Biology subjects

Paton, B.

Publications and source records attributed to Paton, B..

8 recordsLinked to original sources

bioRxiv2025

Region-specific associations between cerebral arterial elasticity and grey matter volume: Evidence for lateral prefrontal vulnerability

BackgroundAgeing is associated with increased cardiovascular health risks and disproportionate atrophy in frontotemporal brain. Regional cerebral arterial elasticity correlates with regional grey matter volume, with stronger associations frontally and in older adults. Cardiorespiratory fitness (CRF) is linked to preserved brain structure and greater arterial elasticity, while sex differences exist in the timing of vascular versus structural changes. This study examines whether regional cerebral arterial elasticity in frontotemporal cortex mediates the association between age and corresponding grey matter volume decline, and whether sex and/or CRF moderate this relationship. MethodsWe analysed data from 162 healthy adults (60-70 years) with structural MRI and diffuse optical tomography of the cerebral arterial pulse (Pulse-DOT) from the ACTIVate cohort. CRF was estimated from demographic and physiological measures. Pulse Relaxation Function (PReFx), an optical index of regional arterial elasticity, was measured across 28 frontal, temporal, and parietal regions of interest (ROI). Grey matter (GM) volume was quantified for corresponding ROIs. Mediation and moderated mediation models tested whether PReFx mediated the relationship between age and GM volume at bilateral frontal and temporal ROIs, and whether biological sex or CRF moderated this relationship. ResultsRegional bivariate correlations identified associations between age, PReFx and GM volume across multiple ROIs, which PReFx and GM volume being associated primarily in left frontal and temporal areas. PReFx partially mediated the effect of age on GM volume in a left mid-inferior frontal ROI, accounting for approximately 16% of the total age effect and this relationship was only evident in females. Although higher CRF was associated with greater PReFx, it did not moderate the relationship between age, PReFx and GM volume. ConclusionsConsistent with cascade models of neurovascular aging, cerebral arterial stiffening was found to partially explain the effect of increasing age on frontal GM volume, even in this highly age-restricted and high functioning cohort. This effect was only significant over the left prefrontal cortex, consistent with greater vulnerability of frontal brain and associated cognitive functions. It was also exclusively present in females, who had better cardiovascular health, larger grey matter volume and greater arterial elasticity than males. These findings are consistent with pulse-DOT measures of cerebral arterial elasticity being more sensitive to subclinical brain structural variability.

neuroscience↗

bioRxiv2024

Spurious correlations in surface-based functional brain imaging

The study of functional MRI data is increasingly performed after mapping from volumetric voxels to surface vertices. Processing pipelines commonly used to achieve this mapping produce meshes with uneven vertex spacing, with closer neighbours in sulci compared to gyri. Consequently, correlations between the fMRI time series of neighbouring sulcal vertices are stronger than expected. However, the causes, extent, and impacts of this bias are not well understood or widely appreciated. We explain the origins of these biases, and using in-silico models of fMRI data, illustrate how they lead to spurious results. The bias leads to leakage of anatomical cortical folding information into fMRI time series. We show that many common analyses can be affected by this "gyral bias", including test-retest reliability, fingerprinting, functional parcellations, regional homogeneity, and brain-behaviour associations. Finally, we provide recommendations to avoid or remedy this spatial bias.

neuroscience↗

bioRxiv2024

Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes

The brain expresses activity in complex spatiotemporal patterns, reflected in the influence of spatially distributed cytoarchitectural, biochemical, and genetic properties. The correspondence between these multimodal "brain maps" may reflect underlying causal pathways and is hence a topic of substantial interest. However, these maps possess intrinsic smoothness (spatial autocorrelation, SA) which can inflate spurious cross-correlations, leading to false positive associations. Identifying true associations requires knowledge about the distribution of correlations that arise by chance in the presence of SA. This null distribution can be generated from an ensemble of surrogate brain maps that preserve the intrinsic SA but break the correlations between maps. The present work introduces the "eigenstrapping" method, which uses a spectral decomposition of cortical and subcortical surfaces in terms of geometric eigenmodes, and then randomly rotating these modes to produce SA-preserving surrogate brain maps. It is shown that these surrogates appropriately represent the null distribution of chance pairwise correlations, with similar or superior false positive control to current state-of-the-art procedures. Eigenstrapping is fast, eschews the need for parametric assumptions about the nature of a maps SA, and works with maps defined on smooth surfaces with or without a boundary. Moreover, it generalizes to broader classes of null models than existing techniques, offering a unified approach for inference on cortical and subcortical maps, spatiotemporal processes, and complex patterns possessing higher-order correlations.

