Biology subjects

Cirstian, R.

Publications and source records attributed to Cirstian, R..

4 recordsLinked to original sources

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

Linking White-Matter Development to Clinical Variation in Autism: Longitudinal Normative Modelling of FA in the EU-AIMS LEAP Cohort

BackgroundAutism is characterised by neurobiological and clinical heterogeneity, which often limits the sensitivity of traditional group comparisons. Longitudinal normative modelling offers a way to capture heterogeneity and to describe an individuals brain-behaviour relationships beyond diagnostic categories. MethodsWe analysed diffusion MRI scans from 3 waves of the EU-AIMS Longitudinal European Autism Project (LEAP). Using normative models of white matter fractional anisotropy (FA) trained on a neurotypical reference cohort, we computed individual deviations relative to age-typical norms. Our sample included 544 individuals (Wave 1: 189 autistic 162 non-autistic; Wave 2: 162 autistic136 non-autistic; Wave 3: 145 autistic 84 non-autistic), aged 7-37 years, with mean intervals of [~]1.5 years (W1-W2) and [~]6 years (W2-W3). We first tested for group level differences between autistic and non-autistic participants in FA deviations across 48 white matter tracts and then examined the associations between these deviation scores and sensory, adaptive, cognitive, and behavioural measures across development within the autism group. ResultsGroup-wise comparisons revealed no significant differences in FA deviations between autistic and non-autistic participants. Within the autism group, multivariate analysis (CCA) showed significant (p<0.05) cross-sectional brain-behaviour associations at Wave 1 (r=0.26) and Wave 2 (r=0.32), but not at Wave 3. Longitudinally, we found significant multivariate associations only for the W1-W3 interval (r=0.36). Follow-up univariate correlations supported these findings, with Wave 2 showing widespread moderate (r{approx}0.35) associations while longitudinally, the univariate associations were generally weaker (r{approx}0.2). Across analyses, higher FA was generally associated with higher Vineland Adaptive Behaviour Scales (VABS) and lower Short Sensory Profile (SSP) scores. Associations with the Autism Diagnostic Observation Schedule (ADOS) were mixed, and the Social Responsiveness Scale (SRS) and Repetitive Behaviour Scale (RBS) showed wave-dependent relationships, with specific tract involvement varying across timepoints. ConclusionsWhite-matter organisation in autism does not differ from that of non-autistic on average but rather follows a dimensional architecture within the autism phenotype. We showed that person-specific deviations from shared neurodevelopmental pathways are linked to behavioural variation among autistic individuals, which cannot be captured by group-level approaches alone. Longitudinal normative modelling provides a practical approach to understand and quantify this heterogeneity. EU-AIMS LEAP GroupZuzana Suchomelova, Mee Rim Oh, Chirag Mehra, Mei Lin Law, Sanjana Gandhi, Laura Bravo Balsa, Maria Dauvermann, Isabel Yorke, Beth Oakley, Rosemary J. Holt, Edward Bullock, Yumnah Kahn, Esme Hayes, Leona Strauss, Noel Lam, Anouk Dykstra, Kim Lamers, Marije Mars, Lucas Geelen, Lotte Beckers, Anna Praat, Feline van Aagten, Sjors Reith, Viola Hollestein, Sanne Kluin, Natalie Forde, Jill Naaijen, Anna Kaiser, Sarah Baumeister, Pascal-M. Aggensteiner, Jumana Ahmad, Sara Ambrosino, Bonnie Auyeung, Tobias Banaschewski, Simon Baron-Cohen, Sarah Baumeister, Christian F. Beckmann, Sven Bolte, Thomas Bourgeron, Carsten Bours, Michael Brammer, Daniel Brandeis, Claudia Brogna, Yvette de Bruijn, Jan K. Buitelaar, Bhismadev Chakrabarti, Tony Charman, Ineke Cornelissen, Daisy Crawley, Flavio DellAcqua, Guillaume Dumas, Sarah Durston, Christine Ecker, Jessica Faulkner, Vincent Frouin, Pilar Garces, David Goyard, Lindsay Ham, Hannah Hayward, Joerg Hipp, Rosemary J. Holt, Mark H. Johnson, Emily J. H. Jones, Prantik Kundu, Meng-Chuan Lai, Xavier Liogier Dardhuy, Michael V. Lombardo, Eva Loth, David J. Lythgoe, Rene Mandl, Andre Marquand, Luke Mason, Maarten Mennes, Andreas Meyer-Lindenberg, Carolin Moessnang, Nico Mueller, Declan G. M. Murphy, Laurence ODwyer, Marianne Oldehinkel, Bob Oranje, Gahan Pandina, Antonio M. Persico, Jack Price, Annika Rausch, Barbara Ruggeri, Amber N. V. Ruigrok, Jessica Sabet, Roberto Sacco, Antonia San Jose Caceres, Emily Simonoff, Will Spooren, Julian Tillmann, Roberto Toro, Heike Tost, Jack Waldman, Steve C. R. Williams, Caroline Wooldridge, Iva Ilioska, Ting Mei, Marcel P. Zwiers. Key PointsO_ST_ABSQuestionC_ST_ABSIn the context of heterogeneity in autism, what do individual differences in white-matter development reveal about brain-behaviour relationships over time? FindingsIn this study, we found significant relationships between individual white-matter deviations and clinical traits in autism both cross-sectionally as well as longitudinally. Increased adaptive functioning and decreased sensory processing were associated with higher FA in long-association and commissural pathways, including the cingulum bundle, superior longitudinal fasciculus, and corpus-callosum, with longitudinal effects most evident over a 7.5-year interval. MeaningDevelopmentally informed normative modelling of white matter offers a pathway toward biologically grounded stratification for precision medicine in autism.

