Biology subjects

Kia, S. M.

Publications and source records attributed to Kia, S. M..

14 recordsLinked to original sources

bioRxiv2026

Accelerated neural aging as a systems-level vulnerability for tinnitus

Tinnitus, the perception of sound without an external source, affects 10-15% of individuals, yet its neural mechanisms remain poorly understood. Building on evidence that chronological age predicts tinnitus risk beyond hearing loss, we tested the hypothesis that accelerated neural aging increases susceptibility to tinnitus by integrating cross-sectional resting-state MEG with longitudinal structural imaging data. In a large MEG dataset, spectral parametrization revealed that age-neural relationships were amplified in the tinnitus group compared to age-, sex- & hearing-matched controls. Complementing these findings, prospective analyses using data from the UK Biobank showed that among participants without tinnitus at baseline, stronger age-related declines in white-matter density predicted later tinnitus onset. Collectively, these converging functional and structural findings support accelerated brain aging as a key risk factor for tinnitus.

neuroscience↗

bioRxiv2026

Distributional and Centile Calibration of Diffusion Tensor Imaging Normative Models as Training Sample Sizes Increase to 40,000 Subjects

Normative modeling (NM) is a powerful framework for quantifying individual deviations in brain structure and function relative to a population reference. However, its clinical utility depends on well-calibrated models trained on heterogeneous datasets such as those found in neuroimaging. Here, we systematically examine the effect of training sample size on the distributional and centile calibration of hierarchical Bayesian regression (HBR)-based NMs. Using multisite 3D diffusion MRI scans of the brain from 54,583 subjects, spanning almost the entire lifespan (age: 4-91 years), we trained NMs of white matter fractional anisotropy, a key microstructural metric, on subsamples ranging from 5,000 to 40,000 subjects. HBR was modeled with a Sinh-Arcsinh likelihood. Model calibration was evaluated using Kernelized Stein Discrepancy (KSD) to assess distributional agreement of Z-scores with the standard normal distribution; we also used Mean Absolute Centile Error (MACE) to quantify centile accuracy. Both metrics showed consistent and substantial improvements as the training sample size increased, indicating reduced posterior uncertainty and improved estimation of distributional parameters, particularly at the centile extremes. These results demonstrate that large training cohorts are essential for well-calibrated NMs derived from heterogeneous neuroimaging data and highlight the importance of large-scale data aggregation for reliable individual-level inference.

bioinformatics↗

bioRxiv2025

Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

Current electroencephalogram (EEG) denoising methods struggle to remove the complex physiological and environmental artifacts typical of real-world settings, which both hinders the isolation of true neural activity and limits the technologys translational potential. We present the Generalized Eigenvalue De-Artifacting Instrument (GEDAI), a novel algorithm for denoising highly contaminated EEG. GEDAI employs leadfield filtering to selectively remove noise and artifacts that diverge from a theoretically defined EEG forward model. This approach offers unique advantages over existing solutions, including 1) denoising of highly corrupt recordings without "clean" reference data, 2) single-step correction of artifactual epochs and bad channels, 3) unsupervised detection of brain and noise components based on the signal and noise subspace alignment index (SENSAI). In ground-truth simulations with synthetic and empirical EEG contaminated with realistic artifacts (EOG, EMG, noise), GEDAI globally outperformed leading denoising techniques based on principal component analysis (ASR) and independent component analysis (IClabel, MARA), revealing large effect sizes in challenging scenarios with simultaneous artifact mixtures, low signal-to-noise ratio (-9 dB), and high temporal contamination (up to 100%). Its superior denoising also enhanced neurobehavioral predictions, yielding highest accuracies in ERP classification and brain fingerprinting. GEDAIs autonomy, computational speed and noise-resilience could find future applications in 1) real- world medical, mobile and dry electrode EEG recordings 2) magnetoenecephalography (MEG) denoising (given the shared M/EEG forward model), and 3) real-time brain-computer interfaces (BCIs). The Matlab code for GEDAI is available as an open-source EEGLAB plugin at https://github.com/neurotuning/GEDAI-master

neuroscience↗

bioRxiv2025

MEGaNorm: Normative Modeling of MEG Brain Oscillations Across the Human Lifespan

Normative modeling provides a principled framework for quantifying individual deviations from typical brain development and is increasingly used to study heterogeneity in neuropsychiatric conditions. While widely applied to structural phenotypes, functional normative models remain underdeveloped. Here, we introduce MEGaNorm, the first normative modeling framework for charting lifespan trajectories of resting-state magnetoencephalography (MEG) brain oscillations. Using a large, multi-site dataset comprising 1,846 individuals aged 6-88 and spanning three MEG systems, we model relative oscillatory power in canonical frequency bands using hierarchical Bayesian regression, accounting for age, sex, and site effects. To support interpretation at multiple scales, we introduce Neuro-Oscillo Charts, visual tools that summarize normative trajectories at the population level and quantify individual-level deviations, enabling personalized assessment of functional brain dynamics. Applying this framework to a Parkinsons disease cohort (n = 160), we show that normative deviation scores reveal disease-related abnormalities and uncover a continuum of patients in theta-beta deviation space. This work provides the first lifespan-encompassing normative reference for MEG oscillations, enabling population-level characterization and individualized benchmarking. All models and tools are openly available and designed for federated, continual adaptation as new data become available, offering a scalable resource for precision neuropsychiatry.

