Biology subjects

Woollams, A.

Publications and source records attributed to Woollams, A..

5 recordsLinked to original sources

bioRxiv2026

Thalamic Nuclei Functional Controllability Accounts for Cognitive Impairment in Multiple Sclerosis Over and Above Structural Damage

BackgroundThe thalamus has emerged as a key region involved in cognitive dysfunction in multiple sclerosis (MS). While previous studies have identified associations between thalamic structural damage, altered functional connectivity, and cognitive performance, the specific contributions of individual thalamic nuclei and the added value of integrating structural and functional metrics remain poorly understood. MethodsT1-weighted MRI, diffusion MRI, resting-state fMRI, and neuropsychological data were collected from 102 individuals with MS and 27 healthy controls. Thalamic grey matter volume, white matter microstructural integrity, and functional controllability were calculated for each nucleus and compared between individuals with MS and healthy controls, as well as between MS cognitive subgroups. Partial Spearman correlations were used to examine the relationship between imaging metrics across the three modalities, and also between imaging metrics and cognitive performance in MS. Sparse canonical correlation analysis models were used to examine the covariance between thalamic imaging metrics and cognitive performance in MS. ResultsWidespread atrophy and microstructural damage were observed across all thalamic nuclei in individuals with MS, regardless of cognitive status. In contrast, alterations in functional controllability were more spatially specific, primarily affecting the medial dorsal anterior nuclei, and were most pronounced in cognitively impaired individuals. These functional controllability metrics were independent of grey matter volume, white matter integrity, and lesion load. Combining thalamic functional controllability with structural metrics yielded a stronger association with cognitive performance in MS than either modality alone. ConclusionThis study provides novel evidence that functional controllability in the thalamus, particularly within the medial dorsal anterior nuclei, plays a critical role in cognitive impairment in MS. By applying a network control framework, our findings offer a dynamic systems perspective that extends beyond traditional connectivity analyses, capturing the thalamuss role in supporting flexible cognitive transitions. The integration of structural and functional controllability metrics enhances the ability to characterise individual differences in cognitive performance and may inform future efforts to identify biomarkers of cognitive dysfunction in MS.

neuroscience↗

bioRxiv2026

Thalamic Nuclei Functional Controllability Explains Cognition Over and Above Grey and White Matter Structure

IntroductionThe thalamic nuclei play a crucial role in regulating information flow to the cortex and supports diverse cognitive functions. Although previous studies have linked thalamic structural and functional characteristics to cognition, these measures do not fully capture the thalamuss role in dynamic control, which is essential for complex cognitive processes. Moreover, it remains unclear how these different metrics relate to each other in the way they account for cognition. MethodsT1-weighted MRI, diffusion MRI, resting-state fMRI, and neuropsychological data were obtained from 419 unrelated participants in the Human Connectome Project. We measured grey matter volume, white matter integrity, and functional controllability of each thalamic nucleus to examine their associations with cognitive performance across domains identified through clustering analysis of the neuropsychological data. We also assessed the relationships among these structural and functional metrics and evaluated their individual and combined contributions in capturing covariance with performance in various cognitive domains. ResultsSignificant correlations were observed between thalamic grey matter volume and white matter integrity; however, thalamic functional controllability showed no significant association with either structural metric. White matter integrity demonstrated the strongest association with sequence working memory and language processing. In contrast, thalamic controllability metrics accounted more for performance in executive function, reasoning and encoding, visuospatial processing, and impulse control, outperforming the combination of grey and white matter structural metrics. ConclusionThis study highlights the critical role of the thalamus from a dynamic control perspective, demonstrating that thalamic structural and functional metrics provide complementary rather than redundant information related to cognitive performance. These findings underscore a promising new direction for understanding the complex and dynamic contributions of the thalamus to human cognition.

neuroscience↗

bioRxiv2023

Thalamic network controllability changes and cognitive impairment in multiple sclerosis

Recent research suggests that individuals with multiple sclerosis (MS) and cognitive impairment exhibit more effortful and less efficient transitions in brain network activity. Previous studies further highlight the increased vulnerability of specific regions, particularly the thalamus, to disease-related damage. This study investigates whether MS affects the controllability of specific brain regions in driving network activity transitions across the brain and examines the relationship between these changes and cognitive impairment in patients. Resting-state functional MRI and neuropsychological data were collected from 102 MS and 27 healthy controls. Functional network controllability analysis was performed to quantify how specific regions influence transitions between brain activity patterns or states. Disease alterations in controllability were assessed in the main dataset and then replicated in an independent dataset of 95 MS and 45 healthy controls. Controllability metrics were then used to distinguish MS from healthy controls and predict cognitive status. MS-specific controllability changes were observed in the subcortical network, particularly the thalamus, which were further confirmed in the replication dataset. Cognitively impaired patients showed significantly greater difficulty in the thalamus steering brain transitions towards difficult-to-reach states, which are typically associated with high-energy-cost cognitive functions. Thalamic network controllability proved more effective than thalamic volume in distinguishing MS from healthy controls (AUC = 88.3%), and in predicting cognitive status in MS (AUC = 80.7%). This study builds on previous research highlighting early thalamic damage in MS, aiming to demonstrate how this damage disrupts activity transitions across the cerebrum and may predict cognitive deficits. Our findings suggest that the thalamus in MS becomes less capable of facilitating broader brain activity transitions essential for high-energy-cost cognitive functions, implying a potential pathological mechanism that links thalamic functional changes to cognitive impairment in MS.

neuroscience↗

bioRxiv2023

Impact of brain parcellation on prediction error in models of cognition and demographics

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with 5 different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, Executive Function, Self-regulation, Language, Encoding and Sequence Processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme proved superior at predictive modelling across all cognitive domains and demographics. In addition, although parcellation schemes impacted the global organisation of each connectivity type, this difference could not account for the out-of-sample prediction performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by higher resolution of parcellation likely disrupts model generalisability.

neuroscience↗

bioRxiv2021

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function.

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve predictive models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify components reflecting five cognitive domains: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression (PCR) approach was then used to fit predictive models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Self-regulation, Encoding and Sequence Processing were best modelled by FC, whereas Executive Function and Language were best modelled by CC. The present study demonstrates that integrating structural and functional connectivity can help predict cognitive performance, but that the added explanatory value may be (cognitive) domain-specific. Implications of these results for studies of the brain basis of cognition in health and disease are discussed. HighlightsO_LIWe assessed the relationship between cognitive domains and structural, functional and combined structural-functional connectivity. C_LIO_LIWe found that Executive Function and Language components were best predicted by combined models of functional and structural connectivity. C_LIO_LISelf-regulation, Encoding and Sequence Processing were best predicted by functional connectivity alone. C_LIO_LIOur findings provide insight into separable contributions of functional, structural and combined connectivity to different cognitive domains. C_LI

neuroscience↗