neuroscience↗

bioRxiv2024

Lifespan Normative Models of White Matter Fractional Anisotropy: Applications to Early Psychosis

This study presents large-scale normative models of white matter (WM) organization across the lifespan, using diffusion MRI data from over 25,000 healthy individuals aged 0-100 years. These models capture lifespan trajectories and inter-individual variation in fractional anisotropy (FA), a marker of white matter integrity. By addressing non-Gaussian data distributions, race, and site effects, the models offer reference baselines across diverse ages, ethnicities, and scanning conditions. We applied these FA models to the HCP Early Psychosis cohort and performed a multivariate analysis to map symptoms onto deviations from multimodal normative models using multi-view sparse canonical correlation analysis (msCCA). Our results reveal extensive white matter heterogeneity in psychosis, which is not captured by group-level analyses, with key regions identified, including the right uncinate fasciculus and thalami. These normative models offer valuable tools for individualized WM deviation identification, improving precision in psychiatric assessments. All models are publicly available for community use. TeaserLifespan models of white matter offer insights into brain health, providing tools for tracking individual deviations across ages.

neuroscience↗

bioRxiv2023

Objective QC for diffusion MRI data: artefact detection using normative modelling

Diffusion MRI is a neuroimaging modality used to evaluate brain structure at a microscopic level and can be exploited to map white matter fibre bundles and microstructure in the brain. One common issue is the presence of artefacts, such as acquisition artefacts, physiological artefacts, distortions or image processing-related artefacts. These may lead to problems with other downstream processes and can bias subsequent analyses. In this work we use normative modelling to create a semi-automated pipeline for detecting diffusion imaging artefacts and errors by modelling 24 white matter imaging derived phenotypes from the UK Biobank dataset. The considered features comprised 4 microstructural features (from models with different complexity such as fractional anisotropy and mean diffusivity from a diffusion tensor model and parameters from neurite orientation, dispersion and density models), each within six pre-selected white matter tracts of various sizes and geometrical complexity (corpus callosum, bilateral corticospinal tract and uncinate fasciculus and fornix). Our method was compared to two traditional quality control approaches: a visual quality control protocol performed on 500 subjects and quantitative quality control using metrics derived from image pre-processing. The normative modelling framework proves to be comprehensive and efficient in detecting diffusion imaging artefacts arising from various sources (such as susceptibility induced distortions or motion), as well as outliers resulting from inaccurate processing (such as erroneous spatial registrations). This is an important contribution by virtue of this methods ability to identify the two problem sources (i) image artefacts and (ii) processing errors, which subsequently allows for a better understanding of our data and informs on inclusion/exclusion criteria of participants.

neuroscience↗