1 (Presenilin 1) are among the most common causes of early-onset familial AD (fAD). While changes in neuronal excitability are believed to be early indicators of AD progression, the link between PSEN1 mutations and neuronal excitability remains to be fully elucidated. This study examined induced pluripotent stem cell (iPSC)-derived NGN2 induced neurons (iNs) from fAD patients with PSEN1 mutations S290C or A246E, alongside CRISPR-corrected isogenic cell lines, to investigate early changes in excitability. Electrophysiological profiling revealed reduced excitability in both PSEN1 mutant iNs compared to their isogenic controls. Neurons bearing S290C and A246E mutations exhibited divergent passive membrane properties compared to isogenic controls, suggesting distinct effects of PSEN1 mutations on neuronal excitability. Additionally, both PSEN1 backgrounds exhibited higher current density of voltage-gated potassium (Kv) channels relative to their isogenic iNs, while displaying comparable voltage-gated sodium (Nav) channel current density. This suggests that the Nav/Kv imbalance contributes to impaired neuronal firing in fAD iNs. Deciphering these early cellular and molecular changes in AD is crucial for understanding the disease pathogenesis.

cell biology↗

bioRxiv2022

Brain structural covariances in the ageing brain in the UK Biobank

Brain structural covariances or pairwise correlations describe how morphologic properties of brain regions are related to one another across individuals. Although it is reported that brain structural covariance changes during brain development, it is not clear how structural covariance relates to the ageing process. Here we investigated the human brain structural covariances of cortical thickness and subcortical volumes in the ageing brain and their associations with age, cognition, and longevity polygenic risk score (longevity-PRS) by using cross-sectional data from the UK Biobank (N = 42075, aged 45-83 years, M/F=19752 /22323). The sample of participants was divided into 84 non-overlapping groups based on their age. The older the age group, the greater the variability in the whole brain structural covariance. However, there was a differential rate of age-related increase of variance between males and females. The variance of females started lower than those of males and then increased with age with a greater gradient than that of males. There was a consistent and significant enrichment of pairwise correlations within the occipital lobe in ageing process. The cortical thickness and subcortical covariances in older groups were significantly different from those in the youngest group. Sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations were significantly associated with age after Bonferroni correction. Specifically, with an increasing age, most decreased cortical thickness correlations were found between the regions within the frontal lobe as well as between the frontal lobe regions and regions in other lobes, while pairwise correlations within occipital lobe regions were all strengthening. Most of these correlations were also associated with global cognition and weakly associated with longevity-PRS. These findings revealed that the structural covariance was not stable during ageing. Given the thinning of the cortex and the volumetric reduction of subcortical structures seen in the ageing process, an increased pairwise correlation between the brain regions in the older brain suggested a strengthened coordinated decline between the brain regions involved. However, some of the brain regions demonstrated a differentiated rate of decline which was shown as the inversed or reduced pairwise correlations between these regions.

neuroscience↗

bioRxiv2020

Genetic and environmental determinants of variation in the plasma lipidome of older Australian twins

The critical role of blood lipids in a broad range of health and disease states is well recognised, while an understanding of the complex genetic regulation of lipid homeostasis is emerging. Traditional blood lipids (LDL-C, HDL-C and triglycerides) are known to be substantially regulated by genetic variation. Less well explored is the interplay of genetics and environment within the broader blood lipidome. Here we use the twin model to examine heritability of the plasma lipidome among healthy older aged twins and explore gene expression and epigenetic (DNA methylation) associations of these lipids. Heritability of 209 plasma lipids quantified by liquid chromatography coupled mass spectrometry (LC-MS) was assessed in 75 monozygotic and 55 dizygotic twin pairs enrolled in the Older Australian Twins Study (OATS), aged 69-93 years. Only 27/209 lipids (13.3%) were significantly heritable under the classical ACE twin model (h2 = 0.28-0.59). Ceramides (Cer) and triglycerides (TG) were most heritable, while sphingomyelins (SM) and most phospholipids, especially lysophospholipids, were not significantly heritable. Lipid levels correlated with 3731 transcripts. Relative to non-significantly heritable TGs, heritable TGs had a greater number of associations with gene transcripts, which were not directly associated with lipid metabolism, but with immune function, signalling and transcriptional regulation. Genome-wide average DNA methylation (GWAM) levels accounted for a proportion of variability in some non-heritable lipids, especially lysophosphatidylcholine (LPC). We found a complex interplay of genetic and environmental influences on the ageing plasma lipidome, with most of the variation controlled by unique environmental influences.

genetics↗

bioRxiv2020

Age-related changes of Peak width Skeletonized Mean Diffusivity (PSMD) across the adult life span: a multi-cohort study

Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and meta-analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing DTI metrics. For each individual MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40 and 50, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process.

neuroscience↗

bioRxiv2017

More Highly Myelinated White Matter Tracts are Associated with Faster Processing Speed in Healthy Adults

The objective of this study was to investigate whether the myelin content of white matter tracts is predictive of cogni-tive processing speed and whether such associations are modulated by age. Associations between myelin content and processing speed was assessed in 570 community-living individuals (277 middle-age, 293 older-age). Myelin content was measured using the mean T1w/T2w magnetic resonance ratio, in six white matter tracts (anterior corona radiata, superior corona radiata, pontine crossing tract, anterior limb of the internal capsule, genu of the corpus callosum, and splenium of the corpus callosum). Processing speed was estimated by extracting a principal component from 5 sep-arate tests of processing speed. It was found that myelin content of the bilateral anterior limb of the internal capsule and left splenium of the corpus callosum were significant predictors of processing speed, even after controlling for socio-demographic, health and genetic variables and correcting for multiple comparisons. A 1 SD increase in the myelin content of the anterior limb of the internal capsule was associated with 2.53% increase in processing speed and within the left splenium of the corpus callosum with a 2.20% increase in processing speed. In addition, significant differences in myelin content between middle-age and older participants were found in all six white matter tracts. The present results indicate that myelin content, estimated in vivo using a neuroimaging approach in healthy older adults is sufficiently precise to predict variability in processing speed in behavioural measures.

neuroscience↗

bioRxiv2017

The independent influences of age and education on functional brain networks and cognition in healthy older adults

Healthy ageing is accompanied by a constellation of changes in cognitive processes and alterations in functional brain networks. The relationships between brain networks and cognition during ageing in later life are moderated by demographic and environmental factors, such as prior education, in a poorly understood manner. Using multivariate analyses, we identify three latent patterns (or modes) linking resting-state functional connectivity to demographic and cognitive measures in 101 cognitively-normal elders. The first mode (p=0.00043) captures an opposing association between age and core cognitive processes such as attention and processing speed on functional connectivity patterns. The functional subnetwork expressed by this mode links bilateral sensorimotor and visual regions through key areas such as the parietal operculum. A strong, independent association between years of education and functional connectivity loads onto a second mode (p=0.012), characterised by the involvement of key hub-regions. A third mode (p=0.041) captures weak, residual brain-behaviour relations. Our findings suggest that circuits supporting lower-level cognitive processes are most sensitive to the influence of age in healthy older adults. Education, and to a lesser extent, executive functions, load independently onto functional networks - suggesting that the moderating effect of education acts upon networks distinct from those vulnerable with ageing. This has important implications in understanding the contribution of education to cognitive reserve during healthy ageing.

neuroscience↗