Sir John Aston
Heather Shappell
Hidden Semi-Markov Models with Covariate-Dependent Dwell Times: Methods and Application to Brain Network Dynamics in Weight Loss
Hidden semi-Markov models (HSMMs) extend hidden Markov models by explicitly modeling state dwell times, providing greater flexibility for characterizing temporal dynamics. However, existing HSMM formulations for dynamic brain network analyses typically assume that dwell-time distributions are homogeneous across subjects, limiting their ability to assess associations with covariates. In this work, we extend the HSMM framework so that dwell times can depend on subject-level covariates. We do this by introducing a regression structure for the dwell-time distributions, considering both Poisson and Gamma models. Parameters are estimated using an expectation–maximization algorithm, and we use bootstrap resampling across participants to obtain standard errors and carry out inference on the regression coefficients. We first evaluate the approach in simulations, where it is able to recover covariate effects across a range of settings. We then apply the model to a study of weight loss in older adults, focusing on how Power of Food Scale scores relate to the amount of time individuals spend in different brain network states during a food cue task.
Thomas Yeo
TBD
Joanna Bayer
Camille Maumet
Damien Wasserman
Alejeandro de la Vega
NiCLIP: Neuroimaging contrastive language-image pretraining model for predicting text from brain activation images
We present NiCLIP, a contrastive language–image model for predicting cognitive tasks, concepts, and domains from brain activation patterns. Trained on more than 23,000 neuroscience articles, NiCLIP improves functional decoding by combining large language models with text-to-brain alignment, outperforming baseline LLM approaches and benefiting from full-text articles and curated cognitive ontologies. It accurately decodes group-level activation maps across multiple Human Connectome Project domains and helps characterize the functional roles of key brain regions.
Bertrand Thirion
Rezvan Farahibozorg
Dylan Nielson
Interpretable factorization of clinical questionnaires
One of the major goals of neuroimaging has been to understand the biological basis of psychiatric disorders. As much as we focus on improving neuroimaging, progress will also depend on having high quality measures of psychopathology. Psychopathology is typically measured through questionnaire scales or subscales. The creation and validation of these scales has canonically relied on factor analysis, but the resulting factors are not guaranteed to be interpretable, and are subject to confounding effects. Additionally, missing data is a common problem in the large datasets necessary for discovery or validation of latent factors of psychopathology. The use of factor analysis therefore requires some form of imputation. We overcome these limitations with a non-negative matrix factorization tailored for questionnaire data, Interpretability Constrained Questionnaire Factorization (ICQF). This method promotes factor interpretability by identifying a sparsely defined set of factors while constraining both weights and loadings to be between 0 and 1. Non-negativity means that factors are strictly additive, preventing the interpretational difficulties posed by cancellation of effects with negative weights. Incorporating a masking matrix allows us to handle missing data (random or non-random) without a separate imputation step. Our optimization procedure has theoretical convergence guarantees, and we have an automated procedure to determine latent dimensionality. We have validated these procedures in realistic synthetic data, as well as in the Healthy Brain Network and Adolescent Brain and Cognitive Development studies. ICQF provides more interpretable factors (as evaluated by domain experts) while preserving diagnostic information across a range of disorders, and outperforming competing methods in smaller datasets.
Mandy Mejia
TBC
Valentina Pacella
Elizabeth DuPre
James Pang
Linking brain structure and function through computational modelling: Advantages and pitfalls
Understanding how brain structure gives rise to large-scale neural dynamics remains a central challenge in neuroscience. Computational models provide a powerful framework for linking structural features of the brain, such as geometry and connectivity, to observable patterns of activity measured with multimodal neuroimaging. In this talk, I will discuss how whole-brain models are constructed, the assumptions they rely on, and what they have revealed about the relationship between structure and function in both human and non-human brains. I will provide examples of how modelling can move beyond correlation to provide mechanistic explanations. I will also present recent work from our team showing how structural constraints, such as cortical geometry and connectivity, shape large-scale wave-like brain dynamics. I will conclude by discussing both the opportunities and pitfalls of current modelling approaches, and providing an outlook on how integrating multiscale biological data may help advance the next generation of computational brain models.