Biology subjects

Voon, V.

Publications and source records attributed to Voon, V..

5 recordsLinked to original sources

bioRxiv2025

Executive control in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium

ObjectiveObsessive-compulsive disorder (OCD) is associated with impaired executive function and altered activity in associated neural circuits, contributing to reduced goal-directed behavior. To investigate neural activation during executive control, we conducted a mega-analysis in the ENIGMA-OCD consortium pooling individual participant data from 475 individuals with OCD and 345 healthy controls across 15 fMRI tasks collected worldwide. MethodsIndividual participant data was uniformly processed using HALFpipe to construct voxelwise statistical images of executive control and task load contrasts. Parameter estimates extracted from regions of interest were entered into multilevel Bayesian models to examine regional and whole-brain effects of diagnosis, and, within OCD, the influence of medication status, symptom severity, and age of onset on task activation. ResultsWe observed a robust task activation pattern across individuals with OCD and control participants in executive control regions across tasks. Relative to controls, individuals with OCD showed moderate to very strong evidence of weaker activation of the dorsolateral prefrontal cortex, precuneus, frontal eye fields, and inferior parietal lobule during executive control (all positive posterior probabilities [P+]<0.1). Individuals with OCD also showed stronger activation in regions of the default mode network during executive function relative to controls. We found little evidence for differential activation during executive control in task- positive regions related to disease onset, severity and medication status. ConclusionIn the first mega-analysis of fMRI studies of executive function in OCD, we found strong evidence of weaker frontoparietal activation during executive control tasks. Our findings also suggest a failure of default mode network regions to appropriately disengage during task performance in OCD.

neuroscience↗

bioRxiv2024

Moving towards precision TMS? Evaluating individual differences and reproducibility of personalized stimulation targets in UK Biobank

ObjectivePersonalized transcranial magnetic stimulation (TMS) targeting, guided by functional connectivity (FC), shows potential in treating depression. The present study aims to map individual FC peak location using UK Biobank, to evaluate individual differences and reproducibility of FC-based targets. MethodsWe analyzed UK Biobank resting-state fMRI (rfMRI) of 35,423 participants, identifying individual FC peak locations on the dorsolateral prefrontal cortex (DLPFC) that functionally connected to the subcallosal cingulate, amygdala, and ventromedial prefrontal cortex, respectively. Euclidean distance between each participants individual peak and group-average peak was calculated. With follow-up rfMRI of 1341 participants, within-subject FC peak location changes were calculated. We also compared common TMS targets and random locations for their median distance to individual peaks in a permutation test. ResultsSeed-based FC analyses revealed large differences in the individual FC peak location on DLPFC: the mean distance from the individual peaks to group-average peak ranged from 14.24 to 29.92mm; 70% to 94% of participants were >10mm away from the group-average peak and potentially located outside of the TMS effective area with common TMS coils. Similar variability was observed in within-subject peak locations across two fMRI assessments. Common TMS targets and the group-average FC peak showed no significant difference in median distances to individual FC peaks when compared to random locations. ConclusionsFC peak location shows wide inter- and intra-individual variability. We emphasize a role for individualized TMS neuronavigation targeting but emphasize the need for more reliable biomarker studies.

neuroscience↗

bioRxiv2018

Brain responses to anticipating and receiving beer: Comparing light, at-risk, and dependent alcohol users

BackgroundImpaired brain processing of alcohol-related rewards has been suggested to play a central role in alcohol use disorder. Yet, evidence remains inconsistent, and mainly originates from studies in which participants passively observe alcohol cues or taste alcohol. Here we designed a protocol in which beer consumption was predicted by incentive cues and contingent on instrumental action, closer to real life situations. We predicted that anticipating and receiving beer (compared with water) would elicit activity in the brain reward network, and that this activity would correlate with drinking level across participants.\n\nMethodsThe sample consisted of 150 beer-drinking males, aged 18-25 years. Three groups were defined based on AUDIT scores: light drinkers (n=40), at-risk drinkers (n=63), and dependent drinkers (n=47). fMRI measures were obtained while participants engaged in the Beer Incentive Delay task involving beer- and water-predicting cues, followed by real sips of beer or water.\n\nResultsDuring anticipation, outcome notification and delivery of beer compared with water, higher activity was found in a reward-related brain network including the medial prefrontal cortex, orbitofrontal cortex and amygdala. Yet, no activity was observed in the striatum, and no differences were found between the groups.\n\nConclusionsOur results reveal that anticipating, obtaining and tasting beer activates parts of the brain reward network, but that these brain responses do not differentiate between different drinking levels. We speculate that other factors, such as cognitive control or sensitivity to social context, may be more discriminant predictors of drinking behaviour in young adults.

neuroscience↗

bioRxiv2018

Modulation of Resting Connectivity Between the Mesial Frontal Cortex and Basal Ganglia

The mesial prefrontal cortex, cingulate cortex and the ventral striatum are key nodes of the human mesial fronto-striatal circuit involved in decision-making and executive function and pathological disorders. Here we ask whether deep wide-field repetitive transcranial magnetic stimulation (rTMS) targeting the mesial prefrontal cortex (MPFC) influences resting state functional connectivity. In Study 1, we examined functional connectivity using resting state multi-echo and independent components analysis in 154 healthy subjects to characterize default connectivity in the MPFC and mid-cingulate cortex (MCC). In Study 2, we used inhibitory, 1 Hz deep rTMS with the H7-coil targeting MPFC and dorsal anterior cingulate (dACC) in a separate group of 20 healthy volunteers and examined pre-and post-TMS functional connectivity using seed-based and independent components analysis. In Study 1, we show that MPFC and MCC have distinct patterns of functional connectivity with MPFC-ventral striatum showing negative, whereas MCC-ventral striatum showing positive functional connectivity. Low-frequency rTMS decreased functional connectivity of MPFC and dACC with the ventral striatum. We further showed enhanced connectivity between MCC and ventral striatum. These findings emphasize how deep inhibitory rTMS using the H7-coil can influence underlying network functional connectivity by decreasing connectivity of the targeted MPFC regions, thus potentially enhancing response inhibition and decreasing drug cue reactivity processes relevant to addictions. The unexpected finding of enhanced default connectivity between MCC and ventral striatum may be related to the decreased influence and connectivity between the MPFC and MCC. These findings are highly relevant to the treatment of disorders relying on the mesioprefrontal-cingulo-striatal circuit.

neuroscience↗

bioRxiv2017

Classification and characterisation of brain network changes in chronic back pain: a multicenter study.

Chronic pain is a common and often disabling condition, and is thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Here, we investigated brain network architecture using resting state fMRI data collected from chronic back pain patients in UK and Japan (41 patients, 56 controls). Using a machine learning approach (support vector machine), we found that brain network patterns reliably classified chronic pain patients in a third, independent open data set with an accuracy of 63%, whilst 68% was attained in cross validation of all data. We then developed a deep learning classifier using a conditional variational autoencoder, which also yield yielded 63% generalisation and 68% cross-validation accuracy. Given the existence of reliable network changes, we next studied the graph topology of the network, and found consistent evidence of hub disruption based on clustering and betweenness centrality of brain nodes in pain patients. To examine this in more detail, we developed a multislice modularity algorithm to identify a consensus pattern of modular reorganisation of brain nodes across the entire data set. This revealed evidence of significant changes in the modular identity of several brain regions, most notably including broad regions of bilateral sensorimotor cortex, subregions of which also contributed to classifier performance. These results provide evidence of consistent and characteristic brain network changes in chronic pain, and highlight extensive reorganisaton of the network architecture of sensorimotor cortex.

neuroscience↗