Buckova, B. R. | Search bioRxiv

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

Amplitude entropy captures chimera-like behavior in epileptic seizure dynamics

Epilepsy is a neurological disease characterized by epileptic seizures, which commonly manifest with pronounced frequency and amplitude changes in the EEG signal. In the case of focal seizures, initially localized pathological activity spreads from a so-called "onset zone" to a wider network of brain areas. Chimeras, defined as states of simultaneously occurring coherent and incoherent dynamics in symmetrically coupled networks are increasingly invoked for characterization of seizures. In particular, chimera-like states have been observed during the transition from a normal (asynchronous) to a seizure (synchronous) network state. However, chimeras in epilepsy have only been investigated with respect to the varying phases of oscillators. We propose a novel method to capture the characteristic pronounced changes in the recorded EEG amplitude during seizures by estimating chimera-like states directly from the signals in a frequency- and time-resolved manner. We test the method on a publicly available intracranial EEG dataset of 16 patients with focal epilepsy. We show that the proposed measure, titled Amplitude Entropy, is sensitive to the altered brain dynamics during seizure, demonstrating its significant increases during seizure as compared to before and after seizure. This finding is robust across patients, their seizures, and different frequency bands. In the future, Amplitude Entropy could serve not only as a feature for seizure detection, but also help in characterizing amplitude chimeras in other networked systems with characteristic amplitude dynamics.

neuroscience↗

bioRxiv2023

Using normative models pre-trained on cross-sectional data to evaluate longitudinal changes in neuroimaging data

Longitudinal neuroimaging studies offer valuable insight into intricate dynamics of brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully harness the potential of longitudinal neuroimaging data, we have to develop and refine methodologies that are adapted to longitudinal designs, considering the complex interplay between population variation and individual dynamics. We build on normative modelling framework, which enables the evaluation of an individuals position compared to a population standard. We extend this framework to evaluate an individuals longitudinal change compared to the longitudinal change reflected by the (population) standard dynamics. Thus, we exploit the existing normative models pre-trained on over 58,000 individuals and adapt the framework so that they can also be used in the evaluation of longitudinal studies. Specifically, we introduce a quantitative metric termed "z-diff" score, which serves as an indicator of a temporal change of an individual compared to a population standard. Notably, our framework offers advantages such as flexibility in dataset size and ease of implementation. To illustrate our approach, we applied it to a longitudinal dataset of 98 patients diagnosed with early-stage schizophrenia who underwent MRI examinations shortly after diagnosis and one year later. Compared to cross-sectional analyses, which showed global thinning of grey matter at the first visit, our method revealed a significant normalisation of grey matter thickness in the frontal lobe over time. Furthermore, this result was not observed when using more traditional methods of longitudinal analysis, making our approach more sensitive to temporal changes. Overall, our framework presents a flexible and effective methodology for analysing longitudinal neuroimaging data, providing insights into the progression of a disease that would otherwise be missed when using more traditional approaches.

neuroscience↗