neuroscience↗

bioRxiv2024

Lifespan Normative Modeling of Brain Microstructure

Normative models of brain metrics based on large populations could be extremely valuable for detecting brain abnormalities in patients with a variety of disorders, including degenerative, psychiatric and neurodevelopmental conditions, but no such models exist for the brains white matter (WM) microstructure. Here we present a large-scale normative model of brain WM microstructure - based on 19 international diffusion MRI datasets covering almost the entire lifespan (totaling N=54,583 individuals; age: 4-91 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol and used hierarchical Bayesian regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex. We extracted the average lifespan trajectories and corresponding centile curves for each WM region. We illustrate the utility of the method by applying it to detect and visualize profiles of WM microstructural deviations in a variety of contexts: in mild cognitive impairment, Alzheimers disease, and 22q11.2 deletion syndrome - a neurogenetic condition that markedly increases risk for schizophrenia. The resulting large-scale model provides a common reference to identify disease effects on the brains microstructure in individuals or groups, and to compare disorders, and discover factors affecting WM abnormalities. The derived normative models are a valuable resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.

neuroscience↗

bioRxiv2024

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

BackgroundInter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. MethodsWe applied Warped Bayesian Linear Regression normative models to T1- weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter- regional connections, specific brain regions, and spatially extended brain networks. ResultsThe specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. ConclusionsThe prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

neuroscience↗

bioRxiv2023

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using hierarchical Bayesian regression from N=40,435 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N=5,985) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born pre-term.

neuroscience↗

bioRxiv2022

Non-Gaussian Normative Modelling With Hierarchical Bayesian Regression

Normative modelling is an emerging technique for parsing heterogeneity in clinical cohorts. This can be implemented in practice using hierarchical Bayesian regression, which provides an elegant probabilistic solution to handle site variation in a federated learning framework. However, applications of this method to date have employed a Gaussian assumption, which may be restrictive in some applications. We have extended the hierarchical Bayesian regression framework to flexibly model non-Gaussian data with heteroskdastic skewness and kurtosis. To this end, we employ a flexible distribution from the sinh-arcsinh (SHASH) family, and introduce a novel reparameterisation and a Markov chain Monte Carlo sampling approach to perform inference in this model. Using a large neuroimaging dataset collected at 82 different sites, we show that the results achieved with this extension are equivalent or better than a warped Bayesian linear regression baseline model on most datasets, whilst providing better control over the parameters governing the shape of distributions that approach is able to model. We also demonstrate that the attained flexibility is useful for accurately modelling highly nonlinear relationships between aging and imaging derived phenotypes, which shows that the extension is important for pushing the field of normative modelling forward. All methods described here are available in the open-source pcntoolkit. HighlightsO_LIWe extended the Hierarchical Bayesian Regression framework for normative modelling C_LIO_LIOur extension allows modelling data with heteroskedastic skewness and kurtosis C_LIO_LIWe developed a reparameterization of the SHASH distribution, suitable for sampling C_LIO_LIWe provide the first implementation of the SHASH distribution in a fully Bayesian framework C_LIO_LIResults show that the extension outperforms current methods on various measures C_LI

neuroscience↗

bioRxiv2021

The Normative Modeling Framework for Computational Psychiatry

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus "healthy" control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of down-stream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling. The protocol takes approximately 1-3 hours to complete.

neuroscience↗

bioRxiv2021

Charting Brain Growth and Aging at High Spatial Precision

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.

neuroscience↗

bioRxiv2021

Normative modeling of neuroimaging data using generalized additive models of location scale and shape

Normative modeling aims to quantify the degree to which an individuals brain deviates from a reference sample with respect to one or more variables, which can be used as a potential biomarker of a healthy brain and as a tool to study heterogeneity of psychiatric disorders. The application of normative models is hindered by methodological challenges and lacks standards for the usage and evaluation of normative models. In this paper, we present generalized additive models for location scale and shape (GAMLSS) for normative modeling of neuroimaging data, a flexible modeling framework that can model heteroskedasticity, non-linear effects of variables, and hierarchical structure of the data. It can model non-Gaussian distributions, and it allows for an automatic model order selection, thus improving the accuracy of normative models while mitigating problems of overfitting. Furthermore, we describe measures and diagnostic tools suitable for evaluating normative models and step-by-step examples of normative modeling, including fitting several candidate models, selecting the best models, and transferring them to new scan sites.

neuroscience↗

bioRxiv2021

Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression

AO_SCPLOWBSTRACTC_SCPLOWClinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) for multi-site normative modeling. The proposed method completes the life-cycle of normative modeling by providing the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed federated framework closes the technical loop for applying normative modeling across multiple sites in a decentralized manner. This will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.

bioinformatics↗

bioRxiv2021

Non-linearity matters: a deep learning solution to the generalization of hidden brain patterns across population cohorts

Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ( latent indices) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.

neuroscience↗

bioRxiv2021

Accommodating site variation in neuroimaging data using hierarchical and Bayesian models

AO_SCPLOWBSTRACTC_SCPLOWThe potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance related to age and sex as biological variation of interest. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90 % of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modelling where the primary interest is in accurate modelling of inter-subject variation and statistical quantification of deviations from a reference model. 1 HighlightsO_LIDevelopment and presentation of normative modeling approach based on hierarchical Bayesian modeling that can be applied to large multi-site neuroimaging data sets. C_LIO_LIComparison of performance of Hierarchical Bayesian model including site as predictor to several common ways to harmonize for multi-site effects. C_LIO_LIPresentation of normative modeling as site correction tool. C_LI

bioinformatics↗