neuroscience↗

bioRxiv2020

Faster model updating in autism during early sensory processing

BackgroundRecent theories of autism propose that a core deficit in autism would be a less context-sensitive weighting of prediction errors. There is also first support for this hypothesis on an early sensory level. However, an open question is whether this decreased context-sensitivity is caused by faster updating of ones model of the world (i.e. higher weighting of new information), proposed by predictive coding theories, or slower model updating. Here, we differentiated between these two hypotheses by investigating how first impressions shape the mismatch negativity (MMN), reflecting early sensory prediction error processing. MethodsAn autism and matched control group (both n=27) were compared on the multi-timescale MMN paradigm, in which tones were presented that were either standard (frequently occurring) or deviant (rare), and these roles reversed every block. A well-replicated observation is that the initial model (i.e. the standard and deviant sound in the first block) influences MMN amplitudes in later blocks. If autism is characterized by faster model updating, we hypothesized that their MMN amplitudes would be less influenced by the initial context. ResultsWe found that MMN responses in the autism group did not differ between the initial deviant and initial standard sounds as they did in the control group. ConclusionsThese results show that individuals with autism are less influenced by initial contexts, confirming that autism is characterized by faster updating of sensory models, as proposed by predictive coding accounts of autism.

neuroscience↗

bioRxiv2019

Hierarchical learning of statistical regularities over multiple timescales of sound sequence processing: A dynamic causal modelling study

The nervous system is endowed with predictive capabilities, updating neural activity to reflect recent stimulus statistics in a manner which optimises processing of expected future states. This process has previously been formulated within a predictive coding framework, where sensory input is either \"explained away\" by accurate top-down predictions, or leads to a salient prediction error which triggers an update to the existing prediction when inaccurate. However, exactly how the brain optimises predictive processes in the stochastic and multi-faceted real-world environment remains unclear. Auditory evoked potentials have proven a useful measure of monitoring unsupervised learning of patterning in sound sequences through modulations of the mismatch negativity component which is associated with \"change detection\" and widely used as a proxy for indexing learnt regularities. Here we used dynamic causal modelling to analyse scalp-recorded auditory evoked potentials collected during presentation of sound sequences consisting of multiple, nested regularities and extend on previous observations of pattern learning restricted to the scalp level or based on single-outcome events. Patterns included the regular characteristics of the two tones presented, consistency in their relative probabilities as either common standard (p = .875) or rare deviant (p = .125), and the regular rate at which these tone probabilities alternated. Significant changes in connectivity reflecting a drop in the precision of prediction errors based on learnt patterns were observed at three points in the sound sequence, corresponding to the three hierarchical levels of nested regularities: (1) when an unexpected \"deviant\" sound was encountered; (2) when the probabilities of the two tonal states altered; and (3) when there was a change in rate at which probabilities in tonal state changed. These observations provide further evidence of simultaneous pattern learning over multiple timescales, reflected through changes in neural activity below the scalp.\n\nAuthor summaryOur physical environment is comprised of regularities which give structure to our world. This consistency provides the basis for experiential learning, where we can increasingly master our interactions with our surroundings based on prior experience. This type of learning also guides how we sense and perceive the world. The sensory system is known to reduce responses to regular and predictable patterns of input, and conserve neural resources for processing input which is new and unexpected. Temporal pattern learning is particularly important for auditory processing, in disentangling overlapping sound streams and deciphering the information value of sound. For example, understanding human language requires an exquisite sensitivity to the rhythm and tempo of speech sounds. Here we elucidate the sensitivity of the auditory system to concurrent temporal patterning during a sound sequence consisting of nested patterns over three timescales. We used dynamic causal modelling to demonstrate that the auditory system monitors short, intermediate and longer-timescale patterns in sound simultaneously. We also show that these timescales are each represented by distinct connections between different brain areas. These findings support complex interactions between different areas of the brain as responsible for the ability to learn sophisticated patterns in sound even without conscious attention.

neuroscience↗

bioRxiv2018

Transdiagnostic variations in impulsivity and compulsivity in obsessive-compulsive disorder and gambling disorder correlate with effective connectivity in cortical-striatal-thalamic-cortical circuits.

