Biology subjects

Zabihi, M.

Publications and source records attributed to Zabihi, M..

11 recordsLinked to original sources

bioRxiv2026

A normative reference for large-scale human brain dynamics across the lifespan

Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

neuroscience↗

bioRxiv2026

Protocol Update: The Normative Modelling Paradigm for Computational Psychiatry

Normative Modelling ( brain growth charting) is now a well-established method for computational psychiatry and involves charting centiles of variation across a population in terms of mappings between biology and behavior, providing statistical inferences at the level of the individual. These models have helped the field to move away from case-control analysis toward individual-level analysis. Correspondingly, normative modelling has now been applied to chart brain development and ageing in many populations and has been used to quantify individual deviations across various neurological and psychiatric conditions. This has been supported by large-scale models that are openly accessible for diverse brain imaging modalities. As normative modelling continues to grow, several recent methodological developments, such as non-Gaussian models, longitudinal models, and federated learning, have been implemented in different software tools, including the Predictive Clinical Neuroscience toolkit (PCNtoolkit). In this protocol update, we provide: (i) a revised overview of this methodological landscape; (ii) an update to our 2022 standardised analytical protocol for normative modelling of neuroimaging data, including options for federated and longitudinal normative models; (iii) practical guidance suited to both novice and experienced practitioners supported by open-source code examples implemented in the refactored version of PCNtoolkit; and (iv) updated models for cortical thickness, surface area, volumetric data, functional connectivity and diffusion-weighted imaging for use by the community.

neuroscience↗

bioRxiv2025

Latent Representations of Early Brain Development: A Multivariate Normative Model of Brian Structure and Behaviour

Individual variation in neurodevelopment plays a central role in shaping cognitive abilities and behavioural profiles, influencing both typical functioning and risk for neurodevelopmental conditions. While much research has focused on characterising trajectories of brain structure changes during development, this typically entails assessing brain regions individually, overlooking the multivariate nature of neuroimaging data. In this study, we trained an autoencoder to map latent representations of childrens brain development using T1-weighted MRI scans from a paediatric cohort (n = 564, 55% male, mean age 4.90 years, age range [0.11,15.38]). This approach enabled us to establish multivariate normative models of brain structure, providing a more comprehensive framework for understanding neurodevelopmental variation. The latent space representation from this model effectively captured demographic variables (age and sex), while preserving both global and local structural features. The model accurately reconstructed the data, having mean reconstruction of 0.04 {+/-} 0.01, whilst also capturing demographic features with classification accuracy for sex of 84% {+/-} 4%, and a mean absolute error of 0.79 {+/-} 0.06 years for age prediction, highlighting its sensitivity to developmental changes. Further, we validated the approach using correlation analysis to show that deviations from the latent norms were significantly associated with multiple cognitive and behavioural measures, suggesting that variations in brain structure may reflect individual differences in neurodevelopment. Finally, we generated reference brain images that represent typical development and used them to visualise structural differences in individuals who deviate from this normative pattern. Our findings demonstrate that semi-supervised autoencoders, combined with multivariate normative modelling, offer a framework for characterizing neurodevelopmental trajectories. This approach can identify meaningful deviations associated with cognition and behaviour and has potential future applications across the lifespan.

developmental biology↗

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↗

bioRxiv2022

Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury

Accurately predicting emergence from disorders of consciousness (DoC) after acute brain injury can profoundly influence mortality, acute management, and rehabilitation planning. While recent advances in functional neuroimaging and stimulus-based EEG offer the potential to enrich shared decision-making, their procedural sophistication and expense limit widespread availability or repeated performance. We investigated continuous EEG (cEEG) within a passive, "resting-state" framework to provide continuously updated predictions of DoC recovery at 24-, 48-, and 72-hour prediction horizons. To develop robust, continuous prediction models from a large population of patients with acute brain injury (ABI), we leveraged a recently described pragmatic approach transforming Glasgow Coma Scale assessment sub-score combinations into frequently assessed DoC diagnoses: coma, vegetative state, minimally conscious state with or without language, and post-injury confusional or recovered states. We retrospectively identified consecutive patients undergoing cEEG following acute traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), or intracerebral hemorrhage (ICH). Models continuously predicting DoC diagnosis for multiple prediction horizons were evaluated utilizing recent clinical assessments with or without cEEG information, which comprised a comprehensive EEG feature set of 288 time, frequency, and time-frequency characteristics computed from consecutive 5-minute EEG epochs, with 6 additional features capturing each EEG features temporal dynamics. Features were fed into a predictive model developed with cross-validation; the ordinal DoC diagnosis was discriminated using an ensemble of XGBoost binary classifiers. For 201 ABI patients (46 TBI, 140 SAH, 15 ICH patients comprising 27,280 cEEG-hours with concomitant clinical assessments), cEEG-augmented models accurately predicted the future DoC diagnosis at 24 hours (one-vs-rest AU-ROC, 92.4%; weighted-F1 84.1%), 48 hours (one-vs-rest AU-ROC=88%, weighted-F1=80%) and 72 hours (one-vs-rest AU-ROC=86.3%, weighted-F1=76.6%). Models were robust to utilizing different ordinal cut-points for the DoC prediction target and evaluating additional models derived from specific sub-populations using a confound-isolating cross-validation framework. The most robust features across evaluation configurations included Petrosian fractal dimension, relative power of high to low (gamma-beta to delta-alpha) EEG frequency spectra, energy within the 12-35 Hz frequency band in the short-time Fourier transform domain, and wavelet entropy. The cEEG-augmented model exceeded the performance of models using preceding clinical assessments, continuously predicting future DoC diagnosis with one-vs-rest AU-ROC in the range of 84.3-92.4% while utilizing approaches to limit overfitting. The proposed continuous, resting-state cEEG prediction method represents a promising tool to predict DoC emergence in ABI patients. Enabling these methods prospectively would represent a new paradigm of continuous prognostic monitoring for predicting coma recovery and assessing treatment response.

neuroscience↗

bioRxiv2022

The Extremes of Normative Modelling

For many problems in neuroimaging, the most informative features occur in the tail of the distribution. For example, when considering psychiatric disorders as deviations from a norm, the tails of the distribution are considerably more informative than the bulk of the distribution for understanding risk, for stratifying and predicting such disorders and for anomaly detection. Yet, most statistical methods used in neuroimaging focus on modelling the bulk and fail to adequately capture extreme values occurring in the tails. To address this, we propose a framework that combines normative models with multivariate extreme value statistics to accurately model extreme deviations of a reference cohort for individual participants. Normative models are now widely used in clinical neuroscience and similar to the employment of normative growth charts in pediatric medicine to track a childs weight in relation to their age, normative models can be used with neuroimaging measurements to quantify individual neurophenotypic deviations from a reference cohort. However, formal statistical treatment of how to model the extreme deviations from these models has been lacking until now. In this article we provide such an approach, inspired by applications of extreme value statistics in meteorology. Since the presentation of extreme value statistics is usually quite technical, we begin with a non-technical introduction to the fundamental principles of extreme value statistics, to accurately map the tails of the normative distribution for biological markers, including mapping multivariate tail dependence across multiple markers. Next, we give a demonstration of this approach to the UK Biobank dataset and demonstrate how extreme values can be used to accurately estimate risk and detect atypicality. This framework provides a valuable tool for the statistical modelling of extreme deviations in neurobiological data, which could provide us with more accurate and effective diagnostic tools for neurological and psychiatric disorders.

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

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↗

bioRxiv2020

Atypical brain asymmetry in autism - a candidate for clinically meaningful stratification

BackgroundAutism Spectrum Disorder (henceforth autism) is a highly heterogeneous neurodevelopmental condition with few effective treatments for core and associated features. To make progress we need to both identify and validate neural markers that help to parse heterogeneity to tailor therapies to specific neurobiological profiles. Atypical hemispheric lateralization is a stable feature across studies in autism, however its potential of lateralization as a neural stratification marker has not been widely examined. MethodsIn order to dissect heterogeneity in lateralization in autism, we used the large EU-AIMS Longitudinal European Autism Project dataset comprising 352 individuals with autism and 233 neurotypical (NT) controls as well as a replication dataset from ABIDE (513 autism, 691 NT) using a promising approach that moves beyond mean-group comparisons. We derived grey matter voxelwise laterality values for each subject and modelled individual deviations from the normative pattern of brain laterality across age using normative modeling. ResultsResults showed that individuals with autism had highly individualized patterns of both extreme right- and leftward deviations, particularly in language-, motor- and visuospatial regions, associated with symptom severity. Language delay (LD) explained most variance in extreme rightward patterns, whereas core autism symptom severity explained most variance in extreme leftward patterns. Follow-up analyses showed that a stepwise pattern emerged with individuals with autism with LD showing more pronounced rightward deviations than autism individuals without LD. ConclusionOur analyses corroborate the need for novel (dimensional) approaches to delineate the heterogeneous neuroanatomy in autism, and indicate atypical lateralization may constitute a neurophenotype for clinically meaningful stratification in autism.

neuroscience↗

bioRxiv2018

Dissecting the heterogeneous cortical anatomy of autism spectrum disorder using normative models

BackgroundThe neuroanatomical basis of autism spectrum disorder (ASD) has remained elusive, mostly due to high biological and clinical heterogeneity among diagnosed individuals. Despite considerable effort towards understanding ASD using neuroimaging biomarkers, heterogeneity remains a barrier, partly because studies mostly employ case-control approaches, which assume that the clinical group is homogeneous.\n\nMethodsHere, we used an innovative normative modelling approach to parse biological heterogeneity in ASD. We aimed to dissect the neuroanatomy of ASD by mapping the deviations from a typical pattern of neuroanatomical development at the level of the individual and to show the necessity to look beyond the case-control paradigm to understand the neurobiology of ASD. We first estimated a vertex-wise normative model of cortical thickness development using Gaussian process regression, then mapped the deviation of each participant from the typical pattern. For this we employed a heterogeneous cross-sectional sample of 206 typically developing (TD) individuals (127 male), and 321 individuals (232 male) with ASD (aged 6-31).\n\nResultsWe found few case-control differences but the ASD cohort showed highly individualized patterns of deviations in cortical thickness that were widespread across the brain. These deviations correlated with severity of repetitive behaviors and social communicative symptoms, although only repetitive behaviors survived corrections for multiple testing.\n\nConclusionsOur results: (i) reinforce the notion that individuals with ASD show distinct, highly individualized trajectories of brain development and (ii) show that by focusing on common effects (i.e. the average ASD participant), the case-control approach disguises considerable inter-individual variation crucial for precision medicine.

neuroscience↗