BackgroundIndividual differences in impulsivity and compulsivity is thought to underlie vulnerability to a broad range of disorders and are closely tied to cortical-striatal-thalamic-cortical (CSTC) function. However, whether impulsivity and compulsivity in clinical disorders is continuous with the healthy population and explains CSTC dysfunction across different disorders remains unclear.\n\nMethodsWe characterized the relationship between CSTC effective connectivity, estimated using dynamic causal modelling of functional magnetic resonance imaging data, and dimensional phenotypes of impulsivity and compulsivity in two symptomatically distinct but phenotypically related disorders, obsessive-compulsive disorder (OCD) and gambling disorder (GD). 487 online participants provided data for modelling of dimensional phenotypes. These data were combined with 34 OCD patients, 22 GD patients, and 39 healthy controls, who underwent functional magnetic resonance imaging.\n\nResultsThree core dimensions were identified: disinhibition, impulsivity, and compulsivity. Patients scores on these dimensions were continuously distributed with the healthy participants, supporting a continuum model of psychopathology. Across all participants, higher disinhibition correlated with lower bottom-up connectivity in the dorsal circuit and increased bottom-up connectivity in the ventral circuit, and higher compulsivity correlated with reduced bottom-up connectivity in the dorsal circuit. Similar changes in effective connectivity were observed with increasing clinical severity that were not accounted for by phenotypic variation, demonstrating convergence towards behaviourally and clinically relevant changes in brain dynamics. Effective connectivity did not differ as a function of traditional diagnostic labels.\n\nConclusionsCSTC dysfunction across OCD and GD is better characterized by dimensional phenotypes than diagnostic comparisons, supporting investigation of quantitative liability phenotypes.

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bioRxiv2017

Deep Brain Stimulation for Parkinson’s disease changes perception in the Rubber Hand Illusion

Parkinsons disease (PD) alters cortico-basal ganglia-thalamic circuitry and susceptibility to an illusion of bodily awareness, the Rubber Hand Illusion (RHI). Bodily awareness is thought to result from multisensory integration in a predominantly cortical network; the role of subcortical connections is unknown. We studied the effect of modulating cortico-subcortical circuitry on multisensory integration for bodily awareness in PD patients treated with subthalamic nucleus (STN) deep brain stimulation (DBS) using the RHI experiment. Typically, synchronous visuo-tactile cues induce a false perception of touch on the rubber hand as if it were the subjects hand, whereas asynchronous visuo-tactile cues do not. However, we found that in the asynchronous condition, patients in the off-stimulation state did not reject the RHI as strongly as healthy controls; switching on STN-DBS partially normalised their responses. Patients in the off-stimulation state also misjudged the position of their hand, indicating it to be closer to the rubber hand than controls. However, STN-DBS did not affect proprioceptive judgements or subsequent arm movements altered by the perceptual effects of the illusion. Our findings support the idea that the STN and subcortical connections have a key role in multisensory integration for bodily awareness. Decisionmaking in multisensory bodily illusions is discussed.

neuroscience↗

bioRxiv2016

Resting state functional coupling between the ascending synchronising system, limbic system and the default mode network via theta oscillations

In order to better understand dysfunction in dementia and psychiatric illnesses, the underlying neuronal systems that give rise to normal memory and cognitive processes need to be better understood. Based on electrophysiological recordings in animals, theta oscillations have been proposed as an intrinsic mechanism for the orchestration of memory functions, especially episodic and autobiographical memory. Theta oscillations are controlled by the ascending synchronising system, a set of nucleui in the pontine tegmentum and basal forebrain. At a network level, the default mode network has been shown to be responsible for episodic and autobiographical.\n\nUsing resting state fMRI data, we show using an ICA approach, seed based connectivity and dynamic causal modelling that the ascending synchronising system is coupled to the medial temporal lobe nodes including the hippocampus and parahippocampal gyrus and with the default mode network. Our results provide thus support the role of theta oscillations in memory function and coordination at a network level.\n\nHighlightsO_LIResting state functional coupling between the DMN, MTL and ascending synchronising system.\nC_LIO_LITheta oscillations may be the basis of this coupling given the role of these structures in control of theta.\nC_LIO_LITheta oscillations have been implicated in memory, cognition and predictive coding.\nC_LIO_LIDMN, MTL and ASS are implicated in Alzheimers disease.\nC_LI\n\nAbbreviations

neuroscience↗